Datasets:

rockmiin

/

ml-codeparrot-train

like 0

kastnerkyle/pylearn2

pylearn2/scripts/datasets/make_mnistplus.py

5

8862

""" Script to generate the MNIST+ dataset. The purpose of this dataset is to make a more challenging MNIST-like dataset, with multiple factors of variation. These factors can serve to evaluate a model's performance at learning invariant features, or its ability to disentangle factors of variation in a multi-task classification setting. The dataset is stored under $PYLEARN2_DATA_PATH. The dataset variants are created as follows. For each MNIST image, we: 1. Perform a random rotation of the image (optional) 2. Rescale the image from 28x28 to 48x48, yielding variable `image`. 3.1 Extract a random patch `textured_patch` from a fixed or random image of the Brodatz texture dataset. 3.2 Generate mask of MNIST digit outline, by thresholding MNIST digit at 0.1 3.3 Fuse MNIST digit and textured patch as follows: textured_patch[mask] <= image[mask]; image <= textured_patch; 4. Randomly select position of light source (optional) 5. Perform embossing operation, given fixed lighting position obtained in 4. """ import numpy import pickle import pylab as pl from copy import copy from optparse import OptionParser from pylearn2.datasets import mnist from pylearn2.utils import string_utils import warnings try: from PIL import Image except ImportError: warnings.warn("Couldn't import Image from PIL, so far make_mnistplus " "is only supported with PIL") OUTPUT_SIZE = 48 DOWN_SAMPLE = 1 def to_array(img): """ Convert PIL.Image to numpy.ndarray. :param img: numpy.ndarray """ return numpy.array(img.getdata()) / 255. def to_img(arr, os): """ Convert numpy.ndarray to PIL.Image :param arr: numpy.ndarray :param os: integer, size of output image. """ return Image.fromarray(arr.reshape(os, os) * 255.) def emboss(img, azi=45., ele=18., dep=2): """ Perform embossing of image `img`. :param img: numpy.ndarray, matrix representing image to emboss. :param azi: azimuth (in degrees) :param ele: elevation (in degrees) :param dep: depth, (0-100) """ # defining azimuth, elevation, and depth ele = (ele * 2 * numpy.pi) / 360. azi = (azi * 2 * numpy.pi) / 360. a = numpy.asarray(img).astype('float') # find the gradient grad = numpy.gradient(a) # (it is two arrays: grad_x and grad_y) grad_x, grad_y = grad # getting the unit incident ray gd = numpy.cos(ele) # length of projection of ray on ground plane dx = gd * numpy.cos(azi) dy = gd * numpy.sin(azi) dz = numpy.sin(ele) # adjusting the gradient by the "depth" factor # (I think this is how GIMP defines it) grad_x = grad_x * dep / 100. grad_y = grad_y * dep / 100. # finding the unit normal vectors for the image leng = numpy.sqrt(grad_x**2 + grad_y**2 + 1.) uni_x = grad_x/leng uni_y = grad_y/leng uni_z = 1./leng # take the dot product a2 = 255 * (dx*uni_x + dy*uni_y + dz*uni_z) # avoid overflow a2 = a2.clip(0, 255) # you must convert back to uint8 /before/ converting to an image return Image.fromarray(a2.astype('uint8')) def extract_patch(textid, os, downsample): """ Extract a patch of texture #textid of Brodatz dataset. :param textid: id of texture image to load. :param os: size of MNIST+ output images. :param downsample: integer, downsampling factor. """ temp = '${PYLEARN2_DATA_PATH}/textures/brodatz/D%i.gif' % textid fname = string_utils.preprocess(temp) img_i = Image.open(fname) img_i = img_i.resize((img_i.size[0]/downsample, img_i.size[1]/downsample), Image.BILINEAR) x = numpy.random.randint(0, img_i.size[0] - os) y = numpy.random.randint(0, img_i.size[1] - os) patch = img_i.crop((x, y, x+os, y+os)) return patch, (x, y) def gendata(enable, os, downsample, textid=None, seed=2313, verbose=False): """ Generate the MNIST+ dataset. :param enable: dictionary of flags with keys ['texture', 'azimuth', 'rotation', 'elevation'] to enable/disable a given factor of variation. :param textid: if enable['texture'], id number of the Brodatz texture to load. If textid is None, we load a random texture for each MNIST image. :param os: output size (width and height) of MNIST+ images. :param downsample: factor by which to downsample texture. :param seed: integer for seeding RNG. :param verbose: bool """ rng = numpy.random.RandomState(seed) data = mnist.MNIST('train') test = mnist.MNIST('test') data.X = numpy.vstack((data.X, test.X)) data.y = numpy.hstack((data.y, test.y)) del test output = {} output['data'] = numpy.zeros((len(data.X), os*os)) output['label'] = numpy.zeros(len(data.y)) if enable['azimuth']: output['azimuth'] = numpy.zeros(len(data.y)) if enable['elevation']: output['elevation'] = numpy.zeros(len(data.y)) if enable['rotation']: output['rotation'] = numpy.zeros(len(data.y)) if enable['texture']: output['texture_id'] = numpy.zeros(len(data.y)) output['texture_pos'] = numpy.zeros((len(data.y), 2)) for i in xrange(len(data.X)): # get MNIST image frgd_img = to_img(data.X[i], 28) frgd_img = frgd_img.convert('L') if enable['rotation']: rot = rng.randint(0, 360) output['rotation'][i] = rot frgd_img = frgd_img.rotate(rot, Image.BILINEAR) frgd_img = frgd_img.resize((os, os), Image.BILINEAR) if enable['texture']: if textid is None: # extract patch from texture database. Note that texture #14 # does not exist. textid = 14 while textid == 14: textid = rng.randint(1, 113) patch_img, (px, py) = extract_patch(textid, os, downsample) patch_arr = to_array(patch_img) # store output details output['texture_id'][i] = textid output['texture_pos'][i] = (px, py) # generate binary mask for digit outline frgd_arr = to_array(frgd_img) mask_arr = frgd_arr > 0.1 # copy contents of masked-MNIST image into background texture blend_arr = copy(patch_arr) blend_arr[mask_arr] = frgd_arr[mask_arr] # this now because the image to emboss frgd_img = to_img(blend_arr, os) azi = 45 if enable['azimuth']: azi = rng.randint(0, 360) output['azimuth'][i] = azi ele = 18. if enable['elevation']: ele = rng.randint(0, 60) output['elevation'][i] = ele mboss_img = emboss(frgd_img, azi=azi, ele=ele) mboss_arr = to_array(mboss_img) output['data'][i] = mboss_arr output['label'][i] = data.y[i] if verbose: pl.imshow(mboss_arr.reshape(os, os)) pl.gray() pl.show() fname = 'mnistplus' if enable['azimuth']: fname += "_azi" if enable['rotation']: fname += "_rot" if enable['texture']: fname += "_tex" fp = open(fname+'.pkl','w') pickle.dump(output, fp, protocol=pickle.HIGHEST_PROTOCOL) fp.close() if __name__ == '__main__': parser = OptionParser() parser.add_option('-v', action='store_true', dest='verbose') parser.add_option('--azimuth', action='store_true', dest='azimuth', help='Enable random azimuth for light-source used in embossing.') parser.add_option('--elevation', action='store_true', dest='elevation', help='Enable random elevation for light-source used in embossing.') parser.add_option('--rotation', action='store_true', dest='rotation', help='Randomly rotate MNIST digit prior to embossing.') parser.add_option('--texture', action='store_true', dest='texture', help='Perform joint embossing of fused {MNIST + Texture} image.') parser.add_option('--textid', action='store', type='int', dest='textid', help='If specified, use a single texture ID for all MNIST images.', default=None) parser.add_option('--output_size', action='store', type='int', dest='os', help='Integer specifying size of (square) output images.', default=OUTPUT_SIZE) parser.add_option('--downsample', action='store', type='int', dest='downsample', default=DOWN_SAMPLE, help='Downsampling factor for Brodatz textures.') (opts, args) = parser.parse_args() enable = {'texture': opts.texture, 'azimuth': opts.azimuth, 'rotation': opts.rotation, 'elevation': opts.elevation} gendata(enable=enable, os=opts.os, downsample=opts.downsample, verbose=opts.verbose, textid=opts.textid)

bsd-3-clause

nicproulx/mne-python

tutorials/plot_brainstorm_auditory.py

3

16597

# -*- coding: utf-8 -*- """ ==================================== Brainstorm auditory tutorial dataset ==================================== Here we compute the evoked from raw for the auditory Brainstorm tutorial dataset. For comparison, see [1]_ and: http://neuroimage.usc.edu/brainstorm/Tutorials/Auditory Experiment: - One subject, 2 acquisition runs 6 minutes each. - Each run contains 200 regular beeps and 40 easy deviant beeps. - Random ISI: between 0.7s and 1.7s seconds, uniformly distributed. - Button pressed when detecting a deviant with the right index finger. The specifications of this dataset were discussed initially on the `FieldTrip bug tracker <http://bugzilla.fcdonders.nl/show_bug.cgi?id=2300>`_. References ---------- .. [1] Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM. Brainstorm: A User-Friendly Application for MEG/EEG Analysis. Computational Intelligence and Neuroscience, vol. 2011, Article ID 879716, 13 pages, 2011. doi:10.1155/2011/879716 """ # Authors: Mainak Jas <mainak.jas@telecom-paristech.fr> # Eric Larson <larson.eric.d@gmail.com> # Jaakko Leppakangas <jaeilepp@student.jyu.fi> # # License: BSD (3-clause) import os.path as op import pandas as pd import numpy as np import mne from mne import combine_evoked from mne.minimum_norm import apply_inverse from mne.datasets.brainstorm import bst_auditory from mne.io import read_raw_ctf from mne.filter import notch_filter, filter_data print(__doc__) ############################################################################### # To reduce memory consumption and running time, some of the steps are # precomputed. To run everything from scratch change this to False. With # ``use_precomputed = False`` running time of this script can be several # minutes even on a fast computer. use_precomputed = True ############################################################################### # The data was collected with a CTF 275 system at 2400 Hz and low-pass # filtered at 600 Hz. Here the data and empty room data files are read to # construct instances of :class:`mne.io.Raw`. data_path = bst_auditory.data_path() subject = 'bst_auditory' subjects_dir = op.join(data_path, 'subjects') raw_fname1 = op.join(data_path, 'MEG', 'bst_auditory', 'S01_AEF_20131218_01.ds') raw_fname2 = op.join(data_path, 'MEG', 'bst_auditory', 'S01_AEF_20131218_02.ds') erm_fname = op.join(data_path, 'MEG', 'bst_auditory', 'S01_Noise_20131218_01.ds') ############################################################################### # In the memory saving mode we use ``preload=False`` and use the memory # efficient IO which loads the data on demand. However, filtering and some # other functions require the data to be preloaded in the memory. preload = not use_precomputed raw = read_raw_ctf(raw_fname1, preload=preload) n_times_run1 = raw.n_times mne.io.concatenate_raws([raw, read_raw_ctf(raw_fname2, preload=preload)]) raw_erm = read_raw_ctf(erm_fname, preload=preload) ############################################################################### # Data channel array consisted of 274 MEG axial gradiometers, 26 MEG reference # sensors and 2 EEG electrodes (Cz and Pz). # In addition: # # - 1 stim channel for marking presentation times for the stimuli # - 1 audio channel for the sent signal # - 1 response channel for recording the button presses # - 1 ECG bipolar # - 2 EOG bipolar (vertical and horizontal) # - 12 head tracking channels # - 20 unused channels # # The head tracking channels and the unused channels are marked as misc # channels. Here we define the EOG and ECG channels. raw.set_channel_types({'HEOG': 'eog', 'VEOG': 'eog', 'ECG': 'ecg'}) if not use_precomputed: # Leave out the two EEG channels for easier computation of forward. raw.pick_types(meg=True, eeg=False, stim=True, misc=True, eog=True, ecg=True) ############################################################################### # For noise reduction, a set of bad segments have been identified and stored # in csv files. The bad segments are later used to reject epochs that overlap # with them. # The file for the second run also contains some saccades. The saccades are # removed by using SSP. We use pandas to read the data from the csv files. You # can also view the files with your favorite text editor. annotations_df = pd.DataFrame() offset = n_times_run1 for idx in [1, 2]: csv_fname = op.join(data_path, 'MEG', 'bst_auditory', 'events_bad_0%s.csv' % idx) df = pd.read_csv(csv_fname, header=None, names=['onset', 'duration', 'id', 'label']) print('Events from run {0}:'.format(idx)) print(df) df['onset'] += offset * (idx - 1) annotations_df = pd.concat([annotations_df, df], axis=0) saccades_events = df[df['label'] == 'saccade'].values[:, :3].astype(int) # Conversion from samples to times: onsets = annotations_df['onset'].values / raw.info['sfreq'] durations = annotations_df['duration'].values / raw.info['sfreq'] descriptions = annotations_df['label'].values annotations = mne.Annotations(onsets, durations, descriptions) raw.annotations = annotations del onsets, durations, descriptions ############################################################################### # Here we compute the saccade and EOG projectors for magnetometers and add # them to the raw data. The projectors are added to both runs. saccade_epochs = mne.Epochs(raw, saccades_events, 1, 0., 0.5, preload=True, reject_by_annotation=False) projs_saccade = mne.compute_proj_epochs(saccade_epochs, n_mag=1, n_eeg=0, desc_prefix='saccade') if use_precomputed: proj_fname = op.join(data_path, 'MEG', 'bst_auditory', 'bst_auditory-eog-proj.fif') projs_eog = mne.read_proj(proj_fname)[0] else: projs_eog, _ = mne.preprocessing.compute_proj_eog(raw.load_data(), n_mag=1, n_eeg=0) raw.add_proj(projs_saccade) raw.add_proj(projs_eog) del saccade_epochs, saccades_events, projs_eog, projs_saccade # To save memory ############################################################################### # Visually inspect the effects of projections. Click on 'proj' button at the # bottom right corner to toggle the projectors on/off. EOG events can be # plotted by adding the event list as a keyword argument. As the bad segments # and saccades were added as annotations to the raw data, they are plotted as # well. raw.plot(block=True) ############################################################################### # Typical preprocessing step is the removal of power line artifact (50 Hz or # 60 Hz). Here we notch filter the data at 60, 120 and 180 to remove the # original 60 Hz artifact and the harmonics. The power spectra are plotted # before and after the filtering to show the effect. The drop after 600 Hz # appears because the data was filtered during the acquisition. In memory # saving mode we do the filtering at evoked stage, which is not something you # usually would do. if not use_precomputed: meg_picks = mne.pick_types(raw.info, meg=True, eeg=False) raw.plot_psd(tmax=np.inf, picks=meg_picks) notches = np.arange(60, 181, 60) raw.notch_filter(notches) raw.plot_psd(tmax=np.inf, picks=meg_picks) ############################################################################### # We also lowpass filter the data at 100 Hz to remove the hf components. if not use_precomputed: raw.filter(None, 100., h_trans_bandwidth=0.5, filter_length='10s', phase='zero-double') ############################################################################### # Epoching and averaging. # First some parameters are defined and events extracted from the stimulus # channel (UPPT001). The rejection thresholds are defined as peak-to-peak # values and are in T / m for gradiometers, T for magnetometers and # V for EOG and EEG channels. tmin, tmax = -0.1, 0.5 event_id = dict(standard=1, deviant=2) reject = dict(mag=4e-12, eog=250e-6) # find events events = mne.find_events(raw, stim_channel='UPPT001') ############################################################################### # The event timing is adjusted by comparing the trigger times on detected # sound onsets on channel UADC001-4408. sound_data = raw[raw.ch_names.index('UADC001-4408')][0][0] onsets = np.where(np.abs(sound_data) > 2. * np.std(sound_data))[0] min_diff = int(0.5 * raw.info['sfreq']) diffs = np.concatenate([[min_diff + 1], np.diff(onsets)]) onsets = onsets[diffs > min_diff] assert len(onsets) == len(events) diffs = 1000. * (events[:, 0] - onsets) / raw.info['sfreq'] print('Trigger delay removed (μ ± σ): %0.1f ± %0.1f ms' % (np.mean(diffs), np.std(diffs))) events[:, 0] = onsets del sound_data, diffs ############################################################################### # We mark a set of bad channels that seem noisier than others. This can also # be done interactively with ``raw.plot`` by clicking the channel name # (or the line). The marked channels are added as bad when the browser window # is closed. raw.info['bads'] = ['MLO52-4408', 'MRT51-4408', 'MLO42-4408', 'MLO43-4408'] ############################################################################### # The epochs (trials) are created for MEG channels. First we find the picks # for MEG and EOG channels. Then the epochs are constructed using these picks. # The epochs overlapping with annotated bad segments are also rejected by # default. To turn off rejection by bad segments (as was done earlier with # saccades) you can use keyword ``reject_by_annotation=False``. picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True, exclude='bads') epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0), reject=reject, preload=False, proj=True) ############################################################################### # We only use first 40 good epochs from each run. Since we first drop the bad # epochs, the indices of the epochs are no longer same as in the original # epochs collection. Investigation of the event timings reveals that first # epoch from the second run corresponds to index 182. epochs.drop_bad() epochs_standard = mne.concatenate_epochs([epochs['standard'][range(40)], epochs['standard'][182:222]]) epochs_standard.load_data() # Resampling to save memory. epochs_standard.resample(600, npad='auto') epochs_deviant = epochs['deviant'].load_data() epochs_deviant.resample(600, npad='auto') del epochs, picks ############################################################################### # The averages for each conditions are computed. evoked_std = epochs_standard.average() evoked_dev = epochs_deviant.average() del epochs_standard, epochs_deviant ############################################################################### # Typical preprocessing step is the removal of power line artifact (50 Hz or # 60 Hz). Here we notch filter the data at 60, 120 and 180 to remove the # original 60 Hz artifact and the harmonics. Normally this would be done to # raw data (with :func:`mne.io.Raw.filter`), but to reduce memory consumption # of this tutorial, we do it at evoked stage. if use_precomputed: sfreq = evoked_std.info['sfreq'] notches = [60, 120, 180] for evoked in (evoked_std, evoked_dev): evoked.data[:] = notch_filter(evoked.data, sfreq, notches) evoked.data[:] = filter_data(evoked.data, sfreq, l_freq=None, h_freq=100.) ############################################################################### # Here we plot the ERF of standard and deviant conditions. In both conditions # we can see the P50 and N100 responses. The mismatch negativity is visible # only in the deviant condition around 100-200 ms. P200 is also visible around # 170 ms in both conditions but much stronger in the standard condition. P300 # is visible in deviant condition only (decision making in preparation of the # button press). You can view the topographies from a certain time span by # painting an area with clicking and holding the left mouse button. evoked_std.plot(window_title='Standard', gfp=True) evoked_dev.plot(window_title='Deviant', gfp=True) ############################################################################### # Show activations as topography figures. times = np.arange(0.05, 0.301, 0.025) evoked_std.plot_topomap(times=times, title='Standard') evoked_dev.plot_topomap(times=times, title='Deviant') ############################################################################### # We can see the MMN effect more clearly by looking at the difference between # the two conditions. P50 and N100 are no longer visible, but MMN/P200 and # P300 are emphasised. evoked_difference = combine_evoked([evoked_dev, -evoked_std], weights='equal') evoked_difference.plot(window_title='Difference', gfp=True) ############################################################################### # Source estimation. # We compute the noise covariance matrix from the empty room measurement # and use it for the other runs. reject = dict(mag=4e-12) cov = mne.compute_raw_covariance(raw_erm, reject=reject) cov.plot(raw_erm.info) del raw_erm ############################################################################### # The transformation is read from a file. More information about coregistering # the data, see :ref:`ch_interactive_analysis` or # :func:`mne.gui.coregistration`. trans_fname = op.join(data_path, 'MEG', 'bst_auditory', 'bst_auditory-trans.fif') trans = mne.read_trans(trans_fname) ############################################################################### # To save time and memory, the forward solution is read from a file. Set # ``use_precomputed=False`` in the beginning of this script to build the # forward solution from scratch. The head surfaces for constructing a BEM # solution are read from a file. Since the data only contains MEG channels, we # only need the inner skull surface for making the forward solution. For more # information: :ref:`CHDBBCEJ`, :func:`mne.setup_source_space`, # :ref:`create_bem_model`, :func:`mne.bem.make_watershed_bem`. if use_precomputed: fwd_fname = op.join(data_path, 'MEG', 'bst_auditory', 'bst_auditory-meg-oct-6-fwd.fif') fwd = mne.read_forward_solution(fwd_fname) else: src = mne.setup_source_space(subject, spacing='ico4', subjects_dir=subjects_dir, overwrite=True) model = mne.make_bem_model(subject=subject, ico=4, conductivity=[0.3], subjects_dir=subjects_dir) bem = mne.make_bem_solution(model) fwd = mne.make_forward_solution(evoked_std.info, trans=trans, src=src, bem=bem) inv = mne.minimum_norm.make_inverse_operator(evoked_std.info, fwd, cov) snr = 3.0 lambda2 = 1.0 / snr ** 2 del fwd ############################################################################### # The sources are computed using dSPM method and plotted on an inflated brain # surface. For interactive controls over the image, use keyword # ``time_viewer=True``. # Standard condition. stc_standard = mne.minimum_norm.apply_inverse(evoked_std, inv, lambda2, 'dSPM') brain = stc_standard.plot(subjects_dir=subjects_dir, subject=subject, surface='inflated', time_viewer=False, hemi='lh', initial_time=0.1, time_unit='s') del stc_standard, brain ############################################################################### # Deviant condition. stc_deviant = mne.minimum_norm.apply_inverse(evoked_dev, inv, lambda2, 'dSPM') brain = stc_deviant.plot(subjects_dir=subjects_dir, subject=subject, surface='inflated', time_viewer=False, hemi='lh', initial_time=0.1, time_unit='s') del stc_deviant, brain ############################################################################### # Difference. stc_difference = apply_inverse(evoked_difference, inv, lambda2, 'dSPM') brain = stc_difference.plot(subjects_dir=subjects_dir, subject=subject, surface='inflated', time_viewer=False, hemi='lh', initial_time=0.15, time_unit='s')

bsd-3-clause

alvarouc/polyssifier

polyssifier/poly_utils.py

1

10338

from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import LinearSVC, SVC from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import (LogisticRegression, LinearRegression, BayesianRidge, Ridge, Lasso, ElasticNet, Lars, LassoLars, OrthogonalMatchingPursuit, PassiveAggressiveRegressor) from sklearn.naive_bayes import GaussianNB from sklearn.neural_network import MLPClassifier as MLP from sklearn.gaussian_process import GaussianProcessRegressor import collections import numpy as np from sklearn.feature_selection import SelectKBest, f_regression from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.gaussian_process.kernels import RBF class MyVoter(object): """ Voter Classifier Receives fitted classifiers and runs majority voting """ def __init__(self, estimators): ''' estimators: List of fitted classifiers ''' self.estimators_ = estimators def predict(self, X): predictions = np.asarray( [clf.predict(X) for clf in self.estimators_]).T maj = np.apply_along_axis( lambda x: np.argmax(np.bincount(x)), axis=1, arr=predictions.astype('int')) return maj class MyRegressionAverager(object): """ Regression averager Receives fitted regressors and averages the predictions of the regressors. """ def __init__(self, estimators): ''' estimators: List of fitted regressors ''' self.estimators_ = estimators def predict(self, X): predictions = np.asarray( [reg.predict(X) for reg in self.estimators_]).T avg = np.average(predictions, axis=1) return avg class MyRegressionMedianer(object): """ Regression averager Receives fitted regressors and averages the predictions of the regressors. """ def __init__(self, estimators): ''' estimators: List of fitted regressors ''' self.estimators_ = estimators def predict(self, X): predictions = np.asarray( [reg.predict(X) for reg in self.estimators_]).T avg = np.median(predictions, axis=1) return avg def build_classifiers(exclude, scale, feature_selection, nCols): ''' Input: - exclude: list of names of classifiers to exclude from the analysis - scale: True or False. Scale data before fitting classifier - feature_selection: True or False. Run feature selection before fitting classifier - nCols: Number of columns in input dataset to classifiers Output: Dictionary with classifier name as keys. - 'clf': Classifier object - 'parameters': Dictionary with parameters of 'clf' as keys ''' classifiers = collections.OrderedDict() if 'Multilayer Perceptron' not in exclude: classifiers['Multilayer Perceptron'] = { 'clf': MLP(), 'parameters': {'hidden_layer_sizes': [(100, 50), (50, 25)], 'max_iter': [500]} } if 'Nearest Neighbors' not in exclude: classifiers['Nearest Neighbors'] = { 'clf': KNeighborsClassifier(), 'parameters': {'n_neighbors': [1, 5, 10, 20]}} if 'SVM' not in exclude: classifiers['SVM'] = { 'clf': SVC(C=1, probability=True, cache_size=10000, class_weight='balanced'), 'parameters': {'kernel': ['rbf', 'poly'], 'C': [0.01, 0.1, 1]}} if 'Linear SVM' not in exclude: classifiers['Linear SVM'] = { 'clf': LinearSVC(dual=False, class_weight='balanced'), 'parameters': {'C': [0.01, 0.1, 1], 'penalty': ['l1', 'l2']}} if 'Decision Tree' not in exclude: classifiers['Decision Tree'] = { 'clf': DecisionTreeClassifier(max_depth=None, max_features='auto'), 'parameters': {}} if 'Random Forest' not in exclude: classifiers['Random Forest'] = { 'clf': RandomForestClassifier(max_depth=None, n_estimators=10, max_features='auto'), 'parameters': {'n_estimators': list(range(5, 20))}} if 'Logistic Regression' not in exclude: classifiers['Logistic Regression'] = { 'clf': LogisticRegression(fit_intercept=True, solver='lbfgs', penalty='l2'), 'parameters': {'C': [0.001, 0.1, 1]}} if 'Naive Bayes' not in exclude: classifiers['Naive Bayes'] = { 'clf': GaussianNB(), 'parameters': {}} # classifiers['Voting'] = {} def name(x): """ :param x: The name of the classifier :return: The class of the final estimator in lower case form """ return x['clf']._final_estimator.__class__.__name__.lower() for key, val in classifiers.items(): if not scale and not feature_selection: break steps = [] if scale: steps.append(StandardScaler()) if feature_selection: steps.append(SelectKBest(f_regression, k='all')) steps.append(classifiers[key]['clf']) classifiers[key]['clf'] = make_pipeline(*steps) # Reorganize paramenter list for grid search new_dict = {} for keyp in classifiers[key]['parameters']: new_dict[name(classifiers[key]) + '__' + keyp] = classifiers[key]['parameters'][keyp] classifiers[key]['parameters'] = new_dict if nCols > 5 and feature_selection: classifiers[key]['parameters']['selectkbest__k'] = np.linspace( np.round(nCols / 5), nCols, 5).astype('int').tolist() return classifiers def build_regressors(exclude, scale, feature_selection, nCols): ''' This method builds an ordered dictionary of regressors, where the key is the name of the regressor and the value of each key contains a standard dictionary with two keys itself. The first key called 'reg' points to the regression object, which is created by scikit learn. The second key called 'parameters' points to another regular map containing the parameters which are associated with the particular regression model. These parameters are used by grid search in polyssifier.py when finding the best model. If parameters are not defined then grid search is not performed on that particular regression model, so the model's default parameters are used instead to find the best model for the particular data. ''' regressors = collections.OrderedDict() if 'Linear Regression' not in exclude: regressors['Linear Regression'] = { 'reg': LinearRegression(), 'parameters': {} # Best to leave default parameters } if 'Bayesian Ridge' not in exclude: regressors['Bayesian Ridge'] = { 'reg': BayesianRidge(), 'parameters': {} # Investigate if alpha and lambda parameters should be changed } if 'PassiveAggressiveRegressor' not in exclude: regressors['PassiveAggressiveRegressor'] = { 'reg': PassiveAggressiveRegressor(), 'parameters': {'C': [0.5, 1.0, 1.5] } } if 'GaussianProcessRegressor' not in exclude: regressors['GaussianProcessRegressor'] = { 'reg': GaussianProcessRegressor(), 'parameters': { 'alpha': [0.01, 0.1, 1.0, 10.0], 'kernel': [RBF(x) for x in [0.01, 1.0, 100.0, 1000.0]], } } if 'Ridge' not in exclude: regressors['Ridge'] = { 'reg': Ridge(), 'parameters': { 'alpha': [0.25, 0.50, 0.75, 1.00] } } if 'Lasso' not in exclude: regressors['Lasso'] = { 'reg': Lasso(), 'parameters': { 'alpha': [0.25, 0.50, 0.75, 1.00] } } if 'Lars' not in exclude: regressors['Lars'] = { 'reg': Lars(), 'parameters': {} # Best to leave the default parameters } if 'LassoLars' not in exclude: regressors['LassoLars'] = { 'reg': LassoLars(), 'parameters': {'alpha': [0.25, 0.50, 0.75, 1.00, 10.0]} } if 'OrthogonalMatchingPursuit' not in exclude: regressors['OrthogonalMatchingPursuit'] = { 'reg': OrthogonalMatchingPursuit(), 'parameters': {} # Best to leave default parameters } if 'ElasticNet' not in exclude: regressors['ElasticNet'] = { 'reg': ElasticNet(), 'parameters': {'alpha': [0.25, 0.50, 0.75, 1.00], 'l1_ratio': [0.25, 0.50, 0.75, 1.00]} } def name(x): """ :param x: The name of the regressor :return: The class of the final regression estimator in lower case form """ return x['reg']._final_estimator.__class__.__name__.lower() for key, val in regressors.items(): if not scale and not feature_selection: break steps = [] if scale: steps.append(StandardScaler()) if feature_selection: steps.append(SelectKBest(f_regression, k='all')) steps.append(regressors[key]['reg']) regressors[key]['reg'] = make_pipeline(*steps) # Reorganize paramenter list for grid search new_dict = {} for keyp in regressors[key]['parameters']: new_dict[name(regressors[key]) + '__' + keyp] = regressors[key]['parameters'][keyp] regressors[key]['parameters'] = new_dict if nCols > 5 and feature_selection: regressors[key]['parameters']['selectkbest__k'] = np.linspace( np.round(nCols / 5), nCols, 5).astype('int').tolist() return regressors

gpl-2.0

UMWRG/HydraPlatform

HydraServer/python/HydraServer/plugins/timeseries_functions.py

2

5406

# (c) Copyright 2013, 2014, University of Manchester # # HydraPlatform is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # HydraPlatform is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with HydraPlatform. If not, see <http://www.gnu.org/licenses/> # from spyne.decorator import rpc from spyne.model.primitive import Integer, Unicode, AnyDict from HydraServer.soap_server.hydra_base import HydraService from HydraServer.lib.data import get_dataset from HydraLib.HydraException import HydraError from HydraServer.util import get_val import logging import numpy import json log = logging.getLogger(__name__) op_map = { 'add' : lambda x, y: numpy.add(x, y), 'subtract' : lambda x, y: numpy.subtract(x, y), 'multiply' : lambda x, y: numpy.multiply(x, y), 'divide' : lambda x, y: numpy.divide(x, y), 'avg' : lambda x : numpy.mean(x), 'stddev' : lambda x : numpy.std(x), } class Service(HydraService): __service_name__ = "TimeseriesService" @rpc(Integer(min_occurs=2, max_occurs='unbounded'), _returns=Unicode) def subtract_datasets(ctx, dataset_ids): """ Subtract the value of dataset[1] from the value of dataset[0]. subtract dataset[2] from result etc. Rules: 1: The datasets must be of the same type 2: The datasets must be numerical 3: If timeseries, the timesteps must match. The result is a new value, NOT a new dataset. It is up to the client to create a new datasets with the resulting value if they wish to do so. """ return _perform_op_on_datasets('subtract', dataset_ids, **ctx.in_header.__dict__) @rpc(Integer(min_occurs=2, max_occurs='unbounded'), _returns=Unicode) def add_datasets(ctx, dataset_ids): """ Add the value of dataset[0] to the value of dataset[1] etc. Rules: 1: The datasets must be of the same type 2: The datasets must be numerical 3: If timeseries, the timesteps must match. The result is a new value, NOT a new dataset. It is up to the client to create a new datasets with the resulting value if they wish to do so. """ return _perform_op_on_datasets('add', dataset_ids, **ctx.in_header.__dict__) @rpc(Integer(min_occurs=2, max_occurs='unbounded'), _returns=Unicode) def multiply_datasets(ctx, dataset_ids): """ Multiply the value of dataset[0] by the value of dataset[1] and the result by the value of dataset[2] etc. Rules: 1: The datasets must be of the same type 2: The datasets must be numerical 3: If timeseries, the timesteps must match. The result is a new value, NOT a new dataset. It is up to the client to create a new datasets with the resulting value if they wish to do so. """ return _perform_op_on_datasets('multiply', dataset_ids, **ctx.in_header.__dict__) @rpc(Integer(min_occurs=2, max_occurs='unbounded'), _returns=Unicode) def divide_datasets(ctx, dataset_ids): """ Divide the value of dataset[0] by the value of dataset[1], the result of which is divided by the value of dataset[2] etc. Rules: 1: The datasets must be of the same type 2: The datasets must be numerical 3: If timeseries, the timesteps must match. The result is a new value, NOT a new dataset. It is up to the client to create a new datasets with the resulting value if they wish to do so. """ return _perform_op_on_datasets('divide', dataset_ids, **ctx.in_header.__dict__) def _perform_op_on_datasets(op, dataset_ids, **kwargs): datasets = [] for dataset_id in dataset_ids: datasets.append(get_dataset(dataset_id, **kwargs)) data_type = None vals = [] for d in datasets: if data_type is None: data_type = d.data_type if data_type == 'descriptor': raise HydraError("Data must be numerical") else: if d.data_type != d.data_type: raise HydraError("Data types do not match.") dataset_val = get_val(d) if data_type == 'timeseries': dataset_val = dataset_val.astype('float') vals.append(dataset_val) _op = op_map[op] op_result = vals[0] for v in vals[1:]: try: op_result = _op(op_result, v) except: raise HydraError("Unable to perform operation %s on values %s and %s" %(op, op_result, v)) if data_type == 'timeseries': return op_result.to_json(date_format='iso', date_unit='ns') elif data_type == 'array': return json.dumps(list(op_result)) else: return json.dumps(str(op_result))

gpl-3.0

kastnerkyle/pylearn2

pylearn2/scripts/datasets/step_through_small_norb.py

49

3123

#! /usr/bin/env python """ A script for sequentially stepping through SmallNORB, viewing each image and its label. Intended as a demonstration of how to iterate through NORB images, and as a way of testing SmallNORB's StereoViewConverter. If you just want an image viewer, consider pylearn2/scripts/show_binocular_grayscale_images.py, which is not specific to SmallNORB. """ __author__ = "Matthew Koichi Grimes" __copyright__ = "Copyright 2010-2014, Universite de Montreal" __credits__ = __author__ __license__ = "3-clause BSD" __maintainer__ = __author__ __email__ = "mkg alum mit edu (@..)" import argparse, pickle, sys from matplotlib import pyplot from pylearn2.datasets.norb import SmallNORB from pylearn2.utils import safe_zip def main(): def parse_args(): parser = argparse.ArgumentParser( description="Step-through visualizer for SmallNORB dataset") parser.add_argument("--which_set", default='train', required=True, help=("'train', 'test', or the path to a " "SmallNORB .pkl file")) return parser.parse_args() def load_norb(args): if args.which_set in ('test', 'train'): return SmallNORB(args.which_set, True) else: norb_file = open(args.which_set) return pickle.load(norb_file) args = parse_args() norb = load_norb(args) topo_space = norb.view_converter.topo_space # does not include label space vec_space = norb.get_data_specs()[0].components[0] figure, axes = pyplot.subplots(1, 2, squeeze=True) figure.suptitle("Press space to step through, or 'q' to quit.") def draw_and_increment(iterator): """ Draws the image pair currently pointed at by the iterator, then increments the iterator. """ def draw(batch_pair): for axis, image_batch in safe_zip(axes, batch_pair): assert image_batch.shape[0] == 1 grayscale_image = image_batch[0, :, :, 0] axis.imshow(grayscale_image, cmap='gray') figure.canvas.draw() def get_values_and_increment(iterator): try: vec_stereo_pair, labels = norb_iter.next() except StopIteration: return (None, None) topo_stereo_pair = vec_space.np_format_as(vec_stereo_pair, topo_space) return topo_stereo_pair, labels batch_pair, labels = get_values_and_increment(norb_iter) draw(batch_pair) norb_iter = norb.iterator(mode='sequential', batch_size=1, data_specs=norb.get_data_specs()) def on_key_press(event): if event.key == ' ': draw_and_increment(norb_iter) if event.key == 'q': sys.exit(0) figure.canvas.mpl_connect('key_press_event', on_key_press) draw_and_increment(norb_iter) pyplot.show() if __name__ == "__main__": main()

bsd-3-clause

OpenMined/PySyft

benchmarks/macro_executor.py

1

3935

# stdlib from datetime import date import json import os from pathlib import Path import subprocess from time import time from typing import Dict from typing import List from typing import Optional from typing import Tuple # third party import pyarrow.parquet as pq # syft absolute import syft as sy from syft.core.adp.data_subject_list import DataSubjectList from syft.core.node.common.node_service.user_manager.user_messages import ( UpdateUserMessage, ) from syft.util import download_file from syft.util import get_root_data_path benchmark_report: dict = {} today = date.today() date = today.strftime("%B %d, %Y") benchmark_report["date"] = date def get_git_revision_short_hash() -> str: return ( subprocess.check_output(["git", "rev-parse", "--short", "HEAD"]) .decode("ascii") .strip() ) benchmark_report["git_revision_hash"] = get_git_revision_short_hash() def download_spicy_bird_benchmark( sizes: Optional[List[str]] = None, ) -> Tuple[Dict[str, Path], List[str]]: sizes = sizes if sizes else ["100K", "250K", "500K", "750K", "1M", "1B"] file_suffix = "_rows_dataset_sample.parquet" BASE_URL = "https://raw.githubusercontent.com/madhavajay/datasets/main/spicy_bird/" folder_name = "spicy_bird" dataset_path = get_root_data_path() / folder_name paths = [] for size in sizes: filename = f"{size}{file_suffix}" full_path = dataset_path / filename url = f"{BASE_URL}{filename}" if not os.path.exists(full_path): print(url) path = download_file(url=url, full_path=full_path) else: path = Path(full_path) paths.append(path) return dict(zip(sizes, paths)), sizes key_size = "1B" files, ordered_sizes = download_spicy_bird_benchmark(sizes=[key_size]) data_file = files[key_size] benchmark_report["data_row_size"] = key_size t0 = time() df = pq.read_table(data_file) end_time = time() tf = round(time() - t0, 4) print(f"Time taken to read parquet file: {round(tf, 2)} seconds") benchmark_report["read_parquet"] = tf t0 = time() impressions = df["impressions"].to_numpy() data_subjects = DataSubjectList.from_series(df["user_id"]) tf = round(time() - t0, 4) benchmark_report["data_subject_list_creation"] = tf print(f"Time taken to create inputs for Syft Tensor: {round(tf,2)} seconds") t0 = time() tweets_data = sy.Tensor(impressions).annotate_with_dp_metadata( lower_bound=70, upper_bound=2000, data_subjects=data_subjects ) tf = round(time() - t0, 4) print(f"Time taken to make Private Syft Tensor: {round(tf,2)} seconds") benchmark_report["make_private_syft_tensor"] = tf # login to domain domain_node = sy.login(email="info@openmined.org", password="changethis", port=9082) # Upgrade admins budget content = {"user_id": 1, "budget": 9_999_999} domain_node._perform_grid_request(grid_msg=UpdateUserMessage, content=content) dataset_name = "1B Tweets dataset" t0 = time() domain_node.load_dataset( assets={"1B Tweets dataset": tweets_data}, name=dataset_name, description=" Tweets- 1B rows", ) tf = round(time() - t0, 3) print(f"Time taken to load {dataset_name} dataset: {tf} seconds") benchmark_report["load_dataset"] = tf data = domain_node.datasets[-1]["1B Tweets dataset"] print(data) sum_result = data.sum() try: t0 = time() sum_result.block tf = round(time() - t0, 3) except Exception as e: print(e) print(f"Time taken to get sum: {tf} seconds") benchmark_report["get_sum"] = tf # Sum result publish published_result = sum_result.publish(sigma=1e6) t0 = time() published_result.block tf = round(time() - t0, 3) print(f"Time taken to publish: {tf} seconds") benchmark_report["publish"] = tf print(benchmark_report) benchmark_report_json = json.dumps(benchmark_report, indent=4) print(benchmark_report_json) with open("macro_benchmark.json", "w") as outfile: outfile.write(benchmark_report_json)

apache-2.0

shangwuhencc/scikit-learn

sklearn/gaussian_process/tests/test_gaussian_process.py

265

6813

""" Testing for Gaussian Process module (sklearn.gaussian_process) """ # Author: Vincent Dubourg <vincent.dubourg@gmail.com> # Licence: BSD 3 clause from nose.tools import raises from nose.tools import assert_true import numpy as np from sklearn.gaussian_process import GaussianProcess from sklearn.gaussian_process import regression_models as regression from sklearn.gaussian_process import correlation_models as correlation from sklearn.datasets import make_regression from sklearn.utils.testing import assert_greater f = lambda x: x * np.sin(x) X = np.atleast_2d([1., 3., 5., 6., 7., 8.]).T X2 = np.atleast_2d([2., 4., 5.5, 6.5, 7.5]).T y = f(X).ravel() def test_1d(regr=regression.constant, corr=correlation.squared_exponential, random_start=10, beta0=None): # MLE estimation of a one-dimensional Gaussian Process model. # Check random start optimization. # Test the interpolating property. gp = GaussianProcess(regr=regr, corr=corr, beta0=beta0, theta0=1e-2, thetaL=1e-4, thetaU=1e-1, random_start=random_start, verbose=False).fit(X, y) y_pred, MSE = gp.predict(X, eval_MSE=True) y2_pred, MSE2 = gp.predict(X2, eval_MSE=True) assert_true(np.allclose(y_pred, y) and np.allclose(MSE, 0.) and np.allclose(MSE2, 0., atol=10)) def test_2d(regr=regression.constant, corr=correlation.squared_exponential, random_start=10, beta0=None): # MLE estimation of a two-dimensional Gaussian Process model accounting for # anisotropy. Check random start optimization. # Test the interpolating property. b, kappa, e = 5., .5, .1 g = lambda x: b - x[:, 1] - kappa * (x[:, 0] - e) ** 2. X = np.array([[-4.61611719, -6.00099547], [4.10469096, 5.32782448], [0.00000000, -0.50000000], [-6.17289014, -4.6984743], [1.3109306, -6.93271427], [-5.03823144, 3.10584743], [-2.87600388, 6.74310541], [5.21301203, 4.26386883]]) y = g(X).ravel() thetaL = [1e-4] * 2 thetaU = [1e-1] * 2 gp = GaussianProcess(regr=regr, corr=corr, beta0=beta0, theta0=[1e-2] * 2, thetaL=thetaL, thetaU=thetaU, random_start=random_start, verbose=False) gp.fit(X, y) y_pred, MSE = gp.predict(X, eval_MSE=True) assert_true(np.allclose(y_pred, y) and np.allclose(MSE, 0.)) eps = np.finfo(gp.theta_.dtype).eps assert_true(np.all(gp.theta_ >= thetaL - eps)) # Lower bounds of hyperparameters assert_true(np.all(gp.theta_ <= thetaU + eps)) # Upper bounds of hyperparameters def test_2d_2d(regr=regression.constant, corr=correlation.squared_exponential, random_start=10, beta0=None): # MLE estimation of a two-dimensional Gaussian Process model accounting for # anisotropy. Check random start optimization. # Test the GP interpolation for 2D output b, kappa, e = 5., .5, .1 g = lambda x: b - x[:, 1] - kappa * (x[:, 0] - e) ** 2. f = lambda x: np.vstack((g(x), g(x))).T X = np.array([[-4.61611719, -6.00099547], [4.10469096, 5.32782448], [0.00000000, -0.50000000], [-6.17289014, -4.6984743], [1.3109306, -6.93271427], [-5.03823144, 3.10584743], [-2.87600388, 6.74310541], [5.21301203, 4.26386883]]) y = f(X) gp = GaussianProcess(regr=regr, corr=corr, beta0=beta0, theta0=[1e-2] * 2, thetaL=[1e-4] * 2, thetaU=[1e-1] * 2, random_start=random_start, verbose=False) gp.fit(X, y) y_pred, MSE = gp.predict(X, eval_MSE=True) assert_true(np.allclose(y_pred, y) and np.allclose(MSE, 0.)) @raises(ValueError) def test_wrong_number_of_outputs(): gp = GaussianProcess() gp.fit([[1, 2, 3], [4, 5, 6]], [1, 2, 3]) def test_more_builtin_correlation_models(random_start=1): # Repeat test_1d and test_2d for several built-in correlation # models specified as strings. all_corr = ['absolute_exponential', 'squared_exponential', 'cubic', 'linear'] for corr in all_corr: test_1d(regr='constant', corr=corr, random_start=random_start) test_2d(regr='constant', corr=corr, random_start=random_start) test_2d_2d(regr='constant', corr=corr, random_start=random_start) def test_ordinary_kriging(): # Repeat test_1d and test_2d with given regression weights (beta0) for # different regression models (Ordinary Kriging). test_1d(regr='linear', beta0=[0., 0.5]) test_1d(regr='quadratic', beta0=[0., 0.5, 0.5]) test_2d(regr='linear', beta0=[0., 0.5, 0.5]) test_2d(regr='quadratic', beta0=[0., 0.5, 0.5, 0.5, 0.5, 0.5]) test_2d_2d(regr='linear', beta0=[0., 0.5, 0.5]) test_2d_2d(regr='quadratic', beta0=[0., 0.5, 0.5, 0.5, 0.5, 0.5]) def test_no_normalize(): gp = GaussianProcess(normalize=False).fit(X, y) y_pred = gp.predict(X) assert_true(np.allclose(y_pred, y)) def test_random_starts(): # Test that an increasing number of random-starts of GP fitting only # increases the reduced likelihood function of the optimal theta. n_samples, n_features = 50, 3 np.random.seed(0) rng = np.random.RandomState(0) X = rng.randn(n_samples, n_features) * 2 - 1 y = np.sin(X).sum(axis=1) + np.sin(3 * X).sum(axis=1) best_likelihood = -np.inf for random_start in range(1, 5): gp = GaussianProcess(regr="constant", corr="squared_exponential", theta0=[1e-0] * n_features, thetaL=[1e-4] * n_features, thetaU=[1e+1] * n_features, random_start=random_start, random_state=0, verbose=False).fit(X, y) rlf = gp.reduced_likelihood_function()[0] assert_greater(rlf, best_likelihood - np.finfo(np.float32).eps) best_likelihood = rlf def test_mse_solving(): # test the MSE estimate to be sane. # non-regression test for ignoring off-diagonals of feature covariance, # testing with nugget that renders covariance useless, only # using the mean function, with low effective rank of data gp = GaussianProcess(corr='absolute_exponential', theta0=1e-4, thetaL=1e-12, thetaU=1e-2, nugget=1e-2, optimizer='Welch', regr="linear", random_state=0) X, y = make_regression(n_informative=3, n_features=60, noise=50, random_state=0, effective_rank=1) gp.fit(X, y) assert_greater(1000, gp.predict(X, eval_MSE=True)[1].mean())

bsd-3-clause

lakshayg/tensorflow

tensorflow/contrib/slim/python/slim/data/dataset_data_provider.py

53

4253

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """A DataProvider that provides data from a Dataset. DatasetDataProviders provide data from datasets. The provide can be configured to use multiple readers simultaneously or read via a single reader. Additionally, the data being read can be optionally shuffled. For example, to read data using a single thread without shuffling: pascal_voc_data_provider = DatasetDataProvider( slim.datasets.pascal_voc.get_split('train'), shuffle=False) images, labels = pascal_voc_data_provider.get(['images', 'labels']) To read data using multiple readers simultaneous with shuffling: pascal_voc_data_provider = DatasetDataProvider( slim.datasets.pascal_voc.Dataset(), num_readers=10, shuffle=True) images, labels = pascal_voc_data_provider.get(['images', 'labels']) Equivalently, one may request different fields of the same sample separately: [images] = pascal_voc_data_provider.get(['images']) [labels] = pascal_voc_data_provider.get(['labels']) """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.contrib.slim.python.slim.data import data_provider from tensorflow.contrib.slim.python.slim.data import parallel_reader class DatasetDataProvider(data_provider.DataProvider): def __init__(self, dataset, num_readers=1, reader_kwargs=None, shuffle=True, num_epochs=None, common_queue_capacity=256, common_queue_min=128, record_key='record_key', seed=None, scope=None): """Creates a DatasetDataProvider. Note: if `num_epochs` is not `None`, local counter `epochs` will be created by relevant function. Use `local_variables_initializer()` to initialize local variables. Args: dataset: An instance of the Dataset class. num_readers: The number of parallel readers to use. reader_kwargs: An optional dict of kwargs for the reader. shuffle: Whether to shuffle the data sources and common queue when reading. num_epochs: The number of times each data source is read. If left as None, the data will be cycled through indefinitely. common_queue_capacity: The capacity of the common queue. common_queue_min: The minimum number of elements in the common queue after a dequeue. record_key: The item name to use for the dataset record keys in the provided tensors. seed: The seed to use if shuffling. scope: Optional name scope for the ops. Raises: ValueError: If `record_key` matches one of the items in the dataset. """ key, data = parallel_reader.parallel_read( dataset.data_sources, reader_class=dataset.reader, num_epochs=num_epochs, num_readers=num_readers, reader_kwargs=reader_kwargs, shuffle=shuffle, capacity=common_queue_capacity, min_after_dequeue=common_queue_min, seed=seed, scope=scope) items = dataset.decoder.list_items() tensors = dataset.decoder.decode(data, items) items_to_tensors = dict(zip(items, tensors)) if record_key in items_to_tensors: raise ValueError('The item name used for `record_key` cannot also be ' 'used for a dataset item: %s', record_key) items_to_tensors[record_key] = key super(DatasetDataProvider, self).__init__( items_to_tensors=items_to_tensors, num_samples=dataset.num_samples)

apache-2.0

wavycloud/pyboto3

pyboto3/glue.py

1

692979

''' The MIT License (MIT) Copyright (c) 2016 WavyCloud Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ''' def batch_create_partition(CatalogId=None, DatabaseName=None, TableName=None, PartitionInputList=None): """ Creates one or more partitions in a batch operation. See also: AWS API Documentation Exceptions :example: response = client.batch_create_partition( CatalogId='string', DatabaseName='string', TableName='string', PartitionInputList=[ { 'Values': [ 'string', ], 'LastAccessTime': datetime(2015, 1, 1), 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'Parameters': { 'string': 'string' }, 'LastAnalyzedTime': datetime(2015, 1, 1) }, ] ) :type CatalogId: string :param CatalogId: The ID of the catalog in which the partition is to be created. Currently, this should be the AWS account ID. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe name of the metadata database in which the partition is to be created.\n :type TableName: string :param TableName: [REQUIRED]\nThe name of the metadata table in which the partition is to be created.\n :type PartitionInputList: list :param PartitionInputList: [REQUIRED]\nA list of PartitionInput structures that define the partitions to be created.\n\n(dict) --The structure used to create and update a partition.\n\nValues (list) --The values of the partition. Although this parameter is not required by the SDK, you must specify this parameter for a valid input.\nThe values for the keys for the new partition must be passed as an array of String objects that must be ordered in the same order as the partition keys appearing in the Amazon S3 prefix. Otherwise AWS Glue will add the values to the wrong keys.\n\n(string) --\n\n\nLastAccessTime (datetime) --The last time at which the partition was accessed.\n\nStorageDescriptor (dict) --Provides information about the physical location where the partition is stored.\n\nColumns (list) --A list of the Columns in the table.\n\n(dict) --A column in a Table .\n\nName (string) -- [REQUIRED]The name of the Column .\n\nType (string) --The data type of the Column .\n\nComment (string) --A free-form text comment.\n\nParameters (dict) --These key-value pairs define properties associated with the column.\n\n(string) --\n(string) --\n\n\n\n\n\n\n\n\nLocation (string) --The physical location of the table. By default, this takes the form of the warehouse location, followed by the database location in the warehouse, followed by the table name.\n\nInputFormat (string) --The input format: SequenceFileInputFormat (binary), or TextInputFormat , or a custom format.\n\nOutputFormat (string) --The output format: SequenceFileOutputFormat (binary), or IgnoreKeyTextOutputFormat , or a custom format.\n\nCompressed (boolean) --\nTrue if the data in the table is compressed, or False if not.\n\nNumberOfBuckets (integer) --Must be specified if the table contains any dimension columns.\n\nSerdeInfo (dict) --The serialization/deserialization (SerDe) information.\n\nName (string) --Name of the SerDe.\n\nSerializationLibrary (string) --Usually the class that implements the SerDe. An example is org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe .\n\nParameters (dict) --These key-value pairs define initialization parameters for the SerDe.\n\n(string) --\n(string) --\n\n\n\n\n\n\nBucketColumns (list) --A list of reducer grouping columns, clustering columns, and bucketing columns in the table.\n\n(string) --\n\n\nSortColumns (list) --A list specifying the sort order of each bucket in the table.\n\n(dict) --Specifies the sort order of a sorted column.\n\nColumn (string) -- [REQUIRED]The name of the column.\n\nSortOrder (integer) -- [REQUIRED]Indicates that the column is sorted in ascending order (== 1 ), or in descending order (==0 ).\n\n\n\n\n\nParameters (dict) --The user-supplied properties in key-value form.\n\n(string) --\n(string) --\n\n\n\n\nSkewedInfo (dict) --The information about values that appear frequently in a column (skewed values).\n\nSkewedColumnNames (list) --A list of names of columns that contain skewed values.\n\n(string) --\n\n\nSkewedColumnValues (list) --A list of values that appear so frequently as to be considered skewed.\n\n(string) --\n\n\nSkewedColumnValueLocationMaps (dict) --A mapping of skewed values to the columns that contain them.\n\n(string) --\n(string) --\n\n\n\n\n\n\nStoredAsSubDirectories (boolean) --\nTrue if the table data is stored in subdirectories, or False if not.\n\n\n\nParameters (dict) --These key-value pairs define partition parameters.\n\n(string) --\n(string) --\n\n\n\n\nLastAnalyzedTime (datetime) --The last time at which column statistics were computed for this partition.\n\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'Errors': [ { 'PartitionValues': [ 'string', ], 'ErrorDetail': { 'ErrorCode': 'string', 'ErrorMessage': 'string' } }, ] } Response Structure (dict) -- Errors (list) -- The errors encountered when trying to create the requested partitions. (dict) -- Contains information about a partition error. PartitionValues (list) -- The values that define the partition. (string) -- ErrorDetail (dict) -- The details about the partition error. ErrorCode (string) -- The code associated with this error. ErrorMessage (string) -- A message describing the error. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.GlueEncryptionException :return: { 'Errors': [ { 'PartitionValues': [ 'string', ], 'ErrorDetail': { 'ErrorCode': 'string', 'ErrorMessage': 'string' } }, ] } :returns: (string) -- """ pass def batch_delete_connection(CatalogId=None, ConnectionNameList=None): """ Deletes a list of connection definitions from the Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.batch_delete_connection( CatalogId='string', ConnectionNameList=[ 'string', ] ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog in which the connections reside. If none is provided, the AWS account ID is used by default. :type ConnectionNameList: list :param ConnectionNameList: [REQUIRED]\nA list of names of the connections to delete.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax { 'Succeeded': [ 'string', ], 'Errors': { 'string': { 'ErrorCode': 'string', 'ErrorMessage': 'string' } } } Response Structure (dict) -- Succeeded (list) -- A list of names of the connection definitions that were successfully deleted. (string) -- Errors (dict) -- A map of the names of connections that were not successfully deleted to error details. (string) -- (dict) -- Contains details about an error. ErrorCode (string) -- The code associated with this error. ErrorMessage (string) -- A message describing the error. Exceptions Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'Succeeded': [ 'string', ], 'Errors': { 'string': { 'ErrorCode': 'string', 'ErrorMessage': 'string' } } } :returns: (string) -- """ pass def batch_delete_partition(CatalogId=None, DatabaseName=None, TableName=None, PartitionsToDelete=None): """ Deletes one or more partitions in a batch operation. See also: AWS API Documentation Exceptions :example: response = client.batch_delete_partition( CatalogId='string', DatabaseName='string', TableName='string', PartitionsToDelete=[ { 'Values': [ 'string', ] }, ] ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the partition to be deleted resides. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe name of the catalog database in which the table in question resides.\n :type TableName: string :param TableName: [REQUIRED]\nThe name of the table that contains the partitions to be deleted.\n :type PartitionsToDelete: list :param PartitionsToDelete: [REQUIRED]\nA list of PartitionInput structures that define the partitions to be deleted.\n\n(dict) --Contains a list of values defining partitions.\n\nValues (list) -- [REQUIRED]The list of values.\n\n(string) --\n\n\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'Errors': [ { 'PartitionValues': [ 'string', ], 'ErrorDetail': { 'ErrorCode': 'string', 'ErrorMessage': 'string' } }, ] } Response Structure (dict) -- Errors (list) -- The errors encountered when trying to delete the requested partitions. (dict) -- Contains information about a partition error. PartitionValues (list) -- The values that define the partition. (string) -- ErrorDetail (dict) -- The details about the partition error. ErrorCode (string) -- The code associated with this error. ErrorMessage (string) -- A message describing the error. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'Errors': [ { 'PartitionValues': [ 'string', ], 'ErrorDetail': { 'ErrorCode': 'string', 'ErrorMessage': 'string' } }, ] } :returns: (string) -- """ pass def batch_delete_table(CatalogId=None, DatabaseName=None, TablesToDelete=None): """ Deletes multiple tables at once. See also: AWS API Documentation Exceptions :example: response = client.batch_delete_table( CatalogId='string', DatabaseName='string', TablesToDelete=[ 'string', ] ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the table resides. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe name of the catalog database in which the tables to delete reside. For Hive compatibility, this name is entirely lowercase.\n :type TablesToDelete: list :param TablesToDelete: [REQUIRED]\nA list of the table to delete.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax { 'Errors': [ { 'TableName': 'string', 'ErrorDetail': { 'ErrorCode': 'string', 'ErrorMessage': 'string' } }, ] } Response Structure (dict) -- Errors (list) -- A list of errors encountered in attempting to delete the specified tables. (dict) -- An error record for table operations. TableName (string) -- The name of the table. For Hive compatibility, this must be entirely lowercase. ErrorDetail (dict) -- The details about the error. ErrorCode (string) -- The code associated with this error. ErrorMessage (string) -- A message describing the error. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'Errors': [ { 'TableName': 'string', 'ErrorDetail': { 'ErrorCode': 'string', 'ErrorMessage': 'string' } }, ] } :returns: Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException """ pass def batch_delete_table_version(CatalogId=None, DatabaseName=None, TableName=None, VersionIds=None): """ Deletes a specified batch of versions of a table. See also: AWS API Documentation Exceptions :example: response = client.batch_delete_table_version( CatalogId='string', DatabaseName='string', TableName='string', VersionIds=[ 'string', ] ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the tables reside. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe database in the catalog in which the table resides. For Hive compatibility, this name is entirely lowercase.\n :type TableName: string :param TableName: [REQUIRED]\nThe name of the table. For Hive compatibility, this name is entirely lowercase.\n :type VersionIds: list :param VersionIds: [REQUIRED]\nA list of the IDs of versions to be deleted. A VersionId is a string representation of an integer. Each version is incremented by 1.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax { 'Errors': [ { 'TableName': 'string', 'VersionId': 'string', 'ErrorDetail': { 'ErrorCode': 'string', 'ErrorMessage': 'string' } }, ] } Response Structure (dict) -- Errors (list) -- A list of errors encountered while trying to delete the specified table versions. (dict) -- An error record for table-version operations. TableName (string) -- The name of the table in question. VersionId (string) -- The ID value of the version in question. A VersionID is a string representation of an integer. Each version is incremented by 1. ErrorDetail (dict) -- The details about the error. ErrorCode (string) -- The code associated with this error. ErrorMessage (string) -- A message describing the error. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'Errors': [ { 'TableName': 'string', 'VersionId': 'string', 'ErrorDetail': { 'ErrorCode': 'string', 'ErrorMessage': 'string' } }, ] } :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException """ pass def batch_get_crawlers(CrawlerNames=None): """ Returns a list of resource metadata for a given list of crawler names. After calling the ListCrawlers operation, you can call this operation to access the data to which you have been granted permissions. This operation supports all IAM permissions, including permission conditions that uses tags. See also: AWS API Documentation Exceptions :example: response = client.batch_get_crawlers( CrawlerNames=[ 'string', ] ) :type CrawlerNames: list :param CrawlerNames: [REQUIRED]\nA list of crawler names, which might be the names returned from the ListCrawlers operation.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax{ 'Crawlers': [ { 'Name': 'string', 'Role': 'string', 'Targets': { 'S3Targets': [ { 'Path': 'string', 'Exclusions': [ 'string', ] }, ], 'JdbcTargets': [ { 'ConnectionName': 'string', 'Path': 'string', 'Exclusions': [ 'string', ] }, ], 'DynamoDBTargets': [ { 'Path': 'string' }, ], 'CatalogTargets': [ { 'DatabaseName': 'string', 'Tables': [ 'string', ] }, ] }, 'DatabaseName': 'string', 'Description': 'string', 'Classifiers': [ 'string', ], 'SchemaChangePolicy': { 'UpdateBehavior': 'LOG'|'UPDATE_IN_DATABASE', 'DeleteBehavior': 'LOG'|'DELETE_FROM_DATABASE'|'DEPRECATE_IN_DATABASE' }, 'State': 'READY'|'RUNNING'|'STOPPING', 'TablePrefix': 'string', 'Schedule': { 'ScheduleExpression': 'string', 'State': 'SCHEDULED'|'NOT_SCHEDULED'|'TRANSITIONING' }, 'CrawlElapsedTime': 123, 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'LastCrawl': { 'Status': 'SUCCEEDED'|'CANCELLED'|'FAILED', 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string', 'MessagePrefix': 'string', 'StartTime': datetime(2015, 1, 1) }, 'Version': 123, 'Configuration': 'string', 'CrawlerSecurityConfiguration': 'string' }, ], 'CrawlersNotFound': [ 'string', ] } Response Structure (dict) -- Crawlers (list) --A list of crawler definitions. (dict) --Specifies a crawler program that examines a data source and uses classifiers to try to determine its schema. If successful, the crawler records metadata concerning the data source in the AWS Glue Data Catalog. Name (string) --The name of the crawler. Role (string) --The Amazon Resource Name (ARN) of an IAM role that\'s used to access customer resources, such as Amazon Simple Storage Service (Amazon S3) data. Targets (dict) --A collection of targets to crawl. S3Targets (list) --Specifies Amazon Simple Storage Service (Amazon S3) targets. (dict) --Specifies a data store in Amazon Simple Storage Service (Amazon S3). Path (string) --The path to the Amazon S3 target. Exclusions (list) --A list of glob patterns used to exclude from the crawl. For more information, see Catalog Tables with a Crawler . (string) -- JdbcTargets (list) --Specifies JDBC targets. (dict) --Specifies a JDBC data store to crawl. ConnectionName (string) --The name of the connection to use to connect to the JDBC target. Path (string) --The path of the JDBC target. Exclusions (list) --A list of glob patterns used to exclude from the crawl. For more information, see Catalog Tables with a Crawler . (string) -- DynamoDBTargets (list) --Specifies Amazon DynamoDB targets. (dict) --Specifies an Amazon DynamoDB table to crawl. Path (string) --The name of the DynamoDB table to crawl. CatalogTargets (list) --Specifies AWS Glue Data Catalog targets. (dict) --Specifies an AWS Glue Data Catalog target. DatabaseName (string) --The name of the database to be synchronized. Tables (list) --A list of the tables to be synchronized. (string) -- DatabaseName (string) --The name of the database in which the crawler\'s output is stored. Description (string) --A description of the crawler. Classifiers (list) --A list of UTF-8 strings that specify the custom classifiers that are associated with the crawler. (string) -- SchemaChangePolicy (dict) --The policy that specifies update and delete behaviors for the crawler. UpdateBehavior (string) --The update behavior when the crawler finds a changed schema. DeleteBehavior (string) --The deletion behavior when the crawler finds a deleted object. State (string) --Indicates whether the crawler is running, or whether a run is pending. TablePrefix (string) --The prefix added to the names of tables that are created. Schedule (dict) --For scheduled crawlers, the schedule when the crawler runs. ScheduleExpression (string) --A cron expression used to specify the schedule. For more information, see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, specify cron(15 12 * * ? *) . State (string) --The state of the schedule. CrawlElapsedTime (integer) --If the crawler is running, contains the total time elapsed since the last crawl began. CreationTime (datetime) --The time that the crawler was created. LastUpdated (datetime) --The time that the crawler was last updated. LastCrawl (dict) --The status of the last crawl, and potentially error information if an error occurred. Status (string) --Status of the last crawl. ErrorMessage (string) --If an error occurred, the error information about the last crawl. LogGroup (string) --The log group for the last crawl. LogStream (string) --The log stream for the last crawl. MessagePrefix (string) --The prefix for a message about this crawl. StartTime (datetime) --The time at which the crawl started. Version (integer) --The version of the crawler. Configuration (string) --Crawler configuration information. This versioned JSON string allows users to specify aspects of a crawler\'s behavior. For more information, see Configuring a Crawler . CrawlerSecurityConfiguration (string) --The name of the SecurityConfiguration structure to be used by this crawler. CrawlersNotFound (list) --A list of names of crawlers that were not found. (string) -- Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException :return: { 'Crawlers': [ { 'Name': 'string', 'Role': 'string', 'Targets': { 'S3Targets': [ { 'Path': 'string', 'Exclusions': [ 'string', ] }, ], 'JdbcTargets': [ { 'ConnectionName': 'string', 'Path': 'string', 'Exclusions': [ 'string', ] }, ], 'DynamoDBTargets': [ { 'Path': 'string' }, ], 'CatalogTargets': [ { 'DatabaseName': 'string', 'Tables': [ 'string', ] }, ] }, 'DatabaseName': 'string', 'Description': 'string', 'Classifiers': [ 'string', ], 'SchemaChangePolicy': { 'UpdateBehavior': 'LOG'|'UPDATE_IN_DATABASE', 'DeleteBehavior': 'LOG'|'DELETE_FROM_DATABASE'|'DEPRECATE_IN_DATABASE' }, 'State': 'READY'|'RUNNING'|'STOPPING', 'TablePrefix': 'string', 'Schedule': { 'ScheduleExpression': 'string', 'State': 'SCHEDULED'|'NOT_SCHEDULED'|'TRANSITIONING' }, 'CrawlElapsedTime': 123, 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'LastCrawl': { 'Status': 'SUCCEEDED'|'CANCELLED'|'FAILED', 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string', 'MessagePrefix': 'string', 'StartTime': datetime(2015, 1, 1) }, 'Version': 123, 'Configuration': 'string', 'CrawlerSecurityConfiguration': 'string' }, ], 'CrawlersNotFound': [ 'string', ] } :returns: (string) -- """ pass def batch_get_dev_endpoints(DevEndpointNames=None): """ Returns a list of resource metadata for a given list of development endpoint names. After calling the ListDevEndpoints operation, you can call this operation to access the data to which you have been granted permissions. This operation supports all IAM permissions, including permission conditions that uses tags. See also: AWS API Documentation Exceptions :example: response = client.batch_get_dev_endpoints( DevEndpointNames=[ 'string', ] ) :type DevEndpointNames: list :param DevEndpointNames: [REQUIRED]\nThe list of DevEndpoint names, which might be the names returned from the ListDevEndpoint operation.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax{ 'DevEndpoints': [ { 'EndpointName': 'string', 'RoleArn': 'string', 'SecurityGroupIds': [ 'string', ], 'SubnetId': 'string', 'YarnEndpointAddress': 'string', 'PrivateAddress': 'string', 'ZeppelinRemoteSparkInterpreterPort': 123, 'PublicAddress': 'string', 'Status': 'string', 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'GlueVersion': 'string', 'NumberOfWorkers': 123, 'NumberOfNodes': 123, 'AvailabilityZone': 'string', 'VpcId': 'string', 'ExtraPythonLibsS3Path': 'string', 'ExtraJarsS3Path': 'string', 'FailureReason': 'string', 'LastUpdateStatus': 'string', 'CreatedTimestamp': datetime(2015, 1, 1), 'LastModifiedTimestamp': datetime(2015, 1, 1), 'PublicKey': 'string', 'PublicKeys': [ 'string', ], 'SecurityConfiguration': 'string', 'Arguments': { 'string': 'string' } }, ], 'DevEndpointsNotFound': [ 'string', ] } Response Structure (dict) -- DevEndpoints (list) --A list of DevEndpoint definitions. (dict) --A development endpoint where a developer can remotely debug extract, transform, and load (ETL) scripts. EndpointName (string) --The name of the DevEndpoint . RoleArn (string) --The Amazon Resource Name (ARN) of the IAM role used in this DevEndpoint . SecurityGroupIds (list) --A list of security group identifiers used in this DevEndpoint . (string) -- SubnetId (string) --The subnet ID for this DevEndpoint . YarnEndpointAddress (string) --The YARN endpoint address used by this DevEndpoint . PrivateAddress (string) --A private IP address to access the DevEndpoint within a VPC if the DevEndpoint is created within one. The PrivateAddress field is present only when you create the DevEndpoint within your VPC. ZeppelinRemoteSparkInterpreterPort (integer) --The Apache Zeppelin port for the remote Apache Spark interpreter. PublicAddress (string) --The public IP address used by this DevEndpoint . The PublicAddress field is present only when you create a non-virtual private cloud (VPC) DevEndpoint . Status (string) --The current status of this DevEndpoint . WorkerType (string) --The type of predefined worker that is allocated to the development endpoint. Accepts a value of Standard, G.1X, or G.2X. For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. For the G.1X worker type, each worker maps to 1 DPU (4 vCPU, 16 GB of memory, 64 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs. For the G.2X worker type, each worker maps to 2 DPU (8 vCPU, 32 GB of memory, 128 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs. Known issue: when a development endpoint is created with the G.2X WorkerType configuration, the Spark drivers for the development endpoint will run on 4 vCPU, 16 GB of memory, and a 64 GB disk. GlueVersion (string) --Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for running your ETL scripts on development endpoints. For more information about the available AWS Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide. Development endpoints that are created without specifying a Glue version default to Glue 0.9. You can specify a version of Python support for development endpoints by using the Arguments parameter in the CreateDevEndpoint or UpdateDevEndpoint APIs. If no arguments are provided, the version defaults to Python 2. NumberOfWorkers (integer) --The number of workers of a defined workerType that are allocated to the development endpoint. The maximum number of workers you can define are 299 for G.1X , and 149 for G.2X . NumberOfNodes (integer) --The number of AWS Glue Data Processing Units (DPUs) allocated to this DevEndpoint . AvailabilityZone (string) --The AWS Availability Zone where this DevEndpoint is located. VpcId (string) --The ID of the virtual private cloud (VPC) used by this DevEndpoint . ExtraPythonLibsS3Path (string) --The paths to one or more Python libraries in an Amazon S3 bucket that should be loaded in your DevEndpoint . Multiple values must be complete paths separated by a comma. Note You can only use pure Python libraries with a DevEndpoint . Libraries that rely on C extensions, such as the pandas Python data analysis library, are not currently supported. ExtraJarsS3Path (string) --The path to one or more Java .jar files in an S3 bucket that should be loaded in your DevEndpoint . Note You can only use pure Java/Scala libraries with a DevEndpoint . FailureReason (string) --The reason for a current failure in this DevEndpoint . LastUpdateStatus (string) --The status of the last update. CreatedTimestamp (datetime) --The point in time at which this DevEndpoint was created. LastModifiedTimestamp (datetime) --The point in time at which this DevEndpoint was last modified. PublicKey (string) --The public key to be used by this DevEndpoint for authentication. This attribute is provided for backward compatibility because the recommended attribute to use is public keys. PublicKeys (list) --A list of public keys to be used by the DevEndpoints for authentication. Using this attribute is preferred over a single public key because the public keys allow you to have a different private key per client. Note If you previously created an endpoint with a public key, you must remove that key to be able to set a list of public keys. Call the UpdateDevEndpoint API operation with the public key content in the deletePublicKeys attribute, and the list of new keys in the addPublicKeys attribute. (string) -- SecurityConfiguration (string) --The name of the SecurityConfiguration structure to be used with this DevEndpoint . Arguments (dict) --A map of arguments used to configure the DevEndpoint . Valid arguments are: "--enable-glue-datacatalog": "" "GLUE_PYTHON_VERSION": "3" "GLUE_PYTHON_VERSION": "2" You can specify a version of Python support for development endpoints by using the Arguments parameter in the CreateDevEndpoint or UpdateDevEndpoint APIs. If no arguments are provided, the version defaults to Python 2. (string) -- (string) -- DevEndpointsNotFound (list) --A list of DevEndpoints not found. (string) -- Exceptions Glue.Client.exceptions.AccessDeniedException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException :return: { 'DevEndpoints': [ { 'EndpointName': 'string', 'RoleArn': 'string', 'SecurityGroupIds': [ 'string', ], 'SubnetId': 'string', 'YarnEndpointAddress': 'string', 'PrivateAddress': 'string', 'ZeppelinRemoteSparkInterpreterPort': 123, 'PublicAddress': 'string', 'Status': 'string', 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'GlueVersion': 'string', 'NumberOfWorkers': 123, 'NumberOfNodes': 123, 'AvailabilityZone': 'string', 'VpcId': 'string', 'ExtraPythonLibsS3Path': 'string', 'ExtraJarsS3Path': 'string', 'FailureReason': 'string', 'LastUpdateStatus': 'string', 'CreatedTimestamp': datetime(2015, 1, 1), 'LastModifiedTimestamp': datetime(2015, 1, 1), 'PublicKey': 'string', 'PublicKeys': [ 'string', ], 'SecurityConfiguration': 'string', 'Arguments': { 'string': 'string' } }, ], 'DevEndpointsNotFound': [ 'string', ] } :returns: (string) -- """ pass def batch_get_jobs(JobNames=None): """ Returns a list of resource metadata for a given list of job names. After calling the ListJobs operation, you can call this operation to access the data to which you have been granted permissions. This operation supports all IAM permissions, including permission conditions that uses tags. See also: AWS API Documentation Exceptions :example: response = client.batch_get_jobs( JobNames=[ 'string', ] ) :type JobNames: list :param JobNames: [REQUIRED]\nA list of job names, which might be the names returned from the ListJobs operation.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax{ 'Jobs': [ { 'Name': 'string', 'Description': 'string', 'LogUri': 'string', 'Role': 'string', 'CreatedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'ExecutionProperty': { 'MaxConcurrentRuns': 123 }, 'Command': { 'Name': 'string', 'ScriptLocation': 'string', 'PythonVersion': 'string' }, 'DefaultArguments': { 'string': 'string' }, 'NonOverridableArguments': { 'string': 'string' }, 'Connections': { 'Connections': [ 'string', ] }, 'MaxRetries': 123, 'AllocatedCapacity': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ], 'JobsNotFound': [ 'string', ] } Response Structure (dict) -- Jobs (list) --A list of job definitions. (dict) --Specifies a job definition. Name (string) --The name you assign to this job definition. Description (string) --A description of the job. LogUri (string) --This field is reserved for future use. Role (string) --The name or Amazon Resource Name (ARN) of the IAM role associated with this job. CreatedOn (datetime) --The time and date that this job definition was created. LastModifiedOn (datetime) --The last point in time when this job definition was modified. ExecutionProperty (dict) --An ExecutionProperty specifying the maximum number of concurrent runs allowed for this job. MaxConcurrentRuns (integer) --The maximum number of concurrent runs allowed for the job. The default is 1. An error is returned when this threshold is reached. The maximum value you can specify is controlled by a service limit. Command (dict) --The JobCommand that executes this job. Name (string) --The name of the job command. For an Apache Spark ETL job, this must be glueetl . For a Python shell job, it must be pythonshell . ScriptLocation (string) --Specifies the Amazon Simple Storage Service (Amazon S3) path to a script that executes a job. PythonVersion (string) --The Python version being used to execute a Python shell job. Allowed values are 2 or 3. DefaultArguments (dict) --The default arguments for this job, specified as name-value pairs. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- NonOverridableArguments (dict) --Non-overridable arguments for this job, specified as name-value pairs. (string) -- (string) -- Connections (dict) --The connections used for this job. Connections (list) --A list of connections used by the job. (string) -- MaxRetries (integer) --The maximum number of times to retry this job after a JobRun fails. AllocatedCapacity (integer) --This field is deprecated. Use MaxCapacity instead. The number of AWS Glue data processing units (DPUs) allocated to runs of this job. You can allocate from 2 to 100 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . Timeout (integer) --The job timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). MaxCapacity (float) --The number of AWS Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . Do not set Max Capacity if using WorkerType and NumberOfWorkers . The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job or an Apache Spark ETL job: When you specify a Python shell job (JobCommand.Name ="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU. When you specify an Apache Spark ETL job (JobCommand.Name ="glueetl"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation. WorkerType (string) --The type of predefined worker that is allocated when a job runs. Accepts a value of Standard, G.1X, or G.2X. For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. For the G.1X worker type, each worker maps to 1 DPU (4 vCPU, 16 GB of memory, 64 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs. For the G.2X worker type, each worker maps to 2 DPU (8 vCPU, 32 GB of memory, 128 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs. NumberOfWorkers (integer) --The number of workers of a defined workerType that are allocated when a job runs. The maximum number of workers you can define are 299 for G.1X , and 149 for G.2X . SecurityConfiguration (string) --The name of the SecurityConfiguration structure to be used with this job. NotificationProperty (dict) --Specifies configuration properties of a job notification. NotifyDelayAfter (integer) --After a job run starts, the number of minutes to wait before sending a job run delay notification. GlueVersion (string) --Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for jobs of type Spark. For more information about the available AWS Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide. Jobs that are created without specifying a Glue version default to Glue 0.9. JobsNotFound (list) --A list of names of jobs not found. (string) -- Exceptions Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException :return: { 'Jobs': [ { 'Name': 'string', 'Description': 'string', 'LogUri': 'string', 'Role': 'string', 'CreatedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'ExecutionProperty': { 'MaxConcurrentRuns': 123 }, 'Command': { 'Name': 'string', 'ScriptLocation': 'string', 'PythonVersion': 'string' }, 'DefaultArguments': { 'string': 'string' }, 'NonOverridableArguments': { 'string': 'string' }, 'Connections': { 'Connections': [ 'string', ] }, 'MaxRetries': 123, 'AllocatedCapacity': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ], 'JobsNotFound': [ 'string', ] } :returns: (string) -- (string) -- """ pass def batch_get_partition(CatalogId=None, DatabaseName=None, TableName=None, PartitionsToGet=None): """ Retrieves partitions in a batch request. See also: AWS API Documentation Exceptions :example: response = client.batch_get_partition( CatalogId='string', DatabaseName='string', TableName='string', PartitionsToGet=[ { 'Values': [ 'string', ] }, ] ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the partitions in question reside. If none is supplied, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe name of the catalog database where the partitions reside.\n :type TableName: string :param TableName: [REQUIRED]\nThe name of the partitions\' table.\n :type PartitionsToGet: list :param PartitionsToGet: [REQUIRED]\nA list of partition values identifying the partitions to retrieve.\n\n(dict) --Contains a list of values defining partitions.\n\nValues (list) -- [REQUIRED]The list of values.\n\n(string) --\n\n\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'Partitions': [ { 'Values': [ 'string', ], 'DatabaseName': 'string', 'TableName': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastAccessTime': datetime(2015, 1, 1), 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'Parameters': { 'string': 'string' }, 'LastAnalyzedTime': datetime(2015, 1, 1) }, ], 'UnprocessedKeys': [ { 'Values': [ 'string', ] }, ] } Response Structure (dict) -- Partitions (list) -- A list of the requested partitions. (dict) -- Represents a slice of table data. Values (list) -- The values of the partition. (string) -- DatabaseName (string) -- The name of the catalog database in which to create the partition. TableName (string) -- The name of the database table in which to create the partition. CreationTime (datetime) -- The time at which the partition was created. LastAccessTime (datetime) -- The last time at which the partition was accessed. StorageDescriptor (dict) -- Provides information about the physical location where the partition is stored. Columns (list) -- A list of the Columns in the table. (dict) -- A column in a Table . Name (string) -- The name of the Column . Type (string) -- The data type of the Column . Comment (string) -- A free-form text comment. Parameters (dict) -- These key-value pairs define properties associated with the column. (string) -- (string) -- Location (string) -- The physical location of the table. By default, this takes the form of the warehouse location, followed by the database location in the warehouse, followed by the table name. InputFormat (string) -- The input format: SequenceFileInputFormat (binary), or TextInputFormat , or a custom format. OutputFormat (string) -- The output format: SequenceFileOutputFormat (binary), or IgnoreKeyTextOutputFormat , or a custom format. Compressed (boolean) -- True if the data in the table is compressed, or False if not. NumberOfBuckets (integer) -- Must be specified if the table contains any dimension columns. SerdeInfo (dict) -- The serialization/deserialization (SerDe) information. Name (string) -- Name of the SerDe. SerializationLibrary (string) -- Usually the class that implements the SerDe. An example is org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe . Parameters (dict) -- These key-value pairs define initialization parameters for the SerDe. (string) -- (string) -- BucketColumns (list) -- A list of reducer grouping columns, clustering columns, and bucketing columns in the table. (string) -- SortColumns (list) -- A list specifying the sort order of each bucket in the table. (dict) -- Specifies the sort order of a sorted column. Column (string) -- The name of the column. SortOrder (integer) -- Indicates that the column is sorted in ascending order (== 1 ), or in descending order (==0 ). Parameters (dict) -- The user-supplied properties in key-value form. (string) -- (string) -- SkewedInfo (dict) -- The information about values that appear frequently in a column (skewed values). SkewedColumnNames (list) -- A list of names of columns that contain skewed values. (string) -- SkewedColumnValues (list) -- A list of values that appear so frequently as to be considered skewed. (string) -- SkewedColumnValueLocationMaps (dict) -- A mapping of skewed values to the columns that contain them. (string) -- (string) -- StoredAsSubDirectories (boolean) -- True if the table data is stored in subdirectories, or False if not. Parameters (dict) -- These key-value pairs define partition parameters. (string) -- (string) -- LastAnalyzedTime (datetime) -- The last time at which column statistics were computed for this partition. UnprocessedKeys (list) -- A list of the partition values in the request for which partitions were not returned. (dict) -- Contains a list of values defining partitions. Values (list) -- The list of values. (string) -- Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.GlueEncryptionException :return: { 'Partitions': [ { 'Values': [ 'string', ], 'DatabaseName': 'string', 'TableName': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastAccessTime': datetime(2015, 1, 1), 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'Parameters': { 'string': 'string' }, 'LastAnalyzedTime': datetime(2015, 1, 1) }, ], 'UnprocessedKeys': [ { 'Values': [ 'string', ] }, ] } :returns: (string) -- """ pass def batch_get_triggers(TriggerNames=None): """ Returns a list of resource metadata for a given list of trigger names. After calling the ListTriggers operation, you can call this operation to access the data to which you have been granted permissions. This operation supports all IAM permissions, including permission conditions that uses tags. See also: AWS API Documentation Exceptions :example: response = client.batch_get_triggers( TriggerNames=[ 'string', ] ) :type TriggerNames: list :param TriggerNames: [REQUIRED]\nA list of trigger names, which may be the names returned from the ListTriggers operation.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax{ 'Triggers': [ { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } }, ], 'TriggersNotFound': [ 'string', ] } Response Structure (dict) -- Triggers (list) --A list of trigger definitions. (dict) --Information about a specific trigger. Name (string) --The name of the trigger. WorkflowName (string) --The name of the workflow associated with the trigger. Id (string) --Reserved for future use. Type (string) --The type of trigger that this is. State (string) --The current state of the trigger. Description (string) --A description of this trigger. Schedule (string) --A cron expression used to specify the schedule (see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, you would specify: cron(15 12 * * ? *) . Actions (list) --The actions initiated by this trigger. (dict) --Defines an action to be initiated by a trigger. JobName (string) --The name of a job to be executed. Arguments (dict) --The job arguments used when this trigger fires. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- Timeout (integer) --The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. SecurityConfiguration (string) --The name of the SecurityConfiguration structure to be used with this action. NotificationProperty (dict) --Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) --After a job run starts, the number of minutes to wait before sending a job run delay notification. CrawlerName (string) --The name of the crawler to be used with this action. Predicate (dict) --The predicate of this trigger, which defines when it will fire. Logical (string) --An optional field if only one condition is listed. If multiple conditions are listed, then this field is required. Conditions (list) --A list of the conditions that determine when the trigger will fire. (dict) --Defines a condition under which a trigger fires. LogicalOperator (string) --A logical operator. JobName (string) --The name of the job whose JobRuns this condition applies to, and on which this trigger waits. State (string) --The condition state. Currently, the only job states that a trigger can listen for are SUCCEEDED , STOPPED , FAILED , and TIMEOUT . The only crawler states that a trigger can listen for are SUCCEEDED , FAILED , and CANCELLED . CrawlerName (string) --The name of the crawler to which this condition applies. CrawlState (string) --The state of the crawler to which this condition applies. TriggersNotFound (list) --A list of names of triggers not found. (string) -- Exceptions Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException :return: { 'Triggers': [ { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } }, ], 'TriggersNotFound': [ 'string', ] } :returns: (string) -- (string) -- """ pass def batch_get_workflows(Names=None, IncludeGraph=None): """ Returns a list of resource metadata for a given list of workflow names. After calling the ListWorkflows operation, you can call this operation to access the data to which you have been granted permissions. This operation supports all IAM permissions, including permission conditions that uses tags. See also: AWS API Documentation Exceptions :example: response = client.batch_get_workflows( Names=[ 'string', ], IncludeGraph=True|False ) :type Names: list :param Names: [REQUIRED]\nA list of workflow names, which may be the names returned from the ListWorkflows operation.\n\n(string) --\n\n :type IncludeGraph: boolean :param IncludeGraph: Specifies whether to include a graph when returning the workflow resource metadata. :rtype: dict ReturnsResponse Syntax { 'Workflows': [ { 'Name': 'string', 'Description': 'string', 'DefaultRunProperties': { 'string': 'string' }, 'CreatedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'LastRun': { 'Name': 'string', 'WorkflowRunId': 'string', 'WorkflowRunProperties': { 'string': 'string' }, 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'Status': 'RUNNING'|'COMPLETED'|'STOPPING'|'STOPPED', 'Statistics': { 'TotalActions': 123, 'TimeoutActions': 123, 'FailedActions': 123, 'StoppedActions': 123, 'SucceededActions': 123, 'RunningActions': 123 }, 'Graph': { 'Nodes': [ { 'Type': 'CRAWLER'|'JOB'|'TRIGGER', 'Name': 'string', 'UniqueId': 'string', 'TriggerDetails': { 'Trigger': { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } } }, 'JobDetails': { 'JobRuns': [ { 'Id': 'string', 'Attempt': 123, 'PreviousRunId': 'string', 'TriggerName': 'string', 'JobName': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'JobRunState': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'Arguments': { 'string': 'string' }, 'ErrorMessage': 'string', 'PredecessorRuns': [ { 'JobName': 'string', 'RunId': 'string' }, ], 'AllocatedCapacity': 123, 'ExecutionTime': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'LogGroupName': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ] }, 'CrawlerDetails': { 'Crawls': [ { 'State': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED', 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string' }, ] } }, ], 'Edges': [ { 'SourceId': 'string', 'DestinationId': 'string' }, ] } }, 'Graph': { 'Nodes': [ { 'Type': 'CRAWLER'|'JOB'|'TRIGGER', 'Name': 'string', 'UniqueId': 'string', 'TriggerDetails': { 'Trigger': { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } } }, 'JobDetails': { 'JobRuns': [ { 'Id': 'string', 'Attempt': 123, 'PreviousRunId': 'string', 'TriggerName': 'string', 'JobName': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'JobRunState': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'Arguments': { 'string': 'string' }, 'ErrorMessage': 'string', 'PredecessorRuns': [ { 'JobName': 'string', 'RunId': 'string' }, ], 'AllocatedCapacity': 123, 'ExecutionTime': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'LogGroupName': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ] }, 'CrawlerDetails': { 'Crawls': [ { 'State': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED', 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string' }, ] } }, ], 'Edges': [ { 'SourceId': 'string', 'DestinationId': 'string' }, ] } }, ], 'MissingWorkflows': [ 'string', ] } Response Structure (dict) -- Workflows (list) -- A list of workflow resource metadata. (dict) -- A workflow represents a flow in which AWS Glue components should be executed to complete a logical task. Name (string) -- The name of the workflow representing the flow. Description (string) -- A description of the workflow. DefaultRunProperties (dict) -- A collection of properties to be used as part of each execution of the workflow. (string) -- (string) -- CreatedOn (datetime) -- The date and time when the workflow was created. LastModifiedOn (datetime) -- The date and time when the workflow was last modified. LastRun (dict) -- The information about the last execution of the workflow. Name (string) -- Name of the workflow which was executed. WorkflowRunId (string) -- The ID of this workflow run. WorkflowRunProperties (dict) -- The workflow run properties which were set during the run. (string) -- (string) -- StartedOn (datetime) -- The date and time when the workflow run was started. CompletedOn (datetime) -- The date and time when the workflow run completed. Status (string) -- The status of the workflow run. Statistics (dict) -- The statistics of the run. TotalActions (integer) -- Total number of Actions in the workflow run. TimeoutActions (integer) -- Total number of Actions which timed out. FailedActions (integer) -- Total number of Actions which have failed. StoppedActions (integer) -- Total number of Actions which have stopped. SucceededActions (integer) -- Total number of Actions which have succeeded. RunningActions (integer) -- Total number Actions in running state. Graph (dict) -- The graph representing all the AWS Glue components that belong to the workflow as nodes and directed connections between them as edges. Nodes (list) -- A list of the the AWS Glue components belong to the workflow represented as nodes. (dict) -- A node represents an AWS Glue component like Trigger, Job etc. which is part of a workflow. Type (string) -- The type of AWS Glue component represented by the node. Name (string) -- The name of the AWS Glue component represented by the node. UniqueId (string) -- The unique Id assigned to the node within the workflow. TriggerDetails (dict) -- Details of the Trigger when the node represents a Trigger. Trigger (dict) -- The information of the trigger represented by the trigger node. Name (string) -- The name of the trigger. WorkflowName (string) -- The name of the workflow associated with the trigger. Id (string) -- Reserved for future use. Type (string) -- The type of trigger that this is. State (string) -- The current state of the trigger. Description (string) -- A description of this trigger. Schedule (string) -- A cron expression used to specify the schedule (see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, you would specify: cron(15 12 * * ? *) . Actions (list) -- The actions initiated by this trigger. (dict) -- Defines an action to be initiated by a trigger. JobName (string) -- The name of a job to be executed. Arguments (dict) -- The job arguments used when this trigger fires. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- Timeout (integer) -- The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this action. NotificationProperty (dict) -- Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) -- After a job run starts, the number of minutes to wait before sending a job run delay notification. CrawlerName (string) -- The name of the crawler to be used with this action. Predicate (dict) -- The predicate of this trigger, which defines when it will fire. Logical (string) -- An optional field if only one condition is listed. If multiple conditions are listed, then this field is required. Conditions (list) -- A list of the conditions that determine when the trigger will fire. (dict) -- Defines a condition under which a trigger fires. LogicalOperator (string) -- A logical operator. JobName (string) -- The name of the job whose JobRuns this condition applies to, and on which this trigger waits. State (string) -- The condition state. Currently, the only job states that a trigger can listen for are SUCCEEDED , STOPPED , FAILED , and TIMEOUT . The only crawler states that a trigger can listen for are SUCCEEDED , FAILED , and CANCELLED . CrawlerName (string) -- The name of the crawler to which this condition applies. CrawlState (string) -- The state of the crawler to which this condition applies. JobDetails (dict) -- Details of the Job when the node represents a Job. JobRuns (list) -- The information for the job runs represented by the job node. (dict) -- Contains information about a job run. Id (string) -- The ID of this job run. Attempt (integer) -- The number of the attempt to run this job. PreviousRunId (string) -- The ID of the previous run of this job. For example, the JobRunId specified in the StartJobRun action. TriggerName (string) -- The name of the trigger that started this job run. JobName (string) -- The name of the job definition being used in this run. StartedOn (datetime) -- The date and time at which this job run was started. LastModifiedOn (datetime) -- The last time that this job run was modified. CompletedOn (datetime) -- The date and time that this job run completed. JobRunState (string) -- The current state of the job run. For more information about the statuses of jobs that have terminated abnormally, see AWS Glue Job Run Statuses . Arguments (dict) -- The job arguments associated with this run. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- ErrorMessage (string) -- An error message associated with this job run. PredecessorRuns (list) -- A list of predecessors to this job run. (dict) -- A job run that was used in the predicate of a conditional trigger that triggered this job run. JobName (string) -- The name of the job definition used by the predecessor job run. RunId (string) -- The job-run ID of the predecessor job run. AllocatedCapacity (integer) -- This field is deprecated. Use MaxCapacity instead. The number of AWS Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be allocated; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . ExecutionTime (integer) -- The amount of time (in seconds) that the job run consumed resources. Timeout (integer) -- The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. MaxCapacity (float) -- The number of AWS Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . Do not set Max Capacity if using WorkerType and NumberOfWorkers . The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job or an Apache Spark ETL job: When you specify a Python shell job (JobCommand.Name ="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU. When you specify an Apache Spark ETL job (JobCommand.Name ="glueetl"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation. WorkerType (string) -- The type of predefined worker that is allocated when a job runs. Accepts a value of Standard, G.1X, or G.2X. For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. For the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker. For the G.2X worker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker. NumberOfWorkers (integer) -- The number of workers of a defined workerType that are allocated when a job runs. The maximum number of workers you can define are 299 for G.1X , and 149 for G.2X . SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this job run. LogGroupName (string) -- The name of the log group for secure logging that can be server-side encrypted in Amazon CloudWatch using AWS KMS. This name can be /aws-glue/jobs/ , in which case the default encryption is NONE . If you add a role name and SecurityConfiguration name (in other words, /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/ ), then that security configuration is used to encrypt the log group. NotificationProperty (dict) -- Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) -- After a job run starts, the number of minutes to wait before sending a job run delay notification. GlueVersion (string) -- Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for jobs of type Spark. For more information about the available AWS Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide. Jobs that are created without specifying a Glue version default to Glue 0.9. CrawlerDetails (dict) -- Details of the crawler when the node represents a crawler. Crawls (list) -- A list of crawls represented by the crawl node. (dict) -- The details of a crawl in the workflow. State (string) -- The state of the crawler. StartedOn (datetime) -- The date and time on which the crawl started. CompletedOn (datetime) -- The date and time on which the crawl completed. ErrorMessage (string) -- The error message associated with the crawl. LogGroup (string) -- The log group associated with the crawl. LogStream (string) -- The log stream associated with the crawl. Edges (list) -- A list of all the directed connections between the nodes belonging to the workflow. (dict) -- An edge represents a directed connection between two AWS Glue components which are part of the workflow the edge belongs to. SourceId (string) -- The unique of the node within the workflow where the edge starts. DestinationId (string) -- The unique of the node within the workflow where the edge ends. Graph (dict) -- The graph representing all the AWS Glue components that belong to the workflow as nodes and directed connections between them as edges. Nodes (list) -- A list of the the AWS Glue components belong to the workflow represented as nodes. (dict) -- A node represents an AWS Glue component like Trigger, Job etc. which is part of a workflow. Type (string) -- The type of AWS Glue component represented by the node. Name (string) -- The name of the AWS Glue component represented by the node. UniqueId (string) -- The unique Id assigned to the node within the workflow. TriggerDetails (dict) -- Details of the Trigger when the node represents a Trigger. Trigger (dict) -- The information of the trigger represented by the trigger node. Name (string) -- The name of the trigger. WorkflowName (string) -- The name of the workflow associated with the trigger. Id (string) -- Reserved for future use. Type (string) -- The type of trigger that this is. State (string) -- The current state of the trigger. Description (string) -- A description of this trigger. Schedule (string) -- A cron expression used to specify the schedule (see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, you would specify: cron(15 12 * * ? *) . Actions (list) -- The actions initiated by this trigger. (dict) -- Defines an action to be initiated by a trigger. JobName (string) -- The name of a job to be executed. Arguments (dict) -- The job arguments used when this trigger fires. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- Timeout (integer) -- The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this action. NotificationProperty (dict) -- Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) -- After a job run starts, the number of minutes to wait before sending a job run delay notification. CrawlerName (string) -- The name of the crawler to be used with this action. Predicate (dict) -- The predicate of this trigger, which defines when it will fire. Logical (string) -- An optional field if only one condition is listed. If multiple conditions are listed, then this field is required. Conditions (list) -- A list of the conditions that determine when the trigger will fire. (dict) -- Defines a condition under which a trigger fires. LogicalOperator (string) -- A logical operator. JobName (string) -- The name of the job whose JobRuns this condition applies to, and on which this trigger waits. State (string) -- The condition state. Currently, the only job states that a trigger can listen for are SUCCEEDED , STOPPED , FAILED , and TIMEOUT . The only crawler states that a trigger can listen for are SUCCEEDED , FAILED , and CANCELLED . CrawlerName (string) -- The name of the crawler to which this condition applies. CrawlState (string) -- The state of the crawler to which this condition applies. JobDetails (dict) -- Details of the Job when the node represents a Job. JobRuns (list) -- The information for the job runs represented by the job node. (dict) -- Contains information about a job run. Id (string) -- The ID of this job run. Attempt (integer) -- The number of the attempt to run this job. PreviousRunId (string) -- The ID of the previous run of this job. For example, the JobRunId specified in the StartJobRun action. TriggerName (string) -- The name of the trigger that started this job run. JobName (string) -- The name of the job definition being used in this run. StartedOn (datetime) -- The date and time at which this job run was started. LastModifiedOn (datetime) -- The last time that this job run was modified. CompletedOn (datetime) -- The date and time that this job run completed. JobRunState (string) -- The current state of the job run. For more information about the statuses of jobs that have terminated abnormally, see AWS Glue Job Run Statuses . Arguments (dict) -- The job arguments associated with this run. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- ErrorMessage (string) -- An error message associated with this job run. PredecessorRuns (list) -- A list of predecessors to this job run. (dict) -- A job run that was used in the predicate of a conditional trigger that triggered this job run. JobName (string) -- The name of the job definition used by the predecessor job run. RunId (string) -- The job-run ID of the predecessor job run. AllocatedCapacity (integer) -- This field is deprecated. Use MaxCapacity instead. The number of AWS Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be allocated; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . ExecutionTime (integer) -- The amount of time (in seconds) that the job run consumed resources. Timeout (integer) -- The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. MaxCapacity (float) -- The number of AWS Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . Do not set Max Capacity if using WorkerType and NumberOfWorkers . The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job or an Apache Spark ETL job: When you specify a Python shell job (JobCommand.Name ="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU. When you specify an Apache Spark ETL job (JobCommand.Name ="glueetl"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation. WorkerType (string) -- The type of predefined worker that is allocated when a job runs. Accepts a value of Standard, G.1X, or G.2X. For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. For the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker. For the G.2X worker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker. NumberOfWorkers (integer) -- The number of workers of a defined workerType that are allocated when a job runs. The maximum number of workers you can define are 299 for G.1X , and 149 for G.2X . SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this job run. LogGroupName (string) -- The name of the log group for secure logging that can be server-side encrypted in Amazon CloudWatch using AWS KMS. This name can be /aws-glue/jobs/ , in which case the default encryption is NONE . If you add a role name and SecurityConfiguration name (in other words, /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/ ), then that security configuration is used to encrypt the log group. NotificationProperty (dict) -- Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) -- After a job run starts, the number of minutes to wait before sending a job run delay notification. GlueVersion (string) -- Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for jobs of type Spark. For more information about the available AWS Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide. Jobs that are created without specifying a Glue version default to Glue 0.9. CrawlerDetails (dict) -- Details of the crawler when the node represents a crawler. Crawls (list) -- A list of crawls represented by the crawl node. (dict) -- The details of a crawl in the workflow. State (string) -- The state of the crawler. StartedOn (datetime) -- The date and time on which the crawl started. CompletedOn (datetime) -- The date and time on which the crawl completed. ErrorMessage (string) -- The error message associated with the crawl. LogGroup (string) -- The log group associated with the crawl. LogStream (string) -- The log stream associated with the crawl. Edges (list) -- A list of all the directed connections between the nodes belonging to the workflow. (dict) -- An edge represents a directed connection between two AWS Glue components which are part of the workflow the edge belongs to. SourceId (string) -- The unique of the node within the workflow where the edge starts. DestinationId (string) -- The unique of the node within the workflow where the edge ends. MissingWorkflows (list) -- A list of names of workflows not found. (string) -- Exceptions Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException :return: { 'Workflows': [ { 'Name': 'string', 'Description': 'string', 'DefaultRunProperties': { 'string': 'string' }, 'CreatedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'LastRun': { 'Name': 'string', 'WorkflowRunId': 'string', 'WorkflowRunProperties': { 'string': 'string' }, 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'Status': 'RUNNING'|'COMPLETED'|'STOPPING'|'STOPPED', 'Statistics': { 'TotalActions': 123, 'TimeoutActions': 123, 'FailedActions': 123, 'StoppedActions': 123, 'SucceededActions': 123, 'RunningActions': 123 }, 'Graph': { 'Nodes': [ { 'Type': 'CRAWLER'|'JOB'|'TRIGGER', 'Name': 'string', 'UniqueId': 'string', 'TriggerDetails': { 'Trigger': { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } } }, 'JobDetails': { 'JobRuns': [ { 'Id': 'string', 'Attempt': 123, 'PreviousRunId': 'string', 'TriggerName': 'string', 'JobName': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'JobRunState': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'Arguments': { 'string': 'string' }, 'ErrorMessage': 'string', 'PredecessorRuns': [ { 'JobName': 'string', 'RunId': 'string' }, ], 'AllocatedCapacity': 123, 'ExecutionTime': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'LogGroupName': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ] }, 'CrawlerDetails': { 'Crawls': [ { 'State': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED', 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string' }, ] } }, ], 'Edges': [ { 'SourceId': 'string', 'DestinationId': 'string' }, ] } }, 'Graph': { 'Nodes': [ { 'Type': 'CRAWLER'|'JOB'|'TRIGGER', 'Name': 'string', 'UniqueId': 'string', 'TriggerDetails': { 'Trigger': { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } } }, 'JobDetails': { 'JobRuns': [ { 'Id': 'string', 'Attempt': 123, 'PreviousRunId': 'string', 'TriggerName': 'string', 'JobName': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'JobRunState': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'Arguments': { 'string': 'string' }, 'ErrorMessage': 'string', 'PredecessorRuns': [ { 'JobName': 'string', 'RunId': 'string' }, ], 'AllocatedCapacity': 123, 'ExecutionTime': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'LogGroupName': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ] }, 'CrawlerDetails': { 'Crawls': [ { 'State': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED', 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string' }, ] } }, ], 'Edges': [ { 'SourceId': 'string', 'DestinationId': 'string' }, ] } }, ], 'MissingWorkflows': [ 'string', ] } :returns: (string) -- (string) -- """ pass def batch_stop_job_run(JobName=None, JobRunIds=None): """ Stops one or more job runs for a specified job definition. See also: AWS API Documentation Exceptions :example: response = client.batch_stop_job_run( JobName='string', JobRunIds=[ 'string', ] ) :type JobName: string :param JobName: [REQUIRED]\nThe name of the job definition for which to stop job runs.\n :type JobRunIds: list :param JobRunIds: [REQUIRED]\nA list of the JobRunIds that should be stopped for that job definition.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax { 'SuccessfulSubmissions': [ { 'JobName': 'string', 'JobRunId': 'string' }, ], 'Errors': [ { 'JobName': 'string', 'JobRunId': 'string', 'ErrorDetail': { 'ErrorCode': 'string', 'ErrorMessage': 'string' } }, ] } Response Structure (dict) -- SuccessfulSubmissions (list) -- A list of the JobRuns that were successfully submitted for stopping. (dict) -- Records a successful request to stop a specified JobRun . JobName (string) -- The name of the job definition used in the job run that was stopped. JobRunId (string) -- The JobRunId of the job run that was stopped. Errors (list) -- A list of the errors that were encountered in trying to stop JobRuns , including the JobRunId for which each error was encountered and details about the error. (dict) -- Records an error that occurred when attempting to stop a specified job run. JobName (string) -- The name of the job definition that is used in the job run in question. JobRunId (string) -- The JobRunId of the job run in question. ErrorDetail (dict) -- Specifies details about the error that was encountered. ErrorCode (string) -- The code associated with this error. ErrorMessage (string) -- A message describing the error. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'SuccessfulSubmissions': [ { 'JobName': 'string', 'JobRunId': 'string' }, ], 'Errors': [ { 'JobName': 'string', 'JobRunId': 'string', 'ErrorDetail': { 'ErrorCode': 'string', 'ErrorMessage': 'string' } }, ] } :returns: Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException """ pass def can_paginate(operation_name=None): """ Check if an operation can be paginated. :type operation_name: string :param operation_name: The operation name. This is the same name\nas the method name on the client. For example, if the\nmethod name is create_foo, and you\'d normally invoke the\noperation as client.create_foo(**kwargs), if the\ncreate_foo operation can be paginated, you can use the\ncall client.get_paginator('create_foo'). """ pass def cancel_ml_task_run(TransformId=None, TaskRunId=None): """ Cancels (stops) a task run. Machine learning task runs are asynchronous tasks that AWS Glue runs on your behalf as part of various machine learning workflows. You can cancel a machine learning task run at any time by calling CancelMLTaskRun with a task run\'s parent transform\'s TransformID and the task run\'s TaskRunId . See also: AWS API Documentation Exceptions :example: response = client.cancel_ml_task_run( TransformId='string', TaskRunId='string' ) :type TransformId: string :param TransformId: [REQUIRED]\nThe unique identifier of the machine learning transform.\n :type TaskRunId: string :param TaskRunId: [REQUIRED]\nA unique identifier for the task run.\n :rtype: dict ReturnsResponse Syntax { 'TransformId': 'string', 'TaskRunId': 'string', 'Status': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT' } Response Structure (dict) -- TransformId (string) -- The unique identifier of the machine learning transform. TaskRunId (string) -- The unique identifier for the task run. Status (string) -- The status for this run. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException :return: { 'TransformId': 'string', 'TaskRunId': 'string', 'Status': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT' } :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException """ pass def create_classifier(GrokClassifier=None, XMLClassifier=None, JsonClassifier=None, CsvClassifier=None): """ Creates a classifier in the user\'s account. This can be a GrokClassifier , an XMLClassifier , a JsonClassifier , or a CsvClassifier , depending on which field of the request is present. See also: AWS API Documentation Exceptions :example: response = client.create_classifier( GrokClassifier={ 'Classification': 'string', 'Name': 'string', 'GrokPattern': 'string', 'CustomPatterns': 'string' }, XMLClassifier={ 'Classification': 'string', 'Name': 'string', 'RowTag': 'string' }, JsonClassifier={ 'Name': 'string', 'JsonPath': 'string' }, CsvClassifier={ 'Name': 'string', 'Delimiter': 'string', 'QuoteSymbol': 'string', 'ContainsHeader': 'UNKNOWN'|'PRESENT'|'ABSENT', 'Header': [ 'string', ], 'DisableValueTrimming': True|False, 'AllowSingleColumn': True|False } ) :type GrokClassifier: dict :param GrokClassifier: A GrokClassifier object specifying the classifier to create.\n\nClassification (string) -- [REQUIRED]An identifier of the data format that the classifier matches, such as Twitter, JSON, Omniture logs, Amazon CloudWatch Logs, and so on.\n\nName (string) -- [REQUIRED]The name of the new classifier.\n\nGrokPattern (string) -- [REQUIRED]The grok pattern used by this classifier.\n\nCustomPatterns (string) --Optional custom grok patterns used by this classifier.\n\n\n :type XMLClassifier: dict :param XMLClassifier: An XMLClassifier object specifying the classifier to create.\n\nClassification (string) -- [REQUIRED]An identifier of the data format that the classifier matches.\n\nName (string) -- [REQUIRED]The name of the classifier.\n\nRowTag (string) --The XML tag designating the element that contains each record in an XML document being parsed. This can\'t identify a self-closing element (closed by /> ). An empty row element that contains only attributes can be parsed as long as it ends with a closing tag (for example, <row item_a='A' item_b='B'></row> is okay, but <row item_a='A' item_b='B' /> is not).\n\n\n :type JsonClassifier: dict :param JsonClassifier: A JsonClassifier object specifying the classifier to create.\n\nName (string) -- [REQUIRED]The name of the classifier.\n\nJsonPath (string) -- [REQUIRED]A JsonPath string defining the JSON data for the classifier to classify. AWS Glue supports a subset of JsonPath , as described in Writing JsonPath Custom Classifiers .\n\n\n :type CsvClassifier: dict :param CsvClassifier: A CsvClassifier object specifying the classifier to create.\n\nName (string) -- [REQUIRED]The name of the classifier.\n\nDelimiter (string) --A custom symbol to denote what separates each column entry in the row.\n\nQuoteSymbol (string) --A custom symbol to denote what combines content into a single column value. Must be different from the column delimiter.\n\nContainsHeader (string) --Indicates whether the CSV file contains a header.\n\nHeader (list) --A list of strings representing column names.\n\n(string) --\n\n\nDisableValueTrimming (boolean) --Specifies not to trim values before identifying the type of column values. The default value is true.\n\nAllowSingleColumn (boolean) --Enables the processing of files that contain only one column.\n\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: (dict) -- """ pass def create_connection(CatalogId=None, ConnectionInput=None): """ Creates a connection definition in the Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.create_connection( CatalogId='string', ConnectionInput={ 'Name': 'string', 'Description': 'string', 'ConnectionType': 'JDBC'|'SFTP'|'MONGODB'|'KAFKA', 'MatchCriteria': [ 'string', ], 'ConnectionProperties': { 'string': 'string' }, 'PhysicalConnectionRequirements': { 'SubnetId': 'string', 'SecurityGroupIdList': [ 'string', ], 'AvailabilityZone': 'string' } } ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog in which to create the connection. If none is provided, the AWS account ID is used by default. :type ConnectionInput: dict :param ConnectionInput: [REQUIRED]\nA ConnectionInput object defining the connection to create.\n\nName (string) -- [REQUIRED]The name of the connection.\n\nDescription (string) --The description of the connection.\n\nConnectionType (string) -- [REQUIRED]The type of the connection. Currently, these types are supported:\n\nJDBC - Designates a connection to a database through Java Database Connectivity (JDBC).\nKAFKA - Designates a connection to an Apache Kafka streaming platform.\nMONGODB - Designates a connection to a MongoDB document database.\n\nSFTP is not supported.\n\nMatchCriteria (list) --A list of criteria that can be used in selecting this connection.\n\n(string) --\n\n\nConnectionProperties (dict) -- [REQUIRED]These key-value pairs define parameters for the connection.\n\n(string) --\n(string) --\n\n\n\n\nPhysicalConnectionRequirements (dict) --A map of physical connection requirements, such as virtual private cloud (VPC) and SecurityGroup , that are needed to successfully make this connection.\n\nSubnetId (string) --The subnet ID used by the connection.\n\nSecurityGroupIdList (list) --The security group ID list used by the connection.\n\n(string) --\n\n\nAvailabilityZone (string) --The connection\'s Availability Zone. This field is redundant because the specified subnet implies the Availability Zone to be used. Currently the field must be populated, but it will be deprecated in the future.\n\n\n\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.GlueEncryptionException :return: {} :returns: (dict) -- """ pass def create_crawler(Name=None, Role=None, DatabaseName=None, Description=None, Targets=None, Schedule=None, Classifiers=None, TablePrefix=None, SchemaChangePolicy=None, Configuration=None, CrawlerSecurityConfiguration=None, Tags=None): """ Creates a new crawler with specified targets, role, configuration, and optional schedule. At least one crawl target must be specified, in the s3Targets field, the jdbcTargets field, or the DynamoDBTargets field. See also: AWS API Documentation Exceptions :example: response = client.create_crawler( Name='string', Role='string', DatabaseName='string', Description='string', Targets={ 'S3Targets': [ { 'Path': 'string', 'Exclusions': [ 'string', ] }, ], 'JdbcTargets': [ { 'ConnectionName': 'string', 'Path': 'string', 'Exclusions': [ 'string', ] }, ], 'DynamoDBTargets': [ { 'Path': 'string' }, ], 'CatalogTargets': [ { 'DatabaseName': 'string', 'Tables': [ 'string', ] }, ] }, Schedule='string', Classifiers=[ 'string', ], TablePrefix='string', SchemaChangePolicy={ 'UpdateBehavior': 'LOG'|'UPDATE_IN_DATABASE', 'DeleteBehavior': 'LOG'|'DELETE_FROM_DATABASE'|'DEPRECATE_IN_DATABASE' }, Configuration='string', CrawlerSecurityConfiguration='string', Tags={ 'string': 'string' } ) :type Name: string :param Name: [REQUIRED]\nName of the new crawler.\n :type Role: string :param Role: [REQUIRED]\nThe IAM role or Amazon Resource Name (ARN) of an IAM role used by the new crawler to access customer resources.\n :type DatabaseName: string :param DatabaseName: The AWS Glue database where results are written, such as: arn:aws:daylight:us-east-1::database/sometable/* . :type Description: string :param Description: A description of the new crawler. :type Targets: dict :param Targets: [REQUIRED]\nA list of collection of targets to crawl.\n\nS3Targets (list) --Specifies Amazon Simple Storage Service (Amazon S3) targets.\n\n(dict) --Specifies a data store in Amazon Simple Storage Service (Amazon S3).\n\nPath (string) --The path to the Amazon S3 target.\n\nExclusions (list) --A list of glob patterns used to exclude from the crawl. For more information, see Catalog Tables with a Crawler .\n\n(string) --\n\n\n\n\n\n\nJdbcTargets (list) --Specifies JDBC targets.\n\n(dict) --Specifies a JDBC data store to crawl.\n\nConnectionName (string) --The name of the connection to use to connect to the JDBC target.\n\nPath (string) --The path of the JDBC target.\n\nExclusions (list) --A list of glob patterns used to exclude from the crawl. For more information, see Catalog Tables with a Crawler .\n\n(string) --\n\n\n\n\n\n\nDynamoDBTargets (list) --Specifies Amazon DynamoDB targets.\n\n(dict) --Specifies an Amazon DynamoDB table to crawl.\n\nPath (string) --The name of the DynamoDB table to crawl.\n\n\n\n\n\nCatalogTargets (list) --Specifies AWS Glue Data Catalog targets.\n\n(dict) --Specifies an AWS Glue Data Catalog target.\n\nDatabaseName (string) -- [REQUIRED]The name of the database to be synchronized.\n\nTables (list) -- [REQUIRED]A list of the tables to be synchronized.\n\n(string) --\n\n\n\n\n\n\n\n :type Schedule: string :param Schedule: A cron expression used to specify the schedule. For more information, see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, specify cron(15 12 * * ? *) . :type Classifiers: list :param Classifiers: A list of custom classifiers that the user has registered. By default, all built-in classifiers are included in a crawl, but these custom classifiers always override the default classifiers for a given classification.\n\n(string) --\n\n :type TablePrefix: string :param TablePrefix: The table prefix used for catalog tables that are created. :type SchemaChangePolicy: dict :param SchemaChangePolicy: The policy for the crawler\'s update and deletion behavior.\n\nUpdateBehavior (string) --The update behavior when the crawler finds a changed schema.\n\nDeleteBehavior (string) --The deletion behavior when the crawler finds a deleted object.\n\n\n :type Configuration: string :param Configuration: The crawler configuration information. This versioned JSON string allows users to specify aspects of a crawler\'s behavior. For more information, see Configuring a Crawler . :type CrawlerSecurityConfiguration: string :param CrawlerSecurityConfiguration: The name of the SecurityConfiguration structure to be used by this crawler. :type Tags: dict :param Tags: The tags to use with this crawler request. You can use tags to limit access to the crawler. For more information, see AWS Tags in AWS Glue .\n\n(string) --\n(string) --\n\n\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException :return: {} :returns: (dict) -- """ pass def create_database(CatalogId=None, DatabaseInput=None): """ Creates a new database in a Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.create_database( CatalogId='string', DatabaseInput={ 'Name': 'string', 'Description': 'string', 'LocationUri': 'string', 'Parameters': { 'string': 'string' }, 'CreateTableDefaultPermissions': [ { 'Principal': { 'DataLakePrincipalIdentifier': 'string' }, 'Permissions': [ 'ALL'|'SELECT'|'ALTER'|'DROP'|'DELETE'|'INSERT'|'CREATE_DATABASE'|'CREATE_TABLE'|'DATA_LOCATION_ACCESS', ] }, ] } ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog in which to create the database. If none is provided, the AWS account ID is used by default. :type DatabaseInput: dict :param DatabaseInput: [REQUIRED]\nThe metadata for the database.\n\nName (string) -- [REQUIRED]The name of the database. For Hive compatibility, this is folded to lowercase when it is stored.\n\nDescription (string) --A description of the database.\n\nLocationUri (string) --The location of the database (for example, an HDFS path).\n\nParameters (dict) --These key-value pairs define parameters and properties of the database.\nThese key-value pairs define parameters and properties of the database.\n\n(string) --\n(string) --\n\n\n\n\nCreateTableDefaultPermissions (list) --Creates a set of default permissions on the table for principals.\n\n(dict) --Permissions granted to a principal.\n\nPrincipal (dict) --The principal who is granted permissions.\n\nDataLakePrincipalIdentifier (string) --An identifier for the AWS Lake Formation principal.\n\n\n\nPermissions (list) --The permissions that are granted to the principal.\n\n(string) --\n\n\n\n\n\n\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.GlueEncryptionException :return: {} :returns: (dict) -- """ pass def create_dev_endpoint(EndpointName=None, RoleArn=None, SecurityGroupIds=None, SubnetId=None, PublicKey=None, PublicKeys=None, NumberOfNodes=None, WorkerType=None, GlueVersion=None, NumberOfWorkers=None, ExtraPythonLibsS3Path=None, ExtraJarsS3Path=None, SecurityConfiguration=None, Tags=None, Arguments=None): """ Creates a new development endpoint. See also: AWS API Documentation Exceptions :example: response = client.create_dev_endpoint( EndpointName='string', RoleArn='string', SecurityGroupIds=[ 'string', ], SubnetId='string', PublicKey='string', PublicKeys=[ 'string', ], NumberOfNodes=123, WorkerType='Standard'|'G.1X'|'G.2X', GlueVersion='string', NumberOfWorkers=123, ExtraPythonLibsS3Path='string', ExtraJarsS3Path='string', SecurityConfiguration='string', Tags={ 'string': 'string' }, Arguments={ 'string': 'string' } ) :type EndpointName: string :param EndpointName: [REQUIRED]\nThe name to be assigned to the new DevEndpoint .\n :type RoleArn: string :param RoleArn: [REQUIRED]\nThe IAM role for the DevEndpoint .\n :type SecurityGroupIds: list :param SecurityGroupIds: Security group IDs for the security groups to be used by the new DevEndpoint .\n\n(string) --\n\n :type SubnetId: string :param SubnetId: The subnet ID for the new DevEndpoint to use. :type PublicKey: string :param PublicKey: The public key to be used by this DevEndpoint for authentication. This attribute is provided for backward compatibility because the recommended attribute to use is public keys. :type PublicKeys: list :param PublicKeys: A list of public keys to be used by the development endpoints for authentication. The use of this attribute is preferred over a single public key because the public keys allow you to have a different private key per client.\n\nNote\nIf you previously created an endpoint with a public key, you must remove that key to be able to set a list of public keys. Call the UpdateDevEndpoint API with the public key content in the deletePublicKeys attribute, and the list of new keys in the addPublicKeys attribute.\n\n\n(string) --\n\n :type NumberOfNodes: integer :param NumberOfNodes: The number of AWS Glue Data Processing Units (DPUs) to allocate to this DevEndpoint . :type WorkerType: string :param WorkerType: The type of predefined worker that is allocated to the development endpoint. Accepts a value of Standard, G.1X, or G.2X.\n\nFor the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker.\nFor the G.1X worker type, each worker maps to 1 DPU (4 vCPU, 16 GB of memory, 64 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs.\nFor the G.2X worker type, each worker maps to 2 DPU (8 vCPU, 32 GB of memory, 128 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs.\n\nKnown issue: when a development endpoint is created with the G.2X WorkerType configuration, the Spark drivers for the development endpoint will run on 4 vCPU, 16 GB of memory, and a 64 GB disk.\n :type GlueVersion: string :param GlueVersion: Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for running your ETL scripts on development endpoints.\nFor more information about the available AWS Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide.\nDevelopment endpoints that are created without specifying a Glue version default to Glue 0.9.\nYou can specify a version of Python support for development endpoints by using the Arguments parameter in the CreateDevEndpoint or UpdateDevEndpoint APIs. If no arguments are provided, the version defaults to Python 2.\n :type NumberOfWorkers: integer :param NumberOfWorkers: The number of workers of a defined workerType that are allocated to the development endpoint.\nThe maximum number of workers you can define are 299 for G.1X , and 149 for G.2X .\n :type ExtraPythonLibsS3Path: string :param ExtraPythonLibsS3Path: The paths to one or more Python libraries in an Amazon S3 bucket that should be loaded in your DevEndpoint . Multiple values must be complete paths separated by a comma.\n\nNote\nYou can only use pure Python libraries with a DevEndpoint . Libraries that rely on C extensions, such as the pandas Python data analysis library, are not yet supported.\n\n :type ExtraJarsS3Path: string :param ExtraJarsS3Path: The path to one or more Java .jar files in an S3 bucket that should be loaded in your DevEndpoint . :type SecurityConfiguration: string :param SecurityConfiguration: The name of the SecurityConfiguration structure to be used with this DevEndpoint . :type Tags: dict :param Tags: The tags to use with this DevEndpoint. You may use tags to limit access to the DevEndpoint. For more information about tags in AWS Glue, see AWS Tags in AWS Glue in the developer guide.\n\n(string) --\n(string) --\n\n\n\n :type Arguments: dict :param Arguments: A map of arguments used to configure the DevEndpoint .\n\n(string) --\n(string) --\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'EndpointName': 'string', 'Status': 'string', 'SecurityGroupIds': [ 'string', ], 'SubnetId': 'string', 'RoleArn': 'string', 'YarnEndpointAddress': 'string', 'ZeppelinRemoteSparkInterpreterPort': 123, 'NumberOfNodes': 123, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'GlueVersion': 'string', 'NumberOfWorkers': 123, 'AvailabilityZone': 'string', 'VpcId': 'string', 'ExtraPythonLibsS3Path': 'string', 'ExtraJarsS3Path': 'string', 'FailureReason': 'string', 'SecurityConfiguration': 'string', 'CreatedTimestamp': datetime(2015, 1, 1), 'Arguments': { 'string': 'string' } } Response Structure (dict) -- EndpointName (string) -- The name assigned to the new DevEndpoint . Status (string) -- The current status of the new DevEndpoint . SecurityGroupIds (list) -- The security groups assigned to the new DevEndpoint . (string) -- SubnetId (string) -- The subnet ID assigned to the new DevEndpoint . RoleArn (string) -- The Amazon Resource Name (ARN) of the role assigned to the new DevEndpoint . YarnEndpointAddress (string) -- The address of the YARN endpoint used by this DevEndpoint . ZeppelinRemoteSparkInterpreterPort (integer) -- The Apache Zeppelin port for the remote Apache Spark interpreter. NumberOfNodes (integer) -- The number of AWS Glue Data Processing Units (DPUs) allocated to this DevEndpoint. WorkerType (string) -- The type of predefined worker that is allocated to the development endpoint. May be a value of Standard, G.1X, or G.2X. GlueVersion (string) -- Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for running your ETL scripts on development endpoints. NumberOfWorkers (integer) -- The number of workers of a defined workerType that are allocated to the development endpoint. AvailabilityZone (string) -- The AWS Availability Zone where this DevEndpoint is located. VpcId (string) -- The ID of the virtual private cloud (VPC) used by this DevEndpoint . ExtraPythonLibsS3Path (string) -- The paths to one or more Python libraries in an S3 bucket that will be loaded in your DevEndpoint . ExtraJarsS3Path (string) -- Path to one or more Java .jar files in an S3 bucket that will be loaded in your DevEndpoint . FailureReason (string) -- The reason for a current failure in this DevEndpoint . SecurityConfiguration (string) -- The name of the SecurityConfiguration structure being used with this DevEndpoint . CreatedTimestamp (datetime) -- The point in time at which this DevEndpoint was created. Arguments (dict) -- The map of arguments used to configure this DevEndpoint . Valid arguments are: "--enable-glue-datacatalog": "" "GLUE_PYTHON_VERSION": "3" "GLUE_PYTHON_VERSION": "2" You can specify a version of Python support for development endpoints by using the Arguments parameter in the CreateDevEndpoint or UpdateDevEndpoint APIs. If no arguments are provided, the version defaults to Python 2. (string) -- (string) -- Exceptions Glue.Client.exceptions.AccessDeniedException Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.IdempotentParameterMismatchException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.ValidationException Glue.Client.exceptions.ResourceNumberLimitExceededException :return: { 'EndpointName': 'string', 'Status': 'string', 'SecurityGroupIds': [ 'string', ], 'SubnetId': 'string', 'RoleArn': 'string', 'YarnEndpointAddress': 'string', 'ZeppelinRemoteSparkInterpreterPort': 123, 'NumberOfNodes': 123, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'GlueVersion': 'string', 'NumberOfWorkers': 123, 'AvailabilityZone': 'string', 'VpcId': 'string', 'ExtraPythonLibsS3Path': 'string', 'ExtraJarsS3Path': 'string', 'FailureReason': 'string', 'SecurityConfiguration': 'string', 'CreatedTimestamp': datetime(2015, 1, 1), 'Arguments': { 'string': 'string' } } :returns: (string) -- """ pass def create_job(Name=None, Description=None, LogUri=None, Role=None, ExecutionProperty=None, Command=None, DefaultArguments=None, NonOverridableArguments=None, Connections=None, MaxRetries=None, AllocatedCapacity=None, Timeout=None, MaxCapacity=None, SecurityConfiguration=None, Tags=None, NotificationProperty=None, GlueVersion=None, NumberOfWorkers=None, WorkerType=None): """ Creates a new job definition. See also: AWS API Documentation Exceptions :example: response = client.create_job( Name='string', Description='string', LogUri='string', Role='string', ExecutionProperty={ 'MaxConcurrentRuns': 123 }, Command={ 'Name': 'string', 'ScriptLocation': 'string', 'PythonVersion': 'string' }, DefaultArguments={ 'string': 'string' }, NonOverridableArguments={ 'string': 'string' }, Connections={ 'Connections': [ 'string', ] }, MaxRetries=123, AllocatedCapacity=123, Timeout=123, MaxCapacity=123.0, SecurityConfiguration='string', Tags={ 'string': 'string' }, NotificationProperty={ 'NotifyDelayAfter': 123 }, GlueVersion='string', NumberOfWorkers=123, WorkerType='Standard'|'G.1X'|'G.2X' ) :type Name: string :param Name: [REQUIRED]\nThe name you assign to this job definition. It must be unique in your account.\n :type Description: string :param Description: Description of the job being defined. :type LogUri: string :param LogUri: This field is reserved for future use. :type Role: string :param Role: [REQUIRED]\nThe name or Amazon Resource Name (ARN) of the IAM role associated with this job.\n :type ExecutionProperty: dict :param ExecutionProperty: An ExecutionProperty specifying the maximum number of concurrent runs allowed for this job.\n\nMaxConcurrentRuns (integer) --The maximum number of concurrent runs allowed for the job. The default is 1. An error is returned when this threshold is reached. The maximum value you can specify is controlled by a service limit.\n\n\n :type Command: dict :param Command: [REQUIRED]\nThe JobCommand that executes this job.\n\nName (string) --The name of the job command. For an Apache Spark ETL job, this must be glueetl . For a Python shell job, it must be pythonshell .\n\nScriptLocation (string) --Specifies the Amazon Simple Storage Service (Amazon S3) path to a script that executes a job.\n\nPythonVersion (string) --The Python version being used to execute a Python shell job. Allowed values are 2 or 3.\n\n\n :type DefaultArguments: dict :param DefaultArguments: The default arguments for this job.\nYou can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes.\nFor information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide.\nFor information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide.\n\n(string) --\n(string) --\n\n\n\n :type NonOverridableArguments: dict :param NonOverridableArguments: Non-overridable arguments for this job, specified as name-value pairs.\n\n(string) --\n(string) --\n\n\n\n :type Connections: dict :param Connections: The connections used for this job.\n\nConnections (list) --A list of connections used by the job.\n\n(string) --\n\n\n\n :type MaxRetries: integer :param MaxRetries: The maximum number of times to retry this job if it fails. :type AllocatedCapacity: integer :param AllocatedCapacity: This parameter is deprecated. Use MaxCapacity instead.\nThe number of AWS Glue data processing units (DPUs) to allocate to this Job. You can allocate from 2 to 100 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page .\n :type Timeout: integer :param Timeout: The job timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). :type MaxCapacity: float :param MaxCapacity: The number of AWS Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page .\nDo not set Max Capacity if using WorkerType and NumberOfWorkers .\nThe value that can be allocated for MaxCapacity depends on whether you are running a Python shell job or an Apache Spark ETL job:\n\nWhen you specify a Python shell job (JobCommand.Name ='pythonshell'), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU.\nWhen you specify an Apache Spark ETL job (JobCommand.Name ='glueetl'), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation.\n\n :type SecurityConfiguration: string :param SecurityConfiguration: The name of the SecurityConfiguration structure to be used with this job. :type Tags: dict :param Tags: The tags to use with this job. You may use tags to limit access to the job. For more information about tags in AWS Glue, see AWS Tags in AWS Glue in the developer guide.\n\n(string) --\n(string) --\n\n\n\n :type NotificationProperty: dict :param NotificationProperty: Specifies configuration properties of a job notification.\n\nNotifyDelayAfter (integer) --After a job run starts, the number of minutes to wait before sending a job run delay notification.\n\n\n :type GlueVersion: string :param GlueVersion: Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for jobs of type Spark.\nFor more information about the available AWS Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide.\nJobs that are created without specifying a Glue version default to Glue 0.9.\n :type NumberOfWorkers: integer :param NumberOfWorkers: The number of workers of a defined workerType that are allocated when a job runs.\nThe maximum number of workers you can define are 299 for G.1X , and 149 for G.2X .\n :type WorkerType: string :param WorkerType: The type of predefined worker that is allocated when a job runs. Accepts a value of Standard, G.1X, or G.2X.\n\nFor the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker.\nFor the G.1X worker type, each worker maps to 1 DPU (4 vCPU, 16 GB of memory, 64 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs.\nFor the G.2X worker type, each worker maps to 2 DPU (8 vCPU, 32 GB of memory, 128 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs.\n\n :rtype: dict ReturnsResponse Syntax { 'Name': 'string' } Response Structure (dict) -- Name (string) -- The unique name that was provided for this job definition. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.IdempotentParameterMismatchException Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.ConcurrentModificationException :return: { 'Name': 'string' } :returns: Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.IdempotentParameterMismatchException Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.ConcurrentModificationException """ pass def create_ml_transform(Name=None, Description=None, InputRecordTables=None, Parameters=None, Role=None, GlueVersion=None, MaxCapacity=None, WorkerType=None, NumberOfWorkers=None, Timeout=None, MaxRetries=None, Tags=None): """ Creates an AWS Glue machine learning transform. This operation creates the transform and all the necessary parameters to train it. Call this operation as the first step in the process of using a machine learning transform (such as the FindMatches transform) for deduplicating data. You can provide an optional Description , in addition to the parameters that you want to use for your algorithm. You must also specify certain parameters for the tasks that AWS Glue runs on your behalf as part of learning from your data and creating a high-quality machine learning transform. These parameters include Role , and optionally, AllocatedCapacity , Timeout , and MaxRetries . For more information, see Jobs . See also: AWS API Documentation Exceptions :example: response = client.create_ml_transform( Name='string', Description='string', InputRecordTables=[ { 'DatabaseName': 'string', 'TableName': 'string', 'CatalogId': 'string', 'ConnectionName': 'string' }, ], Parameters={ 'TransformType': 'FIND_MATCHES', 'FindMatchesParameters': { 'PrimaryKeyColumnName': 'string', 'PrecisionRecallTradeoff': 123.0, 'AccuracyCostTradeoff': 123.0, 'EnforceProvidedLabels': True|False } }, Role='string', GlueVersion='string', MaxCapacity=123.0, WorkerType='Standard'|'G.1X'|'G.2X', NumberOfWorkers=123, Timeout=123, MaxRetries=123, Tags={ 'string': 'string' } ) :type Name: string :param Name: [REQUIRED]\nThe unique name that you give the transform when you create it.\n :type Description: string :param Description: A description of the machine learning transform that is being defined. The default is an empty string. :type InputRecordTables: list :param InputRecordTables: [REQUIRED]\nA list of AWS Glue table definitions used by the transform.\n\n(dict) --The database and table in the AWS Glue Data Catalog that is used for input or output data.\n\nDatabaseName (string) -- [REQUIRED]A database name in the AWS Glue Data Catalog.\n\nTableName (string) -- [REQUIRED]A table name in the AWS Glue Data Catalog.\n\nCatalogId (string) --A unique identifier for the AWS Glue Data Catalog.\n\nConnectionName (string) --The name of the connection to the AWS Glue Data Catalog.\n\n\n\n\n :type Parameters: dict :param Parameters: [REQUIRED]\nThe algorithmic parameters that are specific to the transform type used. Conditionally dependent on the transform type.\n\nTransformType (string) -- [REQUIRED]The type of machine learning transform.\nFor information about the types of machine learning transforms, see Creating Machine Learning Transforms .\n\nFindMatchesParameters (dict) --The parameters for the find matches algorithm.\n\nPrimaryKeyColumnName (string) --The name of a column that uniquely identifies rows in the source table. Used to help identify matching records.\n\nPrecisionRecallTradeoff (float) --The value selected when tuning your transform for a balance between precision and recall. A value of 0.5 means no preference; a value of 1.0 means a bias purely for precision, and a value of 0.0 means a bias for recall. Because this is a tradeoff, choosing values close to 1.0 means very low recall, and choosing values close to 0.0 results in very low precision.\nThe precision metric indicates how often your model is correct when it predicts a match.\nThe recall metric indicates that for an actual match, how often your model predicts the match.\n\nAccuracyCostTradeoff (float) --The value that is selected when tuning your transform for a balance between accuracy and cost. A value of 0.5 means that the system balances accuracy and cost concerns. A value of 1.0 means a bias purely for accuracy, which typically results in a higher cost, sometimes substantially higher. A value of 0.0 means a bias purely for cost, which results in a less accurate FindMatches transform, sometimes with unacceptable accuracy.\nAccuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall.\nCost measures how many compute resources, and thus money, are consumed to run the transform.\n\nEnforceProvidedLabels (boolean) --The value to switch on or off to force the output to match the provided labels from users. If the value is True , the find matches transform forces the output to match the provided labels. The results override the normal conflation results. If the value is False , the find matches transform does not ensure all the labels provided are respected, and the results rely on the trained model.\nNote that setting this value to true may increase the conflation execution time.\n\n\n\n\n :type Role: string :param Role: [REQUIRED]\nThe name or Amazon Resource Name (ARN) of the IAM role with the required permissions. The required permissions include both AWS Glue service role permissions to AWS Glue resources, and Amazon S3 permissions required by the transform.\n\nThis role needs AWS Glue service role permissions to allow access to resources in AWS Glue. See Attach a Policy to IAM Users That Access AWS Glue .\nThis role needs permission to your Amazon Simple Storage Service (Amazon S3) sources, targets, temporary directory, scripts, and any libraries used by the task run for this transform.\n\n :type GlueVersion: string :param GlueVersion: This value determines which version of AWS Glue this machine learning transform is compatible with. Glue 1.0 is recommended for most customers. If the value is not set, the Glue compatibility defaults to Glue 0.9. For more information, see AWS Glue Versions in the developer guide. :type MaxCapacity: float :param MaxCapacity: The number of AWS Glue data processing units (DPUs) that are allocated to task runs for this transform. You can allocate from 2 to 100 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page .\n\nMaxCapacity is a mutually exclusive option with NumberOfWorkers and WorkerType .\n\nIf either NumberOfWorkers or WorkerType is set, then MaxCapacity cannot be set.\nIf MaxCapacity is set then neither NumberOfWorkers or WorkerType can be set.\nIf WorkerType is set, then NumberOfWorkers is required (and vice versa).\nMaxCapacity and NumberOfWorkers must both be at least 1.\n\nWhen the WorkerType field is set to a value other than Standard , the MaxCapacity field is set automatically and becomes read-only.\nWhen the WorkerType field is set to a value other than Standard , the MaxCapacity field is set automatically and becomes read-only.\n :type WorkerType: string :param WorkerType: The type of predefined worker that is allocated when this task runs. Accepts a value of Standard, G.1X, or G.2X.\n\nFor the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker.\nFor the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker.\nFor the G.2X worker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker.\n\n\nMaxCapacity is a mutually exclusive option with NumberOfWorkers and WorkerType .\n\nIf either NumberOfWorkers or WorkerType is set, then MaxCapacity cannot be set.\nIf MaxCapacity is set then neither NumberOfWorkers or WorkerType can be set.\nIf WorkerType is set, then NumberOfWorkers is required (and vice versa).\nMaxCapacity and NumberOfWorkers must both be at least 1.\n\n :type NumberOfWorkers: integer :param NumberOfWorkers: The number of workers of a defined workerType that are allocated when this task runs.\nIf WorkerType is set, then NumberOfWorkers is required (and vice versa).\n :type Timeout: integer :param Timeout: The timeout of the task run for this transform in minutes. This is the maximum time that a task run for this transform can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). :type MaxRetries: integer :param MaxRetries: The maximum number of times to retry a task for this transform after a task run fails. :type Tags: dict :param Tags: The tags to use with this machine learning transform. You may use tags to limit access to the machine learning transform. For more information about tags in AWS Glue, see AWS Tags in AWS Glue in the developer guide.\n\n(string) --\n(string) --\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'TransformId': 'string' } Response Structure (dict) -- TransformId (string) -- A unique identifier that is generated for the transform. Exceptions Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.AccessDeniedException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.IdempotentParameterMismatchException :return: { 'TransformId': 'string' } :returns: Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.AccessDeniedException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.IdempotentParameterMismatchException """ pass def create_partition(CatalogId=None, DatabaseName=None, TableName=None, PartitionInput=None): """ Creates a new partition. See also: AWS API Documentation Exceptions :example: response = client.create_partition( CatalogId='string', DatabaseName='string', TableName='string', PartitionInput={ 'Values': [ 'string', ], 'LastAccessTime': datetime(2015, 1, 1), 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'Parameters': { 'string': 'string' }, 'LastAnalyzedTime': datetime(2015, 1, 1) } ) :type CatalogId: string :param CatalogId: The AWS account ID of the catalog in which the partition is to be created. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe name of the metadata database in which the partition is to be created.\n :type TableName: string :param TableName: [REQUIRED]\nThe name of the metadata table in which the partition is to be created.\n :type PartitionInput: dict :param PartitionInput: [REQUIRED]\nA PartitionInput structure defining the partition to be created.\n\nValues (list) --The values of the partition. Although this parameter is not required by the SDK, you must specify this parameter for a valid input.\nThe values for the keys for the new partition must be passed as an array of String objects that must be ordered in the same order as the partition keys appearing in the Amazon S3 prefix. Otherwise AWS Glue will add the values to the wrong keys.\n\n(string) --\n\n\nLastAccessTime (datetime) --The last time at which the partition was accessed.\n\nStorageDescriptor (dict) --Provides information about the physical location where the partition is stored.\n\nColumns (list) --A list of the Columns in the table.\n\n(dict) --A column in a Table .\n\nName (string) -- [REQUIRED]The name of the Column .\n\nType (string) --The data type of the Column .\n\nComment (string) --A free-form text comment.\n\nParameters (dict) --These key-value pairs define properties associated with the column.\n\n(string) --\n(string) --\n\n\n\n\n\n\n\n\nLocation (string) --The physical location of the table. By default, this takes the form of the warehouse location, followed by the database location in the warehouse, followed by the table name.\n\nInputFormat (string) --The input format: SequenceFileInputFormat (binary), or TextInputFormat , or a custom format.\n\nOutputFormat (string) --The output format: SequenceFileOutputFormat (binary), or IgnoreKeyTextOutputFormat , or a custom format.\n\nCompressed (boolean) --\nTrue if the data in the table is compressed, or False if not.\n\nNumberOfBuckets (integer) --Must be specified if the table contains any dimension columns.\n\nSerdeInfo (dict) --The serialization/deserialization (SerDe) information.\n\nName (string) --Name of the SerDe.\n\nSerializationLibrary (string) --Usually the class that implements the SerDe. An example is org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe .\n\nParameters (dict) --These key-value pairs define initialization parameters for the SerDe.\n\n(string) --\n(string) --\n\n\n\n\n\n\nBucketColumns (list) --A list of reducer grouping columns, clustering columns, and bucketing columns in the table.\n\n(string) --\n\n\nSortColumns (list) --A list specifying the sort order of each bucket in the table.\n\n(dict) --Specifies the sort order of a sorted column.\n\nColumn (string) -- [REQUIRED]The name of the column.\n\nSortOrder (integer) -- [REQUIRED]Indicates that the column is sorted in ascending order (== 1 ), or in descending order (==0 ).\n\n\n\n\n\nParameters (dict) --The user-supplied properties in key-value form.\n\n(string) --\n(string) --\n\n\n\n\nSkewedInfo (dict) --The information about values that appear frequently in a column (skewed values).\n\nSkewedColumnNames (list) --A list of names of columns that contain skewed values.\n\n(string) --\n\n\nSkewedColumnValues (list) --A list of values that appear so frequently as to be considered skewed.\n\n(string) --\n\n\nSkewedColumnValueLocationMaps (dict) --A mapping of skewed values to the columns that contain them.\n\n(string) --\n(string) --\n\n\n\n\n\n\nStoredAsSubDirectories (boolean) --\nTrue if the table data is stored in subdirectories, or False if not.\n\n\n\nParameters (dict) --These key-value pairs define partition parameters.\n\n(string) --\n(string) --\n\n\n\n\nLastAnalyzedTime (datetime) --The last time at which column statistics were computed for this partition.\n\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.GlueEncryptionException :return: {} :returns: (dict) -- """ pass def create_script(DagNodes=None, DagEdges=None, Language=None): """ Transforms a directed acyclic graph (DAG) into code. See also: AWS API Documentation Exceptions :example: response = client.create_script( DagNodes=[ { 'Id': 'string', 'NodeType': 'string', 'Args': [ { 'Name': 'string', 'Value': 'string', 'Param': True|False }, ], 'LineNumber': 123 }, ], DagEdges=[ { 'Source': 'string', 'Target': 'string', 'TargetParameter': 'string' }, ], Language='PYTHON'|'SCALA' ) :type DagNodes: list :param DagNodes: A list of the nodes in the DAG.\n\n(dict) --Represents a node in a directed acyclic graph (DAG)\n\nId (string) -- [REQUIRED]A node identifier that is unique within the node\'s graph.\n\nNodeType (string) -- [REQUIRED]The type of node that this is.\n\nArgs (list) -- [REQUIRED]Properties of the node, in the form of name-value pairs.\n\n(dict) --An argument or property of a node.\n\nName (string) -- [REQUIRED]The name of the argument or property.\n\nValue (string) -- [REQUIRED]The value of the argument or property.\n\nParam (boolean) --True if the value is used as a parameter.\n\n\n\n\n\nLineNumber (integer) --The line number of the node.\n\n\n\n\n :type DagEdges: list :param DagEdges: A list of the edges in the DAG.\n\n(dict) --Represents a directional edge in a directed acyclic graph (DAG).\n\nSource (string) -- [REQUIRED]The ID of the node at which the edge starts.\n\nTarget (string) -- [REQUIRED]The ID of the node at which the edge ends.\n\nTargetParameter (string) --The target of the edge.\n\n\n\n\n :type Language: string :param Language: The programming language of the resulting code from the DAG. :rtype: dict ReturnsResponse Syntax { 'PythonScript': 'string', 'ScalaCode': 'string' } Response Structure (dict) -- PythonScript (string) -- The Python script generated from the DAG. ScalaCode (string) -- The Scala code generated from the DAG. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'PythonScript': 'string', 'ScalaCode': 'string' } :returns: Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException """ pass def create_security_configuration(Name=None, EncryptionConfiguration=None): """ Creates a new security configuration. A security configuration is a set of security properties that can be used by AWS Glue. You can use a security configuration to encrypt data at rest. For information about using security configurations in AWS Glue, see Encrypting Data Written by Crawlers, Jobs, and Development Endpoints . See also: AWS API Documentation Exceptions :example: response = client.create_security_configuration( Name='string', EncryptionConfiguration={ 'S3Encryption': [ { 'S3EncryptionMode': 'DISABLED'|'SSE-KMS'|'SSE-S3', 'KmsKeyArn': 'string' }, ], 'CloudWatchEncryption': { 'CloudWatchEncryptionMode': 'DISABLED'|'SSE-KMS', 'KmsKeyArn': 'string' }, 'JobBookmarksEncryption': { 'JobBookmarksEncryptionMode': 'DISABLED'|'CSE-KMS', 'KmsKeyArn': 'string' } } ) :type Name: string :param Name: [REQUIRED]\nThe name for the new security configuration.\n :type EncryptionConfiguration: dict :param EncryptionConfiguration: [REQUIRED]\nThe encryption configuration for the new security configuration.\n\nS3Encryption (list) --The encryption configuration for Amazon Simple Storage Service (Amazon S3) data.\n\n(dict) --Specifies how Amazon Simple Storage Service (Amazon S3) data should be encrypted.\n\nS3EncryptionMode (string) --The encryption mode to use for Amazon S3 data.\n\nKmsKeyArn (string) --The Amazon Resource Name (ARN) of the KMS key to be used to encrypt the data.\n\n\n\n\n\nCloudWatchEncryption (dict) --The encryption configuration for Amazon CloudWatch.\n\nCloudWatchEncryptionMode (string) --The encryption mode to use for CloudWatch data.\n\nKmsKeyArn (string) --The Amazon Resource Name (ARN) of the KMS key to be used to encrypt the data.\n\n\n\nJobBookmarksEncryption (dict) --The encryption configuration for job bookmarks.\n\nJobBookmarksEncryptionMode (string) --The encryption mode to use for job bookmarks data.\n\nKmsKeyArn (string) --The Amazon Resource Name (ARN) of the KMS key to be used to encrypt the data.\n\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'Name': 'string', 'CreatedTimestamp': datetime(2015, 1, 1) } Response Structure (dict) -- Name (string) -- The name assigned to the new security configuration. CreatedTimestamp (datetime) -- The time at which the new security configuration was created. Exceptions Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException :return: { 'Name': 'string', 'CreatedTimestamp': datetime(2015, 1, 1) } :returns: Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException """ pass def create_table(CatalogId=None, DatabaseName=None, TableInput=None): """ Creates a new table definition in the Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.create_table( CatalogId='string', DatabaseName='string', TableInput={ 'Name': 'string', 'Description': 'string', 'Owner': 'string', 'LastAccessTime': datetime(2015, 1, 1), 'LastAnalyzedTime': datetime(2015, 1, 1), 'Retention': 123, 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'PartitionKeys': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'ViewOriginalText': 'string', 'ViewExpandedText': 'string', 'TableType': 'string', 'Parameters': { 'string': 'string' } } ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog in which to create the Table . If none is supplied, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe catalog database in which to create the new table. For Hive compatibility, this name is entirely lowercase.\n :type TableInput: dict :param TableInput: [REQUIRED]\nThe TableInput object that defines the metadata table to create in the catalog.\n\nName (string) -- [REQUIRED]The table name. For Hive compatibility, this is folded to lowercase when it is stored.\n\nDescription (string) --A description of the table.\n\nOwner (string) --The table owner.\n\nLastAccessTime (datetime) --The last time that the table was accessed.\n\nLastAnalyzedTime (datetime) --The last time that column statistics were computed for this table.\n\nRetention (integer) --The retention time for this table.\n\nStorageDescriptor (dict) --A storage descriptor containing information about the physical storage of this table.\n\nColumns (list) --A list of the Columns in the table.\n\n(dict) --A column in a Table .\n\nName (string) -- [REQUIRED]The name of the Column .\n\nType (string) --The data type of the Column .\n\nComment (string) --A free-form text comment.\n\nParameters (dict) --These key-value pairs define properties associated with the column.\n\n(string) --\n(string) --\n\n\n\n\n\n\n\n\nLocation (string) --The physical location of the table. By default, this takes the form of the warehouse location, followed by the database location in the warehouse, followed by the table name.\n\nInputFormat (string) --The input format: SequenceFileInputFormat (binary), or TextInputFormat , or a custom format.\n\nOutputFormat (string) --The output format: SequenceFileOutputFormat (binary), or IgnoreKeyTextOutputFormat , or a custom format.\n\nCompressed (boolean) --\nTrue if the data in the table is compressed, or False if not.\n\nNumberOfBuckets (integer) --Must be specified if the table contains any dimension columns.\n\nSerdeInfo (dict) --The serialization/deserialization (SerDe) information.\n\nName (string) --Name of the SerDe.\n\nSerializationLibrary (string) --Usually the class that implements the SerDe. An example is org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe .\n\nParameters (dict) --These key-value pairs define initialization parameters for the SerDe.\n\n(string) --\n(string) --\n\n\n\n\n\n\nBucketColumns (list) --A list of reducer grouping columns, clustering columns, and bucketing columns in the table.\n\n(string) --\n\n\nSortColumns (list) --A list specifying the sort order of each bucket in the table.\n\n(dict) --Specifies the sort order of a sorted column.\n\nColumn (string) -- [REQUIRED]The name of the column.\n\nSortOrder (integer) -- [REQUIRED]Indicates that the column is sorted in ascending order (== 1 ), or in descending order (==0 ).\n\n\n\n\n\nParameters (dict) --The user-supplied properties in key-value form.\n\n(string) --\n(string) --\n\n\n\n\nSkewedInfo (dict) --The information about values that appear frequently in a column (skewed values).\n\nSkewedColumnNames (list) --A list of names of columns that contain skewed values.\n\n(string) --\n\n\nSkewedColumnValues (list) --A list of values that appear so frequently as to be considered skewed.\n\n(string) --\n\n\nSkewedColumnValueLocationMaps (dict) --A mapping of skewed values to the columns that contain them.\n\n(string) --\n(string) --\n\n\n\n\n\n\nStoredAsSubDirectories (boolean) --\nTrue if the table data is stored in subdirectories, or False if not.\n\n\n\nPartitionKeys (list) --A list of columns by which the table is partitioned. Only primitive types are supported as partition keys.\nWhen you create a table used by Amazon Athena, and you do not specify any partitionKeys , you must at least set the value of partitionKeys to an empty list. For example:\n\n'PartitionKeys': []\n\n(dict) --A column in a Table .\n\nName (string) -- [REQUIRED]The name of the Column .\n\nType (string) --The data type of the Column .\n\nComment (string) --A free-form text comment.\n\nParameters (dict) --These key-value pairs define properties associated with the column.\n\n(string) --\n(string) --\n\n\n\n\n\n\n\n\nViewOriginalText (string) --If the table is a view, the original text of the view; otherwise null .\n\nViewExpandedText (string) --If the table is a view, the expanded text of the view; otherwise null .\n\nTableType (string) --The type of this table (EXTERNAL_TABLE , VIRTUAL_VIEW , etc.).\n\nParameters (dict) --These key-value pairs define properties associated with the table.\n\n(string) --\n(string) --\n\n\n\n\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.GlueEncryptionException :return: {} :returns: (dict) -- """ pass def create_trigger(Name=None, WorkflowName=None, Type=None, Schedule=None, Predicate=None, Actions=None, Description=None, StartOnCreation=None, Tags=None): """ Creates a new trigger. See also: AWS API Documentation Exceptions :example: response = client.create_trigger( Name='string', WorkflowName='string', Type='SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', Schedule='string', Predicate={ 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] }, Actions=[ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], Description='string', StartOnCreation=True|False, Tags={ 'string': 'string' } ) :type Name: string :param Name: [REQUIRED]\nThe name of the trigger.\n :type WorkflowName: string :param WorkflowName: The name of the workflow associated with the trigger. :type Type: string :param Type: [REQUIRED]\nThe type of the new trigger.\n :type Schedule: string :param Schedule: A cron expression used to specify the schedule (see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, you would specify: cron(15 12 * * ? *) .\nThis field is required when the trigger type is SCHEDULED.\n :type Predicate: dict :param Predicate: A predicate to specify when the new trigger should fire.\nThis field is required when the trigger type is CONDITIONAL .\n\nLogical (string) --An optional field if only one condition is listed. If multiple conditions are listed, then this field is required.\n\nConditions (list) --A list of the conditions that determine when the trigger will fire.\n\n(dict) --Defines a condition under which a trigger fires.\n\nLogicalOperator (string) --A logical operator.\n\nJobName (string) --The name of the job whose JobRuns this condition applies to, and on which this trigger waits.\n\nState (string) --The condition state. Currently, the only job states that a trigger can listen for are SUCCEEDED , STOPPED , FAILED , and TIMEOUT . The only crawler states that a trigger can listen for are SUCCEEDED , FAILED , and CANCELLED .\n\nCrawlerName (string) --The name of the crawler to which this condition applies.\n\nCrawlState (string) --The state of the crawler to which this condition applies.\n\n\n\n\n\n\n :type Actions: list :param Actions: [REQUIRED]\nThe actions initiated by this trigger when it fires.\n\n(dict) --Defines an action to be initiated by a trigger.\n\nJobName (string) --The name of a job to be executed.\n\nArguments (dict) --The job arguments used when this trigger fires. For this job run, they replace the default arguments set in the job definition itself.\nYou can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes.\nFor information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide.\nFor information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide.\n\n(string) --\n(string) --\n\n\n\n\nTimeout (integer) --The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job.\n\nSecurityConfiguration (string) --The name of the SecurityConfiguration structure to be used with this action.\n\nNotificationProperty (dict) --Specifies configuration properties of a job run notification.\n\nNotifyDelayAfter (integer) --After a job run starts, the number of minutes to wait before sending a job run delay notification.\n\n\n\nCrawlerName (string) --The name of the crawler to be used with this action.\n\n\n\n\n :type Description: string :param Description: A description of the new trigger. :type StartOnCreation: boolean :param StartOnCreation: Set to true to start SCHEDULED and CONDITIONAL triggers when created. True is not supported for ON_DEMAND triggers. :type Tags: dict :param Tags: The tags to use with this trigger. You may use tags to limit access to the trigger. For more information about tags in AWS Glue, see AWS Tags in AWS Glue in the developer guide.\n\n(string) --\n(string) --\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'Name': 'string' } Response Structure (dict) -- Name (string) -- The name of the trigger. Exceptions Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.IdempotentParameterMismatchException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.ConcurrentModificationException :return: { 'Name': 'string' } :returns: Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.IdempotentParameterMismatchException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.ConcurrentModificationException """ pass def create_user_defined_function(CatalogId=None, DatabaseName=None, FunctionInput=None): """ Creates a new function definition in the Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.create_user_defined_function( CatalogId='string', DatabaseName='string', FunctionInput={ 'FunctionName': 'string', 'ClassName': 'string', 'OwnerName': 'string', 'OwnerType': 'USER'|'ROLE'|'GROUP', 'ResourceUris': [ { 'ResourceType': 'JAR'|'FILE'|'ARCHIVE', 'Uri': 'string' }, ] } ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog in which to create the function. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe name of the catalog database in which to create the function.\n :type FunctionInput: dict :param FunctionInput: [REQUIRED]\nA FunctionInput object that defines the function to create in the Data Catalog.\n\nFunctionName (string) --The name of the function.\n\nClassName (string) --The Java class that contains the function code.\n\nOwnerName (string) --The owner of the function.\n\nOwnerType (string) --The owner type.\n\nResourceUris (list) --The resource URIs for the function.\n\n(dict) --The URIs for function resources.\n\nResourceType (string) --The type of the resource.\n\nUri (string) --The URI for accessing the resource.\n\n\n\n\n\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.GlueEncryptionException :return: {} :returns: (dict) -- """ pass def create_workflow(Name=None, Description=None, DefaultRunProperties=None, Tags=None): """ Creates a new workflow. See also: AWS API Documentation Exceptions :example: response = client.create_workflow( Name='string', Description='string', DefaultRunProperties={ 'string': 'string' }, Tags={ 'string': 'string' } ) :type Name: string :param Name: [REQUIRED]\nThe name to be assigned to the workflow. It should be unique within your account.\n :type Description: string :param Description: A description of the workflow. :type DefaultRunProperties: dict :param DefaultRunProperties: A collection of properties to be used as part of each execution of the workflow.\n\n(string) --\n(string) --\n\n\n\n :type Tags: dict :param Tags: The tags to be used with this workflow.\n\n(string) --\n(string) --\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'Name': 'string' } Response Structure (dict) -- Name (string) -- The name of the workflow which was provided as part of the request. Exceptions Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.ConcurrentModificationException :return: { 'Name': 'string' } :returns: Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.ConcurrentModificationException """ pass def delete_classifier(Name=None): """ Removes a classifier from the Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.delete_classifier( Name='string' ) :type Name: string :param Name: [REQUIRED]\nName of the classifier to remove.\n :rtype: dict ReturnsResponse Syntax{} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.OperationTimeoutException """ pass def delete_connection(CatalogId=None, ConnectionName=None): """ Deletes a connection from the Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.delete_connection( CatalogId='string', ConnectionName='string' ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog in which the connection resides. If none is provided, the AWS account ID is used by default. :type ConnectionName: string :param ConnectionName: [REQUIRED]\nThe name of the connection to delete.\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: (dict) -- """ pass def delete_crawler(Name=None): """ Removes a specified crawler from the AWS Glue Data Catalog, unless the crawler state is RUNNING . See also: AWS API Documentation Exceptions :example: response = client.delete_crawler( Name='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the crawler to remove.\n :rtype: dict ReturnsResponse Syntax{} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.CrawlerRunningException Glue.Client.exceptions.SchedulerTransitioningException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.CrawlerRunningException Glue.Client.exceptions.SchedulerTransitioningException Glue.Client.exceptions.OperationTimeoutException """ pass def delete_database(CatalogId=None, Name=None): """ Removes a specified database from a Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.delete_database( CatalogId='string', Name='string' ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog in which the database resides. If none is provided, the AWS account ID is used by default. :type Name: string :param Name: [REQUIRED]\nThe name of the database to delete. For Hive compatibility, this must be all lowercase.\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: (dict) -- """ pass def delete_dev_endpoint(EndpointName=None): """ Deletes a specified development endpoint. See also: AWS API Documentation Exceptions :example: response = client.delete_dev_endpoint( EndpointName='string' ) :type EndpointName: string :param EndpointName: [REQUIRED]\nThe name of the DevEndpoint .\n :rtype: dict ReturnsResponse Syntax{} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException :return: {} :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException """ pass def delete_job(JobName=None): """ Deletes a specified job definition. If the job definition is not found, no exception is thrown. See also: AWS API Documentation Exceptions :example: response = client.delete_job( JobName='string' ) :type JobName: string :param JobName: [REQUIRED]\nThe name of the job definition to delete.\n :rtype: dict ReturnsResponse Syntax{ 'JobName': 'string' } Response Structure (dict) -- JobName (string) --The name of the job definition that was deleted. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'JobName': 'string' } """ pass def delete_ml_transform(TransformId=None): """ Deletes an AWS Glue machine learning transform. Machine learning transforms are a special type of transform that use machine learning to learn the details of the transformation to be performed by learning from examples provided by humans. These transformations are then saved by AWS Glue. If you no longer need a transform, you can delete it by calling DeleteMLTransforms . However, any AWS Glue jobs that still reference the deleted transform will no longer succeed. See also: AWS API Documentation Exceptions :example: response = client.delete_ml_transform( TransformId='string' ) :type TransformId: string :param TransformId: [REQUIRED]\nThe unique identifier of the transform to delete.\n :rtype: dict ReturnsResponse Syntax{ 'TransformId': 'string' } Response Structure (dict) -- TransformId (string) --The unique identifier of the transform that was deleted. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException :return: { 'TransformId': 'string' } """ pass def delete_partition(CatalogId=None, DatabaseName=None, TableName=None, PartitionValues=None): """ Deletes a specified partition. See also: AWS API Documentation Exceptions :example: response = client.delete_partition( CatalogId='string', DatabaseName='string', TableName='string', PartitionValues=[ 'string', ] ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the partition to be deleted resides. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe name of the catalog database in which the table in question resides.\n :type TableName: string :param TableName: [REQUIRED]\nThe name of the table that contains the partition to be deleted.\n :type PartitionValues: list :param PartitionValues: [REQUIRED]\nThe values that define the partition.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: (dict) -- """ pass def delete_resource_policy(PolicyHashCondition=None): """ Deletes a specified policy. See also: AWS API Documentation Exceptions :example: response = client.delete_resource_policy( PolicyHashCondition='string' ) :type PolicyHashCondition: string :param PolicyHashCondition: The hash value returned when this policy was set. :rtype: dict ReturnsResponse Syntax{} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.ConditionCheckFailureException :return: {} :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.ConditionCheckFailureException """ pass def delete_security_configuration(Name=None): """ Deletes a specified security configuration. See also: AWS API Documentation Exceptions :example: response = client.delete_security_configuration( Name='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the security configuration to delete.\n :rtype: dict ReturnsResponse Syntax{} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException """ pass def delete_table(CatalogId=None, DatabaseName=None, Name=None): """ Removes a table definition from the Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.delete_table( CatalogId='string', DatabaseName='string', Name='string' ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the table resides. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe name of the catalog database in which the table resides. For Hive compatibility, this name is entirely lowercase.\n :type Name: string :param Name: [REQUIRED]\nThe name of the table to be deleted. For Hive compatibility, this name is entirely lowercase.\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: (dict) -- """ pass def delete_table_version(CatalogId=None, DatabaseName=None, TableName=None, VersionId=None): """ Deletes a specified version of a table. See also: AWS API Documentation Exceptions :example: response = client.delete_table_version( CatalogId='string', DatabaseName='string', TableName='string', VersionId='string' ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the tables reside. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe database in the catalog in which the table resides. For Hive compatibility, this name is entirely lowercase.\n :type TableName: string :param TableName: [REQUIRED]\nThe name of the table. For Hive compatibility, this name is entirely lowercase.\n :type VersionId: string :param VersionId: [REQUIRED]\nThe ID of the table version to be deleted. A VersionID is a string representation of an integer. Each version is incremented by 1.\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: (dict) -- """ pass def delete_trigger(Name=None): """ Deletes a specified trigger. If the trigger is not found, no exception is thrown. See also: AWS API Documentation Exceptions :example: response = client.delete_trigger( Name='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the trigger to delete.\n :rtype: dict ReturnsResponse Syntax{ 'Name': 'string' } Response Structure (dict) -- Name (string) --The name of the trigger that was deleted. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ConcurrentModificationException :return: { 'Name': 'string' } """ pass def delete_user_defined_function(CatalogId=None, DatabaseName=None, FunctionName=None): """ Deletes an existing function definition from the Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.delete_user_defined_function( CatalogId='string', DatabaseName='string', FunctionName='string' ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the function to be deleted is located. If none is supplied, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe name of the catalog database where the function is located.\n :type FunctionName: string :param FunctionName: [REQUIRED]\nThe name of the function definition to be deleted.\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: (dict) -- """ pass def delete_workflow(Name=None): """ Deletes a workflow. See also: AWS API Documentation Exceptions :example: response = client.delete_workflow( Name='string' ) :type Name: string :param Name: [REQUIRED]\nName of the workflow to be deleted.\n :rtype: dict ReturnsResponse Syntax{ 'Name': 'string' } Response Structure (dict) -- Name (string) --Name of the workflow specified in input. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ConcurrentModificationException :return: { 'Name': 'string' } """ pass def generate_presigned_url(ClientMethod=None, Params=None, ExpiresIn=None, HttpMethod=None): """ Generate a presigned url given a client, its method, and arguments :type ClientMethod: string :param ClientMethod: The client method to presign for :type Params: dict :param Params: The parameters normally passed to\nClientMethod. :type ExpiresIn: int :param ExpiresIn: The number of seconds the presigned url is valid\nfor. By default it expires in an hour (3600 seconds) :type HttpMethod: string :param HttpMethod: The http method to use on the generated url. By\ndefault, the http method is whatever is used in the method\'s model. """ pass def get_catalog_import_status(CatalogId=None): """ Retrieves the status of a migration operation. See also: AWS API Documentation Exceptions :example: response = client.get_catalog_import_status( CatalogId='string' ) :type CatalogId: string :param CatalogId: The ID of the catalog to migrate. Currently, this should be the AWS account ID. :rtype: dict ReturnsResponse Syntax{ 'ImportStatus': { 'ImportCompleted': True|False, 'ImportTime': datetime(2015, 1, 1), 'ImportedBy': 'string' } } Response Structure (dict) -- ImportStatus (dict) --The status of the specified catalog migration. ImportCompleted (boolean) -- True if the migration has completed, or False otherwise. ImportTime (datetime) --The time that the migration was started. ImportedBy (string) --The name of the person who initiated the migration. Exceptions Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'ImportStatus': { 'ImportCompleted': True|False, 'ImportTime': datetime(2015, 1, 1), 'ImportedBy': 'string' } } """ pass def get_classifier(Name=None): """ Retrieve a classifier by name. See also: AWS API Documentation Exceptions :example: response = client.get_classifier( Name='string' ) :type Name: string :param Name: [REQUIRED]\nName of the classifier to retrieve.\n :rtype: dict ReturnsResponse Syntax{ 'Classifier': { 'GrokClassifier': { 'Name': 'string', 'Classification': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'Version': 123, 'GrokPattern': 'string', 'CustomPatterns': 'string' }, 'XMLClassifier': { 'Name': 'string', 'Classification': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'Version': 123, 'RowTag': 'string' }, 'JsonClassifier': { 'Name': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'Version': 123, 'JsonPath': 'string' }, 'CsvClassifier': { 'Name': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'Version': 123, 'Delimiter': 'string', 'QuoteSymbol': 'string', 'ContainsHeader': 'UNKNOWN'|'PRESENT'|'ABSENT', 'Header': [ 'string', ], 'DisableValueTrimming': True|False, 'AllowSingleColumn': True|False } } } Response Structure (dict) -- Classifier (dict) --The requested classifier. GrokClassifier (dict) --A classifier that uses grok . Name (string) --The name of the classifier. Classification (string) --An identifier of the data format that the classifier matches, such as Twitter, JSON, Omniture logs, and so on. CreationTime (datetime) --The time that this classifier was registered. LastUpdated (datetime) --The time that this classifier was last updated. Version (integer) --The version of this classifier. GrokPattern (string) --The grok pattern applied to a data store by this classifier. For more information, see built-in patterns in Writing Custom Classifiers . CustomPatterns (string) --Optional custom grok patterns defined by this classifier. For more information, see custom patterns in Writing Custom Classifiers . XMLClassifier (dict) --A classifier for XML content. Name (string) --The name of the classifier. Classification (string) --An identifier of the data format that the classifier matches. CreationTime (datetime) --The time that this classifier was registered. LastUpdated (datetime) --The time that this classifier was last updated. Version (integer) --The version of this classifier. RowTag (string) --The XML tag designating the element that contains each record in an XML document being parsed. This can\'t identify a self-closing element (closed by /> ). An empty row element that contains only attributes can be parsed as long as it ends with a closing tag (for example, <row item_a="A" item_b="B"></row> is okay, but <row item_a="A" item_b="B" /> is not). JsonClassifier (dict) --A classifier for JSON content. Name (string) --The name of the classifier. CreationTime (datetime) --The time that this classifier was registered. LastUpdated (datetime) --The time that this classifier was last updated. Version (integer) --The version of this classifier. JsonPath (string) --A JsonPath string defining the JSON data for the classifier to classify. AWS Glue supports a subset of JsonPath , as described in Writing JsonPath Custom Classifiers . CsvClassifier (dict) --A classifier for comma-separated values (CSV). Name (string) --The name of the classifier. CreationTime (datetime) --The time that this classifier was registered. LastUpdated (datetime) --The time that this classifier was last updated. Version (integer) --The version of this classifier. Delimiter (string) --A custom symbol to denote what separates each column entry in the row. QuoteSymbol (string) --A custom symbol to denote what combines content into a single column value. It must be different from the column delimiter. ContainsHeader (string) --Indicates whether the CSV file contains a header. Header (list) --A list of strings representing column names. (string) -- DisableValueTrimming (boolean) --Specifies not to trim values before identifying the type of column values. The default value is true . AllowSingleColumn (boolean) --Enables the processing of files that contain only one column. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.OperationTimeoutException :return: { 'Classifier': { 'GrokClassifier': { 'Name': 'string', 'Classification': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'Version': 123, 'GrokPattern': 'string', 'CustomPatterns': 'string' }, 'XMLClassifier': { 'Name': 'string', 'Classification': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'Version': 123, 'RowTag': 'string' }, 'JsonClassifier': { 'Name': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'Version': 123, 'JsonPath': 'string' }, 'CsvClassifier': { 'Name': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'Version': 123, 'Delimiter': 'string', 'QuoteSymbol': 'string', 'ContainsHeader': 'UNKNOWN'|'PRESENT'|'ABSENT', 'Header': [ 'string', ], 'DisableValueTrimming': True|False, 'AllowSingleColumn': True|False } } } :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.OperationTimeoutException """ pass def get_classifiers(MaxResults=None, NextToken=None): """ Lists all classifier objects in the Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.get_classifiers( MaxResults=123, NextToken='string' ) :type MaxResults: integer :param MaxResults: The size of the list to return (optional). :type NextToken: string :param NextToken: An optional continuation token. :rtype: dict ReturnsResponse Syntax { 'Classifiers': [ { 'GrokClassifier': { 'Name': 'string', 'Classification': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'Version': 123, 'GrokPattern': 'string', 'CustomPatterns': 'string' }, 'XMLClassifier': { 'Name': 'string', 'Classification': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'Version': 123, 'RowTag': 'string' }, 'JsonClassifier': { 'Name': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'Version': 123, 'JsonPath': 'string' }, 'CsvClassifier': { 'Name': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'Version': 123, 'Delimiter': 'string', 'QuoteSymbol': 'string', 'ContainsHeader': 'UNKNOWN'|'PRESENT'|'ABSENT', 'Header': [ 'string', ], 'DisableValueTrimming': True|False, 'AllowSingleColumn': True|False } }, ], 'NextToken': 'string' } Response Structure (dict) -- Classifiers (list) -- The requested list of classifier objects. (dict) -- Classifiers are triggered during a crawl task. A classifier checks whether a given file is in a format it can handle. If it is, the classifier creates a schema in the form of a StructType object that matches that data format. You can use the standard classifiers that AWS Glue provides, or you can write your own classifiers to best categorize your data sources and specify the appropriate schemas to use for them. A classifier can be a grok classifier, an XML classifier, a JSON classifier, or a custom CSV classifier, as specified in one of the fields in the Classifier object. GrokClassifier (dict) -- A classifier that uses grok . Name (string) -- The name of the classifier. Classification (string) -- An identifier of the data format that the classifier matches, such as Twitter, JSON, Omniture logs, and so on. CreationTime (datetime) -- The time that this classifier was registered. LastUpdated (datetime) -- The time that this classifier was last updated. Version (integer) -- The version of this classifier. GrokPattern (string) -- The grok pattern applied to a data store by this classifier. For more information, see built-in patterns in Writing Custom Classifiers . CustomPatterns (string) -- Optional custom grok patterns defined by this classifier. For more information, see custom patterns in Writing Custom Classifiers . XMLClassifier (dict) -- A classifier for XML content. Name (string) -- The name of the classifier. Classification (string) -- An identifier of the data format that the classifier matches. CreationTime (datetime) -- The time that this classifier was registered. LastUpdated (datetime) -- The time that this classifier was last updated. Version (integer) -- The version of this classifier. RowTag (string) -- The XML tag designating the element that contains each record in an XML document being parsed. This can\'t identify a self-closing element (closed by /> ). An empty row element that contains only attributes can be parsed as long as it ends with a closing tag (for example, <row item_a="A" item_b="B"></row> is okay, but <row item_a="A" item_b="B" /> is not). JsonClassifier (dict) -- A classifier for JSON content. Name (string) -- The name of the classifier. CreationTime (datetime) -- The time that this classifier was registered. LastUpdated (datetime) -- The time that this classifier was last updated. Version (integer) -- The version of this classifier. JsonPath (string) -- A JsonPath string defining the JSON data for the classifier to classify. AWS Glue supports a subset of JsonPath , as described in Writing JsonPath Custom Classifiers . CsvClassifier (dict) -- A classifier for comma-separated values (CSV). Name (string) -- The name of the classifier. CreationTime (datetime) -- The time that this classifier was registered. LastUpdated (datetime) -- The time that this classifier was last updated. Version (integer) -- The version of this classifier. Delimiter (string) -- A custom symbol to denote what separates each column entry in the row. QuoteSymbol (string) -- A custom symbol to denote what combines content into a single column value. It must be different from the column delimiter. ContainsHeader (string) -- Indicates whether the CSV file contains a header. Header (list) -- A list of strings representing column names. (string) -- DisableValueTrimming (boolean) -- Specifies not to trim values before identifying the type of column values. The default value is true . AllowSingleColumn (boolean) -- Enables the processing of files that contain only one column. NextToken (string) -- A continuation token. Exceptions Glue.Client.exceptions.OperationTimeoutException :return: { 'Classifiers': [ { 'GrokClassifier': { 'Name': 'string', 'Classification': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'Version': 123, 'GrokPattern': 'string', 'CustomPatterns': 'string' }, 'XMLClassifier': { 'Name': 'string', 'Classification': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'Version': 123, 'RowTag': 'string' }, 'JsonClassifier': { 'Name': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'Version': 123, 'JsonPath': 'string' }, 'CsvClassifier': { 'Name': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'Version': 123, 'Delimiter': 'string', 'QuoteSymbol': 'string', 'ContainsHeader': 'UNKNOWN'|'PRESENT'|'ABSENT', 'Header': [ 'string', ], 'DisableValueTrimming': True|False, 'AllowSingleColumn': True|False } }, ], 'NextToken': 'string' } :returns: (string) -- """ pass def get_connection(CatalogId=None, Name=None, HidePassword=None): """ Retrieves a connection definition from the Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.get_connection( CatalogId='string', Name='string', HidePassword=True|False ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog in which the connection resides. If none is provided, the AWS account ID is used by default. :type Name: string :param Name: [REQUIRED]\nThe name of the connection definition to retrieve.\n :type HidePassword: boolean :param HidePassword: Allows you to retrieve the connection metadata without returning the password. For instance, the AWS Glue console uses this flag to retrieve the connection, and does not display the password. Set this parameter when the caller might not have permission to use the AWS KMS key to decrypt the password, but it does have permission to access the rest of the connection properties. :rtype: dict ReturnsResponse Syntax { 'Connection': { 'Name': 'string', 'Description': 'string', 'ConnectionType': 'JDBC'|'SFTP'|'MONGODB'|'KAFKA', 'MatchCriteria': [ 'string', ], 'ConnectionProperties': { 'string': 'string' }, 'PhysicalConnectionRequirements': { 'SubnetId': 'string', 'SecurityGroupIdList': [ 'string', ], 'AvailabilityZone': 'string' }, 'CreationTime': datetime(2015, 1, 1), 'LastUpdatedTime': datetime(2015, 1, 1), 'LastUpdatedBy': 'string' } } Response Structure (dict) -- Connection (dict) -- The requested connection definition. Name (string) -- The name of the connection definition. Description (string) -- The description of the connection. ConnectionType (string) -- The type of the connection. Currently, only JDBC is supported; SFTP is not supported. MatchCriteria (list) -- A list of criteria that can be used in selecting this connection. (string) -- ConnectionProperties (dict) -- These key-value pairs define parameters for the connection: HOST - The host URI: either the fully qualified domain name (FQDN) or the IPv4 address of the database host. PORT - The port number, between 1024 and 65535, of the port on which the database host is listening for database connections. USER_NAME - The name under which to log in to the database. The value string for USER_NAME is "USERNAME ". PASSWORD - A password, if one is used, for the user name. ENCRYPTED_PASSWORD - When you enable connection password protection by setting ConnectionPasswordEncryption in the Data Catalog encryption settings, this field stores the encrypted password. JDBC_DRIVER_JAR_URI - The Amazon Simple Storage Service (Amazon S3) path of the JAR file that contains the JDBC driver to use. JDBC_DRIVER_CLASS_NAME - The class name of the JDBC driver to use. JDBC_ENGINE - The name of the JDBC engine to use. JDBC_ENGINE_VERSION - The version of the JDBC engine to use. CONFIG_FILES - (Reserved for future use.) INSTANCE_ID - The instance ID to use. JDBC_CONNECTION_URL - The URL for connecting to a JDBC data source. JDBC_ENFORCE_SSL - A Boolean string (true, false) specifying whether Secure Sockets Layer (SSL) with hostname matching is enforced for the JDBC connection on the client. The default is false. CUSTOM_JDBC_CERT - An Amazon S3 location specifying the customer\'s root certificate. AWS Glue uses this root certificate to validate the customer\xe2\x80\x99s certificate when connecting to the customer database. AWS Glue only handles X.509 certificates. The certificate provided must be DER-encoded and supplied in Base64 encoding PEM format. SKIP_CUSTOM_JDBC_CERT_VALIDATION - By default, this is false . AWS Glue validates the Signature algorithm and Subject Public Key Algorithm for the customer certificate. The only permitted algorithms for the Signature algorithm are SHA256withRSA, SHA384withRSA or SHA512withRSA. For the Subject Public Key Algorithm, the key length must be at least 2048. You can set the value of this property to true to skip AWS Glue\xe2\x80\x99s validation of the customer certificate. CUSTOM_JDBC_CERT_STRING - A custom JDBC certificate string which is used for domain match or distinguished name match to prevent a man-in-the-middle attack. In Oracle database, this is used as the SSL_SERVER_CERT_DN ; in Microsoft SQL Server, this is used as the hostNameInCertificate . CONNECTION_URL - The URL for connecting to a general (non-JDBC) data source. KAFKA_BOOTSTRAP_SERVERS - A comma-separated list of host and port pairs that are the addresses of the Apache Kafka brokers in a Kafka cluster to which a Kafka client will connect to and bootstrap itself. (string) -- (string) -- PhysicalConnectionRequirements (dict) -- A map of physical connection requirements, such as virtual private cloud (VPC) and SecurityGroup , that are needed to make this connection successfully. SubnetId (string) -- The subnet ID used by the connection. SecurityGroupIdList (list) -- The security group ID list used by the connection. (string) -- AvailabilityZone (string) -- The connection\'s Availability Zone. This field is redundant because the specified subnet implies the Availability Zone to be used. Currently the field must be populated, but it will be deprecated in the future. CreationTime (datetime) -- The time that this connection definition was created. LastUpdatedTime (datetime) -- The last time that this connection definition was updated. LastUpdatedBy (string) -- The user, group, or role that last updated this connection definition. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.GlueEncryptionException :return: { 'Connection': { 'Name': 'string', 'Description': 'string', 'ConnectionType': 'JDBC'|'SFTP'|'MONGODB'|'KAFKA', 'MatchCriteria': [ 'string', ], 'ConnectionProperties': { 'string': 'string' }, 'PhysicalConnectionRequirements': { 'SubnetId': 'string', 'SecurityGroupIdList': [ 'string', ], 'AvailabilityZone': 'string' }, 'CreationTime': datetime(2015, 1, 1), 'LastUpdatedTime': datetime(2015, 1, 1), 'LastUpdatedBy': 'string' } } :returns: (string) -- """ pass def get_connections(CatalogId=None, Filter=None, HidePassword=None, NextToken=None, MaxResults=None): """ Retrieves a list of connection definitions from the Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.get_connections( CatalogId='string', Filter={ 'MatchCriteria': [ 'string', ], 'ConnectionType': 'JDBC'|'SFTP'|'MONGODB'|'KAFKA' }, HidePassword=True|False, NextToken='string', MaxResults=123 ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog in which the connections reside. If none is provided, the AWS account ID is used by default. :type Filter: dict :param Filter: A filter that controls which connections are returned.\n\nMatchCriteria (list) --A criteria string that must match the criteria recorded in the connection definition for that connection definition to be returned.\n\n(string) --\n\n\nConnectionType (string) --The type of connections to return. Currently, only JDBC is supported; SFTP is not supported.\n\n\n :type HidePassword: boolean :param HidePassword: Allows you to retrieve the connection metadata without returning the password. For instance, the AWS Glue console uses this flag to retrieve the connection, and does not display the password. Set this parameter when the caller might not have permission to use the AWS KMS key to decrypt the password, but it does have permission to access the rest of the connection properties. :type NextToken: string :param NextToken: A continuation token, if this is a continuation call. :type MaxResults: integer :param MaxResults: The maximum number of connections to return in one response. :rtype: dict ReturnsResponse Syntax { 'ConnectionList': [ { 'Name': 'string', 'Description': 'string', 'ConnectionType': 'JDBC'|'SFTP'|'MONGODB'|'KAFKA', 'MatchCriteria': [ 'string', ], 'ConnectionProperties': { 'string': 'string' }, 'PhysicalConnectionRequirements': { 'SubnetId': 'string', 'SecurityGroupIdList': [ 'string', ], 'AvailabilityZone': 'string' }, 'CreationTime': datetime(2015, 1, 1), 'LastUpdatedTime': datetime(2015, 1, 1), 'LastUpdatedBy': 'string' }, ], 'NextToken': 'string' } Response Structure (dict) -- ConnectionList (list) -- A list of requested connection definitions. (dict) -- Defines a connection to a data source. Name (string) -- The name of the connection definition. Description (string) -- The description of the connection. ConnectionType (string) -- The type of the connection. Currently, only JDBC is supported; SFTP is not supported. MatchCriteria (list) -- A list of criteria that can be used in selecting this connection. (string) -- ConnectionProperties (dict) -- These key-value pairs define parameters for the connection: HOST - The host URI: either the fully qualified domain name (FQDN) or the IPv4 address of the database host. PORT - The port number, between 1024 and 65535, of the port on which the database host is listening for database connections. USER_NAME - The name under which to log in to the database. The value string for USER_NAME is "USERNAME ". PASSWORD - A password, if one is used, for the user name. ENCRYPTED_PASSWORD - When you enable connection password protection by setting ConnectionPasswordEncryption in the Data Catalog encryption settings, this field stores the encrypted password. JDBC_DRIVER_JAR_URI - The Amazon Simple Storage Service (Amazon S3) path of the JAR file that contains the JDBC driver to use. JDBC_DRIVER_CLASS_NAME - The class name of the JDBC driver to use. JDBC_ENGINE - The name of the JDBC engine to use. JDBC_ENGINE_VERSION - The version of the JDBC engine to use. CONFIG_FILES - (Reserved for future use.) INSTANCE_ID - The instance ID to use. JDBC_CONNECTION_URL - The URL for connecting to a JDBC data source. JDBC_ENFORCE_SSL - A Boolean string (true, false) specifying whether Secure Sockets Layer (SSL) with hostname matching is enforced for the JDBC connection on the client. The default is false. CUSTOM_JDBC_CERT - An Amazon S3 location specifying the customer\'s root certificate. AWS Glue uses this root certificate to validate the customer\xe2\x80\x99s certificate when connecting to the customer database. AWS Glue only handles X.509 certificates. The certificate provided must be DER-encoded and supplied in Base64 encoding PEM format. SKIP_CUSTOM_JDBC_CERT_VALIDATION - By default, this is false . AWS Glue validates the Signature algorithm and Subject Public Key Algorithm for the customer certificate. The only permitted algorithms for the Signature algorithm are SHA256withRSA, SHA384withRSA or SHA512withRSA. For the Subject Public Key Algorithm, the key length must be at least 2048. You can set the value of this property to true to skip AWS Glue\xe2\x80\x99s validation of the customer certificate. CUSTOM_JDBC_CERT_STRING - A custom JDBC certificate string which is used for domain match or distinguished name match to prevent a man-in-the-middle attack. In Oracle database, this is used as the SSL_SERVER_CERT_DN ; in Microsoft SQL Server, this is used as the hostNameInCertificate . CONNECTION_URL - The URL for connecting to a general (non-JDBC) data source. KAFKA_BOOTSTRAP_SERVERS - A comma-separated list of host and port pairs that are the addresses of the Apache Kafka brokers in a Kafka cluster to which a Kafka client will connect to and bootstrap itself. (string) -- (string) -- PhysicalConnectionRequirements (dict) -- A map of physical connection requirements, such as virtual private cloud (VPC) and SecurityGroup , that are needed to make this connection successfully. SubnetId (string) -- The subnet ID used by the connection. SecurityGroupIdList (list) -- The security group ID list used by the connection. (string) -- AvailabilityZone (string) -- The connection\'s Availability Zone. This field is redundant because the specified subnet implies the Availability Zone to be used. Currently the field must be populated, but it will be deprecated in the future. CreationTime (datetime) -- The time that this connection definition was created. LastUpdatedTime (datetime) -- The last time that this connection definition was updated. LastUpdatedBy (string) -- The user, group, or role that last updated this connection definition. NextToken (string) -- A continuation token, if the list of connections returned does not include the last of the filtered connections. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.GlueEncryptionException :return: { 'ConnectionList': [ { 'Name': 'string', 'Description': 'string', 'ConnectionType': 'JDBC'|'SFTP'|'MONGODB'|'KAFKA', 'MatchCriteria': [ 'string', ], 'ConnectionProperties': { 'string': 'string' }, 'PhysicalConnectionRequirements': { 'SubnetId': 'string', 'SecurityGroupIdList': [ 'string', ], 'AvailabilityZone': 'string' }, 'CreationTime': datetime(2015, 1, 1), 'LastUpdatedTime': datetime(2015, 1, 1), 'LastUpdatedBy': 'string' }, ], 'NextToken': 'string' } :returns: (string) -- """ pass def get_crawler(Name=None): """ Retrieves metadata for a specified crawler. See also: AWS API Documentation Exceptions :example: response = client.get_crawler( Name='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the crawler to retrieve metadata for.\n :rtype: dict ReturnsResponse Syntax{ 'Crawler': { 'Name': 'string', 'Role': 'string', 'Targets': { 'S3Targets': [ { 'Path': 'string', 'Exclusions': [ 'string', ] }, ], 'JdbcTargets': [ { 'ConnectionName': 'string', 'Path': 'string', 'Exclusions': [ 'string', ] }, ], 'DynamoDBTargets': [ { 'Path': 'string' }, ], 'CatalogTargets': [ { 'DatabaseName': 'string', 'Tables': [ 'string', ] }, ] }, 'DatabaseName': 'string', 'Description': 'string', 'Classifiers': [ 'string', ], 'SchemaChangePolicy': { 'UpdateBehavior': 'LOG'|'UPDATE_IN_DATABASE', 'DeleteBehavior': 'LOG'|'DELETE_FROM_DATABASE'|'DEPRECATE_IN_DATABASE' }, 'State': 'READY'|'RUNNING'|'STOPPING', 'TablePrefix': 'string', 'Schedule': { 'ScheduleExpression': 'string', 'State': 'SCHEDULED'|'NOT_SCHEDULED'|'TRANSITIONING' }, 'CrawlElapsedTime': 123, 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'LastCrawl': { 'Status': 'SUCCEEDED'|'CANCELLED'|'FAILED', 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string', 'MessagePrefix': 'string', 'StartTime': datetime(2015, 1, 1) }, 'Version': 123, 'Configuration': 'string', 'CrawlerSecurityConfiguration': 'string' } } Response Structure (dict) -- Crawler (dict) --The metadata for the specified crawler. Name (string) --The name of the crawler. Role (string) --The Amazon Resource Name (ARN) of an IAM role that\'s used to access customer resources, such as Amazon Simple Storage Service (Amazon S3) data. Targets (dict) --A collection of targets to crawl. S3Targets (list) --Specifies Amazon Simple Storage Service (Amazon S3) targets. (dict) --Specifies a data store in Amazon Simple Storage Service (Amazon S3). Path (string) --The path to the Amazon S3 target. Exclusions (list) --A list of glob patterns used to exclude from the crawl. For more information, see Catalog Tables with a Crawler . (string) -- JdbcTargets (list) --Specifies JDBC targets. (dict) --Specifies a JDBC data store to crawl. ConnectionName (string) --The name of the connection to use to connect to the JDBC target. Path (string) --The path of the JDBC target. Exclusions (list) --A list of glob patterns used to exclude from the crawl. For more information, see Catalog Tables with a Crawler . (string) -- DynamoDBTargets (list) --Specifies Amazon DynamoDB targets. (dict) --Specifies an Amazon DynamoDB table to crawl. Path (string) --The name of the DynamoDB table to crawl. CatalogTargets (list) --Specifies AWS Glue Data Catalog targets. (dict) --Specifies an AWS Glue Data Catalog target. DatabaseName (string) --The name of the database to be synchronized. Tables (list) --A list of the tables to be synchronized. (string) -- DatabaseName (string) --The name of the database in which the crawler\'s output is stored. Description (string) --A description of the crawler. Classifiers (list) --A list of UTF-8 strings that specify the custom classifiers that are associated with the crawler. (string) -- SchemaChangePolicy (dict) --The policy that specifies update and delete behaviors for the crawler. UpdateBehavior (string) --The update behavior when the crawler finds a changed schema. DeleteBehavior (string) --The deletion behavior when the crawler finds a deleted object. State (string) --Indicates whether the crawler is running, or whether a run is pending. TablePrefix (string) --The prefix added to the names of tables that are created. Schedule (dict) --For scheduled crawlers, the schedule when the crawler runs. ScheduleExpression (string) --A cron expression used to specify the schedule. For more information, see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, specify cron(15 12 * * ? *) . State (string) --The state of the schedule. CrawlElapsedTime (integer) --If the crawler is running, contains the total time elapsed since the last crawl began. CreationTime (datetime) --The time that the crawler was created. LastUpdated (datetime) --The time that the crawler was last updated. LastCrawl (dict) --The status of the last crawl, and potentially error information if an error occurred. Status (string) --Status of the last crawl. ErrorMessage (string) --If an error occurred, the error information about the last crawl. LogGroup (string) --The log group for the last crawl. LogStream (string) --The log stream for the last crawl. MessagePrefix (string) --The prefix for a message about this crawl. StartTime (datetime) --The time at which the crawl started. Version (integer) --The version of the crawler. Configuration (string) --Crawler configuration information. This versioned JSON string allows users to specify aspects of a crawler\'s behavior. For more information, see Configuring a Crawler . CrawlerSecurityConfiguration (string) --The name of the SecurityConfiguration structure to be used by this crawler. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.OperationTimeoutException :return: { 'Crawler': { 'Name': 'string', 'Role': 'string', 'Targets': { 'S3Targets': [ { 'Path': 'string', 'Exclusions': [ 'string', ] }, ], 'JdbcTargets': [ { 'ConnectionName': 'string', 'Path': 'string', 'Exclusions': [ 'string', ] }, ], 'DynamoDBTargets': [ { 'Path': 'string' }, ], 'CatalogTargets': [ { 'DatabaseName': 'string', 'Tables': [ 'string', ] }, ] }, 'DatabaseName': 'string', 'Description': 'string', 'Classifiers': [ 'string', ], 'SchemaChangePolicy': { 'UpdateBehavior': 'LOG'|'UPDATE_IN_DATABASE', 'DeleteBehavior': 'LOG'|'DELETE_FROM_DATABASE'|'DEPRECATE_IN_DATABASE' }, 'State': 'READY'|'RUNNING'|'STOPPING', 'TablePrefix': 'string', 'Schedule': { 'ScheduleExpression': 'string', 'State': 'SCHEDULED'|'NOT_SCHEDULED'|'TRANSITIONING' }, 'CrawlElapsedTime': 123, 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'LastCrawl': { 'Status': 'SUCCEEDED'|'CANCELLED'|'FAILED', 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string', 'MessagePrefix': 'string', 'StartTime': datetime(2015, 1, 1) }, 'Version': 123, 'Configuration': 'string', 'CrawlerSecurityConfiguration': 'string' } } :returns: (string) -- """ pass def get_crawler_metrics(CrawlerNameList=None, MaxResults=None, NextToken=None): """ Retrieves metrics about specified crawlers. See also: AWS API Documentation Exceptions :example: response = client.get_crawler_metrics( CrawlerNameList=[ 'string', ], MaxResults=123, NextToken='string' ) :type CrawlerNameList: list :param CrawlerNameList: A list of the names of crawlers about which to retrieve metrics.\n\n(string) --\n\n :type MaxResults: integer :param MaxResults: The maximum size of a list to return. :type NextToken: string :param NextToken: A continuation token, if this is a continuation call. :rtype: dict ReturnsResponse Syntax { 'CrawlerMetricsList': [ { 'CrawlerName': 'string', 'TimeLeftSeconds': 123.0, 'StillEstimating': True|False, 'LastRuntimeSeconds': 123.0, 'MedianRuntimeSeconds': 123.0, 'TablesCreated': 123, 'TablesUpdated': 123, 'TablesDeleted': 123 }, ], 'NextToken': 'string' } Response Structure (dict) -- CrawlerMetricsList (list) -- A list of metrics for the specified crawler. (dict) -- Metrics for a specified crawler. CrawlerName (string) -- The name of the crawler. TimeLeftSeconds (float) -- The estimated time left to complete a running crawl. StillEstimating (boolean) -- True if the crawler is still estimating how long it will take to complete this run. LastRuntimeSeconds (float) -- The duration of the crawler\'s most recent run, in seconds. MedianRuntimeSeconds (float) -- The median duration of this crawler\'s runs, in seconds. TablesCreated (integer) -- The number of tables created by this crawler. TablesUpdated (integer) -- The number of tables updated by this crawler. TablesDeleted (integer) -- The number of tables deleted by this crawler. NextToken (string) -- A continuation token, if the returned list does not contain the last metric available. Exceptions Glue.Client.exceptions.OperationTimeoutException :return: { 'CrawlerMetricsList': [ { 'CrawlerName': 'string', 'TimeLeftSeconds': 123.0, 'StillEstimating': True|False, 'LastRuntimeSeconds': 123.0, 'MedianRuntimeSeconds': 123.0, 'TablesCreated': 123, 'TablesUpdated': 123, 'TablesDeleted': 123 }, ], 'NextToken': 'string' } :returns: Glue.Client.exceptions.OperationTimeoutException """ pass def get_crawlers(MaxResults=None, NextToken=None): """ Retrieves metadata for all crawlers defined in the customer account. See also: AWS API Documentation Exceptions :example: response = client.get_crawlers( MaxResults=123, NextToken='string' ) :type MaxResults: integer :param MaxResults: The number of crawlers to return on each call. :type NextToken: string :param NextToken: A continuation token, if this is a continuation request. :rtype: dict ReturnsResponse Syntax { 'Crawlers': [ { 'Name': 'string', 'Role': 'string', 'Targets': { 'S3Targets': [ { 'Path': 'string', 'Exclusions': [ 'string', ] }, ], 'JdbcTargets': [ { 'ConnectionName': 'string', 'Path': 'string', 'Exclusions': [ 'string', ] }, ], 'DynamoDBTargets': [ { 'Path': 'string' }, ], 'CatalogTargets': [ { 'DatabaseName': 'string', 'Tables': [ 'string', ] }, ] }, 'DatabaseName': 'string', 'Description': 'string', 'Classifiers': [ 'string', ], 'SchemaChangePolicy': { 'UpdateBehavior': 'LOG'|'UPDATE_IN_DATABASE', 'DeleteBehavior': 'LOG'|'DELETE_FROM_DATABASE'|'DEPRECATE_IN_DATABASE' }, 'State': 'READY'|'RUNNING'|'STOPPING', 'TablePrefix': 'string', 'Schedule': { 'ScheduleExpression': 'string', 'State': 'SCHEDULED'|'NOT_SCHEDULED'|'TRANSITIONING' }, 'CrawlElapsedTime': 123, 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'LastCrawl': { 'Status': 'SUCCEEDED'|'CANCELLED'|'FAILED', 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string', 'MessagePrefix': 'string', 'StartTime': datetime(2015, 1, 1) }, 'Version': 123, 'Configuration': 'string', 'CrawlerSecurityConfiguration': 'string' }, ], 'NextToken': 'string' } Response Structure (dict) -- Crawlers (list) -- A list of crawler metadata. (dict) -- Specifies a crawler program that examines a data source and uses classifiers to try to determine its schema. If successful, the crawler records metadata concerning the data source in the AWS Glue Data Catalog. Name (string) -- The name of the crawler. Role (string) -- The Amazon Resource Name (ARN) of an IAM role that\'s used to access customer resources, such as Amazon Simple Storage Service (Amazon S3) data. Targets (dict) -- A collection of targets to crawl. S3Targets (list) -- Specifies Amazon Simple Storage Service (Amazon S3) targets. (dict) -- Specifies a data store in Amazon Simple Storage Service (Amazon S3). Path (string) -- The path to the Amazon S3 target. Exclusions (list) -- A list of glob patterns used to exclude from the crawl. For more information, see Catalog Tables with a Crawler . (string) -- JdbcTargets (list) -- Specifies JDBC targets. (dict) -- Specifies a JDBC data store to crawl. ConnectionName (string) -- The name of the connection to use to connect to the JDBC target. Path (string) -- The path of the JDBC target. Exclusions (list) -- A list of glob patterns used to exclude from the crawl. For more information, see Catalog Tables with a Crawler . (string) -- DynamoDBTargets (list) -- Specifies Amazon DynamoDB targets. (dict) -- Specifies an Amazon DynamoDB table to crawl. Path (string) -- The name of the DynamoDB table to crawl. CatalogTargets (list) -- Specifies AWS Glue Data Catalog targets. (dict) -- Specifies an AWS Glue Data Catalog target. DatabaseName (string) -- The name of the database to be synchronized. Tables (list) -- A list of the tables to be synchronized. (string) -- DatabaseName (string) -- The name of the database in which the crawler\'s output is stored. Description (string) -- A description of the crawler. Classifiers (list) -- A list of UTF-8 strings that specify the custom classifiers that are associated with the crawler. (string) -- SchemaChangePolicy (dict) -- The policy that specifies update and delete behaviors for the crawler. UpdateBehavior (string) -- The update behavior when the crawler finds a changed schema. DeleteBehavior (string) -- The deletion behavior when the crawler finds a deleted object. State (string) -- Indicates whether the crawler is running, or whether a run is pending. TablePrefix (string) -- The prefix added to the names of tables that are created. Schedule (dict) -- For scheduled crawlers, the schedule when the crawler runs. ScheduleExpression (string) -- A cron expression used to specify the schedule. For more information, see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, specify cron(15 12 * * ? *) . State (string) -- The state of the schedule. CrawlElapsedTime (integer) -- If the crawler is running, contains the total time elapsed since the last crawl began. CreationTime (datetime) -- The time that the crawler was created. LastUpdated (datetime) -- The time that the crawler was last updated. LastCrawl (dict) -- The status of the last crawl, and potentially error information if an error occurred. Status (string) -- Status of the last crawl. ErrorMessage (string) -- If an error occurred, the error information about the last crawl. LogGroup (string) -- The log group for the last crawl. LogStream (string) -- The log stream for the last crawl. MessagePrefix (string) -- The prefix for a message about this crawl. StartTime (datetime) -- The time at which the crawl started. Version (integer) -- The version of the crawler. Configuration (string) -- Crawler configuration information. This versioned JSON string allows users to specify aspects of a crawler\'s behavior. For more information, see Configuring a Crawler . CrawlerSecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used by this crawler. NextToken (string) -- A continuation token, if the returned list has not reached the end of those defined in this customer account. Exceptions Glue.Client.exceptions.OperationTimeoutException :return: { 'Crawlers': [ { 'Name': 'string', 'Role': 'string', 'Targets': { 'S3Targets': [ { 'Path': 'string', 'Exclusions': [ 'string', ] }, ], 'JdbcTargets': [ { 'ConnectionName': 'string', 'Path': 'string', 'Exclusions': [ 'string', ] }, ], 'DynamoDBTargets': [ { 'Path': 'string' }, ], 'CatalogTargets': [ { 'DatabaseName': 'string', 'Tables': [ 'string', ] }, ] }, 'DatabaseName': 'string', 'Description': 'string', 'Classifiers': [ 'string', ], 'SchemaChangePolicy': { 'UpdateBehavior': 'LOG'|'UPDATE_IN_DATABASE', 'DeleteBehavior': 'LOG'|'DELETE_FROM_DATABASE'|'DEPRECATE_IN_DATABASE' }, 'State': 'READY'|'RUNNING'|'STOPPING', 'TablePrefix': 'string', 'Schedule': { 'ScheduleExpression': 'string', 'State': 'SCHEDULED'|'NOT_SCHEDULED'|'TRANSITIONING' }, 'CrawlElapsedTime': 123, 'CreationTime': datetime(2015, 1, 1), 'LastUpdated': datetime(2015, 1, 1), 'LastCrawl': { 'Status': 'SUCCEEDED'|'CANCELLED'|'FAILED', 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string', 'MessagePrefix': 'string', 'StartTime': datetime(2015, 1, 1) }, 'Version': 123, 'Configuration': 'string', 'CrawlerSecurityConfiguration': 'string' }, ], 'NextToken': 'string' } :returns: (string) -- """ pass def get_data_catalog_encryption_settings(CatalogId=None): """ Retrieves the security configuration for a specified catalog. See also: AWS API Documentation Exceptions :example: response = client.get_data_catalog_encryption_settings( CatalogId='string' ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog to retrieve the security configuration for. If none is provided, the AWS account ID is used by default. :rtype: dict ReturnsResponse Syntax{ 'DataCatalogEncryptionSettings': { 'EncryptionAtRest': { 'CatalogEncryptionMode': 'DISABLED'|'SSE-KMS', 'SseAwsKmsKeyId': 'string' }, 'ConnectionPasswordEncryption': { 'ReturnConnectionPasswordEncrypted': True|False, 'AwsKmsKeyId': 'string' } } } Response Structure (dict) -- DataCatalogEncryptionSettings (dict) --The requested security configuration. EncryptionAtRest (dict) --Specifies the encryption-at-rest configuration for the Data Catalog. CatalogEncryptionMode (string) --The encryption-at-rest mode for encrypting Data Catalog data. SseAwsKmsKeyId (string) --The ID of the AWS KMS key to use for encryption at rest. ConnectionPasswordEncryption (dict) --When connection password protection is enabled, the Data Catalog uses a customer-provided key to encrypt the password as part of CreateConnection or UpdateConnection and store it in the ENCRYPTED_PASSWORD field in the connection properties. You can enable catalog encryption or only password encryption. ReturnConnectionPasswordEncrypted (boolean) --When the ReturnConnectionPasswordEncrypted flag is set to "true", passwords remain encrypted in the responses of GetConnection and GetConnections . This encryption takes effect independently from catalog encryption. AwsKmsKeyId (string) --An AWS KMS key that is used to encrypt the connection password. If connection password protection is enabled, the caller of CreateConnection and UpdateConnection needs at least kms:Encrypt permission on the specified AWS KMS key, to encrypt passwords before storing them in the Data Catalog. You can set the decrypt permission to enable or restrict access on the password key according to your security requirements. Exceptions Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException :return: { 'DataCatalogEncryptionSettings': { 'EncryptionAtRest': { 'CatalogEncryptionMode': 'DISABLED'|'SSE-KMS', 'SseAwsKmsKeyId': 'string' }, 'ConnectionPasswordEncryption': { 'ReturnConnectionPasswordEncrypted': True|False, 'AwsKmsKeyId': 'string' } } } """ pass def get_database(CatalogId=None, Name=None): """ Retrieves the definition of a specified database. See also: AWS API Documentation Exceptions :example: response = client.get_database( CatalogId='string', Name='string' ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog in which the database resides. If none is provided, the AWS account ID is used by default. :type Name: string :param Name: [REQUIRED]\nThe name of the database to retrieve. For Hive compatibility, this should be all lowercase.\n :rtype: dict ReturnsResponse Syntax { 'Database': { 'Name': 'string', 'Description': 'string', 'LocationUri': 'string', 'Parameters': { 'string': 'string' }, 'CreateTime': datetime(2015, 1, 1), 'CreateTableDefaultPermissions': [ { 'Principal': { 'DataLakePrincipalIdentifier': 'string' }, 'Permissions': [ 'ALL'|'SELECT'|'ALTER'|'DROP'|'DELETE'|'INSERT'|'CREATE_DATABASE'|'CREATE_TABLE'|'DATA_LOCATION_ACCESS', ] }, ] } } Response Structure (dict) -- Database (dict) -- The definition of the specified database in the Data Catalog. Name (string) -- The name of the database. For Hive compatibility, this is folded to lowercase when it is stored. Description (string) -- A description of the database. LocationUri (string) -- The location of the database (for example, an HDFS path). Parameters (dict) -- These key-value pairs define parameters and properties of the database. (string) -- (string) -- CreateTime (datetime) -- The time at which the metadata database was created in the catalog. CreateTableDefaultPermissions (list) -- Creates a set of default permissions on the table for principals. (dict) -- Permissions granted to a principal. Principal (dict) -- The principal who is granted permissions. DataLakePrincipalIdentifier (string) -- An identifier for the AWS Lake Formation principal. Permissions (list) -- The permissions that are granted to the principal. (string) -- Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.GlueEncryptionException :return: { 'Database': { 'Name': 'string', 'Description': 'string', 'LocationUri': 'string', 'Parameters': { 'string': 'string' }, 'CreateTime': datetime(2015, 1, 1), 'CreateTableDefaultPermissions': [ { 'Principal': { 'DataLakePrincipalIdentifier': 'string' }, 'Permissions': [ 'ALL'|'SELECT'|'ALTER'|'DROP'|'DELETE'|'INSERT'|'CREATE_DATABASE'|'CREATE_TABLE'|'DATA_LOCATION_ACCESS', ] }, ] } } :returns: (string) -- (string) -- """ pass def get_databases(CatalogId=None, NextToken=None, MaxResults=None): """ Retrieves all databases defined in a given Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.get_databases( CatalogId='string', NextToken='string', MaxResults=123 ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog from which to retrieve Databases . If none is provided, the AWS account ID is used by default. :type NextToken: string :param NextToken: A continuation token, if this is a continuation call. :type MaxResults: integer :param MaxResults: The maximum number of databases to return in one response. :rtype: dict ReturnsResponse Syntax { 'DatabaseList': [ { 'Name': 'string', 'Description': 'string', 'LocationUri': 'string', 'Parameters': { 'string': 'string' }, 'CreateTime': datetime(2015, 1, 1), 'CreateTableDefaultPermissions': [ { 'Principal': { 'DataLakePrincipalIdentifier': 'string' }, 'Permissions': [ 'ALL'|'SELECT'|'ALTER'|'DROP'|'DELETE'|'INSERT'|'CREATE_DATABASE'|'CREATE_TABLE'|'DATA_LOCATION_ACCESS', ] }, ] }, ], 'NextToken': 'string' } Response Structure (dict) -- DatabaseList (list) -- A list of Database objects from the specified catalog. (dict) -- The Database object represents a logical grouping of tables that might reside in a Hive metastore or an RDBMS. Name (string) -- The name of the database. For Hive compatibility, this is folded to lowercase when it is stored. Description (string) -- A description of the database. LocationUri (string) -- The location of the database (for example, an HDFS path). Parameters (dict) -- These key-value pairs define parameters and properties of the database. (string) -- (string) -- CreateTime (datetime) -- The time at which the metadata database was created in the catalog. CreateTableDefaultPermissions (list) -- Creates a set of default permissions on the table for principals. (dict) -- Permissions granted to a principal. Principal (dict) -- The principal who is granted permissions. DataLakePrincipalIdentifier (string) -- An identifier for the AWS Lake Formation principal. Permissions (list) -- The permissions that are granted to the principal. (string) -- NextToken (string) -- A continuation token for paginating the returned list of tokens, returned if the current segment of the list is not the last. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.GlueEncryptionException :return: { 'DatabaseList': [ { 'Name': 'string', 'Description': 'string', 'LocationUri': 'string', 'Parameters': { 'string': 'string' }, 'CreateTime': datetime(2015, 1, 1), 'CreateTableDefaultPermissions': [ { 'Principal': { 'DataLakePrincipalIdentifier': 'string' }, 'Permissions': [ 'ALL'|'SELECT'|'ALTER'|'DROP'|'DELETE'|'INSERT'|'CREATE_DATABASE'|'CREATE_TABLE'|'DATA_LOCATION_ACCESS', ] }, ] }, ], 'NextToken': 'string' } :returns: (string) -- (string) -- """ pass def get_dataflow_graph(PythonScript=None): """ Transforms a Python script into a directed acyclic graph (DAG). See also: AWS API Documentation Exceptions :example: response = client.get_dataflow_graph( PythonScript='string' ) :type PythonScript: string :param PythonScript: The Python script to transform. :rtype: dict ReturnsResponse Syntax{ 'DagNodes': [ { 'Id': 'string', 'NodeType': 'string', 'Args': [ { 'Name': 'string', 'Value': 'string', 'Param': True|False }, ], 'LineNumber': 123 }, ], 'DagEdges': [ { 'Source': 'string', 'Target': 'string', 'TargetParameter': 'string' }, ] } Response Structure (dict) -- DagNodes (list) --A list of the nodes in the resulting DAG. (dict) --Represents a node in a directed acyclic graph (DAG) Id (string) --A node identifier that is unique within the node\'s graph. NodeType (string) --The type of node that this is. Args (list) --Properties of the node, in the form of name-value pairs. (dict) --An argument or property of a node. Name (string) --The name of the argument or property. Value (string) --The value of the argument or property. Param (boolean) --True if the value is used as a parameter. LineNumber (integer) --The line number of the node. DagEdges (list) --A list of the edges in the resulting DAG. (dict) --Represents a directional edge in a directed acyclic graph (DAG). Source (string) --The ID of the node at which the edge starts. Target (string) --The ID of the node at which the edge ends. TargetParameter (string) --The target of the edge. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'DagNodes': [ { 'Id': 'string', 'NodeType': 'string', 'Args': [ { 'Name': 'string', 'Value': 'string', 'Param': True|False }, ], 'LineNumber': 123 }, ], 'DagEdges': [ { 'Source': 'string', 'Target': 'string', 'TargetParameter': 'string' }, ] } """ pass def get_dev_endpoint(EndpointName=None): """ Retrieves information about a specified development endpoint. See also: AWS API Documentation Exceptions :example: response = client.get_dev_endpoint( EndpointName='string' ) :type EndpointName: string :param EndpointName: [REQUIRED]\nName of the DevEndpoint to retrieve information for.\n :rtype: dict ReturnsResponse Syntax{ 'DevEndpoint': { 'EndpointName': 'string', 'RoleArn': 'string', 'SecurityGroupIds': [ 'string', ], 'SubnetId': 'string', 'YarnEndpointAddress': 'string', 'PrivateAddress': 'string', 'ZeppelinRemoteSparkInterpreterPort': 123, 'PublicAddress': 'string', 'Status': 'string', 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'GlueVersion': 'string', 'NumberOfWorkers': 123, 'NumberOfNodes': 123, 'AvailabilityZone': 'string', 'VpcId': 'string', 'ExtraPythonLibsS3Path': 'string', 'ExtraJarsS3Path': 'string', 'FailureReason': 'string', 'LastUpdateStatus': 'string', 'CreatedTimestamp': datetime(2015, 1, 1), 'LastModifiedTimestamp': datetime(2015, 1, 1), 'PublicKey': 'string', 'PublicKeys': [ 'string', ], 'SecurityConfiguration': 'string', 'Arguments': { 'string': 'string' } } } Response Structure (dict) -- DevEndpoint (dict) --A DevEndpoint definition. EndpointName (string) --The name of the DevEndpoint . RoleArn (string) --The Amazon Resource Name (ARN) of the IAM role used in this DevEndpoint . SecurityGroupIds (list) --A list of security group identifiers used in this DevEndpoint . (string) -- SubnetId (string) --The subnet ID for this DevEndpoint . YarnEndpointAddress (string) --The YARN endpoint address used by this DevEndpoint . PrivateAddress (string) --A private IP address to access the DevEndpoint within a VPC if the DevEndpoint is created within one. The PrivateAddress field is present only when you create the DevEndpoint within your VPC. ZeppelinRemoteSparkInterpreterPort (integer) --The Apache Zeppelin port for the remote Apache Spark interpreter. PublicAddress (string) --The public IP address used by this DevEndpoint . The PublicAddress field is present only when you create a non-virtual private cloud (VPC) DevEndpoint . Status (string) --The current status of this DevEndpoint . WorkerType (string) --The type of predefined worker that is allocated to the development endpoint. Accepts a value of Standard, G.1X, or G.2X. For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. For the G.1X worker type, each worker maps to 1 DPU (4 vCPU, 16 GB of memory, 64 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs. For the G.2X worker type, each worker maps to 2 DPU (8 vCPU, 32 GB of memory, 128 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs. Known issue: when a development endpoint is created with the G.2X WorkerType configuration, the Spark drivers for the development endpoint will run on 4 vCPU, 16 GB of memory, and a 64 GB disk. GlueVersion (string) --Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for running your ETL scripts on development endpoints. For more information about the available AWS Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide. Development endpoints that are created without specifying a Glue version default to Glue 0.9. You can specify a version of Python support for development endpoints by using the Arguments parameter in the CreateDevEndpoint or UpdateDevEndpoint APIs. If no arguments are provided, the version defaults to Python 2. NumberOfWorkers (integer) --The number of workers of a defined workerType that are allocated to the development endpoint. The maximum number of workers you can define are 299 for G.1X , and 149 for G.2X . NumberOfNodes (integer) --The number of AWS Glue Data Processing Units (DPUs) allocated to this DevEndpoint . AvailabilityZone (string) --The AWS Availability Zone where this DevEndpoint is located. VpcId (string) --The ID of the virtual private cloud (VPC) used by this DevEndpoint . ExtraPythonLibsS3Path (string) --The paths to one or more Python libraries in an Amazon S3 bucket that should be loaded in your DevEndpoint . Multiple values must be complete paths separated by a comma. Note You can only use pure Python libraries with a DevEndpoint . Libraries that rely on C extensions, such as the pandas Python data analysis library, are not currently supported. ExtraJarsS3Path (string) --The path to one or more Java .jar files in an S3 bucket that should be loaded in your DevEndpoint . Note You can only use pure Java/Scala libraries with a DevEndpoint . FailureReason (string) --The reason for a current failure in this DevEndpoint . LastUpdateStatus (string) --The status of the last update. CreatedTimestamp (datetime) --The point in time at which this DevEndpoint was created. LastModifiedTimestamp (datetime) --The point in time at which this DevEndpoint was last modified. PublicKey (string) --The public key to be used by this DevEndpoint for authentication. This attribute is provided for backward compatibility because the recommended attribute to use is public keys. PublicKeys (list) --A list of public keys to be used by the DevEndpoints for authentication. Using this attribute is preferred over a single public key because the public keys allow you to have a different private key per client. Note If you previously created an endpoint with a public key, you must remove that key to be able to set a list of public keys. Call the UpdateDevEndpoint API operation with the public key content in the deletePublicKeys attribute, and the list of new keys in the addPublicKeys attribute. (string) -- SecurityConfiguration (string) --The name of the SecurityConfiguration structure to be used with this DevEndpoint . Arguments (dict) --A map of arguments used to configure the DevEndpoint . Valid arguments are: "--enable-glue-datacatalog": "" "GLUE_PYTHON_VERSION": "3" "GLUE_PYTHON_VERSION": "2" You can specify a version of Python support for development endpoints by using the Arguments parameter in the CreateDevEndpoint or UpdateDevEndpoint APIs. If no arguments are provided, the version defaults to Python 2. (string) -- (string) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException :return: { 'DevEndpoint': { 'EndpointName': 'string', 'RoleArn': 'string', 'SecurityGroupIds': [ 'string', ], 'SubnetId': 'string', 'YarnEndpointAddress': 'string', 'PrivateAddress': 'string', 'ZeppelinRemoteSparkInterpreterPort': 123, 'PublicAddress': 'string', 'Status': 'string', 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'GlueVersion': 'string', 'NumberOfWorkers': 123, 'NumberOfNodes': 123, 'AvailabilityZone': 'string', 'VpcId': 'string', 'ExtraPythonLibsS3Path': 'string', 'ExtraJarsS3Path': 'string', 'FailureReason': 'string', 'LastUpdateStatus': 'string', 'CreatedTimestamp': datetime(2015, 1, 1), 'LastModifiedTimestamp': datetime(2015, 1, 1), 'PublicKey': 'string', 'PublicKeys': [ 'string', ], 'SecurityConfiguration': 'string', 'Arguments': { 'string': 'string' } } } :returns: For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. For the G.1X worker type, each worker maps to 1 DPU (4 vCPU, 16 GB of memory, 64 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs. For the G.2X worker type, each worker maps to 2 DPU (8 vCPU, 32 GB of memory, 128 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs. """ pass def get_dev_endpoints(MaxResults=None, NextToken=None): """ Retrieves all the development endpoints in this AWS account. See also: AWS API Documentation Exceptions :example: response = client.get_dev_endpoints( MaxResults=123, NextToken='string' ) :type MaxResults: integer :param MaxResults: The maximum size of information to return. :type NextToken: string :param NextToken: A continuation token, if this is a continuation call. :rtype: dict ReturnsResponse Syntax { 'DevEndpoints': [ { 'EndpointName': 'string', 'RoleArn': 'string', 'SecurityGroupIds': [ 'string', ], 'SubnetId': 'string', 'YarnEndpointAddress': 'string', 'PrivateAddress': 'string', 'ZeppelinRemoteSparkInterpreterPort': 123, 'PublicAddress': 'string', 'Status': 'string', 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'GlueVersion': 'string', 'NumberOfWorkers': 123, 'NumberOfNodes': 123, 'AvailabilityZone': 'string', 'VpcId': 'string', 'ExtraPythonLibsS3Path': 'string', 'ExtraJarsS3Path': 'string', 'FailureReason': 'string', 'LastUpdateStatus': 'string', 'CreatedTimestamp': datetime(2015, 1, 1), 'LastModifiedTimestamp': datetime(2015, 1, 1), 'PublicKey': 'string', 'PublicKeys': [ 'string', ], 'SecurityConfiguration': 'string', 'Arguments': { 'string': 'string' } }, ], 'NextToken': 'string' } Response Structure (dict) -- DevEndpoints (list) -- A list of DevEndpoint definitions. (dict) -- A development endpoint where a developer can remotely debug extract, transform, and load (ETL) scripts. EndpointName (string) -- The name of the DevEndpoint . RoleArn (string) -- The Amazon Resource Name (ARN) of the IAM role used in this DevEndpoint . SecurityGroupIds (list) -- A list of security group identifiers used in this DevEndpoint . (string) -- SubnetId (string) -- The subnet ID for this DevEndpoint . YarnEndpointAddress (string) -- The YARN endpoint address used by this DevEndpoint . PrivateAddress (string) -- A private IP address to access the DevEndpoint within a VPC if the DevEndpoint is created within one. The PrivateAddress field is present only when you create the DevEndpoint within your VPC. ZeppelinRemoteSparkInterpreterPort (integer) -- The Apache Zeppelin port for the remote Apache Spark interpreter. PublicAddress (string) -- The public IP address used by this DevEndpoint . The PublicAddress field is present only when you create a non-virtual private cloud (VPC) DevEndpoint . Status (string) -- The current status of this DevEndpoint . WorkerType (string) -- The type of predefined worker that is allocated to the development endpoint. Accepts a value of Standard, G.1X, or G.2X. For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. For the G.1X worker type, each worker maps to 1 DPU (4 vCPU, 16 GB of memory, 64 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs. For the G.2X worker type, each worker maps to 2 DPU (8 vCPU, 32 GB of memory, 128 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs. Known issue: when a development endpoint is created with the G.2X WorkerType configuration, the Spark drivers for the development endpoint will run on 4 vCPU, 16 GB of memory, and a 64 GB disk. GlueVersion (string) -- Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for running your ETL scripts on development endpoints. For more information about the available AWS Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide. Development endpoints that are created without specifying a Glue version default to Glue 0.9. You can specify a version of Python support for development endpoints by using the Arguments parameter in the CreateDevEndpoint or UpdateDevEndpoint APIs. If no arguments are provided, the version defaults to Python 2. NumberOfWorkers (integer) -- The number of workers of a defined workerType that are allocated to the development endpoint. The maximum number of workers you can define are 299 for G.1X , and 149 for G.2X . NumberOfNodes (integer) -- The number of AWS Glue Data Processing Units (DPUs) allocated to this DevEndpoint . AvailabilityZone (string) -- The AWS Availability Zone where this DevEndpoint is located. VpcId (string) -- The ID of the virtual private cloud (VPC) used by this DevEndpoint . ExtraPythonLibsS3Path (string) -- The paths to one or more Python libraries in an Amazon S3 bucket that should be loaded in your DevEndpoint . Multiple values must be complete paths separated by a comma. Note You can only use pure Python libraries with a DevEndpoint . Libraries that rely on C extensions, such as the pandas Python data analysis library, are not currently supported. ExtraJarsS3Path (string) -- The path to one or more Java .jar files in an S3 bucket that should be loaded in your DevEndpoint . Note You can only use pure Java/Scala libraries with a DevEndpoint . FailureReason (string) -- The reason for a current failure in this DevEndpoint . LastUpdateStatus (string) -- The status of the last update. CreatedTimestamp (datetime) -- The point in time at which this DevEndpoint was created. LastModifiedTimestamp (datetime) -- The point in time at which this DevEndpoint was last modified. PublicKey (string) -- The public key to be used by this DevEndpoint for authentication. This attribute is provided for backward compatibility because the recommended attribute to use is public keys. PublicKeys (list) -- A list of public keys to be used by the DevEndpoints for authentication. Using this attribute is preferred over a single public key because the public keys allow you to have a different private key per client. Note If you previously created an endpoint with a public key, you must remove that key to be able to set a list of public keys. Call the UpdateDevEndpoint API operation with the public key content in the deletePublicKeys attribute, and the list of new keys in the addPublicKeys attribute. (string) -- SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this DevEndpoint . Arguments (dict) -- A map of arguments used to configure the DevEndpoint . Valid arguments are: "--enable-glue-datacatalog": "" "GLUE_PYTHON_VERSION": "3" "GLUE_PYTHON_VERSION": "2" You can specify a version of Python support for development endpoints by using the Arguments parameter in the CreateDevEndpoint or UpdateDevEndpoint APIs. If no arguments are provided, the version defaults to Python 2. (string) -- (string) -- NextToken (string) -- A continuation token, if not all DevEndpoint definitions have yet been returned. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException :return: { 'DevEndpoints': [ { 'EndpointName': 'string', 'RoleArn': 'string', 'SecurityGroupIds': [ 'string', ], 'SubnetId': 'string', 'YarnEndpointAddress': 'string', 'PrivateAddress': 'string', 'ZeppelinRemoteSparkInterpreterPort': 123, 'PublicAddress': 'string', 'Status': 'string', 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'GlueVersion': 'string', 'NumberOfWorkers': 123, 'NumberOfNodes': 123, 'AvailabilityZone': 'string', 'VpcId': 'string', 'ExtraPythonLibsS3Path': 'string', 'ExtraJarsS3Path': 'string', 'FailureReason': 'string', 'LastUpdateStatus': 'string', 'CreatedTimestamp': datetime(2015, 1, 1), 'LastModifiedTimestamp': datetime(2015, 1, 1), 'PublicKey': 'string', 'PublicKeys': [ 'string', ], 'SecurityConfiguration': 'string', 'Arguments': { 'string': 'string' } }, ], 'NextToken': 'string' } :returns: (string) -- """ pass def get_job(JobName=None): """ Retrieves an existing job definition. See also: AWS API Documentation Exceptions :example: response = client.get_job( JobName='string' ) :type JobName: string :param JobName: [REQUIRED]\nThe name of the job definition to retrieve.\n :rtype: dict ReturnsResponse Syntax{ 'Job': { 'Name': 'string', 'Description': 'string', 'LogUri': 'string', 'Role': 'string', 'CreatedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'ExecutionProperty': { 'MaxConcurrentRuns': 123 }, 'Command': { 'Name': 'string', 'ScriptLocation': 'string', 'PythonVersion': 'string' }, 'DefaultArguments': { 'string': 'string' }, 'NonOverridableArguments': { 'string': 'string' }, 'Connections': { 'Connections': [ 'string', ] }, 'MaxRetries': 123, 'AllocatedCapacity': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' } } Response Structure (dict) -- Job (dict) --The requested job definition. Name (string) --The name you assign to this job definition. Description (string) --A description of the job. LogUri (string) --This field is reserved for future use. Role (string) --The name or Amazon Resource Name (ARN) of the IAM role associated with this job. CreatedOn (datetime) --The time and date that this job definition was created. LastModifiedOn (datetime) --The last point in time when this job definition was modified. ExecutionProperty (dict) --An ExecutionProperty specifying the maximum number of concurrent runs allowed for this job. MaxConcurrentRuns (integer) --The maximum number of concurrent runs allowed for the job. The default is 1. An error is returned when this threshold is reached. The maximum value you can specify is controlled by a service limit. Command (dict) --The JobCommand that executes this job. Name (string) --The name of the job command. For an Apache Spark ETL job, this must be glueetl . For a Python shell job, it must be pythonshell . ScriptLocation (string) --Specifies the Amazon Simple Storage Service (Amazon S3) path to a script that executes a job. PythonVersion (string) --The Python version being used to execute a Python shell job. Allowed values are 2 or 3. DefaultArguments (dict) --The default arguments for this job, specified as name-value pairs. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- NonOverridableArguments (dict) --Non-overridable arguments for this job, specified as name-value pairs. (string) -- (string) -- Connections (dict) --The connections used for this job. Connections (list) --A list of connections used by the job. (string) -- MaxRetries (integer) --The maximum number of times to retry this job after a JobRun fails. AllocatedCapacity (integer) --This field is deprecated. Use MaxCapacity instead. The number of AWS Glue data processing units (DPUs) allocated to runs of this job. You can allocate from 2 to 100 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . Timeout (integer) --The job timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). MaxCapacity (float) --The number of AWS Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . Do not set Max Capacity if using WorkerType and NumberOfWorkers . The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job or an Apache Spark ETL job: When you specify a Python shell job (JobCommand.Name ="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU. When you specify an Apache Spark ETL job (JobCommand.Name ="glueetl"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation. WorkerType (string) --The type of predefined worker that is allocated when a job runs. Accepts a value of Standard, G.1X, or G.2X. For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. For the G.1X worker type, each worker maps to 1 DPU (4 vCPU, 16 GB of memory, 64 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs. For the G.2X worker type, each worker maps to 2 DPU (8 vCPU, 32 GB of memory, 128 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs. NumberOfWorkers (integer) --The number of workers of a defined workerType that are allocated when a job runs. The maximum number of workers you can define are 299 for G.1X , and 149 for G.2X . SecurityConfiguration (string) --The name of the SecurityConfiguration structure to be used with this job. NotificationProperty (dict) --Specifies configuration properties of a job notification. NotifyDelayAfter (integer) --After a job run starts, the number of minutes to wait before sending a job run delay notification. GlueVersion (string) --Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for jobs of type Spark. For more information about the available AWS Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide. Jobs that are created without specifying a Glue version default to Glue 0.9. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'Job': { 'Name': 'string', 'Description': 'string', 'LogUri': 'string', 'Role': 'string', 'CreatedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'ExecutionProperty': { 'MaxConcurrentRuns': 123 }, 'Command': { 'Name': 'string', 'ScriptLocation': 'string', 'PythonVersion': 'string' }, 'DefaultArguments': { 'string': 'string' }, 'NonOverridableArguments': { 'string': 'string' }, 'Connections': { 'Connections': [ 'string', ] }, 'MaxRetries': 123, 'AllocatedCapacity': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' } } :returns: (string) -- (string) -- """ pass def get_job_bookmark(JobName=None, RunId=None): """ Returns information on a job bookmark entry. See also: AWS API Documentation Exceptions :example: response = client.get_job_bookmark( JobName='string', RunId='string' ) :type JobName: string :param JobName: [REQUIRED]\nThe name of the job in question.\n :type RunId: string :param RunId: The unique run identifier associated with this job run. :rtype: dict ReturnsResponse Syntax { 'JobBookmarkEntry': { 'JobName': 'string', 'Version': 123, 'Run': 123, 'Attempt': 123, 'PreviousRunId': 'string', 'RunId': 'string', 'JobBookmark': 'string' } } Response Structure (dict) -- JobBookmarkEntry (dict) -- A structure that defines a point that a job can resume processing. JobName (string) -- The name of the job in question. Version (integer) -- The version of the job. Run (integer) -- The run ID number. Attempt (integer) -- The attempt ID number. PreviousRunId (string) -- The unique run identifier associated with the previous job run. RunId (string) -- The run ID number. JobBookmark (string) -- The bookmark itself. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ValidationException :return: { 'JobBookmarkEntry': { 'JobName': 'string', 'Version': 123, 'Run': 123, 'Attempt': 123, 'PreviousRunId': 'string', 'RunId': 'string', 'JobBookmark': 'string' } } :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ValidationException """ pass def get_job_run(JobName=None, RunId=None, PredecessorsIncluded=None): """ Retrieves the metadata for a given job run. See also: AWS API Documentation Exceptions :example: response = client.get_job_run( JobName='string', RunId='string', PredecessorsIncluded=True|False ) :type JobName: string :param JobName: [REQUIRED]\nName of the job definition being run.\n :type RunId: string :param RunId: [REQUIRED]\nThe ID of the job run.\n :type PredecessorsIncluded: boolean :param PredecessorsIncluded: True if a list of predecessor runs should be returned. :rtype: dict ReturnsResponse Syntax { 'JobRun': { 'Id': 'string', 'Attempt': 123, 'PreviousRunId': 'string', 'TriggerName': 'string', 'JobName': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'JobRunState': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'Arguments': { 'string': 'string' }, 'ErrorMessage': 'string', 'PredecessorRuns': [ { 'JobName': 'string', 'RunId': 'string' }, ], 'AllocatedCapacity': 123, 'ExecutionTime': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'LogGroupName': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' } } Response Structure (dict) -- JobRun (dict) -- The requested job-run metadata. Id (string) -- The ID of this job run. Attempt (integer) -- The number of the attempt to run this job. PreviousRunId (string) -- The ID of the previous run of this job. For example, the JobRunId specified in the StartJobRun action. TriggerName (string) -- The name of the trigger that started this job run. JobName (string) -- The name of the job definition being used in this run. StartedOn (datetime) -- The date and time at which this job run was started. LastModifiedOn (datetime) -- The last time that this job run was modified. CompletedOn (datetime) -- The date and time that this job run completed. JobRunState (string) -- The current state of the job run. For more information about the statuses of jobs that have terminated abnormally, see AWS Glue Job Run Statuses . Arguments (dict) -- The job arguments associated with this run. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- ErrorMessage (string) -- An error message associated with this job run. PredecessorRuns (list) -- A list of predecessors to this job run. (dict) -- A job run that was used in the predicate of a conditional trigger that triggered this job run. JobName (string) -- The name of the job definition used by the predecessor job run. RunId (string) -- The job-run ID of the predecessor job run. AllocatedCapacity (integer) -- This field is deprecated. Use MaxCapacity instead. The number of AWS Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be allocated; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . ExecutionTime (integer) -- The amount of time (in seconds) that the job run consumed resources. Timeout (integer) -- The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. MaxCapacity (float) -- The number of AWS Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . Do not set Max Capacity if using WorkerType and NumberOfWorkers . The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job or an Apache Spark ETL job: When you specify a Python shell job (JobCommand.Name ="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU. When you specify an Apache Spark ETL job (JobCommand.Name ="glueetl"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation. WorkerType (string) -- The type of predefined worker that is allocated when a job runs. Accepts a value of Standard, G.1X, or G.2X. For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. For the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker. For the G.2X worker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker. NumberOfWorkers (integer) -- The number of workers of a defined workerType that are allocated when a job runs. The maximum number of workers you can define are 299 for G.1X , and 149 for G.2X . SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this job run. LogGroupName (string) -- The name of the log group for secure logging that can be server-side encrypted in Amazon CloudWatch using AWS KMS. This name can be /aws-glue/jobs/ , in which case the default encryption is NONE . If you add a role name and SecurityConfiguration name (in other words, /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/ ), then that security configuration is used to encrypt the log group. NotificationProperty (dict) -- Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) -- After a job run starts, the number of minutes to wait before sending a job run delay notification. GlueVersion (string) -- Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for jobs of type Spark. For more information about the available AWS Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide. Jobs that are created without specifying a Glue version default to Glue 0.9. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'JobRun': { 'Id': 'string', 'Attempt': 123, 'PreviousRunId': 'string', 'TriggerName': 'string', 'JobName': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'JobRunState': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'Arguments': { 'string': 'string' }, 'ErrorMessage': 'string', 'PredecessorRuns': [ { 'JobName': 'string', 'RunId': 'string' }, ], 'AllocatedCapacity': 123, 'ExecutionTime': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'LogGroupName': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' } } :returns: (string) -- (string) -- """ pass def get_job_runs(JobName=None, NextToken=None, MaxResults=None): """ Retrieves metadata for all runs of a given job definition. See also: AWS API Documentation Exceptions :example: response = client.get_job_runs( JobName='string', NextToken='string', MaxResults=123 ) :type JobName: string :param JobName: [REQUIRED]\nThe name of the job definition for which to retrieve all job runs.\n :type NextToken: string :param NextToken: A continuation token, if this is a continuation call. :type MaxResults: integer :param MaxResults: The maximum size of the response. :rtype: dict ReturnsResponse Syntax { 'JobRuns': [ { 'Id': 'string', 'Attempt': 123, 'PreviousRunId': 'string', 'TriggerName': 'string', 'JobName': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'JobRunState': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'Arguments': { 'string': 'string' }, 'ErrorMessage': 'string', 'PredecessorRuns': [ { 'JobName': 'string', 'RunId': 'string' }, ], 'AllocatedCapacity': 123, 'ExecutionTime': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'LogGroupName': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ], 'NextToken': 'string' } Response Structure (dict) -- JobRuns (list) -- A list of job-run metadata objects. (dict) -- Contains information about a job run. Id (string) -- The ID of this job run. Attempt (integer) -- The number of the attempt to run this job. PreviousRunId (string) -- The ID of the previous run of this job. For example, the JobRunId specified in the StartJobRun action. TriggerName (string) -- The name of the trigger that started this job run. JobName (string) -- The name of the job definition being used in this run. StartedOn (datetime) -- The date and time at which this job run was started. LastModifiedOn (datetime) -- The last time that this job run was modified. CompletedOn (datetime) -- The date and time that this job run completed. JobRunState (string) -- The current state of the job run. For more information about the statuses of jobs that have terminated abnormally, see AWS Glue Job Run Statuses . Arguments (dict) -- The job arguments associated with this run. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- ErrorMessage (string) -- An error message associated with this job run. PredecessorRuns (list) -- A list of predecessors to this job run. (dict) -- A job run that was used in the predicate of a conditional trigger that triggered this job run. JobName (string) -- The name of the job definition used by the predecessor job run. RunId (string) -- The job-run ID of the predecessor job run. AllocatedCapacity (integer) -- This field is deprecated. Use MaxCapacity instead. The number of AWS Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be allocated; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . ExecutionTime (integer) -- The amount of time (in seconds) that the job run consumed resources. Timeout (integer) -- The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. MaxCapacity (float) -- The number of AWS Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . Do not set Max Capacity if using WorkerType and NumberOfWorkers . The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job or an Apache Spark ETL job: When you specify a Python shell job (JobCommand.Name ="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU. When you specify an Apache Spark ETL job (JobCommand.Name ="glueetl"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation. WorkerType (string) -- The type of predefined worker that is allocated when a job runs. Accepts a value of Standard, G.1X, or G.2X. For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. For the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker. For the G.2X worker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker. NumberOfWorkers (integer) -- The number of workers of a defined workerType that are allocated when a job runs. The maximum number of workers you can define are 299 for G.1X , and 149 for G.2X . SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this job run. LogGroupName (string) -- The name of the log group for secure logging that can be server-side encrypted in Amazon CloudWatch using AWS KMS. This name can be /aws-glue/jobs/ , in which case the default encryption is NONE . If you add a role name and SecurityConfiguration name (in other words, /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/ ), then that security configuration is used to encrypt the log group. NotificationProperty (dict) -- Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) -- After a job run starts, the number of minutes to wait before sending a job run delay notification. GlueVersion (string) -- Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for jobs of type Spark. For more information about the available AWS Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide. Jobs that are created without specifying a Glue version default to Glue 0.9. NextToken (string) -- A continuation token, if not all requested job runs have been returned. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'JobRuns': [ { 'Id': 'string', 'Attempt': 123, 'PreviousRunId': 'string', 'TriggerName': 'string', 'JobName': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'JobRunState': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'Arguments': { 'string': 'string' }, 'ErrorMessage': 'string', 'PredecessorRuns': [ { 'JobName': 'string', 'RunId': 'string' }, ], 'AllocatedCapacity': 123, 'ExecutionTime': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'LogGroupName': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ], 'NextToken': 'string' } :returns: (string) -- (string) -- """ pass def get_jobs(NextToken=None, MaxResults=None): """ Retrieves all current job definitions. See also: AWS API Documentation Exceptions :example: response = client.get_jobs( NextToken='string', MaxResults=123 ) :type NextToken: string :param NextToken: A continuation token, if this is a continuation call. :type MaxResults: integer :param MaxResults: The maximum size of the response. :rtype: dict ReturnsResponse Syntax { 'Jobs': [ { 'Name': 'string', 'Description': 'string', 'LogUri': 'string', 'Role': 'string', 'CreatedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'ExecutionProperty': { 'MaxConcurrentRuns': 123 }, 'Command': { 'Name': 'string', 'ScriptLocation': 'string', 'PythonVersion': 'string' }, 'DefaultArguments': { 'string': 'string' }, 'NonOverridableArguments': { 'string': 'string' }, 'Connections': { 'Connections': [ 'string', ] }, 'MaxRetries': 123, 'AllocatedCapacity': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ], 'NextToken': 'string' } Response Structure (dict) -- Jobs (list) -- A list of job definitions. (dict) -- Specifies a job definition. Name (string) -- The name you assign to this job definition. Description (string) -- A description of the job. LogUri (string) -- This field is reserved for future use. Role (string) -- The name or Amazon Resource Name (ARN) of the IAM role associated with this job. CreatedOn (datetime) -- The time and date that this job definition was created. LastModifiedOn (datetime) -- The last point in time when this job definition was modified. ExecutionProperty (dict) -- An ExecutionProperty specifying the maximum number of concurrent runs allowed for this job. MaxConcurrentRuns (integer) -- The maximum number of concurrent runs allowed for the job. The default is 1. An error is returned when this threshold is reached. The maximum value you can specify is controlled by a service limit. Command (dict) -- The JobCommand that executes this job. Name (string) -- The name of the job command. For an Apache Spark ETL job, this must be glueetl . For a Python shell job, it must be pythonshell . ScriptLocation (string) -- Specifies the Amazon Simple Storage Service (Amazon S3) path to a script that executes a job. PythonVersion (string) -- The Python version being used to execute a Python shell job. Allowed values are 2 or 3. DefaultArguments (dict) -- The default arguments for this job, specified as name-value pairs. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- NonOverridableArguments (dict) -- Non-overridable arguments for this job, specified as name-value pairs. (string) -- (string) -- Connections (dict) -- The connections used for this job. Connections (list) -- A list of connections used by the job. (string) -- MaxRetries (integer) -- The maximum number of times to retry this job after a JobRun fails. AllocatedCapacity (integer) -- This field is deprecated. Use MaxCapacity instead. The number of AWS Glue data processing units (DPUs) allocated to runs of this job. You can allocate from 2 to 100 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . Timeout (integer) -- The job timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). MaxCapacity (float) -- The number of AWS Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . Do not set Max Capacity if using WorkerType and NumberOfWorkers . The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job or an Apache Spark ETL job: When you specify a Python shell job (JobCommand.Name ="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU. When you specify an Apache Spark ETL job (JobCommand.Name ="glueetl"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation. WorkerType (string) -- The type of predefined worker that is allocated when a job runs. Accepts a value of Standard, G.1X, or G.2X. For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. For the G.1X worker type, each worker maps to 1 DPU (4 vCPU, 16 GB of memory, 64 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs. For the G.2X worker type, each worker maps to 2 DPU (8 vCPU, 32 GB of memory, 128 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs. NumberOfWorkers (integer) -- The number of workers of a defined workerType that are allocated when a job runs. The maximum number of workers you can define are 299 for G.1X , and 149 for G.2X . SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this job. NotificationProperty (dict) -- Specifies configuration properties of a job notification. NotifyDelayAfter (integer) -- After a job run starts, the number of minutes to wait before sending a job run delay notification. GlueVersion (string) -- Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for jobs of type Spark. For more information about the available AWS Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide. Jobs that are created without specifying a Glue version default to Glue 0.9. NextToken (string) -- A continuation token, if not all job definitions have yet been returned. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'Jobs': [ { 'Name': 'string', 'Description': 'string', 'LogUri': 'string', 'Role': 'string', 'CreatedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'ExecutionProperty': { 'MaxConcurrentRuns': 123 }, 'Command': { 'Name': 'string', 'ScriptLocation': 'string', 'PythonVersion': 'string' }, 'DefaultArguments': { 'string': 'string' }, 'NonOverridableArguments': { 'string': 'string' }, 'Connections': { 'Connections': [ 'string', ] }, 'MaxRetries': 123, 'AllocatedCapacity': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ], 'NextToken': 'string' } :returns: (string) -- (string) -- """ pass def get_mapping(Source=None, Sinks=None, Location=None): """ Creates mappings. See also: AWS API Documentation Exceptions :example: response = client.get_mapping( Source={ 'DatabaseName': 'string', 'TableName': 'string' }, Sinks=[ { 'DatabaseName': 'string', 'TableName': 'string' }, ], Location={ 'Jdbc': [ { 'Name': 'string', 'Value': 'string', 'Param': True|False }, ], 'S3': [ { 'Name': 'string', 'Value': 'string', 'Param': True|False }, ], 'DynamoDB': [ { 'Name': 'string', 'Value': 'string', 'Param': True|False }, ] } ) :type Source: dict :param Source: [REQUIRED]\nSpecifies the source table.\n\nDatabaseName (string) -- [REQUIRED]The database in which the table metadata resides.\n\nTableName (string) -- [REQUIRED]The name of the table in question.\n\n\n :type Sinks: list :param Sinks: A list of target tables.\n\n(dict) --Specifies a table definition in the AWS Glue Data Catalog.\n\nDatabaseName (string) -- [REQUIRED]The database in which the table metadata resides.\n\nTableName (string) -- [REQUIRED]The name of the table in question.\n\n\n\n\n :type Location: dict :param Location: Parameters for the mapping.\n\nJdbc (list) --A JDBC location.\n\n(dict) --An argument or property of a node.\n\nName (string) -- [REQUIRED]The name of the argument or property.\n\nValue (string) -- [REQUIRED]The value of the argument or property.\n\nParam (boolean) --True if the value is used as a parameter.\n\n\n\n\n\nS3 (list) --An Amazon Simple Storage Service (Amazon S3) location.\n\n(dict) --An argument or property of a node.\n\nName (string) -- [REQUIRED]The name of the argument or property.\n\nValue (string) -- [REQUIRED]The value of the argument or property.\n\nParam (boolean) --True if the value is used as a parameter.\n\n\n\n\n\nDynamoDB (list) --An Amazon DynamoDB table location.\n\n(dict) --An argument or property of a node.\n\nName (string) -- [REQUIRED]The name of the argument or property.\n\nValue (string) -- [REQUIRED]The value of the argument or property.\n\nParam (boolean) --True if the value is used as a parameter.\n\n\n\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'Mapping': [ { 'SourceTable': 'string', 'SourcePath': 'string', 'SourceType': 'string', 'TargetTable': 'string', 'TargetPath': 'string', 'TargetType': 'string' }, ] } Response Structure (dict) -- Mapping (list) -- A list of mappings to the specified targets. (dict) -- Defines a mapping. SourceTable (string) -- The name of the source table. SourcePath (string) -- The source path. SourceType (string) -- The source type. TargetTable (string) -- The target table. TargetPath (string) -- The target path. TargetType (string) -- The target type. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.EntityNotFoundException :return: { 'Mapping': [ { 'SourceTable': 'string', 'SourcePath': 'string', 'SourceType': 'string', 'TargetTable': 'string', 'TargetPath': 'string', 'TargetType': 'string' }, ] } :returns: Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.EntityNotFoundException """ pass def get_ml_task_run(TransformId=None, TaskRunId=None): """ Gets details for a specific task run on a machine learning transform. Machine learning task runs are asynchronous tasks that AWS Glue runs on your behalf as part of various machine learning workflows. You can check the stats of any task run by calling GetMLTaskRun with the TaskRunID and its parent transform\'s TransformID . See also: AWS API Documentation Exceptions :example: response = client.get_ml_task_run( TransformId='string', TaskRunId='string' ) :type TransformId: string :param TransformId: [REQUIRED]\nThe unique identifier of the machine learning transform.\n :type TaskRunId: string :param TaskRunId: [REQUIRED]\nThe unique identifier of the task run.\n :rtype: dict ReturnsResponse Syntax { 'TransformId': 'string', 'TaskRunId': 'string', 'Status': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'LogGroupName': 'string', 'Properties': { 'TaskType': 'EVALUATION'|'LABELING_SET_GENERATION'|'IMPORT_LABELS'|'EXPORT_LABELS'|'FIND_MATCHES', 'ImportLabelsTaskRunProperties': { 'InputS3Path': 'string', 'Replace': True|False }, 'ExportLabelsTaskRunProperties': { 'OutputS3Path': 'string' }, 'LabelingSetGenerationTaskRunProperties': { 'OutputS3Path': 'string' }, 'FindMatchesTaskRunProperties': { 'JobId': 'string', 'JobName': 'string', 'JobRunId': 'string' } }, 'ErrorString': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'ExecutionTime': 123 } Response Structure (dict) -- TransformId (string) -- The unique identifier of the task run. TaskRunId (string) -- The unique run identifier associated with this run. Status (string) -- The status for this task run. LogGroupName (string) -- The names of the log groups that are associated with the task run. Properties (dict) -- The list of properties that are associated with the task run. TaskType (string) -- The type of task run. ImportLabelsTaskRunProperties (dict) -- The configuration properties for an importing labels task run. InputS3Path (string) -- The Amazon Simple Storage Service (Amazon S3) path from where you will import the labels. Replace (boolean) -- Indicates whether to overwrite your existing labels. ExportLabelsTaskRunProperties (dict) -- The configuration properties for an exporting labels task run. OutputS3Path (string) -- The Amazon Simple Storage Service (Amazon S3) path where you will export the labels. LabelingSetGenerationTaskRunProperties (dict) -- The configuration properties for a labeling set generation task run. OutputS3Path (string) -- The Amazon Simple Storage Service (Amazon S3) path where you will generate the labeling set. FindMatchesTaskRunProperties (dict) -- The configuration properties for a find matches task run. JobId (string) -- The job ID for the Find Matches task run. JobName (string) -- The name assigned to the job for the Find Matches task run. JobRunId (string) -- The job run ID for the Find Matches task run. ErrorString (string) -- The error strings that are associated with the task run. StartedOn (datetime) -- The date and time when this task run started. LastModifiedOn (datetime) -- The date and time when this task run was last modified. CompletedOn (datetime) -- The date and time when this task run was completed. ExecutionTime (integer) -- The amount of time (in seconds) that the task run consumed resources. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException :return: { 'TransformId': 'string', 'TaskRunId': 'string', 'Status': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'LogGroupName': 'string', 'Properties': { 'TaskType': 'EVALUATION'|'LABELING_SET_GENERATION'|'IMPORT_LABELS'|'EXPORT_LABELS'|'FIND_MATCHES', 'ImportLabelsTaskRunProperties': { 'InputS3Path': 'string', 'Replace': True|False }, 'ExportLabelsTaskRunProperties': { 'OutputS3Path': 'string' }, 'LabelingSetGenerationTaskRunProperties': { 'OutputS3Path': 'string' }, 'FindMatchesTaskRunProperties': { 'JobId': 'string', 'JobName': 'string', 'JobRunId': 'string' } }, 'ErrorString': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'ExecutionTime': 123 } :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException """ pass def get_ml_task_runs(TransformId=None, NextToken=None, MaxResults=None, Filter=None, Sort=None): """ Gets a list of runs for a machine learning transform. Machine learning task runs are asynchronous tasks that AWS Glue runs on your behalf as part of various machine learning workflows. You can get a sortable, filterable list of machine learning task runs by calling GetMLTaskRuns with their parent transform\'s TransformID and other optional parameters as documented in this section. This operation returns a list of historic runs and must be paginated. See also: AWS API Documentation Exceptions :example: response = client.get_ml_task_runs( TransformId='string', NextToken='string', MaxResults=123, Filter={ 'TaskRunType': 'EVALUATION'|'LABELING_SET_GENERATION'|'IMPORT_LABELS'|'EXPORT_LABELS'|'FIND_MATCHES', 'Status': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'StartedBefore': datetime(2015, 1, 1), 'StartedAfter': datetime(2015, 1, 1) }, Sort={ 'Column': 'TASK_RUN_TYPE'|'STATUS'|'STARTED', 'SortDirection': 'DESCENDING'|'ASCENDING' } ) :type TransformId: string :param TransformId: [REQUIRED]\nThe unique identifier of the machine learning transform.\n :type NextToken: string :param NextToken: A token for pagination of the results. The default is empty. :type MaxResults: integer :param MaxResults: The maximum number of results to return. :type Filter: dict :param Filter: The filter criteria, in the TaskRunFilterCriteria structure, for the task run.\n\nTaskRunType (string) --The type of task run.\n\nStatus (string) --The current status of the task run.\n\nStartedBefore (datetime) --Filter on task runs started before this date.\n\nStartedAfter (datetime) --Filter on task runs started after this date.\n\n\n :type Sort: dict :param Sort: The sorting criteria, in the TaskRunSortCriteria structure, for the task run.\n\nColumn (string) -- [REQUIRED]The column to be used to sort the list of task runs for the machine learning transform.\n\nSortDirection (string) -- [REQUIRED]The sort direction to be used to sort the list of task runs for the machine learning transform.\n\n\n :rtype: dict ReturnsResponse Syntax { 'TaskRuns': [ { 'TransformId': 'string', 'TaskRunId': 'string', 'Status': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'LogGroupName': 'string', 'Properties': { 'TaskType': 'EVALUATION'|'LABELING_SET_GENERATION'|'IMPORT_LABELS'|'EXPORT_LABELS'|'FIND_MATCHES', 'ImportLabelsTaskRunProperties': { 'InputS3Path': 'string', 'Replace': True|False }, 'ExportLabelsTaskRunProperties': { 'OutputS3Path': 'string' }, 'LabelingSetGenerationTaskRunProperties': { 'OutputS3Path': 'string' }, 'FindMatchesTaskRunProperties': { 'JobId': 'string', 'JobName': 'string', 'JobRunId': 'string' } }, 'ErrorString': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'ExecutionTime': 123 }, ], 'NextToken': 'string' } Response Structure (dict) -- TaskRuns (list) -- A list of task runs that are associated with the transform. (dict) -- The sampling parameters that are associated with the machine learning transform. TransformId (string) -- The unique identifier for the transform. TaskRunId (string) -- The unique identifier for this task run. Status (string) -- The current status of the requested task run. LogGroupName (string) -- The names of the log group for secure logging, associated with this task run. Properties (dict) -- Specifies configuration properties associated with this task run. TaskType (string) -- The type of task run. ImportLabelsTaskRunProperties (dict) -- The configuration properties for an importing labels task run. InputS3Path (string) -- The Amazon Simple Storage Service (Amazon S3) path from where you will import the labels. Replace (boolean) -- Indicates whether to overwrite your existing labels. ExportLabelsTaskRunProperties (dict) -- The configuration properties for an exporting labels task run. OutputS3Path (string) -- The Amazon Simple Storage Service (Amazon S3) path where you will export the labels. LabelingSetGenerationTaskRunProperties (dict) -- The configuration properties for a labeling set generation task run. OutputS3Path (string) -- The Amazon Simple Storage Service (Amazon S3) path where you will generate the labeling set. FindMatchesTaskRunProperties (dict) -- The configuration properties for a find matches task run. JobId (string) -- The job ID for the Find Matches task run. JobName (string) -- The name assigned to the job for the Find Matches task run. JobRunId (string) -- The job run ID for the Find Matches task run. ErrorString (string) -- The list of error strings associated with this task run. StartedOn (datetime) -- The date and time that this task run started. LastModifiedOn (datetime) -- The last point in time that the requested task run was updated. CompletedOn (datetime) -- The last point in time that the requested task run was completed. ExecutionTime (integer) -- The amount of time (in seconds) that the task run consumed resources. NextToken (string) -- A pagination token, if more results are available. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException :return: { 'TaskRuns': [ { 'TransformId': 'string', 'TaskRunId': 'string', 'Status': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'LogGroupName': 'string', 'Properties': { 'TaskType': 'EVALUATION'|'LABELING_SET_GENERATION'|'IMPORT_LABELS'|'EXPORT_LABELS'|'FIND_MATCHES', 'ImportLabelsTaskRunProperties': { 'InputS3Path': 'string', 'Replace': True|False }, 'ExportLabelsTaskRunProperties': { 'OutputS3Path': 'string' }, 'LabelingSetGenerationTaskRunProperties': { 'OutputS3Path': 'string' }, 'FindMatchesTaskRunProperties': { 'JobId': 'string', 'JobName': 'string', 'JobRunId': 'string' } }, 'ErrorString': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'ExecutionTime': 123 }, ], 'NextToken': 'string' } :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException """ pass def get_ml_transform(TransformId=None): """ Gets an AWS Glue machine learning transform artifact and all its corresponding metadata. Machine learning transforms are a special type of transform that use machine learning to learn the details of the transformation to be performed by learning from examples provided by humans. These transformations are then saved by AWS Glue. You can retrieve their metadata by calling GetMLTransform . See also: AWS API Documentation Exceptions :example: response = client.get_ml_transform( TransformId='string' ) :type TransformId: string :param TransformId: [REQUIRED]\nThe unique identifier of the transform, generated at the time that the transform was created.\n :rtype: dict ReturnsResponse Syntax{ 'TransformId': 'string', 'Name': 'string', 'Description': 'string', 'Status': 'NOT_READY'|'READY'|'DELETING', 'CreatedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'InputRecordTables': [ { 'DatabaseName': 'string', 'TableName': 'string', 'CatalogId': 'string', 'ConnectionName': 'string' }, ], 'Parameters': { 'TransformType': 'FIND_MATCHES', 'FindMatchesParameters': { 'PrimaryKeyColumnName': 'string', 'PrecisionRecallTradeoff': 123.0, 'AccuracyCostTradeoff': 123.0, 'EnforceProvidedLabels': True|False } }, 'EvaluationMetrics': { 'TransformType': 'FIND_MATCHES', 'FindMatchesMetrics': { 'AreaUnderPRCurve': 123.0, 'Precision': 123.0, 'Recall': 123.0, 'F1': 123.0, 'ConfusionMatrix': { 'NumTruePositives': 123, 'NumFalsePositives': 123, 'NumTrueNegatives': 123, 'NumFalseNegatives': 123 } } }, 'LabelCount': 123, 'Schema': [ { 'Name': 'string', 'DataType': 'string' }, ], 'Role': 'string', 'GlueVersion': 'string', 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'Timeout': 123, 'MaxRetries': 123 } Response Structure (dict) -- TransformId (string) --The unique identifier of the transform, generated at the time that the transform was created. Name (string) --The unique name given to the transform when it was created. Description (string) --A description of the transform. Status (string) --The last known status of the transform (to indicate whether it can be used or not). One of "NOT_READY", "READY", or "DELETING". CreatedOn (datetime) --The date and time when the transform was created. LastModifiedOn (datetime) --The date and time when the transform was last modified. InputRecordTables (list) --A list of AWS Glue table definitions used by the transform. (dict) --The database and table in the AWS Glue Data Catalog that is used for input or output data. DatabaseName (string) --A database name in the AWS Glue Data Catalog. TableName (string) --A table name in the AWS Glue Data Catalog. CatalogId (string) --A unique identifier for the AWS Glue Data Catalog. ConnectionName (string) --The name of the connection to the AWS Glue Data Catalog. Parameters (dict) --The configuration parameters that are specific to the algorithm used. TransformType (string) --The type of machine learning transform. For information about the types of machine learning transforms, see Creating Machine Learning Transforms . FindMatchesParameters (dict) --The parameters for the find matches algorithm. PrimaryKeyColumnName (string) --The name of a column that uniquely identifies rows in the source table. Used to help identify matching records. PrecisionRecallTradeoff (float) --The value selected when tuning your transform for a balance between precision and recall. A value of 0.5 means no preference; a value of 1.0 means a bias purely for precision, and a value of 0.0 means a bias for recall. Because this is a tradeoff, choosing values close to 1.0 means very low recall, and choosing values close to 0.0 results in very low precision. The precision metric indicates how often your model is correct when it predicts a match. The recall metric indicates that for an actual match, how often your model predicts the match. AccuracyCostTradeoff (float) --The value that is selected when tuning your transform for a balance between accuracy and cost. A value of 0.5 means that the system balances accuracy and cost concerns. A value of 1.0 means a bias purely for accuracy, which typically results in a higher cost, sometimes substantially higher. A value of 0.0 means a bias purely for cost, which results in a less accurate FindMatches transform, sometimes with unacceptable accuracy. Accuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall. Cost measures how many compute resources, and thus money, are consumed to run the transform. EnforceProvidedLabels (boolean) --The value to switch on or off to force the output to match the provided labels from users. If the value is True , the find matches transform forces the output to match the provided labels. The results override the normal conflation results. If the value is False , the find matches transform does not ensure all the labels provided are respected, and the results rely on the trained model. Note that setting this value to true may increase the conflation execution time. EvaluationMetrics (dict) --The latest evaluation metrics. TransformType (string) --The type of machine learning transform. FindMatchesMetrics (dict) --The evaluation metrics for the find matches algorithm. AreaUnderPRCurve (float) --The area under the precision/recall curve (AUPRC) is a single number measuring the overall quality of the transform, that is independent of the choice made for precision vs. recall. Higher values indicate that you have a more attractive precision vs. recall tradeoff. For more information, see Precision and recall in Wikipedia. Precision (float) --The precision metric indicates when often your transform is correct when it predicts a match. Specifically, it measures how well the transform finds true positives from the total true positives possible. For more information, see Precision and recall in Wikipedia. Recall (float) --The recall metric indicates that for an actual match, how often your transform predicts the match. Specifically, it measures how well the transform finds true positives from the total records in the source data. For more information, see Precision and recall in Wikipedia. F1 (float) --The maximum F1 metric indicates the transform\'s accuracy between 0 and 1, where 1 is the best accuracy. For more information, see F1 score in Wikipedia. ConfusionMatrix (dict) --The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making. For more information, see Confusion matrix in Wikipedia. NumTruePositives (integer) --The number of matches in the data that the transform correctly found, in the confusion matrix for your transform. NumFalsePositives (integer) --The number of nonmatches in the data that the transform incorrectly classified as a match, in the confusion matrix for your transform. NumTrueNegatives (integer) --The number of nonmatches in the data that the transform correctly rejected, in the confusion matrix for your transform. NumFalseNegatives (integer) --The number of matches in the data that the transform didn\'t find, in the confusion matrix for your transform. LabelCount (integer) --The number of labels available for this transform. Schema (list) --The Map<Column, Type> object that represents the schema that this transform accepts. Has an upper bound of 100 columns. (dict) --A key-value pair representing a column and data type that this transform can run against. The Schema parameter of the MLTransform may contain up to 100 of these structures. Name (string) --The name of the column. DataType (string) --The type of data in the column. Role (string) --The name or Amazon Resource Name (ARN) of the IAM role with the required permissions. GlueVersion (string) --This value determines which version of AWS Glue this machine learning transform is compatible with. Glue 1.0 is recommended for most customers. If the value is not set, the Glue compatibility defaults to Glue 0.9. For more information, see AWS Glue Versions in the developer guide. MaxCapacity (float) --The number of AWS Glue data processing units (DPUs) that are allocated to task runs for this transform. You can allocate from 2 to 100 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . When the WorkerType field is set to a value other than Standard , the MaxCapacity field is set automatically and becomes read-only. WorkerType (string) --The type of predefined worker that is allocated when this task runs. Accepts a value of Standard, G.1X, or G.2X. For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. For the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker. For the G.2X worker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker. NumberOfWorkers (integer) --The number of workers of a defined workerType that are allocated when this task runs. Timeout (integer) --The timeout for a task run for this transform in minutes. This is the maximum time that a task run for this transform can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). MaxRetries (integer) --The maximum number of times to retry a task for this transform after a task run fails. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException :return: { 'TransformId': 'string', 'Name': 'string', 'Description': 'string', 'Status': 'NOT_READY'|'READY'|'DELETING', 'CreatedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'InputRecordTables': [ { 'DatabaseName': 'string', 'TableName': 'string', 'CatalogId': 'string', 'ConnectionName': 'string' }, ], 'Parameters': { 'TransformType': 'FIND_MATCHES', 'FindMatchesParameters': { 'PrimaryKeyColumnName': 'string', 'PrecisionRecallTradeoff': 123.0, 'AccuracyCostTradeoff': 123.0, 'EnforceProvidedLabels': True|False } }, 'EvaluationMetrics': { 'TransformType': 'FIND_MATCHES', 'FindMatchesMetrics': { 'AreaUnderPRCurve': 123.0, 'Precision': 123.0, 'Recall': 123.0, 'F1': 123.0, 'ConfusionMatrix': { 'NumTruePositives': 123, 'NumFalsePositives': 123, 'NumTrueNegatives': 123, 'NumFalseNegatives': 123 } } }, 'LabelCount': 123, 'Schema': [ { 'Name': 'string', 'DataType': 'string' }, ], 'Role': 'string', 'GlueVersion': 'string', 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'Timeout': 123, 'MaxRetries': 123 } :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException """ pass def get_ml_transforms(NextToken=None, MaxResults=None, Filter=None, Sort=None): """ Gets a sortable, filterable list of existing AWS Glue machine learning transforms. Machine learning transforms are a special type of transform that use machine learning to learn the details of the transformation to be performed by learning from examples provided by humans. These transformations are then saved by AWS Glue, and you can retrieve their metadata by calling GetMLTransforms . See also: AWS API Documentation Exceptions :example: response = client.get_ml_transforms( NextToken='string', MaxResults=123, Filter={ 'Name': 'string', 'TransformType': 'FIND_MATCHES', 'Status': 'NOT_READY'|'READY'|'DELETING', 'GlueVersion': 'string', 'CreatedBefore': datetime(2015, 1, 1), 'CreatedAfter': datetime(2015, 1, 1), 'LastModifiedBefore': datetime(2015, 1, 1), 'LastModifiedAfter': datetime(2015, 1, 1), 'Schema': [ { 'Name': 'string', 'DataType': 'string' }, ] }, Sort={ 'Column': 'NAME'|'TRANSFORM_TYPE'|'STATUS'|'CREATED'|'LAST_MODIFIED', 'SortDirection': 'DESCENDING'|'ASCENDING' } ) :type NextToken: string :param NextToken: A paginated token to offset the results. :type MaxResults: integer :param MaxResults: The maximum number of results to return. :type Filter: dict :param Filter: The filter transformation criteria.\n\nName (string) --A unique transform name that is used to filter the machine learning transforms.\n\nTransformType (string) --The type of machine learning transform that is used to filter the machine learning transforms.\n\nStatus (string) --Filters the list of machine learning transforms by the last known status of the transforms (to indicate whether a transform can be used or not). One of 'NOT_READY', 'READY', or 'DELETING'.\n\nGlueVersion (string) --This value determines which version of AWS Glue this machine learning transform is compatible with. Glue 1.0 is recommended for most customers. If the value is not set, the Glue compatibility defaults to Glue 0.9. For more information, see AWS Glue Versions in the developer guide.\n\nCreatedBefore (datetime) --The time and date before which the transforms were created.\n\nCreatedAfter (datetime) --The time and date after which the transforms were created.\n\nLastModifiedBefore (datetime) --Filter on transforms last modified before this date.\n\nLastModifiedAfter (datetime) --Filter on transforms last modified after this date.\n\nSchema (list) --Filters on datasets with a specific schema. The Map<Column, Type> object is an array of key-value pairs representing the schema this transform accepts, where Column is the name of a column, and Type is the type of the data such as an integer or string. Has an upper bound of 100 columns.\n\n(dict) --A key-value pair representing a column and data type that this transform can run against. The Schema parameter of the MLTransform may contain up to 100 of these structures.\n\nName (string) --The name of the column.\n\nDataType (string) --The type of data in the column.\n\n\n\n\n\n\n :type Sort: dict :param Sort: The sorting criteria.\n\nColumn (string) -- [REQUIRED]The column to be used in the sorting criteria that are associated with the machine learning transform.\n\nSortDirection (string) -- [REQUIRED]The sort direction to be used in the sorting criteria that are associated with the machine learning transform.\n\n\n :rtype: dict ReturnsResponse Syntax { 'Transforms': [ { 'TransformId': 'string', 'Name': 'string', 'Description': 'string', 'Status': 'NOT_READY'|'READY'|'DELETING', 'CreatedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'InputRecordTables': [ { 'DatabaseName': 'string', 'TableName': 'string', 'CatalogId': 'string', 'ConnectionName': 'string' }, ], 'Parameters': { 'TransformType': 'FIND_MATCHES', 'FindMatchesParameters': { 'PrimaryKeyColumnName': 'string', 'PrecisionRecallTradeoff': 123.0, 'AccuracyCostTradeoff': 123.0, 'EnforceProvidedLabels': True|False } }, 'EvaluationMetrics': { 'TransformType': 'FIND_MATCHES', 'FindMatchesMetrics': { 'AreaUnderPRCurve': 123.0, 'Precision': 123.0, 'Recall': 123.0, 'F1': 123.0, 'ConfusionMatrix': { 'NumTruePositives': 123, 'NumFalsePositives': 123, 'NumTrueNegatives': 123, 'NumFalseNegatives': 123 } } }, 'LabelCount': 123, 'Schema': [ { 'Name': 'string', 'DataType': 'string' }, ], 'Role': 'string', 'GlueVersion': 'string', 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'Timeout': 123, 'MaxRetries': 123 }, ], 'NextToken': 'string' } Response Structure (dict) -- Transforms (list) -- A list of machine learning transforms. (dict) -- A structure for a machine learning transform. TransformId (string) -- The unique transform ID that is generated for the machine learning transform. The ID is guaranteed to be unique and does not change. Name (string) -- A user-defined name for the machine learning transform. Names are not guaranteed unique and can be changed at any time. Description (string) -- A user-defined, long-form description text for the machine learning transform. Descriptions are not guaranteed to be unique and can be changed at any time. Status (string) -- The current status of the machine learning transform. CreatedOn (datetime) -- A timestamp. The time and date that this machine learning transform was created. LastModifiedOn (datetime) -- A timestamp. The last point in time when this machine learning transform was modified. InputRecordTables (list) -- A list of AWS Glue table definitions used by the transform. (dict) -- The database and table in the AWS Glue Data Catalog that is used for input or output data. DatabaseName (string) -- A database name in the AWS Glue Data Catalog. TableName (string) -- A table name in the AWS Glue Data Catalog. CatalogId (string) -- A unique identifier for the AWS Glue Data Catalog. ConnectionName (string) -- The name of the connection to the AWS Glue Data Catalog. Parameters (dict) -- A TransformParameters object. You can use parameters to tune (customize) the behavior of the machine learning transform by specifying what data it learns from and your preference on various tradeoffs (such as precious vs. recall, or accuracy vs. cost). TransformType (string) -- The type of machine learning transform. For information about the types of machine learning transforms, see Creating Machine Learning Transforms . FindMatchesParameters (dict) -- The parameters for the find matches algorithm. PrimaryKeyColumnName (string) -- The name of a column that uniquely identifies rows in the source table. Used to help identify matching records. PrecisionRecallTradeoff (float) -- The value selected when tuning your transform for a balance between precision and recall. A value of 0.5 means no preference; a value of 1.0 means a bias purely for precision, and a value of 0.0 means a bias for recall. Because this is a tradeoff, choosing values close to 1.0 means very low recall, and choosing values close to 0.0 results in very low precision. The precision metric indicates how often your model is correct when it predicts a match. The recall metric indicates that for an actual match, how often your model predicts the match. AccuracyCostTradeoff (float) -- The value that is selected when tuning your transform for a balance between accuracy and cost. A value of 0.5 means that the system balances accuracy and cost concerns. A value of 1.0 means a bias purely for accuracy, which typically results in a higher cost, sometimes substantially higher. A value of 0.0 means a bias purely for cost, which results in a less accurate FindMatches transform, sometimes with unacceptable accuracy. Accuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall. Cost measures how many compute resources, and thus money, are consumed to run the transform. EnforceProvidedLabels (boolean) -- The value to switch on or off to force the output to match the provided labels from users. If the value is True , the find matches transform forces the output to match the provided labels. The results override the normal conflation results. If the value is False , the find matches transform does not ensure all the labels provided are respected, and the results rely on the trained model. Note that setting this value to true may increase the conflation execution time. EvaluationMetrics (dict) -- An EvaluationMetrics object. Evaluation metrics provide an estimate of the quality of your machine learning transform. TransformType (string) -- The type of machine learning transform. FindMatchesMetrics (dict) -- The evaluation metrics for the find matches algorithm. AreaUnderPRCurve (float) -- The area under the precision/recall curve (AUPRC) is a single number measuring the overall quality of the transform, that is independent of the choice made for precision vs. recall. Higher values indicate that you have a more attractive precision vs. recall tradeoff. For more information, see Precision and recall in Wikipedia. Precision (float) -- The precision metric indicates when often your transform is correct when it predicts a match. Specifically, it measures how well the transform finds true positives from the total true positives possible. For more information, see Precision and recall in Wikipedia. Recall (float) -- The recall metric indicates that for an actual match, how often your transform predicts the match. Specifically, it measures how well the transform finds true positives from the total records in the source data. For more information, see Precision and recall in Wikipedia. F1 (float) -- The maximum F1 metric indicates the transform\'s accuracy between 0 and 1, where 1 is the best accuracy. For more information, see F1 score in Wikipedia. ConfusionMatrix (dict) -- The confusion matrix shows you what your transform is predicting accurately and what types of errors it is making. For more information, see Confusion matrix in Wikipedia. NumTruePositives (integer) -- The number of matches in the data that the transform correctly found, in the confusion matrix for your transform. NumFalsePositives (integer) -- The number of nonmatches in the data that the transform incorrectly classified as a match, in the confusion matrix for your transform. NumTrueNegatives (integer) -- The number of nonmatches in the data that the transform correctly rejected, in the confusion matrix for your transform. NumFalseNegatives (integer) -- The number of matches in the data that the transform didn\'t find, in the confusion matrix for your transform. LabelCount (integer) -- A count identifier for the labeling files generated by AWS Glue for this transform. As you create a better transform, you can iteratively download, label, and upload the labeling file. Schema (list) -- A map of key-value pairs representing the columns and data types that this transform can run against. Has an upper bound of 100 columns. (dict) -- A key-value pair representing a column and data type that this transform can run against. The Schema parameter of the MLTransform may contain up to 100 of these structures. Name (string) -- The name of the column. DataType (string) -- The type of data in the column. Role (string) -- The name or Amazon Resource Name (ARN) of the IAM role with the required permissions. The required permissions include both AWS Glue service role permissions to AWS Glue resources, and Amazon S3 permissions required by the transform. This role needs AWS Glue service role permissions to allow access to resources in AWS Glue. See Attach a Policy to IAM Users That Access AWS Glue . This role needs permission to your Amazon Simple Storage Service (Amazon S3) sources, targets, temporary directory, scripts, and any libraries used by the task run for this transform. GlueVersion (string) -- This value determines which version of AWS Glue this machine learning transform is compatible with. Glue 1.0 is recommended for most customers. If the value is not set, the Glue compatibility defaults to Glue 0.9. For more information, see AWS Glue Versions in the developer guide. MaxCapacity (float) -- The number of AWS Glue data processing units (DPUs) that are allocated to task runs for this transform. You can allocate from 2 to 100 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . MaxCapacity is a mutually exclusive option with NumberOfWorkers and WorkerType . If either NumberOfWorkers or WorkerType is set, then MaxCapacity cannot be set. If MaxCapacity is set then neither NumberOfWorkers or WorkerType can be set. If WorkerType is set, then NumberOfWorkers is required (and vice versa). MaxCapacity and NumberOfWorkers must both be at least 1. When the WorkerType field is set to a value other than Standard , the MaxCapacity field is set automatically and becomes read-only. WorkerType (string) -- The type of predefined worker that is allocated when a task of this transform runs. Accepts a value of Standard, G.1X, or G.2X. For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. For the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker. For the G.2X worker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker. MaxCapacity is a mutually exclusive option with NumberOfWorkers and WorkerType . If either NumberOfWorkers or WorkerType is set, then MaxCapacity cannot be set. If MaxCapacity is set then neither NumberOfWorkers or WorkerType can be set. If WorkerType is set, then NumberOfWorkers is required (and vice versa). MaxCapacity and NumberOfWorkers must both be at least 1. NumberOfWorkers (integer) -- The number of workers of a defined workerType that are allocated when a task of the transform runs. If WorkerType is set, then NumberOfWorkers is required (and vice versa). Timeout (integer) -- The timeout in minutes of the machine learning transform. MaxRetries (integer) -- The maximum number of times to retry after an MLTaskRun of the machine learning transform fails. NextToken (string) -- A pagination token, if more results are available. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException :return: { 'Transforms': [ { 'TransformId': 'string', 'Name': 'string', 'Description': 'string', 'Status': 'NOT_READY'|'READY'|'DELETING', 'CreatedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'InputRecordTables': [ { 'DatabaseName': 'string', 'TableName': 'string', 'CatalogId': 'string', 'ConnectionName': 'string' }, ], 'Parameters': { 'TransformType': 'FIND_MATCHES', 'FindMatchesParameters': { 'PrimaryKeyColumnName': 'string', 'PrecisionRecallTradeoff': 123.0, 'AccuracyCostTradeoff': 123.0, 'EnforceProvidedLabels': True|False } }, 'EvaluationMetrics': { 'TransformType': 'FIND_MATCHES', 'FindMatchesMetrics': { 'AreaUnderPRCurve': 123.0, 'Precision': 123.0, 'Recall': 123.0, 'F1': 123.0, 'ConfusionMatrix': { 'NumTruePositives': 123, 'NumFalsePositives': 123, 'NumTrueNegatives': 123, 'NumFalseNegatives': 123 } } }, 'LabelCount': 123, 'Schema': [ { 'Name': 'string', 'DataType': 'string' }, ], 'Role': 'string', 'GlueVersion': 'string', 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'Timeout': 123, 'MaxRetries': 123 }, ], 'NextToken': 'string' } :returns: This role needs AWS Glue service role permissions to allow access to resources in AWS Glue. See Attach a Policy to IAM Users That Access AWS Glue . This role needs permission to your Amazon Simple Storage Service (Amazon S3) sources, targets, temporary directory, scripts, and any libraries used by the task run for this transform. """ pass def get_paginator(operation_name=None): """ Create a paginator for an operation. :type operation_name: string :param operation_name: The operation name. This is the same name\nas the method name on the client. For example, if the\nmethod name is create_foo, and you\'d normally invoke the\noperation as client.create_foo(**kwargs), if the\ncreate_foo operation can be paginated, you can use the\ncall client.get_paginator('create_foo'). :rtype: L{botocore.paginate.Paginator} ReturnsA paginator object. """ pass def get_partition(CatalogId=None, DatabaseName=None, TableName=None, PartitionValues=None): """ Retrieves information about a specified partition. See also: AWS API Documentation Exceptions :example: response = client.get_partition( CatalogId='string', DatabaseName='string', TableName='string', PartitionValues=[ 'string', ] ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the partition in question resides. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe name of the catalog database where the partition resides.\n :type TableName: string :param TableName: [REQUIRED]\nThe name of the partition\'s table.\n :type PartitionValues: list :param PartitionValues: [REQUIRED]\nThe values that define the partition.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax { 'Partition': { 'Values': [ 'string', ], 'DatabaseName': 'string', 'TableName': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastAccessTime': datetime(2015, 1, 1), 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'Parameters': { 'string': 'string' }, 'LastAnalyzedTime': datetime(2015, 1, 1) } } Response Structure (dict) -- Partition (dict) -- The requested information, in the form of a Partition object. Values (list) -- The values of the partition. (string) -- DatabaseName (string) -- The name of the catalog database in which to create the partition. TableName (string) -- The name of the database table in which to create the partition. CreationTime (datetime) -- The time at which the partition was created. LastAccessTime (datetime) -- The last time at which the partition was accessed. StorageDescriptor (dict) -- Provides information about the physical location where the partition is stored. Columns (list) -- A list of the Columns in the table. (dict) -- A column in a Table . Name (string) -- The name of the Column . Type (string) -- The data type of the Column . Comment (string) -- A free-form text comment. Parameters (dict) -- These key-value pairs define properties associated with the column. (string) -- (string) -- Location (string) -- The physical location of the table. By default, this takes the form of the warehouse location, followed by the database location in the warehouse, followed by the table name. InputFormat (string) -- The input format: SequenceFileInputFormat (binary), or TextInputFormat , or a custom format. OutputFormat (string) -- The output format: SequenceFileOutputFormat (binary), or IgnoreKeyTextOutputFormat , or a custom format. Compressed (boolean) -- True if the data in the table is compressed, or False if not. NumberOfBuckets (integer) -- Must be specified if the table contains any dimension columns. SerdeInfo (dict) -- The serialization/deserialization (SerDe) information. Name (string) -- Name of the SerDe. SerializationLibrary (string) -- Usually the class that implements the SerDe. An example is org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe . Parameters (dict) -- These key-value pairs define initialization parameters for the SerDe. (string) -- (string) -- BucketColumns (list) -- A list of reducer grouping columns, clustering columns, and bucketing columns in the table. (string) -- SortColumns (list) -- A list specifying the sort order of each bucket in the table. (dict) -- Specifies the sort order of a sorted column. Column (string) -- The name of the column. SortOrder (integer) -- Indicates that the column is sorted in ascending order (== 1 ), or in descending order (==0 ). Parameters (dict) -- The user-supplied properties in key-value form. (string) -- (string) -- SkewedInfo (dict) -- The information about values that appear frequently in a column (skewed values). SkewedColumnNames (list) -- A list of names of columns that contain skewed values. (string) -- SkewedColumnValues (list) -- A list of values that appear so frequently as to be considered skewed. (string) -- SkewedColumnValueLocationMaps (dict) -- A mapping of skewed values to the columns that contain them. (string) -- (string) -- StoredAsSubDirectories (boolean) -- True if the table data is stored in subdirectories, or False if not. Parameters (dict) -- These key-value pairs define partition parameters. (string) -- (string) -- LastAnalyzedTime (datetime) -- The last time at which column statistics were computed for this partition. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.GlueEncryptionException :return: { 'Partition': { 'Values': [ 'string', ], 'DatabaseName': 'string', 'TableName': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastAccessTime': datetime(2015, 1, 1), 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'Parameters': { 'string': 'string' }, 'LastAnalyzedTime': datetime(2015, 1, 1) } } :returns: (string) -- """ pass def get_partitions(CatalogId=None, DatabaseName=None, TableName=None, Expression=None, NextToken=None, Segment=None, MaxResults=None): """ Retrieves information about the partitions in a table. See also: AWS API Documentation Exceptions :example: response = client.get_partitions( CatalogId='string', DatabaseName='string', TableName='string', Expression='string', NextToken='string', Segment={ 'SegmentNumber': 123, 'TotalSegments': 123 }, MaxResults=123 ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the partitions in question reside. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe name of the catalog database where the partitions reside.\n :type TableName: string :param TableName: [REQUIRED]\nThe name of the partitions\' table.\n :type Expression: string :param Expression: An expression that filters the partitions to be returned.\nThe expression uses SQL syntax similar to the SQL WHERE filter clause. The SQL statement parser JSQLParser parses the expression.\n\nOperators : The following are the operators that you can use in the Expression API call:\n=\n\nChecks whether the values of the two operands are equal; if yes, then the condition becomes true.\nExample: Assume \'variable a\' holds 10 and \'variable b\' holds 20.\n(a = b) is not true.\n\n< >\nChecks whether the values of two operands are equal; if the values are not equal, then the condition becomes true.\nExample: (a < > b) is true.\n\n>\nChecks whether the value of the left operand is greater than the value of the right operand; if yes, then the condition becomes true.\nExample: (a > b) is not true.\n\n<\nChecks whether the value of the left operand is less than the value of the right operand; if yes, then the condition becomes true.\nExample: (a < b) is true.\n\n>=\nChecks whether the value of the left operand is greater than or equal to the value of the right operand; if yes, then the condition becomes true.\nExample: (a >= b) is not true.\n\n<=\nChecks whether the value of the left operand is less than or equal to the value of the right operand; if yes, then the condition becomes true.\nExample: (a <= b) is true.\n\nAND, OR, IN, BETWEEN, LIKE, NOT, IS NULL\nLogical operators.\n\nSupported Partition Key Types : The following are the supported partition keys.\n\nstring\ndate\ntimestamp\nint\nbigint\nlong\ntinyint\nsmallint\ndecimal\n\nIf an invalid type is encountered, an exception is thrown.\nThe following list shows the valid operators on each type. When you define a crawler, the partitionKey type is created as a STRING , to be compatible with the catalog partitions.\n\nSample API Call :\n :type NextToken: string :param NextToken: A continuation token, if this is not the first call to retrieve these partitions. :type Segment: dict :param Segment: The segment of the table\'s partitions to scan in this request.\n\nSegmentNumber (integer) -- [REQUIRED]The zero-based index number of the segment. For example, if the total number of segments is 4, SegmentNumber values range from 0 through 3.\n\nTotalSegments (integer) -- [REQUIRED]The total number of segments.\n\n\n :type MaxResults: integer :param MaxResults: The maximum number of partitions to return in a single response. :rtype: dict ReturnsResponse Syntax { 'Partitions': [ { 'Values': [ 'string', ], 'DatabaseName': 'string', 'TableName': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastAccessTime': datetime(2015, 1, 1), 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'Parameters': { 'string': 'string' }, 'LastAnalyzedTime': datetime(2015, 1, 1) }, ], 'NextToken': 'string' } Response Structure (dict) -- Partitions (list) -- A list of requested partitions. (dict) -- Represents a slice of table data. Values (list) -- The values of the partition. (string) -- DatabaseName (string) -- The name of the catalog database in which to create the partition. TableName (string) -- The name of the database table in which to create the partition. CreationTime (datetime) -- The time at which the partition was created. LastAccessTime (datetime) -- The last time at which the partition was accessed. StorageDescriptor (dict) -- Provides information about the physical location where the partition is stored. Columns (list) -- A list of the Columns in the table. (dict) -- A column in a Table . Name (string) -- The name of the Column . Type (string) -- The data type of the Column . Comment (string) -- A free-form text comment. Parameters (dict) -- These key-value pairs define properties associated with the column. (string) -- (string) -- Location (string) -- The physical location of the table. By default, this takes the form of the warehouse location, followed by the database location in the warehouse, followed by the table name. InputFormat (string) -- The input format: SequenceFileInputFormat (binary), or TextInputFormat , or a custom format. OutputFormat (string) -- The output format: SequenceFileOutputFormat (binary), or IgnoreKeyTextOutputFormat , or a custom format. Compressed (boolean) -- True if the data in the table is compressed, or False if not. NumberOfBuckets (integer) -- Must be specified if the table contains any dimension columns. SerdeInfo (dict) -- The serialization/deserialization (SerDe) information. Name (string) -- Name of the SerDe. SerializationLibrary (string) -- Usually the class that implements the SerDe. An example is org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe . Parameters (dict) -- These key-value pairs define initialization parameters for the SerDe. (string) -- (string) -- BucketColumns (list) -- A list of reducer grouping columns, clustering columns, and bucketing columns in the table. (string) -- SortColumns (list) -- A list specifying the sort order of each bucket in the table. (dict) -- Specifies the sort order of a sorted column. Column (string) -- The name of the column. SortOrder (integer) -- Indicates that the column is sorted in ascending order (== 1 ), or in descending order (==0 ). Parameters (dict) -- The user-supplied properties in key-value form. (string) -- (string) -- SkewedInfo (dict) -- The information about values that appear frequently in a column (skewed values). SkewedColumnNames (list) -- A list of names of columns that contain skewed values. (string) -- SkewedColumnValues (list) -- A list of values that appear so frequently as to be considered skewed. (string) -- SkewedColumnValueLocationMaps (dict) -- A mapping of skewed values to the columns that contain them. (string) -- (string) -- StoredAsSubDirectories (boolean) -- True if the table data is stored in subdirectories, or False if not. Parameters (dict) -- These key-value pairs define partition parameters. (string) -- (string) -- LastAnalyzedTime (datetime) -- The last time at which column statistics were computed for this partition. NextToken (string) -- A continuation token, if the returned list of partitions does not include the last one. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.GlueEncryptionException :return: { 'Partitions': [ { 'Values': [ 'string', ], 'DatabaseName': 'string', 'TableName': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastAccessTime': datetime(2015, 1, 1), 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'Parameters': { 'string': 'string' }, 'LastAnalyzedTime': datetime(2015, 1, 1) }, ], 'NextToken': 'string' } :returns: (string) -- """ pass def get_plan(Mapping=None, Source=None, Sinks=None, Location=None, Language=None): """ Gets code to perform a specified mapping. See also: AWS API Documentation Exceptions :example: response = client.get_plan( Mapping=[ { 'SourceTable': 'string', 'SourcePath': 'string', 'SourceType': 'string', 'TargetTable': 'string', 'TargetPath': 'string', 'TargetType': 'string' }, ], Source={ 'DatabaseName': 'string', 'TableName': 'string' }, Sinks=[ { 'DatabaseName': 'string', 'TableName': 'string' }, ], Location={ 'Jdbc': [ { 'Name': 'string', 'Value': 'string', 'Param': True|False }, ], 'S3': [ { 'Name': 'string', 'Value': 'string', 'Param': True|False }, ], 'DynamoDB': [ { 'Name': 'string', 'Value': 'string', 'Param': True|False }, ] }, Language='PYTHON'|'SCALA' ) :type Mapping: list :param Mapping: [REQUIRED]\nThe list of mappings from a source table to target tables.\n\n(dict) --Defines a mapping.\n\nSourceTable (string) --The name of the source table.\n\nSourcePath (string) --The source path.\n\nSourceType (string) --The source type.\n\nTargetTable (string) --The target table.\n\nTargetPath (string) --The target path.\n\nTargetType (string) --The target type.\n\n\n\n\n :type Source: dict :param Source: [REQUIRED]\nThe source table.\n\nDatabaseName (string) -- [REQUIRED]The database in which the table metadata resides.\n\nTableName (string) -- [REQUIRED]The name of the table in question.\n\n\n :type Sinks: list :param Sinks: The target tables.\n\n(dict) --Specifies a table definition in the AWS Glue Data Catalog.\n\nDatabaseName (string) -- [REQUIRED]The database in which the table metadata resides.\n\nTableName (string) -- [REQUIRED]The name of the table in question.\n\n\n\n\n :type Location: dict :param Location: The parameters for the mapping.\n\nJdbc (list) --A JDBC location.\n\n(dict) --An argument or property of a node.\n\nName (string) -- [REQUIRED]The name of the argument or property.\n\nValue (string) -- [REQUIRED]The value of the argument or property.\n\nParam (boolean) --True if the value is used as a parameter.\n\n\n\n\n\nS3 (list) --An Amazon Simple Storage Service (Amazon S3) location.\n\n(dict) --An argument or property of a node.\n\nName (string) -- [REQUIRED]The name of the argument or property.\n\nValue (string) -- [REQUIRED]The value of the argument or property.\n\nParam (boolean) --True if the value is used as a parameter.\n\n\n\n\n\nDynamoDB (list) --An Amazon DynamoDB table location.\n\n(dict) --An argument or property of a node.\n\nName (string) -- [REQUIRED]The name of the argument or property.\n\nValue (string) -- [REQUIRED]The value of the argument or property.\n\nParam (boolean) --True if the value is used as a parameter.\n\n\n\n\n\n\n :type Language: string :param Language: The programming language of the code to perform the mapping. :rtype: dict ReturnsResponse Syntax { 'PythonScript': 'string', 'ScalaCode': 'string' } Response Structure (dict) -- PythonScript (string) -- A Python script to perform the mapping. ScalaCode (string) -- The Scala code to perform the mapping. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'PythonScript': 'string', 'ScalaCode': 'string' } :returns: Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException """ pass def get_resource_policy(): """ Retrieves a specified resource policy. See also: AWS API Documentation Exceptions :example: response = client.get_resource_policy() :rtype: dict ReturnsResponse Syntax{ 'PolicyInJson': 'string', 'PolicyHash': 'string', 'CreateTime': datetime(2015, 1, 1), 'UpdateTime': datetime(2015, 1, 1) } Response Structure (dict) -- PolicyInJson (string) --Contains the requested policy document, in JSON format. PolicyHash (string) --Contains the hash value associated with this policy. CreateTime (datetime) --The date and time at which the policy was created. UpdateTime (datetime) --The date and time at which the policy was last updated. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException :return: { 'PolicyInJson': 'string', 'PolicyHash': 'string', 'CreateTime': datetime(2015, 1, 1), 'UpdateTime': datetime(2015, 1, 1) } """ pass def get_security_configuration(Name=None): """ Retrieves a specified security configuration. See also: AWS API Documentation Exceptions :example: response = client.get_security_configuration( Name='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the security configuration to retrieve.\n :rtype: dict ReturnsResponse Syntax{ 'SecurityConfiguration': { 'Name': 'string', 'CreatedTimeStamp': datetime(2015, 1, 1), 'EncryptionConfiguration': { 'S3Encryption': [ { 'S3EncryptionMode': 'DISABLED'|'SSE-KMS'|'SSE-S3', 'KmsKeyArn': 'string' }, ], 'CloudWatchEncryption': { 'CloudWatchEncryptionMode': 'DISABLED'|'SSE-KMS', 'KmsKeyArn': 'string' }, 'JobBookmarksEncryption': { 'JobBookmarksEncryptionMode': 'DISABLED'|'CSE-KMS', 'KmsKeyArn': 'string' } } } } Response Structure (dict) -- SecurityConfiguration (dict) --The requested security configuration. Name (string) --The name of the security configuration. CreatedTimeStamp (datetime) --The time at which this security configuration was created. EncryptionConfiguration (dict) --The encryption configuration associated with this security configuration. S3Encryption (list) --The encryption configuration for Amazon Simple Storage Service (Amazon S3) data. (dict) --Specifies how Amazon Simple Storage Service (Amazon S3) data should be encrypted. S3EncryptionMode (string) --The encryption mode to use for Amazon S3 data. KmsKeyArn (string) --The Amazon Resource Name (ARN) of the KMS key to be used to encrypt the data. CloudWatchEncryption (dict) --The encryption configuration for Amazon CloudWatch. CloudWatchEncryptionMode (string) --The encryption mode to use for CloudWatch data. KmsKeyArn (string) --The Amazon Resource Name (ARN) of the KMS key to be used to encrypt the data. JobBookmarksEncryption (dict) --The encryption configuration for job bookmarks. JobBookmarksEncryptionMode (string) --The encryption mode to use for job bookmarks data. KmsKeyArn (string) --The Amazon Resource Name (ARN) of the KMS key to be used to encrypt the data. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'SecurityConfiguration': { 'Name': 'string', 'CreatedTimeStamp': datetime(2015, 1, 1), 'EncryptionConfiguration': { 'S3Encryption': [ { 'S3EncryptionMode': 'DISABLED'|'SSE-KMS'|'SSE-S3', 'KmsKeyArn': 'string' }, ], 'CloudWatchEncryption': { 'CloudWatchEncryptionMode': 'DISABLED'|'SSE-KMS', 'KmsKeyArn': 'string' }, 'JobBookmarksEncryption': { 'JobBookmarksEncryptionMode': 'DISABLED'|'CSE-KMS', 'KmsKeyArn': 'string' } } } } """ pass def get_security_configurations(MaxResults=None, NextToken=None): """ Retrieves a list of all security configurations. See also: AWS API Documentation Exceptions :example: response = client.get_security_configurations( MaxResults=123, NextToken='string' ) :type MaxResults: integer :param MaxResults: The maximum number of results to return. :type NextToken: string :param NextToken: A continuation token, if this is a continuation call. :rtype: dict ReturnsResponse Syntax { 'SecurityConfigurations': [ { 'Name': 'string', 'CreatedTimeStamp': datetime(2015, 1, 1), 'EncryptionConfiguration': { 'S3Encryption': [ { 'S3EncryptionMode': 'DISABLED'|'SSE-KMS'|'SSE-S3', 'KmsKeyArn': 'string' }, ], 'CloudWatchEncryption': { 'CloudWatchEncryptionMode': 'DISABLED'|'SSE-KMS', 'KmsKeyArn': 'string' }, 'JobBookmarksEncryption': { 'JobBookmarksEncryptionMode': 'DISABLED'|'CSE-KMS', 'KmsKeyArn': 'string' } } }, ], 'NextToken': 'string' } Response Structure (dict) -- SecurityConfigurations (list) -- A list of security configurations. (dict) -- Specifies a security configuration. Name (string) -- The name of the security configuration. CreatedTimeStamp (datetime) -- The time at which this security configuration was created. EncryptionConfiguration (dict) -- The encryption configuration associated with this security configuration. S3Encryption (list) -- The encryption configuration for Amazon Simple Storage Service (Amazon S3) data. (dict) -- Specifies how Amazon Simple Storage Service (Amazon S3) data should be encrypted. S3EncryptionMode (string) -- The encryption mode to use for Amazon S3 data. KmsKeyArn (string) -- The Amazon Resource Name (ARN) of the KMS key to be used to encrypt the data. CloudWatchEncryption (dict) -- The encryption configuration for Amazon CloudWatch. CloudWatchEncryptionMode (string) -- The encryption mode to use for CloudWatch data. KmsKeyArn (string) -- The Amazon Resource Name (ARN) of the KMS key to be used to encrypt the data. JobBookmarksEncryption (dict) -- The encryption configuration for job bookmarks. JobBookmarksEncryptionMode (string) -- The encryption mode to use for job bookmarks data. KmsKeyArn (string) -- The Amazon Resource Name (ARN) of the KMS key to be used to encrypt the data. NextToken (string) -- A continuation token, if there are more security configurations to return. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'SecurityConfigurations': [ { 'Name': 'string', 'CreatedTimeStamp': datetime(2015, 1, 1), 'EncryptionConfiguration': { 'S3Encryption': [ { 'S3EncryptionMode': 'DISABLED'|'SSE-KMS'|'SSE-S3', 'KmsKeyArn': 'string' }, ], 'CloudWatchEncryption': { 'CloudWatchEncryptionMode': 'DISABLED'|'SSE-KMS', 'KmsKeyArn': 'string' }, 'JobBookmarksEncryption': { 'JobBookmarksEncryptionMode': 'DISABLED'|'CSE-KMS', 'KmsKeyArn': 'string' } } }, ], 'NextToken': 'string' } :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException """ pass def get_table(CatalogId=None, DatabaseName=None, Name=None): """ Retrieves the Table definition in a Data Catalog for a specified table. See also: AWS API Documentation Exceptions :example: response = client.get_table( CatalogId='string', DatabaseName='string', Name='string' ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the table resides. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe name of the database in the catalog in which the table resides. For Hive compatibility, this name is entirely lowercase.\n :type Name: string :param Name: [REQUIRED]\nThe name of the table for which to retrieve the definition. For Hive compatibility, this name is entirely lowercase.\n :rtype: dict ReturnsResponse Syntax { 'Table': { 'Name': 'string', 'DatabaseName': 'string', 'Description': 'string', 'Owner': 'string', 'CreateTime': datetime(2015, 1, 1), 'UpdateTime': datetime(2015, 1, 1), 'LastAccessTime': datetime(2015, 1, 1), 'LastAnalyzedTime': datetime(2015, 1, 1), 'Retention': 123, 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'PartitionKeys': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'ViewOriginalText': 'string', 'ViewExpandedText': 'string', 'TableType': 'string', 'Parameters': { 'string': 'string' }, 'CreatedBy': 'string', 'IsRegisteredWithLakeFormation': True|False } } Response Structure (dict) -- Table (dict) -- The Table object that defines the specified table. Name (string) -- The table name. For Hive compatibility, this must be entirely lowercase. DatabaseName (string) -- The name of the database where the table metadata resides. For Hive compatibility, this must be all lowercase. Description (string) -- A description of the table. Owner (string) -- The owner of the table. CreateTime (datetime) -- The time when the table definition was created in the Data Catalog. UpdateTime (datetime) -- The last time that the table was updated. LastAccessTime (datetime) -- The last time that the table was accessed. This is usually taken from HDFS, and might not be reliable. LastAnalyzedTime (datetime) -- The last time that column statistics were computed for this table. Retention (integer) -- The retention time for this table. StorageDescriptor (dict) -- A storage descriptor containing information about the physical storage of this table. Columns (list) -- A list of the Columns in the table. (dict) -- A column in a Table . Name (string) -- The name of the Column . Type (string) -- The data type of the Column . Comment (string) -- A free-form text comment. Parameters (dict) -- These key-value pairs define properties associated with the column. (string) -- (string) -- Location (string) -- The physical location of the table. By default, this takes the form of the warehouse location, followed by the database location in the warehouse, followed by the table name. InputFormat (string) -- The input format: SequenceFileInputFormat (binary), or TextInputFormat , or a custom format. OutputFormat (string) -- The output format: SequenceFileOutputFormat (binary), or IgnoreKeyTextOutputFormat , or a custom format. Compressed (boolean) -- True if the data in the table is compressed, or False if not. NumberOfBuckets (integer) -- Must be specified if the table contains any dimension columns. SerdeInfo (dict) -- The serialization/deserialization (SerDe) information. Name (string) -- Name of the SerDe. SerializationLibrary (string) -- Usually the class that implements the SerDe. An example is org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe . Parameters (dict) -- These key-value pairs define initialization parameters for the SerDe. (string) -- (string) -- BucketColumns (list) -- A list of reducer grouping columns, clustering columns, and bucketing columns in the table. (string) -- SortColumns (list) -- A list specifying the sort order of each bucket in the table. (dict) -- Specifies the sort order of a sorted column. Column (string) -- The name of the column. SortOrder (integer) -- Indicates that the column is sorted in ascending order (== 1 ), or in descending order (==0 ). Parameters (dict) -- The user-supplied properties in key-value form. (string) -- (string) -- SkewedInfo (dict) -- The information about values that appear frequently in a column (skewed values). SkewedColumnNames (list) -- A list of names of columns that contain skewed values. (string) -- SkewedColumnValues (list) -- A list of values that appear so frequently as to be considered skewed. (string) -- SkewedColumnValueLocationMaps (dict) -- A mapping of skewed values to the columns that contain them. (string) -- (string) -- StoredAsSubDirectories (boolean) -- True if the table data is stored in subdirectories, or False if not. PartitionKeys (list) -- A list of columns by which the table is partitioned. Only primitive types are supported as partition keys. When you create a table used by Amazon Athena, and you do not specify any partitionKeys , you must at least set the value of partitionKeys to an empty list. For example: "PartitionKeys": [] (dict) -- A column in a Table . Name (string) -- The name of the Column . Type (string) -- The data type of the Column . Comment (string) -- A free-form text comment. Parameters (dict) -- These key-value pairs define properties associated with the column. (string) -- (string) -- ViewOriginalText (string) -- If the table is a view, the original text of the view; otherwise null . ViewExpandedText (string) -- If the table is a view, the expanded text of the view; otherwise null . TableType (string) -- The type of this table (EXTERNAL_TABLE , VIRTUAL_VIEW , etc.). Parameters (dict) -- These key-value pairs define properties associated with the table. (string) -- (string) -- CreatedBy (string) -- The person or entity who created the table. IsRegisteredWithLakeFormation (boolean) -- Indicates whether the table has been registered with AWS Lake Formation. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.GlueEncryptionException :return: { 'Table': { 'Name': 'string', 'DatabaseName': 'string', 'Description': 'string', 'Owner': 'string', 'CreateTime': datetime(2015, 1, 1), 'UpdateTime': datetime(2015, 1, 1), 'LastAccessTime': datetime(2015, 1, 1), 'LastAnalyzedTime': datetime(2015, 1, 1), 'Retention': 123, 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'PartitionKeys': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'ViewOriginalText': 'string', 'ViewExpandedText': 'string', 'TableType': 'string', 'Parameters': { 'string': 'string' }, 'CreatedBy': 'string', 'IsRegisteredWithLakeFormation': True|False } } :returns: (string) -- (string) -- """ pass def get_table_version(CatalogId=None, DatabaseName=None, TableName=None, VersionId=None): """ Retrieves a specified version of a table. See also: AWS API Documentation Exceptions :example: response = client.get_table_version( CatalogId='string', DatabaseName='string', TableName='string', VersionId='string' ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the tables reside. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe database in the catalog in which the table resides. For Hive compatibility, this name is entirely lowercase.\n :type TableName: string :param TableName: [REQUIRED]\nThe name of the table. For Hive compatibility, this name is entirely lowercase.\n :type VersionId: string :param VersionId: The ID value of the table version to be retrieved. A VersionID is a string representation of an integer. Each version is incremented by 1. :rtype: dict ReturnsResponse Syntax { 'TableVersion': { 'Table': { 'Name': 'string', 'DatabaseName': 'string', 'Description': 'string', 'Owner': 'string', 'CreateTime': datetime(2015, 1, 1), 'UpdateTime': datetime(2015, 1, 1), 'LastAccessTime': datetime(2015, 1, 1), 'LastAnalyzedTime': datetime(2015, 1, 1), 'Retention': 123, 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'PartitionKeys': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'ViewOriginalText': 'string', 'ViewExpandedText': 'string', 'TableType': 'string', 'Parameters': { 'string': 'string' }, 'CreatedBy': 'string', 'IsRegisteredWithLakeFormation': True|False }, 'VersionId': 'string' } } Response Structure (dict) -- TableVersion (dict) -- The requested table version. Table (dict) -- The table in question. Name (string) -- The table name. For Hive compatibility, this must be entirely lowercase. DatabaseName (string) -- The name of the database where the table metadata resides. For Hive compatibility, this must be all lowercase. Description (string) -- A description of the table. Owner (string) -- The owner of the table. CreateTime (datetime) -- The time when the table definition was created in the Data Catalog. UpdateTime (datetime) -- The last time that the table was updated. LastAccessTime (datetime) -- The last time that the table was accessed. This is usually taken from HDFS, and might not be reliable. LastAnalyzedTime (datetime) -- The last time that column statistics were computed for this table. Retention (integer) -- The retention time for this table. StorageDescriptor (dict) -- A storage descriptor containing information about the physical storage of this table. Columns (list) -- A list of the Columns in the table. (dict) -- A column in a Table . Name (string) -- The name of the Column . Type (string) -- The data type of the Column . Comment (string) -- A free-form text comment. Parameters (dict) -- These key-value pairs define properties associated with the column. (string) -- (string) -- Location (string) -- The physical location of the table. By default, this takes the form of the warehouse location, followed by the database location in the warehouse, followed by the table name. InputFormat (string) -- The input format: SequenceFileInputFormat (binary), or TextInputFormat , or a custom format. OutputFormat (string) -- The output format: SequenceFileOutputFormat (binary), or IgnoreKeyTextOutputFormat , or a custom format. Compressed (boolean) -- True if the data in the table is compressed, or False if not. NumberOfBuckets (integer) -- Must be specified if the table contains any dimension columns. SerdeInfo (dict) -- The serialization/deserialization (SerDe) information. Name (string) -- Name of the SerDe. SerializationLibrary (string) -- Usually the class that implements the SerDe. An example is org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe . Parameters (dict) -- These key-value pairs define initialization parameters for the SerDe. (string) -- (string) -- BucketColumns (list) -- A list of reducer grouping columns, clustering columns, and bucketing columns in the table. (string) -- SortColumns (list) -- A list specifying the sort order of each bucket in the table. (dict) -- Specifies the sort order of a sorted column. Column (string) -- The name of the column. SortOrder (integer) -- Indicates that the column is sorted in ascending order (== 1 ), or in descending order (==0 ). Parameters (dict) -- The user-supplied properties in key-value form. (string) -- (string) -- SkewedInfo (dict) -- The information about values that appear frequently in a column (skewed values). SkewedColumnNames (list) -- A list of names of columns that contain skewed values. (string) -- SkewedColumnValues (list) -- A list of values that appear so frequently as to be considered skewed. (string) -- SkewedColumnValueLocationMaps (dict) -- A mapping of skewed values to the columns that contain them. (string) -- (string) -- StoredAsSubDirectories (boolean) -- True if the table data is stored in subdirectories, or False if not. PartitionKeys (list) -- A list of columns by which the table is partitioned. Only primitive types are supported as partition keys. When you create a table used by Amazon Athena, and you do not specify any partitionKeys , you must at least set the value of partitionKeys to an empty list. For example: "PartitionKeys": [] (dict) -- A column in a Table . Name (string) -- The name of the Column . Type (string) -- The data type of the Column . Comment (string) -- A free-form text comment. Parameters (dict) -- These key-value pairs define properties associated with the column. (string) -- (string) -- ViewOriginalText (string) -- If the table is a view, the original text of the view; otherwise null . ViewExpandedText (string) -- If the table is a view, the expanded text of the view; otherwise null . TableType (string) -- The type of this table (EXTERNAL_TABLE , VIRTUAL_VIEW , etc.). Parameters (dict) -- These key-value pairs define properties associated with the table. (string) -- (string) -- CreatedBy (string) -- The person or entity who created the table. IsRegisteredWithLakeFormation (boolean) -- Indicates whether the table has been registered with AWS Lake Formation. VersionId (string) -- The ID value that identifies this table version. A VersionId is a string representation of an integer. Each version is incremented by 1. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.GlueEncryptionException :return: { 'TableVersion': { 'Table': { 'Name': 'string', 'DatabaseName': 'string', 'Description': 'string', 'Owner': 'string', 'CreateTime': datetime(2015, 1, 1), 'UpdateTime': datetime(2015, 1, 1), 'LastAccessTime': datetime(2015, 1, 1), 'LastAnalyzedTime': datetime(2015, 1, 1), 'Retention': 123, 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'PartitionKeys': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'ViewOriginalText': 'string', 'ViewExpandedText': 'string', 'TableType': 'string', 'Parameters': { 'string': 'string' }, 'CreatedBy': 'string', 'IsRegisteredWithLakeFormation': True|False }, 'VersionId': 'string' } } :returns: (string) -- (string) -- """ pass def get_table_versions(CatalogId=None, DatabaseName=None, TableName=None, NextToken=None, MaxResults=None): """ Retrieves a list of strings that identify available versions of a specified table. See also: AWS API Documentation Exceptions :example: response = client.get_table_versions( CatalogId='string', DatabaseName='string', TableName='string', NextToken='string', MaxResults=123 ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the tables reside. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe database in the catalog in which the table resides. For Hive compatibility, this name is entirely lowercase.\n :type TableName: string :param TableName: [REQUIRED]\nThe name of the table. For Hive compatibility, this name is entirely lowercase.\n :type NextToken: string :param NextToken: A continuation token, if this is not the first call. :type MaxResults: integer :param MaxResults: The maximum number of table versions to return in one response. :rtype: dict ReturnsResponse Syntax { 'TableVersions': [ { 'Table': { 'Name': 'string', 'DatabaseName': 'string', 'Description': 'string', 'Owner': 'string', 'CreateTime': datetime(2015, 1, 1), 'UpdateTime': datetime(2015, 1, 1), 'LastAccessTime': datetime(2015, 1, 1), 'LastAnalyzedTime': datetime(2015, 1, 1), 'Retention': 123, 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'PartitionKeys': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'ViewOriginalText': 'string', 'ViewExpandedText': 'string', 'TableType': 'string', 'Parameters': { 'string': 'string' }, 'CreatedBy': 'string', 'IsRegisteredWithLakeFormation': True|False }, 'VersionId': 'string' }, ], 'NextToken': 'string' } Response Structure (dict) -- TableVersions (list) -- A list of strings identifying available versions of the specified table. (dict) -- Specifies a version of a table. Table (dict) -- The table in question. Name (string) -- The table name. For Hive compatibility, this must be entirely lowercase. DatabaseName (string) -- The name of the database where the table metadata resides. For Hive compatibility, this must be all lowercase. Description (string) -- A description of the table. Owner (string) -- The owner of the table. CreateTime (datetime) -- The time when the table definition was created in the Data Catalog. UpdateTime (datetime) -- The last time that the table was updated. LastAccessTime (datetime) -- The last time that the table was accessed. This is usually taken from HDFS, and might not be reliable. LastAnalyzedTime (datetime) -- The last time that column statistics were computed for this table. Retention (integer) -- The retention time for this table. StorageDescriptor (dict) -- A storage descriptor containing information about the physical storage of this table. Columns (list) -- A list of the Columns in the table. (dict) -- A column in a Table . Name (string) -- The name of the Column . Type (string) -- The data type of the Column . Comment (string) -- A free-form text comment. Parameters (dict) -- These key-value pairs define properties associated with the column. (string) -- (string) -- Location (string) -- The physical location of the table. By default, this takes the form of the warehouse location, followed by the database location in the warehouse, followed by the table name. InputFormat (string) -- The input format: SequenceFileInputFormat (binary), or TextInputFormat , or a custom format. OutputFormat (string) -- The output format: SequenceFileOutputFormat (binary), or IgnoreKeyTextOutputFormat , or a custom format. Compressed (boolean) -- True if the data in the table is compressed, or False if not. NumberOfBuckets (integer) -- Must be specified if the table contains any dimension columns. SerdeInfo (dict) -- The serialization/deserialization (SerDe) information. Name (string) -- Name of the SerDe. SerializationLibrary (string) -- Usually the class that implements the SerDe. An example is org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe . Parameters (dict) -- These key-value pairs define initialization parameters for the SerDe. (string) -- (string) -- BucketColumns (list) -- A list of reducer grouping columns, clustering columns, and bucketing columns in the table. (string) -- SortColumns (list) -- A list specifying the sort order of each bucket in the table. (dict) -- Specifies the sort order of a sorted column. Column (string) -- The name of the column. SortOrder (integer) -- Indicates that the column is sorted in ascending order (== 1 ), or in descending order (==0 ). Parameters (dict) -- The user-supplied properties in key-value form. (string) -- (string) -- SkewedInfo (dict) -- The information about values that appear frequently in a column (skewed values). SkewedColumnNames (list) -- A list of names of columns that contain skewed values. (string) -- SkewedColumnValues (list) -- A list of values that appear so frequently as to be considered skewed. (string) -- SkewedColumnValueLocationMaps (dict) -- A mapping of skewed values to the columns that contain them. (string) -- (string) -- StoredAsSubDirectories (boolean) -- True if the table data is stored in subdirectories, or False if not. PartitionKeys (list) -- A list of columns by which the table is partitioned. Only primitive types are supported as partition keys. When you create a table used by Amazon Athena, and you do not specify any partitionKeys , you must at least set the value of partitionKeys to an empty list. For example: "PartitionKeys": [] (dict) -- A column in a Table . Name (string) -- The name of the Column . Type (string) -- The data type of the Column . Comment (string) -- A free-form text comment. Parameters (dict) -- These key-value pairs define properties associated with the column. (string) -- (string) -- ViewOriginalText (string) -- If the table is a view, the original text of the view; otherwise null . ViewExpandedText (string) -- If the table is a view, the expanded text of the view; otherwise null . TableType (string) -- The type of this table (EXTERNAL_TABLE , VIRTUAL_VIEW , etc.). Parameters (dict) -- These key-value pairs define properties associated with the table. (string) -- (string) -- CreatedBy (string) -- The person or entity who created the table. IsRegisteredWithLakeFormation (boolean) -- Indicates whether the table has been registered with AWS Lake Formation. VersionId (string) -- The ID value that identifies this table version. A VersionId is a string representation of an integer. Each version is incremented by 1. NextToken (string) -- A continuation token, if the list of available versions does not include the last one. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.GlueEncryptionException :return: { 'TableVersions': [ { 'Table': { 'Name': 'string', 'DatabaseName': 'string', 'Description': 'string', 'Owner': 'string', 'CreateTime': datetime(2015, 1, 1), 'UpdateTime': datetime(2015, 1, 1), 'LastAccessTime': datetime(2015, 1, 1), 'LastAnalyzedTime': datetime(2015, 1, 1), 'Retention': 123, 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'PartitionKeys': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'ViewOriginalText': 'string', 'ViewExpandedText': 'string', 'TableType': 'string', 'Parameters': { 'string': 'string' }, 'CreatedBy': 'string', 'IsRegisteredWithLakeFormation': True|False }, 'VersionId': 'string' }, ], 'NextToken': 'string' } :returns: (string) -- (string) -- """ pass def get_tables(CatalogId=None, DatabaseName=None, Expression=None, NextToken=None, MaxResults=None): """ Retrieves the definitions of some or all of the tables in a given Database . See also: AWS API Documentation Exceptions :example: response = client.get_tables( CatalogId='string', DatabaseName='string', Expression='string', NextToken='string', MaxResults=123 ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the tables reside. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe database in the catalog whose tables to list. For Hive compatibility, this name is entirely lowercase.\n :type Expression: string :param Expression: A regular expression pattern. If present, only those tables whose names match the pattern are returned. :type NextToken: string :param NextToken: A continuation token, included if this is a continuation call. :type MaxResults: integer :param MaxResults: The maximum number of tables to return in a single response. :rtype: dict ReturnsResponse Syntax { 'TableList': [ { 'Name': 'string', 'DatabaseName': 'string', 'Description': 'string', 'Owner': 'string', 'CreateTime': datetime(2015, 1, 1), 'UpdateTime': datetime(2015, 1, 1), 'LastAccessTime': datetime(2015, 1, 1), 'LastAnalyzedTime': datetime(2015, 1, 1), 'Retention': 123, 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'PartitionKeys': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'ViewOriginalText': 'string', 'ViewExpandedText': 'string', 'TableType': 'string', 'Parameters': { 'string': 'string' }, 'CreatedBy': 'string', 'IsRegisteredWithLakeFormation': True|False }, ], 'NextToken': 'string' } Response Structure (dict) -- TableList (list) -- A list of the requested Table objects. (dict) -- Represents a collection of related data organized in columns and rows. Name (string) -- The table name. For Hive compatibility, this must be entirely lowercase. DatabaseName (string) -- The name of the database where the table metadata resides. For Hive compatibility, this must be all lowercase. Description (string) -- A description of the table. Owner (string) -- The owner of the table. CreateTime (datetime) -- The time when the table definition was created in the Data Catalog. UpdateTime (datetime) -- The last time that the table was updated. LastAccessTime (datetime) -- The last time that the table was accessed. This is usually taken from HDFS, and might not be reliable. LastAnalyzedTime (datetime) -- The last time that column statistics were computed for this table. Retention (integer) -- The retention time for this table. StorageDescriptor (dict) -- A storage descriptor containing information about the physical storage of this table. Columns (list) -- A list of the Columns in the table. (dict) -- A column in a Table . Name (string) -- The name of the Column . Type (string) -- The data type of the Column . Comment (string) -- A free-form text comment. Parameters (dict) -- These key-value pairs define properties associated with the column. (string) -- (string) -- Location (string) -- The physical location of the table. By default, this takes the form of the warehouse location, followed by the database location in the warehouse, followed by the table name. InputFormat (string) -- The input format: SequenceFileInputFormat (binary), or TextInputFormat , or a custom format. OutputFormat (string) -- The output format: SequenceFileOutputFormat (binary), or IgnoreKeyTextOutputFormat , or a custom format. Compressed (boolean) -- True if the data in the table is compressed, or False if not. NumberOfBuckets (integer) -- Must be specified if the table contains any dimension columns. SerdeInfo (dict) -- The serialization/deserialization (SerDe) information. Name (string) -- Name of the SerDe. SerializationLibrary (string) -- Usually the class that implements the SerDe. An example is org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe . Parameters (dict) -- These key-value pairs define initialization parameters for the SerDe. (string) -- (string) -- BucketColumns (list) -- A list of reducer grouping columns, clustering columns, and bucketing columns in the table. (string) -- SortColumns (list) -- A list specifying the sort order of each bucket in the table. (dict) -- Specifies the sort order of a sorted column. Column (string) -- The name of the column. SortOrder (integer) -- Indicates that the column is sorted in ascending order (== 1 ), or in descending order (==0 ). Parameters (dict) -- The user-supplied properties in key-value form. (string) -- (string) -- SkewedInfo (dict) -- The information about values that appear frequently in a column (skewed values). SkewedColumnNames (list) -- A list of names of columns that contain skewed values. (string) -- SkewedColumnValues (list) -- A list of values that appear so frequently as to be considered skewed. (string) -- SkewedColumnValueLocationMaps (dict) -- A mapping of skewed values to the columns that contain them. (string) -- (string) -- StoredAsSubDirectories (boolean) -- True if the table data is stored in subdirectories, or False if not. PartitionKeys (list) -- A list of columns by which the table is partitioned. Only primitive types are supported as partition keys. When you create a table used by Amazon Athena, and you do not specify any partitionKeys , you must at least set the value of partitionKeys to an empty list. For example: "PartitionKeys": [] (dict) -- A column in a Table . Name (string) -- The name of the Column . Type (string) -- The data type of the Column . Comment (string) -- A free-form text comment. Parameters (dict) -- These key-value pairs define properties associated with the column. (string) -- (string) -- ViewOriginalText (string) -- If the table is a view, the original text of the view; otherwise null . ViewExpandedText (string) -- If the table is a view, the expanded text of the view; otherwise null . TableType (string) -- The type of this table (EXTERNAL_TABLE , VIRTUAL_VIEW , etc.). Parameters (dict) -- These key-value pairs define properties associated with the table. (string) -- (string) -- CreatedBy (string) -- The person or entity who created the table. IsRegisteredWithLakeFormation (boolean) -- Indicates whether the table has been registered with AWS Lake Formation. NextToken (string) -- A continuation token, present if the current list segment is not the last. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.GlueEncryptionException :return: { 'TableList': [ { 'Name': 'string', 'DatabaseName': 'string', 'Description': 'string', 'Owner': 'string', 'CreateTime': datetime(2015, 1, 1), 'UpdateTime': datetime(2015, 1, 1), 'LastAccessTime': datetime(2015, 1, 1), 'LastAnalyzedTime': datetime(2015, 1, 1), 'Retention': 123, 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'PartitionKeys': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'ViewOriginalText': 'string', 'ViewExpandedText': 'string', 'TableType': 'string', 'Parameters': { 'string': 'string' }, 'CreatedBy': 'string', 'IsRegisteredWithLakeFormation': True|False }, ], 'NextToken': 'string' } :returns: (string) -- (string) -- """ pass def get_tags(ResourceArn=None): """ Retrieves a list of tags associated with a resource. See also: AWS API Documentation Exceptions :example: response = client.get_tags( ResourceArn='string' ) :type ResourceArn: string :param ResourceArn: [REQUIRED]\nThe Amazon Resource Name (ARN) of the resource for which to retrieve tags.\n :rtype: dict ReturnsResponse Syntax{ 'Tags': { 'string': 'string' } } Response Structure (dict) -- Tags (dict) --The requested tags. (string) -- (string) -- Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.EntityNotFoundException :return: { 'Tags': { 'string': 'string' } } :returns: Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.EntityNotFoundException """ pass def get_trigger(Name=None): """ Retrieves the definition of a trigger. See also: AWS API Documentation Exceptions :example: response = client.get_trigger( Name='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the trigger to retrieve.\n :rtype: dict ReturnsResponse Syntax{ 'Trigger': { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } } } Response Structure (dict) -- Trigger (dict) --The requested trigger definition. Name (string) --The name of the trigger. WorkflowName (string) --The name of the workflow associated with the trigger. Id (string) --Reserved for future use. Type (string) --The type of trigger that this is. State (string) --The current state of the trigger. Description (string) --A description of this trigger. Schedule (string) --A cron expression used to specify the schedule (see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, you would specify: cron(15 12 * * ? *) . Actions (list) --The actions initiated by this trigger. (dict) --Defines an action to be initiated by a trigger. JobName (string) --The name of a job to be executed. Arguments (dict) --The job arguments used when this trigger fires. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- Timeout (integer) --The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. SecurityConfiguration (string) --The name of the SecurityConfiguration structure to be used with this action. NotificationProperty (dict) --Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) --After a job run starts, the number of minutes to wait before sending a job run delay notification. CrawlerName (string) --The name of the crawler to be used with this action. Predicate (dict) --The predicate of this trigger, which defines when it will fire. Logical (string) --An optional field if only one condition is listed. If multiple conditions are listed, then this field is required. Conditions (list) --A list of the conditions that determine when the trigger will fire. (dict) --Defines a condition under which a trigger fires. LogicalOperator (string) --A logical operator. JobName (string) --The name of the job whose JobRuns this condition applies to, and on which this trigger waits. State (string) --The condition state. Currently, the only job states that a trigger can listen for are SUCCEEDED , STOPPED , FAILED , and TIMEOUT . The only crawler states that a trigger can listen for are SUCCEEDED , FAILED , and CANCELLED . CrawlerName (string) --The name of the crawler to which this condition applies. CrawlState (string) --The state of the crawler to which this condition applies. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'Trigger': { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } } } :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException """ pass def get_triggers(NextToken=None, DependentJobName=None, MaxResults=None): """ Gets all the triggers associated with a job. See also: AWS API Documentation Exceptions :example: response = client.get_triggers( NextToken='string', DependentJobName='string', MaxResults=123 ) :type NextToken: string :param NextToken: A continuation token, if this is a continuation call. :type DependentJobName: string :param DependentJobName: The name of the job to retrieve triggers for. The trigger that can start this job is returned, and if there is no such trigger, all triggers are returned. :type MaxResults: integer :param MaxResults: The maximum size of the response. :rtype: dict ReturnsResponse Syntax { 'Triggers': [ { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } }, ], 'NextToken': 'string' } Response Structure (dict) -- Triggers (list) -- A list of triggers for the specified job. (dict) -- Information about a specific trigger. Name (string) -- The name of the trigger. WorkflowName (string) -- The name of the workflow associated with the trigger. Id (string) -- Reserved for future use. Type (string) -- The type of trigger that this is. State (string) -- The current state of the trigger. Description (string) -- A description of this trigger. Schedule (string) -- A cron expression used to specify the schedule (see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, you would specify: cron(15 12 * * ? *) . Actions (list) -- The actions initiated by this trigger. (dict) -- Defines an action to be initiated by a trigger. JobName (string) -- The name of a job to be executed. Arguments (dict) -- The job arguments used when this trigger fires. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- Timeout (integer) -- The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this action. NotificationProperty (dict) -- Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) -- After a job run starts, the number of minutes to wait before sending a job run delay notification. CrawlerName (string) -- The name of the crawler to be used with this action. Predicate (dict) -- The predicate of this trigger, which defines when it will fire. Logical (string) -- An optional field if only one condition is listed. If multiple conditions are listed, then this field is required. Conditions (list) -- A list of the conditions that determine when the trigger will fire. (dict) -- Defines a condition under which a trigger fires. LogicalOperator (string) -- A logical operator. JobName (string) -- The name of the job whose JobRuns this condition applies to, and on which this trigger waits. State (string) -- The condition state. Currently, the only job states that a trigger can listen for are SUCCEEDED , STOPPED , FAILED , and TIMEOUT . The only crawler states that a trigger can listen for are SUCCEEDED , FAILED , and CANCELLED . CrawlerName (string) -- The name of the crawler to which this condition applies. CrawlState (string) -- The state of the crawler to which this condition applies. NextToken (string) -- A continuation token, if not all the requested triggers have yet been returned. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'Triggers': [ { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } }, ], 'NextToken': 'string' } :returns: (string) -- (string) -- """ pass def get_user_defined_function(CatalogId=None, DatabaseName=None, FunctionName=None): """ Retrieves a specified function definition from the Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.get_user_defined_function( CatalogId='string', DatabaseName='string', FunctionName='string' ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the function to be retrieved is located. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe name of the catalog database where the function is located.\n :type FunctionName: string :param FunctionName: [REQUIRED]\nThe name of the function.\n :rtype: dict ReturnsResponse Syntax { 'UserDefinedFunction': { 'FunctionName': 'string', 'ClassName': 'string', 'OwnerName': 'string', 'OwnerType': 'USER'|'ROLE'|'GROUP', 'CreateTime': datetime(2015, 1, 1), 'ResourceUris': [ { 'ResourceType': 'JAR'|'FILE'|'ARCHIVE', 'Uri': 'string' }, ] } } Response Structure (dict) -- UserDefinedFunction (dict) -- The requested function definition. FunctionName (string) -- The name of the function. ClassName (string) -- The Java class that contains the function code. OwnerName (string) -- The owner of the function. OwnerType (string) -- The owner type. CreateTime (datetime) -- The time at which the function was created. ResourceUris (list) -- The resource URIs for the function. (dict) -- The URIs for function resources. ResourceType (string) -- The type of the resource. Uri (string) -- The URI for accessing the resource. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.GlueEncryptionException :return: { 'UserDefinedFunction': { 'FunctionName': 'string', 'ClassName': 'string', 'OwnerName': 'string', 'OwnerType': 'USER'|'ROLE'|'GROUP', 'CreateTime': datetime(2015, 1, 1), 'ResourceUris': [ { 'ResourceType': 'JAR'|'FILE'|'ARCHIVE', 'Uri': 'string' }, ] } } :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.GlueEncryptionException """ pass def get_user_defined_functions(CatalogId=None, DatabaseName=None, Pattern=None, NextToken=None, MaxResults=None): """ Retrieves multiple function definitions from the Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.get_user_defined_functions( CatalogId='string', DatabaseName='string', Pattern='string', NextToken='string', MaxResults=123 ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the functions to be retrieved are located. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: The name of the catalog database where the functions are located. :type Pattern: string :param Pattern: [REQUIRED]\nAn optional function-name pattern string that filters the function definitions returned.\n :type NextToken: string :param NextToken: A continuation token, if this is a continuation call. :type MaxResults: integer :param MaxResults: The maximum number of functions to return in one response. :rtype: dict ReturnsResponse Syntax { 'UserDefinedFunctions': [ { 'FunctionName': 'string', 'ClassName': 'string', 'OwnerName': 'string', 'OwnerType': 'USER'|'ROLE'|'GROUP', 'CreateTime': datetime(2015, 1, 1), 'ResourceUris': [ { 'ResourceType': 'JAR'|'FILE'|'ARCHIVE', 'Uri': 'string' }, ] }, ], 'NextToken': 'string' } Response Structure (dict) -- UserDefinedFunctions (list) -- A list of requested function definitions. (dict) -- Represents the equivalent of a Hive user-defined function (UDF ) definition. FunctionName (string) -- The name of the function. ClassName (string) -- The Java class that contains the function code. OwnerName (string) -- The owner of the function. OwnerType (string) -- The owner type. CreateTime (datetime) -- The time at which the function was created. ResourceUris (list) -- The resource URIs for the function. (dict) -- The URIs for function resources. ResourceType (string) -- The type of the resource. Uri (string) -- The URI for accessing the resource. NextToken (string) -- A continuation token, if the list of functions returned does not include the last requested function. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.GlueEncryptionException :return: { 'UserDefinedFunctions': [ { 'FunctionName': 'string', 'ClassName': 'string', 'OwnerName': 'string', 'OwnerType': 'USER'|'ROLE'|'GROUP', 'CreateTime': datetime(2015, 1, 1), 'ResourceUris': [ { 'ResourceType': 'JAR'|'FILE'|'ARCHIVE', 'Uri': 'string' }, ] }, ], 'NextToken': 'string' } :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.GlueEncryptionException """ pass def get_waiter(waiter_name=None): """ Returns an object that can wait for some condition. :type waiter_name: str :param waiter_name: The name of the waiter to get. See the waiters\nsection of the service docs for a list of available waiters. :rtype: botocore.waiter.Waiter """ pass def get_workflow(Name=None, IncludeGraph=None): """ Retrieves resource metadata for a workflow. See also: AWS API Documentation Exceptions :example: response = client.get_workflow( Name='string', IncludeGraph=True|False ) :type Name: string :param Name: [REQUIRED]\nThe name of the workflow to retrieve.\n :type IncludeGraph: boolean :param IncludeGraph: Specifies whether to include a graph when returning the workflow resource metadata. :rtype: dict ReturnsResponse Syntax { 'Workflow': { 'Name': 'string', 'Description': 'string', 'DefaultRunProperties': { 'string': 'string' }, 'CreatedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'LastRun': { 'Name': 'string', 'WorkflowRunId': 'string', 'WorkflowRunProperties': { 'string': 'string' }, 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'Status': 'RUNNING'|'COMPLETED'|'STOPPING'|'STOPPED', 'Statistics': { 'TotalActions': 123, 'TimeoutActions': 123, 'FailedActions': 123, 'StoppedActions': 123, 'SucceededActions': 123, 'RunningActions': 123 }, 'Graph': { 'Nodes': [ { 'Type': 'CRAWLER'|'JOB'|'TRIGGER', 'Name': 'string', 'UniqueId': 'string', 'TriggerDetails': { 'Trigger': { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } } }, 'JobDetails': { 'JobRuns': [ { 'Id': 'string', 'Attempt': 123, 'PreviousRunId': 'string', 'TriggerName': 'string', 'JobName': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'JobRunState': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'Arguments': { 'string': 'string' }, 'ErrorMessage': 'string', 'PredecessorRuns': [ { 'JobName': 'string', 'RunId': 'string' }, ], 'AllocatedCapacity': 123, 'ExecutionTime': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'LogGroupName': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ] }, 'CrawlerDetails': { 'Crawls': [ { 'State': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED', 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string' }, ] } }, ], 'Edges': [ { 'SourceId': 'string', 'DestinationId': 'string' }, ] } }, 'Graph': { 'Nodes': [ { 'Type': 'CRAWLER'|'JOB'|'TRIGGER', 'Name': 'string', 'UniqueId': 'string', 'TriggerDetails': { 'Trigger': { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } } }, 'JobDetails': { 'JobRuns': [ { 'Id': 'string', 'Attempt': 123, 'PreviousRunId': 'string', 'TriggerName': 'string', 'JobName': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'JobRunState': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'Arguments': { 'string': 'string' }, 'ErrorMessage': 'string', 'PredecessorRuns': [ { 'JobName': 'string', 'RunId': 'string' }, ], 'AllocatedCapacity': 123, 'ExecutionTime': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'LogGroupName': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ] }, 'CrawlerDetails': { 'Crawls': [ { 'State': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED', 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string' }, ] } }, ], 'Edges': [ { 'SourceId': 'string', 'DestinationId': 'string' }, ] } } } Response Structure (dict) -- Workflow (dict) -- The resource metadata for the workflow. Name (string) -- The name of the workflow representing the flow. Description (string) -- A description of the workflow. DefaultRunProperties (dict) -- A collection of properties to be used as part of each execution of the workflow. (string) -- (string) -- CreatedOn (datetime) -- The date and time when the workflow was created. LastModifiedOn (datetime) -- The date and time when the workflow was last modified. LastRun (dict) -- The information about the last execution of the workflow. Name (string) -- Name of the workflow which was executed. WorkflowRunId (string) -- The ID of this workflow run. WorkflowRunProperties (dict) -- The workflow run properties which were set during the run. (string) -- (string) -- StartedOn (datetime) -- The date and time when the workflow run was started. CompletedOn (datetime) -- The date and time when the workflow run completed. Status (string) -- The status of the workflow run. Statistics (dict) -- The statistics of the run. TotalActions (integer) -- Total number of Actions in the workflow run. TimeoutActions (integer) -- Total number of Actions which timed out. FailedActions (integer) -- Total number of Actions which have failed. StoppedActions (integer) -- Total number of Actions which have stopped. SucceededActions (integer) -- Total number of Actions which have succeeded. RunningActions (integer) -- Total number Actions in running state. Graph (dict) -- The graph representing all the AWS Glue components that belong to the workflow as nodes and directed connections between them as edges. Nodes (list) -- A list of the the AWS Glue components belong to the workflow represented as nodes. (dict) -- A node represents an AWS Glue component like Trigger, Job etc. which is part of a workflow. Type (string) -- The type of AWS Glue component represented by the node. Name (string) -- The name of the AWS Glue component represented by the node. UniqueId (string) -- The unique Id assigned to the node within the workflow. TriggerDetails (dict) -- Details of the Trigger when the node represents a Trigger. Trigger (dict) -- The information of the trigger represented by the trigger node. Name (string) -- The name of the trigger. WorkflowName (string) -- The name of the workflow associated with the trigger. Id (string) -- Reserved for future use. Type (string) -- The type of trigger that this is. State (string) -- The current state of the trigger. Description (string) -- A description of this trigger. Schedule (string) -- A cron expression used to specify the schedule (see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, you would specify: cron(15 12 * * ? *) . Actions (list) -- The actions initiated by this trigger. (dict) -- Defines an action to be initiated by a trigger. JobName (string) -- The name of a job to be executed. Arguments (dict) -- The job arguments used when this trigger fires. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- Timeout (integer) -- The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this action. NotificationProperty (dict) -- Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) -- After a job run starts, the number of minutes to wait before sending a job run delay notification. CrawlerName (string) -- The name of the crawler to be used with this action. Predicate (dict) -- The predicate of this trigger, which defines when it will fire. Logical (string) -- An optional field if only one condition is listed. If multiple conditions are listed, then this field is required. Conditions (list) -- A list of the conditions that determine when the trigger will fire. (dict) -- Defines a condition under which a trigger fires. LogicalOperator (string) -- A logical operator. JobName (string) -- The name of the job whose JobRuns this condition applies to, and on which this trigger waits. State (string) -- The condition state. Currently, the only job states that a trigger can listen for are SUCCEEDED , STOPPED , FAILED , and TIMEOUT . The only crawler states that a trigger can listen for are SUCCEEDED , FAILED , and CANCELLED . CrawlerName (string) -- The name of the crawler to which this condition applies. CrawlState (string) -- The state of the crawler to which this condition applies. JobDetails (dict) -- Details of the Job when the node represents a Job. JobRuns (list) -- The information for the job runs represented by the job node. (dict) -- Contains information about a job run. Id (string) -- The ID of this job run. Attempt (integer) -- The number of the attempt to run this job. PreviousRunId (string) -- The ID of the previous run of this job. For example, the JobRunId specified in the StartJobRun action. TriggerName (string) -- The name of the trigger that started this job run. JobName (string) -- The name of the job definition being used in this run. StartedOn (datetime) -- The date and time at which this job run was started. LastModifiedOn (datetime) -- The last time that this job run was modified. CompletedOn (datetime) -- The date and time that this job run completed. JobRunState (string) -- The current state of the job run. For more information about the statuses of jobs that have terminated abnormally, see AWS Glue Job Run Statuses . Arguments (dict) -- The job arguments associated with this run. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- ErrorMessage (string) -- An error message associated with this job run. PredecessorRuns (list) -- A list of predecessors to this job run. (dict) -- A job run that was used in the predicate of a conditional trigger that triggered this job run. JobName (string) -- The name of the job definition used by the predecessor job run. RunId (string) -- The job-run ID of the predecessor job run. AllocatedCapacity (integer) -- This field is deprecated. Use MaxCapacity instead. The number of AWS Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be allocated; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . ExecutionTime (integer) -- The amount of time (in seconds) that the job run consumed resources. Timeout (integer) -- The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. MaxCapacity (float) -- The number of AWS Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . Do not set Max Capacity if using WorkerType and NumberOfWorkers . The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job or an Apache Spark ETL job: When you specify a Python shell job (JobCommand.Name ="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU. When you specify an Apache Spark ETL job (JobCommand.Name ="glueetl"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation. WorkerType (string) -- The type of predefined worker that is allocated when a job runs. Accepts a value of Standard, G.1X, or G.2X. For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. For the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker. For the G.2X worker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker. NumberOfWorkers (integer) -- The number of workers of a defined workerType that are allocated when a job runs. The maximum number of workers you can define are 299 for G.1X , and 149 for G.2X . SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this job run. LogGroupName (string) -- The name of the log group for secure logging that can be server-side encrypted in Amazon CloudWatch using AWS KMS. This name can be /aws-glue/jobs/ , in which case the default encryption is NONE . If you add a role name and SecurityConfiguration name (in other words, /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/ ), then that security configuration is used to encrypt the log group. NotificationProperty (dict) -- Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) -- After a job run starts, the number of minutes to wait before sending a job run delay notification. GlueVersion (string) -- Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for jobs of type Spark. For more information about the available AWS Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide. Jobs that are created without specifying a Glue version default to Glue 0.9. CrawlerDetails (dict) -- Details of the crawler when the node represents a crawler. Crawls (list) -- A list of crawls represented by the crawl node. (dict) -- The details of a crawl in the workflow. State (string) -- The state of the crawler. StartedOn (datetime) -- The date and time on which the crawl started. CompletedOn (datetime) -- The date and time on which the crawl completed. ErrorMessage (string) -- The error message associated with the crawl. LogGroup (string) -- The log group associated with the crawl. LogStream (string) -- The log stream associated with the crawl. Edges (list) -- A list of all the directed connections between the nodes belonging to the workflow. (dict) -- An edge represents a directed connection between two AWS Glue components which are part of the workflow the edge belongs to. SourceId (string) -- The unique of the node within the workflow where the edge starts. DestinationId (string) -- The unique of the node within the workflow where the edge ends. Graph (dict) -- The graph representing all the AWS Glue components that belong to the workflow as nodes and directed connections between them as edges. Nodes (list) -- A list of the the AWS Glue components belong to the workflow represented as nodes. (dict) -- A node represents an AWS Glue component like Trigger, Job etc. which is part of a workflow. Type (string) -- The type of AWS Glue component represented by the node. Name (string) -- The name of the AWS Glue component represented by the node. UniqueId (string) -- The unique Id assigned to the node within the workflow. TriggerDetails (dict) -- Details of the Trigger when the node represents a Trigger. Trigger (dict) -- The information of the trigger represented by the trigger node. Name (string) -- The name of the trigger. WorkflowName (string) -- The name of the workflow associated with the trigger. Id (string) -- Reserved for future use. Type (string) -- The type of trigger that this is. State (string) -- The current state of the trigger. Description (string) -- A description of this trigger. Schedule (string) -- A cron expression used to specify the schedule (see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, you would specify: cron(15 12 * * ? *) . Actions (list) -- The actions initiated by this trigger. (dict) -- Defines an action to be initiated by a trigger. JobName (string) -- The name of a job to be executed. Arguments (dict) -- The job arguments used when this trigger fires. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- Timeout (integer) -- The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this action. NotificationProperty (dict) -- Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) -- After a job run starts, the number of minutes to wait before sending a job run delay notification. CrawlerName (string) -- The name of the crawler to be used with this action. Predicate (dict) -- The predicate of this trigger, which defines when it will fire. Logical (string) -- An optional field if only one condition is listed. If multiple conditions are listed, then this field is required. Conditions (list) -- A list of the conditions that determine when the trigger will fire. (dict) -- Defines a condition under which a trigger fires. LogicalOperator (string) -- A logical operator. JobName (string) -- The name of the job whose JobRuns this condition applies to, and on which this trigger waits. State (string) -- The condition state. Currently, the only job states that a trigger can listen for are SUCCEEDED , STOPPED , FAILED , and TIMEOUT . The only crawler states that a trigger can listen for are SUCCEEDED , FAILED , and CANCELLED . CrawlerName (string) -- The name of the crawler to which this condition applies. CrawlState (string) -- The state of the crawler to which this condition applies. JobDetails (dict) -- Details of the Job when the node represents a Job. JobRuns (list) -- The information for the job runs represented by the job node. (dict) -- Contains information about a job run. Id (string) -- The ID of this job run. Attempt (integer) -- The number of the attempt to run this job. PreviousRunId (string) -- The ID of the previous run of this job. For example, the JobRunId specified in the StartJobRun action. TriggerName (string) -- The name of the trigger that started this job run. JobName (string) -- The name of the job definition being used in this run. StartedOn (datetime) -- The date and time at which this job run was started. LastModifiedOn (datetime) -- The last time that this job run was modified. CompletedOn (datetime) -- The date and time that this job run completed. JobRunState (string) -- The current state of the job run. For more information about the statuses of jobs that have terminated abnormally, see AWS Glue Job Run Statuses . Arguments (dict) -- The job arguments associated with this run. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- ErrorMessage (string) -- An error message associated with this job run. PredecessorRuns (list) -- A list of predecessors to this job run. (dict) -- A job run that was used in the predicate of a conditional trigger that triggered this job run. JobName (string) -- The name of the job definition used by the predecessor job run. RunId (string) -- The job-run ID of the predecessor job run. AllocatedCapacity (integer) -- This field is deprecated. Use MaxCapacity instead. The number of AWS Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be allocated; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . ExecutionTime (integer) -- The amount of time (in seconds) that the job run consumed resources. Timeout (integer) -- The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. MaxCapacity (float) -- The number of AWS Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . Do not set Max Capacity if using WorkerType and NumberOfWorkers . The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job or an Apache Spark ETL job: When you specify a Python shell job (JobCommand.Name ="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU. When you specify an Apache Spark ETL job (JobCommand.Name ="glueetl"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation. WorkerType (string) -- The type of predefined worker that is allocated when a job runs. Accepts a value of Standard, G.1X, or G.2X. For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. For the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker. For the G.2X worker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker. NumberOfWorkers (integer) -- The number of workers of a defined workerType that are allocated when a job runs. The maximum number of workers you can define are 299 for G.1X , and 149 for G.2X . SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this job run. LogGroupName (string) -- The name of the log group for secure logging that can be server-side encrypted in Amazon CloudWatch using AWS KMS. This name can be /aws-glue/jobs/ , in which case the default encryption is NONE . If you add a role name and SecurityConfiguration name (in other words, /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/ ), then that security configuration is used to encrypt the log group. NotificationProperty (dict) -- Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) -- After a job run starts, the number of minutes to wait before sending a job run delay notification. GlueVersion (string) -- Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for jobs of type Spark. For more information about the available AWS Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide. Jobs that are created without specifying a Glue version default to Glue 0.9. CrawlerDetails (dict) -- Details of the crawler when the node represents a crawler. Crawls (list) -- A list of crawls represented by the crawl node. (dict) -- The details of a crawl in the workflow. State (string) -- The state of the crawler. StartedOn (datetime) -- The date and time on which the crawl started. CompletedOn (datetime) -- The date and time on which the crawl completed. ErrorMessage (string) -- The error message associated with the crawl. LogGroup (string) -- The log group associated with the crawl. LogStream (string) -- The log stream associated with the crawl. Edges (list) -- A list of all the directed connections between the nodes belonging to the workflow. (dict) -- An edge represents a directed connection between two AWS Glue components which are part of the workflow the edge belongs to. SourceId (string) -- The unique of the node within the workflow where the edge starts. DestinationId (string) -- The unique of the node within the workflow where the edge ends. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'Workflow': { 'Name': 'string', 'Description': 'string', 'DefaultRunProperties': { 'string': 'string' }, 'CreatedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'LastRun': { 'Name': 'string', 'WorkflowRunId': 'string', 'WorkflowRunProperties': { 'string': 'string' }, 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'Status': 'RUNNING'|'COMPLETED'|'STOPPING'|'STOPPED', 'Statistics': { 'TotalActions': 123, 'TimeoutActions': 123, 'FailedActions': 123, 'StoppedActions': 123, 'SucceededActions': 123, 'RunningActions': 123 }, 'Graph': { 'Nodes': [ { 'Type': 'CRAWLER'|'JOB'|'TRIGGER', 'Name': 'string', 'UniqueId': 'string', 'TriggerDetails': { 'Trigger': { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } } }, 'JobDetails': { 'JobRuns': [ { 'Id': 'string', 'Attempt': 123, 'PreviousRunId': 'string', 'TriggerName': 'string', 'JobName': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'JobRunState': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'Arguments': { 'string': 'string' }, 'ErrorMessage': 'string', 'PredecessorRuns': [ { 'JobName': 'string', 'RunId': 'string' }, ], 'AllocatedCapacity': 123, 'ExecutionTime': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'LogGroupName': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ] }, 'CrawlerDetails': { 'Crawls': [ { 'State': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED', 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string' }, ] } }, ], 'Edges': [ { 'SourceId': 'string', 'DestinationId': 'string' }, ] } }, 'Graph': { 'Nodes': [ { 'Type': 'CRAWLER'|'JOB'|'TRIGGER', 'Name': 'string', 'UniqueId': 'string', 'TriggerDetails': { 'Trigger': { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } } }, 'JobDetails': { 'JobRuns': [ { 'Id': 'string', 'Attempt': 123, 'PreviousRunId': 'string', 'TriggerName': 'string', 'JobName': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'JobRunState': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'Arguments': { 'string': 'string' }, 'ErrorMessage': 'string', 'PredecessorRuns': [ { 'JobName': 'string', 'RunId': 'string' }, ], 'AllocatedCapacity': 123, 'ExecutionTime': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'LogGroupName': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ] }, 'CrawlerDetails': { 'Crawls': [ { 'State': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED', 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string' }, ] } }, ], 'Edges': [ { 'SourceId': 'string', 'DestinationId': 'string' }, ] } } } :returns: (string) -- (string) -- """ pass def get_workflow_run(Name=None, RunId=None, IncludeGraph=None): """ Retrieves the metadata for a given workflow run. See also: AWS API Documentation Exceptions :example: response = client.get_workflow_run( Name='string', RunId='string', IncludeGraph=True|False ) :type Name: string :param Name: [REQUIRED]\nName of the workflow being run.\n :type RunId: string :param RunId: [REQUIRED]\nThe ID of the workflow run.\n :type IncludeGraph: boolean :param IncludeGraph: Specifies whether to include the workflow graph in response or not. :rtype: dict ReturnsResponse Syntax { 'Run': { 'Name': 'string', 'WorkflowRunId': 'string', 'WorkflowRunProperties': { 'string': 'string' }, 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'Status': 'RUNNING'|'COMPLETED'|'STOPPING'|'STOPPED', 'Statistics': { 'TotalActions': 123, 'TimeoutActions': 123, 'FailedActions': 123, 'StoppedActions': 123, 'SucceededActions': 123, 'RunningActions': 123 }, 'Graph': { 'Nodes': [ { 'Type': 'CRAWLER'|'JOB'|'TRIGGER', 'Name': 'string', 'UniqueId': 'string', 'TriggerDetails': { 'Trigger': { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } } }, 'JobDetails': { 'JobRuns': [ { 'Id': 'string', 'Attempt': 123, 'PreviousRunId': 'string', 'TriggerName': 'string', 'JobName': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'JobRunState': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'Arguments': { 'string': 'string' }, 'ErrorMessage': 'string', 'PredecessorRuns': [ { 'JobName': 'string', 'RunId': 'string' }, ], 'AllocatedCapacity': 123, 'ExecutionTime': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'LogGroupName': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ] }, 'CrawlerDetails': { 'Crawls': [ { 'State': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED', 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string' }, ] } }, ], 'Edges': [ { 'SourceId': 'string', 'DestinationId': 'string' }, ] } } } Response Structure (dict) -- Run (dict) -- The requested workflow run metadata. Name (string) -- Name of the workflow which was executed. WorkflowRunId (string) -- The ID of this workflow run. WorkflowRunProperties (dict) -- The workflow run properties which were set during the run. (string) -- (string) -- StartedOn (datetime) -- The date and time when the workflow run was started. CompletedOn (datetime) -- The date and time when the workflow run completed. Status (string) -- The status of the workflow run. Statistics (dict) -- The statistics of the run. TotalActions (integer) -- Total number of Actions in the workflow run. TimeoutActions (integer) -- Total number of Actions which timed out. FailedActions (integer) -- Total number of Actions which have failed. StoppedActions (integer) -- Total number of Actions which have stopped. SucceededActions (integer) -- Total number of Actions which have succeeded. RunningActions (integer) -- Total number Actions in running state. Graph (dict) -- The graph representing all the AWS Glue components that belong to the workflow as nodes and directed connections between them as edges. Nodes (list) -- A list of the the AWS Glue components belong to the workflow represented as nodes. (dict) -- A node represents an AWS Glue component like Trigger, Job etc. which is part of a workflow. Type (string) -- The type of AWS Glue component represented by the node. Name (string) -- The name of the AWS Glue component represented by the node. UniqueId (string) -- The unique Id assigned to the node within the workflow. TriggerDetails (dict) -- Details of the Trigger when the node represents a Trigger. Trigger (dict) -- The information of the trigger represented by the trigger node. Name (string) -- The name of the trigger. WorkflowName (string) -- The name of the workflow associated with the trigger. Id (string) -- Reserved for future use. Type (string) -- The type of trigger that this is. State (string) -- The current state of the trigger. Description (string) -- A description of this trigger. Schedule (string) -- A cron expression used to specify the schedule (see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, you would specify: cron(15 12 * * ? *) . Actions (list) -- The actions initiated by this trigger. (dict) -- Defines an action to be initiated by a trigger. JobName (string) -- The name of a job to be executed. Arguments (dict) -- The job arguments used when this trigger fires. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- Timeout (integer) -- The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this action. NotificationProperty (dict) -- Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) -- After a job run starts, the number of minutes to wait before sending a job run delay notification. CrawlerName (string) -- The name of the crawler to be used with this action. Predicate (dict) -- The predicate of this trigger, which defines when it will fire. Logical (string) -- An optional field if only one condition is listed. If multiple conditions are listed, then this field is required. Conditions (list) -- A list of the conditions that determine when the trigger will fire. (dict) -- Defines a condition under which a trigger fires. LogicalOperator (string) -- A logical operator. JobName (string) -- The name of the job whose JobRuns this condition applies to, and on which this trigger waits. State (string) -- The condition state. Currently, the only job states that a trigger can listen for are SUCCEEDED , STOPPED , FAILED , and TIMEOUT . The only crawler states that a trigger can listen for are SUCCEEDED , FAILED , and CANCELLED . CrawlerName (string) -- The name of the crawler to which this condition applies. CrawlState (string) -- The state of the crawler to which this condition applies. JobDetails (dict) -- Details of the Job when the node represents a Job. JobRuns (list) -- The information for the job runs represented by the job node. (dict) -- Contains information about a job run. Id (string) -- The ID of this job run. Attempt (integer) -- The number of the attempt to run this job. PreviousRunId (string) -- The ID of the previous run of this job. For example, the JobRunId specified in the StartJobRun action. TriggerName (string) -- The name of the trigger that started this job run. JobName (string) -- The name of the job definition being used in this run. StartedOn (datetime) -- The date and time at which this job run was started. LastModifiedOn (datetime) -- The last time that this job run was modified. CompletedOn (datetime) -- The date and time that this job run completed. JobRunState (string) -- The current state of the job run. For more information about the statuses of jobs that have terminated abnormally, see AWS Glue Job Run Statuses . Arguments (dict) -- The job arguments associated with this run. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- ErrorMessage (string) -- An error message associated with this job run. PredecessorRuns (list) -- A list of predecessors to this job run. (dict) -- A job run that was used in the predicate of a conditional trigger that triggered this job run. JobName (string) -- The name of the job definition used by the predecessor job run. RunId (string) -- The job-run ID of the predecessor job run. AllocatedCapacity (integer) -- This field is deprecated. Use MaxCapacity instead. The number of AWS Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be allocated; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . ExecutionTime (integer) -- The amount of time (in seconds) that the job run consumed resources. Timeout (integer) -- The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. MaxCapacity (float) -- The number of AWS Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . Do not set Max Capacity if using WorkerType and NumberOfWorkers . The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job or an Apache Spark ETL job: When you specify a Python shell job (JobCommand.Name ="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU. When you specify an Apache Spark ETL job (JobCommand.Name ="glueetl"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation. WorkerType (string) -- The type of predefined worker that is allocated when a job runs. Accepts a value of Standard, G.1X, or G.2X. For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. For the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker. For the G.2X worker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker. NumberOfWorkers (integer) -- The number of workers of a defined workerType that are allocated when a job runs. The maximum number of workers you can define are 299 for G.1X , and 149 for G.2X . SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this job run. LogGroupName (string) -- The name of the log group for secure logging that can be server-side encrypted in Amazon CloudWatch using AWS KMS. This name can be /aws-glue/jobs/ , in which case the default encryption is NONE . If you add a role name and SecurityConfiguration name (in other words, /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/ ), then that security configuration is used to encrypt the log group. NotificationProperty (dict) -- Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) -- After a job run starts, the number of minutes to wait before sending a job run delay notification. GlueVersion (string) -- Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for jobs of type Spark. For more information about the available AWS Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide. Jobs that are created without specifying a Glue version default to Glue 0.9. CrawlerDetails (dict) -- Details of the crawler when the node represents a crawler. Crawls (list) -- A list of crawls represented by the crawl node. (dict) -- The details of a crawl in the workflow. State (string) -- The state of the crawler. StartedOn (datetime) -- The date and time on which the crawl started. CompletedOn (datetime) -- The date and time on which the crawl completed. ErrorMessage (string) -- The error message associated with the crawl. LogGroup (string) -- The log group associated with the crawl. LogStream (string) -- The log stream associated with the crawl. Edges (list) -- A list of all the directed connections between the nodes belonging to the workflow. (dict) -- An edge represents a directed connection between two AWS Glue components which are part of the workflow the edge belongs to. SourceId (string) -- The unique of the node within the workflow where the edge starts. DestinationId (string) -- The unique of the node within the workflow where the edge ends. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'Run': { 'Name': 'string', 'WorkflowRunId': 'string', 'WorkflowRunProperties': { 'string': 'string' }, 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'Status': 'RUNNING'|'COMPLETED'|'STOPPING'|'STOPPED', 'Statistics': { 'TotalActions': 123, 'TimeoutActions': 123, 'FailedActions': 123, 'StoppedActions': 123, 'SucceededActions': 123, 'RunningActions': 123 }, 'Graph': { 'Nodes': [ { 'Type': 'CRAWLER'|'JOB'|'TRIGGER', 'Name': 'string', 'UniqueId': 'string', 'TriggerDetails': { 'Trigger': { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } } }, 'JobDetails': { 'JobRuns': [ { 'Id': 'string', 'Attempt': 123, 'PreviousRunId': 'string', 'TriggerName': 'string', 'JobName': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'JobRunState': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'Arguments': { 'string': 'string' }, 'ErrorMessage': 'string', 'PredecessorRuns': [ { 'JobName': 'string', 'RunId': 'string' }, ], 'AllocatedCapacity': 123, 'ExecutionTime': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'LogGroupName': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ] }, 'CrawlerDetails': { 'Crawls': [ { 'State': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED', 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string' }, ] } }, ], 'Edges': [ { 'SourceId': 'string', 'DestinationId': 'string' }, ] } } } :returns: (string) -- (string) -- """ pass def get_workflow_run_properties(Name=None, RunId=None): """ Retrieves the workflow run properties which were set during the run. See also: AWS API Documentation Exceptions :example: response = client.get_workflow_run_properties( Name='string', RunId='string' ) :type Name: string :param Name: [REQUIRED]\nName of the workflow which was run.\n :type RunId: string :param RunId: [REQUIRED]\nThe ID of the workflow run whose run properties should be returned.\n :rtype: dict ReturnsResponse Syntax { 'RunProperties': { 'string': 'string' } } Response Structure (dict) -- RunProperties (dict) -- The workflow run properties which were set during the specified run. (string) -- (string) -- Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'RunProperties': { 'string': 'string' } } :returns: (string) -- (string) -- """ pass def get_workflow_runs(Name=None, IncludeGraph=None, NextToken=None, MaxResults=None): """ Retrieves metadata for all runs of a given workflow. See also: AWS API Documentation Exceptions :example: response = client.get_workflow_runs( Name='string', IncludeGraph=True|False, NextToken='string', MaxResults=123 ) :type Name: string :param Name: [REQUIRED]\nName of the workflow whose metadata of runs should be returned.\n :type IncludeGraph: boolean :param IncludeGraph: Specifies whether to include the workflow graph in response or not. :type NextToken: string :param NextToken: The maximum size of the response. :type MaxResults: integer :param MaxResults: The maximum number of workflow runs to be included in the response. :rtype: dict ReturnsResponse Syntax { 'Runs': [ { 'Name': 'string', 'WorkflowRunId': 'string', 'WorkflowRunProperties': { 'string': 'string' }, 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'Status': 'RUNNING'|'COMPLETED'|'STOPPING'|'STOPPED', 'Statistics': { 'TotalActions': 123, 'TimeoutActions': 123, 'FailedActions': 123, 'StoppedActions': 123, 'SucceededActions': 123, 'RunningActions': 123 }, 'Graph': { 'Nodes': [ { 'Type': 'CRAWLER'|'JOB'|'TRIGGER', 'Name': 'string', 'UniqueId': 'string', 'TriggerDetails': { 'Trigger': { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } } }, 'JobDetails': { 'JobRuns': [ { 'Id': 'string', 'Attempt': 123, 'PreviousRunId': 'string', 'TriggerName': 'string', 'JobName': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'JobRunState': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'Arguments': { 'string': 'string' }, 'ErrorMessage': 'string', 'PredecessorRuns': [ { 'JobName': 'string', 'RunId': 'string' }, ], 'AllocatedCapacity': 123, 'ExecutionTime': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'LogGroupName': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ] }, 'CrawlerDetails': { 'Crawls': [ { 'State': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED', 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string' }, ] } }, ], 'Edges': [ { 'SourceId': 'string', 'DestinationId': 'string' }, ] } }, ], 'NextToken': 'string' } Response Structure (dict) -- Runs (list) -- A list of workflow run metadata objects. (dict) -- A workflow run is an execution of a workflow providing all the runtime information. Name (string) -- Name of the workflow which was executed. WorkflowRunId (string) -- The ID of this workflow run. WorkflowRunProperties (dict) -- The workflow run properties which were set during the run. (string) -- (string) -- StartedOn (datetime) -- The date and time when the workflow run was started. CompletedOn (datetime) -- The date and time when the workflow run completed. Status (string) -- The status of the workflow run. Statistics (dict) -- The statistics of the run. TotalActions (integer) -- Total number of Actions in the workflow run. TimeoutActions (integer) -- Total number of Actions which timed out. FailedActions (integer) -- Total number of Actions which have failed. StoppedActions (integer) -- Total number of Actions which have stopped. SucceededActions (integer) -- Total number of Actions which have succeeded. RunningActions (integer) -- Total number Actions in running state. Graph (dict) -- The graph representing all the AWS Glue components that belong to the workflow as nodes and directed connections between them as edges. Nodes (list) -- A list of the the AWS Glue components belong to the workflow represented as nodes. (dict) -- A node represents an AWS Glue component like Trigger, Job etc. which is part of a workflow. Type (string) -- The type of AWS Glue component represented by the node. Name (string) -- The name of the AWS Glue component represented by the node. UniqueId (string) -- The unique Id assigned to the node within the workflow. TriggerDetails (dict) -- Details of the Trigger when the node represents a Trigger. Trigger (dict) -- The information of the trigger represented by the trigger node. Name (string) -- The name of the trigger. WorkflowName (string) -- The name of the workflow associated with the trigger. Id (string) -- Reserved for future use. Type (string) -- The type of trigger that this is. State (string) -- The current state of the trigger. Description (string) -- A description of this trigger. Schedule (string) -- A cron expression used to specify the schedule (see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, you would specify: cron(15 12 * * ? *) . Actions (list) -- The actions initiated by this trigger. (dict) -- Defines an action to be initiated by a trigger. JobName (string) -- The name of a job to be executed. Arguments (dict) -- The job arguments used when this trigger fires. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- Timeout (integer) -- The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this action. NotificationProperty (dict) -- Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) -- After a job run starts, the number of minutes to wait before sending a job run delay notification. CrawlerName (string) -- The name of the crawler to be used with this action. Predicate (dict) -- The predicate of this trigger, which defines when it will fire. Logical (string) -- An optional field if only one condition is listed. If multiple conditions are listed, then this field is required. Conditions (list) -- A list of the conditions that determine when the trigger will fire. (dict) -- Defines a condition under which a trigger fires. LogicalOperator (string) -- A logical operator. JobName (string) -- The name of the job whose JobRuns this condition applies to, and on which this trigger waits. State (string) -- The condition state. Currently, the only job states that a trigger can listen for are SUCCEEDED , STOPPED , FAILED , and TIMEOUT . The only crawler states that a trigger can listen for are SUCCEEDED , FAILED , and CANCELLED . CrawlerName (string) -- The name of the crawler to which this condition applies. CrawlState (string) -- The state of the crawler to which this condition applies. JobDetails (dict) -- Details of the Job when the node represents a Job. JobRuns (list) -- The information for the job runs represented by the job node. (dict) -- Contains information about a job run. Id (string) -- The ID of this job run. Attempt (integer) -- The number of the attempt to run this job. PreviousRunId (string) -- The ID of the previous run of this job. For example, the JobRunId specified in the StartJobRun action. TriggerName (string) -- The name of the trigger that started this job run. JobName (string) -- The name of the job definition being used in this run. StartedOn (datetime) -- The date and time at which this job run was started. LastModifiedOn (datetime) -- The last time that this job run was modified. CompletedOn (datetime) -- The date and time that this job run completed. JobRunState (string) -- The current state of the job run. For more information about the statuses of jobs that have terminated abnormally, see AWS Glue Job Run Statuses . Arguments (dict) -- The job arguments associated with this run. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- ErrorMessage (string) -- An error message associated with this job run. PredecessorRuns (list) -- A list of predecessors to this job run. (dict) -- A job run that was used in the predicate of a conditional trigger that triggered this job run. JobName (string) -- The name of the job definition used by the predecessor job run. RunId (string) -- The job-run ID of the predecessor job run. AllocatedCapacity (integer) -- This field is deprecated. Use MaxCapacity instead. The number of AWS Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be allocated; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . ExecutionTime (integer) -- The amount of time (in seconds) that the job run consumed resources. Timeout (integer) -- The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. MaxCapacity (float) -- The number of AWS Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page . Do not set Max Capacity if using WorkerType and NumberOfWorkers . The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job or an Apache Spark ETL job: When you specify a Python shell job (JobCommand.Name ="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU. When you specify an Apache Spark ETL job (JobCommand.Name ="glueetl"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation. WorkerType (string) -- The type of predefined worker that is allocated when a job runs. Accepts a value of Standard, G.1X, or G.2X. For the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. For the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker. For the G.2X worker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker. NumberOfWorkers (integer) -- The number of workers of a defined workerType that are allocated when a job runs. The maximum number of workers you can define are 299 for G.1X , and 149 for G.2X . SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this job run. LogGroupName (string) -- The name of the log group for secure logging that can be server-side encrypted in Amazon CloudWatch using AWS KMS. This name can be /aws-glue/jobs/ , in which case the default encryption is NONE . If you add a role name and SecurityConfiguration name (in other words, /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/ ), then that security configuration is used to encrypt the log group. NotificationProperty (dict) -- Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) -- After a job run starts, the number of minutes to wait before sending a job run delay notification. GlueVersion (string) -- Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for jobs of type Spark. For more information about the available AWS Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide. Jobs that are created without specifying a Glue version default to Glue 0.9. CrawlerDetails (dict) -- Details of the crawler when the node represents a crawler. Crawls (list) -- A list of crawls represented by the crawl node. (dict) -- The details of a crawl in the workflow. State (string) -- The state of the crawler. StartedOn (datetime) -- The date and time on which the crawl started. CompletedOn (datetime) -- The date and time on which the crawl completed. ErrorMessage (string) -- The error message associated with the crawl. LogGroup (string) -- The log group associated with the crawl. LogStream (string) -- The log stream associated with the crawl. Edges (list) -- A list of all the directed connections between the nodes belonging to the workflow. (dict) -- An edge represents a directed connection between two AWS Glue components which are part of the workflow the edge belongs to. SourceId (string) -- The unique of the node within the workflow where the edge starts. DestinationId (string) -- The unique of the node within the workflow where the edge ends. NextToken (string) -- A continuation token, if not all requested workflow runs have been returned. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'Runs': [ { 'Name': 'string', 'WorkflowRunId': 'string', 'WorkflowRunProperties': { 'string': 'string' }, 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'Status': 'RUNNING'|'COMPLETED'|'STOPPING'|'STOPPED', 'Statistics': { 'TotalActions': 123, 'TimeoutActions': 123, 'FailedActions': 123, 'StoppedActions': 123, 'SucceededActions': 123, 'RunningActions': 123 }, 'Graph': { 'Nodes': [ { 'Type': 'CRAWLER'|'JOB'|'TRIGGER', 'Name': 'string', 'UniqueId': 'string', 'TriggerDetails': { 'Trigger': { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } } }, 'JobDetails': { 'JobRuns': [ { 'Id': 'string', 'Attempt': 123, 'PreviousRunId': 'string', 'TriggerName': 'string', 'JobName': 'string', 'StartedOn': datetime(2015, 1, 1), 'LastModifiedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'JobRunState': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'Arguments': { 'string': 'string' }, 'ErrorMessage': 'string', 'PredecessorRuns': [ { 'JobName': 'string', 'RunId': 'string' }, ], 'AllocatedCapacity': 123, 'ExecutionTime': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'LogGroupName': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' }, ] }, 'CrawlerDetails': { 'Crawls': [ { 'State': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED', 'StartedOn': datetime(2015, 1, 1), 'CompletedOn': datetime(2015, 1, 1), 'ErrorMessage': 'string', 'LogGroup': 'string', 'LogStream': 'string' }, ] } }, ], 'Edges': [ { 'SourceId': 'string', 'DestinationId': 'string' }, ] } }, ], 'NextToken': 'string' } :returns: (string) -- (string) -- """ pass def import_catalog_to_glue(CatalogId=None): """ Imports an existing Amazon Athena Data Catalog to AWS Glue See also: AWS API Documentation Exceptions :example: response = client.import_catalog_to_glue( CatalogId='string' ) :type CatalogId: string :param CatalogId: The ID of the catalog to import. Currently, this should be the AWS account ID. :rtype: dict ReturnsResponse Syntax{} Response Structure (dict) -- Exceptions Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException """ pass def list_crawlers(MaxResults=None, NextToken=None, Tags=None): """ Retrieves the names of all crawler resources in this AWS account, or the resources with the specified tag. This operation allows you to see which resources are available in your account, and their names. This operation takes the optional Tags field, which you can use as a filter on the response so that tagged resources can be retrieved as a group. If you choose to use tags filtering, only resources with the tag are retrieved. See also: AWS API Documentation Exceptions :example: response = client.list_crawlers( MaxResults=123, NextToken='string', Tags={ 'string': 'string' } ) :type MaxResults: integer :param MaxResults: The maximum size of a list to return. :type NextToken: string :param NextToken: A continuation token, if this is a continuation request. :type Tags: dict :param Tags: Specifies to return only these tagged resources.\n\n(string) --\n(string) --\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'CrawlerNames': [ 'string', ], 'NextToken': 'string' } Response Structure (dict) -- CrawlerNames (list) -- The names of all crawlers in the account, or the crawlers with the specified tags. (string) -- NextToken (string) -- A continuation token, if the returned list does not contain the last metric available. Exceptions Glue.Client.exceptions.OperationTimeoutException :return: { 'CrawlerNames': [ 'string', ], 'NextToken': 'string' } :returns: (string) -- """ pass def list_dev_endpoints(NextToken=None, MaxResults=None, Tags=None): """ Retrieves the names of all DevEndpoint resources in this AWS account, or the resources with the specified tag. This operation allows you to see which resources are available in your account, and their names. This operation takes the optional Tags field, which you can use as a filter on the response so that tagged resources can be retrieved as a group. If you choose to use tags filtering, only resources with the tag are retrieved. See also: AWS API Documentation Exceptions :example: response = client.list_dev_endpoints( NextToken='string', MaxResults=123, Tags={ 'string': 'string' } ) :type NextToken: string :param NextToken: A continuation token, if this is a continuation request. :type MaxResults: integer :param MaxResults: The maximum size of a list to return. :type Tags: dict :param Tags: Specifies to return only these tagged resources.\n\n(string) --\n(string) --\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'DevEndpointNames': [ 'string', ], 'NextToken': 'string' } Response Structure (dict) -- DevEndpointNames (list) -- The names of all the DevEndpoint s in the account, or the DevEndpoint s with the specified tags. (string) -- NextToken (string) -- A continuation token, if the returned list does not contain the last metric available. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'DevEndpointNames': [ 'string', ], 'NextToken': 'string' } :returns: (string) -- """ pass def list_jobs(NextToken=None, MaxResults=None, Tags=None): """ Retrieves the names of all job resources in this AWS account, or the resources with the specified tag. This operation allows you to see which resources are available in your account, and their names. This operation takes the optional Tags field, which you can use as a filter on the response so that tagged resources can be retrieved as a group. If you choose to use tags filtering, only resources with the tag are retrieved. See also: AWS API Documentation Exceptions :example: response = client.list_jobs( NextToken='string', MaxResults=123, Tags={ 'string': 'string' } ) :type NextToken: string :param NextToken: A continuation token, if this is a continuation request. :type MaxResults: integer :param MaxResults: The maximum size of a list to return. :type Tags: dict :param Tags: Specifies to return only these tagged resources.\n\n(string) --\n(string) --\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'JobNames': [ 'string', ], 'NextToken': 'string' } Response Structure (dict) -- JobNames (list) -- The names of all jobs in the account, or the jobs with the specified tags. (string) -- NextToken (string) -- A continuation token, if the returned list does not contain the last metric available. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'JobNames': [ 'string', ], 'NextToken': 'string' } :returns: (string) -- """ pass def list_ml_transforms(NextToken=None, MaxResults=None, Filter=None, Sort=None, Tags=None): """ Retrieves a sortable, filterable list of existing AWS Glue machine learning transforms in this AWS account, or the resources with the specified tag. This operation takes the optional Tags field, which you can use as a filter of the responses so that tagged resources can be retrieved as a group. If you choose to use tag filtering, only resources with the tags are retrieved. See also: AWS API Documentation Exceptions :example: response = client.list_ml_transforms( NextToken='string', MaxResults=123, Filter={ 'Name': 'string', 'TransformType': 'FIND_MATCHES', 'Status': 'NOT_READY'|'READY'|'DELETING', 'GlueVersion': 'string', 'CreatedBefore': datetime(2015, 1, 1), 'CreatedAfter': datetime(2015, 1, 1), 'LastModifiedBefore': datetime(2015, 1, 1), 'LastModifiedAfter': datetime(2015, 1, 1), 'Schema': [ { 'Name': 'string', 'DataType': 'string' }, ] }, Sort={ 'Column': 'NAME'|'TRANSFORM_TYPE'|'STATUS'|'CREATED'|'LAST_MODIFIED', 'SortDirection': 'DESCENDING'|'ASCENDING' }, Tags={ 'string': 'string' } ) :type NextToken: string :param NextToken: A continuation token, if this is a continuation request. :type MaxResults: integer :param MaxResults: The maximum size of a list to return. :type Filter: dict :param Filter: A TransformFilterCriteria used to filter the machine learning transforms.\n\nName (string) --A unique transform name that is used to filter the machine learning transforms.\n\nTransformType (string) --The type of machine learning transform that is used to filter the machine learning transforms.\n\nStatus (string) --Filters the list of machine learning transforms by the last known status of the transforms (to indicate whether a transform can be used or not). One of 'NOT_READY', 'READY', or 'DELETING'.\n\nGlueVersion (string) --This value determines which version of AWS Glue this machine learning transform is compatible with. Glue 1.0 is recommended for most customers. If the value is not set, the Glue compatibility defaults to Glue 0.9. For more information, see AWS Glue Versions in the developer guide.\n\nCreatedBefore (datetime) --The time and date before which the transforms were created.\n\nCreatedAfter (datetime) --The time and date after which the transforms were created.\n\nLastModifiedBefore (datetime) --Filter on transforms last modified before this date.\n\nLastModifiedAfter (datetime) --Filter on transforms last modified after this date.\n\nSchema (list) --Filters on datasets with a specific schema. The Map<Column, Type> object is an array of key-value pairs representing the schema this transform accepts, where Column is the name of a column, and Type is the type of the data such as an integer or string. Has an upper bound of 100 columns.\n\n(dict) --A key-value pair representing a column and data type that this transform can run against. The Schema parameter of the MLTransform may contain up to 100 of these structures.\n\nName (string) --The name of the column.\n\nDataType (string) --The type of data in the column.\n\n\n\n\n\n\n :type Sort: dict :param Sort: A TransformSortCriteria used to sort the machine learning transforms.\n\nColumn (string) -- [REQUIRED]The column to be used in the sorting criteria that are associated with the machine learning transform.\n\nSortDirection (string) -- [REQUIRED]The sort direction to be used in the sorting criteria that are associated with the machine learning transform.\n\n\n :type Tags: dict :param Tags: Specifies to return only these tagged resources.\n\n(string) --\n(string) --\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'TransformIds': [ 'string', ], 'NextToken': 'string' } Response Structure (dict) -- TransformIds (list) -- The identifiers of all the machine learning transforms in the account, or the machine learning transforms with the specified tags. (string) -- NextToken (string) -- A continuation token, if the returned list does not contain the last metric available. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException :return: { 'TransformIds': [ 'string', ], 'NextToken': 'string' } :returns: (string) -- """ pass def list_triggers(NextToken=None, DependentJobName=None, MaxResults=None, Tags=None): """ Retrieves the names of all trigger resources in this AWS account, or the resources with the specified tag. This operation allows you to see which resources are available in your account, and their names. This operation takes the optional Tags field, which you can use as a filter on the response so that tagged resources can be retrieved as a group. If you choose to use tags filtering, only resources with the tag are retrieved. See also: AWS API Documentation Exceptions :example: response = client.list_triggers( NextToken='string', DependentJobName='string', MaxResults=123, Tags={ 'string': 'string' } ) :type NextToken: string :param NextToken: A continuation token, if this is a continuation request. :type DependentJobName: string :param DependentJobName: The name of the job for which to retrieve triggers. The trigger that can start this job is returned. If there is no such trigger, all triggers are returned. :type MaxResults: integer :param MaxResults: The maximum size of a list to return. :type Tags: dict :param Tags: Specifies to return only these tagged resources.\n\n(string) --\n(string) --\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'TriggerNames': [ 'string', ], 'NextToken': 'string' } Response Structure (dict) -- TriggerNames (list) -- The names of all triggers in the account, or the triggers with the specified tags. (string) -- NextToken (string) -- A continuation token, if the returned list does not contain the last metric available. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'TriggerNames': [ 'string', ], 'NextToken': 'string' } :returns: (string) -- """ pass def list_workflows(NextToken=None, MaxResults=None): """ Lists names of workflows created in the account. See also: AWS API Documentation Exceptions :example: response = client.list_workflows( NextToken='string', MaxResults=123 ) :type NextToken: string :param NextToken: A continuation token, if this is a continuation request. :type MaxResults: integer :param MaxResults: The maximum size of a list to return. :rtype: dict ReturnsResponse Syntax { 'Workflows': [ 'string', ], 'NextToken': 'string' } Response Structure (dict) -- Workflows (list) -- List of names of workflows in the account. (string) -- NextToken (string) -- A continuation token, if not all workflow names have been returned. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'Workflows': [ 'string', ], 'NextToken': 'string' } :returns: (string) -- """ pass def put_data_catalog_encryption_settings(CatalogId=None, DataCatalogEncryptionSettings=None): """ Sets the security configuration for a specified catalog. After the configuration has been set, the specified encryption is applied to every catalog write thereafter. See also: AWS API Documentation Exceptions :example: response = client.put_data_catalog_encryption_settings( CatalogId='string', DataCatalogEncryptionSettings={ 'EncryptionAtRest': { 'CatalogEncryptionMode': 'DISABLED'|'SSE-KMS', 'SseAwsKmsKeyId': 'string' }, 'ConnectionPasswordEncryption': { 'ReturnConnectionPasswordEncrypted': True|False, 'AwsKmsKeyId': 'string' } } ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog to set the security configuration for. If none is provided, the AWS account ID is used by default. :type DataCatalogEncryptionSettings: dict :param DataCatalogEncryptionSettings: [REQUIRED]\nThe security configuration to set.\n\nEncryptionAtRest (dict) --Specifies the encryption-at-rest configuration for the Data Catalog.\n\nCatalogEncryptionMode (string) -- [REQUIRED]The encryption-at-rest mode for encrypting Data Catalog data.\n\nSseAwsKmsKeyId (string) --The ID of the AWS KMS key to use for encryption at rest.\n\n\n\nConnectionPasswordEncryption (dict) --When connection password protection is enabled, the Data Catalog uses a customer-provided key to encrypt the password as part of CreateConnection or UpdateConnection and store it in the ENCRYPTED_PASSWORD field in the connection properties. You can enable catalog encryption or only password encryption.\n\nReturnConnectionPasswordEncrypted (boolean) -- [REQUIRED]When the ReturnConnectionPasswordEncrypted flag is set to 'true', passwords remain encrypted in the responses of GetConnection and GetConnections . This encryption takes effect independently from catalog encryption.\n\nAwsKmsKeyId (string) --An AWS KMS key that is used to encrypt the connection password.\nIf connection password protection is enabled, the caller of CreateConnection and UpdateConnection needs at least kms:Encrypt permission on the specified AWS KMS key, to encrypt passwords before storing them in the Data Catalog.\nYou can set the decrypt permission to enable or restrict access on the password key according to your security requirements.\n\n\n\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: (dict) -- """ pass def put_resource_policy(PolicyInJson=None, PolicyHashCondition=None, PolicyExistsCondition=None): """ Sets the Data Catalog resource policy for access control. See also: AWS API Documentation Exceptions :example: response = client.put_resource_policy( PolicyInJson='string', PolicyHashCondition='string', PolicyExistsCondition='MUST_EXIST'|'NOT_EXIST'|'NONE' ) :type PolicyInJson: string :param PolicyInJson: [REQUIRED]\nContains the policy document to set, in JSON format.\n :type PolicyHashCondition: string :param PolicyHashCondition: The hash value returned when the previous policy was set using PutResourcePolicy . Its purpose is to prevent concurrent modifications of a policy. Do not use this parameter if no previous policy has been set. :type PolicyExistsCondition: string :param PolicyExistsCondition: A value of MUST_EXIST is used to update a policy. A value of NOT_EXIST is used to create a new policy. If a value of NONE or a null value is used, the call will not depend on the existence of a policy. :rtype: dict ReturnsResponse Syntax { 'PolicyHash': 'string' } Response Structure (dict) -- PolicyHash (string) -- A hash of the policy that has just been set. This must be included in a subsequent call that overwrites or updates this policy. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.ConditionCheckFailureException :return: { 'PolicyHash': 'string' } :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.ConditionCheckFailureException """ pass def put_workflow_run_properties(Name=None, RunId=None, RunProperties=None): """ Puts the specified workflow run properties for the given workflow run. If a property already exists for the specified run, then it overrides the value otherwise adds the property to existing properties. See also: AWS API Documentation Exceptions :example: response = client.put_workflow_run_properties( Name='string', RunId='string', RunProperties={ 'string': 'string' } ) :type Name: string :param Name: [REQUIRED]\nName of the workflow which was run.\n :type RunId: string :param RunId: [REQUIRED]\nThe ID of the workflow run for which the run properties should be updated.\n :type RunProperties: dict :param RunProperties: [REQUIRED]\nThe properties to put for the specified run.\n\n(string) --\n(string) --\n\n\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.AlreadyExistsException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.ConcurrentModificationException :return: {} :returns: (dict) -- """ pass def reset_job_bookmark(JobName=None, RunId=None): """ Resets a bookmark entry. See also: AWS API Documentation Exceptions :example: response = client.reset_job_bookmark( JobName='string', RunId='string' ) :type JobName: string :param JobName: [REQUIRED]\nThe name of the job in question.\n :type RunId: string :param RunId: The unique run identifier associated with this job run. :rtype: dict ReturnsResponse Syntax { 'JobBookmarkEntry': { 'JobName': 'string', 'Version': 123, 'Run': 123, 'Attempt': 123, 'PreviousRunId': 'string', 'RunId': 'string', 'JobBookmark': 'string' } } Response Structure (dict) -- JobBookmarkEntry (dict) -- The reset bookmark entry. JobName (string) -- The name of the job in question. Version (integer) -- The version of the job. Run (integer) -- The run ID number. Attempt (integer) -- The attempt ID number. PreviousRunId (string) -- The unique run identifier associated with the previous job run. RunId (string) -- The run ID number. JobBookmark (string) -- The bookmark itself. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException :return: { 'JobBookmarkEntry': { 'JobName': 'string', 'Version': 123, 'Run': 123, 'Attempt': 123, 'PreviousRunId': 'string', 'RunId': 'string', 'JobBookmark': 'string' } } :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException """ pass def search_tables(CatalogId=None, NextToken=None, Filters=None, SearchText=None, SortCriteria=None, MaxResults=None): """ Searches a set of tables based on properties in the table metadata as well as on the parent database. You can search against text or filter conditions. You can only get tables that you have access to based on the security policies defined in Lake Formation. You need at least a read-only access to the table for it to be returned. If you do not have access to all the columns in the table, these columns will not be searched against when returning the list of tables back to you. If you have access to the columns but not the data in the columns, those columns and the associated metadata for those columns will be included in the search. See also: AWS API Documentation Exceptions :example: response = client.search_tables( CatalogId='string', NextToken='string', Filters=[ { 'Key': 'string', 'Value': 'string', 'Comparator': 'EQUALS'|'GREATER_THAN'|'LESS_THAN'|'GREATER_THAN_EQUALS'|'LESS_THAN_EQUALS' }, ], SearchText='string', SortCriteria=[ { 'FieldName': 'string', 'Sort': 'ASC'|'DESC' }, ], MaxResults=123 ) :type CatalogId: string :param CatalogId: A unique identifier, consisting of `` account_id /datalake`` . :type NextToken: string :param NextToken: A continuation token, included if this is a continuation call. :type Filters: list :param Filters: A list of key-value pairs, and a comparator used to filter the search results. Returns all entities matching the predicate.\n\n(dict) --Defines a property predicate.\n\nKey (string) --The key of the property.\n\nValue (string) --The value of the property.\n\nComparator (string) --The comparator used to compare this property to others.\n\n\n\n\n :type SearchText: string :param SearchText: A string used for a text search.\nSpecifying a value in quotes filters based on an exact match to the value.\n :type SortCriteria: list :param SortCriteria: A list of criteria for sorting the results by a field name, in an ascending or descending order.\n\n(dict) --Specifies a field to sort by and a sort order.\n\nFieldName (string) --The name of the field on which to sort.\n\nSort (string) --An ascending or descending sort.\n\n\n\n\n :type MaxResults: integer :param MaxResults: The maximum number of tables to return in a single response. :rtype: dict ReturnsResponse Syntax { 'NextToken': 'string', 'TableList': [ { 'Name': 'string', 'DatabaseName': 'string', 'Description': 'string', 'Owner': 'string', 'CreateTime': datetime(2015, 1, 1), 'UpdateTime': datetime(2015, 1, 1), 'LastAccessTime': datetime(2015, 1, 1), 'LastAnalyzedTime': datetime(2015, 1, 1), 'Retention': 123, 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'PartitionKeys': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'ViewOriginalText': 'string', 'ViewExpandedText': 'string', 'TableType': 'string', 'Parameters': { 'string': 'string' }, 'CreatedBy': 'string', 'IsRegisteredWithLakeFormation': True|False }, ] } Response Structure (dict) -- NextToken (string) -- A continuation token, present if the current list segment is not the last. TableList (list) -- A list of the requested Table objects. The SearchTables response returns only the tables that you have access to. (dict) -- Represents a collection of related data organized in columns and rows. Name (string) -- The table name. For Hive compatibility, this must be entirely lowercase. DatabaseName (string) -- The name of the database where the table metadata resides. For Hive compatibility, this must be all lowercase. Description (string) -- A description of the table. Owner (string) -- The owner of the table. CreateTime (datetime) -- The time when the table definition was created in the Data Catalog. UpdateTime (datetime) -- The last time that the table was updated. LastAccessTime (datetime) -- The last time that the table was accessed. This is usually taken from HDFS, and might not be reliable. LastAnalyzedTime (datetime) -- The last time that column statistics were computed for this table. Retention (integer) -- The retention time for this table. StorageDescriptor (dict) -- A storage descriptor containing information about the physical storage of this table. Columns (list) -- A list of the Columns in the table. (dict) -- A column in a Table . Name (string) -- The name of the Column . Type (string) -- The data type of the Column . Comment (string) -- A free-form text comment. Parameters (dict) -- These key-value pairs define properties associated with the column. (string) -- (string) -- Location (string) -- The physical location of the table. By default, this takes the form of the warehouse location, followed by the database location in the warehouse, followed by the table name. InputFormat (string) -- The input format: SequenceFileInputFormat (binary), or TextInputFormat , or a custom format. OutputFormat (string) -- The output format: SequenceFileOutputFormat (binary), or IgnoreKeyTextOutputFormat , or a custom format. Compressed (boolean) -- True if the data in the table is compressed, or False if not. NumberOfBuckets (integer) -- Must be specified if the table contains any dimension columns. SerdeInfo (dict) -- The serialization/deserialization (SerDe) information. Name (string) -- Name of the SerDe. SerializationLibrary (string) -- Usually the class that implements the SerDe. An example is org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe . Parameters (dict) -- These key-value pairs define initialization parameters for the SerDe. (string) -- (string) -- BucketColumns (list) -- A list of reducer grouping columns, clustering columns, and bucketing columns in the table. (string) -- SortColumns (list) -- A list specifying the sort order of each bucket in the table. (dict) -- Specifies the sort order of a sorted column. Column (string) -- The name of the column. SortOrder (integer) -- Indicates that the column is sorted in ascending order (== 1 ), or in descending order (==0 ). Parameters (dict) -- The user-supplied properties in key-value form. (string) -- (string) -- SkewedInfo (dict) -- The information about values that appear frequently in a column (skewed values). SkewedColumnNames (list) -- A list of names of columns that contain skewed values. (string) -- SkewedColumnValues (list) -- A list of values that appear so frequently as to be considered skewed. (string) -- SkewedColumnValueLocationMaps (dict) -- A mapping of skewed values to the columns that contain them. (string) -- (string) -- StoredAsSubDirectories (boolean) -- True if the table data is stored in subdirectories, or False if not. PartitionKeys (list) -- A list of columns by which the table is partitioned. Only primitive types are supported as partition keys. When you create a table used by Amazon Athena, and you do not specify any partitionKeys , you must at least set the value of partitionKeys to an empty list. For example: "PartitionKeys": [] (dict) -- A column in a Table . Name (string) -- The name of the Column . Type (string) -- The data type of the Column . Comment (string) -- A free-form text comment. Parameters (dict) -- These key-value pairs define properties associated with the column. (string) -- (string) -- ViewOriginalText (string) -- If the table is a view, the original text of the view; otherwise null . ViewExpandedText (string) -- If the table is a view, the expanded text of the view; otherwise null . TableType (string) -- The type of this table (EXTERNAL_TABLE , VIRTUAL_VIEW , etc.). Parameters (dict) -- These key-value pairs define properties associated with the table. (string) -- (string) -- CreatedBy (string) -- The person or entity who created the table. IsRegisteredWithLakeFormation (boolean) -- Indicates whether the table has been registered with AWS Lake Formation. Exceptions Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException :return: { 'NextToken': 'string', 'TableList': [ { 'Name': 'string', 'DatabaseName': 'string', 'Description': 'string', 'Owner': 'string', 'CreateTime': datetime(2015, 1, 1), 'UpdateTime': datetime(2015, 1, 1), 'LastAccessTime': datetime(2015, 1, 1), 'LastAnalyzedTime': datetime(2015, 1, 1), 'Retention': 123, 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'PartitionKeys': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'ViewOriginalText': 'string', 'ViewExpandedText': 'string', 'TableType': 'string', 'Parameters': { 'string': 'string' }, 'CreatedBy': 'string', 'IsRegisteredWithLakeFormation': True|False }, ] } :returns: (string) -- (string) -- """ pass def start_crawler(Name=None): """ Starts a crawl using the specified crawler, regardless of what is scheduled. If the crawler is already running, returns a CrawlerRunningException . See also: AWS API Documentation Exceptions :example: response = client.start_crawler( Name='string' ) :type Name: string :param Name: [REQUIRED]\nName of the crawler to start.\n :rtype: dict ReturnsResponse Syntax{} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.CrawlerRunningException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.CrawlerRunningException Glue.Client.exceptions.OperationTimeoutException """ pass def start_crawler_schedule(CrawlerName=None): """ Changes the schedule state of the specified crawler to SCHEDULED , unless the crawler is already running or the schedule state is already SCHEDULED . See also: AWS API Documentation Exceptions :example: response = client.start_crawler_schedule( CrawlerName='string' ) :type CrawlerName: string :param CrawlerName: [REQUIRED]\nName of the crawler to schedule.\n :rtype: dict ReturnsResponse Syntax{} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.SchedulerRunningException Glue.Client.exceptions.SchedulerTransitioningException Glue.Client.exceptions.NoScheduleException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.SchedulerRunningException Glue.Client.exceptions.SchedulerTransitioningException Glue.Client.exceptions.NoScheduleException Glue.Client.exceptions.OperationTimeoutException """ pass def start_export_labels_task_run(TransformId=None, OutputS3Path=None): """ Begins an asynchronous task to export all labeled data for a particular transform. This task is the only label-related API call that is not part of the typical active learning workflow. You typically use StartExportLabelsTaskRun when you want to work with all of your existing labels at the same time, such as when you want to remove or change labels that were previously submitted as truth. This API operation accepts the TransformId whose labels you want to export and an Amazon Simple Storage Service (Amazon S3) path to export the labels to. The operation returns a TaskRunId . You can check on the status of your task run by calling the GetMLTaskRun API. See also: AWS API Documentation Exceptions :example: response = client.start_export_labels_task_run( TransformId='string', OutputS3Path='string' ) :type TransformId: string :param TransformId: [REQUIRED]\nThe unique identifier of the machine learning transform.\n :type OutputS3Path: string :param OutputS3Path: [REQUIRED]\nThe Amazon S3 path where you export the labels.\n :rtype: dict ReturnsResponse Syntax { 'TaskRunId': 'string' } Response Structure (dict) -- TaskRunId (string) -- The unique identifier for the task run. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException :return: { 'TaskRunId': 'string' } :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException """ pass def start_import_labels_task_run(TransformId=None, InputS3Path=None, ReplaceAllLabels=None): """ Enables you to provide additional labels (examples of truth) to be used to teach the machine learning transform and improve its quality. This API operation is generally used as part of the active learning workflow that starts with the StartMLLabelingSetGenerationTaskRun call and that ultimately results in improving the quality of your machine learning transform. After the StartMLLabelingSetGenerationTaskRun finishes, AWS Glue machine learning will have generated a series of questions for humans to answer. (Answering these questions is often called \'labeling\' in the machine learning workflows). In the case of the FindMatches transform, these questions are of the form, \xe2\x80\x9cWhat is the correct way to group these rows together into groups composed entirely of matching records?\xe2\x80\x9d After the labeling process is finished, users upload their answers/labels with a call to StartImportLabelsTaskRun . After StartImportLabelsTaskRun finishes, all future runs of the machine learning transform use the new and improved labels and perform a higher-quality transformation. By default, StartMLLabelingSetGenerationTaskRun continually learns from and combines all labels that you upload unless you set Replace to true. If you set Replace to true, StartImportLabelsTaskRun deletes and forgets all previously uploaded labels and learns only from the exact set that you upload. Replacing labels can be helpful if you realize that you previously uploaded incorrect labels, and you believe that they are having a negative effect on your transform quality. You can check on the status of your task run by calling the GetMLTaskRun operation. See also: AWS API Documentation Exceptions :example: response = client.start_import_labels_task_run( TransformId='string', InputS3Path='string', ReplaceAllLabels=True|False ) :type TransformId: string :param TransformId: [REQUIRED]\nThe unique identifier of the machine learning transform.\n :type InputS3Path: string :param InputS3Path: [REQUIRED]\nThe Amazon Simple Storage Service (Amazon S3) path from where you import the labels.\n :type ReplaceAllLabels: boolean :param ReplaceAllLabels: Indicates whether to overwrite your existing labels. :rtype: dict ReturnsResponse Syntax { 'TaskRunId': 'string' } Response Structure (dict) -- TaskRunId (string) -- The unique identifier for the task run. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.InternalServiceException :return: { 'TaskRunId': 'string' } :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.InternalServiceException """ pass def start_job_run(JobName=None, JobRunId=None, Arguments=None, AllocatedCapacity=None, Timeout=None, MaxCapacity=None, SecurityConfiguration=None, NotificationProperty=None, WorkerType=None, NumberOfWorkers=None): """ Starts a job run using a job definition. See also: AWS API Documentation Exceptions :example: response = client.start_job_run( JobName='string', JobRunId='string', Arguments={ 'string': 'string' }, AllocatedCapacity=123, Timeout=123, MaxCapacity=123.0, SecurityConfiguration='string', NotificationProperty={ 'NotifyDelayAfter': 123 }, WorkerType='Standard'|'G.1X'|'G.2X', NumberOfWorkers=123 ) :type JobName: string :param JobName: [REQUIRED]\nThe name of the job definition to use.\n :type JobRunId: string :param JobRunId: The ID of a previous JobRun to retry. :type Arguments: dict :param Arguments: The job arguments specifically for this run. For this job run, they replace the default arguments set in the job definition itself.\nYou can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes.\nFor information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide.\nFor information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide.\n\n(string) --\n(string) --\n\n\n\n :type AllocatedCapacity: integer :param AllocatedCapacity: This field is deprecated. Use MaxCapacity instead.\nThe number of AWS Glue data processing units (DPUs) to allocate to this JobRun. From 2 to 100 DPUs can be allocated; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page .\n :type Timeout: integer :param Timeout: The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. :type MaxCapacity: float :param MaxCapacity: The number of AWS Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page .\nDo not set Max Capacity if using WorkerType and NumberOfWorkers .\nThe value that can be allocated for MaxCapacity depends on whether you are running a Python shell job, or an Apache Spark ETL job:\n\nWhen you specify a Python shell job (JobCommand.Name ='pythonshell'), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU.\nWhen you specify an Apache Spark ETL job (JobCommand.Name ='glueetl'), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation.\n\n :type SecurityConfiguration: string :param SecurityConfiguration: The name of the SecurityConfiguration structure to be used with this job run. :type NotificationProperty: dict :param NotificationProperty: Specifies configuration properties of a job run notification.\n\nNotifyDelayAfter (integer) --After a job run starts, the number of minutes to wait before sending a job run delay notification.\n\n\n :type WorkerType: string :param WorkerType: The type of predefined worker that is allocated when a job runs. Accepts a value of Standard, G.1X, or G.2X.\n\nFor the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker.\nFor the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker.\nFor the G.2X worker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker.\n\n :type NumberOfWorkers: integer :param NumberOfWorkers: The number of workers of a defined workerType that are allocated when a job runs.\nThe maximum number of workers you can define are 299 for G.1X , and 149 for G.2X .\n :rtype: dict ReturnsResponse Syntax { 'JobRunId': 'string' } Response Structure (dict) -- JobRunId (string) -- The ID assigned to this job run. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.ConcurrentRunsExceededException :return: { 'JobRunId': 'string' } :returns: Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.ConcurrentRunsExceededException """ pass def start_ml_evaluation_task_run(TransformId=None): """ Starts a task to estimate the quality of the transform. When you provide label sets as examples of truth, AWS Glue machine learning uses some of those examples to learn from them. The rest of the labels are used as a test to estimate quality. Returns a unique identifier for the run. You can call GetMLTaskRun to get more information about the stats of the EvaluationTaskRun . See also: AWS API Documentation Exceptions :example: response = client.start_ml_evaluation_task_run( TransformId='string' ) :type TransformId: string :param TransformId: [REQUIRED]\nThe unique identifier of the machine learning transform.\n :rtype: dict ReturnsResponse Syntax{ 'TaskRunId': 'string' } Response Structure (dict) -- TaskRunId (string) --The unique identifier associated with this run. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.ConcurrentRunsExceededException Glue.Client.exceptions.MLTransformNotReadyException :return: { 'TaskRunId': 'string' } """ pass def start_ml_labeling_set_generation_task_run(TransformId=None, OutputS3Path=None): """ Starts the active learning workflow for your machine learning transform to improve the transform\'s quality by generating label sets and adding labels. When the StartMLLabelingSetGenerationTaskRun finishes, AWS Glue will have generated a "labeling set" or a set of questions for humans to answer. In the case of the FindMatches transform, these questions are of the form, \xe2\x80\x9cWhat is the correct way to group these rows together into groups composed entirely of matching records?\xe2\x80\x9d After the labeling process is finished, you can upload your labels with a call to StartImportLabelsTaskRun . After StartImportLabelsTaskRun finishes, all future runs of the machine learning transform will use the new and improved labels and perform a higher-quality transformation. See also: AWS API Documentation Exceptions :example: response = client.start_ml_labeling_set_generation_task_run( TransformId='string', OutputS3Path='string' ) :type TransformId: string :param TransformId: [REQUIRED]\nThe unique identifier of the machine learning transform.\n :type OutputS3Path: string :param OutputS3Path: [REQUIRED]\nThe Amazon Simple Storage Service (Amazon S3) path where you generate the labeling set.\n :rtype: dict ReturnsResponse Syntax { 'TaskRunId': 'string' } Response Structure (dict) -- TaskRunId (string) -- The unique run identifier that is associated with this task run. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.ConcurrentRunsExceededException :return: { 'TaskRunId': 'string' } :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.ConcurrentRunsExceededException """ pass def start_trigger(Name=None): """ Starts an existing trigger. See Triggering Jobs for information about how different types of trigger are started. See also: AWS API Documentation Exceptions :example: response = client.start_trigger( Name='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the trigger to start.\n :rtype: dict ReturnsResponse Syntax{ 'Name': 'string' } Response Structure (dict) -- Name (string) --The name of the trigger that was started. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.ConcurrentRunsExceededException :return: { 'Name': 'string' } """ pass def start_workflow_run(Name=None): """ Starts a new run of the specified workflow. See also: AWS API Documentation Exceptions :example: response = client.start_workflow_run( Name='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the workflow to start.\n :rtype: dict ReturnsResponse Syntax{ 'RunId': 'string' } Response Structure (dict) -- RunId (string) --An Id for the new run. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.ConcurrentRunsExceededException :return: { 'RunId': 'string' } """ pass def stop_crawler(Name=None): """ If the specified crawler is running, stops the crawl. See also: AWS API Documentation Exceptions :example: response = client.stop_crawler( Name='string' ) :type Name: string :param Name: [REQUIRED]\nName of the crawler to stop.\n :rtype: dict ReturnsResponse Syntax{} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.CrawlerNotRunningException Glue.Client.exceptions.CrawlerStoppingException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.CrawlerNotRunningException Glue.Client.exceptions.CrawlerStoppingException Glue.Client.exceptions.OperationTimeoutException """ pass def stop_crawler_schedule(CrawlerName=None): """ Sets the schedule state of the specified crawler to NOT_SCHEDULED , but does not stop the crawler if it is already running. See also: AWS API Documentation Exceptions :example: response = client.stop_crawler_schedule( CrawlerName='string' ) :type CrawlerName: string :param CrawlerName: [REQUIRED]\nName of the crawler whose schedule state to set.\n :rtype: dict ReturnsResponse Syntax{} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.SchedulerNotRunningException Glue.Client.exceptions.SchedulerTransitioningException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.SchedulerNotRunningException Glue.Client.exceptions.SchedulerTransitioningException Glue.Client.exceptions.OperationTimeoutException """ pass def stop_trigger(Name=None): """ Stops a specified trigger. See also: AWS API Documentation Exceptions :example: response = client.stop_trigger( Name='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the trigger to stop.\n :rtype: dict ReturnsResponse Syntax{ 'Name': 'string' } Response Structure (dict) -- Name (string) --The name of the trigger that was stopped. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ConcurrentModificationException :return: { 'Name': 'string' } """ pass def stop_workflow_run(Name=None, RunId=None): """ Stops the execution of the specified workflow run. See also: AWS API Documentation Exceptions :example: response = client.stop_workflow_run( Name='string', RunId='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the workflow to stop.\n :type RunId: string :param RunId: [REQUIRED]\nThe ID of the workflow run to stop.\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.IllegalWorkflowStateException :return: {} :returns: (dict) -- """ pass def tag_resource(ResourceArn=None, TagsToAdd=None): """ Adds tags to a resource. A tag is a label you can assign to an AWS resource. In AWS Glue, you can tag only certain resources. For information about what resources you can tag, see AWS Tags in AWS Glue . See also: AWS API Documentation Exceptions :example: response = client.tag_resource( ResourceArn='string', TagsToAdd={ 'string': 'string' } ) :type ResourceArn: string :param ResourceArn: [REQUIRED]\nThe ARN of the AWS Glue resource to which to add the tags. For more information about AWS Glue resource ARNs, see the AWS Glue ARN string pattern .\n :type TagsToAdd: dict :param TagsToAdd: [REQUIRED]\nTags to add to this resource.\n\n(string) --\n(string) --\n\n\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.EntityNotFoundException :return: {} :returns: (dict) -- """ pass def untag_resource(ResourceArn=None, TagsToRemove=None): """ Removes tags from a resource. See also: AWS API Documentation Exceptions :example: response = client.untag_resource( ResourceArn='string', TagsToRemove=[ 'string', ] ) :type ResourceArn: string :param ResourceArn: [REQUIRED]\nThe Amazon Resource Name (ARN) of the resource from which to remove the tags.\n :type TagsToRemove: list :param TagsToRemove: [REQUIRED]\nTags to remove from this resource.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.EntityNotFoundException :return: {} :returns: (dict) -- """ pass def update_classifier(GrokClassifier=None, XMLClassifier=None, JsonClassifier=None, CsvClassifier=None): """ Modifies an existing classifier (a GrokClassifier , an XMLClassifier , a JsonClassifier , or a CsvClassifier , depending on which field is present). See also: AWS API Documentation Exceptions :example: response = client.update_classifier( GrokClassifier={ 'Name': 'string', 'Classification': 'string', 'GrokPattern': 'string', 'CustomPatterns': 'string' }, XMLClassifier={ 'Name': 'string', 'Classification': 'string', 'RowTag': 'string' }, JsonClassifier={ 'Name': 'string', 'JsonPath': 'string' }, CsvClassifier={ 'Name': 'string', 'Delimiter': 'string', 'QuoteSymbol': 'string', 'ContainsHeader': 'UNKNOWN'|'PRESENT'|'ABSENT', 'Header': [ 'string', ], 'DisableValueTrimming': True|False, 'AllowSingleColumn': True|False } ) :type GrokClassifier: dict :param GrokClassifier: A GrokClassifier object with updated fields.\n\nName (string) -- [REQUIRED]The name of the GrokClassifier .\n\nClassification (string) --An identifier of the data format that the classifier matches, such as Twitter, JSON, Omniture logs, Amazon CloudWatch Logs, and so on.\n\nGrokPattern (string) --The grok pattern used by this classifier.\n\nCustomPatterns (string) --Optional custom grok patterns used by this classifier.\n\n\n :type XMLClassifier: dict :param XMLClassifier: An XMLClassifier object with updated fields.\n\nName (string) -- [REQUIRED]The name of the classifier.\n\nClassification (string) --An identifier of the data format that the classifier matches.\n\nRowTag (string) --The XML tag designating the element that contains each record in an XML document being parsed. This cannot identify a self-closing element (closed by /> ). An empty row element that contains only attributes can be parsed as long as it ends with a closing tag (for example, <row item_a='A' item_b='B'></row> is okay, but <row item_a='A' item_b='B' /> is not).\n\n\n :type JsonClassifier: dict :param JsonClassifier: A JsonClassifier object with updated fields.\n\nName (string) -- [REQUIRED]The name of the classifier.\n\nJsonPath (string) --A JsonPath string defining the JSON data for the classifier to classify. AWS Glue supports a subset of JsonPath , as described in Writing JsonPath Custom Classifiers .\n\n\n :type CsvClassifier: dict :param CsvClassifier: A CsvClassifier object with updated fields.\n\nName (string) -- [REQUIRED]The name of the classifier.\n\nDelimiter (string) --A custom symbol to denote what separates each column entry in the row.\n\nQuoteSymbol (string) --A custom symbol to denote what combines content into a single column value. It must be different from the column delimiter.\n\nContainsHeader (string) --Indicates whether the CSV file contains a header.\n\nHeader (list) --A list of strings representing column names.\n\n(string) --\n\n\nDisableValueTrimming (boolean) --Specifies not to trim values before identifying the type of column values. The default value is true.\n\nAllowSingleColumn (boolean) --Enables the processing of files that contain only one column.\n\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.VersionMismatchException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: (dict) -- """ pass def update_connection(CatalogId=None, Name=None, ConnectionInput=None): """ Updates a connection definition in the Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.update_connection( CatalogId='string', Name='string', ConnectionInput={ 'Name': 'string', 'Description': 'string', 'ConnectionType': 'JDBC'|'SFTP'|'MONGODB'|'KAFKA', 'MatchCriteria': [ 'string', ], 'ConnectionProperties': { 'string': 'string' }, 'PhysicalConnectionRequirements': { 'SubnetId': 'string', 'SecurityGroupIdList': [ 'string', ], 'AvailabilityZone': 'string' } } ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog in which the connection resides. If none is provided, the AWS account ID is used by default. :type Name: string :param Name: [REQUIRED]\nThe name of the connection definition to update.\n :type ConnectionInput: dict :param ConnectionInput: [REQUIRED]\nA ConnectionInput object that redefines the connection in question.\n\nName (string) -- [REQUIRED]The name of the connection.\n\nDescription (string) --The description of the connection.\n\nConnectionType (string) -- [REQUIRED]The type of the connection. Currently, these types are supported:\n\nJDBC - Designates a connection to a database through Java Database Connectivity (JDBC).\nKAFKA - Designates a connection to an Apache Kafka streaming platform.\nMONGODB - Designates a connection to a MongoDB document database.\n\nSFTP is not supported.\n\nMatchCriteria (list) --A list of criteria that can be used in selecting this connection.\n\n(string) --\n\n\nConnectionProperties (dict) -- [REQUIRED]These key-value pairs define parameters for the connection.\n\n(string) --\n(string) --\n\n\n\n\nPhysicalConnectionRequirements (dict) --A map of physical connection requirements, such as virtual private cloud (VPC) and SecurityGroup , that are needed to successfully make this connection.\n\nSubnetId (string) --The subnet ID used by the connection.\n\nSecurityGroupIdList (list) --The security group ID list used by the connection.\n\n(string) --\n\n\nAvailabilityZone (string) --The connection\'s Availability Zone. This field is redundant because the specified subnet implies the Availability Zone to be used. Currently the field must be populated, but it will be deprecated in the future.\n\n\n\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.GlueEncryptionException :return: {} :returns: (dict) -- """ pass def update_crawler(Name=None, Role=None, DatabaseName=None, Description=None, Targets=None, Schedule=None, Classifiers=None, TablePrefix=None, SchemaChangePolicy=None, Configuration=None, CrawlerSecurityConfiguration=None): """ Updates a crawler. If a crawler is running, you must stop it using StopCrawler before updating it. See also: AWS API Documentation Exceptions :example: response = client.update_crawler( Name='string', Role='string', DatabaseName='string', Description='string', Targets={ 'S3Targets': [ { 'Path': 'string', 'Exclusions': [ 'string', ] }, ], 'JdbcTargets': [ { 'ConnectionName': 'string', 'Path': 'string', 'Exclusions': [ 'string', ] }, ], 'DynamoDBTargets': [ { 'Path': 'string' }, ], 'CatalogTargets': [ { 'DatabaseName': 'string', 'Tables': [ 'string', ] }, ] }, Schedule='string', Classifiers=[ 'string', ], TablePrefix='string', SchemaChangePolicy={ 'UpdateBehavior': 'LOG'|'UPDATE_IN_DATABASE', 'DeleteBehavior': 'LOG'|'DELETE_FROM_DATABASE'|'DEPRECATE_IN_DATABASE' }, Configuration='string', CrawlerSecurityConfiguration='string' ) :type Name: string :param Name: [REQUIRED]\nName of the new crawler.\n :type Role: string :param Role: The IAM role or Amazon Resource Name (ARN) of an IAM role that is used by the new crawler to access customer resources. :type DatabaseName: string :param DatabaseName: The AWS Glue database where results are stored, such as: arn:aws:daylight:us-east-1::database/sometable/* . :type Description: string :param Description: A description of the new crawler. :type Targets: dict :param Targets: A list of targets to crawl.\n\nS3Targets (list) --Specifies Amazon Simple Storage Service (Amazon S3) targets.\n\n(dict) --Specifies a data store in Amazon Simple Storage Service (Amazon S3).\n\nPath (string) --The path to the Amazon S3 target.\n\nExclusions (list) --A list of glob patterns used to exclude from the crawl. For more information, see Catalog Tables with a Crawler .\n\n(string) --\n\n\n\n\n\n\nJdbcTargets (list) --Specifies JDBC targets.\n\n(dict) --Specifies a JDBC data store to crawl.\n\nConnectionName (string) --The name of the connection to use to connect to the JDBC target.\n\nPath (string) --The path of the JDBC target.\n\nExclusions (list) --A list of glob patterns used to exclude from the crawl. For more information, see Catalog Tables with a Crawler .\n\n(string) --\n\n\n\n\n\n\nDynamoDBTargets (list) --Specifies Amazon DynamoDB targets.\n\n(dict) --Specifies an Amazon DynamoDB table to crawl.\n\nPath (string) --The name of the DynamoDB table to crawl.\n\n\n\n\n\nCatalogTargets (list) --Specifies AWS Glue Data Catalog targets.\n\n(dict) --Specifies an AWS Glue Data Catalog target.\n\nDatabaseName (string) -- [REQUIRED]The name of the database to be synchronized.\n\nTables (list) -- [REQUIRED]A list of the tables to be synchronized.\n\n(string) --\n\n\n\n\n\n\n\n :type Schedule: string :param Schedule: A cron expression used to specify the schedule. For more information, see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, specify cron(15 12 * * ? *) . :type Classifiers: list :param Classifiers: A list of custom classifiers that the user has registered. By default, all built-in classifiers are included in a crawl, but these custom classifiers always override the default classifiers for a given classification.\n\n(string) --\n\n :type TablePrefix: string :param TablePrefix: The table prefix used for catalog tables that are created. :type SchemaChangePolicy: dict :param SchemaChangePolicy: The policy for the crawler\'s update and deletion behavior.\n\nUpdateBehavior (string) --The update behavior when the crawler finds a changed schema.\n\nDeleteBehavior (string) --The deletion behavior when the crawler finds a deleted object.\n\n\n :type Configuration: string :param Configuration: The crawler configuration information. This versioned JSON string allows users to specify aspects of a crawler\'s behavior. For more information, see Configuring a Crawler . :type CrawlerSecurityConfiguration: string :param CrawlerSecurityConfiguration: The name of the SecurityConfiguration structure to be used by this crawler. :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.VersionMismatchException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.CrawlerRunningException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: (dict) -- """ pass def update_crawler_schedule(CrawlerName=None, Schedule=None): """ Updates the schedule of a crawler using a cron expression. See also: AWS API Documentation Exceptions :example: response = client.update_crawler_schedule( CrawlerName='string', Schedule='string' ) :type CrawlerName: string :param CrawlerName: [REQUIRED]\nThe name of the crawler whose schedule to update.\n :type Schedule: string :param Schedule: The updated cron expression used to specify the schedule. For more information, see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, specify cron(15 12 * * ? *) . :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.VersionMismatchException Glue.Client.exceptions.SchedulerTransitioningException Glue.Client.exceptions.OperationTimeoutException :return: {} :returns: (dict) -- """ pass def update_database(CatalogId=None, Name=None, DatabaseInput=None): """ Updates an existing database definition in a Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.update_database( CatalogId='string', Name='string', DatabaseInput={ 'Name': 'string', 'Description': 'string', 'LocationUri': 'string', 'Parameters': { 'string': 'string' }, 'CreateTableDefaultPermissions': [ { 'Principal': { 'DataLakePrincipalIdentifier': 'string' }, 'Permissions': [ 'ALL'|'SELECT'|'ALTER'|'DROP'|'DELETE'|'INSERT'|'CREATE_DATABASE'|'CREATE_TABLE'|'DATA_LOCATION_ACCESS', ] }, ] } ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog in which the metadata database resides. If none is provided, the AWS account ID is used by default. :type Name: string :param Name: [REQUIRED]\nThe name of the database to update in the catalog. For Hive compatibility, this is folded to lowercase.\n :type DatabaseInput: dict :param DatabaseInput: [REQUIRED]\nA DatabaseInput object specifying the new definition of the metadata database in the catalog.\n\nName (string) -- [REQUIRED]The name of the database. For Hive compatibility, this is folded to lowercase when it is stored.\n\nDescription (string) --A description of the database.\n\nLocationUri (string) --The location of the database (for example, an HDFS path).\n\nParameters (dict) --These key-value pairs define parameters and properties of the database.\nThese key-value pairs define parameters and properties of the database.\n\n(string) --\n(string) --\n\n\n\n\nCreateTableDefaultPermissions (list) --Creates a set of default permissions on the table for principals.\n\n(dict) --Permissions granted to a principal.\n\nPrincipal (dict) --The principal who is granted permissions.\n\nDataLakePrincipalIdentifier (string) --An identifier for the AWS Lake Formation principal.\n\n\n\nPermissions (list) --The permissions that are granted to the principal.\n\n(string) --\n\n\n\n\n\n\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.GlueEncryptionException :return: {} :returns: (dict) -- """ pass def update_dev_endpoint(EndpointName=None, PublicKey=None, AddPublicKeys=None, DeletePublicKeys=None, CustomLibraries=None, UpdateEtlLibraries=None, DeleteArguments=None, AddArguments=None): """ Updates a specified development endpoint. See also: AWS API Documentation Exceptions :example: response = client.update_dev_endpoint( EndpointName='string', PublicKey='string', AddPublicKeys=[ 'string', ], DeletePublicKeys=[ 'string', ], CustomLibraries={ 'ExtraPythonLibsS3Path': 'string', 'ExtraJarsS3Path': 'string' }, UpdateEtlLibraries=True|False, DeleteArguments=[ 'string', ], AddArguments={ 'string': 'string' } ) :type EndpointName: string :param EndpointName: [REQUIRED]\nThe name of the DevEndpoint to be updated.\n :type PublicKey: string :param PublicKey: The public key for the DevEndpoint to use. :type AddPublicKeys: list :param AddPublicKeys: The list of public keys for the DevEndpoint to use.\n\n(string) --\n\n :type DeletePublicKeys: list :param DeletePublicKeys: The list of public keys to be deleted from the DevEndpoint .\n\n(string) --\n\n :type CustomLibraries: dict :param CustomLibraries: Custom Python or Java libraries to be loaded in the DevEndpoint .\n\nExtraPythonLibsS3Path (string) --The paths to one or more Python libraries in an Amazon Simple Storage Service (Amazon S3) bucket that should be loaded in your DevEndpoint . Multiple values must be complete paths separated by a comma.\n\nNote\nYou can only use pure Python libraries with a DevEndpoint . Libraries that rely on C extensions, such as the pandas Python data analysis library, are not currently supported.\n\n\nExtraJarsS3Path (string) --The path to one or more Java .jar files in an S3 bucket that should be loaded in your DevEndpoint .\n\nNote\nYou can only use pure Java/Scala libraries with a DevEndpoint .\n\n\n\n :type UpdateEtlLibraries: boolean :param UpdateEtlLibraries: True if the list of custom libraries to be loaded in the development endpoint needs to be updated, or False if otherwise. :type DeleteArguments: list :param DeleteArguments: The list of argument keys to be deleted from the map of arguments used to configure the DevEndpoint .\n\n(string) --\n\n :type AddArguments: dict :param AddArguments: The map of arguments to add the map of arguments used to configure the DevEndpoint .\nValid arguments are:\n\n'--enable-glue-datacatalog': ''\n'GLUE_PYTHON_VERSION': '3'\n'GLUE_PYTHON_VERSION': '2'\n\nYou can specify a version of Python support for development endpoints by using the Arguments parameter in the CreateDevEndpoint or UpdateDevEndpoint APIs. If no arguments are provided, the version defaults to Python 2.\n\n(string) --\n(string) --\n\n\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.ValidationException :return: {} :returns: (dict) -- """ pass def update_job(JobName=None, JobUpdate=None): """ Updates an existing job definition. See also: AWS API Documentation Exceptions :example: response = client.update_job( JobName='string', JobUpdate={ 'Description': 'string', 'LogUri': 'string', 'Role': 'string', 'ExecutionProperty': { 'MaxConcurrentRuns': 123 }, 'Command': { 'Name': 'string', 'ScriptLocation': 'string', 'PythonVersion': 'string' }, 'DefaultArguments': { 'string': 'string' }, 'NonOverridableArguments': { 'string': 'string' }, 'Connections': { 'Connections': [ 'string', ] }, 'MaxRetries': 123, 'AllocatedCapacity': 123, 'Timeout': 123, 'MaxCapacity': 123.0, 'WorkerType': 'Standard'|'G.1X'|'G.2X', 'NumberOfWorkers': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'GlueVersion': 'string' } ) :type JobName: string :param JobName: [REQUIRED]\nThe name of the job definition to update.\n :type JobUpdate: dict :param JobUpdate: [REQUIRED]\nSpecifies the values with which to update the job definition.\n\nDescription (string) --Description of the job being defined.\n\nLogUri (string) --This field is reserved for future use.\n\nRole (string) --The name or Amazon Resource Name (ARN) of the IAM role associated with this job (required).\n\nExecutionProperty (dict) --An ExecutionProperty specifying the maximum number of concurrent runs allowed for this job.\n\nMaxConcurrentRuns (integer) --The maximum number of concurrent runs allowed for the job. The default is 1. An error is returned when this threshold is reached. The maximum value you can specify is controlled by a service limit.\n\n\n\nCommand (dict) --The JobCommand that executes this job (required).\n\nName (string) --The name of the job command. For an Apache Spark ETL job, this must be glueetl . For a Python shell job, it must be pythonshell .\n\nScriptLocation (string) --Specifies the Amazon Simple Storage Service (Amazon S3) path to a script that executes a job.\n\nPythonVersion (string) --The Python version being used to execute a Python shell job. Allowed values are 2 or 3.\n\n\n\nDefaultArguments (dict) --The default arguments for this job.\nYou can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes.\nFor information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide.\nFor information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide.\n\n(string) --\n(string) --\n\n\n\n\nNonOverridableArguments (dict) --Non-overridable arguments for this job, specified as name-value pairs.\n\n(string) --\n(string) --\n\n\n\n\nConnections (dict) --The connections used for this job.\n\nConnections (list) --A list of connections used by the job.\n\n(string) --\n\n\n\n\nMaxRetries (integer) --The maximum number of times to retry this job if it fails.\n\nAllocatedCapacity (integer) --This field is deprecated. Use MaxCapacity instead.\nThe number of AWS Glue data processing units (DPUs) to allocate to this job. You can allocate from 2 to 100 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page .\n\nTimeout (integer) --The job timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours).\n\nMaxCapacity (float) --The number of AWS Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page .\nDo not set Max Capacity if using WorkerType and NumberOfWorkers .\nThe value that can be allocated for MaxCapacity depends on whether you are running a Python shell job or an Apache Spark ETL job:\n\nWhen you specify a Python shell job (JobCommand.Name ='pythonshell'), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU.\nWhen you specify an Apache Spark ETL job (JobCommand.Name ='glueetl'), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation.\n\n\nWorkerType (string) --The type of predefined worker that is allocated when a job runs. Accepts a value of Standard, G.1X, or G.2X.\n\nFor the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker.\nFor the G.1X worker type, each worker maps to 1 DPU (4 vCPU, 16 GB of memory, 64 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs.\nFor the G.2X worker type, each worker maps to 2 DPU (8 vCPU, 32 GB of memory, 128 GB disk), and provides 1 executor per worker. We recommend this worker type for memory-intensive jobs.\n\n\nNumberOfWorkers (integer) --The number of workers of a defined workerType that are allocated when a job runs.\nThe maximum number of workers you can define are 299 for G.1X , and 149 for G.2X .\n\nSecurityConfiguration (string) --The name of the SecurityConfiguration structure to be used with this job.\n\nNotificationProperty (dict) --Specifies the configuration properties of a job notification.\n\nNotifyDelayAfter (integer) --After a job run starts, the number of minutes to wait before sending a job run delay notification.\n\n\n\nGlueVersion (string) --Glue version determines the versions of Apache Spark and Python that AWS Glue supports. The Python version indicates the version supported for jobs of type Spark.\nFor more information about the available AWS Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide.\n\n\n :rtype: dict ReturnsResponse Syntax { 'JobName': 'string' } Response Structure (dict) -- JobName (string) -- Returns the name of the updated job definition. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ConcurrentModificationException :return: { 'JobName': 'string' } :returns: Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ConcurrentModificationException """ pass def update_ml_transform(TransformId=None, Name=None, Description=None, Parameters=None, Role=None, GlueVersion=None, MaxCapacity=None, WorkerType=None, NumberOfWorkers=None, Timeout=None, MaxRetries=None): """ Updates an existing machine learning transform. Call this operation to tune the algorithm parameters to achieve better results. After calling this operation, you can call the StartMLEvaluationTaskRun operation to assess how well your new parameters achieved your goals (such as improving the quality of your machine learning transform, or making it more cost-effective). See also: AWS API Documentation Exceptions :example: response = client.update_ml_transform( TransformId='string', Name='string', Description='string', Parameters={ 'TransformType': 'FIND_MATCHES', 'FindMatchesParameters': { 'PrimaryKeyColumnName': 'string', 'PrecisionRecallTradeoff': 123.0, 'AccuracyCostTradeoff': 123.0, 'EnforceProvidedLabels': True|False } }, Role='string', GlueVersion='string', MaxCapacity=123.0, WorkerType='Standard'|'G.1X'|'G.2X', NumberOfWorkers=123, Timeout=123, MaxRetries=123 ) :type TransformId: string :param TransformId: [REQUIRED]\nA unique identifier that was generated when the transform was created.\n :type Name: string :param Name: The unique name that you gave the transform when you created it. :type Description: string :param Description: A description of the transform. The default is an empty string. :type Parameters: dict :param Parameters: The configuration parameters that are specific to the transform type (algorithm) used. Conditionally dependent on the transform type.\n\nTransformType (string) -- [REQUIRED]The type of machine learning transform.\nFor information about the types of machine learning transforms, see Creating Machine Learning Transforms .\n\nFindMatchesParameters (dict) --The parameters for the find matches algorithm.\n\nPrimaryKeyColumnName (string) --The name of a column that uniquely identifies rows in the source table. Used to help identify matching records.\n\nPrecisionRecallTradeoff (float) --The value selected when tuning your transform for a balance between precision and recall. A value of 0.5 means no preference; a value of 1.0 means a bias purely for precision, and a value of 0.0 means a bias for recall. Because this is a tradeoff, choosing values close to 1.0 means very low recall, and choosing values close to 0.0 results in very low precision.\nThe precision metric indicates how often your model is correct when it predicts a match.\nThe recall metric indicates that for an actual match, how often your model predicts the match.\n\nAccuracyCostTradeoff (float) --The value that is selected when tuning your transform for a balance between accuracy and cost. A value of 0.5 means that the system balances accuracy and cost concerns. A value of 1.0 means a bias purely for accuracy, which typically results in a higher cost, sometimes substantially higher. A value of 0.0 means a bias purely for cost, which results in a less accurate FindMatches transform, sometimes with unacceptable accuracy.\nAccuracy measures how well the transform finds true positives and true negatives. Increasing accuracy requires more machine resources and cost. But it also results in increased recall.\nCost measures how many compute resources, and thus money, are consumed to run the transform.\n\nEnforceProvidedLabels (boolean) --The value to switch on or off to force the output to match the provided labels from users. If the value is True , the find matches transform forces the output to match the provided labels. The results override the normal conflation results. If the value is False , the find matches transform does not ensure all the labels provided are respected, and the results rely on the trained model.\nNote that setting this value to true may increase the conflation execution time.\n\n\n\n\n :type Role: string :param Role: The name or Amazon Resource Name (ARN) of the IAM role with the required permissions. :type GlueVersion: string :param GlueVersion: This value determines which version of AWS Glue this machine learning transform is compatible with. Glue 1.0 is recommended for most customers. If the value is not set, the Glue compatibility defaults to Glue 0.9. For more information, see AWS Glue Versions in the developer guide. :type MaxCapacity: float :param MaxCapacity: The number of AWS Glue data processing units (DPUs) that are allocated to task runs for this transform. You can allocate from 2 to 100 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the AWS Glue pricing page .\nWhen the WorkerType field is set to a value other than Standard , the MaxCapacity field is set automatically and becomes read-only.\n :type WorkerType: string :param WorkerType: The type of predefined worker that is allocated when this task runs. Accepts a value of Standard, G.1X, or G.2X.\n\nFor the Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker.\nFor the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker.\nFor the G.2X worker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker.\n\n :type NumberOfWorkers: integer :param NumberOfWorkers: The number of workers of a defined workerType that are allocated when this task runs. :type Timeout: integer :param Timeout: The timeout for a task run for this transform in minutes. This is the maximum time that a task run for this transform can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). :type MaxRetries: integer :param MaxRetries: The maximum number of times to retry a task for this transform after a task run fails. :rtype: dict ReturnsResponse Syntax { 'TransformId': 'string' } Response Structure (dict) -- TransformId (string) -- The unique identifier for the transform that was updated. Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.AccessDeniedException :return: { 'TransformId': 'string' } :returns: Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.AccessDeniedException """ pass def update_partition(CatalogId=None, DatabaseName=None, TableName=None, PartitionValueList=None, PartitionInput=None): """ Updates a partition. See also: AWS API Documentation Exceptions :example: response = client.update_partition( CatalogId='string', DatabaseName='string', TableName='string', PartitionValueList=[ 'string', ], PartitionInput={ 'Values': [ 'string', ], 'LastAccessTime': datetime(2015, 1, 1), 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'Parameters': { 'string': 'string' }, 'LastAnalyzedTime': datetime(2015, 1, 1) } ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the partition to be updated resides. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe name of the catalog database in which the table in question resides.\n :type TableName: string :param TableName: [REQUIRED]\nThe name of the table in which the partition to be updated is located.\n :type PartitionValueList: list :param PartitionValueList: [REQUIRED]\nA list of the values defining the partition.\n\n(string) --\n\n :type PartitionInput: dict :param PartitionInput: [REQUIRED]\nThe new partition object to update the partition to.\n\nValues (list) --The values of the partition. Although this parameter is not required by the SDK, you must specify this parameter for a valid input.\nThe values for the keys for the new partition must be passed as an array of String objects that must be ordered in the same order as the partition keys appearing in the Amazon S3 prefix. Otherwise AWS Glue will add the values to the wrong keys.\n\n(string) --\n\n\nLastAccessTime (datetime) --The last time at which the partition was accessed.\n\nStorageDescriptor (dict) --Provides information about the physical location where the partition is stored.\n\nColumns (list) --A list of the Columns in the table.\n\n(dict) --A column in a Table .\n\nName (string) -- [REQUIRED]The name of the Column .\n\nType (string) --The data type of the Column .\n\nComment (string) --A free-form text comment.\n\nParameters (dict) --These key-value pairs define properties associated with the column.\n\n(string) --\n(string) --\n\n\n\n\n\n\n\n\nLocation (string) --The physical location of the table. By default, this takes the form of the warehouse location, followed by the database location in the warehouse, followed by the table name.\n\nInputFormat (string) --The input format: SequenceFileInputFormat (binary), or TextInputFormat , or a custom format.\n\nOutputFormat (string) --The output format: SequenceFileOutputFormat (binary), or IgnoreKeyTextOutputFormat , or a custom format.\n\nCompressed (boolean) --\nTrue if the data in the table is compressed, or False if not.\n\nNumberOfBuckets (integer) --Must be specified if the table contains any dimension columns.\n\nSerdeInfo (dict) --The serialization/deserialization (SerDe) information.\n\nName (string) --Name of the SerDe.\n\nSerializationLibrary (string) --Usually the class that implements the SerDe. An example is org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe .\n\nParameters (dict) --These key-value pairs define initialization parameters for the SerDe.\n\n(string) --\n(string) --\n\n\n\n\n\n\nBucketColumns (list) --A list of reducer grouping columns, clustering columns, and bucketing columns in the table.\n\n(string) --\n\n\nSortColumns (list) --A list specifying the sort order of each bucket in the table.\n\n(dict) --Specifies the sort order of a sorted column.\n\nColumn (string) -- [REQUIRED]The name of the column.\n\nSortOrder (integer) -- [REQUIRED]Indicates that the column is sorted in ascending order (== 1 ), or in descending order (==0 ).\n\n\n\n\n\nParameters (dict) --The user-supplied properties in key-value form.\n\n(string) --\n(string) --\n\n\n\n\nSkewedInfo (dict) --The information about values that appear frequently in a column (skewed values).\n\nSkewedColumnNames (list) --A list of names of columns that contain skewed values.\n\n(string) --\n\n\nSkewedColumnValues (list) --A list of values that appear so frequently as to be considered skewed.\n\n(string) --\n\n\nSkewedColumnValueLocationMaps (dict) --A mapping of skewed values to the columns that contain them.\n\n(string) --\n(string) --\n\n\n\n\n\n\nStoredAsSubDirectories (boolean) --\nTrue if the table data is stored in subdirectories, or False if not.\n\n\n\nParameters (dict) --These key-value pairs define partition parameters.\n\n(string) --\n(string) --\n\n\n\n\nLastAnalyzedTime (datetime) --The last time at which column statistics were computed for this partition.\n\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.GlueEncryptionException :return: {} :returns: (dict) -- """ pass def update_table(CatalogId=None, DatabaseName=None, TableInput=None, SkipArchive=None): """ Updates a metadata table in the Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.update_table( CatalogId='string', DatabaseName='string', TableInput={ 'Name': 'string', 'Description': 'string', 'Owner': 'string', 'LastAccessTime': datetime(2015, 1, 1), 'LastAnalyzedTime': datetime(2015, 1, 1), 'Retention': 123, 'StorageDescriptor': { 'Columns': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'Location': 'string', 'InputFormat': 'string', 'OutputFormat': 'string', 'Compressed': True|False, 'NumberOfBuckets': 123, 'SerdeInfo': { 'Name': 'string', 'SerializationLibrary': 'string', 'Parameters': { 'string': 'string' } }, 'BucketColumns': [ 'string', ], 'SortColumns': [ { 'Column': 'string', 'SortOrder': 123 }, ], 'Parameters': { 'string': 'string' }, 'SkewedInfo': { 'SkewedColumnNames': [ 'string', ], 'SkewedColumnValues': [ 'string', ], 'SkewedColumnValueLocationMaps': { 'string': 'string' } }, 'StoredAsSubDirectories': True|False }, 'PartitionKeys': [ { 'Name': 'string', 'Type': 'string', 'Comment': 'string', 'Parameters': { 'string': 'string' } }, ], 'ViewOriginalText': 'string', 'ViewExpandedText': 'string', 'TableType': 'string', 'Parameters': { 'string': 'string' } }, SkipArchive=True|False ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the table resides. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe name of the catalog database in which the table resides. For Hive compatibility, this name is entirely lowercase.\n :type TableInput: dict :param TableInput: [REQUIRED]\nAn updated TableInput object to define the metadata table in the catalog.\n\nName (string) -- [REQUIRED]The table name. For Hive compatibility, this is folded to lowercase when it is stored.\n\nDescription (string) --A description of the table.\n\nOwner (string) --The table owner.\n\nLastAccessTime (datetime) --The last time that the table was accessed.\n\nLastAnalyzedTime (datetime) --The last time that column statistics were computed for this table.\n\nRetention (integer) --The retention time for this table.\n\nStorageDescriptor (dict) --A storage descriptor containing information about the physical storage of this table.\n\nColumns (list) --A list of the Columns in the table.\n\n(dict) --A column in a Table .\n\nName (string) -- [REQUIRED]The name of the Column .\n\nType (string) --The data type of the Column .\n\nComment (string) --A free-form text comment.\n\nParameters (dict) --These key-value pairs define properties associated with the column.\n\n(string) --\n(string) --\n\n\n\n\n\n\n\n\nLocation (string) --The physical location of the table. By default, this takes the form of the warehouse location, followed by the database location in the warehouse, followed by the table name.\n\nInputFormat (string) --The input format: SequenceFileInputFormat (binary), or TextInputFormat , or a custom format.\n\nOutputFormat (string) --The output format: SequenceFileOutputFormat (binary), or IgnoreKeyTextOutputFormat , or a custom format.\n\nCompressed (boolean) --\nTrue if the data in the table is compressed, or False if not.\n\nNumberOfBuckets (integer) --Must be specified if the table contains any dimension columns.\n\nSerdeInfo (dict) --The serialization/deserialization (SerDe) information.\n\nName (string) --Name of the SerDe.\n\nSerializationLibrary (string) --Usually the class that implements the SerDe. An example is org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe .\n\nParameters (dict) --These key-value pairs define initialization parameters for the SerDe.\n\n(string) --\n(string) --\n\n\n\n\n\n\nBucketColumns (list) --A list of reducer grouping columns, clustering columns, and bucketing columns in the table.\n\n(string) --\n\n\nSortColumns (list) --A list specifying the sort order of each bucket in the table.\n\n(dict) --Specifies the sort order of a sorted column.\n\nColumn (string) -- [REQUIRED]The name of the column.\n\nSortOrder (integer) -- [REQUIRED]Indicates that the column is sorted in ascending order (== 1 ), or in descending order (==0 ).\n\n\n\n\n\nParameters (dict) --The user-supplied properties in key-value form.\n\n(string) --\n(string) --\n\n\n\n\nSkewedInfo (dict) --The information about values that appear frequently in a column (skewed values).\n\nSkewedColumnNames (list) --A list of names of columns that contain skewed values.\n\n(string) --\n\n\nSkewedColumnValues (list) --A list of values that appear so frequently as to be considered skewed.\n\n(string) --\n\n\nSkewedColumnValueLocationMaps (dict) --A mapping of skewed values to the columns that contain them.\n\n(string) --\n(string) --\n\n\n\n\n\n\nStoredAsSubDirectories (boolean) --\nTrue if the table data is stored in subdirectories, or False if not.\n\n\n\nPartitionKeys (list) --A list of columns by which the table is partitioned. Only primitive types are supported as partition keys.\nWhen you create a table used by Amazon Athena, and you do not specify any partitionKeys , you must at least set the value of partitionKeys to an empty list. For example:\n\n'PartitionKeys': []\n\n(dict) --A column in a Table .\n\nName (string) -- [REQUIRED]The name of the Column .\n\nType (string) --The data type of the Column .\n\nComment (string) --A free-form text comment.\n\nParameters (dict) --These key-value pairs define properties associated with the column.\n\n(string) --\n(string) --\n\n\n\n\n\n\n\n\nViewOriginalText (string) --If the table is a view, the original text of the view; otherwise null .\n\nViewExpandedText (string) --If the table is a view, the expanded text of the view; otherwise null .\n\nTableType (string) --The type of this table (EXTERNAL_TABLE , VIRTUAL_VIEW , etc.).\n\nParameters (dict) --These key-value pairs define properties associated with the table.\n\n(string) --\n(string) --\n\n\n\n\n\n :type SkipArchive: boolean :param SkipArchive: By default, UpdateTable always creates an archived version of the table before updating it. However, if skipArchive is set to true, UpdateTable does not create the archived version. :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ConcurrentModificationException Glue.Client.exceptions.ResourceNumberLimitExceededException Glue.Client.exceptions.GlueEncryptionException :return: {} :returns: (dict) -- """ pass def update_trigger(Name=None, TriggerUpdate=None): """ Updates a trigger definition. See also: AWS API Documentation Exceptions :example: response = client.update_trigger( Name='string', TriggerUpdate={ 'Name': 'string', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } } ) :type Name: string :param Name: [REQUIRED]\nThe name of the trigger to update.\n :type TriggerUpdate: dict :param TriggerUpdate: [REQUIRED]\nThe new values with which to update the trigger.\n\nName (string) --Reserved for future use.\n\nDescription (string) --A description of this trigger.\n\nSchedule (string) --A cron expression used to specify the schedule (see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, you would specify: cron(15 12 * * ? *) .\n\nActions (list) --The actions initiated by this trigger.\n\n(dict) --Defines an action to be initiated by a trigger.\n\nJobName (string) --The name of a job to be executed.\n\nArguments (dict) --The job arguments used when this trigger fires. For this job run, they replace the default arguments set in the job definition itself.\nYou can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes.\nFor information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide.\nFor information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide.\n\n(string) --\n(string) --\n\n\n\n\nTimeout (integer) --The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job.\n\nSecurityConfiguration (string) --The name of the SecurityConfiguration structure to be used with this action.\n\nNotificationProperty (dict) --Specifies configuration properties of a job run notification.\n\nNotifyDelayAfter (integer) --After a job run starts, the number of minutes to wait before sending a job run delay notification.\n\n\n\nCrawlerName (string) --The name of the crawler to be used with this action.\n\n\n\n\n\nPredicate (dict) --The predicate of this trigger, which defines when it will fire.\n\nLogical (string) --An optional field if only one condition is listed. If multiple conditions are listed, then this field is required.\n\nConditions (list) --A list of the conditions that determine when the trigger will fire.\n\n(dict) --Defines a condition under which a trigger fires.\n\nLogicalOperator (string) --A logical operator.\n\nJobName (string) --The name of the job whose JobRuns this condition applies to, and on which this trigger waits.\n\nState (string) --The condition state. Currently, the only job states that a trigger can listen for are SUCCEEDED , STOPPED , FAILED , and TIMEOUT . The only crawler states that a trigger can listen for are SUCCEEDED , FAILED , and CANCELLED .\n\nCrawlerName (string) --The name of the crawler to which this condition applies.\n\nCrawlState (string) --The state of the crawler to which this condition applies.\n\n\n\n\n\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'Trigger': { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } } } Response Structure (dict) -- Trigger (dict) -- The resulting trigger definition. Name (string) -- The name of the trigger. WorkflowName (string) -- The name of the workflow associated with the trigger. Id (string) -- Reserved for future use. Type (string) -- The type of trigger that this is. State (string) -- The current state of the trigger. Description (string) -- A description of this trigger. Schedule (string) -- A cron expression used to specify the schedule (see Time-Based Schedules for Jobs and Crawlers . For example, to run something every day at 12:15 UTC, you would specify: cron(15 12 * * ? *) . Actions (list) -- The actions initiated by this trigger. (dict) -- Defines an action to be initiated by a trigger. JobName (string) -- The name of a job to be executed. Arguments (dict) -- The job arguments used when this trigger fires. For this job run, they replace the default arguments set in the job definition itself. You can specify arguments here that your own job-execution script consumes, as well as arguments that AWS Glue itself consumes. For information about how to specify and consume your own Job arguments, see the Calling AWS Glue APIs in Python topic in the developer guide. For information about the key-value pairs that AWS Glue consumes to set up your job, see the Special Parameters Used by AWS Glue topic in the developer guide. (string) -- (string) -- Timeout (integer) -- The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours). This overrides the timeout value set in the parent job. SecurityConfiguration (string) -- The name of the SecurityConfiguration structure to be used with this action. NotificationProperty (dict) -- Specifies configuration properties of a job run notification. NotifyDelayAfter (integer) -- After a job run starts, the number of minutes to wait before sending a job run delay notification. CrawlerName (string) -- The name of the crawler to be used with this action. Predicate (dict) -- The predicate of this trigger, which defines when it will fire. Logical (string) -- An optional field if only one condition is listed. If multiple conditions are listed, then this field is required. Conditions (list) -- A list of the conditions that determine when the trigger will fire. (dict) -- Defines a condition under which a trigger fires. LogicalOperator (string) -- A logical operator. JobName (string) -- The name of the job whose JobRuns this condition applies to, and on which this trigger waits. State (string) -- The condition state. Currently, the only job states that a trigger can listen for are SUCCEEDED , STOPPED , FAILED , and TIMEOUT . The only crawler states that a trigger can listen for are SUCCEEDED , FAILED , and CANCELLED . CrawlerName (string) -- The name of the crawler to which this condition applies. CrawlState (string) -- The state of the crawler to which this condition applies. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ConcurrentModificationException :return: { 'Trigger': { 'Name': 'string', 'WorkflowName': 'string', 'Id': 'string', 'Type': 'SCHEDULED'|'CONDITIONAL'|'ON_DEMAND', 'State': 'CREATING'|'CREATED'|'ACTIVATING'|'ACTIVATED'|'DEACTIVATING'|'DEACTIVATED'|'DELETING'|'UPDATING', 'Description': 'string', 'Schedule': 'string', 'Actions': [ { 'JobName': 'string', 'Arguments': { 'string': 'string' }, 'Timeout': 123, 'SecurityConfiguration': 'string', 'NotificationProperty': { 'NotifyDelayAfter': 123 }, 'CrawlerName': 'string' }, ], 'Predicate': { 'Logical': 'AND'|'ANY', 'Conditions': [ { 'LogicalOperator': 'EQUALS', 'JobName': 'string', 'State': 'STARTING'|'RUNNING'|'STOPPING'|'STOPPED'|'SUCCEEDED'|'FAILED'|'TIMEOUT', 'CrawlerName': 'string', 'CrawlState': 'RUNNING'|'CANCELLING'|'CANCELLED'|'SUCCEEDED'|'FAILED' }, ] } } } :returns: (string) -- (string) -- """ pass def update_user_defined_function(CatalogId=None, DatabaseName=None, FunctionName=None, FunctionInput=None): """ Updates an existing function definition in the Data Catalog. See also: AWS API Documentation Exceptions :example: response = client.update_user_defined_function( CatalogId='string', DatabaseName='string', FunctionName='string', FunctionInput={ 'FunctionName': 'string', 'ClassName': 'string', 'OwnerName': 'string', 'OwnerType': 'USER'|'ROLE'|'GROUP', 'ResourceUris': [ { 'ResourceType': 'JAR'|'FILE'|'ARCHIVE', 'Uri': 'string' }, ] } ) :type CatalogId: string :param CatalogId: The ID of the Data Catalog where the function to be updated is located. If none is provided, the AWS account ID is used by default. :type DatabaseName: string :param DatabaseName: [REQUIRED]\nThe name of the catalog database where the function to be updated is located.\n :type FunctionName: string :param FunctionName: [REQUIRED]\nThe name of the function.\n :type FunctionInput: dict :param FunctionInput: [REQUIRED]\nA FunctionInput object that redefines the function in the Data Catalog.\n\nFunctionName (string) --The name of the function.\n\nClassName (string) --The Java class that contains the function code.\n\nOwnerName (string) --The owner of the function.\n\nOwnerType (string) --The owner type.\n\nResourceUris (list) --The resource URIs for the function.\n\n(dict) --The URIs for function resources.\n\nResourceType (string) --The type of the resource.\n\nUri (string) --The URI for accessing the resource.\n\n\n\n\n\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.GlueEncryptionException :return: {} :returns: (dict) -- """ pass def update_workflow(Name=None, Description=None, DefaultRunProperties=None): """ Updates an existing workflow. See also: AWS API Documentation Exceptions :example: response = client.update_workflow( Name='string', Description='string', DefaultRunProperties={ 'string': 'string' } ) :type Name: string :param Name: [REQUIRED]\nName of the workflow to be updated.\n :type Description: string :param Description: The description of the workflow. :type DefaultRunProperties: dict :param DefaultRunProperties: A collection of properties to be used as part of each execution of the workflow.\n\n(string) --\n(string) --\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'Name': 'string' } Response Structure (dict) -- Name (string) -- The name of the workflow which was specified in input. Exceptions Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ConcurrentModificationException :return: { 'Name': 'string' } :returns: Glue.Client.exceptions.InvalidInputException Glue.Client.exceptions.EntityNotFoundException Glue.Client.exceptions.InternalServiceException Glue.Client.exceptions.OperationTimeoutException Glue.Client.exceptions.ConcurrentModificationException """ pass

mit

shangwuhencc/scikit-learn

examples/linear_model/plot_ransac.py

249

1673

""" =========================================== Robust linear model estimation using RANSAC =========================================== In this example we see how to robustly fit a linear model to faulty data using the RANSAC algorithm. """ import numpy as np from matplotlib import pyplot as plt from sklearn import linear_model, datasets n_samples = 1000 n_outliers = 50 X, y, coef = datasets.make_regression(n_samples=n_samples, n_features=1, n_informative=1, noise=10, coef=True, random_state=0) # Add outlier data np.random.seed(0) X[:n_outliers] = 3 + 0.5 * np.random.normal(size=(n_outliers, 1)) y[:n_outliers] = -3 + 10 * np.random.normal(size=n_outliers) # Fit line using all data model = linear_model.LinearRegression() model.fit(X, y) # Robustly fit linear model with RANSAC algorithm model_ransac = linear_model.RANSACRegressor(linear_model.LinearRegression()) model_ransac.fit(X, y) inlier_mask = model_ransac.inlier_mask_ outlier_mask = np.logical_not(inlier_mask) # Predict data of estimated models line_X = np.arange(-5, 5) line_y = model.predict(line_X[:, np.newaxis]) line_y_ransac = model_ransac.predict(line_X[:, np.newaxis]) # Compare estimated coefficients print("Estimated coefficients (true, normal, RANSAC):") print(coef, model.coef_, model_ransac.estimator_.coef_) plt.plot(X[inlier_mask], y[inlier_mask], '.g', label='Inliers') plt.plot(X[outlier_mask], y[outlier_mask], '.r', label='Outliers') plt.plot(line_X, line_y, '-k', label='Linear regressor') plt.plot(line_X, line_y_ransac, '-b', label='RANSAC regressor') plt.legend(loc='lower right') plt.show()

bsd-3-clause

CredoReference/edx-platform

lms/djangoapps/course_api/blocks/tests/test_serializers.py

4

8709

""" Tests for Course Blocks serializers """ from mock import MagicMock from lms.djangoapps.course_blocks.api import get_course_block_access_transformers, get_course_blocks from openedx.core.djangoapps.content.block_structure.transformers import BlockStructureTransformers from student.roles import CourseStaffRole from student.tests.factories import UserFactory from xmodule.modulestore import ModuleStoreEnum from xmodule.modulestore.tests.django_utils import SharedModuleStoreTestCase from xmodule.modulestore.tests.factories import ToyCourseFactory from ..serializers import BlockDictSerializer, BlockSerializer from ..transformers.blocks_api import BlocksAPITransformer from .helpers import deserialize_usage_key class TestBlockSerializerBase(SharedModuleStoreTestCase): """ Base class for testing BlockSerializer and BlockDictSerializer """ shard = 4 @classmethod def setUpClass(cls): super(TestBlockSerializerBase, cls).setUpClass() cls.course = ToyCourseFactory.create() # Hide the html block key = cls.course.id.make_usage_key('html', 'secret:toylab') cls.html_block = cls.store.get_item(key) cls.html_block.visible_to_staff_only = True cls.store.update_item(cls.html_block, ModuleStoreEnum.UserID.test) def setUp(self): super(TestBlockSerializerBase, self).setUp() self.user = UserFactory.create() blocks_api_transformer = BlocksAPITransformer( block_types_to_count=['video'], requested_student_view_data=['video'], ) self.transformers = BlockStructureTransformers( get_course_block_access_transformers() + [blocks_api_transformer] ) self.block_structure = get_course_blocks( self.user, self.course.location, self.transformers, ) self.serializer_context = { 'request': MagicMock(), 'block_structure': self.block_structure, 'requested_fields': ['type'], } def assert_basic_block(self, block_key_string, serialized_block): """ Verifies the given serialized_block when basic fields are requested. """ block_key = deserialize_usage_key(block_key_string, self.course.id) self.assertEquals( self.block_structure.get_xblock_field(block_key, 'category'), serialized_block['type'], ) self.assertEquals( set(serialized_block.iterkeys()), {'id', 'block_id', 'type', 'lms_web_url', 'student_view_url'}, ) def add_additional_requested_fields(self, context=None): """ Adds additional fields to the requested_fields context for the serializer. """ if context is None: context = self.serializer_context context['requested_fields'].extend([ 'children', 'display_name', 'graded', 'format', 'block_counts', 'student_view_data', 'student_view_multi_device', 'lti_url', 'visible_to_staff_only', ]) def assert_extended_block(self, serialized_block): """ Verifies the given serialized_block when additional fields are requested. """ self.assertLessEqual( { 'id', 'type', 'lms_web_url', 'student_view_url', 'display_name', 'graded', 'student_view_multi_device', 'lti_url', 'visible_to_staff_only', }, set(serialized_block.iterkeys()), ) # video blocks should have student_view_data if serialized_block['type'] == 'video': self.assertIn('student_view_data', serialized_block) # html blocks should have student_view_multi_device set to True if serialized_block['type'] == 'html': self.assertIn('student_view_multi_device', serialized_block) self.assertTrue(serialized_block['student_view_multi_device']) # chapters with video should have block_counts if serialized_block['type'] == 'chapter': if serialized_block['display_name'] not in ('poll_test', 'handout_container'): self.assertIn('block_counts', serialized_block) else: self.assertNotIn('block_counts', serialized_block) def create_staff_context(self): """ Create staff user and course blocks accessible by that user """ # Create a staff user to be able to test visible_to_staff_only staff_user = UserFactory.create() CourseStaffRole(self.course.location.course_key).add_users(staff_user) block_structure = get_course_blocks( staff_user, self.course.location, self.transformers, ) return { 'request': MagicMock(), 'block_structure': block_structure, 'requested_fields': ['type'], } def assert_staff_fields(self, serialized_block): """ Test fields accessed by a staff user """ if serialized_block['id'] == unicode(self.html_block.location): self.assertTrue(serialized_block['visible_to_staff_only']) else: self.assertFalse(serialized_block['visible_to_staff_only']) class TestBlockSerializer(TestBlockSerializerBase): """ Tests the BlockSerializer class, which returns a list of blocks. """ shard = 4 def create_serializer(self, context=None): """ creates a BlockSerializer """ if context is None: context = self.serializer_context return BlockSerializer( context['block_structure'], many=True, context=context, ) def test_basic(self): serializer = self.create_serializer() for serialized_block in serializer.data: self.assert_basic_block(serialized_block['id'], serialized_block) self.assertEquals(len(serializer.data), 28) def test_additional_requested_fields(self): self.add_additional_requested_fields() serializer = self.create_serializer() for serialized_block in serializer.data: self.assert_extended_block(serialized_block) self.assertEquals(len(serializer.data), 28) def test_staff_fields(self): """ Test fields accessed by a staff user """ context = self.create_staff_context() self.add_additional_requested_fields(context) serializer = self.create_serializer(context) for serialized_block in serializer.data: self.assert_extended_block(serialized_block) self.assert_staff_fields(serialized_block) self.assertEquals(len(serializer.data), 29) class TestBlockDictSerializer(TestBlockSerializerBase): """ Tests the BlockDictSerializer class, which returns a dict of blocks key-ed by its block_key. """ shard = 4 def create_serializer(self, context=None): """ creates a BlockDictSerializer """ if context is None: context = self.serializer_context return BlockDictSerializer( context['block_structure'], many=False, context=context, ) def test_basic(self): serializer = self.create_serializer() # verify root self.assertEquals(serializer.data['root'], unicode(self.block_structure.root_block_usage_key)) # verify blocks for block_key_string, serialized_block in serializer.data['blocks'].iteritems(): self.assertEquals(serialized_block['id'], block_key_string) self.assert_basic_block(block_key_string, serialized_block) self.assertEquals(len(serializer.data['blocks']), 28) def test_additional_requested_fields(self): self.add_additional_requested_fields() serializer = self.create_serializer() for serialized_block in serializer.data['blocks'].itervalues(): self.assert_extended_block(serialized_block) self.assertEquals(len(serializer.data['blocks']), 28) def test_staff_fields(self): """ Test fields accessed by a staff user """ context = self.create_staff_context() self.add_additional_requested_fields(context) serializer = self.create_serializer(context) for serialized_block in serializer.data['blocks'].itervalues(): self.assert_extended_block(serialized_block) self.assert_staff_fields(serialized_block) self.assertEquals(len(serializer.data['blocks']), 29)

agpl-3.0

nicproulx/mne-python

tutorials/plot_artifacts_detection.py

5

5377

""" .. _tut_artifacts_detect: Introduction to artifacts and artifact detection ================================================ Since MNE supports the data of many different acquisition systems, the particular artifacts in your data might behave very differently from the artifacts you can observe in our tutorials and examples. Therefore you should be aware of the different approaches and of the variability of artifact rejection (automatic/manual) procedures described onwards. At the end consider always to visually inspect your data after artifact rejection or correction. Background: what is an artifact? -------------------------------- Artifacts are signal interference that can be endogenous (biological) and exogenous (environmental). Typical biological artifacts are head movements, eye blinks or eye movements, heart beats. The most common environmental artifact is due to the power line, the so-called *line noise*. How to handle artifacts? ------------------------ MNE deals with artifacts by first identifying them, and subsequently removing them. Detection of artifacts can be done visually, or using automatic routines (or a combination of both). After you know what the artifacts are, you need remove them. This can be done by: - *ignoring* the piece of corrupted data - *fixing* the corrupted data For the artifact detection the functions MNE provides depend on whether your data is continuous (Raw) or epoch-based (Epochs) and depending on whether your data is stored on disk or already in memory. Detecting the artifacts without reading the complete data into memory allows you to work with datasets that are too large to fit in memory all at once. Detecting the artifacts in continuous data allows you to apply filters (e.g. a band-pass filter to zoom in on the muscle artifacts on the temporal channels) without having to worry about edge effects due to the filter (i.e. filter ringing). Having the data in memory after segmenting/epoching is however a very efficient way of browsing through the data which helps in visualizing. So to conclude, there is not a single most optimal manner to detect the artifacts: it just depends on the data properties and your own preferences. In this tutorial we show how to detect artifacts visually and automatically. For how to correct artifacts by rejection see :ref:`tut_artifacts_reject`. To discover how to correct certain artifacts by filtering see :ref:`tut_artifacts_filter` and to learn how to correct artifacts with subspace methods like SSP and ICA see :ref:`tut_artifacts_correct_ssp` and :ref:`tut_artifacts_correct_ica`. Artifacts Detection ------------------- This tutorial discusses a couple of major artifacts that most analyses have to deal with and demonstrates how to detect them. """ import numpy as np import mne from mne.datasets import sample from mne.preprocessing import create_ecg_epochs, create_eog_epochs # getting some data ready data_path = sample.data_path() raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif' raw = mne.io.read_raw_fif(raw_fname, preload=True) ############################################################################### # Low frequency drifts and line noise (raw.copy().pick_types(meg='mag') .del_proj(0) .plot(duration=60, n_channels=100, remove_dc=False)) ############################################################################### # we see high amplitude undulations in low frequencies, spanning across tens of # seconds raw.plot_psd(tmax=np.inf, fmax=250) ############################################################################### # On MEG sensors we see narrow frequency peaks at 60, 120, 180, 240 Hz, # related to line noise. # But also some high amplitude signals between 25 and 32 Hz, hinting at other # biological artifacts such as ECG. These can be most easily detected in the # time domain using MNE helper functions # # See :ref:`tut_artifacts_filter`. ############################################################################### # ECG # --- # # finds ECG events, creates epochs, averages and plots average_ecg = create_ecg_epochs(raw).average() print('We found %i ECG events' % average_ecg.nave) average_ecg.plot_joint() ############################################################################### # we can see typical time courses and non dipolar topographies # not the order of magnitude of the average artifact related signal and # compare this to what you observe for brain signals ############################################################################### # EOG # --- average_eog = create_eog_epochs(raw).average() print('We found %i EOG events' % average_eog.nave) average_eog.plot_joint() ############################################################################### # Knowing these artifact patterns is of paramount importance when # judging about the quality of artifact removal techniques such as SSP or ICA. # As a rule of thumb you need artifact amplitudes orders of magnitude higher # than your signal of interest and you need a few of such events in order # to find decompositions that allow you to estimate and remove patterns related # to artifacts. # # Consider the following tutorials for correcting this class of artifacts: # - :ref:`tut_artifacts_filter` # - :ref:`tut_artifacts_correct_ica` # - :ref:`tut_artifacts_correct_ssp`

bsd-3-clause

shangwuhencc/scikit-learn

examples/exercises/plot_iris_exercise.py

320

1602

""" ================================ SVM Exercise ================================ A tutorial exercise for using different SVM kernels. This exercise is used in the :ref:`using_kernels_tut` part of the :ref:`supervised_learning_tut` section of the :ref:`stat_learn_tut_index`. """ print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import datasets, svm iris = datasets.load_iris() X = iris.data y = iris.target X = X[y != 0, :2] y = y[y != 0] n_sample = len(X) np.random.seed(0) order = np.random.permutation(n_sample) X = X[order] y = y[order].astype(np.float) X_train = X[:.9 * n_sample] y_train = y[:.9 * n_sample] X_test = X[.9 * n_sample:] y_test = y[.9 * n_sample:] # fit the model for fig_num, kernel in enumerate(('linear', 'rbf', 'poly')): clf = svm.SVC(kernel=kernel, gamma=10) clf.fit(X_train, y_train) plt.figure(fig_num) plt.clf() plt.scatter(X[:, 0], X[:, 1], c=y, zorder=10, cmap=plt.cm.Paired) # Circle out the test data plt.scatter(X_test[:, 0], X_test[:, 1], s=80, facecolors='none', zorder=10) plt.axis('tight') x_min = X[:, 0].min() x_max = X[:, 0].max() y_min = X[:, 1].min() y_max = X[:, 1].max() XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j] Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()]) # Put the result into a color plot Z = Z.reshape(XX.shape) plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired) plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'], levels=[-.5, 0, .5]) plt.title(kernel) plt.show()

bsd-3-clause

NUKnightLab/panda

panda/migrations/0025_add_subscription_permissions.py

6

14192

# -*- coding: utf-8 -*- import datetime from south.db import db from south.v2 import DataMigration from django.db import models class Migration(DataMigration): def forwards(self, orm): """ This migration will fail if run against a clean database (fresh setup) This is fine because the permission will be installed from the fixture. """ try: group = orm['auth.group'].objects.get(name='panda_user') perm = orm['auth.permission'].objects.get(codename='add_searchsubscription') group.permissions.add(perm) perm = orm['auth.permission'].objects.get(codename='delete_searchsubscription') group.permissions.add(perm) except: pass def backwards(self, orm): group = orm['auth.group'].objects.get(name='panda_user') perm = orm['auth.permission'].objects.get(codename='add_searchsubscription') group.permissions.remove(perm) perm = orm['auth.permission'].objects.get(codename='delete_searchsubscription') group.permissions.remove(perm) models = { 'auth.group': { 'Meta': {'object_name': 'Group'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}), 'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}) }, 'auth.permission': { 'Meta': {'ordering': "('content_type__app_label', 'content_type__model', 'codename')", 'unique_together': "(('content_type', 'codename'),)", 'object_name': 'Permission'}, 'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, 'auth.user': { 'Meta': {'object_name': 'User'}, 'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}), 'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}) }, 'contenttypes.contenttype': { 'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"}, 'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, 'panda.activitylog': { 'Meta': {'unique_together': "(('user', 'when'),)", 'object_name': 'ActivityLog'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'activity_logs'", 'to': "orm['auth.User']"}), 'when': ('django.db.models.fields.DateField', [], {'auto_now': 'True', 'blank': 'True'}) }, 'panda.category': { 'Meta': {'object_name': 'Category'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '64'}), 'slug': ('django.db.models.fields.SlugField', [], {'max_length': '256'}) }, 'panda.dataset': { 'Meta': {'ordering': "['-creation_date']", 'object_name': 'Dataset'}, 'categories': ('django.db.models.fields.related.ManyToManyField', [], {'blank': 'True', 'related_name': "'datasets'", 'null': 'True', 'symmetrical': 'False', 'to': "orm['panda.Category']"}), 'column_schema': ('panda.fields.JSONField', [], {'default': 'None', 'null': 'True'}), 'creation_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True'}), 'creator': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'datasets'", 'to': "orm['auth.User']"}), 'current_task': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['panda.TaskStatus']", 'null': 'True', 'blank': 'True'}), 'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'initial_upload': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'initial_upload_for'", 'null': 'True', 'to': "orm['panda.DataUpload']"}), 'last_modification': ('django.db.models.fields.TextField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}), 'last_modified': ('django.db.models.fields.DateTimeField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}), 'last_modified_by': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']", 'null': 'True', 'blank': 'True'}), 'locked': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'locked_at': ('django.db.models.fields.DateTimeField', [], {'default': 'None', 'null': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '256'}), 'row_count': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'sample_data': ('panda.fields.JSONField', [], {'default': 'None', 'null': 'True'}), 'slug': ('django.db.models.fields.SlugField', [], {'max_length': '256'}) }, 'panda.dataupload': { 'Meta': {'ordering': "['creation_date']", 'object_name': 'DataUpload'}, 'columns': ('panda.fields.JSONField', [], {'null': 'True'}), 'creation_date': ('django.db.models.fields.DateTimeField', [], {}), 'creator': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}), 'data_type': ('django.db.models.fields.CharField', [], {'max_length': '4', 'null': 'True', 'blank': 'True'}), 'dataset': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'data_uploads'", 'null': 'True', 'to': "orm['panda.Dataset']"}), 'dialect': ('panda.fields.JSONField', [], {'null': 'True'}), 'encoding': ('django.db.models.fields.CharField', [], {'default': "'utf-8'", 'max_length': '32'}), 'filename': ('django.db.models.fields.CharField', [], {'max_length': '256'}), 'guessed_types': ('panda.fields.JSONField', [], {'null': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'imported': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'original_filename': ('django.db.models.fields.CharField', [], {'max_length': '256'}), 'sample_data': ('panda.fields.JSONField', [], {'null': 'True'}), 'size': ('django.db.models.fields.IntegerField', [], {}) }, 'panda.export': { 'Meta': {'ordering': "['creation_date']", 'object_name': 'Export'}, 'creation_date': ('django.db.models.fields.DateTimeField', [], {}), 'creator': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}), 'dataset': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'exports'", 'null': 'True', 'to': "orm['panda.Dataset']"}), 'filename': ('django.db.models.fields.CharField', [], {'max_length': '256'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'original_filename': ('django.db.models.fields.CharField', [], {'max_length': '256'}), 'size': ('django.db.models.fields.IntegerField', [], {}) }, 'panda.notification': { 'Meta': {'ordering': "['-sent_at']", 'object_name': 'Notification'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'message': ('django.db.models.fields.TextField', [], {}), 'read_at': ('django.db.models.fields.DateTimeField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}), 'recipient': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'notifications'", 'to': "orm['auth.User']"}), 'sent_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'type': ('django.db.models.fields.CharField', [], {'default': "'Info'", 'max_length': '16'}), 'url': ('django.db.models.fields.URLField', [], {'default': 'None', 'max_length': '200', 'null': 'True'}) }, 'panda.relatedupload': { 'Meta': {'ordering': "['creation_date']", 'object_name': 'RelatedUpload'}, 'creation_date': ('django.db.models.fields.DateTimeField', [], {}), 'creator': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}), 'dataset': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'related_uploads'", 'to': "orm['panda.Dataset']"}), 'filename': ('django.db.models.fields.CharField', [], {'max_length': '256'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'original_filename': ('django.db.models.fields.CharField', [], {'max_length': '256'}), 'size': ('django.db.models.fields.IntegerField', [], {}) }, 'panda.searchlog': { 'Meta': {'object_name': 'SearchLog'}, 'dataset': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'related_name': "'searches'", 'null': 'True', 'to': "orm['panda.Dataset']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'query': ('django.db.models.fields.CharField', [], {'max_length': '256'}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'search_logs'", 'to': "orm['auth.User']"}), 'when': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}) }, 'panda.searchsubscription': { 'Meta': {'object_name': 'SearchSubscription'}, 'dataset': ('django.db.models.fields.related.ForeignKey', [], {'default': 'None', 'related_name': "'subscribed_searches'", 'null': 'True', 'to': "orm['panda.Dataset']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'last_run': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'query': ('django.db.models.fields.CharField', [], {'max_length': '256'}), 'query_human': ('django.db.models.fields.TextField', [], {}), 'query_url': ('django.db.models.fields.CharField', [], {'max_length': '256'}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'subscribed_searches'", 'to': "orm['auth.User']"}) }, 'panda.taskstatus': { 'Meta': {'object_name': 'TaskStatus'}, 'creator': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'tasks'", 'null': 'True', 'to': "orm['auth.User']"}), 'end': ('django.db.models.fields.DateTimeField', [], {'null': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'message': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'start': ('django.db.models.fields.DateTimeField', [], {'null': 'True'}), 'status': ('django.db.models.fields.CharField', [], {'default': "'PENDING'", 'max_length': '50'}), 'task_description': ('django.db.models.fields.TextField', [], {}), 'task_name': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'traceback': ('django.db.models.fields.TextField', [], {'default': 'None', 'null': 'True', 'blank': 'True'}) }, 'panda.userprofile': { 'Meta': {'object_name': 'UserProfile'}, 'activation_key': ('django.db.models.fields.CharField', [], {'max_length': '40', 'null': 'True', 'blank': 'True'}), 'activation_key_expiration': ('django.db.models.fields.DateTimeField', [], {}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'user': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['auth.User']", 'unique': 'True'}) } } complete_apps = ['panda'] symmetrical = True

mit

nicproulx/mne-python

tutorials/plot_stats_cluster_methods.py

5

8797

# doc:slow-example """ .. _tut_stats_cluster_methods: ====================================================== Permutation t-test on toy data with spatial clustering ====================================================== Following the illustrative example of Ridgway et al. 2012 [1]_, this demonstrates some basic ideas behind both the "hat" variance adjustment method, as well as threshold-free cluster enhancement (TFCE) [2]_ methods in mne-python. This toy dataset consists of a 40 x 40 square with a "signal" present in the center (at pixel [20, 20]) with white noise added and a 5-pixel-SD normal smoothing kernel applied. In the top row plot the T statistic over space, peaking toward the center. Note that it has peaky edges. Second, with the "hat" variance correction/regularization, the peak becomes correctly centered. Third, the TFCE approach also corrects for these edge artifacts. Fourth, the the two methods combined provide a tighter estimate, for better or worse. Now considering multiple-comparisons corrected statistics on these variables, note that a non-cluster test (e.g., FDR or Bonferroni) would mis-localize the peak due to sharpness in the T statistic driven by low-variance pixels toward the edge of the plateau. Standard clustering (first plot in the second row) identifies the correct region, but the whole area must be declared significant, so no peak analysis can be done. Also, the peak is broad. In this method, all significances are family-wise error rate (FWER) corrected, and the method is non-parametric so assumptions of Gaussian data distributions (which do actually hold for this example) don't need to be satisfied. Adding the "hat" technique tightens the estimate of significant activity (second plot). The TFCE approach (third plot) allows analyzing each significant point independently, but still has a broadened estimate. Note that this is also FWER corrected. Finally, combining the TFCE and "hat" methods tightens the area declared significant (again FWER corrected), and allows for evaluation of each point independently instead of as a single, broad cluster. .. note:: This example does quite a bit of processing, so even on a fast machine it can take a few minutes to complete. """ # Authors: Eric Larson <larson.eric.d@gmail.com> # License: BSD (3-clause) import numpy as np from scipy import stats from functools import partial import matplotlib.pyplot as plt # this changes hidden MPL vars: from mpl_toolkits.mplot3d import Axes3D # noqa from mne.stats import (spatio_temporal_cluster_1samp_test, bonferroni_correction, ttest_1samp_no_p) try: from sklearn.feature_extraction.image import grid_to_graph except ImportError: from scikits.learn.feature_extraction.image import grid_to_graph print(__doc__) ############################################################################### # Set parameters # -------------- width = 40 n_subjects = 10 signal_mean = 100 signal_sd = 100 noise_sd = 0.01 gaussian_sd = 5 sigma = 1e-3 # sigma for the "hat" method threshold = -stats.distributions.t.ppf(0.05, n_subjects - 1) threshold_tfce = dict(start=0, step=0.2) n_permutations = 1024 # number of clustering permutations (1024 for exact) ############################################################################### # Construct simulated data # ------------------------ # # Make the connectivity matrix just next-neighbor spatially n_src = width * width connectivity = grid_to_graph(width, width) # For each "subject", make a smoothed noisy signal with a centered peak rng = np.random.RandomState(42) X = noise_sd * rng.randn(n_subjects, width, width) # Add a signal at the dead center X[:, width // 2, width // 2] = signal_mean + rng.randn(n_subjects) * signal_sd # Spatially smooth with a 2D Gaussian kernel size = width // 2 - 1 gaussian = np.exp(-(np.arange(-size, size + 1) ** 2 / float(gaussian_sd ** 2))) for si in range(X.shape[0]): for ri in range(X.shape[1]): X[si, ri, :] = np.convolve(X[si, ri, :], gaussian, 'same') for ci in range(X.shape[2]): X[si, :, ci] = np.convolve(X[si, :, ci], gaussian, 'same') ############################################################################### # Do some statistics # ------------------ # # .. note:: # X needs to be a multi-dimensional array of shape # samples (subjects) x time x space, so we permute dimensions: X = X.reshape((n_subjects, 1, n_src)) ############################################################################### # Now let's do some clustering using the standard method. # # .. note:: # Not specifying a connectivity matrix implies grid-like connectivity, # which we want here: T_obs, clusters, p_values, H0 = \ spatio_temporal_cluster_1samp_test(X, n_jobs=1, threshold=threshold, connectivity=connectivity, tail=1, n_permutations=n_permutations) # Let's put the cluster data in a readable format ps = np.zeros(width * width) for cl, p in zip(clusters, p_values): ps[cl[1]] = -np.log10(p) ps = ps.reshape((width, width)) T_obs = T_obs.reshape((width, width)) # To do a Bonferroni correction on these data is simple: p = stats.distributions.t.sf(T_obs, n_subjects - 1) p_bon = -np.log10(bonferroni_correction(p)[1]) # Now let's do some clustering using the standard method with "hat": stat_fun = partial(ttest_1samp_no_p, sigma=sigma) T_obs_hat, clusters, p_values, H0 = \ spatio_temporal_cluster_1samp_test(X, n_jobs=1, threshold=threshold, connectivity=connectivity, tail=1, n_permutations=n_permutations, stat_fun=stat_fun, buffer_size=None) # Let's put the cluster data in a readable format ps_hat = np.zeros(width * width) for cl, p in zip(clusters, p_values): ps_hat[cl[1]] = -np.log10(p) ps_hat = ps_hat.reshape((width, width)) T_obs_hat = T_obs_hat.reshape((width, width)) # Now the threshold-free cluster enhancement method (TFCE): T_obs_tfce, clusters, p_values, H0 = \ spatio_temporal_cluster_1samp_test(X, n_jobs=1, threshold=threshold_tfce, connectivity=connectivity, tail=1, n_permutations=n_permutations) T_obs_tfce = T_obs_tfce.reshape((width, width)) ps_tfce = -np.log10(p_values.reshape((width, width))) # Now the TFCE with "hat" variance correction: T_obs_tfce_hat, clusters, p_values, H0 = \ spatio_temporal_cluster_1samp_test(X, n_jobs=1, threshold=threshold_tfce, connectivity=connectivity, tail=1, n_permutations=n_permutations, stat_fun=stat_fun, buffer_size=None) T_obs_tfce_hat = T_obs_tfce_hat.reshape((width, width)) ps_tfce_hat = -np.log10(p_values.reshape((width, width))) ############################################################################### # Visualize results # ----------------- fig = plt.figure(facecolor='w') x, y = np.mgrid[0:width, 0:width] kwargs = dict(rstride=1, cstride=1, linewidth=0, cmap='Greens') Ts = [T_obs, T_obs_hat, T_obs_tfce, T_obs_tfce_hat] titles = ['T statistic', 'T with "hat"', 'TFCE statistic', 'TFCE w/"hat" stat'] for ii, (t, title) in enumerate(zip(Ts, titles)): ax = fig.add_subplot(2, 4, ii + 1, projection='3d') ax.plot_surface(x, y, t, **kwargs) ax.set_xticks([]) ax.set_yticks([]) ax.set_title(title) p_lims = [1.3, -np.log10(1.0 / n_permutations)] pvals = [ps, ps_hat, ps_tfce, ps_tfce_hat] titles = ['Standard clustering', 'Clust. w/"hat"', 'Clust. w/TFCE', 'Clust. w/TFCE+"hat"'] axs = [] for ii, (p, title) in enumerate(zip(pvals, titles)): ax = fig.add_subplot(2, 4, 5 + ii) plt.imshow(p, cmap='Purples', vmin=p_lims[0], vmax=p_lims[1]) ax.set_xticks([]) ax.set_yticks([]) ax.set_title(title) axs.append(ax) plt.tight_layout() for ax in axs: cbar = plt.colorbar(ax=ax, shrink=0.75, orientation='horizontal', fraction=0.1, pad=0.025) cbar.set_label('-log10(p)') cbar.set_ticks(p_lims) cbar.set_ticklabels(['%0.1f' % p for p in p_lims]) plt.show() ############################################################################### # References # ---------- # .. [1] Ridgway et al. 2012, "The problem of low variance voxels in # statistical parametric mapping; a new hat avoids a 'haircut'", # NeuroImage. 2012 Feb 1;59(3):2131-41. # # .. [2] Smith and Nichols 2009, "Threshold-free cluster enhancement: # addressing problems of smoothing, threshold dependence, and # localisation in cluster inference", NeuroImage 44 (2009) 83-98.

bsd-3-clause

kastnerkyle/pylearn2

pylearn2/tests/test_theano.py

45

4805

""" Include tests related to Theano. 1) One test on one thing Pylearn2 depend to be done by Theano. 2) One test for a rare corner case crash in Theano that we where not able to reproduce rapidly enough without having this tests depend on Pylearn2. """ __authors__ = "Ian Goodfellow" __copyright__ = "Copyright 2010-2012, Universite de Montreal" __credits__ = ["Ian Goodfellow"] __license__ = "3-clause BSD" __maintainer__ = "LISA Lab" __email__ = "pylearn-dev@googlegroups" import numpy as np import theano from theano import tensor as T import pylearn2 from pylearn2.config import yaml_parse from pylearn2.testing.skip import skip_if_no_gpu def test_grad(): """Tests that the theano grad method returns a list if it is passed a list and a single variable if it is passed a single variable. pylearn2 depends on theano behaving this way but theano developers have repeatedly changed it """ X = T.matrix() y = X.sum() G = T.grad(y, [X]) assert isinstance(G, list) G = T.grad(y, X) assert not isinstance(G, list) def test_biglayer(): """Test a crash during Theano compilation. It would be too long to redo this test without depending on Pylearn2. So we put it here. """ skip_if_no_gpu() yaml_string = """ !obj:pylearn2.train.Train { dataset: &train !obj:pylearn2.testing.datasets.random_one_hot_topological_dense_design_matrix { rng: !obj:numpy.random.RandomState { seed: [2014, 6, 6] }, shape: &input_shape [%(xsize)i, %(ysize)i], channels: 4, axes: ['c', 0, 1, 'b'], num_examples: 128, num_classes: 10 }, model: !obj:pylearn2.models.mlp.MLP { batch_size: 128, layers: [ !obj:pylearn2.models.mlp.FlattenerLayer { raw_layer: !obj:pylearn2.models.mlp.CompositeLayer { layer_name: 'h0', layers: [ !obj:pylearn2.models.mlp.MLP { layer_name: 'h1', layers: [ !obj:pylearn2.models.maxout.MaxoutConvC01B { layer_name: 'conv00', tied_b: 1, W_lr_scale: .05, b_lr_scale: .05, num_channels: 16, num_pieces: 1, kernel_shape: [1, 1], pool_shape: [4, 4], pool_stride: [4, 4], irange: .005, max_kernel_norm: 0.9, } ]}, !obj:pylearn2.models.maxout.Maxout { layer_name: 'max0', W_lr_scale: .1, b_lr_scale: .1, num_units: 16, irange: .005, max_col_norm: 1.9365, num_pieces: 1, } ] } }, !obj:pylearn2.models.mlp.Softmax { max_col_norm: 1.9365, layer_name: 'y', n_classes: 10, irange: .005 } ], input_space: !obj:pylearn2.space.Conv2DSpace { shape: *input_shape, num_channels: 4, axes: ['c', 0, 1, 'b'], }, }, algorithm: !obj:pylearn2.training_algorithms.sgd.SGD { learning_rate: .05, learning_rule: !obj:pylearn2.training_algorithms.learning_rule.Momentum { init_momentum: 0.5, }, monitoring_dataset: { 'train': *train }, termination_criterion: !obj:pylearn2.termination_criteria.EpochCounter { max_epochs: 3 }, }, extensions: [ !obj:pylearn2.training_algorithms.learning_rule.MomentumAdjustor { start: 1, saturate: 250, final_momentum: .7 } ] } """ try: orig_floatX = theano.config.floatX theano.config.floatX = 'float32' theano.sandbox.cuda.use('gpu') x_size, y_size = 4, 4 parameters = {'xsize': x_size, 'ysize': y_size} test = yaml_parse.load(yaml_string % parameters) test.main_loop() finally: theano.config.floatX = orig_floatX theano.sandbox.cuda.unuse()

bsd-3-clause

gacarrillor/QGIS

tests/src/python/featuresourcetestbase.py

8

57101

# -*- coding: utf-8 -*- """QGIS Unit test utils for QgsFeatureSource subclasses. .. note:: This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. """ from builtins import str from builtins import object __author__ = 'Nyall Dawson' __date__ = '2017-05-25' __copyright__ = 'Copyright 2017, The QGIS Project' from qgis.core import ( QgsRectangle, QgsFeatureRequest, QgsFeature, QgsWkbTypes, QgsProject, QgsGeometry, QgsAbstractFeatureIterator, QgsExpressionContextScope, QgsExpressionContext, QgsVectorLayerFeatureSource, QgsCoordinateReferenceSystem, NULL ) from qgis.PyQt.QtCore import QDate, QTime, QDateTime from utilities import compareWkt class FeatureSourceTestCase(object): """ This is a collection of tests for QgsFeatureSources subclasses and kept generic. To make use of it, subclass it and set self.source to a QgsFeatureSource you want to test. Make sure that your source uses the default dataset by converting one of the provided datasets from the folder tests/testdata/source to a dataset your source is able to handle. """ def treat_date_as_datetime(self): return False def treat_datetime_as_string(self): return False def treat_date_as_string(self): return False def treat_time_as_string(self): return False def testCrs(self): self.assertEqual(self.source.sourceCrs().authid(), 'EPSG:4326') def testWkbType(self): self.assertEqual(self.source.wkbType(), QgsWkbTypes.Point) def testFeatureCount(self): self.assertEqual(self.source.featureCount(), 5) self.assertEqual(len(self.source), 5) def testFields(self): fields = self.source.fields() for f in ('pk', 'cnt', 'name', 'name2', 'num_char'): self.assertTrue(fields.lookupField(f) >= 0) def testGetFeatures(self, source=None, extra_features=[], skip_features=[], changed_attributes={}, changed_geometries={}): """ Test that expected results are returned when fetching all features """ # IMPORTANT - we do not use `for f in source.getFeatures()` as we are also # testing that existing attributes & geometry in f are overwritten correctly # (for f in ... uses a new QgsFeature for every iteration) if not source: source = self.source it = source.getFeatures() f = QgsFeature() attributes = {} geometries = {} while it.nextFeature(f): # expect feature to be valid self.assertTrue(f.isValid()) # some source test datasets will include additional attributes which we ignore, # so cherry pick desired attributes attrs = [f['pk'], f['cnt'], f['name'], f['name2'], f['num_char'], f['dt'], f['date'], f['time']] # force the num_char attribute to be text - some sources (e.g., delimited text) will # automatically detect that this attribute contains numbers and set it as a numeric # field attrs[4] = str(attrs[4]) attributes[f['pk']] = attrs geometries[f['pk']] = f.hasGeometry() and f.geometry().asWkt() expected_attributes = {5: [5, -200, NULL, 'NuLl', '5', QDateTime(QDate(2020, 5, 4), QTime(12, 13, 14)) if not self.treat_datetime_as_string() else '2020-05-04 12:13:14', QDate(2020, 5, 2) if not self.treat_date_as_datetime() and not self.treat_date_as_string() else QDateTime(2020, 5, 2, 0, 0, 0) if not self.treat_date_as_string() else '2020-05-02', QTime(12, 13, 1) if not self.treat_time_as_string() else '12:13:01'], 3: [3, 300, 'Pear', 'PEaR', '3', NULL, NULL, NULL], 1: [1, 100, 'Orange', 'oranGe', '1', QDateTime(QDate(2020, 5, 3), QTime(12, 13, 14)) if not self.treat_datetime_as_string() else '2020-05-03 12:13:14', QDate(2020, 5, 3) if not self.treat_date_as_datetime() and not self.treat_date_as_string() else QDateTime(2020, 5, 3, 0, 0, 0) if not self.treat_date_as_string() else '2020-05-03', QTime(12, 13, 14) if not self.treat_time_as_string() else '12:13:14'], 2: [2, 200, 'Apple', 'Apple', '2', QDateTime(QDate(2020, 5, 4), QTime(12, 14, 14)) if not self.treat_datetime_as_string() else '2020-05-04 12:14:14', QDate(2020, 5, 4) if not self.treat_date_as_datetime() and not self.treat_date_as_string() else QDateTime(2020, 5, 4, 0, 0, 0) if not self.treat_date_as_string() else '2020-05-04', QTime(12, 14, 14) if not self.treat_time_as_string() else '12:14:14'], 4: [4, 400, 'Honey', 'Honey', '4', QDateTime(QDate(2021, 5, 4), QTime(13, 13, 14)) if not self.treat_datetime_as_string() else '2021-05-04 13:13:14', QDate(2021, 5, 4) if not self.treat_date_as_datetime() and not self.treat_date_as_string() else QDateTime(2021, 5, 4, 0, 0, 0) if not self.treat_date_as_string() else '2021-05-04', QTime(13, 13, 14) if not self.treat_time_as_string() else '13:13:14']} expected_geometries = {1: 'Point (-70.332 66.33)', 2: 'Point (-68.2 70.8)', 3: None, 4: 'Point(-65.32 78.3)', 5: 'Point(-71.123 78.23)'} for f in extra_features: expected_attributes[f[0]] = f.attributes() if f.hasGeometry(): expected_geometries[f[0]] = f.geometry().asWkt() else: expected_geometries[f[0]] = None for i in skip_features: del expected_attributes[i] del expected_geometries[i] for i, a in changed_attributes.items(): for attr_idx, v in a.items(): expected_attributes[i][attr_idx] = v for i, g, in changed_geometries.items(): if g: expected_geometries[i] = g.asWkt() else: expected_geometries[i] = None self.assertEqual(attributes, expected_attributes, 'Expected {}, got {}'.format(expected_attributes, attributes)) self.assertEqual(len(expected_geometries), len(geometries)) for pk, geom in list(expected_geometries.items()): if geom: assert compareWkt(geom, geometries[pk]), "Geometry {} mismatch Expected:\n{}\nGot:\n{}\n".format(pk, geom, geometries[ pk]) else: self.assertFalse(geometries[pk], 'Expected null geometry for {}'.format(pk)) def assert_query(self, source, expression, expected): request = QgsFeatureRequest().setFilterExpression(expression).setFlags(QgsFeatureRequest.NoGeometry | QgsFeatureRequest.IgnoreStaticNodesDuringExpressionCompilation) result = set([f['pk'] for f in source.getFeatures(request)]) assert set(expected) == result, 'Expected {} and got {} when testing expression "{}"'.format(set(expected), result, expression) self.assertTrue(all(f.isValid() for f in source.getFeatures(request))) # Also check that filter works when referenced fields are not being retrieved by request result = set([f['pk'] for f in source.getFeatures( QgsFeatureRequest().setFilterExpression(expression).setSubsetOfAttributes(['pk'], self.source.fields()).setFlags(QgsFeatureRequest.IgnoreStaticNodesDuringExpressionCompilation))]) assert set( expected) == result, 'Expected {} and got {} when testing expression "{}" using empty attribute subset'.format( set(expected), result, expression) # test that results match QgsFeatureRequest.acceptFeature request = QgsFeatureRequest().setFilterExpression(expression).setFlags(QgsFeatureRequest.IgnoreStaticNodesDuringExpressionCompilation) for f in source.getFeatures(): self.assertEqual(request.acceptFeature(f), f['pk'] in expected) def runGetFeatureTests(self, source): self.assertEqual(len([f for f in source.getFeatures()]), 5) self.assert_query(source, 'name ILIKE \'QGIS\'', []) self.assert_query(source, '"name" IS NULL', [5]) self.assert_query(source, '"name" IS NOT NULL', [1, 2, 3, 4]) self.assert_query(source, '"name" NOT LIKE \'Ap%\'', [1, 3, 4]) self.assert_query(source, '"name" NOT ILIKE \'QGIS\'', [1, 2, 3, 4]) self.assert_query(source, '"name" NOT ILIKE \'pEAR\'', [1, 2, 4]) self.assert_query(source, 'name = \'Apple\'', [2]) # field names themselves are NOT case sensitive -- QGIS expressions don't care about this self.assert_query(source, '\"NaMe\" = \'Apple\'', [2]) self.assert_query(source, 'name <> \'Apple\'', [1, 3, 4]) self.assert_query(source, 'name = \'apple\'', []) self.assert_query(source, '"name" <> \'apple\'', [1, 2, 3, 4]) self.assert_query(source, '(name = \'Apple\') is not null', [1, 2, 3, 4]) self.assert_query(source, 'name LIKE \'Apple\'', [2]) self.assert_query(source, 'name LIKE \'aPple\'', []) self.assert_query(source, 'name LIKE \'Ap_le\'', [2]) self.assert_query(source, 'name LIKE \'Ap\\_le\'', []) self.assert_query(source, 'name ILIKE \'aPple\'', [2]) self.assert_query(source, 'name ILIKE \'%pp%\'', [2]) self.assert_query(source, 'cnt > 0', [1, 2, 3, 4]) self.assert_query(source, '-cnt > 0', [5]) self.assert_query(source, 'cnt < 0', [5]) self.assert_query(source, '-cnt < 0', [1, 2, 3, 4]) self.assert_query(source, 'cnt >= 100', [1, 2, 3, 4]) self.assert_query(source, 'cnt <= 100', [1, 5]) self.assert_query(source, 'pk IN (1, 2, 4, 8)', [1, 2, 4]) self.assert_query(source, 'cnt = 50 * 2', [1]) self.assert_query(source, 'cnt = 150 / 1.5', [1]) self.assert_query(source, 'cnt = 1000 / 10', [1]) self.assert_query(source, 'cnt = 1000/11+10', []) # checks that source isn't rounding int/int self.assert_query(source, 'pk = 9 // 4', [2]) # int division self.assert_query(source, 'cnt = 99 + 1', [1]) self.assert_query(source, 'cnt = 101 - 1', [1]) self.assert_query(source, 'cnt - 1 = 99', [1]) self.assert_query(source, '-cnt - 1 = -101', [1]) self.assert_query(source, '-(-cnt) = 100', [1]) self.assert_query(source, '-(cnt) = -(100)', [1]) self.assert_query(source, 'cnt + 1 = 101', [1]) self.assert_query(source, 'cnt = 1100 % 1000', [1]) self.assert_query(source, '"name" || \' \' || "name" = \'Orange Orange\'', [1]) self.assert_query(source, '"name" || \' \' || "cnt" = \'Orange 100\'', [1]) self.assert_query(source, '\'x\' || "name" IS NOT NULL', [1, 2, 3, 4]) self.assert_query(source, '\'x\' || "name" IS NULL', [5]) self.assert_query(source, 'cnt = 10 ^ 2', [1]) self.assert_query(source, '"name" ~ \'[OP]ra[gne]+\'', [1]) self.assert_query(source, '"name"="name2"', [2, 4]) # mix of matched and non-matched case sensitive names self.assert_query(source, 'true', [1, 2, 3, 4, 5]) self.assert_query(source, 'false', []) # Three value logic self.assert_query(source, 'false and false', []) self.assert_query(source, 'false and true', []) self.assert_query(source, 'false and NULL', []) self.assert_query(source, 'true and false', []) self.assert_query(source, 'true and true', [1, 2, 3, 4, 5]) self.assert_query(source, 'true and NULL', []) self.assert_query(source, 'NULL and false', []) self.assert_query(source, 'NULL and true', []) self.assert_query(source, 'NULL and NULL', []) self.assert_query(source, 'false or false', []) self.assert_query(source, 'false or true', [1, 2, 3, 4, 5]) self.assert_query(source, 'false or NULL', []) self.assert_query(source, 'true or false', [1, 2, 3, 4, 5]) self.assert_query(source, 'true or true', [1, 2, 3, 4, 5]) self.assert_query(source, 'true or NULL', [1, 2, 3, 4, 5]) self.assert_query(source, 'NULL or false', []) self.assert_query(source, 'NULL or true', [1, 2, 3, 4, 5]) self.assert_query(source, 'NULL or NULL', []) self.assert_query(source, 'not true', []) self.assert_query(source, 'not false', [1, 2, 3, 4, 5]) self.assert_query(source, 'not null', []) # not self.assert_query(source, 'not name = \'Apple\'', [1, 3, 4]) self.assert_query(source, 'not name IS NULL', [1, 2, 3, 4]) self.assert_query(source, 'not name = \'Apple\' or name = \'Apple\'', [1, 2, 3, 4]) self.assert_query(source, 'not name = \'Apple\' or not name = \'Apple\'', [1, 3, 4]) self.assert_query(source, 'not name = \'Apple\' and pk = 4', [4]) self.assert_query(source, 'not name = \'Apple\' and not pk = 4', [1, 3]) self.assert_query(source, 'not pk IN (1, 2, 4, 8)', [3, 5]) # type conversion - QGIS expressions do not mind that we are comparing a string # against numeric literals self.assert_query(source, 'num_char IN (2, 4, 5)', [2, 4, 5]) # function self.assert_query(source, 'sqrt(pk) >= 2', [4, 5]) self.assert_query(source, 'radians(cnt) < 2', [1, 5]) self.assert_query(source, 'degrees(pk) <= 200', [1, 2, 3]) self.assert_query(source, 'abs(cnt) <= 200', [1, 2, 5]) self.assert_query(source, 'cos(pk) < 0', [2, 3, 4]) self.assert_query(source, 'sin(pk) < 0', [4, 5]) self.assert_query(source, 'tan(pk) < 0', [2, 3, 5]) self.assert_query(source, 'acos(-1) < pk', [4, 5]) self.assert_query(source, 'asin(1) < pk', [2, 3, 4, 5]) self.assert_query(source, 'atan(3.14) < pk', [2, 3, 4, 5]) self.assert_query(source, 'atan2(3.14, pk) < 1', [3, 4, 5]) self.assert_query(source, 'exp(pk) < 10', [1, 2]) self.assert_query(source, 'ln(pk) <= 1', [1, 2]) self.assert_query(source, 'log(3, pk) <= 1', [1, 2, 3]) self.assert_query(source, 'log10(pk) < 0.5', [1, 2, 3]) self.assert_query(source, 'round(3.14) <= pk', [3, 4, 5]) self.assert_query(source, 'round(0.314,1) * 10 = pk', [3]) self.assert_query(source, 'floor(3.14) <= pk', [3, 4, 5]) self.assert_query(source, 'ceil(3.14) <= pk', [4, 5]) self.assert_query(source, 'pk < pi()', [1, 2, 3]) self.assert_query(source, 'round(cnt / 66.67) <= 2', [1, 5]) self.assert_query(source, 'floor(cnt / 66.67) <= 2', [1, 2, 5]) self.assert_query(source, 'ceil(cnt / 66.67) <= 2', [1, 5]) self.assert_query(source, 'pk < pi() / 2', [1]) self.assert_query(source, 'pk = char(51)', [3]) self.assert_query(source, 'pk = coalesce(NULL,3,4)', [3]) self.assert_query(source, 'lower(name) = \'apple\'', [2]) self.assert_query(source, 'upper(name) = \'APPLE\'', [2]) self.assert_query(source, 'name = trim(\' Apple \')', [2]) # geometry # azimuth and touches tests are deactivated because they do not pass for WFS source # self.assert_query(source, 'azimuth($geometry,geom_from_wkt( \'Point (-70 70)\')) < pi()', [1, 5]) self.assert_query(source, 'x($geometry) < -70', [1, 5]) self.assert_query(source, 'y($geometry) > 70', [2, 4, 5]) self.assert_query(source, 'xmin($geometry) < -70', [1, 5]) self.assert_query(source, 'ymin($geometry) > 70', [2, 4, 5]) self.assert_query(source, 'xmax($geometry) < -70', [1, 5]) self.assert_query(source, 'ymax($geometry) > 70', [2, 4, 5]) self.assert_query(source, 'disjoint($geometry,geom_from_wkt( \'Polygon ((-72.2 66.1, -65.2 66.1, -65.2 72.0, -72.2 72.0, -72.2 66.1))\'))', [4, 5]) self.assert_query(source, 'intersects($geometry,geom_from_wkt( \'Polygon ((-72.2 66.1, -65.2 66.1, -65.2 72.0, -72.2 72.0, -72.2 66.1))\'))', [1, 2]) # self.assert_query(source, 'touches($geometry,geom_from_wkt( \'Polygon ((-70.332 66.33, -65.32 66.33, -65.32 78.3, -70.332 78.3, -70.332 66.33))\'))', [1, 4]) self.assert_query(source, 'contains(geom_from_wkt( \'Polygon ((-72.2 66.1, -65.2 66.1, -65.2 72.0, -72.2 72.0, -72.2 66.1))\'),$geometry)', [1, 2]) self.assert_query(source, 'distance($geometry,geom_from_wkt( \'Point (-70 70)\')) > 7', [4, 5]) self.assert_query(source, 'intersects($geometry,geom_from_gml( \'<gml:Polygon srsName="EPSG:4326"><gml:outerBoundaryIs><gml:LinearRing><gml:coordinates>-72.2,66.1 -65.2,66.1 -65.2,72.0 -72.2,72.0 -72.2,66.1</gml:coordinates></gml:LinearRing></gml:outerBoundaryIs></gml:Polygon>\'))', [1, 2]) # between/not between self.assert_query(source, 'cnt BETWEEN -200 AND 200', [1, 2, 5]) self.assert_query(source, 'cnt NOT BETWEEN 100 AND 200', [3, 4, 5]) if self.treat_datetime_as_string(): self.assert_query(source, """dt BETWEEN format_date(make_datetime(2020, 5, 3, 12, 13, 14), 'yyyy-MM-dd hh:mm:ss') AND format_date(make_datetime(2020, 5, 4, 12, 14, 14), 'yyyy-MM-dd hh:mm:ss')""", [1, 2, 5]) self.assert_query(source, """dt NOT BETWEEN format_date(make_datetime(2020, 5, 3, 12, 13, 14), 'yyyy-MM-dd hh:mm:ss') AND format_date(make_datetime(2020, 5, 4, 12, 14, 14), 'yyyy-MM-dd hh:mm:ss')""", [4]) else: self.assert_query(source, 'dt BETWEEN make_datetime(2020, 5, 3, 12, 13, 14) AND make_datetime(2020, 5, 4, 12, 14, 14)', [1, 2, 5]) self.assert_query(source, 'dt NOT BETWEEN make_datetime(2020, 5, 3, 12, 13, 14) AND make_datetime(2020, 5, 4, 12, 14, 14)', [4]) # datetime if self.treat_datetime_as_string(): self.assert_query(source, '"dt" <= format_date(make_datetime(2020, 5, 4, 12, 13, 14), \'yyyy-MM-dd hh:mm:ss\')', [1, 5]) self.assert_query(source, '"dt" < format_date(make_date(2020, 5, 4), \'yyyy-MM-dd hh:mm:ss\')', [1]) self.assert_query(source, '"dt" = format_date(to_datetime(\'000www14ww13ww12www4ww5ww2020\',\'zzzwwwsswwmmwwhhwwwdwwMwwyyyy\'),\'yyyy-MM-dd hh:mm:ss\')', [5]) else: self.assert_query(source, '"dt" <= make_datetime(2020, 5, 4, 12, 13, 14)', [1, 5]) self.assert_query(source, '"dt" < make_date(2020, 5, 4)', [1]) self.assert_query(source, '"dt" = to_datetime(\'000www14ww13ww12www4ww5ww2020\',\'zzzwwwsswwmmwwhhwwwdwwMwwyyyy\')', [5]) self.assert_query(source, '"date" <= make_datetime(2020, 5, 4, 12, 13, 14)', [1, 2, 5]) self.assert_query(source, '"date" >= make_date(2020, 5, 4)', [2, 4]) if not self.treat_date_as_datetime(): self.assert_query(source, '"date" = to_date(\'www4ww5ww2020\',\'wwwdwwMwwyyyy\')', [2]) else: # TODO - we don't have any expression functions which can upgrade a date value to a datetime value! pass if not self.treat_time_as_string(): self.assert_query(source, '"time" >= make_time(12, 14, 14)', [2, 4]) self.assert_query(source, '"time" = to_time(\'000www14ww13ww12www\',\'zzzwwwsswwmmwwhhwww\')', [1]) else: self.assert_query(source, 'to_time("time") >= make_time(12, 14, 14)', [2, 4]) self.assert_query(source, 'to_time("time") = to_time(\'000www14ww13ww12www\',\'zzzwwwsswwmmwwhhwww\')', [1]) # TODO - enable, but needs fixing on Travis due to timezone handling issues # if self.treat_datetime_as_string(): # self.assert_query(source, 'to_datetime("dt", \'yyyy-MM-dd hh:mm:ss\') + make_interval(days:=1) <= make_datetime(2020, 5, 4, 12, 13, 14)', [1]) # self.assert_query(source, 'to_datetime("dt", \'yyyy-MM-dd hh:mm:ss\') + make_interval(days:=0.01) <= make_datetime(2020, 5, 4, 12, 13, 14)', [1, 5]) # else: # self.assert_query(source, '"dt" + make_interval(days:=1) <= make_datetime(2020, 5, 4, 12, 13, 14)', [1]) # self.assert_query(source, '"dt" + make_interval(days:=0.01) <= make_datetime(2020, 5, 4, 12, 13, 14)', [1, 5]) # combination of an uncompilable expression and limit # TODO - move this test to FeatureSourceTestCase # it's currently added in ProviderTestCase, but tests only using a QgsVectorLayer getting features, # i.e. not directly requesting features from the provider. Turns out the WFS provider fails this # and should be fixed - then we can enable this test at the FeatureSourceTestCase level # feature = next(self.source.getFeatures(QgsFeatureRequest().setFilterExpression('pk=4'))) # context = QgsExpressionContext() # scope = QgsExpressionContextScope() # scope.setVariable('parent', feature) # context.appendScope(scope) # request = QgsFeatureRequest() # request.setExpressionContext(context) # request.setFilterExpression('"pk" = attribute(@parent, \'pk\')') # request.setLimit(1) # values = [f['pk'] for f in self.source.getFeatures(request)] # self.assertEqual(values, [4]) def testGetFeaturesExp(self): self.runGetFeatureTests(self.source) def runOrderByTests(self): request = QgsFeatureRequest().addOrderBy('cnt') values = [f['cnt'] for f in self.source.getFeatures(request)] self.assertEqual(values, [-200, 100, 200, 300, 400]) request = QgsFeatureRequest().addOrderBy('cnt', False) values = [f['cnt'] for f in self.source.getFeatures(request)] self.assertEqual(values, [400, 300, 200, 100, -200]) request = QgsFeatureRequest().addOrderBy('name') values = [f['name'] for f in self.source.getFeatures(request)] self.assertEqual(values, ['Apple', 'Honey', 'Orange', 'Pear', NULL]) request = QgsFeatureRequest().addOrderBy('name', True, True) values = [f['name'] for f in self.source.getFeatures(request)] self.assertEqual(values, [NULL, 'Apple', 'Honey', 'Orange', 'Pear']) request = QgsFeatureRequest().addOrderBy('name', False) values = [f['name'] for f in self.source.getFeatures(request)] self.assertEqual(values, [NULL, 'Pear', 'Orange', 'Honey', 'Apple']) request = QgsFeatureRequest().addOrderBy('name', False, False) values = [f['name'] for f in self.source.getFeatures(request)] self.assertEqual(values, ['Pear', 'Orange', 'Honey', 'Apple', NULL]) request = QgsFeatureRequest().addOrderBy('num_char', False) values = [f['pk'] for f in self.source.getFeatures(request)] self.assertEqual(values, [5, 4, 3, 2, 1]) request = QgsFeatureRequest().addOrderBy('dt', False) values = [f['pk'] for f in self.source.getFeatures(request)] self.assertEqual(values, [3, 4, 2, 5, 1]) request = QgsFeatureRequest().addOrderBy('date', False) values = [f['pk'] for f in self.source.getFeatures(request)] self.assertEqual(values, [3, 4, 2, 1, 5]) request = QgsFeatureRequest().addOrderBy('time', False) values = [f['pk'] for f in self.source.getFeatures(request)] self.assertEqual(values, [3, 4, 2, 1, 5]) # Case sensitivity request = QgsFeatureRequest().addOrderBy('name2') values = [f['name2'] for f in self.source.getFeatures(request)] self.assertEqual(values, ['Apple', 'Honey', 'NuLl', 'oranGe', 'PEaR']) # Combination with LIMIT request = QgsFeatureRequest().addOrderBy('pk', False).setLimit(2) values = [f['pk'] for f in self.source.getFeatures(request)] self.assertEqual(values, [5, 4]) # A slightly more complex expression request = QgsFeatureRequest().addOrderBy('pk*2', False) values = [f['pk'] for f in self.source.getFeatures(request)] self.assertEqual(values, [5, 4, 3, 2, 1]) # Order reversing expression request = QgsFeatureRequest().addOrderBy('pk*-1', False) values = [f['pk'] for f in self.source.getFeatures(request)] self.assertEqual(values, [1, 2, 3, 4, 5]) # Type dependent expression request = QgsFeatureRequest().addOrderBy('num_char*2', False) values = [f['pk'] for f in self.source.getFeatures(request)] self.assertEqual(values, [5, 4, 3, 2, 1]) # Order by guaranteed to fail request = QgsFeatureRequest().addOrderBy('not a valid expression*', False) values = [f['pk'] for f in self.source.getFeatures(request)] self.assertEqual(set(values), set([5, 4, 3, 2, 1])) # Multiple order bys and boolean request = QgsFeatureRequest().addOrderBy('pk > 2').addOrderBy('pk', False) values = [f['pk'] for f in self.source.getFeatures(request)] self.assertEqual(values, [2, 1, 5, 4, 3]) # Multiple order bys, one bad, and a limit request = QgsFeatureRequest().addOrderBy('pk', False).addOrderBy('not a valid expression*', False).setLimit(2) values = [f['pk'] for f in self.source.getFeatures(request)] self.assertEqual(values, [5, 4]) # Bad expression first request = QgsFeatureRequest().addOrderBy('not a valid expression*', False).addOrderBy('pk', False).setLimit(2) values = [f['pk'] for f in self.source.getFeatures(request)] self.assertEqual(values, [5, 4]) # Combination with subset of attributes request = QgsFeatureRequest().addOrderBy('num_char', False).setSubsetOfAttributes(['pk'], self.source.fields()) values = [f['pk'] for f in self.source.getFeatures(request)] self.assertEqual(values, [5, 4, 3, 2, 1]) def testOrderBy(self): self.runOrderByTests() def testOpenIteratorAfterSourceRemoval(self): """ Test that removing source after opening an iterator does not crash. All required information should be captured in the iterator's source and there MUST be no links between the iterators and the sources's data source """ if not getattr(self, 'getSource', None): return source = self.getSource() it = source.getFeatures() del source # get the features pks = [] for f in it: pks.append(f['pk']) self.assertEqual(set(pks), {1, 2, 3, 4, 5}) def testGetFeaturesFidTests(self): fids = [f.id() for f in self.source.getFeatures()] assert len(fids) == 5, 'Expected 5 features, got {} instead'.format(len(fids)) for id in fids: features = [f for f in self.source.getFeatures(QgsFeatureRequest().setFilterFid(id))] self.assertEqual(len(features), 1) feature = features[0] self.assertTrue(feature.isValid()) result = [feature.id()] expected = [id] assert result == expected, 'Expected {} and got {} when testing for feature ID filter'.format(expected, result) # test that results match QgsFeatureRequest.acceptFeature request = QgsFeatureRequest().setFilterFid(id) for f in self.source.getFeatures(): self.assertEqual(request.acceptFeature(f), f.id() == id) # bad features it = self.source.getFeatures(QgsFeatureRequest().setFilterFid(-99999999)) feature = QgsFeature(5) feature.setValid(False) self.assertFalse(it.nextFeature(feature)) self.assertFalse(feature.isValid()) def testGetFeaturesFidsTests(self): fids = [f.id() for f in self.source.getFeatures()] self.assertEqual(len(fids), 5) # empty list = no features request = QgsFeatureRequest().setFilterFids([]) result = set([f.id() for f in self.source.getFeatures(request)]) self.assertFalse(result) request = QgsFeatureRequest().setFilterFids([fids[0], fids[2]]) result = set([f.id() for f in self.source.getFeatures(request)]) all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) expected = set([fids[0], fids[2]]) assert result == expected, 'Expected {} and got {} when testing for feature IDs filter'.format(expected, result) self.assertTrue(all_valid) # test that results match QgsFeatureRequest.acceptFeature for f in self.source.getFeatures(): self.assertEqual(request.acceptFeature(f), f.id() in expected) result = set( [f.id() for f in self.source.getFeatures(QgsFeatureRequest().setFilterFids([fids[1], fids[3], fids[4]]))]) expected = set([fids[1], fids[3], fids[4]]) assert result == expected, 'Expected {} and got {} when testing for feature IDs filter'.format(expected, result) # sources should ignore non-existent fids result = set([f.id() for f in self.source.getFeatures( QgsFeatureRequest().setFilterFids([-101, fids[1], -102, fids[3], -103, fids[4], -104]))]) expected = set([fids[1], fids[3], fids[4]]) assert result == expected, 'Expected {} and got {} when testing for feature IDs filter'.format(expected, result) result = set([f.id() for f in self.source.getFeatures(QgsFeatureRequest().setFilterFids([]))]) expected = set([]) assert result == expected, 'Expected {} and got {} when testing for feature IDs filter'.format(expected, result) # Rewind mid-way request = QgsFeatureRequest().setFilterFids([fids[1], fids[3], fids[4]]) feature_it = self.source.getFeatures(request) feature = QgsFeature() feature.setValid(True) self.assertTrue(feature_it.nextFeature(feature)) self.assertIn(feature.id(), [fids[1], fids[3], fids[4]]) first_feature = feature self.assertTrue(feature.isValid()) # rewind self.assertTrue(feature_it.rewind()) self.assertTrue(feature_it.nextFeature(feature)) self.assertEqual(feature.id(), first_feature.id()) self.assertTrue(feature.isValid()) # grab all features self.assertTrue(feature_it.nextFeature(feature)) self.assertTrue(feature_it.nextFeature(feature)) # none left self.assertFalse(feature_it.nextFeature(feature)) self.assertFalse(feature.isValid()) def testGetFeaturesFilterRectTests(self): extent = QgsRectangle(-70, 67, -60, 80) request = QgsFeatureRequest().setFilterRect(extent) features = [f['pk'] for f in self.source.getFeatures(request)] all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) assert set(features) == set([2, 4]), 'Got {} instead'.format(features) self.assertTrue(all_valid) # test that results match QgsFeatureRequest.acceptFeature for f in self.source.getFeatures(): self.assertEqual(request.acceptFeature(f), f['pk'] in set([2, 4])) # test with an empty rectangle extent = QgsRectangle() request = QgsFeatureRequest().setFilterRect(extent) features = [f['pk'] for f in self.source.getFeatures(request)] all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) assert set(features) == set([1, 2, 3, 4, 5]), 'Got {} instead'.format(features) self.assertTrue(all_valid) # ExactIntersection flag set, but no filter rect set. Should be ignored. request = QgsFeatureRequest() request.setFlags(QgsFeatureRequest.ExactIntersect) features = [f['pk'] for f in self.source.getFeatures(request)] all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) assert set(features) == set([1, 2, 3, 4, 5]), 'Got {} instead'.format(features) self.assertTrue(all_valid) def testRectAndExpression(self): extent = QgsRectangle(-70, 67, -60, 80) request = QgsFeatureRequest().setFilterExpression('"cnt">200').setFilterRect(extent) result = set([f['pk'] for f in self.source.getFeatures(request)]) all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) expected = [4] assert set( expected) == result, 'Expected {} and got {} when testing for combination of filterRect and expression'.format( set(expected), result) self.assertTrue(all_valid) # shouldn't matter what order this is done in request = QgsFeatureRequest().setFilterRect(extent).setFilterExpression('"cnt">200') result = set([f['pk'] for f in self.source.getFeatures(request)]) all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) expected = [4] assert set( expected) == result, 'Expected {} and got {} when testing for combination of filterRect and expression'.format( set(expected), result) self.assertTrue(all_valid) # test that results match QgsFeatureRequest.acceptFeature for f in self.source.getFeatures(): self.assertEqual(request.acceptFeature(f), f['pk'] in expected) def testGetFeaturesDistanceWithinTests(self): request = QgsFeatureRequest().setDistanceWithin(QgsGeometry.fromWkt('LineString (-63.2 69.9, -68.47 69.86, -69.74 79.28)'), 1.7) features = [f['pk'] for f in self.source.getFeatures(request)] all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) assert set(features) == set([2, 5]), 'Got {} instead'.format(features) self.assertTrue(all_valid) # test that results match QgsFeatureRequest.acceptFeature for f in self.source.getFeatures(): self.assertEqual(request.acceptFeature(f), f['pk'] in set([2, 5])) request = QgsFeatureRequest().setDistanceWithin(QgsGeometry.fromWkt('LineString (-63.2 69.9, -68.47 69.86, -69.74 79.28)'), 0.6) features = [f['pk'] for f in self.source.getFeatures(request)] all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) assert set(features) == set([2]), 'Got {} instead'.format(features) self.assertTrue(all_valid) # test that results match QgsFeatureRequest.acceptFeature for f in self.source.getFeatures(): self.assertEqual(request.acceptFeature(f), f['pk'] in set([2])) # in different crs request = QgsFeatureRequest().setDestinationCrs(QgsCoordinateReferenceSystem('EPSG:3857'), QgsProject.instance().transformContext()).setDistanceWithin(QgsGeometry.fromWkt('LineString (-7035391 11036245, -7622045 11023301, -7763421 15092839)'), 250000) features = [f['pk'] for f in self.source.getFeatures(request)] all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) self.assertEqual(set(features), {2, 5}) self.assertTrue(all_valid) # point geometry request = QgsFeatureRequest().setDistanceWithin( QgsGeometry.fromWkt('Point (-68.1 78.1)'), 3.6) features = [f['pk'] for f in self.source.getFeatures(request)] all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) assert set(features) == set([4, 5]), 'Got {} instead'.format(features) self.assertTrue(all_valid) # test that results match QgsFeatureRequest.acceptFeature for f in self.source.getFeatures(): self.assertEqual(request.acceptFeature(f), f['pk'] in set([4, 5])) request = QgsFeatureRequest().setDistanceWithin( QgsGeometry.fromWkt('Polygon ((-64.47 79.59, -64.37 73.59, -72.69 73.61, -72.73 68.07, -62.51 68.01, -62.71 79.55, -64.47 79.59))'), 0) features = [f['pk'] for f in self.source.getFeatures(request)] all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) assert set(features) == set([2]), 'Got {} instead'.format(features) self.assertTrue(all_valid) # test that results match QgsFeatureRequest.acceptFeature for f in self.source.getFeatures(): self.assertEqual(request.acceptFeature(f), f['pk'] in set([2])) request = QgsFeatureRequest().setDistanceWithin( QgsGeometry.fromWkt('Polygon ((-64.47 79.59, -64.37 73.59, -72.69 73.61, -72.73 68.07, -62.51 68.01, -62.71 79.55, -64.47 79.59))'), 1.3) features = [f['pk'] for f in self.source.getFeatures(request)] all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) assert set(features) == set([2, 4]), 'Got {} instead'.format(features) self.assertTrue(all_valid) # test that results match QgsFeatureRequest.acceptFeature for f in self.source.getFeatures(): self.assertEqual(request.acceptFeature(f), f['pk'] in set([2, 4])) request = QgsFeatureRequest().setDistanceWithin( QgsGeometry.fromWkt('Polygon ((-64.47 79.59, -64.37 73.59, -72.69 73.61, -72.73 68.07, -62.51 68.01, -62.71 79.55, -64.47 79.59))'), 2.3) features = [f['pk'] for f in self.source.getFeatures(request)] all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) assert set(features) == set([1, 2, 4]), 'Got {} instead'.format(features) self.assertTrue(all_valid) # test that results match QgsFeatureRequest.acceptFeature for f in self.source.getFeatures(): self.assertEqual(request.acceptFeature(f), f['pk'] in set([1, 2, 4])) # test with linestring whose bounding box overlaps all query # points but being only within one of them, which we hope will # be returned NOT as the first one. # This is a test for https://github.com/qgis/QGIS/issues/45352 request = QgsFeatureRequest().setDistanceWithin( QgsGeometry.fromWkt('LINESTRING(-100 80, -100 66, -30 66, -30 80)'), 0.5) features = {f['pk'] for f in self.source.getFeatures(request)} self.assertEqual(features, {1}, "Unexpected return from QgsFeatureRequest with DistanceWithin filter") def testGeomAndAllAttributes(self): """ Test combination of a filter which requires geometry and all attributes """ request = QgsFeatureRequest().setFilterExpression( 'attribute($currentfeature,\'cnt\')>200 and $x>=-70 and $x<=-60').setSubsetOfAttributes([]).setFlags( QgsFeatureRequest.NoGeometry | QgsFeatureRequest.IgnoreStaticNodesDuringExpressionCompilation) result = set([f['pk'] for f in self.source.getFeatures(request)]) all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) self.assertEqual(result, {4}) self.assertTrue(all_valid) request = QgsFeatureRequest().setFilterExpression( 'attribute($currentfeature,\'cnt\')>200 and $x>=-70 and $x<=-60').setFlags(QgsFeatureRequest.IgnoreStaticNodesDuringExpressionCompilation) result = set([f['pk'] for f in self.source.getFeatures(request)]) all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) self.assertEqual(result, {4}) self.assertTrue(all_valid) def testRectAndFids(self): """ Test the combination of a filter rect along with filterfids """ # first get feature ids ids = {f['pk']: f.id() for f in self.source.getFeatures()} extent = QgsRectangle(-70, 67, -60, 80) request = QgsFeatureRequest().setFilterFids([ids[3], ids[4]]).setFilterRect(extent) result = set([f['pk'] for f in self.source.getFeatures(request)]) all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) expected = [4] assert set( expected) == result, 'Expected {} and got {} when testing for combination of filterRect and expression'.format( set(expected), result) self.assertTrue(all_valid) # shouldn't matter what order this is done in request = QgsFeatureRequest().setFilterRect(extent).setFilterFids([ids[3], ids[4]]) result = set([f['pk'] for f in self.source.getFeatures(request)]) all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) expected = [4] assert set( expected) == result, 'Expected {} and got {} when testing for combination of filterRect and expression'.format( set(expected), result) self.assertTrue(all_valid) # test that results match QgsFeatureRequest.acceptFeature for f in self.source.getFeatures(): self.assertEqual(request.acceptFeature(f), f['pk'] in expected) def testGetFeaturesDestinationCrs(self): request = QgsFeatureRequest().setDestinationCrs(QgsCoordinateReferenceSystem('epsg:3785'), QgsProject.instance().transformContext()) features = {f['pk']: f for f in self.source.getFeatures(request)} # test that features have been reprojected self.assertAlmostEqual(features[1].geometry().constGet().x(), -7829322, -5) self.assertAlmostEqual(features[1].geometry().constGet().y(), 9967753, -5) self.assertAlmostEqual(features[2].geometry().constGet().x(), -7591989, -5) self.assertAlmostEqual(features[2].geometry().constGet().y(), 11334232, -5) self.assertFalse(features[3].hasGeometry()) self.assertAlmostEqual(features[4].geometry().constGet().x(), -7271389, -5) self.assertAlmostEqual(features[4].geometry().constGet().y(), 14531322, -5) self.assertAlmostEqual(features[5].geometry().constGet().x(), -7917376, -5) self.assertAlmostEqual(features[5].geometry().constGet().y(), 14493008, -5) # when destination crs is set, filter rect should be in destination crs rect = QgsRectangle(-7650000, 10500000, -7200000, 15000000) request = QgsFeatureRequest().setDestinationCrs(QgsCoordinateReferenceSystem('epsg:3785'), QgsProject.instance().transformContext()).setFilterRect(rect) features = {f['pk']: f for f in self.source.getFeatures(request)} self.assertEqual(set(features.keys()), {2, 4}) # test that features have been reprojected self.assertAlmostEqual(features[2].geometry().constGet().x(), -7591989, -5) self.assertAlmostEqual(features[2].geometry().constGet().y(), 11334232, -5) self.assertAlmostEqual(features[4].geometry().constGet().x(), -7271389, -5) self.assertAlmostEqual(features[4].geometry().constGet().y(), 14531322, -5) # bad rect for transform rect = QgsRectangle(-99999999999, 99999999999, -99999999998, 99999999998) request = QgsFeatureRequest().setDestinationCrs(QgsCoordinateReferenceSystem('epsg:28356'), QgsProject.instance().transformContext()).setFilterRect(rect) features = [f for f in self.source.getFeatures(request)] self.assertFalse(features) def testGetFeaturesLimit(self): it = self.source.getFeatures(QgsFeatureRequest().setLimit(2)) features = [f['pk'] for f in it] assert len(features) == 2, 'Expected two features, got {} instead'.format(len(features)) # fetch one feature feature = QgsFeature() assert not it.nextFeature(feature), 'Expected no feature after limit, got one' it.rewind() features = [f['pk'] for f in it] assert len(features) == 2, 'Expected two features after rewind, got {} instead'.format(len(features)) it.rewind() assert it.nextFeature(feature), 'Expected feature after rewind, got none' it.rewind() features = [f['pk'] for f in it] assert len(features) == 2, 'Expected two features after rewind, got {} instead'.format(len(features)) # test with expression, both with and without compilation try: self.disableCompiler() except AttributeError: pass it = self.source.getFeatures(QgsFeatureRequest().setLimit(2).setFilterExpression('cnt <= 100')) features = [f['pk'] for f in it] assert set(features) == set([1, 5]), 'Expected [1,5] for expression and feature limit, Got {} instead'.format( features) try: self.enableCompiler() except AttributeError: pass it = self.source.getFeatures(QgsFeatureRequest().setLimit(2).setFilterExpression('cnt <= 100')) features = [f['pk'] for f in it] assert set(features) == set([1, 5]), 'Expected [1,5] for expression and feature limit, Got {} instead'.format( features) # limit to more features than exist it = self.source.getFeatures(QgsFeatureRequest().setLimit(3).setFilterExpression('cnt <= 100')) features = [f['pk'] for f in it] assert set(features) == set([1, 5]), 'Expected [1,5] for expression and feature limit, Got {} instead'.format( features) # limit to less features than possible it = self.source.getFeatures(QgsFeatureRequest().setLimit(1).setFilterExpression('cnt <= 100')) features = [f['pk'] for f in it] assert 1 in features or 5 in features, 'Expected either 1 or 5 for expression and feature limit, Got {} instead'.format( features) def testClosedIterators(self): """ Test behavior of closed iterators """ # Test retrieving feature after closing iterator f_it = self.source.getFeatures(QgsFeatureRequest()) fet = QgsFeature() assert f_it.nextFeature(fet), 'Could not fetch feature' assert fet.isValid(), 'Feature is not valid' assert f_it.close(), 'Could not close iterator' self.assertFalse(f_it.nextFeature(fet), 'Fetched feature after iterator closed, expected nextFeature() to return False') self.assertFalse(fet.isValid(), 'Valid feature fetched from closed iterator, should be invalid') # Test rewinding closed iterator self.assertFalse(f_it.rewind(), 'Rewinding closed iterator successful, should not be allowed') def testGetFeaturesSubsetAttributes(self): """ Test that expected results are returned when using subsets of attributes """ tests = {'pk': set([1, 2, 3, 4, 5]), 'cnt': set([-200, 300, 100, 200, 400]), 'name': set(['Pear', 'Orange', 'Apple', 'Honey', NULL]), 'name2': set(['NuLl', 'PEaR', 'oranGe', 'Apple', 'Honey']), 'dt': set([NULL, '2021-05-04 13:13:14' if self.treat_datetime_as_string() else QDateTime(2021, 5, 4, 13, 13, 14) if not self.treat_datetime_as_string() else '2021-05-04 13:13:14', '2020-05-04 12:14:14' if self.treat_datetime_as_string() else QDateTime(2020, 5, 4, 12, 14, 14) if not self.treat_datetime_as_string() else '2020-05-04 12:14:14', '2020-05-04 12:13:14' if self.treat_datetime_as_string() else QDateTime(2020, 5, 4, 12, 13, 14) if not self.treat_datetime_as_string() else '2020-05-04 12:13:14', '2020-05-03 12:13:14' if self.treat_datetime_as_string() else QDateTime(2020, 5, 3, 12, 13, 14) if not self.treat_datetime_as_string() else '2020-05-03 12:13:14']), 'date': set([NULL, '2020-05-02' if self.treat_date_as_string() else QDate(2020, 5, 2) if not self.treat_date_as_datetime() else QDateTime(2020, 5, 2, 0, 0, 0), '2020-05-03' if self.treat_date_as_string() else QDate(2020, 5, 3) if not self.treat_date_as_datetime() else QDateTime(2020, 5, 3, 0, 0, 0), '2020-05-04' if self.treat_date_as_string() else QDate(2020, 5, 4) if not self.treat_date_as_datetime() else QDateTime(2020, 5, 4, 0, 0, 0), '2021-05-04' if self.treat_date_as_string() else QDate(2021, 5, 4) if not self.treat_date_as_datetime() else QDateTime(2021, 5, 4, 0, 0, 0)]), 'time': set([QTime(12, 13, 1) if not self.treat_time_as_string() else '12:13:01', QTime(12, 14, 14) if not self.treat_time_as_string() else '12:14:14', QTime(12, 13, 14) if not self.treat_time_as_string() else '12:13:14', QTime(13, 13, 14) if not self.treat_time_as_string() else '13:13:14', NULL])} for field, expected in list(tests.items()): request = QgsFeatureRequest().setSubsetOfAttributes([field], self.source.fields()) result = set([f[field] for f in self.source.getFeatures(request)]) all_valid = (all(f.isValid() for f in self.source.getFeatures(request))) self.assertEqual(result, expected, 'Expected {}, got {}'.format(expected, result)) self.assertTrue(all_valid) def testGetFeaturesSubsetAttributes2(self): """ Test that other fields are NULL when fetching subsets of attributes """ for field_to_fetch in ['pk', 'cnt', 'name', 'name2', 'dt', 'date', 'time']: for f in self.source.getFeatures( QgsFeatureRequest().setSubsetOfAttributes([field_to_fetch], self.source.fields())): # Check that all other fields are NULL and force name to lower-case for other_field in [field.name() for field in self.source.fields() if field.name().lower() != field_to_fetch]: if other_field == 'pk' or other_field == 'PK': # skip checking the primary key field, as it may be validly fetched by providers to use as feature id continue self.assertEqual(f[other_field], NULL, 'Value for field "{}" was present when it should not have been fetched by request'.format( other_field)) def testGetFeaturesNoGeometry(self): """ Test that no geometry is present when fetching features without geometry""" for f in self.source.getFeatures(QgsFeatureRequest().setFlags(QgsFeatureRequest.NoGeometry)): self.assertFalse(f.hasGeometry(), 'Expected no geometry, got one') self.assertTrue(f.isValid()) def testGetFeaturesWithGeometry(self): """ Test that geometry is present when fetching features without setting NoGeometry flag""" for f in self.source.getFeatures(QgsFeatureRequest()): if f['pk'] == 3: # no geometry for this feature continue assert f.hasGeometry(), 'Expected geometry, got none' self.assertTrue(f.isValid()) def testUniqueValues(self): self.assertEqual(set(self.source.uniqueValues(self.source.fields().lookupField('cnt'))), set([-200, 100, 200, 300, 400])) assert set(['Apple', 'Honey', 'Orange', 'Pear', NULL]) == set( self.source.uniqueValues(self.source.fields().lookupField('name'))), 'Got {}'.format( set(self.source.uniqueValues(self.source.fields().lookupField('name')))) if self.treat_datetime_as_string(): self.assertEqual(set(self.source.uniqueValues(self.source.fields().lookupField('dt'))), set(['2021-05-04 13:13:14', '2020-05-04 12:14:14', '2020-05-04 12:13:14', '2020-05-03 12:13:14', NULL])) else: self.assertEqual(set(self.source.uniqueValues(self.source.fields().lookupField('dt'))), set([QDateTime(2021, 5, 4, 13, 13, 14), QDateTime(2020, 5, 4, 12, 14, 14), QDateTime(2020, 5, 4, 12, 13, 14), QDateTime(2020, 5, 3, 12, 13, 14), NULL])) if self.treat_date_as_string(): self.assertEqual(set(self.source.uniqueValues(self.source.fields().lookupField('date'))), set(['2020-05-03', '2020-05-04', '2021-05-04', '2020-05-02', NULL])) elif self.treat_date_as_datetime(): self.assertEqual(set(self.source.uniqueValues(self.source.fields().lookupField('date'))), set([QDateTime(2020, 5, 3, 0, 0, 0), QDateTime(2020, 5, 4, 0, 0, 0), QDateTime(2021, 5, 4, 0, 0, 0), QDateTime(2020, 5, 2, 0, 0, 0), NULL])) else: self.assertEqual(set(self.source.uniqueValues(self.source.fields().lookupField('date'))), set([QDate(2020, 5, 3), QDate(2020, 5, 4), QDate(2021, 5, 4), QDate(2020, 5, 2), NULL])) if self.treat_time_as_string(): self.assertEqual(set(self.source.uniqueValues(self.source.fields().lookupField('time'))), set(['12:14:14', '13:13:14', '12:13:14', '12:13:01', NULL])) else: self.assertEqual(set(self.source.uniqueValues(self.source.fields().lookupField('time'))), set([QTime(12, 14, 14), QTime(13, 13, 14), QTime(12, 13, 14), QTime(12, 13, 1), NULL])) def testMinimumValue(self): self.assertEqual(self.source.minimumValue(self.source.fields().lookupField('cnt')), -200) self.assertEqual(self.source.minimumValue(self.source.fields().lookupField('name')), 'Apple') if self.treat_datetime_as_string(): self.assertEqual(self.source.minimumValue(self.source.fields().lookupField('dt')), '2020-05-03 12:13:14') else: self.assertEqual(self.source.minimumValue(self.source.fields().lookupField('dt')), QDateTime(QDate(2020, 5, 3), QTime(12, 13, 14))) if self.treat_date_as_string(): self.assertEqual(self.source.minimumValue(self.source.fields().lookupField('date')), '2020-05-02') elif not self.treat_date_as_datetime(): self.assertEqual(self.source.minimumValue(self.source.fields().lookupField('date')), QDate(2020, 5, 2)) else: self.assertEqual(self.source.minimumValue(self.source.fields().lookupField('date')), QDateTime(2020, 5, 2, 0, 0, 0)) if not self.treat_time_as_string(): self.assertEqual(self.source.minimumValue(self.source.fields().lookupField('time')), QTime(12, 13, 1)) else: self.assertEqual(self.source.minimumValue(self.source.fields().lookupField('time')), '12:13:01') def testMaximumValue(self): self.assertEqual(self.source.maximumValue(self.source.fields().lookupField('cnt')), 400) self.assertEqual(self.source.maximumValue(self.source.fields().lookupField('name')), 'Pear') if not self.treat_datetime_as_string(): self.assertEqual(self.source.maximumValue(self.source.fields().lookupField('dt')), QDateTime(QDate(2021, 5, 4), QTime(13, 13, 14))) else: self.assertEqual(self.source.maximumValue(self.source.fields().lookupField('dt')), '2021-05-04 13:13:14') if self.treat_date_as_string(): self.assertEqual(self.source.maximumValue(self.source.fields().lookupField('date')), '2021-05-04') elif not self.treat_date_as_datetime(): self.assertEqual(self.source.maximumValue(self.source.fields().lookupField('date')), QDate(2021, 5, 4)) else: self.assertEqual(self.source.maximumValue(self.source.fields().lookupField('date')), QDateTime(2021, 5, 4, 0, 0, 0)) if not self.treat_time_as_string(): self.assertEqual(self.source.maximumValue(self.source.fields().lookupField('time')), QTime(13, 13, 14)) else: self.assertEqual(self.source.maximumValue(self.source.fields().lookupField('time')), '13:13:14') def testAllFeatureIds(self): ids = set([f.id() for f in self.source.getFeatures()]) self.assertEqual(set(self.source.allFeatureIds()), ids) def testSubsetOfAttributesWithFilterExprWithNonExistingColumn(self): """ Test fix for https://github.com/qgis/QGIS/issues/33878 """ request = QgsFeatureRequest().setSubsetOfAttributes([0]) request.setFilterExpression("non_existing = 1") features = [f for f in self.source.getFeatures(request)] self.assertEqual(len(features), 0)

gpl-2.0

shangwuhencc/scikit-learn

examples/cluster/plot_cluster_iris.py

347

2593

#!/usr/bin/python # -*- coding: utf-8 -*- """ ========================================================= K-means Clustering ========================================================= The plots display firstly what a K-means algorithm would yield using three clusters. It is then shown what the effect of a bad initialization is on the classification process: By setting n_init to only 1 (default is 10), the amount of times that the algorithm will be run with different centroid seeds is reduced. The next plot displays what using eight clusters would deliver and finally the ground truth. """ print(__doc__) # Code source: Gaël Varoquaux # Modified for documentation by Jaques Grobler # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from sklearn.cluster import KMeans from sklearn import datasets np.random.seed(5) centers = [[1, 1], [-1, -1], [1, -1]] iris = datasets.load_iris() X = iris.data y = iris.target estimators = {'k_means_iris_3': KMeans(n_clusters=3), 'k_means_iris_8': KMeans(n_clusters=8), 'k_means_iris_bad_init': KMeans(n_clusters=3, n_init=1, init='random')} fignum = 1 for name, est in estimators.items(): fig = plt.figure(fignum, figsize=(4, 3)) plt.clf() ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134) plt.cla() est.fit(X) labels = est.labels_ ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=labels.astype(np.float)) ax.w_xaxis.set_ticklabels([]) ax.w_yaxis.set_ticklabels([]) ax.w_zaxis.set_ticklabels([]) ax.set_xlabel('Petal width') ax.set_ylabel('Sepal length') ax.set_zlabel('Petal length') fignum = fignum + 1 # Plot the ground truth fig = plt.figure(fignum, figsize=(4, 3)) plt.clf() ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134) plt.cla() for name, label in [('Setosa', 0), ('Versicolour', 1), ('Virginica', 2)]: ax.text3D(X[y == label, 3].mean(), X[y == label, 0].mean() + 1.5, X[y == label, 2].mean(), name, horizontalalignment='center', bbox=dict(alpha=.5, edgecolor='w', facecolor='w')) # Reorder the labels to have colors matching the cluster results y = np.choose(y, [1, 2, 0]).astype(np.float) ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y) ax.w_xaxis.set_ticklabels([]) ax.w_yaxis.set_ticklabels([]) ax.w_zaxis.set_ticklabels([]) ax.set_xlabel('Petal width') ax.set_ylabel('Sepal length') ax.set_zlabel('Petal length') plt.show()

bsd-3-clause

lakshayg/tensorflow

tensorflow/contrib/boosted_trees/examples/mnist.py

61

5840

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== r"""Demonstrates multiclass MNIST TF Boosted trees example. This example demonstrates how to run experiments with TF Boosted Trees on a MNIST dataset. We are using layer by layer boosting with diagonal hessian strategy for multiclass handling, and cross entropy loss. Example Usage: python tensorflow/contrib/boosted_trees/examples/mnist.py \ --output_dir="/tmp/mnist" --depth=4 --learning_rate=0.3 --batch_size=60000 \ --examples_per_layer=60000 --eval_batch_size=10000 --num_eval_steps=1 \ --num_trees=10 --l2=1 --vmodule=training_ops=1 When training is done, accuracy on eval data is reported. Point tensorboard to the directory for the run to see how the training progresses: tensorboard --logdir=/tmp/mnist """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import sys import numpy as np import tensorflow as tf from tensorflow.contrib.boosted_trees.estimator_batch.estimator import GradientBoostedDecisionTreeClassifier from tensorflow.contrib.boosted_trees.proto import learner_pb2 from tensorflow.contrib.learn import learn_runner def get_input_fn(dataset_split, batch_size, capacity=10000, min_after_dequeue=3000): """Input function over MNIST data.""" def _input_fn(): """Prepare features and labels.""" images_batch, labels_batch = tf.train.shuffle_batch( tensors=[dataset_split.images, dataset_split.labels.astype(np.int32)], batch_size=batch_size, capacity=capacity, min_after_dequeue=min_after_dequeue, enqueue_many=True, num_threads=4) features_map = {"images": images_batch} return features_map, labels_batch return _input_fn # Main config - creates a TF Boosted Trees Estimator based on flags. def _get_tfbt(output_dir): """Configures TF Boosted Trees estimator based on flags.""" learner_config = learner_pb2.LearnerConfig() num_classes = 10 learner_config.learning_rate_tuner.fixed.learning_rate = FLAGS.learning_rate learner_config.num_classes = num_classes learner_config.regularization.l1 = 0.0 learner_config.regularization.l2 = FLAGS.l2 / FLAGS.examples_per_layer learner_config.constraints.max_tree_depth = FLAGS.depth growing_mode = learner_pb2.LearnerConfig.LAYER_BY_LAYER learner_config.growing_mode = growing_mode run_config = tf.contrib.learn.RunConfig(save_checkpoints_secs=300) learner_config.multi_class_strategy = ( learner_pb2.LearnerConfig.DIAGONAL_HESSIAN) # Create a TF Boosted trees estimator that can take in custom loss. estimator = GradientBoostedDecisionTreeClassifier( learner_config=learner_config, n_classes=num_classes, examples_per_layer=FLAGS.examples_per_layer, model_dir=output_dir, num_trees=FLAGS.num_trees, center_bias=False, config=run_config) return estimator def _make_experiment_fn(output_dir): """Creates experiment for gradient boosted decision trees.""" data = tf.contrib.learn.datasets.mnist.load_mnist() train_input_fn = get_input_fn(data.train, FLAGS.batch_size) eval_input_fn = get_input_fn(data.validation, FLAGS.eval_batch_size) return tf.contrib.learn.Experiment( estimator=_get_tfbt(output_dir), train_input_fn=train_input_fn, eval_input_fn=eval_input_fn, train_steps=None, eval_steps=FLAGS.num_eval_steps, eval_metrics=None) def main(unused_argv): learn_runner.run( experiment_fn=_make_experiment_fn, output_dir=FLAGS.output_dir, schedule="train_and_evaluate") if __name__ == "__main__": tf.logging.set_verbosity(tf.logging.INFO) parser = argparse.ArgumentParser() # Define the list of flags that users can change. parser.add_argument( "--output_dir", type=str, required=True, help="Choose the dir for the output.") parser.add_argument( "--batch_size", type=int, default=1000, help="The batch size for reading data.") parser.add_argument( "--eval_batch_size", type=int, default=1000, help="Size of the batch for eval.") parser.add_argument( "--num_eval_steps", type=int, default=1, help="The number of steps to run evaluation for.") # Flags for gradient boosted trees config. parser.add_argument( "--depth", type=int, default=4, help="Maximum depth of weak learners.") parser.add_argument( "--l2", type=float, default=1.0, help="l2 regularization per batch.") parser.add_argument( "--learning_rate", type=float, default=0.1, help="Learning rate (shrinkage weight) with which each new tree is added." ) parser.add_argument( "--examples_per_layer", type=int, default=1000, help="Number of examples to accumulate stats for per layer.") parser.add_argument( "--num_trees", type=int, default=None, required=True, help="Number of trees to grow before stopping.") FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

apache-2.0

aalmah/pylearn2

pylearn2/utils/utlc.py

49

7347

"""Several utilities for experimenting upon utlc datasets""" # Standard library imports import logging import os import inspect import zipfile from tempfile import TemporaryFile # Third-party imports import numpy import theano from pylearn2.datasets.utlc import load_ndarray_dataset, load_sparse_dataset from pylearn2.utils import subdict, sharedX logger = logging.getLogger(__name__) ################################################## # Shortcuts and auxiliary functions ################################################## def getboth(dict1, dict2, key, default=None): """ Try to retrieve key from dict1 if exists, otherwise try with dict2. If the key is not found in any of them, raise an exception. Parameters ---------- dict1 : dict WRITEME dict2 : dict WRITEME key : WRITEME default : WRITEME Returns ------- WRITEME """ try: return dict1[key] except KeyError: if default is None: return dict2[key] else: return dict2.get(key, default) ################################################## # Datasets loading and contest facilities ################################################## def load_data(conf): """ Loads a specified dataset according to the parameters in the dictionary Parameters ---------- conf : WRITEME Returns ------- WRITEME """ logger.info('... loading dataset') # Special case for sparse format if conf.get('sparse', False): expected = inspect.getargspec(load_sparse_dataset)[0][1:] data = load_sparse_dataset(conf['dataset'], **subdict(conf, expected)) valid, test = data[1:3] # Sparse TERRY data on LISA servers contains an extra null first row in # valid and test subsets. if conf['dataset'] == 'terry': valid = valid[1:] test = test[1:] assert valid.shape[0] == test.shape[0] == 4096, \ 'Sparse TERRY data loaded has wrong number of examples' if len(data) == 3: return [data[0], valid, test] else: return [data[0], valid, test, data[3]] # Load as the usual ndarray expected = inspect.getargspec(load_ndarray_dataset)[0][1:] data = load_ndarray_dataset(conf['dataset'], **subdict(conf, expected)) # Special case for on-the-fly normalization if conf.get('normalize_on_the_fly', False): return data # Allocate shared variables def shared_dataset(data_x): """Function that loads the dataset into shared variables""" if conf.get('normalize', True): return sharedX(data_x, borrow=True) else: return theano.shared(theano._asarray(data_x), borrow=True) return map(shared_dataset, data) def save_submission(conf, valid_repr, test_repr): """ Create a submission file given a configuration dictionary and a representation for valid and test. Parameters ---------- conf : WRITEME valid_repr : WRITEME test_repr : WRITEME """ logger.info('... creating zipfile') # Ensure the given directory is correct submit_dir = conf['savedir'] if not os.path.exists(submit_dir): os.makedirs(submit_dir) elif not os.path.isdir(submit_dir): raise IOError('savedir %s is not a directory' % submit_dir) basename = os.path.join(submit_dir, conf['dataset'] + '_' + conf['expname']) # If there are too much features, outputs kernel matrices if (valid_repr.shape[1] > valid_repr.shape[0]): valid_repr = numpy.dot(valid_repr, valid_repr.T) test_repr = numpy.dot(test_repr, test_repr.T) # Quantitize data valid_repr = numpy.floor((valid_repr / valid_repr.max())*999) test_repr = numpy.floor((test_repr / test_repr.max())*999) # Store the representations in two temporary files valid_file = TemporaryFile() test_file = TemporaryFile() numpy.savetxt(valid_file, valid_repr, fmt="%.3f") numpy.savetxt(test_file, test_repr, fmt="%.3f") # Reread those files and put them together in a .zip valid_file.seek(0) test_file.seek(0) submission = zipfile.ZipFile(basename + ".zip", "w", compression=zipfile.ZIP_DEFLATED) submission.writestr(basename + '_valid.prepro', valid_file.read()) submission.writestr(basename + '_final.prepro', test_file.read()) submission.close() valid_file.close() test_file.close() def create_submission(conf, transform_valid, transform_test=None, features=None): """ Create a submission file given a configuration dictionary and a computation function. Note that it always reload the datasets to ensure valid & test are not permuted. Parameters ---------- conf : WRITEME transform_valid : WRITEME transform_test : WRITEME features : WRITEME """ if transform_test is None: transform_test = transform_valid # Load the dataset, without permuting valid and test kwargs = subdict(conf, ['dataset', 'normalize', 'normalize_on_the_fly', 'sparse']) kwargs.update(randomize_valid=False, randomize_test=False) valid_set, test_set = load_data(kwargs)[1:3] # Sparse datasets are not stored as Theano shared vars. if not conf.get('sparse', False): valid_set = valid_set.get_value(borrow=True) test_set = test_set.get_value(borrow=True) # Prefilter features, if needed. if features is not None: valid_set = valid_set[:, features] test_set = test_set[:, features] # Valid and test representations valid_repr = transform_valid(valid_set) test_repr = transform_test(test_set) # Convert into text info save_submission(conf, valid_repr, test_repr) ################################################## # Proxies for representation evaluations ################################################## def compute_alc(valid_repr, test_repr): """ Returns the ALC of the valid set VS test set Note: This proxy won't work in the case of transductive learning (This is an assumption) but it seems to be a good proxy in the normal case (i.e only train on training set) Parameters ---------- valid_repr : WRITEME test_repr : WRITEME Returns ------- WRITEME """ # Concatenate the sets, and give different one hot labels for valid and test n_valid = valid_repr.shape[0] n_test = test_repr.shape[0] _labvalid = numpy.hstack((numpy.ones((n_valid, 1)), numpy.zeros((n_valid, 1)))) _labtest = numpy.hstack((numpy.zeros((n_test, 1)), numpy.ones((n_test, 1)))) dataset = numpy.vstack((valid_repr, test_repr)) label = numpy.vstack((_labvalid, _labtest)) logger.info('... computing the ALC') raise NotImplementedError("This got broken by embed no longer being " "where it used to be (if it even still exists, I haven't " "looked for it)") # return embed.score(dataset, label) def lookup_alc(data, transform): """ .. todo:: WRITEME """ valid_repr = transform(data[1].get_value(borrow=True)) test_repr = transform(data[2].get_value(borrow=True)) return compute_alc(valid_repr, test_repr)

bsd-3-clause

ephes/scikit-learn

examples/linear_model/plot_ols_3d.py

347

2040

#!/usr/bin/python # -*- coding: utf-8 -*- """ ========================================================= Sparsity Example: Fitting only features 1 and 2 ========================================================= Features 1 and 2 of the diabetes-dataset are fitted and plotted below. It illustrates that although feature 2 has a strong coefficient on the full model, it does not give us much regarding `y` when compared to just feature 1 """ print(__doc__) # Code source: Gaël Varoquaux # Modified for documentation by Jaques Grobler # License: BSD 3 clause import matplotlib.pyplot as plt import numpy as np from mpl_toolkits.mplot3d import Axes3D from sklearn import datasets, linear_model diabetes = datasets.load_diabetes() indices = (0, 1) X_train = diabetes.data[:-20, indices] X_test = diabetes.data[-20:, indices] y_train = diabetes.target[:-20] y_test = diabetes.target[-20:] ols = linear_model.LinearRegression() ols.fit(X_train, y_train) ############################################################################### # Plot the figure def plot_figs(fig_num, elev, azim, X_train, clf): fig = plt.figure(fig_num, figsize=(4, 3)) plt.clf() ax = Axes3D(fig, elev=elev, azim=azim) ax.scatter(X_train[:, 0], X_train[:, 1], y_train, c='k', marker='+') ax.plot_surface(np.array([[-.1, -.1], [.15, .15]]), np.array([[-.1, .15], [-.1, .15]]), clf.predict(np.array([[-.1, -.1, .15, .15], [-.1, .15, -.1, .15]]).T ).reshape((2, 2)), alpha=.5) ax.set_xlabel('X_1') ax.set_ylabel('X_2') ax.set_zlabel('Y') ax.w_xaxis.set_ticklabels([]) ax.w_yaxis.set_ticklabels([]) ax.w_zaxis.set_ticklabels([]) #Generate the three different figures from different views elev = 43.5 azim = -110 plot_figs(1, elev, azim, X_train, ols) elev = -.5 azim = 0 plot_figs(2, elev, azim, X_train, ols) elev = -.5 azim = 90 plot_figs(3, elev, azim, X_train, ols) plt.show()

bsd-3-clause

roxyboy/scikit-learn

sklearn/utils/multiclass.py

83

12343

# Author: Arnaud Joly, Joel Nothman, Hamzeh Alsalhi # # License: BSD 3 clause """ Multi-class / multi-label utility function ========================================== """ from __future__ import division from collections import Sequence from itertools import chain from scipy.sparse import issparse from scipy.sparse.base import spmatrix from scipy.sparse import dok_matrix from scipy.sparse import lil_matrix import numpy as np from ..externals.six import string_types from .validation import check_array from ..utils.fixes import bincount def _unique_multiclass(y): if hasattr(y, '__array__'): return np.unique(np.asarray(y)) else: return set(y) def _unique_indicator(y): return np.arange(check_array(y, ['csr', 'csc', 'coo']).shape[1]) _FN_UNIQUE_LABELS = { 'binary': _unique_multiclass, 'multiclass': _unique_multiclass, 'multilabel-indicator': _unique_indicator, } def unique_labels(*ys): """Extract an ordered array of unique labels We don't allow: - mix of multilabel and multiclass (single label) targets - mix of label indicator matrix and anything else, because there are no explicit labels) - mix of label indicator matrices of different sizes - mix of string and integer labels At the moment, we also don't allow "multiclass-multioutput" input type. Parameters ---------- *ys : array-likes, Returns ------- out : numpy array of shape [n_unique_labels] An ordered array of unique labels. Examples -------- >>> from sklearn.utils.multiclass import unique_labels >>> unique_labels([3, 5, 5, 5, 7, 7]) array([3, 5, 7]) >>> unique_labels([1, 2, 3, 4], [2, 2, 3, 4]) array([1, 2, 3, 4]) >>> unique_labels([1, 2, 10], [5, 11]) array([ 1, 2, 5, 10, 11]) """ if not ys: raise ValueError('No argument has been passed.') # Check that we don't mix label format ys_types = set(type_of_target(x) for x in ys) if ys_types == set(["binary", "multiclass"]): ys_types = set(["multiclass"]) if len(ys_types) > 1: raise ValueError("Mix type of y not allowed, got types %s" % ys_types) label_type = ys_types.pop() # Check consistency for the indicator format if (label_type == "multilabel-indicator" and len(set(check_array(y, ['csr', 'csc', 'coo']).shape[1] for y in ys)) > 1): raise ValueError("Multi-label binary indicator input with " "different numbers of labels") # Get the unique set of labels _unique_labels = _FN_UNIQUE_LABELS.get(label_type, None) if not _unique_labels: raise ValueError("Unknown label type: %s" % repr(ys)) ys_labels = set(chain.from_iterable(_unique_labels(y) for y in ys)) # Check that we don't mix string type with number type if (len(set(isinstance(label, string_types) for label in ys_labels)) > 1): raise ValueError("Mix of label input types (string and number)") return np.array(sorted(ys_labels)) def _is_integral_float(y): return y.dtype.kind == 'f' and np.all(y.astype(int) == y) def is_multilabel(y): """ Check if ``y`` is in a multilabel format. Parameters ---------- y : numpy array of shape [n_samples] Target values. Returns ------- out : bool, Return ``True``, if ``y`` is in a multilabel format, else ```False``. Examples -------- >>> import numpy as np >>> from sklearn.utils.multiclass import is_multilabel >>> is_multilabel([0, 1, 0, 1]) False >>> is_multilabel([[1], [0, 2], []]) False >>> is_multilabel(np.array([[1, 0], [0, 0]])) True >>> is_multilabel(np.array([[1], [0], [0]])) False >>> is_multilabel(np.array([[1, 0, 0]])) True """ if hasattr(y, '__array__'): y = np.asarray(y) if not (hasattr(y, "shape") and y.ndim == 2 and y.shape[1] > 1): return False if issparse(y): if isinstance(y, (dok_matrix, lil_matrix)): y = y.tocsr() return (len(y.data) == 0 or np.ptp(y.data) == 0 and (y.dtype.kind in 'biu' or # bool, int, uint _is_integral_float(np.unique(y.data)))) else: labels = np.unique(y) return len(labels) < 3 and (y.dtype.kind in 'biu' or # bool, int, uint _is_integral_float(labels)) def type_of_target(y): """Determine the type of data indicated by target `y` Parameters ---------- y : array-like Returns ------- target_type : string One of: * 'continuous': `y` is an array-like of floats that are not all integers, and is 1d or a column vector. * 'continuous-multioutput': `y` is a 2d array of floats that are not all integers, and both dimensions are of size > 1. * 'binary': `y` contains <= 2 discrete values and is 1d or a column vector. * 'multiclass': `y` contains more than two discrete values, is not a sequence of sequences, and is 1d or a column vector. * 'multiclass-multioutput': `y` is a 2d array that contains more than two discrete values, is not a sequence of sequences, and both dimensions are of size > 1. * 'multilabel-indicator': `y` is a label indicator matrix, an array of two dimensions with at least two columns, and at most 2 unique values. * 'unknown': `y` is array-like but none of the above, such as a 3d array, sequence of sequences, or an array of non-sequence objects. Examples -------- >>> import numpy as np >>> type_of_target([0.1, 0.6]) 'continuous' >>> type_of_target([1, -1, -1, 1]) 'binary' >>> type_of_target(['a', 'b', 'a']) 'binary' >>> type_of_target([1.0, 2.0]) 'binary' >>> type_of_target([1, 0, 2]) 'multiclass' >>> type_of_target([1.0, 0.0, 3.0]) 'multiclass' >>> type_of_target(['a', 'b', 'c']) 'multiclass' >>> type_of_target(np.array([[1, 2], [3, 1]])) 'multiclass-multioutput' >>> type_of_target([[1, 2]]) 'multiclass-multioutput' >>> type_of_target(np.array([[1.5, 2.0], [3.0, 1.6]])) 'continuous-multioutput' >>> type_of_target(np.array([[0, 1], [1, 1]])) 'multilabel-indicator' """ valid = ((isinstance(y, (Sequence, spmatrix)) or hasattr(y, '__array__')) and not isinstance(y, string_types)) if not valid: raise ValueError('Expected array-like (array or non-string sequence), ' 'got %r' % y) if is_multilabel(y): return 'multilabel-indicator' try: y = np.asarray(y) except ValueError: # Known to fail in numpy 1.3 for array of arrays return 'unknown' # The old sequence of sequences format try: if (not hasattr(y[0], '__array__') and isinstance(y[0], Sequence) and not isinstance(y[0], string_types)): raise ValueError('You appear to be using a legacy multi-label data' ' representation. Sequence of sequences are no' ' longer supported; use a binary array or sparse' ' matrix instead.') except IndexError: pass # Invalid inputs if y.ndim > 2 or (y.dtype == object and len(y) and not isinstance(y.flat[0], string_types)): return 'unknown' # [[[1, 2]]] or [obj_1] and not ["label_1"] if y.ndim == 2 and y.shape[1] == 0: return 'unknown' # [[]] if y.ndim == 2 and y.shape[1] > 1: suffix = "-multioutput" # [[1, 2], [1, 2]] else: suffix = "" # [1, 2, 3] or [[1], [2], [3]] # check float and contains non-integer float values if y.dtype.kind == 'f' and np.any(y != y.astype(int)): # [.1, .2, 3] or [[.1, .2, 3]] or [[1., .2]] and not [1., 2., 3.] return 'continuous' + suffix if (len(np.unique(y)) > 2) or (y.ndim >= 2 and len(y[0]) > 1): return 'multiclass' + suffix # [1, 2, 3] or [[1., 2., 3]] or [[1, 2]] else: return 'binary' # [1, 2] or [["a"], ["b"]] def _check_partial_fit_first_call(clf, classes=None): """Private helper function for factorizing common classes param logic Estimators that implement the ``partial_fit`` API need to be provided with the list of possible classes at the first call to partial_fit. Subsequent calls to partial_fit should check that ``classes`` is still consistent with a previous value of ``clf.classes_`` when provided. This function returns True if it detects that this was the first call to ``partial_fit`` on ``clf``. In that case the ``classes_`` attribute is also set on ``clf``. """ if getattr(clf, 'classes_', None) is None and classes is None: raise ValueError("classes must be passed on the first call " "to partial_fit.") elif classes is not None: if getattr(clf, 'classes_', None) is not None: if not np.all(clf.classes_ == unique_labels(classes)): raise ValueError( "`classes=%r` is not the same as on last call " "to partial_fit, was: %r" % (classes, clf.classes_)) else: # This is the first call to partial_fit clf.classes_ = unique_labels(classes) return True # classes is None and clf.classes_ has already previously been set: # nothing to do return False def class_distribution(y, sample_weight=None): """Compute class priors from multioutput-multiclass target data Parameters ---------- y : array like or sparse matrix of size (n_samples, n_outputs) The labels for each example. sample_weight : array-like of shape = (n_samples,), optional Sample weights. Returns ------- classes : list of size n_outputs of arrays of size (n_classes,) List of classes for each column. n_classes : list of integrs of size n_outputs Number of classes in each column class_prior : list of size n_outputs of arrays of size (n_classes,) Class distribution of each column. """ classes = [] n_classes = [] class_prior = [] n_samples, n_outputs = y.shape if issparse(y): y = y.tocsc() y_nnz = np.diff(y.indptr) for k in range(n_outputs): col_nonzero = y.indices[y.indptr[k]:y.indptr[k + 1]] # separate sample weights for zero and non-zero elements if sample_weight is not None: nz_samp_weight = np.asarray(sample_weight)[col_nonzero] zeros_samp_weight_sum = (np.sum(sample_weight) - np.sum(nz_samp_weight)) else: nz_samp_weight = None zeros_samp_weight_sum = y.shape[0] - y_nnz[k] classes_k, y_k = np.unique(y.data[y.indptr[k]:y.indptr[k + 1]], return_inverse=True) class_prior_k = bincount(y_k, weights=nz_samp_weight) # An explicit zero was found, combine its wieght with the wieght # of the implicit zeros if 0 in classes_k: class_prior_k[classes_k == 0] += zeros_samp_weight_sum # If an there is an implict zero and it is not in classes and # class_prior, make an entry for it if 0 not in classes_k and y_nnz[k] < y.shape[0]: classes_k = np.insert(classes_k, 0, 0) class_prior_k = np.insert(class_prior_k, 0, zeros_samp_weight_sum) classes.append(classes_k) n_classes.append(classes_k.shape[0]) class_prior.append(class_prior_k / class_prior_k.sum()) else: for k in range(n_outputs): classes_k, y_k = np.unique(y[:, k], return_inverse=True) classes.append(classes_k) n_classes.append(classes_k.shape[0]) class_prior_k = bincount(y_k, weights=sample_weight) class_prior.append(class_prior_k / class_prior_k.sum()) return (classes, n_classes, class_prior)

bsd-3-clause

roxyboy/scikit-learn

sklearn/metrics/pairwise.py

103

42995

# -*- coding: utf-8 -*- # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr> # Mathieu Blondel <mathieu@mblondel.org> # Robert Layton <robertlayton@gmail.com> # Andreas Mueller <amueller@ais.uni-bonn.de> # Philippe Gervais <philippe.gervais@inria.fr> # Lars Buitinck <larsmans@gmail.com> # Joel Nothman <joel.nothman@gmail.com> # License: BSD 3 clause import itertools import numpy as np from scipy.spatial import distance from scipy.sparse import csr_matrix from scipy.sparse import issparse from ..utils import check_array from ..utils import gen_even_slices from ..utils import gen_batches from ..utils.fixes import partial from ..utils.extmath import row_norms, safe_sparse_dot from ..preprocessing import normalize from ..externals.joblib import Parallel from ..externals.joblib import delayed from ..externals.joblib.parallel import cpu_count from .pairwise_fast import _chi2_kernel_fast, _sparse_manhattan # Utility Functions def _return_float_dtype(X, Y): """ 1. If dtype of X and Y is float32, then dtype float32 is returned. 2. Else dtype float is returned. """ if not issparse(X) and not isinstance(X, np.ndarray): X = np.asarray(X) if Y is None: Y_dtype = X.dtype elif not issparse(Y) and not isinstance(Y, np.ndarray): Y = np.asarray(Y) Y_dtype = Y.dtype else: Y_dtype = Y.dtype if X.dtype == Y_dtype == np.float32: dtype = np.float32 else: dtype = np.float return X, Y, dtype def check_pairwise_arrays(X, Y): """ Set X and Y appropriately and checks inputs If Y is None, it is set as a pointer to X (i.e. not a copy). If Y is given, this does not happen. All distance metrics should use this function first to assert that the given parameters are correct and safe to use. Specifically, this function first ensures that both X and Y are arrays, then checks that they are at least two dimensional while ensuring that their elements are floats. Finally, the function checks that the size of the second dimension of the two arrays is equal. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples_a, n_features) Y : {array-like, sparse matrix}, shape (n_samples_b, n_features) Returns ------- safe_X : {array-like, sparse matrix}, shape (n_samples_a, n_features) An array equal to X, guaranteed to be a numpy array. safe_Y : {array-like, sparse matrix}, shape (n_samples_b, n_features) An array equal to Y if Y was not None, guaranteed to be a numpy array. If Y was None, safe_Y will be a pointer to X. """ X, Y, dtype = _return_float_dtype(X, Y) if Y is X or Y is None: X = Y = check_array(X, accept_sparse='csr', dtype=dtype) else: X = check_array(X, accept_sparse='csr', dtype=dtype) Y = check_array(Y, accept_sparse='csr', dtype=dtype) if X.shape[1] != Y.shape[1]: raise ValueError("Incompatible dimension for X and Y matrices: " "X.shape[1] == %d while Y.shape[1] == %d" % ( X.shape[1], Y.shape[1])) return X, Y def check_paired_arrays(X, Y): """ Set X and Y appropriately and checks inputs for paired distances All paired distance metrics should use this function first to assert that the given parameters are correct and safe to use. Specifically, this function first ensures that both X and Y are arrays, then checks that they are at least two dimensional while ensuring that their elements are floats. Finally, the function checks that the size of the dimensions of the two arrays are equal. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples_a, n_features) Y : {array-like, sparse matrix}, shape (n_samples_b, n_features) Returns ------- safe_X : {array-like, sparse matrix}, shape (n_samples_a, n_features) An array equal to X, guaranteed to be a numpy array. safe_Y : {array-like, sparse matrix}, shape (n_samples_b, n_features) An array equal to Y if Y was not None, guaranteed to be a numpy array. If Y was None, safe_Y will be a pointer to X. """ X, Y = check_pairwise_arrays(X, Y) if X.shape != Y.shape: raise ValueError("X and Y should be of same shape. They were " "respectively %r and %r long." % (X.shape, Y.shape)) return X, Y # Pairwise distances def euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False): """ Considering the rows of X (and Y=X) as vectors, compute the distance matrix between each pair of vectors. For efficiency reasons, the euclidean distance between a pair of row vector x and y is computed as:: dist(x, y) = sqrt(dot(x, x) - 2 * dot(x, y) + dot(y, y)) This formulation has two advantages over other ways of computing distances. First, it is computationally efficient when dealing with sparse data. Second, if x varies but y remains unchanged, then the right-most dot product `dot(y, y)` can be pre-computed. However, this is not the most precise way of doing this computation, and the distance matrix returned by this function may not be exactly symmetric as required by, e.g., ``scipy.spatial.distance`` functions. Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples_1, n_features) Y : {array-like, sparse matrix}, shape (n_samples_2, n_features) Y_norm_squared : array-like, shape (n_samples_2, ), optional Pre-computed dot-products of vectors in Y (e.g., ``(Y**2).sum(axis=1)``) squared : boolean, optional Return squared Euclidean distances. Returns ------- distances : {array, sparse matrix}, shape (n_samples_1, n_samples_2) Examples -------- >>> from sklearn.metrics.pairwise import euclidean_distances >>> X = [[0, 1], [1, 1]] >>> # distance between rows of X >>> euclidean_distances(X, X) array([[ 0., 1.], [ 1., 0.]]) >>> # get distance to origin >>> euclidean_distances(X, [[0, 0]]) array([[ 1. ], [ 1.41421356]]) See also -------- paired_distances : distances betweens pairs of elements of X and Y. """ # should not need X_norm_squared because if you could precompute that as # well as Y, then you should just pre-compute the output and not even # call this function. X, Y = check_pairwise_arrays(X, Y) if Y_norm_squared is not None: YY = check_array(Y_norm_squared) if YY.shape != (1, Y.shape[0]): raise ValueError( "Incompatible dimensions for Y and Y_norm_squared") else: YY = row_norms(Y, squared=True)[np.newaxis, :] if X is Y: # shortcut in the common case euclidean_distances(X, X) XX = YY.T else: XX = row_norms(X, squared=True)[:, np.newaxis] distances = safe_sparse_dot(X, Y.T, dense_output=True) distances *= -2 distances += XX distances += YY np.maximum(distances, 0, out=distances) if X is Y: # Ensure that distances between vectors and themselves are set to 0.0. # This may not be the case due to floating point rounding errors. distances.flat[::distances.shape[0] + 1] = 0.0 return distances if squared else np.sqrt(distances, out=distances) def pairwise_distances_argmin_min(X, Y, axis=1, metric="euclidean", batch_size=500, metric_kwargs=None): """Compute minimum distances between one point and a set of points. This function computes for each row in X, the index of the row of Y which is closest (according to the specified distance). The minimal distances are also returned. This is mostly equivalent to calling: (pairwise_distances(X, Y=Y, metric=metric).argmin(axis=axis), pairwise_distances(X, Y=Y, metric=metric).min(axis=axis)) but uses much less memory, and is faster for large arrays. Parameters ---------- X, Y : {array-like, sparse matrix} Arrays containing points. Respective shapes (n_samples1, n_features) and (n_samples2, n_features) batch_size : integer To reduce memory consumption over the naive solution, data are processed in batches, comprising batch_size rows of X and batch_size rows of Y. The default value is quite conservative, but can be changed for fine-tuning. The larger the number, the larger the memory usage. metric : string or callable, default 'euclidean' metric to use for distance computation. Any metric from scikit-learn or scipy.spatial.distance can be used. If metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays as input and return one value indicating the distance between them. This works for Scipy's metrics, but is less efficient than passing the metric name as a string. Distance matrices are not supported. Valid values for metric are: - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan'] - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'] See the documentation for scipy.spatial.distance for details on these metrics. metric_kwargs : dict, optional Keyword arguments to pass to specified metric function. axis : int, optional, default 1 Axis along which the argmin and distances are to be computed. Returns ------- argmin : numpy.ndarray Y[argmin[i], :] is the row in Y that is closest to X[i, :]. distances : numpy.ndarray distances[i] is the distance between the i-th row in X and the argmin[i]-th row in Y. See also -------- sklearn.metrics.pairwise_distances sklearn.metrics.pairwise_distances_argmin """ dist_func = None if metric in PAIRWISE_DISTANCE_FUNCTIONS: dist_func = PAIRWISE_DISTANCE_FUNCTIONS[metric] elif not callable(metric) and not isinstance(metric, str): raise ValueError("'metric' must be a string or a callable") X, Y = check_pairwise_arrays(X, Y) if metric_kwargs is None: metric_kwargs = {} if axis == 0: X, Y = Y, X # Allocate output arrays indices = np.empty(X.shape[0], dtype=np.intp) values = np.empty(X.shape[0]) values.fill(np.infty) for chunk_x in gen_batches(X.shape[0], batch_size): X_chunk = X[chunk_x, :] for chunk_y in gen_batches(Y.shape[0], batch_size): Y_chunk = Y[chunk_y, :] if dist_func is not None: if metric == 'euclidean': # special case, for speed d_chunk = safe_sparse_dot(X_chunk, Y_chunk.T, dense_output=True) d_chunk *= -2 d_chunk += row_norms(X_chunk, squared=True)[:, np.newaxis] d_chunk += row_norms(Y_chunk, squared=True)[np.newaxis, :] np.maximum(d_chunk, 0, d_chunk) else: d_chunk = dist_func(X_chunk, Y_chunk, **metric_kwargs) else: d_chunk = pairwise_distances(X_chunk, Y_chunk, metric=metric, **metric_kwargs) # Update indices and minimum values using chunk min_indices = d_chunk.argmin(axis=1) min_values = d_chunk[np.arange(chunk_x.stop - chunk_x.start), min_indices] flags = values[chunk_x] > min_values indices[chunk_x][flags] = min_indices[flags] + chunk_y.start values[chunk_x][flags] = min_values[flags] if metric == "euclidean" and not metric_kwargs.get("squared", False): np.sqrt(values, values) return indices, values def pairwise_distances_argmin(X, Y, axis=1, metric="euclidean", batch_size=500, metric_kwargs=None): """Compute minimum distances between one point and a set of points. This function computes for each row in X, the index of the row of Y which is closest (according to the specified distance). This is mostly equivalent to calling: pairwise_distances(X, Y=Y, metric=metric).argmin(axis=axis) but uses much less memory, and is faster for large arrays. This function works with dense 2D arrays only. Parameters ---------- X : array-like Arrays containing points. Respective shapes (n_samples1, n_features) and (n_samples2, n_features) Y : array-like Arrays containing points. Respective shapes (n_samples1, n_features) and (n_samples2, n_features) batch_size : integer To reduce memory consumption over the naive solution, data are processed in batches, comprising batch_size rows of X and batch_size rows of Y. The default value is quite conservative, but can be changed for fine-tuning. The larger the number, the larger the memory usage. metric : string or callable metric to use for distance computation. Any metric from scikit-learn or scipy.spatial.distance can be used. If metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays as input and return one value indicating the distance between them. This works for Scipy's metrics, but is less efficient than passing the metric name as a string. Distance matrices are not supported. Valid values for metric are: - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan'] - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'] See the documentation for scipy.spatial.distance for details on these metrics. metric_kwargs : dict keyword arguments to pass to specified metric function. axis : int, optional, default 1 Axis along which the argmin and distances are to be computed. Returns ------- argmin : numpy.ndarray Y[argmin[i], :] is the row in Y that is closest to X[i, :]. See also -------- sklearn.metrics.pairwise_distances sklearn.metrics.pairwise_distances_argmin_min """ if metric_kwargs is None: metric_kwargs = {} return pairwise_distances_argmin_min(X, Y, axis, metric, batch_size, metric_kwargs)[0] def manhattan_distances(X, Y=None, sum_over_features=True, size_threshold=5e8): """ Compute the L1 distances between the vectors in X and Y. With sum_over_features equal to False it returns the componentwise distances. Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : array_like An array with shape (n_samples_X, n_features). Y : array_like, optional An array with shape (n_samples_Y, n_features). sum_over_features : bool, default=True If True the function returns the pairwise distance matrix else it returns the componentwise L1 pairwise-distances. Not supported for sparse matrix inputs. size_threshold : int, default=5e8 Unused parameter. Returns ------- D : array If sum_over_features is False shape is (n_samples_X * n_samples_Y, n_features) and D contains the componentwise L1 pairwise-distances (ie. absolute difference), else shape is (n_samples_X, n_samples_Y) and D contains the pairwise L1 distances. Examples -------- >>> from sklearn.metrics.pairwise import manhattan_distances >>> manhattan_distances(3, 3)#doctest:+ELLIPSIS array([[ 0.]]) >>> manhattan_distances(3, 2)#doctest:+ELLIPSIS array([[ 1.]]) >>> manhattan_distances(2, 3)#doctest:+ELLIPSIS array([[ 1.]]) >>> manhattan_distances([[1, 2], [3, 4]],\ [[1, 2], [0, 3]])#doctest:+ELLIPSIS array([[ 0., 2.], [ 4., 4.]]) >>> import numpy as np >>> X = np.ones((1, 2)) >>> y = 2 * np.ones((2, 2)) >>> manhattan_distances(X, y, sum_over_features=False)#doctest:+ELLIPSIS array([[ 1., 1.], [ 1., 1.]]...) """ X, Y = check_pairwise_arrays(X, Y) if issparse(X) or issparse(Y): if not sum_over_features: raise TypeError("sum_over_features=%r not supported" " for sparse matrices" % sum_over_features) X = csr_matrix(X, copy=False) Y = csr_matrix(Y, copy=False) D = np.zeros((X.shape[0], Y.shape[0])) _sparse_manhattan(X.data, X.indices, X.indptr, Y.data, Y.indices, Y.indptr, X.shape[1], D) return D if sum_over_features: return distance.cdist(X, Y, 'cityblock') D = X[:, np.newaxis, :] - Y[np.newaxis, :, :] D = np.abs(D, D) return D.reshape((-1, X.shape[1])) def cosine_distances(X, Y=None): """ Compute cosine distance between samples in X and Y. Cosine distance is defined as 1.0 minus the cosine similarity. Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : array_like, sparse matrix with shape (n_samples_X, n_features). Y : array_like, sparse matrix (optional) with shape (n_samples_Y, n_features). Returns ------- distance matrix : array An array with shape (n_samples_X, n_samples_Y). See also -------- sklearn.metrics.pairwise.cosine_similarity scipy.spatial.distance.cosine (dense matrices only) """ # 1.0 - cosine_similarity(X, Y) without copy S = cosine_similarity(X, Y) S *= -1 S += 1 return S # Paired distances def paired_euclidean_distances(X, Y): """ Computes the paired euclidean distances between X and Y Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : array-like, shape (n_samples, n_features) Y : array-like, shape (n_samples, n_features) Returns ------- distances : ndarray (n_samples, ) """ X, Y = check_paired_arrays(X, Y) return row_norms(X - Y) def paired_manhattan_distances(X, Y): """Compute the L1 distances between the vectors in X and Y. Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : array-like, shape (n_samples, n_features) Y : array-like, shape (n_samples, n_features) Returns ------- distances : ndarray (n_samples, ) """ X, Y = check_paired_arrays(X, Y) diff = X - Y if issparse(diff): diff.data = np.abs(diff.data) return np.squeeze(np.array(diff.sum(axis=1))) else: return np.abs(diff).sum(axis=-1) def paired_cosine_distances(X, Y): """ Computes the paired cosine distances between X and Y Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : array-like, shape (n_samples, n_features) Y : array-like, shape (n_samples, n_features) Returns ------- distances : ndarray, shape (n_samples, ) Notes ------ The cosine distance is equivalent to the half the squared euclidean distance if each sample is normalized to unit norm """ X, Y = check_paired_arrays(X, Y) return .5 * row_norms(normalize(X) - normalize(Y), squared=True) PAIRED_DISTANCES = { 'cosine': paired_cosine_distances, 'euclidean': paired_euclidean_distances, 'l2': paired_euclidean_distances, 'l1': paired_manhattan_distances, 'manhattan': paired_manhattan_distances, 'cityblock': paired_manhattan_distances} def paired_distances(X, Y, metric="euclidean", **kwds): """ Computes the paired distances between X and Y. Computes the distances between (X[0], Y[0]), (X[1], Y[1]), etc... Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : ndarray (n_samples, n_features) Array 1 for distance computation. Y : ndarray (n_samples, n_features) Array 2 for distance computation. metric : string or callable The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options specified in PAIRED_DISTANCES, including "euclidean", "manhattan", or "cosine". Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them. Returns ------- distances : ndarray (n_samples, ) Examples -------- >>> from sklearn.metrics.pairwise import paired_distances >>> X = [[0, 1], [1, 1]] >>> Y = [[0, 1], [2, 1]] >>> paired_distances(X, Y) array([ 0., 1.]) See also -------- pairwise_distances : pairwise distances. """ if metric in PAIRED_DISTANCES: func = PAIRED_DISTANCES[metric] return func(X, Y) elif callable(metric): # Check the matrix first (it is usually done by the metric) X, Y = check_paired_arrays(X, Y) distances = np.zeros(len(X)) for i in range(len(X)): distances[i] = metric(X[i], Y[i]) return distances else: raise ValueError('Unknown distance %s' % metric) # Kernels def linear_kernel(X, Y=None): """ Compute the linear kernel between X and Y. Read more in the :ref:`User Guide <linear_kernel>`. Parameters ---------- X : array of shape (n_samples_1, n_features) Y : array of shape (n_samples_2, n_features) Returns ------- Gram matrix : array of shape (n_samples_1, n_samples_2) """ X, Y = check_pairwise_arrays(X, Y) return safe_sparse_dot(X, Y.T, dense_output=True) def polynomial_kernel(X, Y=None, degree=3, gamma=None, coef0=1): """ Compute the polynomial kernel between X and Y:: K(X, Y) = (gamma <X, Y> + coef0)^degree Read more in the :ref:`User Guide <polynomial_kernel>`. Parameters ---------- X : ndarray of shape (n_samples_1, n_features) Y : ndarray of shape (n_samples_2, n_features) coef0 : int, default 1 degree : int, default 3 Returns ------- Gram matrix : array of shape (n_samples_1, n_samples_2) """ X, Y = check_pairwise_arrays(X, Y) if gamma is None: gamma = 1.0 / X.shape[1] K = safe_sparse_dot(X, Y.T, dense_output=True) K *= gamma K += coef0 K **= degree return K def sigmoid_kernel(X, Y=None, gamma=None, coef0=1): """ Compute the sigmoid kernel between X and Y:: K(X, Y) = tanh(gamma <X, Y> + coef0) Read more in the :ref:`User Guide <sigmoid_kernel>`. Parameters ---------- X : ndarray of shape (n_samples_1, n_features) Y : ndarray of shape (n_samples_2, n_features) coef0 : int, default 1 Returns ------- Gram matrix: array of shape (n_samples_1, n_samples_2) """ X, Y = check_pairwise_arrays(X, Y) if gamma is None: gamma = 1.0 / X.shape[1] K = safe_sparse_dot(X, Y.T, dense_output=True) K *= gamma K += coef0 np.tanh(K, K) # compute tanh in-place return K def rbf_kernel(X, Y=None, gamma=None): """ Compute the rbf (gaussian) kernel between X and Y:: K(x, y) = exp(-gamma ||x-y||^2) for each pair of rows x in X and y in Y. Read more in the :ref:`User Guide <rbf_kernel>`. Parameters ---------- X : array of shape (n_samples_X, n_features) Y : array of shape (n_samples_Y, n_features) gamma : float Returns ------- kernel_matrix : array of shape (n_samples_X, n_samples_Y) """ X, Y = check_pairwise_arrays(X, Y) if gamma is None: gamma = 1.0 / X.shape[1] K = euclidean_distances(X, Y, squared=True) K *= -gamma np.exp(K, K) # exponentiate K in-place return K def cosine_similarity(X, Y=None, dense_output=True): """Compute cosine similarity between samples in X and Y. Cosine similarity, or the cosine kernel, computes similarity as the normalized dot product of X and Y: K(X, Y) = <X, Y> / (||X||*||Y||) On L2-normalized data, this function is equivalent to linear_kernel. Read more in the :ref:`User Guide <cosine_similarity>`. Parameters ---------- X : ndarray or sparse array, shape: (n_samples_X, n_features) Input data. Y : ndarray or sparse array, shape: (n_samples_Y, n_features) Input data. If ``None``, the output will be the pairwise similarities between all samples in ``X``. dense_output : boolean (optional), default True Whether to return dense output even when the input is sparse. If ``False``, the output is sparse if both input arrays are sparse. Returns ------- kernel matrix : array An array with shape (n_samples_X, n_samples_Y). """ # to avoid recursive import X, Y = check_pairwise_arrays(X, Y) X_normalized = normalize(X, copy=True) if X is Y: Y_normalized = X_normalized else: Y_normalized = normalize(Y, copy=True) K = safe_sparse_dot(X_normalized, Y_normalized.T, dense_output=dense_output) return K def additive_chi2_kernel(X, Y=None): """Computes the additive chi-squared kernel between observations in X and Y The chi-squared kernel is computed between each pair of rows in X and Y. X and Y have to be non-negative. This kernel is most commonly applied to histograms. The chi-squared kernel is given by:: k(x, y) = -Sum [(x - y)^2 / (x + y)] It can be interpreted as a weighted difference per entry. Read more in the :ref:`User Guide <chi2_kernel>`. Notes ----- As the negative of a distance, this kernel is only conditionally positive definite. Parameters ---------- X : array-like of shape (n_samples_X, n_features) Y : array of shape (n_samples_Y, n_features) Returns ------- kernel_matrix : array of shape (n_samples_X, n_samples_Y) References ---------- * Zhang, J. and Marszalek, M. and Lazebnik, S. and Schmid, C. Local features and kernels for classification of texture and object categories: A comprehensive study International Journal of Computer Vision 2007 http://eprints.pascal-network.org/archive/00002309/01/Zhang06-IJCV.pdf See also -------- chi2_kernel : The exponentiated version of the kernel, which is usually preferable. sklearn.kernel_approximation.AdditiveChi2Sampler : A Fourier approximation to this kernel. """ if issparse(X) or issparse(Y): raise ValueError("additive_chi2 does not support sparse matrices.") X, Y = check_pairwise_arrays(X, Y) if (X < 0).any(): raise ValueError("X contains negative values.") if Y is not X and (Y < 0).any(): raise ValueError("Y contains negative values.") result = np.zeros((X.shape[0], Y.shape[0]), dtype=X.dtype) _chi2_kernel_fast(X, Y, result) return result def chi2_kernel(X, Y=None, gamma=1.): """Computes the exponential chi-squared kernel X and Y. The chi-squared kernel is computed between each pair of rows in X and Y. X and Y have to be non-negative. This kernel is most commonly applied to histograms. The chi-squared kernel is given by:: k(x, y) = exp(-gamma Sum [(x - y)^2 / (x + y)]) It can be interpreted as a weighted difference per entry. Read more in the :ref:`User Guide <chi2_kernel>`. Parameters ---------- X : array-like of shape (n_samples_X, n_features) Y : array of shape (n_samples_Y, n_features) gamma : float, default=1. Scaling parameter of the chi2 kernel. Returns ------- kernel_matrix : array of shape (n_samples_X, n_samples_Y) References ---------- * Zhang, J. and Marszalek, M. and Lazebnik, S. and Schmid, C. Local features and kernels for classification of texture and object categories: A comprehensive study International Journal of Computer Vision 2007 http://eprints.pascal-network.org/archive/00002309/01/Zhang06-IJCV.pdf See also -------- additive_chi2_kernel : The additive version of this kernel sklearn.kernel_approximation.AdditiveChi2Sampler : A Fourier approximation to the additive version of this kernel. """ K = additive_chi2_kernel(X, Y) K *= gamma return np.exp(K, K) # Helper functions - distance PAIRWISE_DISTANCE_FUNCTIONS = { # If updating this dictionary, update the doc in both distance_metrics() # and also in pairwise_distances()! 'cityblock': manhattan_distances, 'cosine': cosine_distances, 'euclidean': euclidean_distances, 'l2': euclidean_distances, 'l1': manhattan_distances, 'manhattan': manhattan_distances, } def distance_metrics(): """Valid metrics for pairwise_distances. This function simply returns the valid pairwise distance metrics. It exists to allow for a description of the mapping for each of the valid strings. The valid distance metrics, and the function they map to, are: ============ ==================================== metric Function ============ ==================================== 'cityblock' metrics.pairwise.manhattan_distances 'cosine' metrics.pairwise.cosine_distances 'euclidean' metrics.pairwise.euclidean_distances 'l1' metrics.pairwise.manhattan_distances 'l2' metrics.pairwise.euclidean_distances 'manhattan' metrics.pairwise.manhattan_distances ============ ==================================== Read more in the :ref:`User Guide <metrics>`. """ return PAIRWISE_DISTANCE_FUNCTIONS def _parallel_pairwise(X, Y, func, n_jobs, **kwds): """Break the pairwise matrix in n_jobs even slices and compute them in parallel""" if n_jobs < 0: n_jobs = max(cpu_count() + 1 + n_jobs, 1) if Y is None: Y = X if n_jobs == 1: # Special case to avoid picklability checks in delayed return func(X, Y, **kwds) # TODO: in some cases, backend='threading' may be appropriate fd = delayed(func) ret = Parallel(n_jobs=n_jobs, verbose=0)( fd(X, Y[s], **kwds) for s in gen_even_slices(Y.shape[0], n_jobs)) return np.hstack(ret) def _pairwise_callable(X, Y, metric, **kwds): """Handle the callable case for pairwise_{distances,kernels} """ X, Y = check_pairwise_arrays(X, Y) if X is Y: # Only calculate metric for upper triangle out = np.zeros((X.shape[0], Y.shape[0]), dtype='float') iterator = itertools.combinations(range(X.shape[0]), 2) for i, j in iterator: out[i, j] = metric(X[i], Y[j], **kwds) # Make symmetric # NB: out += out.T will produce incorrect results out = out + out.T # Calculate diagonal # NB: nonzero diagonals are allowed for both metrics and kernels for i in range(X.shape[0]): x = X[i] out[i, i] = metric(x, x, **kwds) else: # Calculate all cells out = np.empty((X.shape[0], Y.shape[0]), dtype='float') iterator = itertools.product(range(X.shape[0]), range(Y.shape[0])) for i, j in iterator: out[i, j] = metric(X[i], Y[j], **kwds) return out _VALID_METRICS = ['euclidean', 'l2', 'l1', 'manhattan', 'cityblock', 'braycurtis', 'canberra', 'chebyshev', 'correlation', 'cosine', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule', "wminkowski"] def pairwise_distances(X, Y=None, metric="euclidean", n_jobs=1, **kwds): """ Compute the distance matrix from a vector array X and optional Y. This method takes either a vector array or a distance matrix, and returns a distance matrix. If the input is a vector array, the distances are computed. If the input is a distances matrix, it is returned instead. This method provides a safe way to take a distance matrix as input, while preserving compatibility with many other algorithms that take a vector array. If Y is given (default is None), then the returned matrix is the pairwise distance between the arrays from both X and Y. Valid values for metric are: - From scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan']. These metrics support sparse matrix inputs. - From scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'] See the documentation for scipy.spatial.distance for details on these metrics. These metrics do not support sparse matrix inputs. Note that in the case of 'cityblock', 'cosine' and 'euclidean' (which are valid scipy.spatial.distance metrics), the scikit-learn implementation will be used, which is faster and has support for sparse matrices (except for 'cityblock'). For a verbose description of the metrics from scikit-learn, see the __doc__ of the sklearn.pairwise.distance_metrics function. Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \ [n_samples_a, n_features] otherwise Array of pairwise distances between samples, or a feature array. Y : array [n_samples_b, n_features], optional An optional second feature array. Only allowed if metric != "precomputed". metric : string, or callable The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options allowed by scipy.spatial.distance.pdist for its metric parameter, or a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS. If metric is "precomputed", X is assumed to be a distance matrix. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them. n_jobs : int The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used. `**kwds` : optional keyword parameters Any further parameters are passed directly to the distance function. If using a scipy.spatial.distance metric, the parameters are still metric dependent. See the scipy docs for usage examples. Returns ------- D : array [n_samples_a, n_samples_a] or [n_samples_a, n_samples_b] A distance matrix D such that D_{i, j} is the distance between the ith and jth vectors of the given matrix X, if Y is None. If Y is not None, then D_{i, j} is the distance between the ith array from X and the jth array from Y. """ if (metric not in _VALID_METRICS and not callable(metric) and metric != "precomputed"): raise ValueError("Unknown metric %s. " "Valid metrics are %s, or 'precomputed', or a " "callable" % (metric, _VALID_METRICS)) if metric == "precomputed": return X elif metric in PAIRWISE_DISTANCE_FUNCTIONS: func = PAIRWISE_DISTANCE_FUNCTIONS[metric] elif callable(metric): func = partial(_pairwise_callable, metric=metric, **kwds) else: if issparse(X) or issparse(Y): raise TypeError("scipy distance metrics do not" " support sparse matrices.") X, Y = check_pairwise_arrays(X, Y) if n_jobs == 1 and X is Y: return distance.squareform(distance.pdist(X, metric=metric, **kwds)) func = partial(distance.cdist, metric=metric, **kwds) return _parallel_pairwise(X, Y, func, n_jobs, **kwds) # Helper functions - distance PAIRWISE_KERNEL_FUNCTIONS = { # If updating this dictionary, update the doc in both distance_metrics() # and also in pairwise_distances()! 'additive_chi2': additive_chi2_kernel, 'chi2': chi2_kernel, 'linear': linear_kernel, 'polynomial': polynomial_kernel, 'poly': polynomial_kernel, 'rbf': rbf_kernel, 'sigmoid': sigmoid_kernel, 'cosine': cosine_similarity, } def kernel_metrics(): """ Valid metrics for pairwise_kernels This function simply returns the valid pairwise distance metrics. It exists, however, to allow for a verbose description of the mapping for each of the valid strings. The valid distance metrics, and the function they map to, are: =============== ======================================== metric Function =============== ======================================== 'additive_chi2' sklearn.pairwise.additive_chi2_kernel 'chi2' sklearn.pairwise.chi2_kernel 'linear' sklearn.pairwise.linear_kernel 'poly' sklearn.pairwise.polynomial_kernel 'polynomial' sklearn.pairwise.polynomial_kernel 'rbf' sklearn.pairwise.rbf_kernel 'sigmoid' sklearn.pairwise.sigmoid_kernel 'cosine' sklearn.pairwise.cosine_similarity =============== ======================================== Read more in the :ref:`User Guide <metrics>`. """ return PAIRWISE_KERNEL_FUNCTIONS KERNEL_PARAMS = { "additive_chi2": (), "chi2": (), "cosine": (), "exp_chi2": frozenset(["gamma"]), "linear": (), "poly": frozenset(["gamma", "degree", "coef0"]), "polynomial": frozenset(["gamma", "degree", "coef0"]), "rbf": frozenset(["gamma"]), "sigmoid": frozenset(["gamma", "coef0"]), } def pairwise_kernels(X, Y=None, metric="linear", filter_params=False, n_jobs=1, **kwds): """Compute the kernel between arrays X and optional array Y. This method takes either a vector array or a kernel matrix, and returns a kernel matrix. If the input is a vector array, the kernels are computed. If the input is a kernel matrix, it is returned instead. This method provides a safe way to take a kernel matrix as input, while preserving compatibility with many other algorithms that take a vector array. If Y is given (default is None), then the returned matrix is the pairwise kernel between the arrays from both X and Y. Valid values for metric are:: ['rbf', 'sigmoid', 'polynomial', 'poly', 'linear', 'cosine'] Read more in the :ref:`User Guide <metrics>`. Parameters ---------- X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \ [n_samples_a, n_features] otherwise Array of pairwise kernels between samples, or a feature array. Y : array [n_samples_b, n_features] A second feature array only if X has shape [n_samples_a, n_features]. metric : string, or callable The metric to use when calculating kernel between instances in a feature array. If metric is a string, it must be one of the metrics in pairwise.PAIRWISE_KERNEL_FUNCTIONS. If metric is "precomputed", X is assumed to be a kernel matrix. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them. n_jobs : int The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used. filter_params: boolean Whether to filter invalid parameters or not. `**kwds` : optional keyword parameters Any further parameters are passed directly to the kernel function. Returns ------- K : array [n_samples_a, n_samples_a] or [n_samples_a, n_samples_b] A kernel matrix K such that K_{i, j} is the kernel between the ith and jth vectors of the given matrix X, if Y is None. If Y is not None, then K_{i, j} is the kernel between the ith array from X and the jth array from Y. Notes ----- If metric is 'precomputed', Y is ignored and X is returned. """ if metric == "precomputed": return X elif metric in PAIRWISE_KERNEL_FUNCTIONS: if filter_params: kwds = dict((k, kwds[k]) for k in kwds if k in KERNEL_PARAMS[metric]) func = PAIRWISE_KERNEL_FUNCTIONS[metric] elif callable(metric): func = partial(_pairwise_callable, metric=metric, **kwds) else: raise ValueError("Unknown kernel %r" % metric) return _parallel_pairwise(X, Y, func, n_jobs, **kwds)

bsd-3-clause

rv816/lightfm

examples/movielens/data.py

11

3560

import itertools import os import zipfile import numpy as np import requests import scipy.sparse as sp def _get_movielens_path(): """ Get path to the movielens dataset file. """ return os.path.join(os.path.dirname(os.path.abspath(__file__)), 'movielens.zip') def _download_movielens(dest_path): """ Download the dataset. """ url = 'http://files.grouplens.org/datasets/movielens/ml-100k.zip' req = requests.get(url, stream=True) with open(dest_path, 'wb') as fd: for chunk in req.iter_content(): fd.write(chunk) def _get_raw_movielens_data(): """ Return the raw lines of the train and test files. """ path = _get_movielens_path() if not os.path.isfile(path): _download_movielens(path) with zipfile.ZipFile(path) as datafile: return (datafile.read('ml-100k/ua.base').decode().split('\n'), datafile.read('ml-100k/ua.test').decode().split('\n')) def _parse(data): """ Parse movielens dataset lines. """ for line in data: if not line: continue uid, iid, rating, timestamp = [int(x) for x in line.split('\t')] yield uid, iid, rating, timestamp def _build_interaction_matrix(rows, cols, data): """ Build the training matrix (no_users, no_items), with ratings >= 4.0 being marked as positive and the rest as negative. """ mat = sp.lil_matrix((rows, cols), dtype=np.int32) for uid, iid, rating, timestamp in data: if rating >= 4.0: mat[uid, iid] = 1.0 else: mat[uid, iid] = -1.0 return mat.tocoo() def _get_movie_raw_metadata(): """ Get raw lines of the genre file. """ path = _get_movielens_path() if not os.path.isfile(path): _download_movielens(path) with zipfile.ZipFile(path) as datafile: return datafile.read('ml-100k/u.item').decode(errors='ignore').split('\n') def get_movielens_item_metadata(use_item_ids): """ Build a matrix of genre features (no_items, no_features). If use_item_ids is True, per-item feeatures will also be used. """ features = {} genre_set = set() for line in _get_movie_raw_metadata(): if not line: continue splt = line.split('|') item_id = int(splt[0]) genres = [idx for idx, val in zip(range(len(splt[5:])), splt[5:]) if int(val) > 0] if use_item_ids: # Add item-specific features too genres.append(item_id) for genre_id in genres: genre_set.add(genre_id) features[item_id] = genres mat = sp.lil_matrix((len(features) + 1, len(genre_set)), dtype=np.int32) for item_id, genre_ids in features.items(): for genre_id in genre_ids: mat[item_id, genre_id] = 1 return mat def get_movielens_data(): """ Return (train_interactions, test_interactions). """ train_data, test_data = _get_raw_movielens_data() uids = set() iids = set() for uid, iid, rating, timestamp in itertools.chain(_parse(train_data), _parse(test_data)): uids.add(uid) iids.add(iid) rows = max(uids) + 1 cols = max(iids) + 1 return (_build_interaction_matrix(rows, cols, _parse(train_data)), _build_interaction_matrix(rows, cols, _parse(test_data)))

apache-2.0

dotsdl/msmbuilder

msmbuilder/tests/test_metzner_mcmc.py

2

3416

import numpy as np from msmbuilder.cluster import NDGrid from msmbuilder.example_datasets import load_doublewell from msmbuilder.msm import BayesianMarkovStateModel from msmbuilder.msm import MarkovStateModel from msmbuilder.msm._metzner_mcmc_fast import metzner_mcmc_fast from msmbuilder.msm._metzner_mcmc_slow import metzner_mcmc_slow def test_1(): Z = np.array([[1, 10, 2], [2, 26, 3], [15, 20, 20]]).astype(np.double) value1 = list(metzner_mcmc_fast(Z, 4, n_thin=1, random_state=0)) value2 = list(metzner_mcmc_slow(Z, 4, n_thin=1, random_state=0)) np.testing.assert_array_almost_equal(np.array(value1), np.array(value2)) value3 = list(metzner_mcmc_fast(Z, 4, n_thin=2, random_state=0)) value4 = list(metzner_mcmc_slow(Z, 4, n_thin=2, random_state=0)) np.testing.assert_array_almost_equal(np.array(value3), np.array(value4)) np.testing.assert_array_almost_equal( np.array(value1)[1::2], np.array(value3)) def test_2(): Z = np.array([[5., 2.], [1., 10.]]) value1 = list(metzner_mcmc_fast(Z, 100, n_thin=1, random_state=0)) value2 = list(metzner_mcmc_slow(Z, 100, n_thin=1, random_state=0)) np.testing.assert_array_almost_equal(np.array(value1), np.array(value2)) assert np.all(np.array(value1) > 0) def test_3(): trajectory = [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0, 0, 2, 2, 2, 0, 0, 0] msm1 = BayesianMarkovStateModel( sampler='metzner', n_steps=1, n_samples=100, n_chains=1, random_state=0) msm1.fit([trajectory]) msm2 = BayesianMarkovStateModel( sampler='metzner_py', n_steps=1, n_samples=100, n_chains=1, random_state=0) msm2.fit([trajectory]) np.testing.assert_array_almost_equal( msm1.all_transmats_, msm2.all_transmats_) assert msm1.all_timescales_.shape == (100, 2) assert msm1.all_eigenvalues_.shape == (100, 3) assert msm1.all_left_eigenvectors_.shape == (100, 3, 3) assert msm1.all_right_eigenvectors_.shape == (100, 3, 3) assert msm1.all_populations_.shape == (100, 3) np.testing.assert_array_almost_equal( msm1.all_populations_.sum(axis=1), np.ones(100)) def test_4(): trajectory = [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 0, 0, 0, 2, 2, 2, 0, 0, 0] msm1 = BayesianMarkovStateModel( n_steps=3, n_samples=10, n_chains=1, random_state=0).fit([trajectory]) assert msm1.all_transmats_.shape[0] == 10 msm2 = BayesianMarkovStateModel( n_steps=4, n_samples=10, n_chains=3, random_state=0).fit([trajectory]) assert msm2.all_transmats_.shape[0] == 10 def test_5(): trjs = load_doublewell(random_state=0)['trajectories'] clusterer = NDGrid(n_bins_per_feature=5) mle_msm = MarkovStateModel(lag_time=100, verbose=False) b_msm = BayesianMarkovStateModel( lag_time=100, n_samples=1000, n_chains=8, n_steps=1000, random_state=0) states = clusterer.fit_transform(trjs) b_msm.fit(states) mle_msm.fit(states) # this is a pretty silly test. it checks that the mean transition # matrix is not so dissimilar from the MLE transition matrix. # This shouldn't necessarily be the case anyways -- the likelihood is # not "symmetric". And the cutoff chosen is just heuristic. assert np.linalg.norm(b_msm.all_transmats_.mean(axis=0) - mle_msm.transmat_) < 1e-2

lgpl-2.1

unreal666/namebench

nb_third_party/dns/rdata.py

215

14860

# Copyright (C) 2001-2007, 2009, 2010 Nominum, Inc. # # Permission to use, copy, modify, and distribute this software and its # documentation for any purpose with or without fee is hereby granted, # provided that the above copyright notice and this permission notice # appear in all copies. # # THE SOFTWARE IS PROVIDED "AS IS" AND NOMINUM DISCLAIMS ALL WARRANTIES # WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL NOMINUM BE LIABLE FOR # ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES # WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN # ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT # OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. """DNS rdata. @var _rdata_modules: A dictionary mapping a (rdclass, rdtype) tuple to the module which implements that type. @type _rdata_modules: dict @var _module_prefix: The prefix to use when forming modules names. The default is 'dns.rdtypes'. Changing this value will break the library. @type _module_prefix: string @var _hex_chunk: At most this many octets that will be represented in each chunk of hexstring that _hexify() produces before whitespace occurs. @type _hex_chunk: int""" import cStringIO import dns.exception import dns.rdataclass import dns.rdatatype import dns.tokenizer _hex_chunksize = 32 def _hexify(data, chunksize=None): """Convert a binary string into its hex encoding, broken up into chunks of I{chunksize} characters separated by a space. @param data: the binary string @type data: string @param chunksize: the chunk size. Default is L{dns.rdata._hex_chunksize} @rtype: string """ if chunksize is None: chunksize = _hex_chunksize hex = data.encode('hex_codec') l = len(hex) if l > chunksize: chunks = [] i = 0 while i < l: chunks.append(hex[i : i + chunksize]) i += chunksize hex = ' '.join(chunks) return hex _base64_chunksize = 32 def _base64ify(data, chunksize=None): """Convert a binary string into its base64 encoding, broken up into chunks of I{chunksize} characters separated by a space. @param data: the binary string @type data: string @param chunksize: the chunk size. Default is L{dns.rdata._base64_chunksize} @rtype: string """ if chunksize is None: chunksize = _base64_chunksize b64 = data.encode('base64_codec') b64 = b64.replace('\n', '') l = len(b64) if l > chunksize: chunks = [] i = 0 while i < l: chunks.append(b64[i : i + chunksize]) i += chunksize b64 = ' '.join(chunks) return b64 __escaped = { '"' : True, '\\' : True, } def _escapify(qstring): """Escape the characters in a quoted string which need it. @param qstring: the string @type qstring: string @returns: the escaped string @rtype: string """ text = '' for c in qstring: if c in __escaped: text += '\\' + c elif ord(c) >= 0x20 and ord(c) < 0x7F: text += c else: text += '\\%03d' % ord(c) return text def _truncate_bitmap(what): """Determine the index of greatest byte that isn't all zeros, and return the bitmap that contains all the bytes less than that index. @param what: a string of octets representing a bitmap. @type what: string @rtype: string """ for i in xrange(len(what) - 1, -1, -1): if what[i] != '\x00': break return ''.join(what[0 : i + 1]) class Rdata(object): """Base class for all DNS rdata types. """ __slots__ = ['rdclass', 'rdtype'] def __init__(self, rdclass, rdtype): """Initialize an rdata. @param rdclass: The rdata class @type rdclass: int @param rdtype: The rdata type @type rdtype: int """ self.rdclass = rdclass self.rdtype = rdtype def covers(self): """DNS SIG/RRSIG rdatas apply to a specific type; this type is returned by the covers() function. If the rdata type is not SIG or RRSIG, dns.rdatatype.NONE is returned. This is useful when creating rdatasets, allowing the rdataset to contain only RRSIGs of a particular type, e.g. RRSIG(NS). @rtype: int """ return dns.rdatatype.NONE def extended_rdatatype(self): """Return a 32-bit type value, the least significant 16 bits of which are the ordinary DNS type, and the upper 16 bits of which are the "covered" type, if any. @rtype: int """ return self.covers() << 16 | self.rdtype def to_text(self, origin=None, relativize=True, **kw): """Convert an rdata to text format. @rtype: string """ raise NotImplementedError def to_wire(self, file, compress = None, origin = None): """Convert an rdata to wire format. @rtype: string """ raise NotImplementedError def to_digestable(self, origin = None): """Convert rdata to a format suitable for digesting in hashes. This is also the DNSSEC canonical form.""" f = cStringIO.StringIO() self.to_wire(f, None, origin) return f.getvalue() def validate(self): """Check that the current contents of the rdata's fields are valid. If you change an rdata by assigning to its fields, it is a good idea to call validate() when you are done making changes. """ dns.rdata.from_text(self.rdclass, self.rdtype, self.to_text()) def __repr__(self): covers = self.covers() if covers == dns.rdatatype.NONE: ctext = '' else: ctext = '(' + dns.rdatatype.to_text(covers) + ')' return '<DNS ' + dns.rdataclass.to_text(self.rdclass) + ' ' + \ dns.rdatatype.to_text(self.rdtype) + ctext + ' rdata: ' + \ str(self) + '>' def __str__(self): return self.to_text() def _cmp(self, other): """Compare an rdata with another rdata of the same rdtype and rdclass. Return < 0 if self < other in the DNSSEC ordering, 0 if self == other, and > 0 if self > other. """ raise NotImplementedError def __eq__(self, other): if not isinstance(other, Rdata): return False if self.rdclass != other.rdclass or \ self.rdtype != other.rdtype: return False return self._cmp(other) == 0 def __ne__(self, other): if not isinstance(other, Rdata): return True if self.rdclass != other.rdclass or \ self.rdtype != other.rdtype: return True return self._cmp(other) != 0 def __lt__(self, other): if not isinstance(other, Rdata) or \ self.rdclass != other.rdclass or \ self.rdtype != other.rdtype: return NotImplemented return self._cmp(other) < 0 def __le__(self, other): if not isinstance(other, Rdata) or \ self.rdclass != other.rdclass or \ self.rdtype != other.rdtype: return NotImplemented return self._cmp(other) <= 0 def __ge__(self, other): if not isinstance(other, Rdata) or \ self.rdclass != other.rdclass or \ self.rdtype != other.rdtype: return NotImplemented return self._cmp(other) >= 0 def __gt__(self, other): if not isinstance(other, Rdata) or \ self.rdclass != other.rdclass or \ self.rdtype != other.rdtype: return NotImplemented return self._cmp(other) > 0 def from_text(cls, rdclass, rdtype, tok, origin = None, relativize = True): """Build an rdata object from text format. @param rdclass: The rdata class @type rdclass: int @param rdtype: The rdata type @type rdtype: int @param tok: The tokenizer @type tok: dns.tokenizer.Tokenizer @param origin: The origin to use for relative names @type origin: dns.name.Name @param relativize: should names be relativized? @type relativize: bool @rtype: dns.rdata.Rdata instance """ raise NotImplementedError from_text = classmethod(from_text) def from_wire(cls, rdclass, rdtype, wire, current, rdlen, origin = None): """Build an rdata object from wire format @param rdclass: The rdata class @type rdclass: int @param rdtype: The rdata type @type rdtype: int @param wire: The wire-format message @type wire: string @param current: The offet in wire of the beginning of the rdata. @type current: int @param rdlen: The length of the wire-format rdata @type rdlen: int @param origin: The origin to use for relative names @type origin: dns.name.Name @rtype: dns.rdata.Rdata instance """ raise NotImplementedError from_wire = classmethod(from_wire) def choose_relativity(self, origin = None, relativize = True): """Convert any domain names in the rdata to the specified relativization. """ pass class GenericRdata(Rdata): """Generate Rdata Class This class is used for rdata types for which we have no better implementation. It implements the DNS "unknown RRs" scheme. """ __slots__ = ['data'] def __init__(self, rdclass, rdtype, data): super(GenericRdata, self).__init__(rdclass, rdtype) self.data = data def to_text(self, origin=None, relativize=True, **kw): return r'\# %d ' % len(self.data) + _hexify(self.data) def from_text(cls, rdclass, rdtype, tok, origin = None, relativize = True): token = tok.get() if not token.is_identifier() or token.value != '\#': raise dns.exception.SyntaxError(r'generic rdata does not start with \#') length = tok.get_int() chunks = [] while 1: token = tok.get() if token.is_eol_or_eof(): break chunks.append(token.value) hex = ''.join(chunks) data = hex.decode('hex_codec') if len(data) != length: raise dns.exception.SyntaxError('generic rdata hex data has wrong length') return cls(rdclass, rdtype, data) from_text = classmethod(from_text) def to_wire(self, file, compress = None, origin = None): file.write(self.data) def from_wire(cls, rdclass, rdtype, wire, current, rdlen, origin = None): return cls(rdclass, rdtype, wire[current : current + rdlen]) from_wire = classmethod(from_wire) def _cmp(self, other): return cmp(self.data, other.data) _rdata_modules = {} _module_prefix = 'dns.rdtypes' def get_rdata_class(rdclass, rdtype): def import_module(name): mod = __import__(name) components = name.split('.') for comp in components[1:]: mod = getattr(mod, comp) return mod mod = _rdata_modules.get((rdclass, rdtype)) rdclass_text = dns.rdataclass.to_text(rdclass) rdtype_text = dns.rdatatype.to_text(rdtype) rdtype_text = rdtype_text.replace('-', '_') if not mod: mod = _rdata_modules.get((dns.rdatatype.ANY, rdtype)) if not mod: try: mod = import_module('.'.join([_module_prefix, rdclass_text, rdtype_text])) _rdata_modules[(rdclass, rdtype)] = mod except ImportError: try: mod = import_module('.'.join([_module_prefix, 'ANY', rdtype_text])) _rdata_modules[(dns.rdataclass.ANY, rdtype)] = mod except ImportError: mod = None if mod: cls = getattr(mod, rdtype_text) else: cls = GenericRdata return cls def from_text(rdclass, rdtype, tok, origin = None, relativize = True): """Build an rdata object from text format. This function attempts to dynamically load a class which implements the specified rdata class and type. If there is no class-and-type-specific implementation, the GenericRdata class is used. Once a class is chosen, its from_text() class method is called with the parameters to this function. @param rdclass: The rdata class @type rdclass: int @param rdtype: The rdata type @type rdtype: int @param tok: The tokenizer @type tok: dns.tokenizer.Tokenizer @param origin: The origin to use for relative names @type origin: dns.name.Name @param relativize: Should names be relativized? @type relativize: bool @rtype: dns.rdata.Rdata instance""" if isinstance(tok, str): tok = dns.tokenizer.Tokenizer(tok) cls = get_rdata_class(rdclass, rdtype) if cls != GenericRdata: # peek at first token token = tok.get() tok.unget(token) if token.is_identifier() and \ token.value == r'\#': # # Known type using the generic syntax. Extract the # wire form from the generic syntax, and then run # from_wire on it. # rdata = GenericRdata.from_text(rdclass, rdtype, tok, origin, relativize) return from_wire(rdclass, rdtype, rdata.data, 0, len(rdata.data), origin) return cls.from_text(rdclass, rdtype, tok, origin, relativize) def from_wire(rdclass, rdtype, wire, current, rdlen, origin = None): """Build an rdata object from wire format This function attempts to dynamically load a class which implements the specified rdata class and type. If there is no class-and-type-specific implementation, the GenericRdata class is used. Once a class is chosen, its from_wire() class method is called with the parameters to this function. @param rdclass: The rdata class @type rdclass: int @param rdtype: The rdata type @type rdtype: int @param wire: The wire-format message @type wire: string @param current: The offet in wire of the beginning of the rdata. @type current: int @param rdlen: The length of the wire-format rdata @type rdlen: int @param origin: The origin to use for relative names @type origin: dns.name.Name @rtype: dns.rdata.Rdata instance""" cls = get_rdata_class(rdclass, rdtype) return cls.from_wire(rdclass, rdtype, wire, current, rdlen, origin)

apache-2.0

ephes/scikit-learn

examples/applications/plot_prediction_latency.py

233

11277

""" ================== Prediction Latency ================== This is an example showing the prediction latency of various scikit-learn estimators. The goal is to measure the latency one can expect when doing predictions either in bulk or atomic (i.e. one by one) mode. The plots represent the distribution of the prediction latency as a boxplot. """ # Authors: Eustache Diemert <eustache@diemert.fr> # License: BSD 3 clause from __future__ import print_function from collections import defaultdict import time import gc import numpy as np import matplotlib.pyplot as plt from scipy.stats import scoreatpercentile from sklearn.datasets.samples_generator import make_regression from sklearn.ensemble.forest import RandomForestRegressor from sklearn.linear_model.ridge import Ridge from sklearn.linear_model.stochastic_gradient import SGDRegressor from sklearn.svm.classes import SVR def _not_in_sphinx(): # Hack to detect whether we are running by the sphinx builder return '__file__' in globals() def atomic_benchmark_estimator(estimator, X_test, verbose=False): """Measure runtime prediction of each instance.""" n_instances = X_test.shape[0] runtimes = np.zeros(n_instances, dtype=np.float) for i in range(n_instances): instance = X_test[i, :] start = time.time() estimator.predict(instance) runtimes[i] = time.time() - start if verbose: print("atomic_benchmark runtimes:", min(runtimes), scoreatpercentile( runtimes, 50), max(runtimes)) return runtimes def bulk_benchmark_estimator(estimator, X_test, n_bulk_repeats, verbose): """Measure runtime prediction of the whole input.""" n_instances = X_test.shape[0] runtimes = np.zeros(n_bulk_repeats, dtype=np.float) for i in range(n_bulk_repeats): start = time.time() estimator.predict(X_test) runtimes[i] = time.time() - start runtimes = np.array(list(map(lambda x: x / float(n_instances), runtimes))) if verbose: print("bulk_benchmark runtimes:", min(runtimes), scoreatpercentile( runtimes, 50), max(runtimes)) return runtimes def benchmark_estimator(estimator, X_test, n_bulk_repeats=30, verbose=False): """ Measure runtimes of prediction in both atomic and bulk mode. Parameters ---------- estimator : already trained estimator supporting `predict()` X_test : test input n_bulk_repeats : how many times to repeat when evaluating bulk mode Returns ------- atomic_runtimes, bulk_runtimes : a pair of `np.array` which contain the runtimes in seconds. """ atomic_runtimes = atomic_benchmark_estimator(estimator, X_test, verbose) bulk_runtimes = bulk_benchmark_estimator(estimator, X_test, n_bulk_repeats, verbose) return atomic_runtimes, bulk_runtimes def generate_dataset(n_train, n_test, n_features, noise=0.1, verbose=False): """Generate a regression dataset with the given parameters.""" if verbose: print("generating dataset...") X, y, coef = make_regression(n_samples=n_train + n_test, n_features=n_features, noise=noise, coef=True) X_train = X[:n_train] y_train = y[:n_train] X_test = X[n_train:] y_test = y[n_train:] idx = np.arange(n_train) np.random.seed(13) np.random.shuffle(idx) X_train = X_train[idx] y_train = y_train[idx] std = X_train.std(axis=0) mean = X_train.mean(axis=0) X_train = (X_train - mean) / std X_test = (X_test - mean) / std std = y_train.std(axis=0) mean = y_train.mean(axis=0) y_train = (y_train - mean) / std y_test = (y_test - mean) / std gc.collect() if verbose: print("ok") return X_train, y_train, X_test, y_test def boxplot_runtimes(runtimes, pred_type, configuration): """ Plot a new `Figure` with boxplots of prediction runtimes. Parameters ---------- runtimes : list of `np.array` of latencies in micro-seconds cls_names : list of estimator class names that generated the runtimes pred_type : 'bulk' or 'atomic' """ fig, ax1 = plt.subplots(figsize=(10, 6)) bp = plt.boxplot(runtimes, ) cls_infos = ['%s\n(%d %s)' % (estimator_conf['name'], estimator_conf['complexity_computer']( estimator_conf['instance']), estimator_conf['complexity_label']) for estimator_conf in configuration['estimators']] plt.setp(ax1, xticklabels=cls_infos) plt.setp(bp['boxes'], color='black') plt.setp(bp['whiskers'], color='black') plt.setp(bp['fliers'], color='red', marker='+') ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5) ax1.set_axisbelow(True) ax1.set_title('Prediction Time per Instance - %s, %d feats.' % ( pred_type.capitalize(), configuration['n_features'])) ax1.set_ylabel('Prediction Time (us)') plt.show() def benchmark(configuration): """Run the whole benchmark.""" X_train, y_train, X_test, y_test = generate_dataset( configuration['n_train'], configuration['n_test'], configuration['n_features']) stats = {} for estimator_conf in configuration['estimators']: print("Benchmarking", estimator_conf['instance']) estimator_conf['instance'].fit(X_train, y_train) gc.collect() a, b = benchmark_estimator(estimator_conf['instance'], X_test) stats[estimator_conf['name']] = {'atomic': a, 'bulk': b} cls_names = [estimator_conf['name'] for estimator_conf in configuration[ 'estimators']] runtimes = [1e6 * stats[clf_name]['atomic'] for clf_name in cls_names] boxplot_runtimes(runtimes, 'atomic', configuration) runtimes = [1e6 * stats[clf_name]['bulk'] for clf_name in cls_names] boxplot_runtimes(runtimes, 'bulk (%d)' % configuration['n_test'], configuration) def n_feature_influence(estimators, n_train, n_test, n_features, percentile): """ Estimate influence of the number of features on prediction time. Parameters ---------- estimators : dict of (name (str), estimator) to benchmark n_train : nber of training instances (int) n_test : nber of testing instances (int) n_features : list of feature-space dimensionality to test (int) percentile : percentile at which to measure the speed (int [0-100]) Returns: -------- percentiles : dict(estimator_name, dict(n_features, percentile_perf_in_us)) """ percentiles = defaultdict(defaultdict) for n in n_features: print("benchmarking with %d features" % n) X_train, y_train, X_test, y_test = generate_dataset(n_train, n_test, n) for cls_name, estimator in estimators.items(): estimator.fit(X_train, y_train) gc.collect() runtimes = bulk_benchmark_estimator(estimator, X_test, 30, False) percentiles[cls_name][n] = 1e6 * scoreatpercentile(runtimes, percentile) return percentiles def plot_n_features_influence(percentiles, percentile): fig, ax1 = plt.subplots(figsize=(10, 6)) colors = ['r', 'g', 'b'] for i, cls_name in enumerate(percentiles.keys()): x = np.array(sorted([n for n in percentiles[cls_name].keys()])) y = np.array([percentiles[cls_name][n] for n in x]) plt.plot(x, y, color=colors[i], ) ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5) ax1.set_axisbelow(True) ax1.set_title('Evolution of Prediction Time with #Features') ax1.set_xlabel('#Features') ax1.set_ylabel('Prediction Time at %d%%-ile (us)' % percentile) plt.show() def benchmark_throughputs(configuration, duration_secs=0.1): """benchmark throughput for different estimators.""" X_train, y_train, X_test, y_test = generate_dataset( configuration['n_train'], configuration['n_test'], configuration['n_features']) throughputs = dict() for estimator_config in configuration['estimators']: estimator_config['instance'].fit(X_train, y_train) start_time = time.time() n_predictions = 0 while (time.time() - start_time) < duration_secs: estimator_config['instance'].predict(X_test[0]) n_predictions += 1 throughputs[estimator_config['name']] = n_predictions / duration_secs return throughputs def plot_benchmark_throughput(throughputs, configuration): fig, ax = plt.subplots(figsize=(10, 6)) colors = ['r', 'g', 'b'] cls_infos = ['%s\n(%d %s)' % (estimator_conf['name'], estimator_conf['complexity_computer']( estimator_conf['instance']), estimator_conf['complexity_label']) for estimator_conf in configuration['estimators']] cls_values = [throughputs[estimator_conf['name']] for estimator_conf in configuration['estimators']] plt.bar(range(len(throughputs)), cls_values, width=0.5, color=colors) ax.set_xticks(np.linspace(0.25, len(throughputs) - 0.75, len(throughputs))) ax.set_xticklabels(cls_infos, fontsize=10) ymax = max(cls_values) * 1.2 ax.set_ylim((0, ymax)) ax.set_ylabel('Throughput (predictions/sec)') ax.set_title('Prediction Throughput for different estimators (%d ' 'features)' % configuration['n_features']) plt.show() ############################################################################### # main code start_time = time.time() # benchmark bulk/atomic prediction speed for various regressors configuration = { 'n_train': int(1e3), 'n_test': int(1e2), 'n_features': int(1e2), 'estimators': [ {'name': 'Linear Model', 'instance': SGDRegressor(penalty='elasticnet', alpha=0.01, l1_ratio=0.25, fit_intercept=True), 'complexity_label': 'non-zero coefficients', 'complexity_computer': lambda clf: np.count_nonzero(clf.coef_)}, {'name': 'RandomForest', 'instance': RandomForestRegressor(), 'complexity_label': 'estimators', 'complexity_computer': lambda clf: clf.n_estimators}, {'name': 'SVR', 'instance': SVR(kernel='rbf'), 'complexity_label': 'support vectors', 'complexity_computer': lambda clf: len(clf.support_vectors_)}, ] } benchmark(configuration) # benchmark n_features influence on prediction speed percentile = 90 percentiles = n_feature_influence({'ridge': Ridge()}, configuration['n_train'], configuration['n_test'], [100, 250, 500], percentile) plot_n_features_influence(percentiles, percentile) # benchmark throughput throughputs = benchmark_throughputs(configuration) plot_benchmark_throughput(throughputs, configuration) stop_time = time.time() print("example run in %.2fs" % (stop_time - start_time))

bsd-3-clause

yinwenpeng/rescale

en/parser/nltk_lite/probability.py

10

59364

# Natural Language Toolkit: Probability and Statistics # # Copyright (C) 2001-2006 University of Pennsylvania # Author: Edward Loper <edloper@gradient.cis.upenn.edu> # Steven Bird <sb@csse.unimelb.edu.au> (additions) # Trevor Cohn <tacohn@cs.mu.oz.au> (additions) # URL: <http://nltk.sf.net> # For license information, see LICENSE.TXT # # $Id: probability.py 3498 2006-10-14 05:30:32Z stevenbird $ _NINF = float('-1e300') """ Classes for representing and processing probabilistic information. The L{FreqDist} class is used to encode X{frequency distributions}, which count the number of times that each outcome of an experiment occurs. The L{ProbDistI} class defines a standard interface for X{probability distributions}, which encode the probability of each outcome for an experiment. There are two types of probability distribution: - X{derived probability distributions} are created from frequency distributions. They attempt to model the probability distribution that generated the frequency distribution. - X{analytic probability distributions} are created directly from parameters (such as variance). The L{ConditionalFreqDist} class and L{ConditionalProbDistI} interface are used to encode conditional distributions. Conditional probability distributions can be derived or analytic; but currently the only implementation of the C{ConditionalProbDistI} interface is L{ConditionalProbDist}, a derived distribution. """ import types, math try: import numpy except: pass ##////////////////////////////////////////////////////// ## Frequency Distributions ##////////////////////////////////////////////////////// class FreqDist(object): """ A frequency distribution for the outcomes of an experiment. A frequency distribution records the number of times each outcome of an experiment has occured. For example, a frequency distribution could be used to record the frequency of each word type in a document. Formally, a frequency distribution can be defined as a function mapping from each sample to the number of times that sample occured as an outcome. Frequency distributions are generally constructed by running a number of experiments, and incrementing the count for a sample every time it is an outcome of an experiment. For example, the following code will produce a frequency distribution that encodes how often each word occurs in a text: >>> fdist = FreqDist() >>> for word in tokenize.whitespace(sent): ... fdist.inc(word) """ def __init__(self): """ Construct a new empty, C{FreqDist}. In particular, the count for every sample is zero. """ self._count = {} self._N = 0 self._Nr_cache = None self._max_cache = None def inc(self, sample, count=1): """ Increment this C{FreqDist}'s count for the given sample. @param sample: The sample whose count should be incremented. @type sample: any @param count: The amount to increment the sample's count by. @type count: C{int} @rtype: None @raise NotImplementedError: If C{sample} is not a supported sample type. """ if count == 0: return self._N += count self._count[sample] = self._count.get(sample,0) + count # Invalidate the Nr cache and max cache. self._Nr_cache = None self._max_cache = None def N(self): """ @return: The total number of sample outcomes that have been recorded by this C{FreqDist}. For the number of unique sample values (or bins) with counts greater than zero, use C{FreqDist.B()}. @rtype: C{int} """ return self._N def B(self): """ @return: The total number of sample values (or X{bins}) that have counts greater than zero. For the total number of sample outcomes recorded, use C{FreqDist.N()}. @rtype: C{int} """ return len(self._count) def samples(self): """ @return: A list of all samples that have been recorded as outcomes by this frequency distribution. Use C{count()} to determine the count for each sample. @rtype: C{list} """ return self._count.keys() def Nr(self, r, bins=None): """ @return: The number of samples with count r. @rtype: C{int} @type r: C{int} @param r: A sample count. @type bins: C{int} @param bins: The number of possible sample outcomes. C{bins} is used to calculate Nr(0). In particular, Nr(0) is C{bins-self.B()}. If C{bins} is not specified, it defaults to C{self.B()} (so Nr(0) will be 0). """ if r < 0: raise IndexError, 'FreqDist.Nr(): r must be non-negative' # Special case for Nr(0): if r == 0: if bins is None: return 0 else: return bins-self.B() # We have to search the entire distribution to find Nr. Since # this is an expensive operation, and is likely to be used # repeatedly, cache the results. if self._Nr_cache is None: self._cache_Nr_values() if r >= len(self._Nr_cache): return 0 return self._Nr_cache[r] def _cache_Nr_values(self): Nr = [0] for sample in self.samples(): c = self._count.get(sample, 0) if c >= len(Nr): Nr += [0]*(c+1-len(Nr)) Nr[c] += 1 self._Nr_cache = Nr def count(self, sample): """ Return the count of a given sample. The count of a sample is defined as the number of times that sample outcome was recorded by this C{FreqDist}. Counts are non-negative integers. @return: The count of a given sample. @rtype: C{int} @param sample: the sample whose count should be returned. @type sample: any. """ return self._count.get(sample, 0) def freq(self, sample): """ Return the frequency of a given sample. The frequency of a sample is defined as the count of that sample divided by the total number of sample outcomes that have been recorded by this C{FreqDist}. The count of a sample is defined as the number of times that sample outcome was recorded by this C{FreqDist}. Frequencies are always real numbers in the range [0, 1]. @return: The frequency of a given sample. @rtype: float @param sample: the sample whose frequency should be returned. @type sample: any """ if self._N is 0: return 0 return float(self._count.get(sample, 0)) / self._N def max(self): """ Return the sample with the greatest number of outcomes in this frequency distribution. If two or more samples have the same number of outcomes, return one of them; which sample is returned is undefined. If no outcomes have occured in this frequency distribution, return C{None}. @return: The sample with the maximum number of outcomes in this frequency distribution. @rtype: any or C{None} """ if self._max_cache is None: best_sample = None best_count = -1 for sample in self._count.keys(): if self._count[sample] > best_count: best_sample = sample best_count = self._count[sample] self._max_cache = best_sample return self._max_cache def sorted_samples(self): """ Return the samples sorted in decreasing order of frequency. Instances with the same count will be arbitrarily ordered. Instances with a count of zero will be omitted. This method is C{O(N^2)}, where C{N} is the number of samples, but will complete in a shorter time on average. @return: The set of samples in sorted order. @rtype: sequence of any """ items = [(-count,sample) for (sample,count) in self._count.items()] items.sort() return [sample for (neg_count,sample) in items] def __repr__(self): """ @return: A string representation of this C{FreqDist}. @rtype: string """ return '<FreqDist with %d samples>' % self.N() def __str__(self): """ @return: A string representation of this C{FreqDist}. @rtype: string """ samples = self.sorted_samples() items = ['%r: %r' % (s, self._count[s]) for s in samples] return '<FreqDist: %s>' % ', '.join(items) def __contains__(self, sample): """ @return: True if the given sample occurs one or more times in this frequency distribution. @rtype: C{boolean} @param sample: The sample to search for. @type sample: any """ return self._count.has_key(sample) ##////////////////////////////////////////////////////// ## Probability Distributions ##////////////////////////////////////////////////////// class ProbDistI(object): """ A probability distribution for the outcomes of an experiment. A probability distribution specifies how likely it is that an experiment will have any given outcome. For example, a probability distribution could be used to predict the probability that a token in a document will have a given type. Formally, a probability distribution can be defined as a function mapping from samples to nonnegative real numbers, such that the sum of every number in the function's range is 1.0. C{ProbDist}s are often used to model the probability distribution of the experiment used to generate a frequency distribution. """ def __init__(self): if self.__class__ == ProbDistI: raise AssertionError, "Interfaces can't be instantiated" def prob(self, sample): """ @return: the probability for a given sample. Probabilities are always real numbers in the range [0, 1]. @rtype: float @param sample: The sample whose probability should be returned. @type sample: any """ raise AssertionError() def logprob(self, sample): """ @return: the natural logarithm of the probability for a given sample. Log probabilities range from negitive infinity to zero. @rtype: float @param sample: The sample whose probability should be returned. @type sample: any """ # Default definition, in terms of prob() p = self.prob(sample) if p == 0: # Use some approximation to infinity. What this does # depends on your system's float implementation. return _NINF else: return math.log(p) def max(self): """ @return: the sample with the greatest probability. If two or more samples have the same probability, return one of them; which sample is returned is undefined. @rtype: any """ raise AssertionError() def samples(self): """ @return: A list of all samples that have nonzero probabilities. Use C{prob} to find the probability of each sample. @rtype: C{list} """ raise AssertionError() class UniformProbDist(ProbDistI): """ A probability distribution that assigns equal probability to each sample in a given set; and a zero probability to all other samples. """ def __init__(self, samples): """ Construct a new uniform probability distribution, that assigns equal probability to each sample in C{samples}. @param samples: The samples that should be given uniform probability. @type samples: C{list} @raise ValueError: If C{samples} is empty. """ if len(samples) == 0: raise ValueError('A Uniform probability distribution must '+ 'have at least one sample.') self._sampleset = set(samples) self._prob = 1.0/len(self._sampleset) self._samples = list(self._sampleset) def prob(self, sample): if sample in self._sampleset: return self._prob else: return 0 def max(self): return self._samples[0] def samples(self): return self._samples def __repr__(self): return '<UniformProbDist with %d samples>' % len(self._sampleset) class DictionaryProbDist(ProbDistI): """ A probability distribution whose probabilities are directly specified by a given dictionary. The given dictionary maps samples to probabilities. """ def __init__(self, prob_dict=None, log=False, normalize=False): """ Construct a new probability distribution from the given dictionary, which maps values to probabilities (or to log probabilities, if C{log} is true). If C{normalize} is true, then the probability values are scaled by a constant factor such that they sum to 1. """ self._prob_dict = prob_dict.copy() self._log = log # Normalize the distribution, if requested. if normalize: if log: value_sum = sum_logs(self._prob_dict.values()) if value_sum <= _NINF: logp = math.log(1.0/len(prob_dict.keys())) for x in prob_dict.keys(): self._prob_dict[x] = logp else: for (x, p) in self._prob_dict.items(): self._prob_dict[x] -= value_sum else: value_sum = sum(self._prob_dict.values()) if value_sum == 0: p = 1.0/len(prob_dict.keys()) for x in prob_dict.keys(): self._prob_dict[x] = p else: norm_factor = 1.0/value_sum for (x, p) in self._prob_dict.items(): self._prob_dict[x] *= norm_factor def prob(self, sample): if self._log: if sample not in self._prob_dict: return 0 else: return math.exp(self._prob_dict[sample]) else: return self._prob_dict.get(sample, 0) def logprob(self, sample): if self._log: return self._prob_dict.get(sample, _NINF) else: if sample not in self._prob_dict: return _NINF else: return math.log(self._prob_dict[sample]) def max(self): if not hasattr(self, '_max'): self._max = max([(p,v) for (v,p) in self._prob_dict.items()])[1] return self._max def samples(self): return self._prob_dict.keys() def __repr__(self): return '<ProbDist with %d samples>' % len(self._prob_dict) class MLEProbDist(ProbDistI): """ The maximum likelihood estimate for the probability distribution of the experiment used to generate a frequency distribution. The X{maximum likelihood estimate} approximates the probability of each sample as the frequency of that sample in the frequency distribution. """ def __init__(self, freqdist): """ Use the maximum likelihood estimate to create a probability distribution for the experiment used to generate C{freqdist}. @type freqdist: C{FreqDist} @param freqdist: The frequency distribution that the probability estimates should be based on. """ if freqdist.N() == 0: raise ValueError('An MLE probability distribution must '+ 'have at least one sample.') self._freqdist = freqdist def freqdist(self): """ @return: The frequency distribution that this probability distribution is based on. @rtype: C{FreqDist} """ return self._freqdist def prob(self, sample): return self._freqdist.freq(sample) def max(self): return self._freqdist.max() def samples(self): return self._freqdist.samples() def __repr__(self): """ @rtype: C{string} @return: A string representation of this C{ProbDist}. """ return '<MLEProbDist based on %d samples>' % self._freqdist.N() class LidstoneProbDist(ProbDistI): """ The Lidstone estimate for the probability distribution of the experiment used to generate a frequency distribution. The C{Lidstone estimate} is paramaterized by a real number M{gamma}, which typically ranges from 0 to 1. The X{Lidstone estimate} approximates the probability of a sample with count M{c} from an experiment with M{N} outcomes and M{B} bins as M{(c+gamma)/(N+B*gamma)}. This is equivalant to adding M{gamma} to the count for each bin, and taking the maximum likelihood estimate of the resulting frequency distribution. """ def __init__(self, freqdist, gamma, bins=None): """ Use the Lidstone estimate to create a probability distribution for the experiment used to generate C{freqdist}. @type freqdist: C{FreqDist} @param freqdist: The frequency distribution that the probability estimates should be based on. @type gamma: C{float} @param gamma: A real number used to paramaterize the estimate. The Lidstone estimate is equivalant to adding M{gamma} to the count for each bin, and taking the maximum likelihood estimate of the resulting frequency distribution. @type bins: C{int} @param bins: The number of sample values that can be generated by the experiment that is described by the probability distribution. This value must be correctly set for the probabilities of the sample values to sum to one. If C{bins} is not specified, it defaults to C{freqdist.B()}. """ if (bins == 0) or (bins is None and freqdist.N() == 0): name = self.__class__.__name__[:-8] raise ValueError('A %s probability distribution ' % name + 'must have at least one bin.') if (bins is not None) and (bins < freqdist.B()): name = self.__class__.__name__[:-8] raise ValueError('\nThe number of bins in a %s must be ' % name + 'greater than or equal to\nthe number of '+ 'bins in the FreqDist used to create it.') self._freqdist = freqdist self._gamma = float(gamma) self._N = self._freqdist.N() if bins is None: bins = freqdist.B() self._bins = bins def freqdist(self): """ @return: The frequency distribution that this probability distribution is based on. @rtype: C{FreqDist} """ return self._freqdist def prob(self, sample): c = self._freqdist.count(sample) return (c + self._gamma) / (self._N + self._bins * self._gamma) def max(self): # For Lidstone distributions, probability is monotonic with # frequency, so the most probable sample is the one that # occurs most frequently. return self._freqdist.max() def samples(self): return self._freqdist.samples() def __repr__(self): """ @rtype: C{string} @return: A string representation of this C{ProbDist}. """ return '<LidstoneProbDist based on %d samples>' % self._freqdist.N() class LaplaceProbDist(LidstoneProbDist): """ The Laplace estimate for the probability distribution of the experiment used to generate a frequency distribution. The X{Lidstone estimate} approximates the probability of a sample with count M{c} from an experiment with M{N} outcomes and M{B} bins as M{(c+1)/(N+B)}. This is equivalant to adding one to the count for each bin, and taking the maximum likelihood estimate of the resulting frequency distribution. """ def __init__(self, freqdist, bins=None): """ Use the Laplace estimate to create a probability distribution for the experiment used to generate C{freqdist}. @type freqdist: C{FreqDist} @param freqdist: The frequency distribution that the probability estimates should be based on. @type bins: C{int} @param bins: The number of sample values that can be generated by the experiment that is described by the probability distribution. This value must be correctly set for the probabilities of the sample values to sum to one. If C{bins} is not specified, it defaults to C{freqdist.B()}. """ LidstoneProbDist.__init__(self, freqdist, 1, bins) def __repr__(self): """ @rtype: C{string} @return: A string representation of this C{ProbDist}. """ return '<LaplaceProbDist based on %d samples>' % self._freqdist.N() class ELEProbDist(LidstoneProbDist): """ The expected likelihood estimate for the probability distribution of the experiment used to generate a frequency distribution. The X{expected likelihood estimate} approximates the probability of a sample with count M{c} from an experiment with M{N} outcomes and M{B} bins as M{(c+0.5)/(N+B/2)}. This is equivalant to adding 0.5 to the count for each bin, and taking the maximum likelihood estimate of the resulting frequency distribution. """ def __init__(self, freqdist, bins=None): """ Use the expected likelihood estimate to create a probability distribution for the experiment used to generate C{freqdist}. @type freqdist: C{FreqDist} @param freqdist: The frequency distribution that the probability estimates should be based on. @type bins: C{int} @param bins: The number of sample values that can be generated by the experiment that is described by the probability distribution. This value must be correctly set for the probabilities of the sample values to sum to one. If C{bins} is not specified, it defaults to C{freqdist.B()}. """ LidstoneProbDist.__init__(self, freqdist, 0.5, bins) def __repr__(self): """ @rtype: C{string} @return: A string representation of this C{ProbDist}. """ return '<ELEProbDist based on %d samples>' % self._freqdist.N() class HeldoutProbDist(ProbDistI): """ The heldout estimate for the probability distribution of the experiment used to generate two frequency distributions. These two frequency distributions are called the "heldout frequency distribution" and the "base frequency distribution." The X{heldout estimate} uses uses the X{heldout frequency distribution} to predict the probability of each sample, given its frequency in the X{base frequency distribution}. In particular, the heldout estimate approximates the probability for a sample that occurs M{r} times in the base distribution as the average frequency in the heldout distribution of all samples that occur M{r} times in the base distribution. This average frequency is M{Tr[r]/(Nr[r]*N)}, where: - M{Tr[r]} is the total count in the heldout distribution for all samples that occur M{r} times in the base distribution. - M{Nr[r]} is the number of samples that occur M{r} times in the base distribution. - M{N} is the number of outcomes recorded by the heldout frequency distribution. In order to increase the efficiency of the C{prob} member function, M{Tr[r]/(Nr[r]*N)} is precomputed for each value of M{r} when the C{HeldoutProbDist} is created. @type _estimate: C{list} of C{float} @ivar _estimate: A list mapping from M{r}, the number of times that a sample occurs in the base distribution, to the probability estimate for that sample. C{_estimate[M{r}]} is calculated by finding the average frequency in the heldout distribution of all samples that occur M{r} times in the base distribution. In particular, C{_estimate[M{r}]} = M{Tr[r]/(Nr[r]*N)}. @type _max_r: C{int} @ivar _max_r: The maximum number of times that any sample occurs in the base distribution. C{_max_r} is used to decide how large C{_estimate} must be. """ def __init__(self, base_fdist, heldout_fdist, bins=None): """ Use the heldout estimate to create a probability distribution for the experiment used to generate C{base_fdist} and C{heldout_fdist}. @type base_fdist: C{FreqDist} @param base_fdist: The base frequency distribution. @type heldout_fdist: C{FreqDist} @param heldout_fdist: The heldout frequency distribution. @type bins: C{int} @param bins: The number of sample values that can be generated by the experiment that is described by the probability distribution. This value must be correctly set for the probabilities of the sample values to sum to one. If C{bins} is not specified, it defaults to C{freqdist.B()}. """ self._base_fdist = base_fdist self._heldout_fdist = heldout_fdist # The max number of times any sample occurs in base_fdist. self._max_r = base_fdist.count(base_fdist.max()) # Calculate Tr, Nr, and N. Tr = self._calculate_Tr() Nr = [base_fdist.Nr(r, bins) for r in range(self._max_r+1)] N = heldout_fdist.N() # Use Tr, Nr, and N to compute the probability estimate for # each value of r. self._estimate = self._calculate_estimate(Tr, Nr, N) def _calculate_Tr(self): """ @return: the list M{Tr}, where M{Tr[r]} is the total count in C{heldout_fdist} for all samples that occur M{r} times in C{base_fdist}. @rtype: C{list} of C{float} """ Tr = [0.0] * (self._max_r+1) for sample in self._heldout_fdist.samples(): r = self._base_fdist.count(sample) Tr[r] += self._heldout_fdist.count(sample) return Tr def _calculate_estimate(self, Tr, Nr, N): """ @return: the list M{estimate}, where M{estimate[r]} is the probability estimate for any sample that occurs M{r} times in the base frequency distribution. In particular, M{estimate[r]} is M{Tr[r]/(N[r]*N)}. In the special case that M{N[r]=0}, M{estimate[r]} will never be used; so we define M{estimate[r]=None} for those cases. @rtype: C{list} of C{float} @type Tr: C{list} of C{float} @param Tr: the list M{Tr}, where M{Tr[r]} is the total count in the heldout distribution for all samples that occur M{r} times in base distribution. @type Nr: C{list} of C{float} @param Nr: The list M{Nr}, where M{Nr[r]} is the number of samples that occur M{r} times in the base distribution. @type N: C{int} @param N: The total number of outcomes recorded by the heldout frequency distribution. """ estimate = [] for r in range(self._max_r+1): if Nr[r] == 0: estimate.append(None) else: estimate.append(Tr[r]/(Nr[r]*N)) return estimate def base_fdist(self): """ @return: The base frequency distribution that this probability distribution is based on. @rtype: C{FreqDist} """ return self._base_fdist def heldout_fdist(self): """ @return: The heldout frequency distribution that this probability distribution is based on. @rtype: C{FreqDist} """ return self._heldout_fdist def prob(self, sample): # Use our precomputed probability estimate. r = self._base_fdist.count(sample) return self._estimate[r] def max(self): # Note: the Heldout estimation is *not* necessarily monotonic; # so this implementation is currently broken. However, it # should give the right answer *most* of the time. :) return self._base_fdist.max() def __repr__(self): """ @rtype: C{string} @return: A string representation of this C{ProbDist}. """ s = '<HeldoutProbDist: %d base samples; %d heldout samples>' return s % (self._base_fdist.N(), self._heldout_fdist.N()) class CrossValidationProbDist(ProbDistI): """ The cross-validation estimate for the probability distribution of the experiment used to generate a set of frequency distribution. The X{cross-validation estimate} for the probability of a sample is found by averaging the held-out estimates for the sample in each pair of frequency distributions. """ def __init__(self, freqdists, bins): """ Use the cross-validation estimate to create a probability distribution for the experiment used to generate C{freqdists}. @type freqdists: C{list} of C{FreqDist} @param freqdists: A list of the frequency distributions generated by the experiment. @type bins: C{int} @param bins: The number of sample values that can be generated by the experiment that is described by the probability distribution. This value must be correctly set for the probabilities of the sample values to sum to one. If C{bins} is not specified, it defaults to C{freqdist.B()}. """ self._freqdists = freqdists # Create a heldout probability distribution for each pair of # frequency distributions in freqdists. self._heldout_probdists = [] for fdist1 in freqdists: for fdist2 in freqdists: if fdist1 is not fdist2: probdist = HeldoutProbDist(fdist1, fdist2, bins) self._heldout_probdists.append(probdist) def freqdists(self): """ @rtype: C{list} of C{FreqDist} @return: The list of frequency distributions that this C{ProbDist} is based on. """ return self._freqdists def prob(self, sample): # Find the average probability estimate returned by each # heldout distribution. prob = 0.0 for heldout_probdist in self._heldout_probdists: prob += heldout_probdist.prob(sample) return prob/len(self._heldout_probdists) def __repr__(self): """ @rtype: C{string} @return: A string representation of this C{ProbDist}. """ return '<CrossValidationProbDist: %d-way>' % len(self._freqdists) class WittenBellProbDist(ProbDistI): """ The Witten-Bell estimate of a probability distribution. This distribution allocates uniform probability mass to as yet unseen events by using the number of events that have only been seen once. The probability mass reserved for unseen events is equal to: - M{T / (N + T)} where M{T} is the number of observed event types and M{N} is the total number of observed events. This equates to the maximum likelihood estimate of a new type event occuring. The remaining probability mass is discounted such that all probability estimates sum to one, yielding: - M{p = T / Z (N + T)}, if count = 0 - M{p = c / (N + T)}, otherwise """ def __init__(self, freqdist, bins=None): """ Creates a distribution of Witten-Bell probability estimates. This distribution allocates uniform probability mass to as yet unseen events by using the number of events that have only been seen once. The probability mass reserved for unseen events is equal to: - M{T / (N + T)} where M{T} is the number of observed event types and M{N} is the total number of observed events. This equates to the maximum likelihood estimate of a new type event occuring. The remaining probability mass is discounted such that all probability estimates sum to one, yielding: - M{p = T / Z (N + T)}, if count = 0 - M{p = c / (N + T)}, otherwise The parameters M{T} and M{N} are taken from the C{freqdist} parameter (the C{B()} and C{N()} values). The normalising factor M{Z} is calculated using these values along with the C{bins} parameter. @param freqdist: The frequency counts upon which to base the estimation. @type freqdist: C{FreqDist} @param bins: The number of possible event types. This must be at least as large as the number of bins in the C{freqdist}. If C{None}, then it's assumed to be equal to that of the C{freqdist} @type bins: C{Int} """ assert bins == None or bins >= freqdist.B(),\ 'Bins parameter must not be less than freqdist.B()' if bins == None: bins = freqdist.B() self._freqdist = freqdist self._T = self._freqdist.B() self._Z = bins - self._freqdist.B() self._N = self._freqdist.N() def prob(self, sample): # inherit docs from ProbDistI c = self._freqdist.count(sample) if c == 0: return self._T / float(self._Z * (self._N + self._T)) else: return c / float(self._N + self._T) def max(self): return self._freqdist.max() def samples(self): return self._freqdist.samples() def freqdist(self): return self._freqdist def __repr__(self): """ @rtype: C{string} @return: A string representation of this C{ProbDist}. """ return '<WittenBellProbDist based on %d samples>' % self._freqdist.N() class GoodTuringProbDist(ProbDistI): """ The Good-Turing estimate of a probability distribution. This method calculates the probability mass to assign to events with zero or low counts based on the number of events with higher counts. It does so by using the smoothed count M{c*}: - M{c* = (c + 1) N(c + 1) / N(c)} where M{c} is the original count, M{N(i)} is the number of event types observed with count M{i}. These smoothed counts are then normalised to yield a probability distribution. """ # TODO - add a cut-off parameter, above which the counts are unmodified # (see J&M p216) def __init__(self, freqdist, bins): """ Creates a Good-Turing probability distribution estimate. This method calculates the probability mass to assign to events with zero or low counts based on the number of events with higher counts. It does so by using the smoothed count M{c*}: - M{c* = (c + 1) N(c + 1) / N(c)} where M{c} is the original count, M{N(i)} is the number of event types observed with count M{i}. These smoothed counts are then normalised to yield a probability distribution. The C{bins} parameter allows C{N(0)} to be estimated. @param freqdist: The frequency counts upon which to base the estimation. @type freqdist: C{FreqDist} @param bins: The number of possible event types. This must be at least as large as the number of bins in the C{freqdist}. If C{None}, then it's taken to be equal to C{freqdist.B()}. @type bins: C{Int} """ assert bins == None or bins >= freqdist.B(),\ 'Bins parameter must not be less than freqdist.B()' if bins == None: bins = freqdist.B() self._freqdist = freqdist self._bins = bins def prob(self, sample): # inherit docs from FreqDist c = self._freqdist.count(sample) nc = self._freqdist.Nr(c, self._bins) ncn = self._freqdist.Nr(c + 1, self._bins) # avoid divide-by-zero errors for sparse datasets if nc == 0 or self._freqdist.N() == 0: return 0.0 return float(c + 1) * ncn / (nc * self._freqdist.N()) def max(self): return self._freqdist.max() def samples(self): return self._freqdist.samples() def freqdist(self): return self._freqdist def __repr__(self): """ @rtype: C{string} @return: A string representation of this C{ProbDist}. """ return '<GoodTuringProbDist based on %d samples>' % self._freqdist.N() class MutableProbDist(ProbDistI): """ An mutable probdist where the probabilities may be easily modified. This simply copies an existing probdist, storing the probability values in a mutable dictionary and providing an update method. """ def __init__(self, prob_dist, samples, store_logs=True): """ Creates the mutable probdist based on the given prob_dist and using the list of samples given. These values are stored as log probabilities if the store_logs flag is set. @param prob_dist: the distribution from which to garner the probabilities @type prob_dist: ProbDist @param samples: the complete set of samples @type samples: sequence of any @param store_logs: whether to store the probabilities as logarithms @type store_logs: bool """ self._samples = samples self._sample_dict = dict([(samples[i], i) for i in range(len(samples))]) try: self._data = numpy.zeros(len(samples), numpy.Float64) except: pass for i in range(len(samples)): if store_logs: self._data[i] = prob_dist.logprob(samples[i]) else: self._data[i] = prob_dist.prob(samples[i]) self._logs = store_logs def samples(self): # inherit documentation return self._samples def prob(self, sample): # inherit documentation i = self._sample_dict.get(sample) if i != None: if self._logs: return exp(self._data[i]) else: return self._data[i] else: return 0.0 def logprob(self, sample): # inherit documentation i = self._sample_dict.get(sample) if i != None: if self._logs: return self._data[i] else: return log(self._data[i]) else: return float('-inf') def update(self, sample, prob, log=True): """ Update the probability for the given sample. This may cause the object to stop being the valid probability distribution - the user must ensure that they update the sample probabilities such that all samples have probabilities between 0 and 1 and that all probabilities sum to one. @param sample: the sample for which to update the probability @type sample: C{any} @param prob: the new probability @type prob: C{float} @param log: is the probability already logged @type log: C{bool} """ i = self._sample_dict.get(sample) assert i != None if self._logs: if log: self._data[i] = prob else: self._data[i] = log(prob) else: if log: self._data[i] = exp(prob) else: self._data[i] = prob ##////////////////////////////////////////////////////// ## Probability Distribution Operations ##////////////////////////////////////////////////////// def log_likelihood(test_pdist, actual_pdist): # Is this right? return sum([actual_pdist.prob(s) * math.log(test_pdist.prob(s)) for s in actual_pdist.samples()]) ##////////////////////////////////////////////////////// ## Conditional Distributions ##////////////////////////////////////////////////////// class ConditionalFreqDist(object): """ A collection of frequency distributions for a single experiment run under different conditions. Conditional frequency distributions are used to record the number of times each sample occured, given the condition under which the experiment was run. For example, a conditional frequency distribution could be used to record the frequency of each word (type) in a document, given its length. Formally, a conditional frequency distribution can be defined as a function that maps from each condition to the C{FreqDist} for the experiment under that condition. The frequency distribution for each condition is accessed using the indexing operator: >>> cfdist[3] <FreqDist with 73 outcomes> >>> cfdist[3].freq('the') 0.4 >>> cfdist[3].count('dog') 2 When the indexing operator is used to access the frequency distribution for a condition that has not been accessed before, C{ConditionalFreqDist} creates a new empty C{FreqDist} for that condition. Conditional frequency distributions are typically constructed by repeatedly running an experiment under a variety of conditions, and incrementing the sample outcome counts for the appropriate conditions. For example, the following code will produce a conditional frequency distribution that encodes how often each word type occurs, given the length of that word type: >>> cfdist = ConditionalFreqDist() >>> for word in tokenize.whitespace(sent): ... condition = len(word) ... cfdist[condition].inc(word) """ def __init__(self): """ Construct a new empty conditional frequency distribution. In particular, the count for every sample, under every condition, is zero. """ self._fdists = {} def __getitem__(self, condition): """ Return the frequency distribution that encodes the frequency of each sample outcome, given that the experiment was run under the given condition. If the frequency distribution for the given condition has not been accessed before, then this will create a new empty C{FreqDist} for that condition. @return: The frequency distribution that encodes the frequency of each sample outcome, given that the experiment was run under the given condition. @rtype: C{FreqDist} @param condition: The condition under which the experiment was run. @type condition: any """ # Create the conditioned freq dist, if it doesn't exist if not self._fdists.has_key(condition): self._fdists[condition] = FreqDist() return self._fdists[condition] def conditions(self): """ @return: A list of the conditions that have been accessed for this C{ConditionalFreqDist}. Use the indexing operator to access the frequency distribution for a given condition. Note that the frequency distributions for some conditions may contain zero sample outcomes. @rtype: C{list} """ return self._fdists.keys() def __repr__(self): """ @return: A string representation of this C{ConditionalFreqDist}. @rtype: C{string} """ n = len(self._fdists) return '<ConditionalFreqDist with %d conditions>' % n class ConditionalProbDistI(object): """ A collection of probability distributions for a single experiment run under different conditions. Conditional probability distributions are used to estimate the likelihood of each sample, given the condition under which the experiment was run. For example, a conditional probability distribution could be used to estimate the probability of each word type in a document, given the length of the word type. Formally, a conditional probability distribution can be defined as a function that maps from each condition to the C{ProbDist} for the experiment under that condition. """ def __init__(self): raise AssertionError, 'ConditionalProbDistI is an interface' def __getitem__(self, condition): """ @return: The probability distribution for the experiment run under the given condition. @rtype: C{ProbDistI} @param condition: The condition whose probability distribution should be returned. @type condition: any """ raise AssertionError def conditions(self): """ @return: A list of the conditions that are represented by this C{ConditionalProbDist}. Use the indexing operator to access the probability distribution for a given condition. @rtype: C{list} """ raise AssertionError # For now, this is the only implementation of ConditionalProbDistI; # but we would want a different implementation if we wanted to build a # conditional probability distribution analytically (e.g., a gaussian # distribution), rather than basing it on an underlying frequency # distribution. class ConditionalProbDist(ConditionalProbDistI): """ A conditional probability distribution modelling the experiments that were used to generate a conditional frequency distribution. A C{ConditoinalProbDist} is constructed from a C{ConditionalFreqDist} and a X{C{ProbDist} factory}: - The B{C{ConditionalFreqDist}} specifies the frequency distribution for each condition. - The B{C{ProbDist} factory} is a function that takes a condition's frequency distribution, and returns its probability distribution. A C{ProbDist} class's name (such as C{MLEProbDist} or C{HeldoutProbDist}) can be used to specify that class's constructor. The first argument to the C{ProbDist} factory is the frequency distribution that it should model; and the remaining arguments are specified by the C{factory_args} parameter to the C{ConditionalProbDist} constructor. For example, the following code constructs a C{ConditionalProbDist}, where the probability distribution for each condition is an C{ELEProbDist} with 10 bins: >>> cpdist = ConditionalProbDist(cfdist, ELEProbDist, 10) >>> print cpdist['run'].max() 'NN' >>> print cpdist['run'].prob('NN') 0.0813 """ def __init__(self, cfdist, probdist_factory, supply_condition=False, *factory_args): """ Construct a new conditional probability distribution, based on the given conditional frequency distribution and C{ProbDist} factory. @type cfdist: L{ConditionalFreqDist} @param cfdist: The C{ConditionalFreqDist} specifying the frequency distribution for each condition. @type probdist_factory: C{class} or C{function} @param probdist_factory: The function or class that maps a condition's frequency distribution to its probability distribution. The function is called with the frequency distribution as its first argument, the condition as its second argument (only if C{supply_condition=True}), and C{factory_args} as its remaining arguments. @type supply_condition: C{bool} @param supply_condition: If true, then pass the condition as the second argument to C{probdist_factory}. @type factory_args: (any) @param factory_args: Extra arguments for C{probdist_factory}. These arguments are usually used to specify extra properties for the probability distributions of individual conditions, such as the number of bins they contain. """ self._probdist_factory = probdist_factory self._cfdist = cfdist self._supply_condition = supply_condition self._factory_args = factory_args self._pdists = {} for c in cfdist.conditions(): if supply_condition: pdist = probdist_factory(cfdist[c], c, *factory_args) else: pdist = probdist_factory(cfdist[c], *factory_args) self._pdists[c] = pdist def __getitem__(self, condition): if not self._pdists.has_key(condition): # If it's a condition we haven't seen, create a new prob # dist from the empty freq dist. Typically, this will # give a uniform prob dist. pdist = self._probdist_factory(FreqDist(), *self._factory_args) self._pdists[condition] = pdist return self._pdists[condition] def conditions(self): return self._pdists.keys() def __repr__(self): """ @return: A string representation of this C{ConditionalProbDist}. @rtype: C{string} """ n = len(self._pdists) return '<ConditionalProbDist with %d conditions>' % n class DictionaryConditionalProbDist(ConditionalProbDistI): """ An alternative ConditionalProbDist that simply wraps a dictionary of ProbDists rather than creating these from FreqDists. """ def __init__(self, probdist_dict): """ @param probdist_dict: a dictionary containing the probdists indexed by the conditions @type probdist_dict: dict any -> probdist """ self._dict = probdist_dict def __getitem__(self, condition): # inherit documentation # this will cause an exception for unseen conditions return self._dict[condition] def conditions(self): # inherit documentation return self._dict.keys() ##////////////////////////////////////////////////////// ## Adding in log-space. ##////////////////////////////////////////////////////// # If the difference is bigger than this, then just take the bigger one: _ADD_LOGS_MAX_DIFF = math.log(1e-30) def add_logs(logx, logy): """ Given two numbers C{logx}=M{log(x)} and C{logy}=M{log(y)}, return M{log(x+y)}. Conceptually, this is the same as returning M{log(exp(C{logx})+exp(C{logy}))}, but the actual implementation avoids overflow errors that could result from direct computation. """ if (logx < logy + _ADD_LOGS_MAX_DIFF): return logy if (logy < logx + _ADD_LOGS_MAX_DIFF): return logx base = min(logx, logy) return base + math.log(math.exp(logx-base) + math.exp(logy-base)) def sum_logs(logs): if len(logs) == 0: # Use some approximation to infinity. What this does # depends on your system's float implementation. return _NINF else: return reduce(add_logs, logs[1:], logs[0]) ##////////////////////////////////////////////////////// ## Probabilistic Mix-in ##////////////////////////////////////////////////////// class ProbabilisticMixIn(object): """ A mix-in class to associate probabilities with other classes (trees, rules, etc.). To use the C{ProbabilisticMixIn} class, define a new class that derives from an existing class and from ProbabilisticMixIn. You will need to define a new constructor for the new class, which explicitly calls the constructors of both its parent classes. For example: >>> class A: ... def __init__(self, x, y): self.data = (x,y) ... >>> class ProbabilisticA(A, ProbabilisticMixIn): ... def __init__(self, x, y, **prob_kwarg): ... A.__init__(self, x, y) ... ProbabilisticMixIn.__init__(self, **prob_kwarg) See the documentation for the ProbabilisticMixIn L{constructor<__init__>} for information about the arguments it expects. You should generally also redefine the string representation methods, the comparison methods, and the hashing method. """ def __init__(self, **kwargs): """ Initialize this object's probability. This initializer should be called by subclass constructors. C{prob} should generally be the first argument for those constructors. @kwparam prob: The probability associated with the object. @type prob: C{float} @kwparam logprob: The log of the probability associated with the object. @type logrpob: C{float} """ if 'prob' in kwargs: if 'logprob' in kwargs: raise TypeError('Must specify either prob or logprob ' '(not both)') else: ProbabilisticMixIn.set_prob(self, kwargs['prob']) elif 'logprob' in kwargs: ProbabilisticMixIn.set_logprob(self, kwargs['logprob']) else: self.__prob = self.__logprob = None def set_prob(self, prob): """ Set the probability associated with this object to C{prob}. @param prob: The new probability @type prob: C{float} """ self.__prob = prob self.__logprob = None def set_logprob(self, logprob): """ Set the log probability associated with this object to C{logprob}. I.e., set the probability associated with this object to C{exp(logprob)}. @param logprob: The new log probability @type logprob: C{float} """ self.__logprob = prob self.__prob = None def prob(self): """ @return: The probability associated with this object. @rtype: C{float} """ if self.__prob is None: if self.__logprob is None: return None self.__prob = math.exp(self.__logprob) return self.__prob def logprob(self): """ @return: C{log(p)}, where C{p} is the probability associated with this object. @rtype: C{float} """ if self.__logprob is None: if self.__prob is None: return None self.__logprob = math.log(self.__prob) return self.__logprob class ImmutableProbabilisticMixIn(ProbabilisticMixIn): def set_prob(self, prob): raise ValueError, '%s is immutable' % self.__class__.__name__ def set_logprob(self, prob): raise ValueError, '%s is immutable' % self.__class__.__name__ ##////////////////////////////////////////////////////// ## Demonstration ##////////////////////////////////////////////////////// def _create_rand_fdist(numsamples, numoutcomes): """ Create a new frequency distribution, with random samples. The samples are numbers from 1 to C{numsamples}, and are generated by summing two numbers, each of which has a uniform distribution. """ import random from math import sqrt fdist = FreqDist() for x in range(numoutcomes): y = (random.randint(1, (1+numsamples)/2) + random.randint(0, numsamples/2)) fdist.inc(y) return fdist def _create_sum_pdist(numsamples): """ Return the true probability distribution for the experiment C{_create_rand_fdist(numsamples, x)}. """ fdist = FreqDist() for x in range(1, (1+numsamples)/2+1): for y in range(0, numsamples/2+1): fdist.inc(x+y) return MLEProbDist(fdist) def demo(numsamples=6, numoutcomes=500): """ A demonstration of frequency distributions and probability distributions. This demonstration creates three frequency distributions with, and uses them to sample a random process with C{numsamples} samples. Each frequency distribution is sampled C{numoutcomes} times. These three frequency distributions are then used to build six probability distributions. Finally, the probability estimates of these distributions are compared to the actual probability of each sample. @type numsamples: C{int} @param numsamples: The number of samples to use in each demo frequency distributions. @type numoutcomes: C{int} @param numoutcomes: The total number of outcomes for each demo frequency distribution. These outcomes are divided into C{numsamples} bins. @rtype: C{None} """ # Randomly sample a stochastic process three times. fdist1 = _create_rand_fdist(numsamples, numoutcomes) fdist2 = _create_rand_fdist(numsamples, numoutcomes) fdist3 = _create_rand_fdist(numsamples, numoutcomes) # Use our samples to create probability distributions. pdists = [ MLEProbDist(fdist1), LidstoneProbDist(fdist1, 0.5, numsamples), HeldoutProbDist(fdist1, fdist2, numsamples), HeldoutProbDist(fdist2, fdist1, numsamples), CrossValidationProbDist([fdist1, fdist2, fdist3], numsamples), _create_sum_pdist(numsamples), ] # Find the probability of each sample. vals = [] for n in range(1,numsamples+1): vals.append(tuple([n, fdist1.freq(n)] + [pdist.prob(n) for pdist in pdists])) # Print the results in a formatted table. print ('%d samples (1-%d); %d outcomes were sampled for each FreqDist' % (numsamples, numsamples, numoutcomes)) print '='*9*(len(pdists)+2) FORMATSTR = ' FreqDist '+ '%8s '*(len(pdists)-1) + '| Actual' print FORMATSTR % tuple([`pdist`[1:9] for pdist in pdists[:-1]]) print '-'*9*(len(pdists)+2) FORMATSTR = '%3d %8.6f ' + '%8.6f '*(len(pdists)-1) + '| %8.6f' for val in vals: print FORMATSTR % val # Print the totals for each column (should all be 1.0) zvals = zip(*vals) def sum(lst): return reduce(lambda x,y:x+y, lst, 0) sums = [sum(val) for val in zvals[1:]] print '-'*9*(len(pdists)+2) FORMATSTR = 'Total ' + '%8.6f '*(len(pdists)) + '| %8.6f' print FORMATSTR % tuple(sums) print '='*9*(len(pdists)+2) # Display the distributions themselves, if they're short enough. if len(`str(fdist1)`) < 70: print ' fdist1:', str(fdist1) print ' fdist2:', str(fdist2) print ' fdist3:', str(fdist3) print if __name__ == '__main__': demo(6, 10) demo(5, 5000)

gpl-3.0

roxyboy/scikit-learn

examples/neighbors/plot_nearest_centroid.py

263

1804

""" =============================== Nearest Centroid Classification =============================== Sample usage of Nearest Centroid classification. It will plot the decision boundaries for each class. """ print(__doc__) import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap from sklearn import datasets from sklearn.neighbors import NearestCentroid n_neighbors = 15 # import some data to play with iris = datasets.load_iris() X = iris.data[:, :2] # we only take the first two features. We could # avoid this ugly slicing by using a two-dim dataset y = iris.target h = .02 # step size in the mesh # Create color maps cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF']) cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF']) for shrinkage in [None, 0.1]: # we create an instance of Neighbours Classifier and fit the data. clf = NearestCentroid(shrink_threshold=shrinkage) clf.fit(X, y) y_pred = clf.predict(X) print(shrinkage, np.mean(y == y_pred)) # Plot the decision boundary. For that, we will assign a color to each # point in the mesh [x_min, m_max]x[y_min, y_max]. x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape(xx.shape) plt.figure() plt.pcolormesh(xx, yy, Z, cmap=cmap_light) # Plot also the training points plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold) plt.title("3-Class classification (shrink_threshold=%r)" % shrinkage) plt.axis('tight') plt.show()

bsd-3-clause

boscotsang/BayesDigitClassify

classify_ternary.py

1

2776

import numpy from sklearn.metrics import confusion_matrix def load_data(): train_labels = [] with open('digitdata/traininglabels', 'rb') as f: for i, line in enumerate(f): train_labels.append(int(line)) train_labels = numpy.array(train_labels, dtype=int) train_x = numpy.zeros((train_labels.shape[0] * 28 * 28)) with open('digitdata/trainingimages', 'rb') as f: for i, line in enumerate(f): for j, char in enumerate(line.strip('\n')): if '+' == char: train_x[i * 28 + j] = 1 if '#' == char: train_x[i * 28 + j] = 2 train_x = numpy.array(train_x, dtype=int).reshape((train_labels.shape[0], 28 * 28)) test_labels = [] with open('digitdata/testlabels', 'rb') as f: for i, line in enumerate(f): test_labels.append(int(line)) test_labels = numpy.array(test_labels, dtype=int) test_x = numpy.zeros((test_labels.shape[0] * 28 * 28)) with open('digitdata/testimages', 'rb') as f: for i, line in enumerate(f): for j, char in enumerate(line.strip('\n')): if '+' == char: test_x[i * 28 + j] = 1 if '#' == char: test_x[i * 28 + j] = 2 test_x = numpy.array(test_x, dtype=int).reshape((test_labels.shape[0], 28 * 28)) return train_x, train_labels, test_x, test_labels class BayesClassifier(object): def __init__(self): self.bayesmatrix = None def fit(self, X, y): bayesmatrix = numpy.ones((10, 3, 28 * 28), dtype=numpy.float64) for k in xrange(10): for i in xrange(3): for j in xrange(X.shape[1]): bayesmatrix[k, i, j] = numpy.sum(X[y==k, j]==i) numclass = numpy.zeros(10) for i in xrange(10): numclass[i] = numpy.sum(y==i) + 1 bayesmatrix += 1 bayesmatrix /= numclass[:, numpy.newaxis, numpy.newaxis] self.bayesmatrix = bayesmatrix def predict(self, X): labels = [] for i in xrange(X.shape[0]): label = numpy.argmax(numpy.sum(numpy.log(self.bayesmatrix[:, 0, X[i, :]==0]), axis=1) + numpy.sum(numpy.log(self.bayesmatrix[:, 1, X[i, :]==1]), axis=1) + numpy.sum(numpy.log(self.bayesmatrix[:, 2, X[i, :]==2]), axis=1)) labels.append(label) return numpy.array(labels) if "__main__" == __name__: X, y, test_x, test_y = load_data() clf = BayesClassifier() clf.fit(X, y) pr = clf.predict(test_x) print "Confusion Matrix" print confusion_matrix(test_y, pr) print "Accuracy" print numpy.sum(pr == test_y) / float(test_y.shape[0])

mit

ephes/scikit-learn

sklearn/utils/tests/test_class_weight.py

139

11909

import numpy as np from sklearn.linear_model import LogisticRegression from sklearn.datasets import make_blobs from sklearn.utils.class_weight import compute_class_weight from sklearn.utils.class_weight import compute_sample_weight from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_warns def test_compute_class_weight(): # Test (and demo) compute_class_weight. y = np.asarray([2, 2, 2, 3, 3, 4]) classes = np.unique(y) cw = assert_warns(DeprecationWarning, compute_class_weight, "auto", classes, y) assert_almost_equal(cw.sum(), classes.shape) assert_true(cw[0] < cw[1] < cw[2]) cw = compute_class_weight("balanced", classes, y) # total effect of samples is preserved class_counts = np.bincount(y)[2:] assert_almost_equal(np.dot(cw, class_counts), y.shape[0]) assert_true(cw[0] < cw[1] < cw[2]) def test_compute_class_weight_not_present(): # Raise error when y does not contain all class labels classes = np.arange(4) y = np.asarray([0, 0, 0, 1, 1, 2]) assert_raises(ValueError, compute_class_weight, "auto", classes, y) assert_raises(ValueError, compute_class_weight, "balanced", classes, y) def test_compute_class_weight_invariance(): # Test that results with class_weight="balanced" is invariant wrt # class imbalance if the number of samples is identical. # The test uses a balanced two class dataset with 100 datapoints. # It creates three versions, one where class 1 is duplicated # resulting in 150 points of class 1 and 50 of class 0, # one where there are 50 points in class 1 and 150 in class 0, # and one where there are 100 points of each class (this one is balanced # again). # With balancing class weights, all three should give the same model. X, y = make_blobs(centers=2, random_state=0) # create dataset where class 1 is duplicated twice X_1 = np.vstack([X] + [X[y == 1]] * 2) y_1 = np.hstack([y] + [y[y == 1]] * 2) # create dataset where class 0 is duplicated twice X_0 = np.vstack([X] + [X[y == 0]] * 2) y_0 = np.hstack([y] + [y[y == 0]] * 2) # cuplicate everything X_ = np.vstack([X] * 2) y_ = np.hstack([y] * 2) # results should be identical logreg1 = LogisticRegression(class_weight="balanced").fit(X_1, y_1) logreg0 = LogisticRegression(class_weight="balanced").fit(X_0, y_0) logreg = LogisticRegression(class_weight="balanced").fit(X_, y_) assert_array_almost_equal(logreg1.coef_, logreg0.coef_) assert_array_almost_equal(logreg.coef_, logreg0.coef_) def test_compute_class_weight_auto_negative(): # Test compute_class_weight when labels are negative # Test with balanced class labels. classes = np.array([-2, -1, 0]) y = np.asarray([-1, -1, 0, 0, -2, -2]) cw = assert_warns(DeprecationWarning, compute_class_weight, "auto", classes, y) assert_almost_equal(cw.sum(), classes.shape) assert_equal(len(cw), len(classes)) assert_array_almost_equal(cw, np.array([1., 1., 1.])) cw = compute_class_weight("balanced", classes, y) assert_equal(len(cw), len(classes)) assert_array_almost_equal(cw, np.array([1., 1., 1.])) # Test with unbalanced class labels. y = np.asarray([-1, 0, 0, -2, -2, -2]) cw = assert_warns(DeprecationWarning, compute_class_weight, "auto", classes, y) assert_almost_equal(cw.sum(), classes.shape) assert_equal(len(cw), len(classes)) assert_array_almost_equal(cw, np.array([0.545, 1.636, 0.818]), decimal=3) cw = compute_class_weight("balanced", classes, y) assert_equal(len(cw), len(classes)) class_counts = np.bincount(y + 2) assert_almost_equal(np.dot(cw, class_counts), y.shape[0]) assert_array_almost_equal(cw, [2. / 3, 2., 1.]) def test_compute_class_weight_auto_unordered(): # Test compute_class_weight when classes are unordered classes = np.array([1, 0, 3]) y = np.asarray([1, 0, 0, 3, 3, 3]) cw = assert_warns(DeprecationWarning, compute_class_weight, "auto", classes, y) assert_almost_equal(cw.sum(), classes.shape) assert_equal(len(cw), len(classes)) assert_array_almost_equal(cw, np.array([1.636, 0.818, 0.545]), decimal=3) cw = compute_class_weight("balanced", classes, y) class_counts = np.bincount(y)[classes] assert_almost_equal(np.dot(cw, class_counts), y.shape[0]) assert_array_almost_equal(cw, [2., 1., 2. / 3]) def test_compute_sample_weight(): # Test (and demo) compute_sample_weight. # Test with balanced classes y = np.asarray([1, 1, 1, 2, 2, 2]) sample_weight = assert_warns(DeprecationWarning, compute_sample_weight, "auto", y) assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.]) sample_weight = compute_sample_weight("balanced", y) assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.]) # Test with user-defined weights sample_weight = compute_sample_weight({1: 2, 2: 1}, y) assert_array_almost_equal(sample_weight, [2., 2., 2., 1., 1., 1.]) # Test with column vector of balanced classes y = np.asarray([[1], [1], [1], [2], [2], [2]]) sample_weight = assert_warns(DeprecationWarning, compute_sample_weight, "auto", y) assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.]) sample_weight = compute_sample_weight("balanced", y) assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.]) # Test with unbalanced classes y = np.asarray([1, 1, 1, 2, 2, 2, 3]) sample_weight = assert_warns(DeprecationWarning, compute_sample_weight, "auto", y) expected_auto = np.asarray([.6, .6, .6, .6, .6, .6, 1.8]) assert_array_almost_equal(sample_weight, expected_auto) sample_weight = compute_sample_weight("balanced", y) expected_balanced = np.array([0.7777, 0.7777, 0.7777, 0.7777, 0.7777, 0.7777, 2.3333]) assert_array_almost_equal(sample_weight, expected_balanced, decimal=4) # Test with `None` weights sample_weight = compute_sample_weight(None, y) assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1., 1.]) # Test with multi-output of balanced classes y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1]]) sample_weight = assert_warns(DeprecationWarning, compute_sample_weight, "auto", y) assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.]) sample_weight = compute_sample_weight("balanced", y) assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.]) # Test with multi-output with user-defined weights y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1]]) sample_weight = compute_sample_weight([{1: 2, 2: 1}, {0: 1, 1: 2}], y) assert_array_almost_equal(sample_weight, [2., 2., 2., 2., 2., 2.]) # Test with multi-output of unbalanced classes y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1], [3, -1]]) sample_weight = assert_warns(DeprecationWarning, compute_sample_weight, "auto", y) assert_array_almost_equal(sample_weight, expected_auto ** 2) sample_weight = compute_sample_weight("balanced", y) assert_array_almost_equal(sample_weight, expected_balanced ** 2, decimal=3) def test_compute_sample_weight_with_subsample(): # Test compute_sample_weight with subsamples specified. # Test with balanced classes and all samples present y = np.asarray([1, 1, 1, 2, 2, 2]) sample_weight = assert_warns(DeprecationWarning, compute_sample_weight, "auto", y) assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.]) sample_weight = compute_sample_weight("balanced", y, range(6)) assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.]) # Test with column vector of balanced classes and all samples present y = np.asarray([[1], [1], [1], [2], [2], [2]]) sample_weight = assert_warns(DeprecationWarning, compute_sample_weight, "auto", y) assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.]) sample_weight = compute_sample_weight("balanced", y, range(6)) assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1.]) # Test with a subsample y = np.asarray([1, 1, 1, 2, 2, 2]) sample_weight = assert_warns(DeprecationWarning, compute_sample_weight, "auto", y, range(4)) assert_array_almost_equal(sample_weight, [.5, .5, .5, 1.5, 1.5, 1.5]) sample_weight = compute_sample_weight("balanced", y, range(4)) assert_array_almost_equal(sample_weight, [2. / 3, 2. / 3, 2. / 3, 2., 2., 2.]) # Test with a bootstrap subsample y = np.asarray([1, 1, 1, 2, 2, 2]) sample_weight = assert_warns(DeprecationWarning, compute_sample_weight, "auto", y, [0, 1, 1, 2, 2, 3]) expected_auto = np.asarray([1 / 3., 1 / 3., 1 / 3., 5 / 3., 5 / 3., 5 / 3.]) assert_array_almost_equal(sample_weight, expected_auto) sample_weight = compute_sample_weight("balanced", y, [0, 1, 1, 2, 2, 3]) expected_balanced = np.asarray([0.6, 0.6, 0.6, 3., 3., 3.]) assert_array_almost_equal(sample_weight, expected_balanced) # Test with a bootstrap subsample for multi-output y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1]]) sample_weight = assert_warns(DeprecationWarning, compute_sample_weight, "auto", y, [0, 1, 1, 2, 2, 3]) assert_array_almost_equal(sample_weight, expected_auto ** 2) sample_weight = compute_sample_weight("balanced", y, [0, 1, 1, 2, 2, 3]) assert_array_almost_equal(sample_weight, expected_balanced ** 2) # Test with a missing class y = np.asarray([1, 1, 1, 2, 2, 2, 3]) sample_weight = assert_warns(DeprecationWarning, compute_sample_weight, "auto", y, range(6)) assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1., 0.]) sample_weight = compute_sample_weight("balanced", y, range(6)) assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1., 0.]) # Test with a missing class for multi-output y = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1], [2, 2]]) sample_weight = assert_warns(DeprecationWarning, compute_sample_weight, "auto", y, range(6)) assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1., 0.]) sample_weight = compute_sample_weight("balanced", y, range(6)) assert_array_almost_equal(sample_weight, [1., 1., 1., 1., 1., 1., 0.]) def test_compute_sample_weight_errors(): # Test compute_sample_weight raises errors expected. # Invalid preset string y = np.asarray([1, 1, 1, 2, 2, 2]) y_ = np.asarray([[1, 0], [1, 0], [1, 0], [2, 1], [2, 1], [2, 1]]) assert_raises(ValueError, compute_sample_weight, "ni", y) assert_raises(ValueError, compute_sample_weight, "ni", y, range(4)) assert_raises(ValueError, compute_sample_weight, "ni", y_) assert_raises(ValueError, compute_sample_weight, "ni", y_, range(4)) # Not "auto" for subsample assert_raises(ValueError, compute_sample_weight, {1: 2, 2: 1}, y, range(4)) # Not a list or preset for multi-output assert_raises(ValueError, compute_sample_weight, {1: 2, 2: 1}, y_) # Incorrect length list for multi-output assert_raises(ValueError, compute_sample_weight, [{1: 2, 2: 1}], y_)

bsd-3-clause

fberanizo/neural_network

tests/ibovespa/mlp.py

1

5708

# -*- coding: utf-8 -*- import sys, os sys.path.insert(0, os.path.abspath('../..')) import unittest, pandas, numpy, datetime, itertools, mlp from sklearn import cross_validation, preprocessing class MLP(unittest.TestCase): """Test cases for Ibovespa tendency problem.""" grid_search = True def test_1(self): """Tests the accuracy of a MLP using k-folds validation method.""" # Read data from CSV files X_train, X_test, y_train, y_test = self.read_data() # Rescales data min_max_scaler = preprocessing.MinMaxScaler() X_train = min_max_scaler.fit_transform(X_train) X_test = min_max_scaler.fit_transform(X_test) y_train = min_max_scaler.fit_transform(y_train) y_test = min_max_scaler.fit_transform(y_test) n_folds = 5 accuracies = map(lambda x: 0, self.hipergrid()) for idx, hiperparams in enumerate(self.hipergrid()): skf = cross_validation.StratifiedKFold(y_train.flatten(), n_folds=n_folds) for fold, (train_index, test_index) in enumerate(skf): self.progress(((1.0+fold)+n_folds*idx)/(len(self.hipergrid())*n_folds)) X_train2, X_test2 = X_train[train_index], X_train[test_index] y_train2, y_test2 = y_train[train_index], y_train[test_index] classifier = mlp.MLP(**hiperparams).fit(X_train2, y_train2) accuracies[idx] += classifier.score(X_test2, y_test2) # Finds which hiperparams give maximum accuracy best_hiperparams = self.hipergrid()[accuracies.index(numpy.max(accuracies))] accuracy = classifier.score(X_test, y_test) print 'Acurácia no cj treino:' + str(numpy.max(accuracies)/n_folds) print 'Acurácia no cj teste:' + str(accuracy) print 'Melhores hiperparâmetros: ' + str(best_hiperparams) def read_data(self): """Reads and processes financial data from CSV files""" ibovespa = "%5EBVSP" america = ["%5EGSPC", "%5EDJI", "%5EMERV", "%5EMXX", "%5EIXIC", "%5EIPSA"] europe = ["%5EFTSE", "%5EGDAXI", "%5EFCHI", "FTSEMIB.MI", "%5EIBEX"] asia = ["%5EN225", "%5EHSI", "%5EBSESN", "%5ESSEC", "%5EJKSE"] continents = 3 stocks_per_continent = 5 time_window = 7 # 7 days prediction_range = 1 # 1 day stocks = america + europe + asia # Request stock data # data = {} # url = "http://ichart.finance.yahoo.com/table.csv?s=STOCK_NAME&g=d&a=0&b=1&c=2016&&ignore=.csv" # for stock_name in america + europe + asia + [ibovespa]: # print stock_name # s = requests.get(url.replace("STOCK_NAME", stock_name)).content # stock = pandas.read_csv(io.StringIO(s.decode('utf-8'))).set_index("Date") # stock.to_csv('input/' + stock_name + '.csv') ibovespa_data = pandas.read_csv('input/' + ibovespa + '.csv', parse_dates=['Date']) stock_data = pandas.DataFrame(data=[], columns=['Date','Open','High','Low','Close','Volume','Adj Close']) for stock in stocks: stock_data = stock_data.append(pandas.read_csv('input/' + stock + '.csv', parse_dates=['Date'])) train = pandas.DataFrame(data=[], columns=['Date', 'Trend']).set_index("Date") test = pandas.DataFrame(data=[], columns=['Date', 'Trend']).set_index("Date") for idx, ibovespa_data in ibovespa_data.iterrows(): trend = 0 if ibovespa_data["Close"] < ibovespa_data["Open"] else 1 start_date = ibovespa_data["Date"] + pandas.Timedelta('-1 days') end_date = ibovespa_data["Date"] + pandas.Timedelta('-1 days') mask = (stock_data['Date'] >= start_date) & (stock_data['Date'] <= end_date) stocks = stock_data.loc[mask]['Close'].tolist() columns = ['Date', 'Trend'] + range(len(stocks)) data = [ibovespa_data["Date"], trend] + stocks row = pandas.DataFrame([data], columns=columns).set_index("Date") # Data from last 3 months is test, the rest is train three_months_ago = pandas.to_datetime('today') + pandas.Timedelta('-90 days') if ibovespa_data["Date"] < three_months_ago: train = train.append(row) else: test = test.append(row) # Removes rows with NaN columns train.dropna(axis=0, how='any', inplace=True) test.dropna(axis=0, how='any', inplace=True) X_train = train[train.columns.tolist()[:-1]].as_matrix() y_train = train[train.columns.tolist()[-1:]].as_matrix() X_test = test[test.columns.tolist()[:-1]].as_matrix() y_test = test[test.columns.tolist()[-1:]].as_matrix() return X_train, X_test, y_train, y_test def hipergrid(self): """Hiperparameters for MLP""" hidden_layer_size = [{'hidden_layer_size':3},{'hidden_layer_size':5},{'hidden_layer_size':7}] learning_rate = [{'learning_rate':0.1},{'learning_rate':0.3},{'learning_rate':1}] grid = [] for hiperparams in itertools.product(hidden_layer_size, learning_rate): d = {} for hiperparam in hiperparams: d.update(hiperparam) grid.append(d) return grid def progress(self, percent): """Prints progress in stdout""" bar_length = 20 hashes = '#' * int(round(percent * bar_length)) spaces = ' ' * (bar_length - len(hashes)) sys.stdout.write("\rPerforming 5-folds grid search: [{0}] {1}%".format(hashes + spaces, int(round(percent * 100)))) sys.stdout.flush() if __name__ == '__main__': unittest.main()

bsd-2-clause

moverlan/LOTlib

LOTlib/Examples/SymbolicRegression/Galileo/Run.py

1

1312

# -*- coding: utf-8 -*- """ This uses Galileo's data on a falling ball. See: http://www.amstat.org/publications/jse/v3n1/datasets.dickey.html See also: Jeffreys, W. H., and Berger, J. O. (1992), "Ockham's Razor and Bayesian Analysis," American Scientist, 80, 64-72 (Erratum, p. 116). """ from LOTlib.Hypotheses.GaussianLOTHypothesis import GaussianLOTHypothesis from LOTlib.FiniteBestSet import FiniteBestSet from LOTlib.Inference.MetropolisHastings import MHSampler from LOTlib.Miscellaneous import qq from Data import data from Grammar import grammar from Utilities import make_h0 def run(*args): """The running function.""" # starting hypothesis -- here this generates at random h0 = GaussianLOTHypothesis(grammar) # We store the top 100 from each run pq = FiniteBestSet(N=100, max=True, key="posterior_score") pq.add(MHSampler(h0, data, STEPS, skip=SKIP)) return pq if __name__ == "__main__": CHAINS = 10 STEPS = 10000000 SKIP = 0 finitesample = FiniteBestSet(max=True) # the finite sample of all results = map(run, [ [None] ] * CHAINS ) # Run on a single core finitesample.merge(results) ## and display for r in finitesample.get_all(decreasing=False, sorted=True): print r.posterior_score, r.prior, r.likelihood, qq(str(r))

gpl-3.0

cpausmit/IntelROCCS

Detox/python/siteProperties.py

3

12964

#==================================================================================================== # C L A S S E S concerning the site description #==================================================================================================== #--------------------------------------------------------------------------------------------------- """ Class: SiteProperties(siteName='') Each site will be fully described for our application in this class. """ #--------------------------------------------------------------------------------------------------- import time, statistics class SiteProperties: "A SiteProperties defines all needed site properties." def __init__(self, siteName): self.name = siteName self.datasetRanks = {} self.rankSum = 0 self.datasetSizes = {} self.dsetIsValid = {} self.dsetIsCustodial = {} self.dsetLastCopy = {} self.dsetIsPartial = {} self.deprecated = {} self.dsetReqTime = {} self.dsetUpdTime = {} self.dsetIsDone = {} self.dsetNotUsedOnTape = {} self.wishList = [] self.datasetsToDelete = [] self.protectedList = [] self.siteSizeGbV = 0 self.spaceTakenV = 0 self.spaceNotUsed = 0 self.spaceLCp = 0 self.space2free = 0 self.deleted = 0 self.protected = 0 self.globalDsetIndex = 0 self.epochTime = int(time.time()) def addDataset(self,dset,rank,size,valid,partial,custodial,depr,reqtime,updtime,wasused,isdone): self.dsetIsValid[dset] = valid self.dsetIsPartial[dset] = partial self.dsetIsCustodial[dset] = custodial self.datasetRanks[dset] = rank self.datasetSizes[dset] = size if depr: self.deprecated[dset] = depr self.spaceTakenV = self.spaceTakenV + size self.dsetIsDone[dset] = isdone self.dsetReqTime[dset] = reqtime self.dsetUpdTime[dset] = updtime self.rankSum = self.rankSum + rank*size if wasused == 0: self.spaceNotUsed = self.spaceNotUsed + size def makeWishList(self, dataPropers, ncopyMin, banInvalid=True): space = 0 self.wishList = [] space2free = self.space2free addedExtra = 0 counter = 0 for datasetName in sorted(self.datasetRanks.keys(), cmp=self.compare): counter = counter + 1 if counter < self.globalDsetIndex: continue if space > (space2free-self.deleted): break if datasetName in self.datasetsToDelete: continue if datasetName in self.protectedList: continue #custodial set can't be on deletion wish list if self.dsetIsCustodial[datasetName] : continue #if dataPropers[datasetName].daysSinceUsed() > 540: if dataPropers[datasetName].isFullOnTape(): #delta = (self.epochTime - self.dsetUpdTime[datasetName])/(60*60*24) if dataPropers[datasetName].getGlobalRank() > 500: #if delta > 500: space = space + self.datasetSizes[datasetName] self.wishList.append(datasetName) dataPropers[datasetName].kickFromPool = True print "exp at " + self.name + ": " + datasetName #print datasetName #addedExtra = addedExtra + 1 continue if "/RECO" in datasetName: delta = (self.epochTime - self.dsetUpdTime[datasetName])/(60*60*24) #if dataPropers[datasetName].daysSinceUsed() > 180 and delta>180: if delta > 180: space = space + self.datasetSizes[datasetName] self.wishList.append(datasetName) dataPropers[datasetName].kickFromPool = True print "RECO " + self.name + ": " + datasetName continue else: continue #non-valid dataset can't be on deletion list if banInvalid == True: if not self.dsetIsValid[datasetName]: continue dataPr = dataPropers[datasetName] if dataPr.nSites() > ncopyMin: space = space + self.datasetSizes[datasetName] self.wishList.append(datasetName) self.globalDsetIndex = counter def hasMoreToDelete(self, dataPropers, ncopyMin, banInvalid): counter = 0 if self.globalDsetIndex >= len(self.datasetRanks.keys()): return False for datasetName in sorted(self.datasetRanks.keys(), cmp=self.compare): counter = counter + 1 if counter < self.globalDsetIndex: continue if '/MINIAOD' in datasetName: ncopyMinTemp = 3 else: ncopyMinTemp = ncopyMin if datasetName in self.datasetsToDelete: continue if datasetName in self.protectedList: continue #custodial set can't be on deletion wish list if self.dsetIsCustodial[datasetName] : continue #non-valid dataset can't be on deletion list if banInvalid == True: if not self.dsetIsValid[datasetName]: continue if datasetName in self.wishList: continue dataPr = dataPropers[datasetName] if dataPr.nSites() <= ncopyMinTemp: continue return True return False def onWishList(self,dset): if dset in self.wishList: return True return False def onProtectedList(self,dset): if dset in self.protectedList: return True return False def wantToDelete(self): if self.deleted < self.space2free: return True else: return False def grantWish(self,dset): if dset in self.protectedList: return False if dset in self.datasetsToDelete: return False #if self.deleted > self.space2free: # return False self.datasetsToDelete.append(dset) self.deleted = self.deleted + self.datasetSizes[dset] return True def revokeWish(self,dset): if dset in self.datasetsToDelete: self.datasetsToDelete.remove(dset) self.deleted = self.deleted - self.datasetSizes[dset] def canBeLastCopy(self,dset,banInvalid): if not banInvalid: return True #can't be partial dataset if dset not in self.dsetIsPartial: return False if self.dsetIsPartial[dset] : return False #can't be non-valid dataset if not self.dsetIsValid[dset]: return False return True def pinDataset(self,dset): if dset in self.datasetsToDelete: return False #can't pin partial dataset if self.dsetIsPartial[dset] : return False #can't pin non-valid dataset if not self.dsetIsValid[dset]: return False self.protectedList.append(dset) self.protected = self.protected + self.datasetSizes[dset] if dset in self.wishList: self.wishList.remove(dset) return True def lastCopySpace(self,datasets,nCopyMin): space = 0 self.dsetLastCopy = {} for dset in self.datasetSizes.keys(): if dset in self.datasetsToDelete: continue dataset = datasets[dset] remaining = dataset.nSites() - dataset.nBeDeleted() if remaining <= nCopyMin: self.dsetLastCopy[dset] = 1 space = space + self.datasetSizes[dset] self.spaceLCp = space def setSiteSize(self,size): self.siteSizeGbV = size def siteSizeGb(self): return self.siteSizeGbV def dsetRank(self,set): return self.datasetRanks[set] def dsetSize(self,set): return self.datasetSizes[set] def isPartial(self,set): return self.dsetIsPartial[set] def siteName(self): return self.name def spaceTaken(self): return self.spaceTakenV def spaceDeleted(self): return self.deleted def spaceProtected(self): return self.protected def spaceFree(self): return self.siteSizeGbV - (self.spaceTakenV - self.deleted) def spaceLastCp(self): return self.spaceLCp def isDeprecated(self,dset): if dset in self.deprecated: return True return False def spaceDeprecated(self): size = 0 for dset in self.deprecated: size = size + self.datasetSizes[dset] return size def spaceIncomplete(self): size = 0; for dset in self.dsetIsPartial: if self.dsetIsPartial[dset]: size = size + self.datasetSizes[dset] return size def spaceCustodial(self): size = 0; for dset in self.dsetIsCustodial: if self.dsetIsCustodial[dset]: size = size + self.datasetSizes[dset] return size def spaceUtouchable(self): size = 0 for dset in self.dsetLastCopy: size = size + self.datasetSizes[dset] for dset in self.dsetIsCustodial: if dset in self.dsetLastCopy: continue if self.dsetIsCustodial[dset]: size = size + self.datasetSizes[dset] return size def nsetsDeprecated(self): nsets = 0 for dset in self.deprecated: nsets = nsets + 1 return nsets def hasDataset(self,dset): if dset in self.datasetRanks: return True else: return False def willDelete(self,dset): if dset in self.datasetsToDelete: return True else: return False def allSets(self): return sorted(self.datasetRanks.keys(), cmp=self.compare) def delTargets(self): return sorted(self.datasetsToDelete, cmp=self.compare) def protectedSets(self): return sorted(self.protectedList, cmp=self.compare) def setSpaceToFree(self,size): self.space2free = size def reqTime(self,dset): return self.dsetReqTime[dset] def dsetLoadTime(self,dset): return (self.dsetUpdTime[dset] - self.dsetReqTime[dset]) def spaceUnused(self): return self.spaceNotUsed def siteRank(self): if self.spaceTakenV == 0: return 0 return self.rankSum/self.spaceTakenV def medianRank(self): if len(self.datasetRanks.values()) > 0: return statistics.median(self.datasetRanks.values()) return 0 def dsetIsStuck(self,dset): if self.dsetIsDone[dset] == 0: reqtime = self.dsetReqTime[dset] if (self.epochTime - reqtime) > 60*60*24*14: return 1 return 0 def considerForStats(self,dset): if self.dsetLoadTime(dset) > 60*60*24*14: return False if self.dsetLoadTime(dset) <= 0: return False if (self.epochTime - self.dsetReqTime[dset]) > 60*60*24*90: return False return True def getDownloadStats(self): loadSize = 0 loadTime = 0 stuck = 0 for dset in self.datasetSizes: if self.dsetIsStuck(dset) == 1: stuck = stuck + 1 continue if not self.considerForStats(dset): continue if self.datasetSizes[dset] > 10: loadSize = loadSize + self.datasetSizes[dset] loadTime = loadTime + self.dsetLoadTime(dset) speed = 0 if loadTime > 0: speed = loadSize/loadTime*(60*60*24) return (speed, loadSize, stuck) def getAverage(self,array): if len(array) < 3: return 0 sortA = sorted(array) diff = 100 prevMean = sortA[len(sortA)/2] prevRms = sortA[len(sortA)-1] - sortA[0] print sortA while diff > 0.01: ave = 0 aveSq = 0 nit = 0 for i in range(1, len(sortA)): if abs(sortA[i] - prevMean) > 1.6*prevRms: continue ave = ave + sortA[i] aveSq = aveSq + sortA[i]*sortA[i] nit = nit + 1 ave = ave/nit rms = math.sqrt(aveSq/nit - ave*ave) diff = abs(ave - prevMean)/prevMean prevMean = ave prevRms = rms return prevMean def compare(self,item1, item2): r1 = self.datasetRanks[item1] r2 = self.datasetRanks[item2] if r1 < r2: return 1 elif r1 > r2: return -1 else: return 0

mit

google-research/selfstudy-adversarial-robustness

defense_randomneuron/model.py

1

2860

# Copyright 2021 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Model of the defense 0.""" import numpy as np import tensorflow as tf from common.framework import DefenseModel, get_checkpoint_abs_path from common.networks import AllConvModel, AllConvModelTorch import common.utils as utils MODEL_PATH = 'checkpoints/baseline/final_checkpoint-1' class RandomDropModel(AllConvModel): def __call__(self, x, training=False): del training for layer in self.layers: x = layer(x) if isinstance(layer, tf.keras.layers.Conv2D): _,a,b,c = x.shape p = tf.abs(x)/tf.reduce_sum(tf.abs(x), axis=(1,2,3), keepdims=True) p_keep = 1-tf.exp(-a*b*c / 3 * p) keep = tf.random.uniform(p_keep.shape)<p_keep x = tf.cast(keep, tf.float32)*x/p_keep return x class Defense(DefenseModel): def __init__(self): self.convnet = RandomDropModel(num_classes=10, num_filters=64, input_shape=[32, 32, 3]) tf.train.Checkpoint(model=self.convnet).restore( get_checkpoint_abs_path(MODEL_PATH)) self.to_tensor = lambda x: x def classify(self, x): preds = [utils.to_numpy(self.convnet(self.to_tensor(x))) for _ in range(10)] return np.mean(preds, axis=0) class RandomDropModelTorch(AllConvModelTorch): def __call__(self, x, training=False): import torch del training for layer in self.layers: x = layer(x) if isinstance(layer, torch.nn.Conv2d): _,a,b,c = x.shape p = torch.abs(x)/torch.sum(torch.abs(x), axis=(1,2,3), keepdims=True) p_keep = 1-torch.exp(-a*b*c / 3 * p) keep = torch.rand(p_keep.shape)<p_keep x = keep.float()*x/p_keep return x class DefenseTorch(Defense): def __init__(self): import torch self.convnet = RandomDropModelTorch(num_classes=10, num_filters=64, input_shape=[3, 32, 32]) self.convnet.load_state_dict( torch.load(get_checkpoint_abs_path(MODEL_PATH) + ".torchmodel")) self.to_tensor = torch.tensor

apache-2.0

roxyboy/scikit-learn

sklearn/neighbors/tests/test_nearest_centroid.py

302

4121

""" Testing for the nearest centroid module. """ import numpy as np from scipy import sparse as sp from numpy.testing import assert_array_equal from numpy.testing import assert_equal from sklearn.neighbors import NearestCentroid from sklearn import datasets from sklearn.metrics.pairwise import pairwise_distances # toy sample X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]] X_csr = sp.csr_matrix(X) # Sparse matrix y = [-1, -1, -1, 1, 1, 1] T = [[-1, -1], [2, 2], [3, 2]] T_csr = sp.csr_matrix(T) true_result = [-1, 1, 1] # also load the iris dataset # and randomly permute it iris = datasets.load_iris() rng = np.random.RandomState(1) perm = rng.permutation(iris.target.size) iris.data = iris.data[perm] iris.target = iris.target[perm] def test_classification_toy(): # Check classification on a toy dataset, including sparse versions. clf = NearestCentroid() clf.fit(X, y) assert_array_equal(clf.predict(T), true_result) # Same test, but with a sparse matrix to fit and test. clf = NearestCentroid() clf.fit(X_csr, y) assert_array_equal(clf.predict(T_csr), true_result) # Fit with sparse, test with non-sparse clf = NearestCentroid() clf.fit(X_csr, y) assert_array_equal(clf.predict(T), true_result) # Fit with non-sparse, test with sparse clf = NearestCentroid() clf.fit(X, y) assert_array_equal(clf.predict(T_csr), true_result) # Fit and predict with non-CSR sparse matrices clf = NearestCentroid() clf.fit(X_csr.tocoo(), y) assert_array_equal(clf.predict(T_csr.tolil()), true_result) def test_precomputed(): clf = NearestCentroid(metric="precomputed") clf.fit(X, y) S = pairwise_distances(T, clf.centroids_) assert_array_equal(clf.predict(S), true_result) def test_iris(): # Check consistency on dataset iris. for metric in ('euclidean', 'cosine'): clf = NearestCentroid(metric=metric).fit(iris.data, iris.target) score = np.mean(clf.predict(iris.data) == iris.target) assert score > 0.9, "Failed with score = " + str(score) def test_iris_shrinkage(): # Check consistency on dataset iris, when using shrinkage. for metric in ('euclidean', 'cosine'): for shrink_threshold in [None, 0.1, 0.5]: clf = NearestCentroid(metric=metric, shrink_threshold=shrink_threshold) clf = clf.fit(iris.data, iris.target) score = np.mean(clf.predict(iris.data) == iris.target) assert score > 0.8, "Failed with score = " + str(score) def test_pickle(): import pickle # classification obj = NearestCentroid() obj.fit(iris.data, iris.target) score = obj.score(iris.data, iris.target) s = pickle.dumps(obj) obj2 = pickle.loads(s) assert_equal(type(obj2), obj.__class__) score2 = obj2.score(iris.data, iris.target) assert_array_equal(score, score2, "Failed to generate same score" " after pickling (classification).") def test_shrinkage_threshold_decoded_y(): clf = NearestCentroid(shrink_threshold=0.01) y_ind = np.asarray(y) y_ind[y_ind == -1] = 0 clf.fit(X, y_ind) centroid_encoded = clf.centroids_ clf.fit(X, y) assert_array_equal(centroid_encoded, clf.centroids_) def test_predict_translated_data(): # Test that NearestCentroid gives same results on translated data rng = np.random.RandomState(0) X = rng.rand(50, 50) y = rng.randint(0, 3, 50) noise = rng.rand(50) clf = NearestCentroid(shrink_threshold=0.1) clf.fit(X, y) y_init = clf.predict(X) clf = NearestCentroid(shrink_threshold=0.1) X_noise = X + noise clf.fit(X_noise, y) y_translate = clf.predict(X_noise) assert_array_equal(y_init, y_translate) def test_manhattan_metric(): # Test the manhattan metric. clf = NearestCentroid(metric='manhattan') clf.fit(X, y) dense_centroid = clf.centroids_ clf.fit(X_csr, y) assert_array_equal(clf.centroids_, dense_centroid) assert_array_equal(dense_centroid, [[-1, -1], [1, 1]])

bsd-3-clause

JustinNoel1/ML-Course

linear-regression/python/linreg.py

1

1895

from sklearn.linear_model import LinearRegression import numpy as np import matplotlib.pyplot as plt from matplotlib import cm from sklearn.metrics import mean_squared_error # Fix the number of samples and our seed NUM_SAMPLES = 200 np.random.seed(42) # Our "true function" def f(x): return 1.5*x + 0.5 #Construct array of (x,f(x))-pairs where x is sampled randomly from unit interval data = np.array([[x,f(x) ] for x in np.random.random(NUM_SAMPLES)]) # Create regular grid of x values and the values of f gridx = np.linspace(0, 1, 200) gridy = np.array([f(x) for x in gridx]) # Add Gaussian noise with sigma=0.6 normaly = data[:,1]+0.6*np.random.randn(NUM_SAMPLES) #Plot the messy data plt.scatter(data[:,0], normaly ) plt.title("Scatter plot of synthetic data with normal errors") #Plot the true function plt.plot(gridx, gridy, label = "True function", color = 'Red') plt.legend(loc = 2) # Save and clear plt.savefig("scatter_normal.png") plt.cla() # Fit linear regressors to increasingly large intervals of data lm = LinearRegression() for i in range(1, NUM_SAMPLES+1): # Fit the regressor lm.fit(data[:i,0].reshape((i,1)), normaly[:i].reshape((i,1))) # Get the predictions on all of the sample points predictions = lm.predict(data[:,0].reshape(NUM_SAMPLES,1)) # Get MSE mse = mean_squared_error(predictions, normaly) # Plot the messy data plt.scatter(data[:,0], normaly) plt.title("Linear regression on {} points with normal error".format(i)) # Plot the true function plt.plot(gridx, gridy, label = "True function", color = 'Red') # Plot the regression line plt.plot(gridx, [lm.coef_[0] * x + lm.intercept_[0] for x in gridx], label = "Linear regressor line MSE = {:0.4f}".format(mse), color = 'Green') plt.legend(loc = 2) # Save and clear plt.savefig("linreg_normal_{:03d}.png".format(i)) plt.cla()

apache-2.0

SKA-INAF/caesar

scripts/skymodel_generator.py

1

65709

#!/usr/bin/env python ################################################## ### MODULE IMPORT ################################################## ## STANDARD MODULES import os import sys import subprocess import string import time import signal from threading import Thread import datetime import numpy as np import random import math ##from ctypes import * ## ASTRO from scipy import ndimage ##import pyfits from astropy.io import fits from astropy.units import Quantity from astropy.modeling.parameters import Parameter from astropy.modeling.core import Fittable2DModel from astropy.modeling.models import Box2D, Gaussian2D, Ring2D, Ellipse2D, TrapezoidDisk2D, Disk2D, AiryDisk2D, Sersic2D #from photutils.datasets import make_noise_image from astropy import wcs ## ROOT import ROOT from ROOT import gSystem, TFile, TTree, gROOT, AddressOf ## CAESAR gSystem.Load('libCaesar') from ROOT import Caesar ## COMMAND-LINE ARG MODULES import getopt import argparse import collections ## Graphics modules import matplotlib.pyplot as plt import pylab ## LOGGER import logging import logging.config logger = logging.getLogger(__name__) logging.basicConfig(format="%(asctime)-15s %(levelname)s - %(message)s",datefmt='%Y-%m-%d %H:%M:%S') logger= logging.getLogger(__name__) logger.setLevel(logging.INFO) ################################################## #### GET SCRIPT ARGS #### def str2bool(v): if v.lower() in ('yes', 'true', 't', 'y', '1'): return True elif v.lower() in ('no', 'false', 'f', 'n', '0'): return False else: raise argparse.ArgumentTypeError('Boolean value expected.') def get_args(): """This function parses and return arguments passed in""" parser = argparse.ArgumentParser(description="Parse args.") # - GENERAL IMAGE OPTIONS parser.add_argument('-nx', '--nx', dest='nx', required=True, type=int, action='store',help='Image width in pixels') parser.add_argument('-ny', '--ny', dest='ny', required=True, type=int, action='store',help='Image height in pixels') parser.add_argument('-marginx', '--marginx', dest='marginx', required=False, type=int, default=0,action='store',help='Image x margin in pixels') parser.add_argument('-marginy', '--marginy', dest='marginy', required=False, type=int, default=0,action='store',help='Image y margin in pixels') parser.add_argument('-pixsize', '--pixsize', dest='pixsize', required=True, type=float, action='store',help='Map pixel size in arcsec') parser.add_argument('-bmaj', '--bmaj', dest='bmaj', required=True, type=float, default=10, action='store',help='Beam bmaj in arcsec (default=5)') parser.add_argument('-bmin', '--bmin', dest='bmin', required=True, type=float, default=5, action='store',help='Beam bmin in arcsec (default=5)') parser.add_argument('-bpa', '--bpa', dest='bpa', required=False, type=float, default=0, action='store',help='Beam bpa in deg (default=0)') parser.add_argument('-crpix1', '--crpix1', dest='crpix1', required=False, type=float, default=1, action='store',help='CRPIX1 fits keyword (default=1)') parser.add_argument('-crpix2', '--crpix2', dest='crpix2', required=False, type=float, default=1, action='store',help='CRPIX2 fits keyword (default=1)') parser.add_argument('-crval1', '--crval1', dest='crval1', required=False, type=float, default=254.851041667, action='store',help='CRVAL1 fits keyword (default=1)') parser.add_argument('-crval2', '--crval2', dest='crval2', required=False, type=float, default=-41.4765888889, action='store',help='CRVAL2 fits keyword (default=1)') #parser.add_argument('-ctype1', '--ctype1', dest='ctype1', required=False, type=str, default='RA---NCP', action='store',help='CTYPE1 fits keyword (default=1)') #parser.add_argument('-ctype2', '--ctype2', dest='ctype2', required=False, type=str, default='DEC--NCP', action='store',help='CTYPE2 fits keyword (default=1)') parser.add_argument('-ctype1', '--ctype1', dest='ctype1', required=False, type=str, default='RA---SIN', action='store',help='CTYPE1 fits keyword (default=1)') parser.add_argument('-ctype2', '--ctype2', dest='ctype2', required=False, type=str, default='DEC--SIN', action='store',help='CTYPE2 fits keyword (default=1)') parser.add_argument('-maskimg', '--maskimg', dest='maskimg', required=False, type=str, default='', action='store',help='FITS image used as mask (not generating sources if mask pixel!=0)') # - BKG OPTIONS parser.add_argument('--bkg', dest='enable_bkg', action='store_true') parser.add_argument('--no-bkg', dest='enable_bkg', action='store_false') parser.set_defaults(enable_bkg=True) parser.add_argument('-bkg_level', '--bkg_level', dest='bkg_level', required=False, type=float, default=10e-6, action='store',help='Bkg level (default=0)') parser.add_argument('-bkg_rms', '--bkg_rms', dest='bkg_rms', required=False, type=float, default=100e-6, action='store',help='Bkg rms (default=0)') # - COMPACT SOURCE OPTIONS parser.add_argument('-npixels_min', '--npixels_min', dest='npixels_min', required=False, type=int, default=5, action='store',help='Minimum number of pixels for a generated source (default=5)') parser.add_argument('--compactsources', dest='enable_compactsources', action='store_true') parser.add_argument('--no-compactsources', dest='enable_compactsources', action='store_false') parser.set_defaults(enable_compactsources=True) parser.add_argument('-nsources', '--nsources', dest='nsources', required=False, type=int, default=0, action='store',help='Compact source number (if >0 overrides the density generation) (default=0)') parser.add_argument('-nx_gen', '--nx_gen', dest='nx_gen', required=False, type=int, default=501, action='store',help='Blob image width in pixels') parser.add_argument('-ny_gen', '--ny_gen', dest='ny_gen', required=False, type=int, default=501, action='store',help='Blob image height in pixels') parser.add_argument('-zmin', '--zmin', dest='zmin', required=False, type=float, default=1, action='store',help='Minimum source significance level in sigmas above the bkg (default=1)') parser.add_argument('-zmax', '--zmax', dest='zmax', required=False, type=float, default=30, action='store',help='Maximum source significance level in sigmas above the bkg (default=30)') parser.add_argument('-source_density', '--source_density', dest='source_density', required=False, type=float, default=1000, action='store',help='Compact source density (default=1000)') parser.add_argument('-bmaj_min', '--bmaj_min', dest='bmaj_min', required=False, type=float, default=4, action='store',help='Gaussian components min bmaj in arcsec (default=4)') parser.add_argument('-bmaj_max', '--bmaj_max', dest='bmaj_max', required=False, type=float, default=10, action='store',help='Gaussian components max bmaj in arcsec (default=10)') parser.add_argument('-bmin_min', '--bmin_min', dest='bmin_min', required=False, type=float, default=4, action='store',help='Gaussian components min bmin in arcsec (default=4)') parser.add_argument('-bmin_max', '--bmin_max', dest='bmin_max', required=False, type=float, default=10, action='store',help='Gaussian components max bmin in arcsec (default=10)') parser.add_argument('-pa_min', '--pa_min', dest='pa_min', required=False, type=float, default=-90, action='store',help='Gaussian components min position angle in deg (default=0)') parser.add_argument('-pa_max', '--pa_max', dest='pa_max', required=False, type=float, default=90, action='store',help='Gaussian components max position angle in deg (default=180)') parser.add_argument('-Smin', '--Smin', dest='Smin', required=False, type=float, default=1.e-6, action='store',help='Minimum source flux in Jy (default=1.e-6)') parser.add_argument('-Smax', '--Smax', dest='Smax', required=False, type=float, default=1, action='store',help='Maximum source flux in Jy (default=1)') parser.add_argument('-Smodel', '--Smodel', dest='Smodel', required=False, type=str, default='uniform', action='store',help='Source flux generation model (default=uniform)') parser.add_argument('-Sslope', '--Sslope', dest='Sslope', required=False, type=float, default=1.6, action='store',help='Slope par in expo source flux generation model (default=1.6)') # - EXTENDED SOURCES parser.add_argument('--extsources', dest='enable_extsources', action='store_true') parser.add_argument('--no-extsources', dest='enable_extsources', action='store_false') parser.set_defaults(enable_extsources=True) parser.add_argument('-ext_nsources', '--ext_nsources', dest='ext_nsources', required=False, type=int, default=0, action='store',help='Extended source number (if >0 overrides the density generation) (default=0)') parser.add_argument('-ext_source_density', '--ext_source_density', dest='ext_source_density', required=False, type=float, default=100, action='store',help='Extended source density (default=1000)') parser.add_argument('-Smin_ext', '--Smin_ext', dest='Smin_ext', required=False, type=float, default=1.e-6, action='store',help='Minimum extended source flux in Jy (default=1.e-6)') parser.add_argument('-Smax_ext', '--Smax_ext', dest='Smax_ext', required=False, type=float, default=1, action='store',help='Maximum extended source flux in Jy (default=1)') parser.add_argument('-zmin_ext', '--zmin_ext', dest='zmin_ext', required=False, type=float, default=0.1, action='store',help='Minimum extended source significance level in sigmas above the bkg (default=0.1)') parser.add_argument('-zmax_ext', '--zmax_ext', dest='zmax_ext', required=False, type=float, default=2, action='store',help='Maximum extended source significance level in sigmas above the bkg (default=2)') parser.add_argument('-ext_scale_min', '--ext_scale_min', dest='ext_scale_min', required=False, type=float, default=10, action='store',help='Minimum extended source size in arcsec (default=10)') parser.add_argument('-ext_scale_max', '--ext_scale_max', dest='ext_scale_max', required=False, type=float, default=3600, action='store',help='Maximum extended source size in arcsec (default=3600)') parser.add_argument('-ext_source_type', '--ext_source_type', dest='ext_source_type', required=False, type=int, default=-1, action='store',help='Extended source type to generate (-1=all types from available models, 1=ring, 2=ellipse, 3=bubble+shell, 4=airy disk (default=-1)') # - SOURCE MODEL OPTIONS parser.add_argument('-ring_rmin', '--ring_rmin', dest='ring_rmin', required=False, type=float, default=0.5, action='store',help='Minimum ring radius in arcsec (default=1)') parser.add_argument('-ring_rmax', '--ring_rmax', dest='ring_rmax', required=False, type=float, default=10, action='store',help='Maximum ring radius in arcsec (default=10)') parser.add_argument('-ring_wmin', '--ring_wmin', dest='ring_wmin', required=False, type=float, default=5, action='store',help='Minimum ring width in arcsec (default=1)') parser.add_argument('-ring_wmax', '--ring_wmax', dest='ring_wmax', required=False, type=float, default=20, action='store',help='Maximum ring width in arcsec (default=10)') parser.add_argument('-ellipse_rmin', '--ellipse_rmin', dest='ellipse_rmin', required=False, type=float, default=0.5, action='store',help='Ellipse bmaj in arcsec (default=1)') parser.add_argument('-ellipse_rmax', '--ellipse_rmax', dest='ellipse_rmax', required=False, type=float, default=10, action='store',help='Ellipse bmin in arcsec (default=10)') parser.add_argument('-disk_shell_ampl_ratio_min', '--disk_shell_ampl_ratio_min', dest='disk_shell_ampl_ratio_min', required=False, type=float, default=0.1, action='store',help='Disk/shell amplitude ratio min (default=0.1)') parser.add_argument('-disk_shell_ampl_ratio_max', '--disk_shell_ampl_ratio_max', dest='disk_shell_ampl_ratio_max', required=False, type=float, default=0.8, action='store',help='Disk/shell amplitude ratio max (default=0.8)') parser.add_argument('-disk_shell_radius_ratio_min', '--disk_shell_radius_ratio_min', dest='disk_shell_radius_ratio_min', required=False, type=float, default=0.6, action='store',help='Disk/shell radius ratio min (default=0.6)') parser.add_argument('-disk_shell_radius_ratio_max', '--disk_shell_radius_ratio_max', dest='disk_shell_radius_ratio_max', required=False, type=float, default=0.9, action='store',help='Disk/shell radius ratio max (default=0.8)') parser.add_argument('-zmin_model', '--zmin_model', dest='model_trunc_zmin', required=False, type=float, default=1, action='store',help='Minimum source significance level in sigmas above the bkg below which source data are set to 0 (default=1)') parser.add_argument('-mask_boxsize', '--mask_boxsize', dest='mask_boxsize', required=False, type=float, default=10, action='store',help='Mask box size in pixels (default=10)') parser.add_argument('-trunc_thr', '--trunc_thr', dest='trunc_thr', required=False, type=float, default=0.01, action='store',help='Source model truncation thr (default=0.01)') parser.add_argument('--truncmodels', dest='truncate_models', action='store_true') parser.add_argument('--no-truncmodels', dest='truncate_models', action='store_false') parser.set_defaults(truncate_models=True) # - OUTPUT FILE OPTIONS parser.add_argument('-outputfile', '--outputfile', dest='outputfile', required=False, type=str, default='simmap.fits',action='store',help='Output filename') parser.add_argument('-outputfile_model', '--outputfile_model', dest='outputfile_model', required=False, type=str, default='skymodel.fits', action='store',help='Model filename') parser.add_argument('-outputfile_sources', '--outputfile_sources', dest='outputfile_sources', required=False, type=str, default='sources.root',action='store',help='Skymodel source ROOT Output filename') parser.add_argument('-outputfile_ds9region', '--outputfile_ds9region', dest='outputfile_ds9region', required=False, type=str, default='dsregion.reg',action='store',help='DS9 source region filename') parser.add_argument('-outputfile_casaregion', '--outputfile_casaregion', dest='outputfile_casaregion', required=False, type=str, default='casa_mask.dat',action='store',help='CASA source region filename') args = parser.parse_args() return args ########################### ## MODELS ########################### class RingSector2D(Fittable2DModel): """ Two dimensional radial symmetric Ring model """ amplitude = Parameter(default=1) x_0 = Parameter(default=0) y_0 = Parameter(default=0) r_in = Parameter(default=1) width = Parameter(default=1) theta_min = Parameter(default=-np.pi) theta_max = Parameter(default=np.pi) def __init__(self, amplitude=amplitude.default, x_0=x_0.default, y_0=y_0.default, r_in=r_in.default, width=width.default, theta_min=theta_min.default, theta_max=theta_max.default, **kwargs): # If outer radius explicitly given, it overrides default width. if width is None: width = self.width.default if theta_min is None: theta_min = self.theta_min.default if theta_max is None: theta_max = self.theta_max.default super(RingSector2D, self).__init__(amplitude=amplitude, x_0=x_0, y_0=y_0, r_in=r_in, width=width, theta_min=theta_min, theta_max=theta_max, **kwargs) @staticmethod def evaluate(x, y, amplitude, x_0, y_0, r_in, width, theta_min, theta_max): """Two dimensional Ring sector model function.""" rr = (x - x_0) ** 2 + (y - y_0) ** 2 theta = np.arctan2(x-x_0,y-y_0) r_range = np.logical_and(rr >= r_in ** 2, rr <= (r_in + width) ** 2) theta_range= np.logical_and(theta>=theta_min, theta<=theta_max) sector_range = np.logical_and(r_range,theta_range) result = np.select([sector_range], [amplitude]) if isinstance(amplitude, Quantity): return Quantity(result, unit=amplitude.unit, copy=False) else: return result @property def bounding_box(self): """ Tuple defining the default ``bounding_box``. ``((y_low, y_high), (x_low, x_high))`` """ dr = self.r_in + self.width return ( (self.y_0 - dr, self.y_0 + dr), (self.x_0 - dr, self.x_0 + dr) ) @property def input_units(self): if self.x_0.unit is None: return None else: return {'x': self.x_0.unit, 'y': self.y_0.unit} def _parameter_units_for_data_units(self, inputs_unit, outputs_unit): # Note that here we need to make sure that x and y are in the same # units otherwise this can lead to issues since rotation is not well # defined. if inputs_unit['x'] != inputs_unit['y']: raise UnitsError("Units of 'x' and 'y' inputs should match") return OrderedDict([('x_0', inputs_unit['x']), ('y_0', inputs_unit['x']), ('r_in', inputs_unit['x']), ('width', inputs_unit['x']), ('amplitude', outputs_unit['z'])]) ########################### ## SIMULATOR CLASS ########################### SIGMA_TO_FWHM= np.sqrt(8*np.log(2)) class SkyMapSimulator(object): """ Sky map simulator class Attributes: nx: image width in pixels ny: image height in pixels pixsize: pixel size in arcsec (default=1) """ def __init__(self, nx, ny, pixsize=1): """ Return a SkyMapGenerator object """ ## Image parameters self.nx = nx #in pixels self.ny = ny # in pixels self.marginx= 0 # in pixels (no margin) self.marginy= 0 # in pixels (no margin) self.pixsize= pixsize # in arcsec self.gridy, self.gridx = np.mgrid[0:ny, 0:nx] self.crpix1= 1 self.crpix2= 1 self.crval1= 254.851041667 self.crval2= -41.4765888889 self.ctype1= 'RA---SIN' self.ctype2= 'DEC--SIN' self.gmask_data= None ## Source model self.truncate_models= True self.trunc_thr= 0.01 # 1% flux truncation at maximum self.trunc_model_zmin= 1 ## Mask box size self.mask_boxsize= 10 # in pixels ## Bkg parameters self.simulate_bkg= True self.bkg_level= 0 # in Jy self.bkg_rms= 10.e-6 # in Jy ## Compact source parameters self.simulate_compact_sources= True #self.nx_gen= 1001 #self.ny_gen= 1001 self.nx_gen= 501 self.ny_gen= 501 self.gridy_gen, self.gridx_gen = np.mgrid[0:self.ny_gen, 0:self.nx_gen] self.ps_list= [] self.nsources= 0 # default is density generator self.source_density= 2000. # in sources/deg^2 self.beam_bmaj= 6.5 # in arcsec self.beam_bmin= 6.5 # in arcsec self.beam_bpa= 0 # in deg self.beam_area= self.compute_beam_area(self.beam_bmaj,self.beam_bmin) # in pixels self.zmin= 1 # in sigmas self.zmax= 30 # in sigmas self.npixels_min= 5 self.beam_bpa_min= -90 # deg self.beam_bpa_max= 90 # deg self.beam_bmaj_min= 4 # arcsec self.beam_bmaj_max= 10 # arcsec self.beam_bmin_min= 4 # arcsec self.beam_bmin_max= 10 # arcsec self.Smin= 1.e-6 # in Jy self.Smax= 1 # in Jy self.Smodel= 'uniform' self.Sslope= 1.6 ## Extended source parameters self.simulate_ext_sources= True self.ext_nsources= 0 # default is density generator self.ext_source_type= -1 # all source models generated self.ext_source_density= 10 # in sources/deg^2 self.Smin_ext= 1.e-6 # in Jy self.Smax_ext= 1 # in Jy self.zmin_ext= 0.5 # in sigmas self.zmax_ext= 5 # in sigmas self.ring_rmin= 2. # in arcsec self.ring_rmax= 10. # in arcsec self.ring_width_min= 5 # in arcsec self.ring_width_max= 10 # in arcsec self.ellipse_rmin= 1 # in arcsec self.ellipse_rmax= 10 # in arcsec self.ellipse_rratiomin= 0.7 # ratio rmin/rmax self.disk_rmin= 2 # in arcsec self.disk_rmax= 10 # in arcsec self.shell_disk_ampl_ratio_min= 0.1 self.shell_disk_ampl_ratio_max= 0.8 self.shell_disk_radius_ratio_min= 0.6 self.shell_disk_radius_ratio_max= 0.9 self.sersic_radius= 10 # in arcsec self.sersic_ellipticity= 0.5 self.sersic_index= 4 ## Map output file self.mapfilename= 'simmap.fits' self.modelfilename= 'skymodel.fits' ## Ascii output file self.source_par_outfile= 'point_sources.dat' ## DS9 output file self.ds9filename= 'ds9region.reg' ## CASA region output file self.casafilename= 'casamask.dat' ## Caesar img & sources self.outfilename= 'SimOutput.root' self.outfile= None self.outtree= None self.cs = None self.caesar_sources= [] self.caesar_img= None def init(self): """ Initialize data """ ## Initialize output tree & file self.outfile= ROOT.TFile(self.outfilename,'RECREATE') self.outtree= ROOT.TTree('SourceInfo','SourceInfo') self.cs = Caesar.Source() self.outtree.Branch('Source',self.cs) def set_mask_box_size(self,boxsize): """ Set mask box size """ if boxsize<=0: raise ValueError('Invalid boxsize specified (shall be larger than 0') self.mask_boxsize= boxsize def set_margins(self,marginx,marginy): """ Set margin in X & Y """ if (marginx<0 or marginy<0 or marginx>=self.nx/2 or marginy>=self.ny/2) : raise ValueError('Invalid margin specified (<0 or larger than image half size!') self.marginx= marginx self.marginy= marginy def set_ref_pix(self,x,y): """ Set reference pixel (CRPIX1,CRPIX2) in FITS output """ self.crpix1= x self.crpix2= y def set_ref_pix_coords(self,x,y): """ Set reference pixel coords (CRPIX1,CRPIX2) in FITS output """ self.crval1= x self.crval2= y def set_coord_system_type(self,x,y): """ Set coord system type (CTYPE1,CTYPE2) in FITS output """ self.ctype1= x self.ctype2= y def set_gen_blob_img_size(self,nx,ny): """ Set size of blob image used for compact source generation (must be odd) """ self.nx_gen= nx self.ny_gen= ny self.gridy_gen, self.gridx_gen = np.mgrid[0:self.ny_gen, 0:self.nx_gen] def enable_compact_sources(self,choice): """ Enable/disable compact source generation """ self.simulate_compact_sources= choice def enable_extended_sources(self,choice): """ Enable/disable extended source generation """ self.simulate_extended_sources= choice def enable_bkg(self,choice): """ Enable/disable bkg generation """ self.simulate_bkg= choice def set_npixels_min(self,value): """ Set the minimum number of pixels for a generated source""" self.npixels_min= value def enable_model_truncation(self,choice): """ Enable/disable continuous model truncation (gaussian, airy disk, ...) """ self.truncate_models= choice def set_model_trunc_significance(self,value): """ Set the significance level below which source model data are truncated """ self.trunc_model_zmin= value def set_model_trunc_thr(self,value): """ Set the flux percentage level for source model truncation """ self.trunc_thr= value def set_ext_source_type(self,value): """ Set the extended source type to be generated (-1=all, 1=ring, 2=ellipse, 3=bubble+shell, 4=airy)""" self.ext_source_type= value def set_ds9region_filename(self,filename): """ Set the output DS9 region filename """ self.ds9filename= filename def set_casaregion_filename(self,filename): """ Set the output CASA region filename """ self.casafilename= filename def set_map_filename(self,filename): """ Set the output map filename """ self.mapfilename= filename def set_model_filename(self,filename): """ Set the output model filename """ self.modelfilename= filename def set_source_filename(self,filename): """ Set the output source ROOT filename """ self.outfilename= filename def set_source_significance_range(self,zmin,zmax): """ Set source significance range """ self.zmin= zmin self.zmax= zmax def set_ext_source_significance_range(self,zmin,zmax): """ Set source significance range """ self.zmin_ext= zmin self.zmax_ext= zmax def set_nsources(self,n): """ Set number of sources to be generated """ if n<0: raise ValueError('Invalid number of sources specified (shall be >=0)') self.nsources= n def set_source_density(self,density): """ Set compact source density in deg^-2 """ self.source_density= density def set_source_flux_rand_model(self,model): """ Set the source flux random model """ self.Smodel= model def set_source_flux_rand_exp_slope(self,slope): """ Set the source flux expo model slope par """ self.Sslope= slope def set_source_flux_range(self,Smin,Smax): """ Set source flux range """ self.Smin= Smin self.Smax= Smax def set_ext_source_flux_range(self,Smin,Smax): """ Set source flux range """ self.Smin_ext= Smin self.Smax_ext= Smax def set_beam_bmaj_range(self,bmaj_min,bmaj_max): """ Set beam bmaj range """ self.beam_bmaj_min= bmaj_min self.beam_bmaj_max= bmaj_max def set_beam_bmin_range(self,bmin_min,bmin_max): """ Set beam bmin range """ self.beam_bmin_min= bmin_min self.beam_bmin_max= bmin_max def set_beam_pa_range(self,pa_min,pa_max): """ Set beam pa range """ self.beam_bpa_min= pa_min self.beam_bpa_max= pa_max def set_ext_nsources(self,n): """ Set number of extended sources to be generated """ if n<0: raise ValueError('Invalid number of sources specified (shall be >=0)') self.ext_nsources= n def set_ext_source_density(self,density): """ Set extended source density in deg^-2 """ self.ext_source_density= density def set_ring_pars(self,rmin,rmax,wmin,wmax): """ Set ring model parameters""" self.ring_rmin= rmin self.ring_rmax= rmax self.ring_width_min= wmin self.ring_width_max= wmax def set_sersic_pars(self,radius,ell,index): """ Set Sersic model pars""" self.sersic_radius= radius self.sersis_ellipticity= ell self.sersic_index= index def set_disk_pars(self,rmin,rmax): """ Set disk model parameters""" self.disk_rmin= rmin self.disk_rmax= rmax def set_disk_shell_pars(self,ampl_ratio_min,ampl_ratio_max,radius_ratio_min,radius_ratio_max): """ Set disk shell model parameters""" self.shell_disk_ampl_ratio_min= ampl_ratio_min self.shell_disk_ampl_ratio_max= ampl_ratio_max self.shell_disk_radius_ratio_min= radius_ratio_min self.shell_disk_radius_ratio_max= radius_ratio_max def set_ellipse_pars(self,rmin,rmax): """ Set ring model parameters""" self.ellipse_rmin= rmin self.ellipse_rmax= rmax def set_bkg_pars(self,bkg_level,bkg_rms): """ Set bkg parameters """ self.bkg_level= bkg_level self.bkg_rms= bkg_rms def set_beam_info(self,Bmaj,Bmin,Bpa): """ Set beam info """ self.beam_bmaj= Bmaj self.beam_bmin= Bmin self.beam_bpa= Bpa self.beam_area= self.compute_beam_area(Bmaj,Bmin) def compute_beam_area(self,Bmaj,Bmin): """ Compute beam area """ A= np.pi*Bmaj*Bmin/(4*np.log(2)) #2d gaussian area with FWHM=fx,fy (in arcsec^2) pixelArea= np.fabs(self.pixsize*self.pixsize) # in arcsec^2 beam_area= A/pixelArea # in pixels return beam_area def compute_beam_sigma(self,fwhm): """ """ sigma= fwhm/(2.*np.sqrt(2.*np.log(2.))) return sigma def check_random_state(self,seed): """ Turn seed into a np.random.RandomState instance """ if seed is None or seed is np.random: return np.random.mtrand._rand if isinstance(seed, (numbers.Integral, np.integer)): return np.random.RandomState(seed) if isinstance(seed, np.random.RandomState): return seed raise ValueError('%r cannot be used to seed a numpy.random.RandomState instance' % seed) def make_gaus_noise_image(self,shape, mean=None, stddev=None,random_state=None): """ Generate gaussian noise numpy array """ if mean is None: raise ValueError('"mean" must be input') if stddev is None: raise ValueError('"stddev" must be input for Gaussian noise') prng = self.check_random_state(random_state) image = prng.normal(loc=mean, scale=stddev, size=shape) return image def generate_bkg(self): """ Generate bkg data """ shape = (self.ny, self.nx) #bkg_data = make_noise_image(shape, type='gaussian', mean=self.bkg_level, stddev=self.bkg_rms) bkg_data = self.make_gaus_noise_image(shape, mean=self.bkg_level, stddev=self.bkg_rms) return bkg_data def generate_blob(self,ampl,x0,y0,sigmax,sigmay,theta,trunc_thr=0.01): """ Generate a blob Arguments: ampl: peak flux in Jy x0, y0: gaussian means in pixels sigmax, sigmay: gaussian sigmas in pixels theta: rotation in degrees trunc_thr: truncation significance threshold """ #modelFcn= Gaussian2D(ampl,x0,y0,sigmax,sigmay,theta=math.radians(theta)) data= Gaussian2D(ampl,x0,y0,sigmax,sigmay,theta=math.radians(theta))(self.gridx, self.gridy) ## Truncate data such that sum(data)_trunc/sum(data)<f f= trunc_thr if self.truncate_models: totFlux= (float)(np.sum(data,axis=None)) #print('Blob total flux=%s' % str(totFlux)) data_vect_sorted= np.ravel(data) data_csum= np.cumsum(data_vect_sorted)/totFlux fluxThr= data_vect_sorted[np.argmin(data_csum<f)] #print('Blob fluxThr=%s' % str(fluxThr)) data[data<fluxThr] = 0 ## Truncate data at minimum significance #ampl_min= (trunc_thr*self.bkg_rms) + self.bkg_level #if self.truncate_models: # data[data<ampl_min] = 0 return data def generate_blob_faster(self,ampl,x0,y0,sigmax,sigmay,theta,trunc_thr=0.01): """ Generate a blob Arguments: ampl: peak flux in Jy x0, y0: gaussian means in pixels sigmax, sigmay: gaussian sigmas in pixels theta: rotation in degrees trunc_thr: truncation significance threshold """ data= Gaussian2D(ampl,x0,y0,sigmax,sigmay,theta=math.radians(theta))(self.gridx_gen, self.gridy_gen) ## Truncate data such that sum(data)_trunc/sum(data)<f f= trunc_thr if self.truncate_models: totFlux= (float)(np.sum(data,axis=None)) data_vect_sorted= np.ravel(data) data_csum= np.cumsum(data_vect_sorted)/totFlux fluxThr= data_vect_sorted[np.argmin(data_csum<f)] data[data<fluxThr] = 0 return data def generate_ring(self,ampl,x0,y0,radius,width): """ Generate a ring Arguments: ampl: peak flux in Jy x0, y0: means in pixels radius: ring radius in pixels width: ring width in pixels """ data= Ring2D(ampl,x0,y0,radius,width)(self.gridx, self.gridy) return data def generate_ring_sector(self,ampl,x0,y0,radius,width,theta_min,theta_max): """ Generate a ring Arguments: ampl: peak flux in Jy x0, y0: means in pixels radius: ring radius in pixels width: ring width in pixels theta_min, theta_max: sector theta min/max in degrees """ data= RingSector2D(ampl,x0,y0,radius,width,np.radians(theta_min),np.radians(theta_max))(self.gridx, self.gridy) return data def generate_bubble(self,ampl,x0,y0,radius,shell_ampl,shell_radius,shell_width,shell_theta_min,shell_theta_max): """ Generate a bubble with a shell """ disk_data= Disk2D(ampl,x0,y0,radius)(self.gridx, self.gridy) shell_data= self.generate_ring_sector(shell_ampl,x0,y0,shell_radius,shell_width,shell_theta_min,shell_theta_max) data= disk_data + shell_data return data def generate_disk(self,ampl,x0,y0,radius): """ Generate a disk """ data= Disk2D(ampl,x0,y0,radius)(self.gridx, self.gridy) return data def generate_ellipse(self,ampl,x0,y0,a,b,theta): """ Generate ellipse """ data= Ellipse2D(ampl,x0,y0,a,b,math.radians(theta))(self.gridx, self.gridy) return data def generate_airy_disk(self,ampl,x0,y0,radius,trunc_thr=0.01): """ Generate Airy disk """ data= AiryDisk2D(amplitude=ampl,x_0=x0,y_0=y0,radius=radius)(self.gridx, self.gridy) totFlux= (float)(np.sum(data,axis=None)) ## Truncate data such that sum(data)_trunc/sum(data)<f f= trunc_thr if self.truncate_models: data_vect_sorted= np.ravel(data) data_csum= np.cumsum(data_vect_sorted)/totFlux fluxThr= data_vect_sorted[np.argmin(data_csum<f)] data[data<fluxThr] = 0 ## Truncate data at minimum significance #ampl_min= (self.zmin_ext*self.bkg_rms) + self.bkg_level #if self.truncate_models: # data[data<ampl_min] = 0 return data def generate_sersic(self,ampl,x0,y0,radius,ell,index,theta,trunc_thr=0.01): """ Generate Sersic model """ data= Sersic2D(amplitude=ampl,x_0=x0,y_0=y0,r_eff=radius,n=index,ellip=ell,theta=math.radians(theta))(self.gridx, self.gridy) totFlux= (float)(np.sum(data,axis=None)) ## Truncate data such that sum(data)_trunc/sum(data)<f f= trunc_thr if self.truncate_models: data_vect_sorted= np.ravel(data) data_csum= np.cumsum(data_vect_sorted)/totFlux fluxThr= data_vect_sorted[np.argmin(data_csum<f)] data[data<fluxThr] = 0 ## Truncate data at minimum significance #ampl_min= (self.zmin_ext*self.bkg_rms) + self.bkg_level #if self.truncate_models: # data[data<ampl_min] = 0 return data def make_caesar_source(self,source_data,source_name,source_id,source_type,source_sim_type,ampl=None,x0=None,y0=None,source_max_scale=None,offsetx=0,offsety=0): """ Create Caesar source from source data array """ # Create Caesar source source= Caesar.Source() # Get source indexes and fill pixels in Caesar source source_indexes= np.column_stack(np.where(source_data!=0)) nRows= (source_data.shape)[0] nCols= (source_data.shape)[1] for index in source_indexes: rowId= index[0] colId= index[1] S= source_data[rowId,colId] ix= colId iy= rowId #iy= nRows-1-rowId gbin= ix + iy*nCols pixel= Caesar.Pixel(gbin,ix,iy,ix+offsetx,iy+offsety,S) source.AddPixel(pixel) # Is at edge if (ix==0) or (ix==nCols-1) or (iy==0) or (iy==nRows-1): source.SetEdgeFlag(True) # Retun None if npixels is too small nPix= source.GetNPixels() if nPix<self.npixels_min: logger.info('Too few pixels (%s) for this source, return None!' % str(nPix)) return None # If true info are not given compute them # - S= count integral # - baricenter of binary map if x0 is None or y0 is None: logger.info('No source true pos given, computing it from data...') data_binary= np.where(source_data!=0,1,0) [y0,x0]= ndimage.measurements.center_of_mass(data_binary) x0+= offsetx y0+= offsety if ampl is None: logger.info('No source true flux given, computing integral from data...') ampl= np.sum(source_data,axis=None) # Set some flags source.SetName(source_name) source.SetId(source_id) source.SetType(source_type) source.SetFlag(Caesar.eFake) source.SetSimType(source_sim_type) if source_max_scale is not None: source.SetSimMaxScale(source_max_scale) source.SetTrueInfo(ampl,x0,y0) # Set flux correction factor fluxCorrection= self.beam_area source.SetBeamFluxIntegral(fluxCorrection) # Compute stats & morph pars source.ComputeStats(); source.ComputeMorphologyParams(); return source def make_caesar_image(self,data): """ Make Caesar image from array data """ # Get source indexes and fill pixels in Caesar source img_indexes= np.column_stack(np.where(data!=0)) nRows= (data.shape)[0] nCols= (data.shape)[1] # Set metadata metadata= Caesar.ImgMetaData() metadata.Nx= self.nx metadata.Ny= self.ny metadata.Cx= (int)(self.crpix1) metadata.Cy= (int)(self.crpix2) metadata.Xc= self.crval1 metadata.Yc= self.crval2 metadata.dX= -self.pixsize/3600. metadata.dY= self.pixsize/3600. metadata.CoordTypeX= self.ctype1 metadata.CoordTypeY= self.ctype2 metadata.BUnit= 'JY/PIXEL' metadata.Bmaj= self.beam_bmaj/3600. metadata.Bmin= self.beam_bmin/3600. metadata.Bpa= self.beam_bpa # Create Caesar image img= Caesar.Image(nCols,nRows,"img") img.SetMetaData(metadata) for index in img_indexes: rowId= index[0] colId= index[1] S= data[rowId,colId] ix= colId iy= rowId #iy= nRows-1-rowId gbin= ix + iy*nCols img.FillPixel(ix,iy,S,True); return img def generate_compact_sources(self): """ Generate list of compact sources in the map. - Uniform spatial distribution - Uniform flux distribution Arguments: density: source density in #sources/deg^2 (e.g. 2000) """ # Compute number of sources to be generated given map area in pixels #area= (self.nx*self.ny)*self.pixsize/(3600.*3600.) # in deg^2 dx_deg= (self.nx-2*self.marginx)*self.pixsize/3600. dy_deg= (self.ny-2*self.marginy)*self.pixsize/3600. #area= ((self.nx-2*self.marginx)*(self.ny-2*self.marginy))*self.pixsize/(3600.*3600.) # in deg^2 area= dx_deg*dy_deg if self.nsources>0: nsources= self.nsources else: # density generator nsources= int(round(self.source_density*area)) #S_min= (self.zmin*self.bkg_rms) + self.bkg_level #S_max= (self.zmax*self.bkg_rms) + self.bkg_level #lgS_min= np.log(S_min) #lgS_max= np.log(S_max) #randomize_flux= False #if self.zmin<self.zmax: # randomize_flux= True S_min= self.Smin # Jy/pixel S_max= self.Smax # Jy/pixel lgS_min= np.log10(S_min) lgS_max= np.log10(S_max) randomize_flux= False if self.Smin<self.Smax: randomize_flux= True ## Set gaus pars generation randomize_gaus= False Bmaj_min= self.beam_bmaj_min Bmaj_max= self.beam_bmaj_max Bmin_min= self.beam_bmin_min Bmin_max= self.beam_bmin_max Pa_min= self.beam_bpa_min Pa_max= self.beam_bpa_max if self.beam_bmaj_min<self.beam_bmaj_max: randomize_gaus= True if self.beam_bmin_min<self.beam_bmin_max: randomize_gaus= True if self.beam_bpa_min<self.beam_bpa_max: randomize_gaus= True logger.info('Generating #%d compact sources in map...' % nsources) # Compute blob sigma pars given beam info sigmax= self.compute_beam_sigma(self.beam_bmaj) sigmay= self.compute_beam_sigma(self.beam_bmin) theta= self.beam_bpa + 90. # NB: BPA is the positional angle of the major axis measuring from North (up) counter clockwise, while theta is measured wrt to x axis source_max_scale= 2*max(self.beam_bmaj,self.beam_bmin) ## Start generation loop sources_data = Box2D(amplitude=0,x_0=0,y_0=0,x_width=2*self.nx, y_width=2*self.ny)(self.gridx, self.gridy) mask_data = Box2D(amplitude=0,x_0=0,y_0=0,x_width=2*self.nx, y_width=2*self.ny)(self.gridx, self.gridy) for index in range(0,nsources): if index%100==0 : logger.info("Generating compact source no. %s/%s ..." % (index+1,nsources)) ## Generate random coordinates #x0= np.random.uniform(0,self.nx) #y0= np.random.uniform(0,self.ny) #x0= np.random.uniform(0,self.nx-1) #y0= np.random.uniform(0,self.ny-1) x0= np.random.uniform(self.marginx,self.nx-self.marginx-1) y0= np.random.uniform(self.marginy,self.ny-self.marginy-1) ix= int(np.round(x0)) iy= int(np.round(y0)) ## Compute amplitude given significance level and bkg ## Generate flux uniform in log #if randomize_flux: # lgS= np.random.uniform(lgS_min,lgS_max) # S= np.exp(lgS) # z= (S-self.bkg_level)/self.bkg_rms #else: # S= (self.zmin*self.bkg_rms) + self.bkg_level # z= self.zmin ## Compute amplitude given significance level and bkg ## Generate flux uniform or expo in log ## Flux are in Jy/pixel if randomize_flux: if self.Smodel=='uniform': lgS= np.random.uniform(lgS_min,lgS_max) elif self.Smodel=='exp': x= np.random.exponential(scale=1./self.Sslope) lgS= x + lgS_min if lgS>lgS_max: continue else: lgS= np.random.uniform(lgS_min,lgS_max) S= np.power(10,lgS) else: S= S_min z= (S-self.bkg_level)/self.bkg_rms ## Generate gaus pars if randomize_gaus: bmin= random.uniform(Bmin_min,Bmin_max) bmaj= random.uniform(bmin,Bmaj_max) pa= random.uniform(Pa_min,Pa_max) else: bmin= self.beam_bmin_min bmaj= self.beam_bmaj_min pa= self.beam_bpa_min sigmax= bmaj/(self.pixsize * SIGMA_TO_FWHM) sigmay= bmin/(self.pixsize * SIGMA_TO_FWHM) theta = 90 + pa # NB: BPA is the positional angle of the major axis measuring from North (up) counter clockwise, while theta is measured wrt to x axis source_max_scale= 2*max(bmaj,bmin) #print("bmaj=%f, bmin=%f, sigmax=%f, sigmay=%f" % (bmaj,bmin,sigmax,sigmay)) ## Generate blob t0 = time.time() #blob_data= self.generate_blob(ampl=S,x0=x0,y0=y0,sigmax=sigmax/self.pixsize,sigmay=sigmay/self.pixsize,theta=theta,trunc_thr=self.trunc_thr) x0_tile_gen= int(self.nx_gen/2.) y0_tile_gen= int(self.ny_gen/2.) #blob_data= self.generate_blob_faster(ampl=S,x0=x0_tile_gen,y0=y0_tile_gen,sigmax=sigmax/self.pixsize,sigmay=sigmay/self.pixsize,theta=theta,trunc_thr=self.trunc_thr) blob_data= self.generate_blob_faster(ampl=S,x0=x0_tile_gen,y0=y0_tile_gen,sigmax=sigmax,sigmay=sigmay,theta=theta,trunc_thr=self.trunc_thr) t1 = time.time() elapsed_time = t1-t0 if blob_data is None: logger.warn('Failed to generate blob (hint: too large trunc threshold), skip and regenerate...') continue logger.info('Generated blob no. %s in %s (s)' % (str(index),str(elapsed_time)) ) ## - Add blob to source data #sources_data+= blob_data xmin_t, xmax_t= 0, self.nx_gen ymin_t, ymax_t= 0, self.ny_gen dx_t= int(self.nx_gen/2.) dy_t= int(self.ny_gen/2.) xmin, ymin = (ix - dx_t), (iy - dy_t) xmax, ymax = (ix + dx_t + 1), (iy + dy_t +1) if xmin<0 and xmax<0: logger.warn('Tile outside mat along x, skip and regenerate!') continue if xmin>self.nx and xmax>self.nx: logger.warn('Tile outside mat along x, skip and regenerate!') continue if ymin<0 and ymax<0: logger.warn('Tile outside mat along y, skip and regenerate!') continue if ymin>self.ny and ymax>self.ny: logger.warn('Tile outside mat along y, skip and regenerate!') continue if xmin<0: xmin= 0 xmin_t= dx_t-ix if ymin<0: ymin= 0 ymin_t= dy_t-iy if xmax>self.nx: xmax= self.nx xmax_t= self.nx - ix + dx_t if ymax>self.ny: ymax= self.ny ymax_t= self.ny - iy + dy_t # - Check if any generated source pixel is masked in the global mask (if provided) if self.gmask_data is not None: has_taken_pixels= np.any(self.gmask_data[ymin:ymax,xmin:xmax]>0) if has_taken_pixels: logger.info('Source pixels have been already taken in provided global mask, regenerate...') continue #sources_data[xmin:xmax,ymin:ymax] += blob_data[xmin_t:xmax_t,ymin_t:ymax_t] sources_data[ymin:ymax,xmin:xmax] += blob_data[ymin_t:ymax_t,xmin_t:xmax_t] ## Set model map mask_data[iy,ix]+= S # Make Caesar source #offset_x= x0 - x0_tile_gen #offset_y= y0 - y0_tile_gen offset_x= ix - x0_tile_gen offset_y= iy - y0_tile_gen source_name= 'S' + str(index+1) source_id= index+1 source_type= Caesar.ePointLike self.ps_list.append([source_name,x0,y0,S]) t0 = time.time() caesar_source= self.make_caesar_source(blob_data,source_name,source_id,source_type,Caesar.eBlobLike,ampl=S,x0=x0,y0=y0,source_max_scale=source_max_scale,offsetx=offset_x,offsety=offset_y) t1 = time.time() elapsed_time = t1-t0 if caesar_source is None: logger.warn('Generate source has too few pixels, skip and regenerate...') continue logger.info('Make Caesar source %s from generated blob in %s (s)' % (source_name,str(elapsed_time)) ) self.caesar_sources.append(caesar_source) logger.info('Source %s: Pos(%s,%s), ix=%s, iy=%s, S=%s' % (source_name,str(x0),str(y0),str(ix),str(iy),str(S))) return [sources_data,mask_data] def generate_extended_sources(self): """ Generate list of extended sources in the map. - Uniform spatial distribution - Uniform flux distribution Arguments: density: source density in #sources/deg^2 (e.g. 2000) """ # Compute number of sources to be generated given map area in pixels dx_deg= (self.nx-2*self.marginx)*self.pixsize/3600. dy_deg= (self.ny-2*self.marginy)*self.pixsize/3600. #area= ((self.nx-2*self.marginx)*(self.ny-2*self.marginy))*self.pixsize/(3600.*3600.) # in deg^2 area= dx_deg*dy_deg if self.ext_nsources>0: nsources= self.ext_nsources else: nsources= int(round(self.ext_source_density*area)) #S_min= (self.zmin_ext*self.bkg_rms) + self.bkg_level #S_max= (self.zmax_ext*self.bkg_rms) + self.bkg_level S_min= self.Smin_ext S_max= self.Smax_ext lgS_min= np.log(S_min) lgS_max= np.log(S_max) randomize_flux= False #if self.zmin_ext<self.zmax_ext: if S_min<S_max: randomize_flux= True logger.info('Generating #%d extended sources in map...' % nsources) logger.debug('zmin_ext=%s, zmax_ext=%s, Smin=%s, Smax=%s' % (str(self.zmin_ext),str(self.zmax_ext),str(S_min),str(S_max)) ) ## Start generation loop sources_data = Box2D(amplitude=0,x_0=0,y_0=0,x_width=2*self.nx, y_width=2*self.ny)(self.gridx, self.gridy) ngen_sources= 0 if self.ext_source_type==-1: nsource_types= 6 else: nsource_types= 1 #for index in range(0,nsources): while (ngen_sources<nsources): if ngen_sources%10==0 : logger.info("Generating extended source no. %s/%s" % (ngen_sources+1,nsources)) ## Generate random coordinates #x0= random.uniform(0,self.nx) #y0= random.uniform(0,self.ny) #x0= np.random.uniform(0,self.nx-1) #y0= np.random.uniform(0,self.ny-1) x0= np.random.uniform(self.marginx,self.nx-self.marginx-1) y0= np.random.uniform(self.marginy,self.ny-self.marginy-1) ## Compute amplitude given significance level and bkg ## Generate flux uniform in log #if randomize_flux: # lgS= np.random.uniform(lgS_min,lgS_max) # S= np.exp(lgS) # z= (S-self.bkg_level)/self.bkg_rms #else: # S= (self.zmin_ext*self.bkg_rms) + self.bkg_level # z= self.zmin_ext if randomize_flux: if self.Smodel=='uniform': lgS= np.random.uniform(lgS_min,lgS_max) elif self.Smodel=='exp': x= np.random.exponential(scale=1./self.Sslope) lgS= x + lgS_min if lgS>lgS_max: continue else: lgS= np.random.uniform(lgS_min,lgS_max) S= np.power(10,lgS) else: S= S_min z= (S-self.bkg_level)/self.bkg_rms ## Generate random type (1=ring, 2=ellipse, ...) if self.ext_source_type==-1: source_sim_type= random.randint(1, nsource_types) else: source_sim_type= self.ext_source_type source_max_scale= 0. if source_sim_type==1: # Ring2D Sector model source_sim_type= Caesar.eRingLike ring_r= random.uniform(self.ring_rmin,self.ring_rmax) ring_w= random.uniform(self.ring_width_min,self.ring_width_max) #source_data= self.generate_ring(S,x0,y0,ring_r/self.pixsize,ring_w/self.pixsize) # convert radius/width from arcsec to pixels theta1= random.uniform(-180,180) theta2= random.uniform(-180,180) theta_min= min(theta1,theta2) theta_max= max(theta1,theta2) dtheta= theta_max-theta_min r= ring_r R= ring_r + ring_w sector_diagonal= np.sqrt( r*r + R*R - 2*r*R*np.cos(np.deg2rad(dtheta)) ) sector_arc= 2*R*np.pi*dtheta/360. source_max_scale= max(max(sector_arc,ring_w),sector_diagonal) source_data= self.generate_ring_sector(S,x0,y0,ring_r/self.pixsize,ring_w/self.pixsize,theta_min,theta_max) # convert radius/width from arcsec to pixels elif source_sim_type==2: # Ellipse 2D model source_sim_type= Caesar.eEllipseLike ellipse_bmaj= random.uniform(self.ellipse_rmin,self.ellipse_rmax) #ellipse_bmin= random.uniform(self.ellipse_rmin,self.ellipse_rmax) ellipse_bmin= random.uniform(max(self.ellipse_rratiomin*ellipse_bmaj,self.ellipse_rmin),self.ellipse_rmax) ellipse_theta= random.uniform(0,360) source_max_scale= max(ellipse_bmaj,ellipse_bmin) source_data= self.generate_ellipse(S,x0,y0,ellipse_bmaj/self.pixsize,ellipse_bmin/self.pixsize,ellipse_theta) # convert radius/width from arcsec to pixels elif source_sim_type==3: # bubble + shell model source_sim_type= Caesar.eBubbleLike bubble_r= random.uniform(self.disk_rmin,self.disk_rmax) shell_excess= random.uniform(self.shell_disk_ampl_ratio_min,self.shell_disk_ampl_ratio_max) shell_S= S*(1+shell_excess) shell_r= random.uniform(bubble_r*self.shell_disk_radius_ratio_min,bubble_r*self.shell_disk_radius_ratio_max) shell_width= random.uniform(0,bubble_r-shell_r) theta1= random.uniform(-180,180) theta2= random.uniform(-180,180) theta_min= min(theta1,theta2) theta_max= max(theta1,theta2) source_max_scale= bubble_r*2 source_data= self.generate_bubble(S,x0,y0,bubble_r,shell_S,shell_r,shell_width,theta_min,theta_max) #elif source_sim_type==4: # Airy disk # source_sim_type= Caesar.eDiskLike # disk_r= random.uniform(self.disk_rmin,self.disk_rmax) # source_data= self.generate_airy_disk(S,x0,y0,disk_r) elif source_sim_type==4: # Sersic source_sim_type= Caesar.eDiskLike sersic_r= random.uniform(self.disk_rmin,self.disk_rmax) sersic_theta= random.uniform(0,360) sersic_ell= random.uniform(0.7,1) source_max_scale= 2*sersic_r ##source_data= self.generate_sersic(S,x0,y0,sersic_r,sersic_ell,self.sersic_index,sersic_theta) source_data= self.generate_sersic(S,x0,y0,sersic_r,sersic_ell,self.sersic_index,sersic_theta,trunc_thr=self.trunc_thr) elif source_sim_type==5: # Gaussian Blob like source_sim_type= Caesar.eBlobLike blob_bmaj= random.uniform(self.ellipse_rmin,self.ellipse_rmax) #blob_bmin= random.uniform(self.ellipse_rmin,self.ellipse_rmax) blob_bmin= random.uniform(max(self.ellipse_rratiomin*blob_bmaj,self.ellipse_rmin),blob_bmaj) blob_theta= random.uniform(0,360) source_max_scale= 2*max(blob_bmin,blob_bmaj) #source_data= self.generate_blob(ampl=S,x0=x0,y0=y0,sigmax=blob_bmaj/self.pixsize,sigmay=blob_bmin/self.pixsize,theta=blob_theta,trunc_thr=self.zmin_ext) source_data= self.generate_blob(ampl=S,x0=x0,y0=y0,sigmax=blob_bmaj/self.pixsize,sigmay=blob_bmin/self.pixsize,theta=blob_theta,trunc_thr=self.trunc_thr) if source_data is None: logger.warn('Failed to generate blob (hint: too large trunc threshold), skip and regenerate...') continue elif source_sim_type==6: # disk model source_sim_type= Caesar.eDiskLike disk_r= random.uniform(self.disk_rmin,self.disk_rmax) source_max_scale= disk_r*2 source_data= self.generate_disk(S,x0,y0,disk_r) else: logger.warn('Invalid source type given!') continue ## Check if source data contains all zeros (e.g. truncation removed all data) if np.count_nonzero(source_data)<=0: logger.warn('Generated extended source data contains all zeros, regenerate...') continue ## Check if source pixels and its contour has been already taken before source_indexes= (source_data!=0) # get all source data pixels (others are 0) source_indexes_xright= (np.roll(source_data,1,axis=1)!=0) source_indexes_xleft= (np.roll(source_data,-1,axis=1)!=0) source_indexes_yright= (np.roll(source_data,1,axis=0)!=0) source_indexes_yleft= (np.roll(source_data,-1,axis=0)!=0) source_mask_indexes= (source_indexes | source_indexes_xright | source_indexes_xleft | source_indexes_yright | source_indexes_yleft) #source_mask= np.where(source_data!=0,1,0) taken_pixels= np.where(sources_data[source_mask_indexes]!=0) # get list of taken pixels in main mask corresponding to this source has_taken_pixels= np.any(taken_pixels) if has_taken_pixels: logger.info('Source pixels have been already taken by a previously generated source, regenerate...') continue # - Check if pixels are taken in the global mask if self.gmask_data is not None: taken_pixels= np.where(self.gmask_data[source_mask_indexes]!=0) # get list of taken pixels in main mask corresponding to this source has_taken_pixels= np.any(taken_pixels) if has_taken_pixels: logger.info('Source pixels have been already taken in the global mask, regenerate...') continue # Add to extended source data and mask sources_data+= source_data ngen_sources+= 1 # Set model map ix= int(np.round(x0)) iy= int(np.round(y0)) # Make Caesar source source_name= 'Sext' + str(ngen_sources) source_id= ngen_sources source_type= Caesar.eExtended caesar_source= self.make_caesar_source(source_data,source_name,source_id,source_type,source_sim_type,None,None,None,source_max_scale) if caesar_source is None: logger.warn('Generate source has too few pixels, skip and regenerate...') continue self.caesar_sources.append(caesar_source) logger.info('Ext Source %s: Pos(%s,%s), ix=%s, iy=%s, S=%s' % (source_name,str(x0),str(y0),str(ix),str(iy),str(S))) return sources_data ##################################### ### GENERATE MAP ## ##################################### def generate_map(self): """ Generate sky map """ ## == INITIALIZE DATA == logger.info('Initializing simulator data...') self.init() # - Check global mask data if given if self.gmask_data is not None: logger.info('Checking global mask data dimensions ...') nx_g= self.gmask_data.shape[1] ny_g= self.gmask_data.shape[0] if nx_g!=self.nx: logger.error("mask nx(%d)!=nx(%d)" % (nx_g,self.nx)) return -1 if ny_g!=self.ny: logger.error("mask ny(%d)!=ny(%d)" % (ny_g,self.ny)) return -1 ## == GENERATE EMPTY IMAGE == data = Box2D(amplitude=0,x_0=0,y_0=0,x_width=2*self.nx, y_width=2*self.ny)(self.gridx, self.gridy) mask_data = Box2D(amplitude=0,x_0=0,y_0=0,x_width=2*self.nx, y_width=2*self.ny)(self.gridx, self.gridy) ## == GENERATE BKG == if self.simulate_bkg: logger.info('Renerating map bkg...') bkg_data= self.generate_bkg() data+= bkg_data ## == GENERATE COMPACT SOURCES == if self.simulate_compact_sources: logger.info('Generating compact sources...') [compact_source_data,compact_source_mask_data] = self.generate_compact_sources() data+= compact_source_data mask_data+= compact_source_mask_data ## == GENERATE EXTENDED SOURCES == if self.simulate_extended_sources: logger.info('Generating extended sources...') ext_source_data = self.generate_extended_sources() data+= ext_source_data mask_data+= ext_source_data ## == MAKE FINAL MAP == logger.info('Creating final map with bkg + sources added...') ## Sum data in cumulative map #data= bkg_data + compact_source_data + ext_source_data #mask_data= compact_source_mask_data + ext_source_data ## Add noise in skymodel if self.simulate_bkg: logger.info('Add noise to skymodel map ...') mask_data+= bkg_data ## Cast data from float64 to float32 data_casted = data.astype(np.float32) mask_data_casted = mask_data.astype(np.float32) ## Convert data from Jy/pixel to Jy/beam ## Jy/pixel= Jy/beam / beamArea(pixels) scaleFactor= self.beam_area data_casted*= scaleFactor ## Create Caesar skymodel image from data (units= Jy/pixel) logger.info('Creating Caesar image from data...') ##self.caesar_img= self.make_caesar_image(data_casted) # set toy sim map data self.caesar_img= self.make_caesar_image(mask_data_casted) # set skymodel map data ## == WRITE MAPS TO FITS FILES == logger.info('Writing images to FITS...') self.write_map(data_casted,self.mapfilename) self.write_source_map(mask_data_casted,self.modelfilename) ## == WRITE IMG & SOURCES TO ROOT FILE == logger.info('Writing image & source collection to ROOT file...') self.save() ## == WRITE DS9 REGION FILE == logger.info('Writing DS9 regions...') self.write_ds9_regions() ## == WRITE ASCII FILE == logger.info('Writing point source parameters to ascii ...') self.write_compact_source_par_list() return 0 def write_compact_source_par_list(self): """ Write cmpact source parameters to ascii file """ # - Open file fout = open(self.source_par_outfile, 'wb') #- Write header header= ("# name x(pix) y(pix) S(Jy/pixel)") fout.write(header) fout.write('\n') for i in range(len(self.ps_list)): name= self.ps_list[i][0] x= self.ps_list[i][1] y= self.ps_list[i][2] S= self.ps_list[i][3] # No need to convert peak flux data= (("%s %s %s %s") % (name,x,y,S) ) fout.write(data) fout.write('\n') fout.flush() def write_ds9_regions(self): """ Write DS9 regions with sim sources """ ## Open file fout = open(self.ds9filename, 'wb') ## Write file header fout.write('global color=white font=\"helvetica 8 normal\" edit=1 move=1 delete=1 include=1\n') fout.write('image\n') ## Write source contour region for item in self.caesar_sources: regionInfo= item.GetDS9Region(True) fout.write(regionInfo) fout.write('\n') fout.close(); def write_casa_mask(self,boxsize=10): """ Write CASA mask file around simulated sources""" ## Create a WCS structure w = wcs.WCS(naxis=2) w.wcs.crpix = [self.crpix1, self.crpix2] w.wcs.cdelt = np.array([-self.pixsize/3600., self.pixsize/3600.]) w.wcs.crval = [self.crval1, self.crval2] w.wcs.ctype = [self.ctype1, self.ctype2] #w.wcs.set_pv([(2, 1, 45.0)]) ## Create mask ascii file with header f = open(str(self.casafilename), 'wb') f.write("#CRTFv0\n") #f.write("global coord = J2000, color=blue\n") ## Create a CASA box around the source for item in self.caesar_sources: ix_min= item.GetIxMin() ix_max= item.GetIxMax() iy_min= item.GetIyMin() iy_max= item.GetIyMax() # Set box coordinates pixcrd = np.array([[max(0,ix_min-boxsize/2.), max(0,iy_min-boxsize/2.)], [min(self.nx-1,ix_max+boxsize/2.), min(self.ny-1,iy_max+boxsize/2.)]], np.float_) # Convert pixel coordinates to world coordinates world = w.wcs_pix2world(pixcrd, 1) print(world) f.write("box [ [{0}deg,{1}deg], [{2}deg,{3}deg] ]\n".format(min(world[0,0],world[1,0]),min(world[0,1],world[1,1]),max(world[0,0],world[1,0]),max(world[0,1],world[1,1]))) # Close ascii file f.close() def draw_map(self,data): """ Draw map data """ plt.imshow(data, origin='lower', cmap="hot") pylab.show() def write_map(self,data,outputfile): """ Write FITS image with sim data """ # Define FITS header header= fits.Header() header.set('SIMPLE','T') header.set('BITPIX','-32') header.set('NAXIS1', str(self.nx)) header.set('NAXIS2', str(self.ny)) #header.set('NAXIS3', 1) #header.set('NAXIS4', 1) header.set('BUNIT', 'JY/BEAM') header.set('BMAJ', self.beam_bmaj/3600.) header.set('BMIN', self.beam_bmin/3600.) header.set('BPA', self.beam_bpa) header.set('BSCALE',1.) header.set('BZERO',0.) header.set('CDELT1',-self.pixsize/3600.) header.set('CDELT2',self.pixsize/3600.) header.set('CTYPE1',self.ctype1) header.set('CTYPE2',self.ctype2) header.set('CRPIX1',self.crpix1) header.set('CRPIX2',self.crpix2) header.set('CRVAL1',self.crval1) header.set('CRVAL2',self.crval2) # Define HDU hdu = fits.PrimaryHDU(data=data,header=header) hdulist = fits.HDUList([hdu]) hdulist.writeto(outputfile,overwrite=True) def write_source_map(self,data,outputfile): """ Write FITS image with sim mask data """ # Define FITS header header= fits.Header() header.set('SIMPLE','T') header.set('BITPIX','-32') header.set('NAXIS1', str(self.nx)) header.set('NAXIS2', str(self.ny)) header.set('BUNIT', 'JY/pixel') header.set('BMAJ', self.beam_bmaj/3600.) header.set('BMIN', self.beam_bmin/3600.) header.set('BPA', self.beam_bpa) header.set('BSCALE',1.) header.set('BZERO',0.) header.set('CDELT1',-self.pixsize/3600.) header.set('CDELT2',self.pixsize/3600.) header.set('CTYPE1',self.ctype1) header.set('CTYPE2',self.ctype2) header.set('CRPIX1',self.crpix1) header.set('CRPIX2',self.crpix2) header.set('CRVAL1',self.crval1) header.set('CRVAL2',self.crval2) # Define HDU hdu = fits.PrimaryHDU(data=data,header=header) hdulist = fits.HDUList([hdu]) hdulist.writeto(outputfile,overwrite=True) def save(self): """ Write img & source collection to ROOT file """ # Loop over sources logger.info('Filling #%s sources to ROOT tree...' % str(len(self.caesar_sources)) ) for item in self.caesar_sources: #self.cs= item item.Copy(self.cs) self.cs.Print() self.outtree.Fill() # Write to file self.outfile.cd() self.caesar_img.Write() self.outtree.Write() self.outfile.Close() # Write CASA mask file self.write_casa_mask(boxsize=self.mask_boxsize) ########################### ############## ## MAIN ## ############## def main(): """Main function""" #=========================== #== Get script args #=========================== logger.info('Get script args') try: args= get_args() except Exception as ex: logger.error("Failed to get and parse options (err=%s)",str(ex)) return 1 # - Image args Nx= args.nx Ny= args.ny marginX= args.marginx marginY= args.marginy pixsize= args.pixsize ctype1= args.ctype1 ctype2= args.ctype2 crpix1= args.crpix1 crpix2= args.crpix2 crval1= args.crval1 crval2= args.crval2 #- Source model model_trunc_zmin= args.model_trunc_zmin trunc_thr= args.trunc_thr truncate_models= args.truncate_models npixels_min= args.npixels_min # - Bkg info args enable_bkg= args.enable_bkg bkg_level= args.bkg_level bkg_rms= args.bkg_rms # - Compact source args enable_compactsources= args.enable_compactsources nx_gen= args.nx_gen ny_gen= args.ny_gen Bmaj= args.bmaj Bmin= args.bmin Bpa= args.bpa Zmin= args.zmin Zmax= args.zmax source_density= args.source_density nsources= args.nsources Smodel= args.Smodel Sslope= args.Sslope Smin= args.Smin Smax= args.Smax bmaj_min= args.bmaj_min bmaj_max= args.bmaj_max bmin_min= args.bmin_min bmin_max= args.bmin_max pa_min= args.pa_min pa_max= args.pa_max # - Extended source args enable_extsources= args.enable_extsources ext_source_type= args.ext_source_type ext_nsources= args.ext_nsources Smin_ext= args.Smin_ext Smax_ext= args.Smax_ext Zmin_ext= args.zmin_ext Zmax_ext= args.zmax_ext ext_source_density= args.ext_source_density ext_scale_min= args.ext_scale_min ext_scale_max= args.ext_scale_max ring_rmin= args.ring_rmin ring_rmax= args.ring_rmax ring_wmin= args.ring_wmin ring_wmax= args.ring_wmax ellipse_rmin= args.ellipse_rmin ellipse_rmax= args.ellipse_rmax disk_shell_ampl_ratio_min= args.disk_shell_ampl_ratio_min disk_shell_ampl_ratio_max= args.disk_shell_ampl_ratio_max disk_shell_radius_ratio_min= args.disk_shell_radius_ratio_min disk_shell_radius_ratio_max= args.disk_shell_radius_ratio_max # - Output args outputfile= args.outputfile mask_outputfile= args.outputfile_model outputfile_sources= args.outputfile_sources outputfile_ds9region= args.outputfile_ds9region outputfile_casaregion= args.outputfile_casaregion mask_boxsize= args.mask_boxsize # - Mask image maskfile= args.maskimg mask_data= None if maskfile!='': hdu= fits.open(maskfile)[0] mask_data= hdu.data print("*** ARGS ***") print("Nx: %s" % Nx) print("Ny: %s" % Ny) print("Margin X: %s" % marginX) print("Margin Y: %s" % marginY) print("pixsize: %s" % pixsize) print("ctype: (%s %s)" % (ctype1,ctype2)) print("crpix: (%s %s)" % (crpix1,crpix2)) print("crval: (%s %s)" % (crval1,crval2)) print("Beam (Bmaj/Bmin/Bpa): (%s,%s,%s)" % (Bmaj, Bmin, Bpa)) print("Enable bkg? %s" % str(enable_bkg) ) print("Bkg info (level,rms): (%s,%s)" % (bkg_level, bkg_rms)) print("Enable compact sources? %s" % str(enable_compactsources) ) print("Source significance range: (%s,%s)" % (Zmin, Zmax)) print("Source density (deg^-2): %s" % source_density) print("Enable extended sources? %s" % str(enable_extsources) ) print("Extended source type %s" %str(ext_source_type) ) print("Extended source flux range: (%s,%s)" % (Smin_ext, Smax_ext)) print("Extended source significance range: (%s,%s)" % (Zmin_ext, Zmax_ext)) print("Extended source density (deg^-2): %s" % ext_source_density) print("Extended source scale min/max: (%s,%s)" % (ext_scale_min, ext_scale_max)) print("Output filename: %s " % outputfile) print("Model trunc thr: %s " % str(trunc_thr)) print("Mask output filename: %s " % mask_outputfile) print("Mask box size: %s " % mask_boxsize) print("************") ## Generate simulated sky map print ('INFO: Generate simulated sky map...') simulator= SkyMapSimulator(Nx,Ny,pixsize) simulator.set_margins(marginX,marginY) simulator.set_ref_pix(crpix1,crpix2) simulator.set_ref_pix_coords(crval1,crval2) simulator.set_coord_system_type(ctype1,ctype2) simulator.set_model_trunc_thr(trunc_thr) simulator.set_model_trunc_significance(model_trunc_zmin) simulator.enable_model_truncation(truncate_models) simulator.set_npixels_min(npixels_min) simulator.set_map_filename(outputfile) simulator.set_model_filename(mask_outputfile) simulator.set_source_filename(outputfile_sources) simulator.set_ds9region_filename(outputfile_ds9region) simulator.set_casaregion_filename(outputfile_casaregion) simulator.enable_bkg(enable_bkg) simulator.set_bkg_pars(bkg_level,bkg_rms) simulator.set_beam_info(Bmaj,Bmin,Bpa) simulator.enable_compact_sources(enable_compactsources) simulator.set_gen_blob_img_size(nx_gen,ny_gen) simulator.set_nsources(nsources) simulator.set_source_flux_rand_model(Smodel) simulator.set_source_flux_rand_exp_slope(Sslope) simulator.set_source_flux_range(Smin,Smax) simulator.set_source_significance_range(Zmin,Zmax) simulator.set_source_density(source_density) simulator.set_beam_bmaj_range(bmaj_min,bmaj_max) simulator.set_beam_bmin_range(bmin_min,bmin_max) simulator.set_beam_pa_range(pa_min,pa_max) simulator.enable_extended_sources(enable_extsources) simulator.set_ext_nsources(ext_nsources) simulator.set_ext_source_type(ext_source_type) simulator.set_ext_source_flux_range(Smin_ext,Smax_ext) simulator.set_ext_source_significance_range(Zmin_ext,Zmax_ext) simulator.set_ext_source_density(ext_source_density) #simulator.set_ring_pars(ring_rmin,ring_rmax,ring_wmin,ring_wmax) simulator.set_ring_pars(ext_scale_min,ext_scale_max,ring_wmin,ring_wmax) #simulator.set_ellipse_pars(ellipse_rmin,ellipse_rmax) simulator.set_ellipse_pars(ext_scale_min,ext_scale_max) simulator.set_disk_pars(ext_scale_min,ext_scale_max) simulator.set_disk_shell_pars(disk_shell_ampl_ratio_min,disk_shell_ampl_ratio_max,disk_shell_radius_ratio_min,disk_shell_radius_ratio_max) simulator.set_mask_box_size(mask_boxsize) if mask_data is not None: simulator.gmask_data= mask_data if simulator.generate_map()<0: print("ERROR: generate map failed!") return 1 return 0 ################### ## MAIN EXEC ## ################### if __name__ == "__main__": sys.exit(main())

gpl-3.0

aalmah/pylearn2

pylearn2/train_extensions/plots.py

34

9617

""" Plot monitoring extensions while training. """ __authors__ = "Laurent Dinh" __copyright__ = "Copyright 2014, Universite de Montreal" __credits__ = ["Laurent Dinh"] __license__ = "3-clause BSD" __maintainer__ = "Laurent Dinh" __email__ = "dinhlaur@iro" import logging import os import os.path import stat import numpy np = numpy from pylearn2.train_extensions import TrainExtension from theano.compat.six.moves import xrange from pylearn2.utils import as_floatX, wraps if os.getenv('DISPLAY') is None: import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import warnings log = logging.getLogger(__name__) def make_readable(fn): """ Make a file readable by all. Practical when the plot is in your public_html. Parameters ---------- fn : str Filename you wish to make public readable. """ st = os.stat(fn) # Create the desired permission st_mode = st.st_mode read_all = stat.S_IRUSR read_all |= stat.S_IRGRP read_all |= stat.S_IROTH # Set the permission os.chmod(fn, st_mode | read_all) def get_best_layout(n_plots): """ Find the best basic layout for a given number of plots. Minimize the perimeter with a minimum area (``n_plots``) for an integer rectangle. Parameters ---------- n_plots : int The number of plots to display Returns ------- n_rows : int Number of rows in the layout n_cols : Number of columns in the layout """ assert n_plots > 0 # Initialize the layout n_rows = 1 n_cols = np.ceil(n_plots*1./n_rows) n_cols = int(n_cols) half_perimeter = n_cols + 1 # Limit the range of possible layouts max_row = np.sqrt(n_plots) max_row = np.round(max_row) max_row = int(max_row) for l in xrange(1, max_row + 1): width = np.ceil(n_plots*1./l) width = int(width) if half_perimeter >= (width + l): n_rows = l n_cols = np.ceil(n_plots*1./n_rows) n_cols = int(n_cols) half_perimeter = n_rows + n_cols return n_rows, n_cols def create_colors(n_colors): """ Create an array of n_colors Parameters ---------- n_colors : int The number of colors to create Returns ------- colors_rgb : np.array An array of shape (n_colors, 3) in RGB format """ # Create the list of color hue colors_hue = np.arange(n_colors) colors_hue = as_floatX(colors_hue) colors_hue *= 1./n_colors # Set the color in HSV format colors_hsv = np.ones((n_colors, 3)) colors_hsv[:, 2] *= .75 colors_hsv[:, 0] = colors_hue # Put in a matplotlib-friendly format colors_hsv = colors_hsv.reshape((1, )+colors_hsv.shape) # Convert to RGB colors_rgb = matplotlib.colors.hsv_to_rgb(colors_hsv) colors_rgb = colors_rgb[0] return colors_rgb class Plotter(object): """ Base class for plotting. Parameters ---------- freq : int, optional The number of epochs before producing plot. Default is None (set by the PlotManager). """ def __init__(self, freq=None): self.filenames = [] self.freq = freq def setup(self, model, dataset, algorithm): """ Setup the plotters. Parameters ---------- model : pylearn2.models.Model The model trained dataset : pylearn2.datasets.Dataset The dataset on which the model is trained algorithm : pylearn2.training_algorithms.TrainingAlgorithm The algorithm the model is trained with """ raise NotImplementedError(str(type(self))+" does not implement setup.") def plot(self): """ The method that draw and save the desired figure, which depend on the object and its attribute. This method is called by the PlotManager object as frequently as the `freq` attribute defines it. """ raise NotImplementedError(str(type(self))+" does not implement plot.") def set_permissions(self, public): """ Make the produced files readable by everyone. Parameters ---------- public : bool If public is True, then the associated files are readable by everyone. """ if public: for filename in self.filenames: make_readable(filename) class Plots(Plotter): """ Plot different monitors. Parameters ---------- channel_names : list of str List of monitor channels to plot save_path : str Filename of the plot file share : float, optional The percentage of epochs shown. Default is .8 (80%) per_second : bool, optional Set if the x-axis is in seconds, in epochs otherwise. Default is False. kwargs : dict Passed on to the superclass. """ def __init__(self, channel_names, save_path, share=.8, per_second=False, ** kwargs): super(Plots, self).__init__(** kwargs) if not save_path.endswith('.png'): save_path += '.png' self.save_path = save_path self.filenames = [self.save_path] self.channel_names = channel_names self.n_colors = len(self.channel_names) self.colors_rgb = create_colors(self.n_colors) self.share = share self.per_second = per_second @wraps(Plotter.setup) def setup(self, model, dataset, algorithm): self.model = model @wraps(Plotter.plot) def plot(self): monitor = self.model.monitor channels = monitor.channels channel_names = self.channel_names # Accumulate the plots plots = np.array(channels[channel_names[0]].val_record) plots = plots.reshape((1, plots.shape[0])) plots = plots.repeat(self.n_colors, axis=0) for i, channel_name in enumerate(channel_names[1:]): plots[i+1] = np.array(channels[channel_name].val_record) # Keep the relevant part n_min = plots.shape[1] n_min -= int(np.ceil(plots.shape[1] * self.share)) plots = plots[:, n_min:] # Get the x axis x = np.arange(plots.shape[1]) x += n_min # Put in seconds if needed if self.per_second: seconds = channels['training_seconds_this_epoch'].val_record seconds = np.array(seconds) seconds = seconds.cumsum() x = seconds[x] # Plot the quantities plt.figure() for i in xrange(self.n_colors): plt.plot(x, plots[i], color=self.colors_rgb[i], alpha=.5) plt.legend(self.channel_names) plt.xlim(x[0], x[-1]) plt.ylim(plots.min(), plots.max()) plt.axis('on') plt.savefig(self.save_path) plt.close() class PlotManager(TrainExtension): """ Class to manage the Plotter classes. Parameters ---------- plots : list of pylearn2.train_extensions.Plotter List of plots to make during training freq : int The default number of epochs before producing plot. public : bool Whether the files are made public or not. Default is true. html_path : str The path where the HTML page is saved. The associated files should be in the same folder. Default is None, then there is no HTML page. """ def __init__(self, plots, freq, public=True, html_path=None): self.plots = plots self.freq = freq # Set a default freq for plot in self.plots: if plot.freq is None: plot.freq = self.freq self.public = public self.html_path = html_path self.filenames = [] self.count = 0 @wraps(TrainExtension.setup) def setup(self, model, dataset, algorithm): for plot in self.plots: plot.setup(model, dataset, algorithm) for filename in plot.filenames: warn = ("/home/www-etud/" in filename) warn |= (os.environ['HOME'] in filename) warn &= ('umontreal' in os.environ['HOSTNAME']) if warn: warnings.warn('YOU MIGHT RUIN THE NFS' 'BY SAVING IN THIS PATH !') self.filenames.append(filename) if self.html_path is not None: header = ('<?xml version="1.0" encoding="UTF-8"?>\n' '<html xmlns="http://www.w3.org/1999/xhtml"' 'xml:lang="en">\n' '\t<body>\n') footer = ('\t</body>\n' '</html>') body = '' for filename in self.filenames: basename = os.path.basename(filename) body += '<img src = "' + basename + '"><br/>\n' with open(self.html_path, 'w') as f: f.write(header + body + footer) f.close() if self.public: make_readable(self.html_path) @wraps(TrainExtension.on_monitor) def on_monitor(self, model, dataset, algorithm): self.count += 1 for plot in self.plots: if self.count % plot.freq == 0: try: plot.plot() plot.set_permissions(self.public) except Exception as e: warnings.warn(str(plot) + ' has failed.\n' + str(e))

bsd-3-clause

a-parhom/edx-platform

lms/djangoapps/course_api/blocks/tests/test_api.py

2

11195

""" Tests for Blocks api.py """ from itertools import product from mock import patch import ddt from django.test.client import RequestFactory from django.test.utils import override_settings import course_blocks.api as course_blocks_api from openedx.core.djangoapps.content.block_structure.api import clear_course_from_cache from openedx.core.djangoapps.content.block_structure.config import STORAGE_BACKING_FOR_CACHE, waffle from student.tests.factories import UserFactory from xmodule.modulestore import ModuleStoreEnum from xmodule.modulestore.tests.django_utils import SharedModuleStoreTestCase from xmodule.modulestore.tests.factories import SampleCourseFactory, check_mongo_calls from xmodule.modulestore.tests.sample_courses import BlockInfo from ..api import get_blocks class TestGetBlocks(SharedModuleStoreTestCase): """ Tests for the get_blocks function """ shard = 4 @classmethod def setUpClass(cls): super(TestGetBlocks, cls).setUpClass() with cls.store.default_store(ModuleStoreEnum.Type.split): cls.course = SampleCourseFactory.create() # hide the html block cls.html_block = cls.store.get_item(cls.course.id.make_usage_key('html', 'html_x1a_1')) cls.html_block.visible_to_staff_only = True cls.store.update_item(cls.html_block, ModuleStoreEnum.UserID.test) def setUp(self): super(TestGetBlocks, self).setUp() self.user = UserFactory.create() self.request = RequestFactory().get("/dummy") self.request.user = self.user def test_basic(self): blocks = get_blocks(self.request, self.course.location, self.user) self.assertEquals(blocks['root'], unicode(self.course.location)) # subtract for (1) the orphaned course About block and (2) the hidden Html block self.assertEquals(len(blocks['blocks']), len(self.store.get_items(self.course.id)) - 2) self.assertNotIn(unicode(self.html_block.location), blocks['blocks']) def test_no_user(self): blocks = get_blocks(self.request, self.course.location) self.assertIn(unicode(self.html_block.location), blocks['blocks']) def test_access_before_api_transformer_order(self): """ Tests the order of transformers: access checks are made before the api transformer is applied. """ blocks = get_blocks(self.request, self.course.location, self.user, nav_depth=5, requested_fields=['nav_depth']) vertical_block = self.store.get_item(self.course.id.make_usage_key('vertical', 'vertical_x1a')) problem_block = self.store.get_item(self.course.id.make_usage_key('problem', 'problem_x1a_1')) vertical_descendants = blocks['blocks'][unicode(vertical_block.location)]['descendants'] self.assertIn(unicode(problem_block.location), vertical_descendants) self.assertNotIn(unicode(self.html_block.location), vertical_descendants) def test_sub_structure(self): sequential_block = self.store.get_item(self.course.id.make_usage_key('sequential', 'sequential_y1')) blocks = get_blocks(self.request, sequential_block.location, self.user) self.assertEquals(blocks['root'], unicode(sequential_block.location)) self.assertEquals(len(blocks['blocks']), 5) for block_type, block_name, is_inside_of_structure in ( ('vertical', 'vertical_y1a', True), ('problem', 'problem_y1a_1', True), ('chapter', 'chapter_y', False), ('sequential', 'sequential_x1', False), ): block = self.store.get_item(self.course.id.make_usage_key(block_type, block_name)) if is_inside_of_structure: self.assertIn(unicode(block.location), blocks['blocks']) else: self.assertNotIn(unicode(block.location), blocks['blocks']) def test_filtering_by_block_types(self): sequential_block = self.store.get_item(self.course.id.make_usage_key('sequential', 'sequential_y1')) # not filtered blocks blocks = get_blocks(self.request, sequential_block.location, self.user, requested_fields=['type']) self.assertEquals(len(blocks['blocks']), 5) found_not_problem = False for block in blocks['blocks'].itervalues(): if block['type'] != 'problem': found_not_problem = True self.assertTrue(found_not_problem) # filtered blocks blocks = get_blocks(self.request, sequential_block.location, self.user, block_types_filter=['problem'], requested_fields=['type']) self.assertEquals(len(blocks['blocks']), 3) for block in blocks['blocks'].itervalues(): self.assertEqual(block['type'], 'problem') # TODO: Remove this class after REVE-52 lands and old-mobile-app traffic falls to < 5% of mobile traffic @ddt.ddt class TestGetBlocksMobileHack(SharedModuleStoreTestCase): """ Tests that requests from the mobile app don't receive empty containers. """ shard = 4 @classmethod def setUpClass(cls): super(TestGetBlocksMobileHack, cls).setUpClass() with cls.store.default_store(ModuleStoreEnum.Type.split): cls.course = SampleCourseFactory.create( block_info_tree=[ BlockInfo('empty_chapter', 'chapter', {}, [ BlockInfo('empty_sequential', 'sequential', {}, [ BlockInfo('empty_vertical', 'vertical', {}, []), ]), ]), BlockInfo('full_chapter', 'chapter', {}, [ BlockInfo('full_sequential', 'sequential', {}, [ BlockInfo('full_vertical', 'vertical', {}, [ BlockInfo('html', 'html', {}, []), ]), ]), ]) ] ) def setUp(self): super(TestGetBlocksMobileHack, self).setUp() self.user = UserFactory.create() self.request = RequestFactory().get("/dummy") self.request.user = self.user @ddt.data( *product([True, False], ['chapter', 'sequential', 'vertical']) ) @ddt.unpack def test_empty_containers(self, is_mobile, container_type): with patch('lms.djangoapps.course_api.blocks.api.is_request_from_mobile_app', return_value=is_mobile): blocks = get_blocks(self.request, self.course.location) full_container_key = self.course.id.make_usage_key(container_type, 'full_{}'.format(container_type)) self.assertIn(str(full_container_key), blocks['blocks']) empty_container_key = self.course.id.make_usage_key(container_type, 'empty_{}'.format(container_type)) assert_containment = self.assertNotIn if is_mobile else self.assertIn assert_containment(str(empty_container_key), blocks['blocks']) @ddt.ddt class TestGetBlocksQueryCountsBase(SharedModuleStoreTestCase): """ Base for the get_blocks tests. """ shard = 4 ENABLED_SIGNALS = ['course_published'] def setUp(self): super(TestGetBlocksQueryCountsBase, self).setUp() self.user = UserFactory.create() self.request = RequestFactory().get("/dummy") self.request.user = self.user def _create_course(self, store_type): """ Creates the sample course in the given store type. """ with self.store.default_store(store_type): return SampleCourseFactory.create() def _get_blocks(self, course, expected_mongo_queries, expected_sql_queries): """ Verifies the number of expected queries when calling get_blocks on the given course. """ with check_mongo_calls(expected_mongo_queries): with self.assertNumQueries(expected_sql_queries): get_blocks(self.request, course.location, self.user) @ddt.ddt class TestGetBlocksQueryCounts(TestGetBlocksQueryCountsBase): """ Tests query counts for the get_blocks function. """ shard = 4 @ddt.data( *product( (ModuleStoreEnum.Type.mongo, ModuleStoreEnum.Type.split), (True, False), ) ) @ddt.unpack def test_query_counts_cached(self, store_type, with_storage_backing): with waffle().override(STORAGE_BACKING_FOR_CACHE, active=with_storage_backing): course = self._create_course(store_type) self._get_blocks( course, expected_mongo_queries=0, expected_sql_queries=9 if with_storage_backing else 8, ) @ddt.data( *product( ((ModuleStoreEnum.Type.mongo, 5), (ModuleStoreEnum.Type.split, 3)), (True, False), ) ) @ddt.unpack def test_query_counts_uncached(self, store_type_tuple, with_storage_backing): store_type, expected_mongo_queries = store_type_tuple with waffle().override(STORAGE_BACKING_FOR_CACHE, active=with_storage_backing): course = self._create_course(store_type) clear_course_from_cache(course.id) if with_storage_backing: num_sql_queries = 19 else: num_sql_queries = 9 self._get_blocks( course, expected_mongo_queries, expected_sql_queries=num_sql_queries, ) @ddt.ddt @override_settings(FIELD_OVERRIDE_PROVIDERS=(course_blocks_api.INDIVIDUAL_STUDENT_OVERRIDE_PROVIDER, )) class TestQueryCountsWithIndividualOverrideProvider(TestGetBlocksQueryCountsBase): """ Tests query counts for the get_blocks function when IndividualStudentOverrideProvider is set. """ shard = 4 @ddt.data( *product( (ModuleStoreEnum.Type.mongo, ModuleStoreEnum.Type.split), (True, False), ) ) @ddt.unpack def test_query_counts_cached(self, store_type, with_storage_backing): with waffle().override(STORAGE_BACKING_FOR_CACHE, active=with_storage_backing): course = self._create_course(store_type) self._get_blocks( course, expected_mongo_queries=0, expected_sql_queries=10 if with_storage_backing else 9, ) @ddt.data( *product( ((ModuleStoreEnum.Type.mongo, 5), (ModuleStoreEnum.Type.split, 3)), (True, False), ) ) @ddt.unpack def test_query_counts_uncached(self, store_type_tuple, with_storage_backing): store_type, expected_mongo_queries = store_type_tuple with waffle().override(STORAGE_BACKING_FOR_CACHE, active=with_storage_backing): course = self._create_course(store_type) clear_course_from_cache(course.id) if with_storage_backing: num_sql_queries = 20 else: num_sql_queries = 10 self._get_blocks( course, expected_mongo_queries, expected_sql_queries=num_sql_queries, )

agpl-3.0

roxyboy/scikit-learn

examples/cluster/plot_segmentation_toy.py

257

3336

""" =========================================== Spectral clustering for image segmentation =========================================== In this example, an image with connected circles is generated and spectral clustering is used to separate the circles. In these settings, the :ref:`spectral_clustering` approach solves the problem know as 'normalized graph cuts': the image is seen as a graph of connected voxels, and the spectral clustering algorithm amounts to choosing graph cuts defining regions while minimizing the ratio of the gradient along the cut, and the volume of the region. As the algorithm tries to balance the volume (ie balance the region sizes), if we take circles with different sizes, the segmentation fails. In addition, as there is no useful information in the intensity of the image, or its gradient, we choose to perform the spectral clustering on a graph that is only weakly informed by the gradient. This is close to performing a Voronoi partition of the graph. In addition, we use the mask of the objects to restrict the graph to the outline of the objects. In this example, we are interested in separating the objects one from the other, and not from the background. """ print(__doc__) # Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org> # Gael Varoquaux <gael.varoquaux@normalesup.org> # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn.feature_extraction import image from sklearn.cluster import spectral_clustering ############################################################################### l = 100 x, y = np.indices((l, l)) center1 = (28, 24) center2 = (40, 50) center3 = (67, 58) center4 = (24, 70) radius1, radius2, radius3, radius4 = 16, 14, 15, 14 circle1 = (x - center1[0]) ** 2 + (y - center1[1]) ** 2 < radius1 ** 2 circle2 = (x - center2[0]) ** 2 + (y - center2[1]) ** 2 < radius2 ** 2 circle3 = (x - center3[0]) ** 2 + (y - center3[1]) ** 2 < radius3 ** 2 circle4 = (x - center4[0]) ** 2 + (y - center4[1]) ** 2 < radius4 ** 2 ############################################################################### # 4 circles img = circle1 + circle2 + circle3 + circle4 mask = img.astype(bool) img = img.astype(float) img += 1 + 0.2 * np.random.randn(*img.shape) # Convert the image into a graph with the value of the gradient on the # edges. graph = image.img_to_graph(img, mask=mask) # Take a decreasing function of the gradient: we take it weakly # dependent from the gradient the segmentation is close to a voronoi graph.data = np.exp(-graph.data / graph.data.std()) # Force the solver to be arpack, since amg is numerically # unstable on this example labels = spectral_clustering(graph, n_clusters=4, eigen_solver='arpack') label_im = -np.ones(mask.shape) label_im[mask] = labels plt.matshow(img) plt.matshow(label_im) ############################################################################### # 2 circles img = circle1 + circle2 mask = img.astype(bool) img = img.astype(float) img += 1 + 0.2 * np.random.randn(*img.shape) graph = image.img_to_graph(img, mask=mask) graph.data = np.exp(-graph.data / graph.data.std()) labels = spectral_clustering(graph, n_clusters=2, eigen_solver='arpack') label_im = -np.ones(mask.shape) label_im[mask] = labels plt.matshow(img) plt.matshow(label_im) plt.show()

bsd-3-clause

aalmah/pylearn2

pylearn2/utils/datasets.py

44

9068

""" Several utilities to evaluate an ALC on the dataset, to iterate over minibatches from a dataset, or to merge three data with given proportions """ # Standard library imports import logging import os import functools from itertools import repeat import warnings # Third-party imports import numpy import scipy from theano.compat.six.moves import reduce, xrange import theano try: from matplotlib import pyplot from mpl_toolkits.mplot3d import Axes3D except ImportError: warnings.warn("Could not import some dependencies.") # Local imports from pylearn2.utils.rng import make_np_rng logger = logging.getLogger(__name__) ################################################## # 3D Visualization ################################################## def do_3d_scatter(x, y, z, figno=None, title=None): """ Generate a 3D scatterplot figure and optionally give it a title. Parameters ---------- x : WRITEME y : WRITEME z : WRITEME figno : WRITEME title : WRITEME """ fig = pyplot.figure(figno) ax = Axes3D(fig) ax.scatter(x, y, z) ax.set_xlabel("X") ax.set_ylabel("Y") ax.set_zlabel("Z") pyplot.suptitle(title) def save_plot(repr, path, name="figure.pdf", title="features"): """ .. todo:: WRITEME """ # TODO : Maybe run a PCA if shape[1] > 3 assert repr.get_value(borrow=True).shape[1] == 3 # Take the first 3 columns x, y, z = repr.get_value(borrow=True).T do_3d_scatter(x, y, z) # Save the produces figure filename = os.path.join(path, name) pyplot.savefig(filename, format="pdf") logger.info('... figure saved: {0}'.format(filename)) ################################################## # Features or examples filtering ################################################## def filter_labels(train, label, classes=None): """ Filter examples of train for which we have labels Parameters ---------- train : WRITEME label : WRITEME classes : WRITEME Returns ------- WRITEME """ if isinstance(train, theano.tensor.sharedvar.SharedVariable): train = train.get_value(borrow=True) if isinstance(label, theano.tensor.sharedvar.SharedVariable): label = label.get_value(borrow=True) if not (isinstance(train, numpy.ndarray) or scipy.sparse.issparse(train)): raise TypeError('train must be a numpy array, a scipy sparse matrix,' ' or a theano shared array') # Examples for which any label is set if classes is not None: label = label[:, classes] # Special case for sparse matrices if scipy.sparse.issparse(train): idx = label.sum(axis=1).nonzero()[0] return (train[idx], label[idx]) # Compress train and label arrays according to condition condition = label.any(axis=1) return tuple(var.compress(condition, axis=0) for var in (train, label)) def nonzero_features(data, combine=None): """ Get features for which there are nonzero entries in the data. Parameters ---------- data : list of matrices List of data matrices, either in sparse format or not. They must have the same number of features (column number). combine : function, optional A function to combine elementwise which features to keep. Default keeps the intersection of each non-zero columns. Returns ------- indices : ndarray object Indices of the nonzero features. Notes ----- I would return a mask (bool array) here, but scipy.sparse doesn't appear to fully support advanced indexing. """ if combine is None: combine = functools.partial(reduce, numpy.logical_and) # Assumes all values are >0, which is the case for all sparse datasets. masks = numpy.asarray([subset.sum(axis=0) for subset in data]).squeeze() nz_feats = combine(masks).nonzero()[0] return nz_feats # TODO: Is this a duplicate? def filter_nonzero(data, combine=None): """ Filter non-zero features of data according to a certain combining function Parameters ---------- data : list of matrices List of data matrices, either in sparse format or not. They must have the same number of features (column number). combine : function A function to combine elementwise which features to keep. Default keeps the intersection of each non-zero columns. Returns ------- indices : ndarray object Indices of the nonzero features. """ nz_feats = nonzero_features(data, combine) return [set[:, nz_feats] for set in data] ################################################## # Iterator object for minibatches of datasets ################################################## class BatchIterator(object): """ Builds an iterator object that can be used to go through the minibatches of a dataset, with respect to the given proportions in conf Parameters ---------- dataset : WRITEME set_proba : WRITEME batch_size : WRITEME seed : WRITEME """ def __init__(self, dataset, set_proba, batch_size, seed=300): # Local shortcuts for array operations flo = numpy.floor sub = numpy.subtract mul = numpy.multiply div = numpy.divide mod = numpy.mod # Record external parameters self.batch_size = batch_size if (isinstance(dataset[0], theano.Variable)): self.dataset = [set.get_value(borrow=True) for set in dataset] else: self.dataset = dataset # Compute maximum number of samples for one loop set_sizes = [set.shape[0] for set in self.dataset] set_batch = [float(self.batch_size) for i in xrange(3)] set_range = div(mul(set_proba, set_sizes), set_batch) set_range = map(int, numpy.ceil(set_range)) # Upper bounds for each minibatch indexes set_limit = numpy.ceil(numpy.divide(set_sizes, set_batch)) self.limit = map(int, set_limit) # Number of rows in the resulting union set_tsign = sub(set_limit, flo(div(set_sizes, set_batch))) set_tsize = mul(set_tsign, flo(div(set_range, set_limit))) l_trun = mul(flo(div(set_range, set_limit)), mod(set_sizes, set_batch)) l_full = mul(sub(set_range, set_tsize), set_batch) self.length = sum(l_full) + sum(l_trun) # Random number generation using a permutation index_tab = [] for i in xrange(3): index_tab.extend(repeat(i, set_range[i])) # Use a deterministic seed self.seed = seed rng = make_np_rng(seed, which_method="permutation") self.permut = rng.permutation(index_tab) def __iter__(self): """Generator function to iterate through all minibatches""" counter = [0, 0, 0] for chosen in self.permut: # Retrieve minibatch from chosen set index = counter[chosen] minibatch = self.dataset[chosen][ index * self.batch_size:(index + 1) * self.batch_size ] # Increment the related counter counter[chosen] = (counter[chosen] + 1) % self.limit[chosen] # Return the computed minibatch yield minibatch def __len__(self): """Return length of the weighted union""" return self.length def by_index(self): """Same generator as __iter__, but yield only the chosen indexes""" counter = [0, 0, 0] for chosen in self.permut: index = counter[chosen] counter[chosen] = (counter[chosen] + 1) % self.limit[chosen] yield chosen, index ################################################## # Miscellaneous ################################################## def minibatch_map(fn, batch_size, input_data, output_data=None, output_width=None): """ Apply a function on input_data, one minibatch at a time. Storage for the output can be provided. If it is the case, it should have appropriate size. If output_data is not provided, then output_width should be specified. Parameters ---------- fn : WRITEME batch_size : WRITEME input_data : WRITEME output_data : WRITEME output_width : WRITEME Returns ------- WRITEME """ if output_width is None: if output_data is None: raise ValueError('output_data or output_width should be provided') output_width = output_data.shape[1] output_length = input_data.shape[0] if output_data is None: output_data = numpy.empty((output_length, output_width)) else: assert output_data.shape[0] == input_data.shape[0], ('output_data ' 'should have the same length as input_data', output_data.shape[0], input_data.shape[0]) for i in xrange(0, output_length, batch_size): output_data[i:i+batch_size] = fn(input_data[i:i+batch_size]) return output_data

bsd-3-clause

gibiansky/tensorflow

tensorflow/examples/learn/iris_with_pipeline.py

13

1854

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Example of DNNClassifier for Iris plant dataset, with pipeline.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from sklearn import cross_validation from sklearn.datasets import load_iris from sklearn.metrics import accuracy_score from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler import tensorflow as tf from tensorflow.contrib import learn def main(unused_argv): iris = load_iris() x_train, x_test, y_train, y_test = cross_validation.train_test_split( iris.data, iris.target, test_size=0.2, random_state=42) # It's useful to scale to ensure Stochastic Gradient Descent # will do the right thing. scaler = StandardScaler() # DNN classifier. classifier = learn.DNNClassifier( feature_columns=learn.infer_real_valued_columns_from_input(x_train), hidden_units=[10, 20, 10], n_classes=3) pipeline = Pipeline([('scaler', scaler), ('DNNclassifier', classifier)]) pipeline.fit(x_train, y_train, DNNclassifier__steps=200) score = accuracy_score(y_test, list(pipeline.predict(x_test))) print('Accuracy: {0:f}'.format(score)) if __name__ == '__main__': tf.app.run()

apache-2.0

roxyboy/scikit-learn

sklearn/decomposition/tests/test_incremental_pca.py

294

8265

"""Tests for Incremental PCA.""" import numpy as np from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_raises from sklearn import datasets from sklearn.decomposition import PCA, IncrementalPCA iris = datasets.load_iris() def test_incremental_pca(): # Incremental PCA on dense arrays. X = iris.data batch_size = X.shape[0] // 3 ipca = IncrementalPCA(n_components=2, batch_size=batch_size) pca = PCA(n_components=2) pca.fit_transform(X) X_transformed = ipca.fit_transform(X) np.testing.assert_equal(X_transformed.shape, (X.shape[0], 2)) assert_almost_equal(ipca.explained_variance_ratio_.sum(), pca.explained_variance_ratio_.sum(), 1) for n_components in [1, 2, X.shape[1]]: ipca = IncrementalPCA(n_components, batch_size=batch_size) ipca.fit(X) cov = ipca.get_covariance() precision = ipca.get_precision() assert_array_almost_equal(np.dot(cov, precision), np.eye(X.shape[1])) def test_incremental_pca_check_projection(): # Test that the projection of data is correct. rng = np.random.RandomState(1999) n, p = 100, 3 X = rng.randn(n, p) * .1 X[:10] += np.array([3, 4, 5]) Xt = 0.1 * rng.randn(1, p) + np.array([3, 4, 5]) # Get the reconstruction of the generated data X # Note that Xt has the same "components" as X, just separated # This is what we want to ensure is recreated correctly Yt = IncrementalPCA(n_components=2).fit(X).transform(Xt) # Normalize Yt /= np.sqrt((Yt ** 2).sum()) # Make sure that the first element of Yt is ~1, this means # the reconstruction worked as expected assert_almost_equal(np.abs(Yt[0][0]), 1., 1) def test_incremental_pca_inverse(): # Test that the projection of data can be inverted. rng = np.random.RandomState(1999) n, p = 50, 3 X = rng.randn(n, p) # spherical data X[:, 1] *= .00001 # make middle component relatively small X += [5, 4, 3] # make a large mean # same check that we can find the original data from the transformed # signal (since the data is almost of rank n_components) ipca = IncrementalPCA(n_components=2, batch_size=10).fit(X) Y = ipca.transform(X) Y_inverse = ipca.inverse_transform(Y) assert_almost_equal(X, Y_inverse, decimal=3) def test_incremental_pca_validation(): # Test that n_components is >=1 and <= n_features. X = [[0, 1], [1, 0]] for n_components in [-1, 0, .99, 3]: assert_raises(ValueError, IncrementalPCA(n_components, batch_size=10).fit, X) def test_incremental_pca_set_params(): # Test that components_ sign is stable over batch sizes. rng = np.random.RandomState(1999) n_samples = 100 n_features = 20 X = rng.randn(n_samples, n_features) X2 = rng.randn(n_samples, n_features) X3 = rng.randn(n_samples, n_features) ipca = IncrementalPCA(n_components=20) ipca.fit(X) # Decreasing number of components ipca.set_params(n_components=10) assert_raises(ValueError, ipca.partial_fit, X2) # Increasing number of components ipca.set_params(n_components=15) assert_raises(ValueError, ipca.partial_fit, X3) # Returning to original setting ipca.set_params(n_components=20) ipca.partial_fit(X) def test_incremental_pca_num_features_change(): # Test that changing n_components will raise an error. rng = np.random.RandomState(1999) n_samples = 100 X = rng.randn(n_samples, 20) X2 = rng.randn(n_samples, 50) ipca = IncrementalPCA(n_components=None) ipca.fit(X) assert_raises(ValueError, ipca.partial_fit, X2) def test_incremental_pca_batch_signs(): # Test that components_ sign is stable over batch sizes. rng = np.random.RandomState(1999) n_samples = 100 n_features = 3 X = rng.randn(n_samples, n_features) all_components = [] batch_sizes = np.arange(10, 20) for batch_size in batch_sizes: ipca = IncrementalPCA(n_components=None, batch_size=batch_size).fit(X) all_components.append(ipca.components_) for i, j in zip(all_components[:-1], all_components[1:]): assert_almost_equal(np.sign(i), np.sign(j), decimal=6) def test_incremental_pca_batch_values(): # Test that components_ values are stable over batch sizes. rng = np.random.RandomState(1999) n_samples = 100 n_features = 3 X = rng.randn(n_samples, n_features) all_components = [] batch_sizes = np.arange(20, 40, 3) for batch_size in batch_sizes: ipca = IncrementalPCA(n_components=None, batch_size=batch_size).fit(X) all_components.append(ipca.components_) for i, j in zip(all_components[:-1], all_components[1:]): assert_almost_equal(i, j, decimal=1) def test_incremental_pca_partial_fit(): # Test that fit and partial_fit get equivalent results. rng = np.random.RandomState(1999) n, p = 50, 3 X = rng.randn(n, p) # spherical data X[:, 1] *= .00001 # make middle component relatively small X += [5, 4, 3] # make a large mean # same check that we can find the original data from the transformed # signal (since the data is almost of rank n_components) batch_size = 10 ipca = IncrementalPCA(n_components=2, batch_size=batch_size).fit(X) pipca = IncrementalPCA(n_components=2, batch_size=batch_size) # Add one to make sure endpoint is included batch_itr = np.arange(0, n + 1, batch_size) for i, j in zip(batch_itr[:-1], batch_itr[1:]): pipca.partial_fit(X[i:j, :]) assert_almost_equal(ipca.components_, pipca.components_, decimal=3) def test_incremental_pca_against_pca_iris(): # Test that IncrementalPCA and PCA are approximate (to a sign flip). X = iris.data Y_pca = PCA(n_components=2).fit_transform(X) Y_ipca = IncrementalPCA(n_components=2, batch_size=25).fit_transform(X) assert_almost_equal(np.abs(Y_pca), np.abs(Y_ipca), 1) def test_incremental_pca_against_pca_random_data(): # Test that IncrementalPCA and PCA are approximate (to a sign flip). rng = np.random.RandomState(1999) n_samples = 100 n_features = 3 X = rng.randn(n_samples, n_features) + 5 * rng.rand(1, n_features) Y_pca = PCA(n_components=3).fit_transform(X) Y_ipca = IncrementalPCA(n_components=3, batch_size=25).fit_transform(X) assert_almost_equal(np.abs(Y_pca), np.abs(Y_ipca), 1) def test_explained_variances(): # Test that PCA and IncrementalPCA calculations match X = datasets.make_low_rank_matrix(1000, 100, tail_strength=0., effective_rank=10, random_state=1999) prec = 3 n_samples, n_features = X.shape for nc in [None, 99]: pca = PCA(n_components=nc).fit(X) ipca = IncrementalPCA(n_components=nc, batch_size=100).fit(X) assert_almost_equal(pca.explained_variance_, ipca.explained_variance_, decimal=prec) assert_almost_equal(pca.explained_variance_ratio_, ipca.explained_variance_ratio_, decimal=prec) assert_almost_equal(pca.noise_variance_, ipca.noise_variance_, decimal=prec) def test_whitening(): # Test that PCA and IncrementalPCA transforms match to sign flip. X = datasets.make_low_rank_matrix(1000, 10, tail_strength=0., effective_rank=2, random_state=1999) prec = 3 n_samples, n_features = X.shape for nc in [None, 9]: pca = PCA(whiten=True, n_components=nc).fit(X) ipca = IncrementalPCA(whiten=True, n_components=nc, batch_size=250).fit(X) Xt_pca = pca.transform(X) Xt_ipca = ipca.transform(X) assert_almost_equal(np.abs(Xt_pca), np.abs(Xt_ipca), decimal=prec) Xinv_ipca = ipca.inverse_transform(Xt_ipca) Xinv_pca = pca.inverse_transform(Xt_pca) assert_almost_equal(X, Xinv_ipca, decimal=prec) assert_almost_equal(X, Xinv_pca, decimal=prec) assert_almost_equal(Xinv_pca, Xinv_ipca, decimal=prec)

bsd-3-clause

ephes/scikit-learn

sklearn/decomposition/tests/test_incremental_pca.py

294

8265

"""Tests for Incremental PCA.""" import numpy as np from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_raises from sklearn import datasets from sklearn.decomposition import PCA, IncrementalPCA iris = datasets.load_iris() def test_incremental_pca(): # Incremental PCA on dense arrays. X = iris.data batch_size = X.shape[0] // 3 ipca = IncrementalPCA(n_components=2, batch_size=batch_size) pca = PCA(n_components=2) pca.fit_transform(X) X_transformed = ipca.fit_transform(X) np.testing.assert_equal(X_transformed.shape, (X.shape[0], 2)) assert_almost_equal(ipca.explained_variance_ratio_.sum(), pca.explained_variance_ratio_.sum(), 1) for n_components in [1, 2, X.shape[1]]: ipca = IncrementalPCA(n_components, batch_size=batch_size) ipca.fit(X) cov = ipca.get_covariance() precision = ipca.get_precision() assert_array_almost_equal(np.dot(cov, precision), np.eye(X.shape[1])) def test_incremental_pca_check_projection(): # Test that the projection of data is correct. rng = np.random.RandomState(1999) n, p = 100, 3 X = rng.randn(n, p) * .1 X[:10] += np.array([3, 4, 5]) Xt = 0.1 * rng.randn(1, p) + np.array([3, 4, 5]) # Get the reconstruction of the generated data X # Note that Xt has the same "components" as X, just separated # This is what we want to ensure is recreated correctly Yt = IncrementalPCA(n_components=2).fit(X).transform(Xt) # Normalize Yt /= np.sqrt((Yt ** 2).sum()) # Make sure that the first element of Yt is ~1, this means # the reconstruction worked as expected assert_almost_equal(np.abs(Yt[0][0]), 1., 1) def test_incremental_pca_inverse(): # Test that the projection of data can be inverted. rng = np.random.RandomState(1999) n, p = 50, 3 X = rng.randn(n, p) # spherical data X[:, 1] *= .00001 # make middle component relatively small X += [5, 4, 3] # make a large mean # same check that we can find the original data from the transformed # signal (since the data is almost of rank n_components) ipca = IncrementalPCA(n_components=2, batch_size=10).fit(X) Y = ipca.transform(X) Y_inverse = ipca.inverse_transform(Y) assert_almost_equal(X, Y_inverse, decimal=3) def test_incremental_pca_validation(): # Test that n_components is >=1 and <= n_features. X = [[0, 1], [1, 0]] for n_components in [-1, 0, .99, 3]: assert_raises(ValueError, IncrementalPCA(n_components, batch_size=10).fit, X) def test_incremental_pca_set_params(): # Test that components_ sign is stable over batch sizes. rng = np.random.RandomState(1999) n_samples = 100 n_features = 20 X = rng.randn(n_samples, n_features) X2 = rng.randn(n_samples, n_features) X3 = rng.randn(n_samples, n_features) ipca = IncrementalPCA(n_components=20) ipca.fit(X) # Decreasing number of components ipca.set_params(n_components=10) assert_raises(ValueError, ipca.partial_fit, X2) # Increasing number of components ipca.set_params(n_components=15) assert_raises(ValueError, ipca.partial_fit, X3) # Returning to original setting ipca.set_params(n_components=20) ipca.partial_fit(X) def test_incremental_pca_num_features_change(): # Test that changing n_components will raise an error. rng = np.random.RandomState(1999) n_samples = 100 X = rng.randn(n_samples, 20) X2 = rng.randn(n_samples, 50) ipca = IncrementalPCA(n_components=None) ipca.fit(X) assert_raises(ValueError, ipca.partial_fit, X2) def test_incremental_pca_batch_signs(): # Test that components_ sign is stable over batch sizes. rng = np.random.RandomState(1999) n_samples = 100 n_features = 3 X = rng.randn(n_samples, n_features) all_components = [] batch_sizes = np.arange(10, 20) for batch_size in batch_sizes: ipca = IncrementalPCA(n_components=None, batch_size=batch_size).fit(X) all_components.append(ipca.components_) for i, j in zip(all_components[:-1], all_components[1:]): assert_almost_equal(np.sign(i), np.sign(j), decimal=6) def test_incremental_pca_batch_values(): # Test that components_ values are stable over batch sizes. rng = np.random.RandomState(1999) n_samples = 100 n_features = 3 X = rng.randn(n_samples, n_features) all_components = [] batch_sizes = np.arange(20, 40, 3) for batch_size in batch_sizes: ipca = IncrementalPCA(n_components=None, batch_size=batch_size).fit(X) all_components.append(ipca.components_) for i, j in zip(all_components[:-1], all_components[1:]): assert_almost_equal(i, j, decimal=1) def test_incremental_pca_partial_fit(): # Test that fit and partial_fit get equivalent results. rng = np.random.RandomState(1999) n, p = 50, 3 X = rng.randn(n, p) # spherical data X[:, 1] *= .00001 # make middle component relatively small X += [5, 4, 3] # make a large mean # same check that we can find the original data from the transformed # signal (since the data is almost of rank n_components) batch_size = 10 ipca = IncrementalPCA(n_components=2, batch_size=batch_size).fit(X) pipca = IncrementalPCA(n_components=2, batch_size=batch_size) # Add one to make sure endpoint is included batch_itr = np.arange(0, n + 1, batch_size) for i, j in zip(batch_itr[:-1], batch_itr[1:]): pipca.partial_fit(X[i:j, :]) assert_almost_equal(ipca.components_, pipca.components_, decimal=3) def test_incremental_pca_against_pca_iris(): # Test that IncrementalPCA and PCA are approximate (to a sign flip). X = iris.data Y_pca = PCA(n_components=2).fit_transform(X) Y_ipca = IncrementalPCA(n_components=2, batch_size=25).fit_transform(X) assert_almost_equal(np.abs(Y_pca), np.abs(Y_ipca), 1) def test_incremental_pca_against_pca_random_data(): # Test that IncrementalPCA and PCA are approximate (to a sign flip). rng = np.random.RandomState(1999) n_samples = 100 n_features = 3 X = rng.randn(n_samples, n_features) + 5 * rng.rand(1, n_features) Y_pca = PCA(n_components=3).fit_transform(X) Y_ipca = IncrementalPCA(n_components=3, batch_size=25).fit_transform(X) assert_almost_equal(np.abs(Y_pca), np.abs(Y_ipca), 1) def test_explained_variances(): # Test that PCA and IncrementalPCA calculations match X = datasets.make_low_rank_matrix(1000, 100, tail_strength=0., effective_rank=10, random_state=1999) prec = 3 n_samples, n_features = X.shape for nc in [None, 99]: pca = PCA(n_components=nc).fit(X) ipca = IncrementalPCA(n_components=nc, batch_size=100).fit(X) assert_almost_equal(pca.explained_variance_, ipca.explained_variance_, decimal=prec) assert_almost_equal(pca.explained_variance_ratio_, ipca.explained_variance_ratio_, decimal=prec) assert_almost_equal(pca.noise_variance_, ipca.noise_variance_, decimal=prec) def test_whitening(): # Test that PCA and IncrementalPCA transforms match to sign flip. X = datasets.make_low_rank_matrix(1000, 10, tail_strength=0., effective_rank=2, random_state=1999) prec = 3 n_samples, n_features = X.shape for nc in [None, 9]: pca = PCA(whiten=True, n_components=nc).fit(X) ipca = IncrementalPCA(whiten=True, n_components=nc, batch_size=250).fit(X) Xt_pca = pca.transform(X) Xt_ipca = ipca.transform(X) assert_almost_equal(np.abs(Xt_pca), np.abs(Xt_ipca), decimal=prec) Xinv_ipca = ipca.inverse_transform(Xt_ipca) Xinv_pca = pca.inverse_transform(Xt_pca) assert_almost_equal(X, Xinv_ipca, decimal=prec) assert_almost_equal(X, Xinv_pca, decimal=prec) assert_almost_equal(Xinv_pca, Xinv_ipca, decimal=prec)

bsd-3-clause

ephes/scikit-learn

sklearn/externals/joblib/__init__.py

85

4795

""" Joblib is a set of tools to provide **lightweight pipelining in Python**. In particular, joblib offers: 1. transparent disk-caching of the output values and lazy re-evaluation (memoize pattern) 2. easy simple parallel computing 3. logging and tracing of the execution Joblib is optimized to be **fast** and **robust** in particular on large data and has specific optimizations for `numpy` arrays. It is **BSD-licensed**. ============================== ============================================ **User documentation**: http://pythonhosted.org/joblib **Download packages**: http://pypi.python.org/pypi/joblib#downloads **Source code**: http://github.com/joblib/joblib **Report issues**: http://github.com/joblib/joblib/issues ============================== ============================================ Vision -------- The vision is to provide tools to easily achieve better performance and reproducibility when working with long running jobs. * **Avoid computing twice the same thing**: code is rerun over an over, for instance when prototyping computational-heavy jobs (as in scientific development), but hand-crafted solution to alleviate this issue is error-prone and often leads to unreproducible results * **Persist to disk transparently**: persisting in an efficient way arbitrary objects containing large data is hard. Using joblib's caching mechanism avoids hand-written persistence and implicitly links the file on disk to the execution context of the original Python object. As a result, joblib's persistence is good for resuming an application status or computational job, eg after a crash. Joblib strives to address these problems while **leaving your code and your flow control as unmodified as possible** (no framework, no new paradigms). Main features ------------------ 1) **Transparent and fast disk-caching of output value:** a memoize or make-like functionality for Python functions that works well for arbitrary Python objects, including very large numpy arrays. Separate persistence and flow-execution logic from domain logic or algorithmic code by writing the operations as a set of steps with well-defined inputs and outputs: Python functions. Joblib can save their computation to disk and rerun it only if necessary:: >>> import numpy as np >>> from sklearn.externals.joblib import Memory >>> mem = Memory(cachedir='/tmp/joblib') >>> import numpy as np >>> a = np.vander(np.arange(3)).astype(np.float) >>> square = mem.cache(np.square) >>> b = square(a) # doctest: +ELLIPSIS ________________________________________________________________________________ [Memory] Calling square... square(array([[ 0., 0., 1.], [ 1., 1., 1.], [ 4., 2., 1.]])) ___________________________________________________________square - 0...s, 0.0min >>> c = square(a) >>> # The above call did not trigger an evaluation 2) **Embarrassingly parallel helper:** to make is easy to write readable parallel code and debug it quickly:: >>> from sklearn.externals.joblib import Parallel, delayed >>> from math import sqrt >>> Parallel(n_jobs=1)(delayed(sqrt)(i**2) for i in range(10)) [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0] 3) **Logging/tracing:** The different functionalities will progressively acquire better logging mechanism to help track what has been ran, and capture I/O easily. In addition, Joblib will provide a few I/O primitives, to easily define define logging and display streams, and provide a way of compiling a report. We want to be able to quickly inspect what has been run. 4) **Fast compressed Persistence**: a replacement for pickle to work efficiently on Python objects containing large data ( *joblib.dump* & *joblib.load* ). .. >>> import shutil ; shutil.rmtree('/tmp/joblib/') """ # PEP0440 compatible formatted version, see: # https://www.python.org/dev/peps/pep-0440/ # # Generic release markers: # X.Y # X.Y.Z # For bugfix releases # # Admissible pre-release markers: # X.YaN # Alpha release # X.YbN # Beta release # X.YrcN # Release Candidate # X.Y # Final release # # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer. # 'X.Y.dev0' is the canonical version of 'X.Y.dev' # __version__ = '0.9.0b3' from .memory import Memory, MemorizedResult from .logger import PrintTime from .logger import Logger from .hashing import hash from .numpy_pickle import dump from .numpy_pickle import load from .parallel import Parallel from .parallel import delayed from .parallel import cpu_count

bsd-3-clause

edx/edx-platform

openedx/features/course_experience/tests/views/test_course_outline.py

2

34442

""" Tests for the Course Outline view and supporting views. """ import datetime import re from unittest.mock import Mock, patch import ddt from completion.waffle import ENABLE_COMPLETION_TRACKING_SWITCH from completion.models import BlockCompletion from completion.test_utils import CompletionWaffleTestMixin from django.contrib.sites.models import Site from django.test import RequestFactory, override_settings from django.urls import reverse from django.utils import timezone from edx_toggles.toggles.testutils import override_waffle_flag, override_waffle_switch from milestones.tests.utils import MilestonesTestCaseMixin from opaque_keys.edx.keys import CourseKey, UsageKey from pyquery import PyQuery as pq from pytz import UTC from waffle.models import Switch from common.djangoapps.course_modes.models import CourseMode from common.djangoapps.course_modes.tests.factories import CourseModeFactory from common.djangoapps.student.tests.factories import StaffFactory from lms.djangoapps.course_api.blocks.transformers.milestones import MilestonesAndSpecialExamsTransformer from lms.djangoapps.gating import api as lms_gating_api from lms.djangoapps.courseware.tests.helpers import MasqueradeMixin from lms.urls import RESET_COURSE_DEADLINES_NAME from openedx.core.djangoapps.course_date_signals.models import SelfPacedRelativeDatesConfig from openedx.core.djangoapps.schedules.models import Schedule from openedx.core.djangoapps.schedules.tests.factories import ScheduleFactory # pylint: disable=unused-import from openedx.core.lib.gating import api as gating_api from openedx.features.content_type_gating.models import ContentTypeGatingConfig from openedx.features.course_experience import RELATIVE_DATES_FLAG from openedx.features.course_experience.views.course_outline import ( DEFAULT_COMPLETION_TRACKING_START, CourseOutlineFragmentView ) from common.djangoapps.student.models import CourseEnrollment from common.djangoapps.student.tests.factories import UserFactory from xmodule.modulestore import ModuleStoreEnum # lint-amnesty, pylint: disable=wrong-import-order from xmodule.modulestore.tests.django_utils import SharedModuleStoreTestCase # lint-amnesty, pylint: disable=wrong-import-order from xmodule.modulestore.tests.factories import CourseFactory, ItemFactory # lint-amnesty, pylint: disable=wrong-import-order from ...utils import get_course_outline_block_tree from .test_course_home import course_home_url TEST_PASSWORD = 'test' GATING_NAMESPACE_QUALIFIER = '.gating' @ddt.ddt class TestCourseOutlinePage(SharedModuleStoreTestCase, MasqueradeMixin): """ Test the course outline view. """ ENABLED_SIGNALS = ['course_published'] @classmethod def setUpClass(cls): # lint-amnesty, pylint: disable=super-method-not-called """ Set up an array of various courses to be tested. """ SelfPacedRelativeDatesConfig.objects.create(enabled=True) # setUpClassAndTestData() already calls setUpClass on SharedModuleStoreTestCase # pylint: disable=super-method-not-called with super().setUpClassAndTestData(): cls.courses = [] course = CourseFactory.create(self_paced=True) with cls.store.bulk_operations(course.id): chapter = ItemFactory.create(category='chapter', parent_location=course.location) sequential = ItemFactory.create(category='sequential', parent_location=chapter.location, graded=True, format="Homework") # lint-amnesty, pylint: disable=line-too-long vertical = ItemFactory.create(category='vertical', parent_location=sequential.location) problem = ItemFactory.create(category='problem', parent_location=vertical.location) course.children = [chapter] chapter.children = [sequential] sequential.children = [vertical] vertical.children = [problem] cls.courses.append(course) course = CourseFactory.create() with cls.store.bulk_operations(course.id): chapter = ItemFactory.create(category='chapter', parent_location=course.location) sequential = ItemFactory.create(category='sequential', parent_location=chapter.location) sequential2 = ItemFactory.create(category='sequential', parent_location=chapter.location) vertical = ItemFactory.create( category='vertical', parent_location=sequential.location, display_name="Vertical 1" ) vertical2 = ItemFactory.create( category='vertical', parent_location=sequential2.location, display_name="Vertical 2" ) course.children = [chapter] chapter.children = [sequential, sequential2] sequential.children = [vertical] sequential2.children = [vertical2] cls.courses.append(course) course = CourseFactory.create() with cls.store.bulk_operations(course.id): chapter = ItemFactory.create(category='chapter', parent_location=course.location) sequential = ItemFactory.create( category='sequential', parent_location=chapter.location, due=datetime.datetime.now(), graded=True, format='Homework', ) vertical = ItemFactory.create(category='vertical', parent_location=sequential.location) course.children = [chapter] chapter.children = [sequential] sequential.children = [vertical] cls.courses.append(course) @classmethod def setUpTestData(cls): # lint-amnesty, pylint: disable=super-method-not-called """Set up and enroll our fake user in the course.""" cls.user = UserFactory(password=TEST_PASSWORD) for course in cls.courses: CourseEnrollment.enroll(cls.user, course.id) Schedule.objects.update(start_date=timezone.now() - datetime.timedelta(days=1)) def setUp(self): """ Set up for the tests. """ super().setUp() self.client.login(username=self.user.username, password=TEST_PASSWORD) @override_waffle_flag(RELATIVE_DATES_FLAG, active=True) def test_outline_details(self): for course in self.courses: url = course_home_url(course) request_factory = RequestFactory() request = request_factory.get(url) request.user = self.user course_block_tree = get_course_outline_block_tree( request, str(course.id), self.user ) response = self.client.get(url) assert course.children for chapter in course_block_tree['children']: self.assertContains(response, chapter['display_name']) assert chapter['children'] for sequential in chapter['children']: self.assertContains(response, sequential['display_name']) if sequential['graded']: print(sequential) self.assertContains(response, sequential['due'].strftime('%Y-%m-%d %H:%M:%S')) self.assertContains(response, sequential['format']) assert sequential['children'] def test_num_graded_problems(self): course = CourseFactory.create() with self.store.bulk_operations(course.id): chapter = ItemFactory.create(category='chapter', parent_location=course.location) sequential = ItemFactory.create(category='sequential', parent_location=chapter.location) problem = ItemFactory.create(category='problem', parent_location=sequential.location) sequential2 = ItemFactory.create(category='sequential', parent_location=chapter.location) problem2 = ItemFactory.create(category='problem', graded=True, has_score=True, parent_location=sequential2.location) sequential3 = ItemFactory.create(category='sequential', parent_location=chapter.location) problem3_1 = ItemFactory.create(category='problem', graded=True, has_score=True, parent_location=sequential3.location) problem3_2 = ItemFactory.create(category='problem', graded=True, has_score=True, parent_location=sequential3.location) course.children = [chapter] chapter.children = [sequential, sequential2, sequential3] sequential.children = [problem] sequential2.children = [problem2] sequential3.children = [problem3_1, problem3_2] CourseEnrollment.enroll(self.user, course.id) url = course_home_url(course) response = self.client.get(url) content = response.content.decode('utf8') self.assertRegex(content, sequential.display_name + r'\s*</h4>') self.assertRegex(content, sequential2.display_name + r'\s*\(1 Question\)\s*</h4>') self.assertRegex(content, sequential3.display_name + r'\s*\(2 Questions\)\s*</h4>') @override_waffle_flag(RELATIVE_DATES_FLAG, active=True) @ddt.data( ([CourseMode.AUDIT, CourseMode.VERIFIED], CourseMode.AUDIT, False, True), ([CourseMode.AUDIT, CourseMode.VERIFIED], CourseMode.VERIFIED, False, True), ([CourseMode.MASTERS], CourseMode.MASTERS, False, True), ([CourseMode.PROFESSIONAL], CourseMode.PROFESSIONAL, True, True), # staff accounts should also see the banner ) @ddt.unpack def test_reset_course_deadlines_banner_shows_for_self_paced_course( self, course_modes, enrollment_mode, is_course_staff, should_display ): ContentTypeGatingConfig.objects.create( enabled=True, enabled_as_of=datetime.datetime(2017, 1, 1, tzinfo=UTC), ) course = self.courses[0] for mode in course_modes: CourseModeFactory.create(course_id=course.id, mode_slug=mode) enrollment = CourseEnrollment.objects.get(course_id=course.id, user=self.user) enrollment.mode = enrollment_mode enrollment.save() enrollment.schedule.start_date = timezone.now() - datetime.timedelta(days=30) enrollment.schedule.save() self.user.is_staff = is_course_staff self.user.save() url = course_home_url(course) response = self.client.get(url) if should_display: self.assertContains(response, '<div class="banner-cta-text"') else: self.assertNotContains(response, '<div class="banner-cta-text"') @override_waffle_flag(RELATIVE_DATES_FLAG, active=True) def test_reset_course_deadlines(self): course = self.courses[0] staff = StaffFactory(course_key=course.id) CourseEnrollment.enroll(staff, course.id) start_date = timezone.now() - datetime.timedelta(days=30) Schedule.objects.update(start_date=start_date) self.client.login(username=staff.username, password=TEST_PASSWORD) self.update_masquerade(course=course, username=self.user.username) post_dict = {'course_id': str(course.id)} self.client.post(reverse(RESET_COURSE_DEADLINES_NAME), post_dict) updated_schedule = Schedule.objects.get(enrollment__user=self.user, enrollment__course_id=course.id) assert updated_schedule.start_date.date() == datetime.datetime.today().date() updated_staff_schedule = Schedule.objects.get(enrollment__user=staff, enrollment__course_id=course.id) assert updated_staff_schedule.start_date == start_date @override_waffle_flag(RELATIVE_DATES_FLAG, active=True) def test_reset_course_deadlines_masquerade_generic_student(self): course = self.courses[0] staff = StaffFactory(course_key=course.id) CourseEnrollment.enroll(staff, course.id) start_date = timezone.now() - datetime.timedelta(days=30) Schedule.objects.update(start_date=start_date) self.client.login(username=staff.username, password=TEST_PASSWORD) self.update_masquerade(course=course) post_dict = {'course_id': str(course.id)} self.client.post(reverse(RESET_COURSE_DEADLINES_NAME), post_dict) updated_student_schedule = Schedule.objects.get(enrollment__user=self.user, enrollment__course_id=course.id) assert updated_student_schedule.start_date == start_date updated_staff_schedule = Schedule.objects.get(enrollment__user=staff, enrollment__course_id=course.id) assert updated_staff_schedule.start_date.date() == datetime.date.today() class TestCourseOutlinePageWithPrerequisites(SharedModuleStoreTestCase, MilestonesTestCaseMixin): """ Test the course outline view with prerequisites. """ TRANSFORMER_CLASS_TO_TEST = MilestonesAndSpecialExamsTransformer @classmethod def setUpClass(cls): """ Creates a test course that can be used for non-destructive tests """ # pylint: disable=super-method-not-called cls.PREREQ_REQUIRED = '(Prerequisite required)' cls.UNLOCKED = 'Unlocked' with super().setUpClassAndTestData(): cls.course, cls.course_blocks = cls.create_test_course() @classmethod def setUpTestData(cls): # lint-amnesty, pylint: disable=super-method-not-called """Set up and enroll our fake user in the course.""" cls.user = UserFactory(password=TEST_PASSWORD) CourseEnrollment.enroll(cls.user, cls.course.id) @classmethod def create_test_course(cls): """Creates a test course.""" course = CourseFactory.create() course.enable_subsection_gating = True course_blocks = {} with cls.store.bulk_operations(course.id): course_blocks['chapter'] = ItemFactory.create( category='chapter', parent_location=course.location ) course_blocks['prerequisite'] = ItemFactory.create( category='sequential', parent_location=course_blocks['chapter'].location, display_name='Prerequisite Exam' ) course_blocks['gated_content'] = ItemFactory.create( category='sequential', parent_location=course_blocks['chapter'].location, display_name='Gated Content' ) course_blocks['prerequisite_vertical'] = ItemFactory.create( category='vertical', parent_location=course_blocks['prerequisite'].location ) course_blocks['gated_content_vertical'] = ItemFactory.create( category='vertical', parent_location=course_blocks['gated_content'].location ) course.children = [course_blocks['chapter']] course_blocks['chapter'].children = [course_blocks['prerequisite'], course_blocks['gated_content']] course_blocks['prerequisite'].children = [course_blocks['prerequisite_vertical']] course_blocks['gated_content'].children = [course_blocks['gated_content_vertical']] if hasattr(cls, 'user'): CourseEnrollment.enroll(cls.user, course.id) return course, course_blocks def setUp(self): """ Set up for the tests. """ super().setUp() self.client.login(username=self.user.username, password=TEST_PASSWORD) def setup_gated_section(self, gated_block, gating_block): """ Test helper to create a gating requirement Args: gated_block: The block the that learner will not have access to until they complete the gating block gating_block: (The prerequisite) The block that must be completed to get access to the gated block """ gating_api.add_prerequisite(self.course.id, str(gating_block.location)) gating_api.set_required_content(self.course.id, gated_block.location, gating_block.location, 100) def test_content_locked(self): """ Test that a sequential/subsection with unmet prereqs correctly indicated that its content is locked """ course = self.course self.setup_gated_section(self.course_blocks['gated_content'], self.course_blocks['prerequisite']) response = self.client.get(course_home_url(course)) assert response.status_code == 200 response_content = pq(response.content) # check lock icon is present lock_icon = response_content('.fa-lock') assert lock_icon, 'lock icon is not present, but should be' subsection = lock_icon.parents('.subsection-text') # check that subsection-title-name is the display name gated_subsection_title = self.course_blocks['gated_content'].display_name assert gated_subsection_title in subsection.children('.subsection-title').html() # check that it says prerequisite required assert 'Prerequisite:' in subsection.children('.details').html() # check that there is not a screen reader message assert not subsection.children('.sr') def test_content_unlocked(self): """ Test that a sequential/subsection with met prereqs correctly indicated that its content is unlocked """ course = self.course self.setup_gated_section(self.course_blocks['gated_content'], self.course_blocks['prerequisite']) # complete the prerequisite to unlock the gated content # this call triggers reevaluation of prerequisites fulfilled by the gating block. with patch('openedx.core.lib.gating.api.get_subsection_completion_percentage', Mock(return_value=100)): lms_gating_api.evaluate_prerequisite( self.course, Mock(location=self.course_blocks['prerequisite'].location, percent_graded=1.0), self.user, ) response = self.client.get(course_home_url(course)) assert response.status_code == 200 response_content = pq(response.content) # check unlock icon is not present unlock_icon = response_content('.fa-unlock') assert not unlock_icon, "unlock icon is present, yet shouldn't be." gated_subsection_title = self.course_blocks['gated_content'].display_name every_subsection_on_outline = response_content('.subsection-title') subsection_has_gated_text = False says_prerequisite_required = False for subsection_contents in every_subsection_on_outline.contents(): subsection_has_gated_text = gated_subsection_title in subsection_contents says_prerequisite_required = "Prerequisite:" in subsection_contents # check that subsection-title-name is the display name of gated content section assert subsection_has_gated_text assert not says_prerequisite_required class TestCourseOutlineResumeCourse(SharedModuleStoreTestCase, CompletionWaffleTestMixin): """ Test start course and resume course for the course outline view. Technically, this mixes course home and course outline tests, but checking the counts of start/resume course should be done together to avoid false positives. """ @classmethod def setUpClass(cls): """ Creates a test course that can be used for non-destructive tests """ # setUpClassAndTestData() already calls setUpClass on SharedModuleStoreTestCase # pylint: disable=super-method-not-called with super().setUpClassAndTestData(): cls.course = cls.create_test_course() @classmethod def setUpTestData(cls): # lint-amnesty, pylint: disable=super-method-not-called """Set up and enroll our fake user in the course.""" cls.user = UserFactory(password=TEST_PASSWORD) CourseEnrollment.enroll(cls.user, cls.course.id) cls.site = Site.objects.get_current() @classmethod def create_test_course(cls): """ Creates a test course. """ course = CourseFactory.create() with cls.store.bulk_operations(course.id): chapter = ItemFactory.create(category='chapter', parent_location=course.location) chapter2 = ItemFactory.create(category='chapter', parent_location=course.location) sequential = ItemFactory.create(category='sequential', parent_location=chapter.location) sequential2 = ItemFactory.create(category='sequential', parent_location=chapter.location) sequential3 = ItemFactory.create(category='sequential', parent_location=chapter2.location) sequential4 = ItemFactory.create(category='sequential', parent_location=chapter2.location) vertical = ItemFactory.create(category='vertical', parent_location=sequential.location) vertical2 = ItemFactory.create(category='vertical', parent_location=sequential2.location) vertical3 = ItemFactory.create(category='vertical', parent_location=sequential3.location) vertical4 = ItemFactory.create(category='vertical', parent_location=sequential4.location) problem = ItemFactory.create(category='problem', parent_location=vertical.location) problem2 = ItemFactory.create(category='problem', parent_location=vertical2.location) problem3 = ItemFactory.create(category='problem', parent_location=vertical3.location) course.children = [chapter, chapter2] chapter.children = [sequential, sequential2] chapter2.children = [sequential3, sequential4] sequential.children = [vertical] sequential2.children = [vertical2] sequential3.children = [vertical3] sequential4.children = [vertical4] vertical.children = [problem] vertical2.children = [problem2] vertical3.children = [problem3] if hasattr(cls, 'user'): CourseEnrollment.enroll(cls.user, course.id) return course def setUp(self): """ Set up for the tests. """ super().setUp() self.client.login(username=self.user.username, password=TEST_PASSWORD) def visit_sequential(self, course, chapter, sequential): """ Navigates to the provided sequential. """ last_accessed_url = reverse( 'courseware_section', kwargs={ 'course_id': str(course.id), 'chapter': chapter.url_name, 'section': sequential.url_name, } ) assert 200 == self.client.get(last_accessed_url).status_code @override_waffle_switch(ENABLE_COMPLETION_TRACKING_SWITCH, active=True) def complete_sequential(self, course, sequential): """ Completes provided sequential. """ course_key = CourseKey.from_string(str(course.id)) # Fake a visit to sequence2/vertical2 block_key = UsageKey.from_string(str(sequential.location)) if block_key.course_key.run is None: # Old mongo keys must be annotated with course run info before calling submit_completion: block_key = block_key.replace(course_key=course_key) completion = 1.0 BlockCompletion.objects.submit_completion( user=self.user, block_key=block_key, completion=completion ) def visit_course_home(self, course, start_count=0, resume_count=0): """ Helper function to navigates to course home page, test for resume buttons :param course: course factory object :param start_count: number of times 'Start Course' should appear :param resume_count: number of times 'Resume Course' should appear :return: response object """ response = self.client.get(course_home_url(course)) assert response.status_code == 200 self.assertContains(response, 'Start Course', count=start_count) self.assertContains(response, 'Resume Course', count=resume_count) return response def test_course_home_completion(self): """ Test that completed blocks appear checked on course home page """ self.override_waffle_switch(True) course = self.course vertical = course.children[0].children[0].children[0] response = self.client.get(course_home_url(course)) content = pq(response.content) assert len(content('.fa-check')) == 0 self.complete_sequential(self.course, vertical) response = self.client.get(course_home_url(course)) content = pq(response.content) # Subsection should be checked. Subsection 4 is also checked because it contains a vertical with no content assert len(content('.fa-check')) == 2 def test_start_course(self): """ Tests that the start course button appears when the course has never been accessed. Technically, this is a course home test, and not a course outline test, but checking the counts of start/resume course should be done together to not get a false positive. """ course = self.course response = self.visit_course_home(course, start_count=1, resume_count=0) content = pq(response.content) problem = course.children[0].children[0].children[0].children[0] assert content('.action-resume-course').attr('href').endswith('/problem/' + problem.url_name) @override_settings(LMS_BASE='test_url:9999') def test_resume_course_with_completion_api(self): """ Tests completion API resume button functionality """ self.override_waffle_switch(True) # Course tree course = self.course problem1 = course.children[0].children[0].children[0].children[0] problem2 = course.children[0].children[1].children[0].children[0] self.complete_sequential(self.course, problem1) # Test for 'resume' link response = self.visit_course_home(course, resume_count=1) # Test for 'resume' link URL - should be problem 1 content = pq(response.content) assert content('.action-resume-course').attr('href').endswith('/problem/' + problem1.url_name) self.complete_sequential(self.course, problem2) # Test for 'resume' link response = self.visit_course_home(course, resume_count=1) # Test for 'resume' link URL - should be problem 2 content = pq(response.content) assert content('.action-resume-course').attr('href').endswith('/problem/' + problem2.url_name) # visit sequential 1, make sure 'Resume Course' URL is robust against 'Last Visited' # (even though I visited seq1/vert1, 'Resume Course' still points to seq2/vert2) self.visit_sequential(course, course.children[0], course.children[0].children[0]) # Test for 'resume' link URL - should be problem 2 (last completed block, NOT last visited) response = self.visit_course_home(course, resume_count=1) content = pq(response.content) assert content('.action-resume-course').attr('href').endswith('/problem/' + problem2.url_name) def test_resume_course_deleted_sequential(self): """ Tests resume course when the last completed sequential is deleted and there is another sequential in the vertical. """ course = self.create_test_course() # first navigate to a sequential to make it the last accessed chapter = course.children[0] assert len(chapter.children) >= 2 sequential = chapter.children[0] sequential2 = chapter.children[1] self.complete_sequential(course, sequential) self.complete_sequential(course, sequential2) # remove one of the sequentials from the chapter with self.store.branch_setting(ModuleStoreEnum.Branch.draft_preferred, course.id): self.store.delete_item(sequential.location, self.user.id) # check resume course buttons response = self.visit_course_home(course, resume_count=1) content = pq(response.content) assert content('.action-resume-course').attr('href').endswith('/sequential/' + sequential2.url_name) def test_resume_course_deleted_sequentials(self): """ Tests resume course when the last completed sequential is deleted and there are no sequentials left in the vertical. """ course = self.create_test_course() # first navigate to a sequential to make it the last accessed chapter = course.children[0] assert len(chapter.children) == 2 sequential = chapter.children[0] self.complete_sequential(course, sequential) # remove all sequentials from chapter with self.store.branch_setting(ModuleStoreEnum.Branch.draft_preferred, course.id): for sequential in chapter.children: self.store.delete_item(sequential.location, self.user.id) # check resume course buttons self.visit_course_home(course, start_count=1, resume_count=0) def test_course_home_for_global_staff(self): """ Tests that staff user can access the course home without being enrolled in the course. """ course = self.course self.user.is_staff = True self.user.save() self.override_waffle_switch(True) CourseEnrollment.get_enrollment(self.user, course.id).delete() response = self.visit_course_home(course, start_count=1, resume_count=0) content = pq(response.content) problem = course.children[0].children[0].children[0].children[0] assert content('.action-resume-course').attr('href').endswith('/problem/' + problem.url_name) @override_waffle_switch(ENABLE_COMPLETION_TRACKING_SWITCH, active=True) def test_course_outline_auto_open(self): """ Tests that the course outline auto-opens to the first subsection in a course if a user has no completion data, and to the last-accessed subsection if a user does have completion data. """ def get_sequential_button(url, is_hidden): is_hidden_string = "is-hidden" if is_hidden else "" return "<olclass=\"outline-itemaccordion-panel" + is_hidden_string + "\"" \ "id=\"" + url + "_contents\"" \ "aria-labelledby=\"" + url + "\"" \ ">" # Course tree course = self.course chapter1 = course.children[0] chapter2 = course.children[1] response_content = self.client.get(course_home_url(course)).content stripped_response = str(re.sub(b"\\s+", b"", response_content), "utf-8") assert get_sequential_button(str(chapter1.location), False) in stripped_response assert get_sequential_button(str(chapter2.location), True) in stripped_response content = pq(response_content) button = content('#expand-collapse-outline-all-button') assert 'Expand All' == button.children()[0].text def test_user_enrolled_after_completion_collection(self): """ Tests that the _completion_data_collection_start() method returns the created time of the waffle switch that enables completion data tracking. """ view = CourseOutlineFragmentView() switch_name = ENABLE_COMPLETION_TRACKING_SWITCH.name switch, _ = Switch.objects.get_or_create(name=switch_name) # pylint: disable=protected-access assert switch.created == view._completion_data_collection_start() switch.delete() def test_user_enrolled_after_completion_collection_default(self): """ Tests that the _completion_data_collection_start() method returns a default constant when no Switch object exists for completion data tracking. """ view = CourseOutlineFragmentView() # pylint: disable=protected-access assert DEFAULT_COMPLETION_TRACKING_START == view._completion_data_collection_start() class TestCourseOutlinePreview(SharedModuleStoreTestCase, MasqueradeMixin): """ Unit tests for staff preview of the course outline. """ def test_preview(self): """ Verify the behavior of preview for the course outline. """ course = CourseFactory.create( start=datetime.datetime.now() - datetime.timedelta(days=30) ) staff_user = StaffFactory(course_key=course.id, password=TEST_PASSWORD) CourseEnrollment.enroll(staff_user, course.id) future_date = datetime.datetime.now() + datetime.timedelta(days=30) with self.store.bulk_operations(course.id): chapter = ItemFactory.create( category='chapter', parent_location=course.location, display_name='First Chapter', ) sequential = ItemFactory.create(category='sequential', parent_location=chapter.location) ItemFactory.create(category='vertical', parent_location=sequential.location) chapter = ItemFactory.create( category='chapter', parent_location=course.location, display_name='Future Chapter', start=future_date, ) sequential = ItemFactory.create(category='sequential', parent_location=chapter.location) ItemFactory.create(category='vertical', parent_location=sequential.location) # Verify that a staff user sees a chapter with a due date in the future self.client.login(username=staff_user.username, password='test') url = course_home_url(course) response = self.client.get(url) assert response.status_code == 200 self.assertContains(response, 'Future Chapter') # Verify that staff masquerading as a learner see the future chapter. self.update_masquerade(course=course, role='student') response = self.client.get(url) assert response.status_code == 200 self.assertContains(response, 'Future Chapter')

agpl-3.0

rgommers/statsmodels

statsmodels/tsa/filters/bk_filter.py

28

3112

from __future__ import absolute_import import numpy as np from scipy.signal import fftconvolve from ._utils import _maybe_get_pandas_wrapper def bkfilter(X, low=6, high=32, K=12): """ Baxter-King bandpass filter Parameters ---------- X : array-like A 1 or 2d ndarray. If 2d, variables are assumed to be in columns. low : float Minimum period for oscillations, ie., Baxter and King suggest that the Burns-Mitchell U.S. business cycle has 6 for quarterly data and 1.5 for annual data. high : float Maximum period for oscillations BK suggest that the U.S. business cycle has 32 for quarterly data and 8 for annual data. K : int Lead-lag length of the filter. Baxter and King propose a truncation length of 12 for quarterly data and 3 for annual data. Returns ------- Y : array Cyclical component of X References ---------- :: Baxter, M. and R. G. King. "Measuring Business Cycles: Approximate Band-Pass Filters for Economic Time Series." *Review of Economics and Statistics*, 1999, 81(4), 575-593. Notes ----- Returns a centered weighted moving average of the original series. Where the weights a[j] are computed :: a[j] = b[j] + theta, for j = 0, +/-1, +/-2, ... +/- K b[0] = (omega_2 - omega_1)/pi b[j] = 1/(pi*j)(sin(omega_2*j)-sin(omega_1*j), for j = +/-1, +/-2,... and theta is a normalizing constant :: theta = -sum(b)/(2K+1) Examples -------- >>> import statsmodels.api as sm >>> import pandas as pd >>> dta = sm.datasets.macrodata.load_pandas().data >>> dates = sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3') >>> index = pd.DatetimeIndex(dates) >>> dta.set_index(index, inplace=True) >>> cycles = sm.tsa.filters.bkfilter(dta[['realinv']], 6, 24, 12) >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots() >>> cycles.plot(ax=ax, style=['r--', 'b-']) >>> plt.show() .. plot:: plots/bkf_plot.py """ #TODO: change the docstring to ..math::? #TODO: allow windowing functions to correct for Gibb's Phenomenon? # adjust bweights (symmetrically) by below before demeaning # Lancosz Sigma Factors np.sinc(2*j/(2.*K+1)) _pandas_wrapper = _maybe_get_pandas_wrapper(X, K, K) X = np.asarray(X) omega_1 = 2.*np.pi/high # convert from freq. to periodicity omega_2 = 2.*np.pi/low bweights = np.zeros(2*K+1) bweights[K] = (omega_2 - omega_1)/np.pi # weight at zero freq. j = np.arange(1,int(K)+1) weights = 1/(np.pi*j)*(np.sin(omega_2*j)-np.sin(omega_1*j)) bweights[K+j] = weights # j is an idx bweights[:K] = weights[::-1] # make symmetric weights bweights -= bweights.mean() # make sure weights sum to zero if X.ndim == 2: bweights = bweights[:,None] X = fftconvolve(X, bweights, mode='valid') # get a centered moving avg/ # convolution if _pandas_wrapper is not None: return _pandas_wrapper(X) return X

bsd-3-clause

jeffreyliu3230/osf.io

website/addons/dataverse/tests/test_client.py

16

8789

from nose.tools import * import mock import unittest from dataverse import Connection, Dataverse, DataverseFile, Dataset from dataverse.exceptions import UnauthorizedError from framework.exceptions import HTTPError from website.addons.dataverse.tests.utils import DataverseAddonTestCase from website.addons.dataverse.tests.utils import create_external_account from website.addons.dataverse.client import ( _connect, get_files, publish_dataset, get_datasets, get_dataset, get_dataverses, get_dataverse, connect_from_settings, connect_or_401, connect_from_settings_or_401, ) from website.addons.dataverse.model import AddonDataverseNodeSettings class TestClient(DataverseAddonTestCase): def setUp(self): super(TestClient, self).setUp() self.host = 'some.host.url' self.token = 'some-fancy-api-token-which-is-long' self.mock_connection = mock.create_autospec(Connection) self.mock_dataverse = mock.create_autospec(Dataverse) self.mock_dataset = mock.create_autospec(Dataset) self.mock_file = mock.create_autospec(DataverseFile) self.mock_file.dataset = self.mock_dataset self.mock_dataset.dataverse = self.mock_dataverse self.mock_dataverse.connection = self.mock_connection @mock.patch('website.addons.dataverse.client.Connection') def test_connect(self, mock_connection): mock_connection.return_value = mock.create_autospec(Connection) c = _connect(self.host, self.token) mock_connection.assert_called_once_with(self.host, self.token) assert_true(c) @mock.patch('website.addons.dataverse.client.Connection') def test_connect_fail(self, mock_connection): mock_connection.side_effect = UnauthorizedError() with assert_raises(UnauthorizedError): _connect(self.host, self.token) mock_connection.assert_called_once_with(self.host, self.token) @mock.patch('website.addons.dataverse.client.Connection') def test_connect_or_401(self, mock_connection): mock_connection.return_value = mock.create_autospec(Connection) c = connect_or_401(self.host, self.token) mock_connection.assert_called_once_with(self.host, self.token) assert_true(c) @mock.patch('website.addons.dataverse.client.Connection') def test_connect_or_401_forbidden(self, mock_connection): mock_connection.side_effect = UnauthorizedError() with assert_raises(HTTPError) as cm: connect_or_401(self.host, self.token) mock_connection.assert_called_once_with(self.host, self.token) assert_equal(cm.exception.code, 401) @mock.patch('website.addons.dataverse.client._connect') def test_connect_from_settings(self, mock_connect): node_settings = AddonDataverseNodeSettings() node_settings.external_account = create_external_account( self.host, self.token, ) connection = connect_from_settings(node_settings) assert_true(connection) mock_connect.assert_called_once_with(self.host, self.token) def test_connect_from_settings_none(self): connection = connect_from_settings(None) assert_is_none(connection) @mock.patch('website.addons.dataverse.client._connect') def test_connect_from_settings_or_401(self, mock_connect): node_settings = AddonDataverseNodeSettings() node_settings.external_account = create_external_account( self.host, self.token, ) connection = connect_from_settings_or_401(node_settings) assert_true(connection) mock_connect.assert_called_once_with(self.host, self.token) def test_connect_from_settings_or_401_none(self): connection = connect_from_settings_or_401(None) assert_is_none(connection) @mock.patch('website.addons.dataverse.client.Connection') def test_connect_from_settings_or_401_forbidden(self, mock_connection): mock_connection.side_effect = UnauthorizedError() node_settings = AddonDataverseNodeSettings() node_settings.external_account = create_external_account( self.host, self.token, ) with assert_raises(HTTPError) as e: connect_from_settings_or_401(node_settings) mock_connection.assert_called_once_with(self.host, self.token) assert_equal(e.exception.code, 401) def test_get_files(self): published = False get_files(self.mock_dataset, published) self.mock_dataset.get_files.assert_called_once_with('latest') def test_get_files_published(self): published = True get_files(self.mock_dataset, published) self.mock_dataset.get_files.assert_called_once_with('latest-published') def test_publish_dataset(self): publish_dataset(self.mock_dataset) self.mock_dataset.publish.assert_called_once_with() def test_publish_dataset_unpublished_dataverse(self): type(self.mock_dataverse).is_published = mock.PropertyMock(return_value=False) with assert_raises(HTTPError) as e: publish_dataset(self.mock_dataset) assert_false(self.mock_dataset.publish.called) assert_equal(e.exception.code, 405) def test_get_datasets(self): mock_dataset1 = mock.create_autospec(Dataset) mock_dataset2 = mock.create_autospec(Dataset) mock_dataset3 = mock.create_autospec(Dataset) mock_dataset1.get_state.return_value = 'DRAFT' mock_dataset2.get_state.return_value = 'RELEASED' mock_dataset3.get_state.return_value = 'DEACCESSIONED' self.mock_dataverse.get_datasets.return_value = [ mock_dataset1, mock_dataset2, mock_dataset3 ] datasets = get_datasets(self.mock_dataverse) self.mock_dataverse.get_datasets.assert_called_once_with() assert_in(mock_dataset1, datasets) assert_in(mock_dataset2, datasets) assert_in(mock_dataset3, datasets) def test_get_datasets_no_dataverse(self): datasets = get_datasets(None) assert_equal(datasets, []) def test_get_dataset(self): self.mock_dataset.get_state.return_value = 'DRAFT' self.mock_dataverse.get_dataset_by_doi.return_value = self.mock_dataset s = get_dataset(self.mock_dataverse, 'My hdl') self.mock_dataverse.get_dataset_by_doi.assert_called_once_with('My hdl') assert_equal(s, self.mock_dataset) def test_get_deaccessioned_dataset(self): self.mock_dataset.get_state.return_value = 'DEACCESSIONED' self.mock_dataverse.get_dataset_by_doi.return_value = self.mock_dataset with assert_raises(HTTPError) as e: s = get_dataset(self.mock_dataverse, 'My hdl') self.mock_dataverse.get_dataset_by_doi.assert_called_once_with('My hdl') assert_equal(e.exception.code, 410) def test_get_bad_dataset(self): error = UnicodeDecodeError('utf-8', b'', 1, 2, 'jeepers') self.mock_dataset.get_state.side_effect = error self.mock_dataverse.get_dataset_by_doi.return_value = self.mock_dataset with assert_raises(HTTPError) as e: s = get_dataset(self.mock_dataverse, 'My hdl') self.mock_dataverse.get_dataset_by_doi.assert_called_once_with('My hdl') assert_equal(e.exception.code, 406) def test_get_dataverses(self): published_dv = mock.create_autospec(Dataverse) unpublished_dv = mock.create_autospec(Dataverse) type(published_dv).is_published = mock.PropertyMock(return_value=True) type(unpublished_dv).is_published = mock.PropertyMock(return_value=False) self.mock_connection.get_dataverses.return_value = [ published_dv, unpublished_dv ] dvs = get_dataverses(self.mock_connection) self.mock_connection.get_dataverses.assert_called_once_with() assert_in(published_dv, dvs) assert_in(unpublished_dv, dvs) assert_equal(len(dvs), 2) def test_get_dataverse(self): type(self.mock_dataverse).is_published = mock.PropertyMock(return_value=True) self.mock_connection.get_dataverse.return_value = self.mock_dataverse d = get_dataverse(self.mock_connection, 'ALIAS') self.mock_connection.get_dataverse.assert_called_once_with('ALIAS') assert_equal(d, self.mock_dataverse) def test_get_unpublished_dataverse(self): type(self.mock_dataverse).is_published = mock.PropertyMock(return_value=False) self.mock_connection.get_dataverse.return_value = self.mock_dataverse d = get_dataverse(self.mock_connection, 'ALIAS') self.mock_connection.get_dataverse.assert_called_once_with('ALIAS') assert_equal(d, self.mock_dataverse)

apache-2.0

gdementen/larray

larray/tests/generate_data.py

2

9260

import os from larray import ndtest, open_excel, Session, X DATA_DIR = os.path.join(os.path.dirname(__file__), 'data') def generate_tests_files(): tests = {'1d': 3, '2d': "a=1..3; b=b0,b1", '2d_classic': "a=a0..a2;b=b0..b2", '3d': "a=1..3; b=b0,b1; c=c0..c2", 'int_labels': "a=0..2; b=0..2; c=0..2", 'missing_values': "a=1..3; b=b0,b1; c=c0..c2", 'unsorted': "a=3..1; b=b1,b0; c=c2..c0", 'position': "a=1..3; b=b0,b1; c=c0..c2"} wb = open_excel(os.path.join(DATA_DIR, 'test.xlsx'), overwrite_file=True) wb_narrow = open_excel(os.path.join(DATA_DIR, 'test_narrow.xlsx'), overwrite_file=True) for name, dim in tests.items(): arr = ndtest(dim) if name == '2d_classic': df = arr.to_frame(fold_last_axis_name=False) # wide format df.to_csv(os.path.join(DATA_DIR, f'test{name}.csv'), sep=',', na_rep='') wb[name] = '' wb[name]['A1'].options().value = df # narrow format df = arr.to_series(name='value') df.to_csv(os.path.join(DATA_DIR, f'test{name}_narrow.csv'), sep=',', na_rep='', header=True) wb_narrow[name] = '' wb_narrow[name]['A1'].options().value = df elif name == 'missing_values': df = arr.to_frame(fold_last_axis_name=True) # wide format df = df.drop([(2, 'b0'), (3, 'b1')]) df.to_csv(os.path.join(DATA_DIR, f'test{name}.csv'), sep=',', na_rep='') wb[name] = '' wb[name]['A1'].options().value = df # narrow format df = arr.to_series(name='value') df = df.drop([(2, 'b0'), (2, 'b1', 'c1'), (3, 'b1')]) df.to_csv(os.path.join(DATA_DIR, f'test{name}_narrow.csv'), sep=',', na_rep='', header=True) wb_narrow[name] = '' wb_narrow[name]['A1'].options().value = df elif name == 'position': # wide format wb[name] = '' wb[name]['D3'] = arr.dump() # narrow format wb_narrow[name] = '' wb_narrow[name]['D3'] = arr.dump(wide=False) else: # wide format arr.to_csv(os.path.join(DATA_DIR, f'test{name}.csv')) wb[name] = arr.dump() # narrow format arr.to_csv(os.path.join(DATA_DIR, f'test{name}_narrow.csv'), wide=False) wb_narrow[name] = arr.dump(wide=False) wb.save() wb.close() wb_narrow.save() wb_narrow.close() def generate_example_files(csv=True, excel=True, hdf5=True): from larray_eurostat import eurostat_get def prepare_eurostat_data(dataset_name, countries): arr = eurostat_get(dataset_name)[X.unit['NR'], X.age['TOTAL'], X.sex['M,F']] arr = arr[X.time[::-1]][2013:2017] arr = arr.rename('sex', 'gender') arr = arr.set_labels(gender='Male,Female') arr = arr.rename('geo', 'country') country_codes = list(countries.keys()) country_names = list(countries.values()) if dataset_name == 'migr_imm1ctz': # example of an array with ambiguous axes arr = arr['COMPLET', X.citizen[country_codes], X.country[country_codes]].astype(int) arr = arr.rename('citizen', 'citizenship') arr = arr.set_labels('citizenship', country_names) arr = arr.set_labels('country', country_names) arr = arr.transpose('country', 'citizenship', 'gender', 'time') else: arr = arr[country_codes].astype(int) arr = arr.set_labels('country', country_names) arr = arr.transpose('country', 'gender', 'time') return arr countries = {'BE': 'Belgium', 'FR': 'France', 'DE': 'Germany'} benelux = {'BE': 'Belgium', 'LU': 'Luxembourg', 'NL': 'Netherlands'} # Arrays population = prepare_eurostat_data('demo_pjan', countries) population.meta.title = 'Population on 1 January by age and sex' population.meta.source = 'table demo_pjan from Eurostat' # ---- population_benelux = prepare_eurostat_data('demo_pjan', benelux) population_benelux.meta.title = 'Population on 1 January by age and sex (Benelux)' population_benelux.meta.source = 'table demo_pjan from Eurostat' # ---- population_5_countries = population.extend('country', population_benelux[['Luxembourg', 'Netherlands']]) population_5_countries.meta.title = 'Population on 1 January by age and sex (Benelux + France + Germany)' population_5_countries.meta.source = 'table demo_pjan from Eurostat' # ---- births = prepare_eurostat_data('demo_fasec', countries) births.meta.title = "Live births by mother's age and newborn's sex" births.meta.source = 'table demo_fasec from Eurostat' # ---- deaths = prepare_eurostat_data('demo_magec', countries) deaths.meta.title = 'Deaths by age and sex' deaths.meta.source = 'table demo_magec from Eurostat' # ---- immigration = prepare_eurostat_data('migr_imm1ctz', benelux) immigration.meta.title = 'Immigration by age group, sex and citizenship' immigration.meta.source = 'table migr_imm1ctz from Eurostat' # Groups even_years = population.time[2014::2] >> 'even_years' odd_years = population.time[2013::2] >> 'odd_years' # Session ses = Session({'country': population.country, 'country_benelux': immigration.country, 'citizenship': immigration.citizenship, 'gender': population.gender, 'time': population.time, 'even_years': even_years, 'odd_years': odd_years, 'population': population, 'population_benelux': population_benelux, 'population_5_countries': population_5_countries, 'births': births, 'deaths': deaths, 'immigration': immigration}) ses.meta.title = 'Demographic datasets for a small selection of countries in Europe' ses.meta.source = 'demo_jpan, demo_fasec, demo_magec and migr_imm1ctz tables from Eurostat' # EUROSTAT DATASET if csv: ses.save(os.path.join(DATA_DIR, 'demography_eurostat')) if excel: ses.save(os.path.join(DATA_DIR, 'demography_eurostat.xlsx')) if hdf5: ses.save(os.path.join(DATA_DIR, 'demography_eurostat.h5')) # EXAMPLE FILES years = population.time[2013:2015] population = population[years] population_narrow = population['Belgium,France'].sum('gender') births = births[years] deaths = deaths[years] immigration = immigration[years] # Dataframes (for testing missing axis/values) df_missing_axis_name = population.to_frame(fold_last_axis_name=False) df_missing_values = population.to_frame(fold_last_axis_name=True) df_missing_values.drop([('France', 'Male'), ('Germany', 'Female')], inplace=True) if csv: examples_dir = os.path.join(DATA_DIR, 'examples') population.to_csv(os.path.join(examples_dir, 'population.csv')) births.to_csv(os.path.join(examples_dir, 'births.csv')) deaths.to_csv(os.path.join(examples_dir, 'deaths.csv')) immigration.to_csv(os.path.join(examples_dir, 'immigration.csv')) df_missing_axis_name.to_csv(os.path.join(examples_dir, 'population_missing_axis_name.csv'), sep=',', na_rep='') df_missing_values.to_csv(os.path.join(examples_dir, 'population_missing_values.csv'), sep=',', na_rep='') population_narrow.to_csv(os.path.join(examples_dir, 'population_narrow_format.csv'), wide=False) if excel: with open_excel(os.path.join(DATA_DIR, 'examples.xlsx'), overwrite_file=True) as wb: wb['population'] = population.dump() wb['births'] = births.dump() wb['deaths'] = deaths.dump() wb['immigration'] = immigration.dump() wb['population_births_deaths'] = population.dump() wb['population_births_deaths']['A9'] = births.dump() wb['population_births_deaths']['A17'] = deaths.dump() wb['population_missing_axis_name'] = '' wb['population_missing_axis_name']['A1'].options().value = df_missing_axis_name wb['population_missing_values'] = '' wb['population_missing_values']['A1'].options().value = df_missing_values # wb['population_narrow_format'] = population_narrow.dump(wide=False) wb.save() population_narrow.to_excel(os.path.join(DATA_DIR, 'examples.xlsx'), 'population_narrow_format', wide=False) Session({'country': population.country, 'gender': population.gender, 'time': population.time, 'population': population}).save(os.path.join(DATA_DIR, 'population_only.xlsx')) Session({'births': births, 'deaths': deaths}).save(os.path.join(DATA_DIR, 'births_and_deaths.xlsx')) if hdf5: examples_h5_file = os.path.join(DATA_DIR, 'examples.h5') population.to_hdf(examples_h5_file, 'population') births.to_hdf(examples_h5_file, 'births') deaths.to_hdf(examples_h5_file, 'deaths') immigration.to_hdf(examples_h5_file, 'immigration') if __name__ == '__main__': # generate_tests_files() generate_example_files()

gpl-3.0

LohithBlaze/scikit-learn

sklearn/utils/tests/test_multiclass.py

128

12853

from __future__ import division import numpy as np import scipy.sparse as sp from itertools import product from sklearn.externals.six.moves import xrange from sklearn.externals.six import iteritems from scipy.sparse import issparse from scipy.sparse import csc_matrix from scipy.sparse import csr_matrix from scipy.sparse import coo_matrix from scipy.sparse import dok_matrix from scipy.sparse import lil_matrix from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_false from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_raises_regex from sklearn.utils.multiclass import unique_labels from sklearn.utils.multiclass import is_multilabel from sklearn.utils.multiclass import type_of_target from sklearn.utils.multiclass import class_distribution class NotAnArray(object): """An object that is convertable to an array. This is useful to simulate a Pandas timeseries.""" def __init__(self, data): self.data = data def __array__(self): return self.data EXAMPLES = { 'multilabel-indicator': [ # valid when the data is formated as sparse or dense, identified # by CSR format when the testing takes place csr_matrix(np.random.RandomState(42).randint(2, size=(10, 10))), csr_matrix(np.array([[0, 1], [1, 0]])), csr_matrix(np.array([[0, 1], [1, 0]], dtype=np.bool)), csr_matrix(np.array([[0, 1], [1, 0]], dtype=np.int8)), csr_matrix(np.array([[0, 1], [1, 0]], dtype=np.uint8)), csr_matrix(np.array([[0, 1], [1, 0]], dtype=np.float)), csr_matrix(np.array([[0, 1], [1, 0]], dtype=np.float32)), csr_matrix(np.array([[0, 0], [0, 0]])), csr_matrix(np.array([[0, 1]])), # Only valid when data is dense np.array([[-1, 1], [1, -1]]), np.array([[-3, 3], [3, -3]]), NotAnArray(np.array([[-3, 3], [3, -3]])), ], 'multiclass': [ [1, 0, 2, 2, 1, 4, 2, 4, 4, 4], np.array([1, 0, 2]), np.array([1, 0, 2], dtype=np.int8), np.array([1, 0, 2], dtype=np.uint8), np.array([1, 0, 2], dtype=np.float), np.array([1, 0, 2], dtype=np.float32), np.array([[1], [0], [2]]), NotAnArray(np.array([1, 0, 2])), [0, 1, 2], ['a', 'b', 'c'], np.array([u'a', u'b', u'c']), np.array([u'a', u'b', u'c'], dtype=object), np.array(['a', 'b', 'c'], dtype=object), ], 'multiclass-multioutput': [ np.array([[1, 0, 2, 2], [1, 4, 2, 4]]), np.array([[1, 0, 2, 2], [1, 4, 2, 4]], dtype=np.int8), np.array([[1, 0, 2, 2], [1, 4, 2, 4]], dtype=np.uint8), np.array([[1, 0, 2, 2], [1, 4, 2, 4]], dtype=np.float), np.array([[1, 0, 2, 2], [1, 4, 2, 4]], dtype=np.float32), np.array([['a', 'b'], ['c', 'd']]), np.array([[u'a', u'b'], [u'c', u'd']]), np.array([[u'a', u'b'], [u'c', u'd']], dtype=object), np.array([[1, 0, 2]]), NotAnArray(np.array([[1, 0, 2]])), ], 'binary': [ [0, 1], [1, 1], [], [0], np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1]), np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1], dtype=np.bool), np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1], dtype=np.int8), np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1], dtype=np.uint8), np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1], dtype=np.float), np.array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1], dtype=np.float32), np.array([[0], [1]]), NotAnArray(np.array([[0], [1]])), [1, -1], [3, 5], ['a'], ['a', 'b'], ['abc', 'def'], np.array(['abc', 'def']), [u'a', u'b'], np.array(['abc', 'def'], dtype=object), ], 'continuous': [ [1e-5], [0, .5], np.array([[0], [.5]]), np.array([[0], [.5]], dtype=np.float32), ], 'continuous-multioutput': [ np.array([[0, .5], [.5, 0]]), np.array([[0, .5], [.5, 0]], dtype=np.float32), np.array([[0, .5]]), ], 'unknown': [ [[]], [()], # sequence of sequences that were'nt supported even before deprecation np.array([np.array([]), np.array([1, 2, 3])], dtype=object), [np.array([]), np.array([1, 2, 3])], [set([1, 2, 3]), set([1, 2])], [frozenset([1, 2, 3]), frozenset([1, 2])], # and also confusable as sequences of sequences [{0: 'a', 1: 'b'}, {0: 'a'}], # empty second dimension np.array([[], []]), # 3d np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]), ] } NON_ARRAY_LIKE_EXAMPLES = [ set([1, 2, 3]), {0: 'a', 1: 'b'}, {0: [5], 1: [5]}, 'abc', frozenset([1, 2, 3]), None, ] MULTILABEL_SEQUENCES = [ [[1], [2], [0, 1]], [(), (2), (0, 1)], np.array([[], [1, 2]], dtype='object'), NotAnArray(np.array([[], [1, 2]], dtype='object')) ] def test_unique_labels(): # Empty iterable assert_raises(ValueError, unique_labels) # Multiclass problem assert_array_equal(unique_labels(xrange(10)), np.arange(10)) assert_array_equal(unique_labels(np.arange(10)), np.arange(10)) assert_array_equal(unique_labels([4, 0, 2]), np.array([0, 2, 4])) # Multilabel indicator assert_array_equal(unique_labels(np.array([[0, 0, 1], [1, 0, 1], [0, 0, 0]])), np.arange(3)) assert_array_equal(unique_labels(np.array([[0, 0, 1], [0, 0, 0]])), np.arange(3)) # Several arrays passed assert_array_equal(unique_labels([4, 0, 2], xrange(5)), np.arange(5)) assert_array_equal(unique_labels((0, 1, 2), (0,), (2, 1)), np.arange(3)) # Border line case with binary indicator matrix assert_raises(ValueError, unique_labels, [4, 0, 2], np.ones((5, 5))) assert_raises(ValueError, unique_labels, np.ones((5, 4)), np.ones((5, 5))) assert_array_equal(unique_labels(np.ones((4, 5)), np.ones((5, 5))), np.arange(5)) def test_unique_labels_non_specific(): # Test unique_labels with a variety of collected examples # Smoke test for all supported format for format in ["binary", "multiclass", "multilabel-indicator"]: for y in EXAMPLES[format]: unique_labels(y) # We don't support those format at the moment for example in NON_ARRAY_LIKE_EXAMPLES: assert_raises(ValueError, unique_labels, example) for y_type in ["unknown", "continuous", 'continuous-multioutput', 'multiclass-multioutput']: for example in EXAMPLES[y_type]: assert_raises(ValueError, unique_labels, example) def test_unique_labels_mixed_types(): # Mix with binary or multiclass and multilabel mix_clf_format = product(EXAMPLES["multilabel-indicator"], EXAMPLES["multiclass"] + EXAMPLES["binary"]) for y_multilabel, y_multiclass in mix_clf_format: assert_raises(ValueError, unique_labels, y_multiclass, y_multilabel) assert_raises(ValueError, unique_labels, y_multilabel, y_multiclass) assert_raises(ValueError, unique_labels, [[1, 2]], [["a", "d"]]) assert_raises(ValueError, unique_labels, ["1", 2]) assert_raises(ValueError, unique_labels, [["1", 2], [1, 3]]) assert_raises(ValueError, unique_labels, [["1", "2"], [2, 3]]) def test_is_multilabel(): for group, group_examples in iteritems(EXAMPLES): if group in ['multilabel-indicator']: dense_assert_, dense_exp = assert_true, 'True' else: dense_assert_, dense_exp = assert_false, 'False' for example in group_examples: # Only mark explicitly defined sparse examples as valid sparse # multilabel-indicators if group == 'multilabel-indicator' and issparse(example): sparse_assert_, sparse_exp = assert_true, 'True' else: sparse_assert_, sparse_exp = assert_false, 'False' if (issparse(example) or (hasattr(example, '__array__') and np.asarray(example).ndim == 2 and np.asarray(example).dtype.kind in 'biuf' and np.asarray(example).shape[1] > 0)): examples_sparse = [sparse_matrix(example) for sparse_matrix in [coo_matrix, csc_matrix, csr_matrix, dok_matrix, lil_matrix]] for exmpl_sparse in examples_sparse: sparse_assert_(is_multilabel(exmpl_sparse), msg=('is_multilabel(%r)' ' should be %s') % (exmpl_sparse, sparse_exp)) # Densify sparse examples before testing if issparse(example): example = example.toarray() dense_assert_(is_multilabel(example), msg='is_multilabel(%r) should be %s' % (example, dense_exp)) def test_type_of_target(): for group, group_examples in iteritems(EXAMPLES): for example in group_examples: assert_equal(type_of_target(example), group, msg=('type_of_target(%r) should be %r, got %r' % (example, group, type_of_target(example)))) for example in NON_ARRAY_LIKE_EXAMPLES: msg_regex = 'Expected array-like \(array or non-string sequence\).*' assert_raises_regex(ValueError, msg_regex, type_of_target, example) for example in MULTILABEL_SEQUENCES: msg = ('You appear to be using a legacy multi-label data ' 'representation. Sequence of sequences are no longer supported;' ' use a binary array or sparse matrix instead.') assert_raises_regex(ValueError, msg, type_of_target, example) def test_class_distribution(): y = np.array([[1, 0, 0, 1], [2, 2, 0, 1], [1, 3, 0, 1], [4, 2, 0, 1], [2, 0, 0, 1], [1, 3, 0, 1]]) # Define the sparse matrix with a mix of implicit and explicit zeros data = np.array([1, 2, 1, 4, 2, 1, 0, 2, 3, 2, 3, 1, 1, 1, 1, 1, 1]) indices = np.array([0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 5, 0, 1, 2, 3, 4, 5]) indptr = np.array([0, 6, 11, 11, 17]) y_sp = sp.csc_matrix((data, indices, indptr), shape=(6, 4)) classes, n_classes, class_prior = class_distribution(y) classes_sp, n_classes_sp, class_prior_sp = class_distribution(y_sp) classes_expected = [[1, 2, 4], [0, 2, 3], [0], [1]] n_classes_expected = [3, 3, 1, 1] class_prior_expected = [[3/6, 2/6, 1/6], [1/3, 1/3, 1/3], [1.0], [1.0]] for k in range(y.shape[1]): assert_array_almost_equal(classes[k], classes_expected[k]) assert_array_almost_equal(n_classes[k], n_classes_expected[k]) assert_array_almost_equal(class_prior[k], class_prior_expected[k]) assert_array_almost_equal(classes_sp[k], classes_expected[k]) assert_array_almost_equal(n_classes_sp[k], n_classes_expected[k]) assert_array_almost_equal(class_prior_sp[k], class_prior_expected[k]) # Test again with explicit sample weights (classes, n_classes, class_prior) = class_distribution(y, [1.0, 2.0, 1.0, 2.0, 1.0, 2.0]) (classes_sp, n_classes_sp, class_prior_sp) = class_distribution(y, [1.0, 2.0, 1.0, 2.0, 1.0, 2.0]) class_prior_expected = [[4/9, 3/9, 2/9], [2/9, 4/9, 3/9], [1.0], [1.0]] for k in range(y.shape[1]): assert_array_almost_equal(classes[k], classes_expected[k]) assert_array_almost_equal(n_classes[k], n_classes_expected[k]) assert_array_almost_equal(class_prior[k], class_prior_expected[k]) assert_array_almost_equal(classes_sp[k], classes_expected[k]) assert_array_almost_equal(n_classes_sp[k], n_classes_expected[k]) assert_array_almost_equal(class_prior_sp[k], class_prior_expected[k])

bsd-3-clause

Diyago/Machine-Learning-scripts

DEEP LEARNING/Kaggle Avito Demand Prediction Challenge/image feat. extraction/avito_deepIQA/deepIQA/evaluate_back.py

1

2442

#!/usr/bin/python2 import argparse import cv2 import numpy as np import six from chainer import cuda from chainer import serializers from sklearn.feature_extraction.image import extract_patches from deepIQA.fr_model import FRModel from deepIQA.nr_model import Model parser = argparse.ArgumentParser(description="evaluate.py") parser.add_argument("INPUT", help="path to input image") parser.add_argument( "REF", default="", nargs="?", help="path to reference image, if omitted NR IQA is assumed", ) parser.add_argument("--model", "-m", default="", help="path to the trained model") parser.add_argument( "--top", choices=("patchwise", "weighted"), default="weighted", help="top layer and loss definition", ) parser.add_argument("--gpu", "-g", default=0, type=int, help="GPU ID") args = parser.parse_args() FR = True if args.REF == "": FR = False if FR: model = FRModel(top=args.top) else: model = Model(top=args.top) cuda.cudnn_enabled = True cuda.check_cuda_available() xp = cuda.cupy serializers.load_hdf5(args.model, model) model.to_gpu() if FR: ref_img = cv2.imread(args.REF) ref_img = cv2.cvtColor(ref_img, cv2.COLOR_BGR2RGB) patches = extract_patches(ref_img, (32, 32, 3), 32) X_ref = np.transpose(patches.reshape((-1, 32, 32, 3)), (0, 3, 1, 2)) img = cv2.imread(args.INPUT) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) patches = extract_patches(img, (32, 32, 3), 32) X = np.transpose(patches.reshape((-1, 32, 32, 3)), (0, 3, 1, 2)) y = [] weights = [] batchsize = min(2000, X.shape[0]) t = xp.zeros((1, 1), np.float32) for i in six.moves.range(0, X.shape[0], batchsize): X_batch = X[i : i + batchsize] X_batch = xp.array(X_batch.astype(np.float32)) if FR: X_ref_batch = X_ref[i : i + batchsize] X_ref_batch = xp.array(X_ref_batch.astype(np.float32)) model.forward( X_batch, X_ref_batch, t, False, n_patches_per_image=X_batch.shape[0] ) else: model.forward(X_batch, t, False, X_batch.shape[0]) y.append(xp.asnumpy(model.y[0].data).reshape((-1,))) weights.append(xp.asnumpy(model.a[0].data).reshape((-1,))) y = np.concatenate(y) weights = np.concatenate(weights) print("%f" % (np.sum(y * weights) / np.sum(weights))) """ --model models/nr_tid_patchwise.model --top patchwise /home/alex/work/py/avito/input/train_jpg/0a0a5a3f22320e0508139273d23f390ca837aef252036034ed640fb939529bd9.jpg """

apache-2.0

mlperf/training_results_v0.5

v0.5.0/google/cloud_v3.8/resnet-tpuv3-8/code/resnet/model/tpu/models/official/amoeba_net/amoeba_net.py

5

15390

# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # pylint: disable=line-too-long r"""TensorFlow AmoebaNet Example. GCP Run Example python amoeba_net.py --data_dir=gs://cloud-tpu-datasets/imagenet-data --model_dir=gs://cloud-tpu-ckpts/models/ameoba_net_x/ \ --drop_connect_keep_prob=1.0 --cell_name=evol_net_x --num_cells=12 --reduction_size=256 --image_size=299 --num_epochs=48 \ --train_batch_size=256 --num_epochs_per_eval=4.0 --lr_decay_value=0.89 --lr_num_epochs_per_decay=1 --alsologtostderr \ --tpu=huangyp-tpu-0 """ # pylint: enable=line-too-long from __future__ import absolute_import from __future__ import division from __future__ import print_function import itertools import math from absl import app from absl import flags import absl.logging as _logging # pylint: disable=unused-import import tensorflow as tf import amoeba_net_model as model_lib # Cloud TPU Cluster Resolvers flags.DEFINE_string( 'tpu', default=None, help='The Cloud TPU to use for training. This should be either the name ' 'used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.') flags.DEFINE_string( 'gcp_project', default=None, help='Project name for the Cloud TPU-enabled project. If not specified, we ' 'will attempt to automatically detect the GCE project from metadata.') flags.DEFINE_string( 'tpu_zone', default=None, help='GCE zone where the Cloud TPU is located in. If not specified, we ' 'will attempt to automatically detect the GCE project from metadata.') # General Parameters flags.DEFINE_integer( 'num_shards', 8, 'Number of shards (TPU cores).') flags.DEFINE_integer( 'distributed_group_size', 1, help='Size of the distributed batch norm. group.' 'Default is normalization over local examples only.' 'When set to a value greater than 1, it will enable' 'a distribtued batch norm. To enable a global batch norm.' 'set distributed_group_size to FLAGS.num_shards') flags.DEFINE_bool( 'use_tpu', True, 'Use TPUs rather than CPU or GPU.') flags.DEFINE_string( 'data_dir', '', 'Directory where input data is stored') flags.DEFINE_string( 'model_dir', None, 'Directory where model output is stored') flags.DEFINE_integer( 'iterations_per_loop', 500, 'Number of iterations per TPU training loop.') flags.DEFINE_integer( 'train_batch_size', 256, 'Global (not per-shard) batch size for training') flags.DEFINE_integer( 'eval_batch_size', 256, 'Global (not per-shard) batch size for evaluation') flags.DEFINE_float( 'num_epochs', 48., 'Number of steps use for training.') flags.DEFINE_float( 'num_epochs_per_eval', 1., 'Number of training epochs to run between evaluations.') flags.DEFINE_string( 'mode', 'train_and_eval', 'Mode to run: train, eval, train_and_eval, predict, or export_savedmodel') flags.DEFINE_integer( 'save_checkpoints_steps', None, 'Interval (in steps) at which the model data ' 'should be checkpointed. Set to 0 to disable.') flags.DEFINE_bool( 'enable_hostcall', True, 'Skip the host_call which is executed every training step. This is' ' generally used for generating training summaries (train loss,' ' learning rate, etc...). When --enable_hostcall=True, there could' ' be a performance drop if host_call function is slow and cannot' ' keep up with the TPU-side computation.') # Model specific parameters flags.DEFINE_bool('use_aux_head', True, 'Include aux head or not.') flags.DEFINE_float( 'aux_scaling', 0.4, 'Scaling factor of aux_head') flags.DEFINE_float( 'batch_norm_decay', 0.9, 'Batch norm decay.') flags.DEFINE_float( 'batch_norm_epsilon', 1e-5, 'Batch norm epsilon.') flags.DEFINE_float( 'dense_dropout_keep_prob', None, 'Dense dropout keep probability.') flags.DEFINE_float( 'drop_connect_keep_prob', 1.0, 'Drop connect keep probability.') flags.DEFINE_string( 'drop_connect_version', None, 'Drop connect version.') flags.DEFINE_string( 'cell_name', 'amoeba_net_d', 'Which network to run.') flags.DEFINE_integer( 'num_cells', 12, 'Total number of cells.') flags.DEFINE_integer( 'reduction_size', 256, 'Default cell reduction size.') flags.DEFINE_integer( 'stem_reduction_size', 32, 'Stem filter size.') flags.DEFINE_float( 'weight_decay', 4e-05, 'Weight decay for slim model.') flags.DEFINE_integer( 'num_label_classes', 1001, 'The number of classes that images fit into.') # Training hyper-parameters flags.DEFINE_float( 'lr', 0.64, 'Learning rate.') flags.DEFINE_string( 'optimizer', 'rmsprop', 'Optimizer (one of sgd, rmsprop, momentum)') flags.DEFINE_float( 'moving_average_decay', 0.9999, 'moving average decay rate') flags.DEFINE_float( 'lr_decay_value', 0.9, 'Exponential decay rate used in learning rate adjustment') flags.DEFINE_integer( 'lr_num_epochs_per_decay', 1, 'Exponential decay epochs used in learning rate adjustment') flags.DEFINE_string( 'lr_decay_method', 'exponential', 'Method of decay: exponential, cosine, constant, stepwise') flags.DEFINE_float( 'lr_warmup_epochs', 3.0, 'Learning rate increased from zero linearly to lr for the first ' 'lr_warmup_epochs.') flags.DEFINE_float('gradient_clipping_by_global_norm', 0, 'gradient_clipping_by_global_norm') flags.DEFINE_integer( 'image_size', 299, 'Size of image, assuming image height and width.') flags.DEFINE_integer( 'num_train_images', 1281167, 'The number of images in the training set.') flags.DEFINE_integer( 'num_eval_images', 50000, 'The number of images in the evaluation set.') flags.DEFINE_bool( 'use_bp16', True, 'If True, use bfloat16 for activations') flags.DEFINE_integer( 'eval_timeout', 60*60*24, 'Maximum seconds between checkpoints before evaluation terminates.') FLAGS = flags.FLAGS def build_run_config(): """Return RunConfig for TPU estimator.""" tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) eval_steps = FLAGS.num_eval_images // FLAGS.eval_batch_size iterations_per_loop = (eval_steps if FLAGS.mode == 'eval' else FLAGS.iterations_per_loop) save_checkpoints_steps = FLAGS.save_checkpoints_steps or iterations_per_loop run_config = tf.contrib.tpu.RunConfig( cluster=tpu_cluster_resolver, model_dir=FLAGS.model_dir, save_checkpoints_steps=save_checkpoints_steps, keep_checkpoint_max=None, tpu_config=tf.contrib.tpu.TPUConfig( iterations_per_loop=iterations_per_loop, num_shards=FLAGS.num_shards, per_host_input_for_training=tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 )) return run_config def build_tensor_serving_input_receiver_fn( shape, batch_size=1, dtype=tf.float32,): """Returns a input_receiver_fn that can be used during serving. This expects examples to come through as float tensors, and simply wraps them as TensorServingInputReceivers. Arguably, this should live in tf.estimator.export. Testing here first. Args: shape: list representing target size of a single example. batch_size: number of input tensors that will be passed for prediction dtype: the expected datatype for the input example Returns: A function that itself returns a TensorServingInputReceiver. """ def serving_input_receiver_fn(): # Prep a placeholder where the input example will be fed in features = tf.placeholder( dtype=dtype, shape=[batch_size] + shape, name='input_tensor') return tf.estimator.export.TensorServingInputReceiver( features=features, receiver_tensors=features) return serving_input_receiver_fn # TODO(ereal): simplify this. def override_with_flags(hparams): """Overrides parameters with flag values.""" override_flag_names = [ 'aux_scaling', 'train_batch_size', 'batch_norm_decay', 'batch_norm_epsilon', 'dense_dropout_keep_prob', 'drop_connect_keep_prob', 'drop_connect_version', 'eval_batch_size', 'gradient_clipping_by_global_norm', 'lr', 'lr_decay_method', 'lr_decay_value', 'lr_num_epochs_per_decay', 'moving_average_decay', 'image_size', 'num_cells', 'reduction_size', 'stem_reduction_size', 'num_epochs', 'num_epochs_per_eval', 'optimizer', 'enable_hostcall', 'use_aux_head', 'use_bp16', 'use_tpu', 'lr_warmup_epochs', 'weight_decay', 'num_shards', 'distributed_group_size', 'num_train_images', 'num_eval_images', 'num_label_classes', ] for flag_name in override_flag_names: flag_value = getattr(FLAGS, flag_name, 'INVALID') if flag_value == 'INVALID': tf.logging.fatal('Unknown flag %s.' % str(flag_name)) if flag_value is not None: _set_or_add_hparam(hparams, flag_name, flag_value) def build_hparams(): """Build tf.Hparams for training Amoeba Net.""" hparams = model_lib.build_hparams(FLAGS.cell_name) override_with_flags(hparams) return hparams def _terminate_eval(): tf.logging.info('Timeout passed with no new checkpoints ... terminating eval') return True def _get_next_checkpoint(): return tf.contrib.training.checkpoints_iterator( FLAGS.model_dir, timeout=FLAGS.eval_timeout, timeout_fn=_terminate_eval) def _set_or_add_hparam(hparams, name, value): if getattr(hparams, name, None) is None: hparams.add_hparam(name, value) else: hparams.set_hparam(name, value) def _load_global_step_from_checkpoint_dir(checkpoint_dir): try: checkpoint_reader = tf.train.NewCheckpointReader( tf.train.latest_checkpoint(checkpoint_dir)) return checkpoint_reader.get_tensor(tf.GraphKeys.GLOBAL_STEP) except: # pylint: disable=bare-except return 0 def main(_): mode = FLAGS.mode data_dir = FLAGS.data_dir model_dir = FLAGS.model_dir hparams = build_hparams() estimator_parmas = {} train_steps_per_epoch = int( math.ceil(hparams.num_train_images / float(hparams.train_batch_size))) eval_steps = hparams.num_eval_images // hparams.eval_batch_size eval_batch_size = (None if mode == 'train' else hparams.eval_batch_size) model = model_lib.AmoebaNetEstimatorModel(hparams, model_dir) if hparams.use_tpu: run_config = build_run_config() image_classifier = tf.contrib.tpu.TPUEstimator( model_fn=model.model_fn, use_tpu=True, config=run_config, params=estimator_parmas, predict_batch_size=eval_batch_size, train_batch_size=hparams.train_batch_size, eval_batch_size=eval_batch_size) else: save_checkpoints_steps = (FLAGS.save_checkpoints_steps or FLAGS.iterations_per_loop) run_config = tf.estimator.RunConfig( model_dir=FLAGS.model_dir, save_checkpoints_steps=save_checkpoints_steps) image_classifier = tf.estimator.Estimator( model_fn=model.model_fn, config=run_config, params=estimator_parmas) # Input pipelines are slightly different (with regards to shuffling and # preprocessing) between training and evaluation. imagenet_train = model_lib.InputPipeline( is_training=True, data_dir=data_dir, hparams=hparams) imagenet_eval = model_lib.InputPipeline( is_training=False, data_dir=data_dir, hparams=hparams) if hparams.moving_average_decay < 1: eval_hooks = [model_lib.LoadEMAHook(model_dir, hparams.moving_average_decay)] else: eval_hooks = [] if mode == 'eval': for checkpoint in _get_next_checkpoint(): tf.logging.info('Starting to evaluate.') try: eval_results = image_classifier.evaluate( input_fn=imagenet_eval.input_fn, steps=eval_steps, hooks=eval_hooks, checkpoint_path=checkpoint) tf.logging.info('Evaluation results: %s' % eval_results) except tf.errors.NotFoundError: # skip checkpoint if it gets deleted prior to evaluation tf.logging.info('Checkpoint %s no longer exists ... skipping') elif mode == 'train_and_eval': current_step = _load_global_step_from_checkpoint_dir(model_dir) tf.logging.info('Starting training at step=%d.' % current_step) train_steps_per_eval = int( hparams.num_epochs_per_eval * train_steps_per_epoch) # Final Evaluation if training is finished. if current_step >= hparams.num_epochs * train_steps_per_epoch: eval_results = image_classifier.evaluate( input_fn=imagenet_eval.input_fn, steps=eval_steps, hooks=eval_hooks) tf.logging.info('Evaluation results: %s' % eval_results) while current_step < hparams.num_epochs * train_steps_per_epoch: image_classifier.train( input_fn=imagenet_train.input_fn, steps=train_steps_per_eval) current_step += train_steps_per_eval tf.logging.info('Starting evaluation at step=%d.' % current_step) eval_results = image_classifier.evaluate( input_fn=imagenet_eval.input_fn, steps=eval_steps, hooks=eval_hooks) tf.logging.info('Evaluation results: %s' % eval_results) elif mode == 'predict': for checkpoint in _get_next_checkpoint(): tf.logging.info('Starting prediction ...') time_hook = model_lib.SessionTimingHook() eval_hooks.append(time_hook) result_iter = image_classifier.predict( input_fn=imagenet_eval.input_fn, hooks=eval_hooks, checkpoint_path=checkpoint, yield_single_examples=False) results = list(itertools.islice(result_iter, eval_steps)) tf.logging.info('Inference speed = {} images per second.'.format( time_hook.compute_speed(len(results) * eval_batch_size))) elif mode == 'export_savedmodel': tf.logging.info('Starting exporting saved model ...') image_classifier.export_saved_model( export_dir_base=model_dir + '/export_savedmodel/', serving_input_receiver_fn=build_tensor_serving_input_receiver_fn( [hparams.image_size, hparams.image_size, 3], batch_size=hparams.eval_batch_size), as_text=True) else: # default to train mode. current_step = _load_global_step_from_checkpoint_dir(model_dir) total_step = int(hparams.num_epochs * train_steps_per_epoch) if current_step < total_step: tf.logging.info('Starting training ...') image_classifier.train( input_fn=imagenet_train.input_fn, steps=total_step-current_step) if __name__ == '__main__': tf.logging.set_verbosity(tf.logging.INFO) app.run(main)

apache-2.0

grehujt/SmallPythonProjects

ClusteringRelatedPosts/solution.py

1

2849

import scipy as sp from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer import nltk.stem stemmer = nltk.stem.SnowballStemmer('english') class StemmedCountVectorizer(CountVectorizer): def build_analyzer(self): analyzer = super(CountVectorizer, self).build_analyzer() return lambda doc: (stemmer.stem(w) for w in analyzer(doc)) class StemmedTfidfVectorizer(TfidfVectorizer): def build_analyzer(self): analyzer = super(StemmedTfidfVectorizer, self).build_analyzer() return lambda doc: (stemmer.stem(w) for w in analyzer(doc)) # vectorizer = CountVectorizer() # vectorizer = CountVectorizer(stop_words='english') # vectorizer = StemmedCountVectorizer(stop_words='english') vectorizer = StemmedTfidfVectorizer(stop_words='english') print vectorizer content = ["How to format my hard disk", " Hard disk format problems "] X = vectorizer.fit_transform(content) print vectorizer.get_feature_names() print X.toarray().T corpus = [ "This is a toy post about machine learning. Actually, it contains not much interesting stuff.", "Imaging databases can get huge.", "Most imaging databases save images permanently.", "Imaging databases store images.", "Imaging databases store images. Imaging databases store images. Imaging databases store images." ] X_train = vectorizer.fit_transform(corpus) print vectorizer.get_feature_names() print X_train.shape, X_train.toarray().T newPost = 'imaging databases' newVec = vectorizer.transform([newPost]) print newVec def dist(v1, v2): # return euclidean distance return sp.linalg.norm((v1 - v2).toarray()) minI, minDist = 0, 1e10 for i in range(X_train.shape[0]): d = dist(X_train[i, :], newVec) print i, d if d < minDist: minDist = d minI = i print 'most related', minI, minDist def dist_norm(v1, v2): v1_normed = v1 / sp.linalg.norm(v1.toarray()) v2_normed = v2 / sp.linalg.norm(v2.toarray()) return sp.linalg.norm((v1_normed - v2_normed).toarray()) minI, minDist = 0, 1e10 for i in range(X_train.shape[0]): d = dist_norm(X_train[i, :], newVec) print i, d if d < minDist: minDist = d minI = i print 'most related', minI, minDist print X_train[1,:] print print X_train[3,:] # stemmer = nltk.stem.SnowballStemmer('english') print stemmer.stem("image") # imag print stemmer.stem("images") # imag print stemmer.stem("imaging") # imag print stemmer.stem("imagination") # imagin def tfidf(word, post, corpus): tf = post.count(word) * 1.0 / len(post) numPosts = len([p for p in corpus if word in p]) idf = sp.log2(len(corpus) * 1.0 / numPosts) return tf * idf a, abb, abc = ['a'], ['a', 'b', 'b'], ['a', 'b', 'c'] corpus = [a, abb, abc] print tfidf('a', a, corpus) print tfidf('b', abb, corpus) print tfidf('c', abc, corpus)

mit

jzt5132/scikit-learn

sklearn/__init__.py

59

3038

""" Machine learning module for Python ================================== sklearn is a Python module integrating classical machine learning algorithms in the tightly-knit world of scientific Python packages (numpy, scipy, matplotlib). It aims to provide simple and efficient solutions to learning problems that are accessible to everybody and reusable in various contexts: machine-learning as a versatile tool for science and engineering. See http://scikit-learn.org for complete documentation. """ import sys import re import warnings # Make sure that DeprecationWarning within this package always gets printed warnings.filterwarnings('always', category=DeprecationWarning, module='^{0}\.'.format(re.escape(__name__))) # PEP0440 compatible formatted version, see: # https://www.python.org/dev/peps/pep-0440/ # # Generic release markers: # X.Y # X.Y.Z # For bugfix releases # # Admissible pre-release markers: # X.YaN # Alpha release # X.YbN # Beta release # X.YrcN # Release Candidate # X.Y # Final release # # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer. # 'X.Y.dev0' is the canonical version of 'X.Y.dev' # __version__ = '0.17.dev0' try: # This variable is injected in the __builtins__ by the build # process. It used to enable importing subpackages of sklearn when # the binaries are not built __SKLEARN_SETUP__ except NameError: __SKLEARN_SETUP__ = False if __SKLEARN_SETUP__: sys.stderr.write('Partial import of sklearn during the build process.\n') # We are not importing the rest of the scikit during the build # process, as it may not be compiled yet else: from . import __check_build from .base import clone __check_build # avoid flakes unused variable error __all__ = ['calibration', 'cluster', 'covariance', 'cross_decomposition', 'cross_validation', 'datasets', 'decomposition', 'dummy', 'ensemble', 'externals', 'feature_extraction', 'feature_selection', 'gaussian_process', 'grid_search', 'isotonic', 'kernel_approximation', 'kernel_ridge', 'lda', 'learning_curve', 'linear_model', 'manifold', 'metrics', 'mixture', 'multiclass', 'naive_bayes', 'neighbors', 'neural_network', 'pipeline', 'preprocessing', 'qda', 'random_projection', 'semi_supervised', 'svm', 'tree', 'discriminant_analysis', # Non-modules: 'clone'] def setup_module(module): """Fixture for the tests to assure globally controllable seeding of RNGs""" import os import numpy as np import random # It could have been provided in the environment _random_seed = os.environ.get('SKLEARN_SEED', None) if _random_seed is None: _random_seed = np.random.uniform() * (2 ** 31 - 1) _random_seed = int(_random_seed) print("I: Seeding RNGs with %r" % _random_seed) np.random.seed(_random_seed) random.seed(_random_seed)

bsd-3-clause

LohithBlaze/scikit-learn

sklearn/tree/tests/test_export.py

75

9318

""" Testing for export functions of decision trees (sklearn.tree.export). """ from numpy.testing import assert_equal from nose.tools import assert_raises from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor from sklearn.tree import export_graphviz from sklearn.externals.six import StringIO # toy sample X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]] y = [-1, -1, -1, 1, 1, 1] y2 = [[-1, 1], [-1, 2], [-1, 3], [1, 1], [1, 2], [1, 3]] w = [1, 1, 1, .5, .5, .5] def test_graphviz_toy(): # Check correctness of export_graphviz clf = DecisionTreeClassifier(max_depth=3, min_samples_split=1, criterion="gini", random_state=2) clf.fit(X, y) # Test export code out = StringIO() export_graphviz(clf, out_file=out) contents1 = out.getvalue() contents2 = 'digraph Tree {\n' \ 'node [shape=box] ;\n' \ '0 [label="X[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n' \ 'value = [3, 3]"] ;\n' \ '1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]"] ;\n' \ '0 -> 1 [labeldistance=2.5, labelangle=45, ' \ 'headlabel="True"] ;\n' \ '2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]"] ;\n' \ '0 -> 2 [labeldistance=2.5, labelangle=-45, ' \ 'headlabel="False"] ;\n' \ '}' assert_equal(contents1, contents2) # Test with feature_names out = StringIO() export_graphviz(clf, out_file=out, feature_names=["feature0", "feature1"]) contents1 = out.getvalue() contents2 = 'digraph Tree {\n' \ 'node [shape=box] ;\n' \ '0 [label="feature0 <= 0.0\\ngini = 0.5\\nsamples = 6\\n' \ 'value = [3, 3]"] ;\n' \ '1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]"] ;\n' \ '0 -> 1 [labeldistance=2.5, labelangle=45, ' \ 'headlabel="True"] ;\n' \ '2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]"] ;\n' \ '0 -> 2 [labeldistance=2.5, labelangle=-45, ' \ 'headlabel="False"] ;\n' \ '}' assert_equal(contents1, contents2) # Test with class_names out = StringIO() export_graphviz(clf, out_file=out, class_names=["yes", "no"]) contents1 = out.getvalue() contents2 = 'digraph Tree {\n' \ 'node [shape=box] ;\n' \ '0 [label="X[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n' \ 'value = [3, 3]\\nclass = yes"] ;\n' \ '1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]\\n' \ 'class = yes"] ;\n' \ '0 -> 1 [labeldistance=2.5, labelangle=45, ' \ 'headlabel="True"] ;\n' \ '2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]\\n' \ 'class = no"] ;\n' \ '0 -> 2 [labeldistance=2.5, labelangle=-45, ' \ 'headlabel="False"] ;\n' \ '}' assert_equal(contents1, contents2) # Test plot_options out = StringIO() export_graphviz(clf, out_file=out, filled=True, impurity=False, proportion=True, special_characters=True, rounded=True) contents1 = out.getvalue() contents2 = 'digraph Tree {\n' \ 'node [shape=box, style="filled, rounded", color="black", ' \ 'fontname=helvetica] ;\n' \ 'edge [fontname=helvetica] ;\n' \ '0 [label=<X<SUB>0</SUB> &le; 0.0<br/>samples = 100.0%<br/>' \ 'value = [0.5, 0.5]>, fillcolor="#e5813900"] ;\n' \ '1 [label=<samples = 50.0%<br/>value = [1.0, 0.0]>, ' \ 'fillcolor="#e58139ff"] ;\n' \ '0 -> 1 [labeldistance=2.5, labelangle=45, ' \ 'headlabel="True"] ;\n' \ '2 [label=<samples = 50.0%<br/>value = [0.0, 1.0]>, ' \ 'fillcolor="#399de5ff"] ;\n' \ '0 -> 2 [labeldistance=2.5, labelangle=-45, ' \ 'headlabel="False"] ;\n' \ '}' assert_equal(contents1, contents2) # Test max_depth out = StringIO() export_graphviz(clf, out_file=out, max_depth=0, class_names=True) contents1 = out.getvalue() contents2 = 'digraph Tree {\n' \ 'node [shape=box] ;\n' \ '0 [label="X[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n' \ 'value = [3, 3]\\nclass = y[0]"] ;\n' \ '1 [label="(...)"] ;\n' \ '0 -> 1 ;\n' \ '2 [label="(...)"] ;\n' \ '0 -> 2 ;\n' \ '}' assert_equal(contents1, contents2) # Test max_depth with plot_options out = StringIO() export_graphviz(clf, out_file=out, max_depth=0, filled=True, node_ids=True) contents1 = out.getvalue() contents2 = 'digraph Tree {\n' \ 'node [shape=box, style="filled", color="black"] ;\n' \ '0 [label="node #0\\nX[0] <= 0.0\\ngini = 0.5\\n' \ 'samples = 6\\nvalue = [3, 3]", fillcolor="#e5813900"] ;\n' \ '1 [label="(...)", fillcolor="#C0C0C0"] ;\n' \ '0 -> 1 ;\n' \ '2 [label="(...)", fillcolor="#C0C0C0"] ;\n' \ '0 -> 2 ;\n' \ '}' assert_equal(contents1, contents2) # Test multi-output with weighted samples clf = DecisionTreeClassifier(max_depth=2, min_samples_split=1, criterion="gini", random_state=2) clf = clf.fit(X, y2, sample_weight=w) out = StringIO() export_graphviz(clf, out_file=out, filled=True, impurity=False) contents1 = out.getvalue() contents2 = 'digraph Tree {\n' \ 'node [shape=box, style="filled", color="black"] ;\n' \ '0 [label="X[0] <= 0.0\\nsamples = 6\\n' \ 'value = [[3.0, 1.5, 0.0]\\n' \ '[1.5, 1.5, 1.5]]", fillcolor="#e5813900"] ;\n' \ '1 [label="X[1] <= -1.5\\nsamples = 3\\n' \ 'value = [[3, 0, 0]\\n[1, 1, 1]]", ' \ 'fillcolor="#e5813965"] ;\n' \ '0 -> 1 [labeldistance=2.5, labelangle=45, ' \ 'headlabel="True"] ;\n' \ '2 [label="samples = 1\\nvalue = [[1, 0, 0]\\n' \ '[0, 0, 1]]", fillcolor="#e58139ff"] ;\n' \ '1 -> 2 ;\n' \ '3 [label="samples = 2\\nvalue = [[2, 0, 0]\\n' \ '[1, 1, 0]]", fillcolor="#e581398c"] ;\n' \ '1 -> 3 ;\n' \ '4 [label="X[0] <= 1.5\\nsamples = 3\\n' \ 'value = [[0.0, 1.5, 0.0]\\n[0.5, 0.5, 0.5]]", ' \ 'fillcolor="#e5813965"] ;\n' \ '0 -> 4 [labeldistance=2.5, labelangle=-45, ' \ 'headlabel="False"] ;\n' \ '5 [label="samples = 2\\nvalue = [[0.0, 1.0, 0.0]\\n' \ '[0.5, 0.5, 0.0]]", fillcolor="#e581398c"] ;\n' \ '4 -> 5 ;\n' \ '6 [label="samples = 1\\nvalue = [[0.0, 0.5, 0.0]\\n' \ '[0.0, 0.0, 0.5]]", fillcolor="#e58139ff"] ;\n' \ '4 -> 6 ;\n' \ '}' assert_equal(contents1, contents2) # Test regression output with plot_options clf = DecisionTreeRegressor(max_depth=3, min_samples_split=1, criterion="mse", random_state=2) clf.fit(X, y) out = StringIO() export_graphviz(clf, out_file=out, filled=True, leaves_parallel=True, rotate=True, rounded=True) contents1 = out.getvalue() contents2 = 'digraph Tree {\n' \ 'node [shape=box, style="filled, rounded", color="black", ' \ 'fontname=helvetica] ;\n' \ 'graph [ranksep=equally, splines=polyline] ;\n' \ 'edge [fontname=helvetica] ;\n' \ 'rankdir=LR ;\n' \ '0 [label="X[0] <= 0.0\\nmse = 1.0\\nsamples = 6\\n' \ 'value = 0.0", fillcolor="#e581397f"] ;\n' \ '1 [label="mse = 0.0\\nsamples = 3\\nvalue = -1.0", ' \ 'fillcolor="#e5813900"] ;\n' \ '0 -> 1 [labeldistance=2.5, labelangle=-45, ' \ 'headlabel="True"] ;\n' \ '2 [label="mse = 0.0\\nsamples = 3\\nvalue = 1.0", ' \ 'fillcolor="#e58139ff"] ;\n' \ '0 -> 2 [labeldistance=2.5, labelangle=45, ' \ 'headlabel="False"] ;\n' \ '{rank=same ; 0} ;\n' \ '{rank=same ; 1; 2} ;\n' \ '}' assert_equal(contents1, contents2) def test_graphviz_errors(): # Check for errors of export_graphviz clf = DecisionTreeClassifier(max_depth=3, min_samples_split=1) clf.fit(X, y) # Check feature_names error out = StringIO() assert_raises(IndexError, export_graphviz, clf, out, feature_names=[]) # Check class_names error out = StringIO() assert_raises(IndexError, export_graphviz, clf, out, class_names=[])

bsd-3-clause

jzt5132/scikit-learn

examples/linear_model/plot_ols_ridge_variance.py

380

2060

#!/usr/bin/python # -*- coding: utf-8 -*- """ ========================================================= Ordinary Least Squares and Ridge Regression Variance ========================================================= Due to the few points in each dimension and the straight line that linear regression uses to follow these points as well as it can, noise on the observations will cause great variance as shown in the first plot. Every line's slope can vary quite a bit for each prediction due to the noise induced in the observations. Ridge regression is basically minimizing a penalised version of the least-squared function. The penalising `shrinks` the value of the regression coefficients. Despite the few data points in each dimension, the slope of the prediction is much more stable and the variance in the line itself is greatly reduced, in comparison to that of the standard linear regression """ print(__doc__) # Code source: Gaël Varoquaux # Modified for documentation by Jaques Grobler # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model X_train = np.c_[.5, 1].T y_train = [.5, 1] X_test = np.c_[0, 2].T np.random.seed(0) classifiers = dict(ols=linear_model.LinearRegression(), ridge=linear_model.Ridge(alpha=.1)) fignum = 1 for name, clf in classifiers.items(): fig = plt.figure(fignum, figsize=(4, 3)) plt.clf() plt.title(name) ax = plt.axes([.12, .12, .8, .8]) for _ in range(6): this_X = .1 * np.random.normal(size=(2, 1)) + X_train clf.fit(this_X, y_train) ax.plot(X_test, clf.predict(X_test), color='.5') ax.scatter(this_X, y_train, s=3, c='.5', marker='o', zorder=10) clf.fit(X_train, y_train) ax.plot(X_test, clf.predict(X_test), linewidth=2, color='blue') ax.scatter(X_train, y_train, s=30, c='r', marker='+', zorder=10) ax.set_xticks(()) ax.set_yticks(()) ax.set_ylim((0, 1.6)) ax.set_xlabel('X') ax.set_ylabel('y') ax.set_xlim(0, 2) fignum += 1 plt.show()

bsd-3-clause

aspidites/django

django/contrib/gis/geoip2/base.py

73

8630

import os import socket import geoip2.database from django.conf import settings from django.core.validators import ipv4_re from django.utils import six from django.utils.ipv6 import is_valid_ipv6_address from .resources import City, Country # Creating the settings dictionary with any settings, if needed. GEOIP_SETTINGS = { 'GEOIP_PATH': getattr(settings, 'GEOIP_PATH', None), 'GEOIP_CITY': getattr(settings, 'GEOIP_CITY', 'GeoLite2-City.mmdb'), 'GEOIP_COUNTRY': getattr(settings, 'GEOIP_COUNTRY', 'GeoLite2-Country.mmdb'), } class GeoIP2Exception(Exception): pass class GeoIP2(object): # The flags for GeoIP memory caching. # Try MODE_MMAP_EXT, MODE_MMAP, MODE_FILE in that order. MODE_AUTO = 0 # Use the C extension with memory map. MODE_MMAP_EXT = 1 # Read from memory map. Pure Python. MODE_MMAP = 2 # Read database as standard file. Pure Python. MODE_FILE = 4 # Load database into memory. Pure Python. MODE_MEMORY = 8 cache_options = {opt: None for opt in (0, 1, 2, 4, 8)} # Paths to the city & country binary databases. _city_file = '' _country_file = '' # Initially, pointers to GeoIP file references are NULL. _city = None _country = None def __init__(self, path=None, cache=0, country=None, city=None): """ Initialize the GeoIP object. No parameters are required to use default settings. Keyword arguments may be passed in to customize the locations of the GeoIP datasets. * path: Base directory to where GeoIP data is located or the full path to where the city or country data files (*.mmdb) are located. Assumes that both the city and country data sets are located in this directory; overrides the GEOIP_PATH setting. * cache: The cache settings when opening up the GeoIP datasets. May be an integer in (0, 1, 2, 4, 8) corresponding to the MODE_AUTO, MODE_MMAP_EXT, MODE_MMAP, MODE_FILE, and MODE_MEMORY, `GeoIPOptions` C API settings, respectively. Defaults to 0, meaning MODE_AUTO. * country: The name of the GeoIP country data file. Defaults to 'GeoLite2-Country.mmdb'; overrides the GEOIP_COUNTRY setting. * city: The name of the GeoIP city data file. Defaults to 'GeoLite2-City.mmdb'; overrides the GEOIP_CITY setting. """ # Checking the given cache option. if cache in self.cache_options: self._cache = cache else: raise GeoIP2Exception('Invalid GeoIP caching option: %s' % cache) # Getting the GeoIP data path. if not path: path = GEOIP_SETTINGS['GEOIP_PATH'] if not path: raise GeoIP2Exception('GeoIP path must be provided via parameter or the GEOIP_PATH setting.') if not isinstance(path, six.string_types): raise TypeError('Invalid path type: %s' % type(path).__name__) if os.path.isdir(path): # Constructing the GeoIP database filenames using the settings # dictionary. If the database files for the GeoLite country # and/or city datasets exist, then try to open them. country_db = os.path.join(path, country or GEOIP_SETTINGS['GEOIP_COUNTRY']) if os.path.isfile(country_db): self._country = geoip2.database.Reader(country_db, mode=cache) self._country_file = country_db city_db = os.path.join(path, city or GEOIP_SETTINGS['GEOIP_CITY']) if os.path.isfile(city_db): self._city = geoip2.database.Reader(city_db, mode=cache) self._city_file = city_db elif os.path.isfile(path): # Otherwise, some detective work will be needed to figure out # whether the given database path is for the GeoIP country or city # databases. reader = geoip2.database.Reader(path, mode=cache) db_type = reader.metadata().database_type if db_type.endswith('City'): # GeoLite City database detected. self._city = reader self._city_file = path elif db_type.endswith('Country'): # GeoIP Country database detected. self._country = reader self._country_file = path else: raise GeoIP2Exception('Unable to recognize database edition: %s' % db_type) else: raise GeoIP2Exception('GeoIP path must be a valid file or directory.') @property def _reader(self): if self._country: return self._country else: return self._city @property def _country_or_city(self): if self._country: return self._country.country else: return self._city.city def __del__(self): # Cleanup any GeoIP file handles lying around. if self._reader: self._reader.close() def _check_query(self, query, country=False, city=False, city_or_country=False): "Helper routine for checking the query and database availability." # Making sure a string was passed in for the query. if not isinstance(query, six.string_types): raise TypeError('GeoIP query must be a string, not type %s' % type(query).__name__) # Extra checks for the existence of country and city databases. if city_or_country and not (self._country or self._city): raise GeoIP2Exception('Invalid GeoIP country and city data files.') elif country and not self._country: raise GeoIP2Exception('Invalid GeoIP country data file: %s' % self._country_file) elif city and not self._city: raise GeoIP2Exception('Invalid GeoIP city data file: %s' % self._city_file) # Return the query string back to the caller. GeoIP2 only takes IP addresses. if not (ipv4_re.match(query) or is_valid_ipv6_address(query)): query = socket.gethostbyname(query) return query def city(self, query): """ Return a dictionary of city information for the given IP address or Fully Qualified Domain Name (FQDN). Some information in the dictionary may be undefined (None). """ enc_query = self._check_query(query, city=True) return City(self._city.city(enc_query)) def country_code(self, query): "Return the country code for the given IP Address or FQDN." enc_query = self._check_query(query, city_or_country=True) return self.country(enc_query)['country_code'] def country_name(self, query): "Return the country name for the given IP Address or FQDN." enc_query = self._check_query(query, city_or_country=True) return self.country(enc_query)['country_name'] def country(self, query): """ Return a dictionary with the country code and name when given an IP address or a Fully Qualified Domain Name (FQDN). For example, both '24.124.1.80' and 'djangoproject.com' are valid parameters. """ # Returning the country code and name enc_query = self._check_query(query, city_or_country=True) return Country(self._country_or_city(enc_query)) # #### Coordinate retrieval routines #### def coords(self, query, ordering=('longitude', 'latitude')): cdict = self.city(query) if cdict is None: return None else: return tuple(cdict[o] for o in ordering) def lon_lat(self, query): "Return a tuple of the (longitude, latitude) for the given query." return self.coords(query) def lat_lon(self, query): "Return a tuple of the (latitude, longitude) for the given query." return self.coords(query, ('latitude', 'longitude')) def geos(self, query): "Return a GEOS Point object for the given query." ll = self.lon_lat(query) if ll: from django.contrib.gis.geos import Point return Point(ll, srid=4326) else: return None # #### GeoIP Database Information Routines #### @property def info(self): "Return information about the GeoIP library and databases in use." meta = self._reader.metadata() return 'GeoIP Library:\n\t%s.%s\n' % (meta.binary_format_major_version, meta.binary_format_minor_version) @classmethod def open(cls, full_path, cache): return GeoIP2(full_path, cache)

bsd-3-clause

qinjian623/dlnotes

tutorials/pytorch/drl/enduro.py

1

5020

import argparse import gym import numpy as np import cv2 from itertools import count import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torch.autograd as autograd from torch.autograd import Variable parser = argparse.ArgumentParser(description='PyTorch REINFORCE example') parser.add_argument('--gamma', type=float, default=0.99, metavar='G', help='discount factor (default: 0.99)') parser.add_argument('--seed', type=int, default=543, metavar='N', help='random seed (default: 543)') parser.add_argument('--render', action='store_true', help='render the environment') parser.add_argument('--log_interval', type=int, default=10, metavar='N', help='interval between training status logs (default: 10)') parser.add_argument('--cuda', action='store_true', help='use CUDA') args = parser.parse_args() torch.manual_seed(args.seed) if not torch.cuda.is_available(): args.cuda = False if torch.cuda.is_available() and args.cuda: torch.cuda.manual_seed(args.seed) env = gym.make('Enduro-v0') env.seed(args.seed) torch.manual_seed(args.seed) class Policy(nn.Module): def __init__(self): super(Policy, self).__init__() self.conv1 = nn.Conv2d(3, 10, kernel_size=3) self.conv2 = nn.Conv2d(10, 20, kernel_size=3) self.conv3 = nn.Conv2d(20, 40, kernel_size=3) self.conv4 = nn.Conv2d(40, 40, kernel_size=3) self.fc1 = nn.Linear(4200, 50) self.fc2 = nn.Linear(50, 6) self.saved_actions = [] self.rewards = [] self.saved_actions = [] self.rewards = [] def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(self.conv2(x)) x = F.relu(F.max_pool2d(self.conv3(x), 2)) x = F.relu(self.conv4(x)) x = x.view(-1, 4200) x = F.relu(self.fc1(x)) action_scores = self.fc2(x) return F.softmax(action_scores) model = Policy() if args.cuda: model.cuda() optimizer = optim.Adam(model.parameters(), lr=1e-2) def select_action(state): state = cv2.resize(state, (40, 103)) state = np.transpose(state, (2, 0, 1)) # print(state.shape) state = torch.from_numpy(state).float().unsqueeze(0) # Norm state /= 255 if args.cuda: state = state.cuda() probs = model(Variable(state)) action = probs.multinomial() model.saved_actions.append(action) return action.data def finish_episode(): R = 0 rewards = [] # Weight sum of rewards for r in model.rewards[::-1]: R = r + args.gamma * R rewards.insert(0, R) rewards = torch.Tensor(rewards) # Norm rewards = (rewards - rewards.mean()) / (rewards.std() + np.finfo(np.float32).eps) # What'is the action? for action, r in zip(model.saved_actions, rewards): # print(action) # print(type(action)) action.reinforce(r) optimizer.zero_grad() autograd.backward(model.saved_actions, [None for _ in model.saved_actions]) optimizer.step() del model.rewards[:] del model.saved_actions[:] running_reward = 0 need_reset = True reward = 0 meaning = env.env.get_action_meanings() for i_episode in count(1): if need_reset: state = env.reset() need_reset = False reward = 0 running_reward = 0 for t in range(500): # Don't infinite loop while learning action = select_action(state) # print(meaning[action[0, 0]]) # ['NOOP', 'FIRE', 'RIGHT', 'LEFT', 'DOWN', # 'DOWNRIGHT', 'DOWNLEFT', 'RIGHTFIRE', 'LEFTFIRE'] # env.step(1) if action[0, 0] <= 3: state, new_reward, done, _ = env.step(action[0, 0]) else: state, new_reward, done, _ = env.step(action[0, 0]+3) if args.render: env.render() # print(env.env.get_action_meanings()) # exit() # print(reward) if done: model.rewards.append(-10000) elif new_reward == 0.0: model.rewards.append(-3000) elif new_reward == reward: model.rewards.append(-500) elif new_reward < reward: model.rewards.append(-2000) else: model.rewards.append(1000) reward = new_reward running_reward += reward if done: print("DONE") # print(reward) # input() need_reset = True break print("Break episode.") # running_reward = running_reward * 0.99 + t * 0.01 finish_episode() if i_episode % args.log_interval == 0: print('Episode {}\tLast length: {:5d}\tAverage length: {:.2f}'.format( i_episode, t, running_reward)) if running_reward > 195: print("Solved! Running reward is now {} and " "the last episode runs to {} time steps!".format(running_reward, t)) break # exit()

gpl-3.0

jzt5132/scikit-learn

examples/mixture/plot_gmm_selection.py

247

3223

""" ================================= Gaussian Mixture Model Selection ================================= This example shows that model selection can be performed with Gaussian Mixture Models using information-theoretic criteria (BIC). Model selection concerns both the covariance type and the number of components in the model. In that case, AIC also provides the right result (not shown to save time), but BIC is better suited if the problem is to identify the right model. Unlike Bayesian procedures, such inferences are prior-free. In that case, the model with 2 components and full covariance (which corresponds to the true generative model) is selected. """ print(__doc__) import itertools import numpy as np from scipy import linalg import matplotlib.pyplot as plt import matplotlib as mpl from sklearn import mixture # Number of samples per component n_samples = 500 # Generate random sample, two components np.random.seed(0) C = np.array([[0., -0.1], [1.7, .4]]) X = np.r_[np.dot(np.random.randn(n_samples, 2), C), .7 * np.random.randn(n_samples, 2) + np.array([-6, 3])] lowest_bic = np.infty bic = [] n_components_range = range(1, 7) cv_types = ['spherical', 'tied', 'diag', 'full'] for cv_type in cv_types: for n_components in n_components_range: # Fit a mixture of Gaussians with EM gmm = mixture.GMM(n_components=n_components, covariance_type=cv_type) gmm.fit(X) bic.append(gmm.bic(X)) if bic[-1] < lowest_bic: lowest_bic = bic[-1] best_gmm = gmm bic = np.array(bic) color_iter = itertools.cycle(['k', 'r', 'g', 'b', 'c', 'm', 'y']) clf = best_gmm bars = [] # Plot the BIC scores spl = plt.subplot(2, 1, 1) for i, (cv_type, color) in enumerate(zip(cv_types, color_iter)): xpos = np.array(n_components_range) + .2 * (i - 2) bars.append(plt.bar(xpos, bic[i * len(n_components_range): (i + 1) * len(n_components_range)], width=.2, color=color)) plt.xticks(n_components_range) plt.ylim([bic.min() * 1.01 - .01 * bic.max(), bic.max()]) plt.title('BIC score per model') xpos = np.mod(bic.argmin(), len(n_components_range)) + .65 +\ .2 * np.floor(bic.argmin() / len(n_components_range)) plt.text(xpos, bic.min() * 0.97 + .03 * bic.max(), '*', fontsize=14) spl.set_xlabel('Number of components') spl.legend([b[0] for b in bars], cv_types) # Plot the winner splot = plt.subplot(2, 1, 2) Y_ = clf.predict(X) for i, (mean, covar, color) in enumerate(zip(clf.means_, clf.covars_, color_iter)): v, w = linalg.eigh(covar) if not np.any(Y_ == i): continue plt.scatter(X[Y_ == i, 0], X[Y_ == i, 1], .8, color=color) # Plot an ellipse to show the Gaussian component angle = np.arctan2(w[0][1], w[0][0]) angle = 180 * angle / np.pi # convert to degrees v *= 4 ell = mpl.patches.Ellipse(mean, v[0], v[1], 180 + angle, color=color) ell.set_clip_box(splot.bbox) ell.set_alpha(.5) splot.add_artist(ell) plt.xlim(-10, 10) plt.ylim(-3, 6) plt.xticks(()) plt.yticks(()) plt.title('Selected GMM: full model, 2 components') plt.subplots_adjust(hspace=.35, bottom=.02) plt.show()

bsd-3-clause

MohammedWasim/scikit-learn

sklearn/mixture/tests/test_gmm.py

199

17427

import unittest import copy import sys from nose.tools import assert_true import numpy as np from numpy.testing import (assert_array_equal, assert_array_almost_equal, assert_raises) from scipy import stats from sklearn import mixture from sklearn.datasets.samples_generator import make_spd_matrix from sklearn.utils.testing import assert_greater from sklearn.utils.testing import assert_raise_message from sklearn.metrics.cluster import adjusted_rand_score from sklearn.externals.six.moves import cStringIO as StringIO rng = np.random.RandomState(0) def test_sample_gaussian(): # Test sample generation from mixture.sample_gaussian where covariance # is diagonal, spherical and full n_features, n_samples = 2, 300 axis = 1 mu = rng.randint(10) * rng.rand(n_features) cv = (rng.rand(n_features) + 1.0) ** 2 samples = mixture.sample_gaussian( mu, cv, covariance_type='diag', n_samples=n_samples) assert_true(np.allclose(samples.mean(axis), mu, atol=1.3)) assert_true(np.allclose(samples.var(axis), cv, atol=1.5)) # the same for spherical covariances cv = (rng.rand() + 1.0) ** 2 samples = mixture.sample_gaussian( mu, cv, covariance_type='spherical', n_samples=n_samples) assert_true(np.allclose(samples.mean(axis), mu, atol=1.5)) assert_true(np.allclose( samples.var(axis), np.repeat(cv, n_features), atol=1.5)) # and for full covariances A = rng.randn(n_features, n_features) cv = np.dot(A.T, A) + np.eye(n_features) samples = mixture.sample_gaussian( mu, cv, covariance_type='full', n_samples=n_samples) assert_true(np.allclose(samples.mean(axis), mu, atol=1.3)) assert_true(np.allclose(np.cov(samples), cv, atol=2.5)) # Numerical stability check: in SciPy 0.12.0 at least, eigh may return # tiny negative values in its second return value. from sklearn.mixture import sample_gaussian x = sample_gaussian([0, 0], [[4, 3], [1, .1]], covariance_type='full', random_state=42) print(x) assert_true(np.isfinite(x).all()) def _naive_lmvnpdf_diag(X, mu, cv): # slow and naive implementation of lmvnpdf ref = np.empty((len(X), len(mu))) stds = np.sqrt(cv) for i, (m, std) in enumerate(zip(mu, stds)): ref[:, i] = np.log(stats.norm.pdf(X, m, std)).sum(axis=1) return ref def test_lmvnpdf_diag(): # test a slow and naive implementation of lmvnpdf and # compare it to the vectorized version (mixture.lmvnpdf) to test # for correctness n_features, n_components, n_samples = 2, 3, 10 mu = rng.randint(10) * rng.rand(n_components, n_features) cv = (rng.rand(n_components, n_features) + 1.0) ** 2 X = rng.randint(10) * rng.rand(n_samples, n_features) ref = _naive_lmvnpdf_diag(X, mu, cv) lpr = mixture.log_multivariate_normal_density(X, mu, cv, 'diag') assert_array_almost_equal(lpr, ref) def test_lmvnpdf_spherical(): n_features, n_components, n_samples = 2, 3, 10 mu = rng.randint(10) * rng.rand(n_components, n_features) spherecv = rng.rand(n_components, 1) ** 2 + 1 X = rng.randint(10) * rng.rand(n_samples, n_features) cv = np.tile(spherecv, (n_features, 1)) reference = _naive_lmvnpdf_diag(X, mu, cv) lpr = mixture.log_multivariate_normal_density(X, mu, spherecv, 'spherical') assert_array_almost_equal(lpr, reference) def test_lmvnpdf_full(): n_features, n_components, n_samples = 2, 3, 10 mu = rng.randint(10) * rng.rand(n_components, n_features) cv = (rng.rand(n_components, n_features) + 1.0) ** 2 X = rng.randint(10) * rng.rand(n_samples, n_features) fullcv = np.array([np.diag(x) for x in cv]) reference = _naive_lmvnpdf_diag(X, mu, cv) lpr = mixture.log_multivariate_normal_density(X, mu, fullcv, 'full') assert_array_almost_equal(lpr, reference) def test_lvmpdf_full_cv_non_positive_definite(): n_features, n_samples = 2, 10 rng = np.random.RandomState(0) X = rng.randint(10) * rng.rand(n_samples, n_features) mu = np.mean(X, 0) cv = np.array([[[-1, 0], [0, 1]]]) expected_message = "'covars' must be symmetric, positive-definite" assert_raise_message(ValueError, expected_message, mixture.log_multivariate_normal_density, X, mu, cv, 'full') def test_GMM_attributes(): n_components, n_features = 10, 4 covariance_type = 'diag' g = mixture.GMM(n_components, covariance_type, random_state=rng) weights = rng.rand(n_components) weights = weights / weights.sum() means = rng.randint(-20, 20, (n_components, n_features)) assert_true(g.n_components == n_components) assert_true(g.covariance_type == covariance_type) g.weights_ = weights assert_array_almost_equal(g.weights_, weights) g.means_ = means assert_array_almost_equal(g.means_, means) covars = (0.1 + 2 * rng.rand(n_components, n_features)) ** 2 g.covars_ = covars assert_array_almost_equal(g.covars_, covars) assert_raises(ValueError, g._set_covars, []) assert_raises(ValueError, g._set_covars, np.zeros((n_components - 2, n_features))) assert_raises(ValueError, mixture.GMM, n_components=20, covariance_type='badcovariance_type') class GMMTester(): do_test_eval = True def _setUp(self): self.n_components = 10 self.n_features = 4 self.weights = rng.rand(self.n_components) self.weights = self.weights / self.weights.sum() self.means = rng.randint(-20, 20, (self.n_components, self.n_features)) self.threshold = -0.5 self.I = np.eye(self.n_features) self.covars = { 'spherical': (0.1 + 2 * rng.rand(self.n_components, self.n_features)) ** 2, 'tied': (make_spd_matrix(self.n_features, random_state=0) + 5 * self.I), 'diag': (0.1 + 2 * rng.rand(self.n_components, self.n_features)) ** 2, 'full': np.array([make_spd_matrix(self.n_features, random_state=0) + 5 * self.I for x in range(self.n_components)])} def test_eval(self): if not self.do_test_eval: return # DPGMM does not support setting the means and # covariances before fitting There is no way of fixing this # due to the variational parameters being more expressive than # covariance matrices g = self.model(n_components=self.n_components, covariance_type=self.covariance_type, random_state=rng) # Make sure the means are far apart so responsibilities.argmax() # picks the actual component used to generate the observations. g.means_ = 20 * self.means g.covars_ = self.covars[self.covariance_type] g.weights_ = self.weights gaussidx = np.repeat(np.arange(self.n_components), 5) n_samples = len(gaussidx) X = rng.randn(n_samples, self.n_features) + g.means_[gaussidx] ll, responsibilities = g.score_samples(X) self.assertEqual(len(ll), n_samples) self.assertEqual(responsibilities.shape, (n_samples, self.n_components)) assert_array_almost_equal(responsibilities.sum(axis=1), np.ones(n_samples)) assert_array_equal(responsibilities.argmax(axis=1), gaussidx) def test_sample(self, n=100): g = self.model(n_components=self.n_components, covariance_type=self.covariance_type, random_state=rng) # Make sure the means are far apart so responsibilities.argmax() # picks the actual component used to generate the observations. g.means_ = 20 * self.means g.covars_ = np.maximum(self.covars[self.covariance_type], 0.1) g.weights_ = self.weights samples = g.sample(n) self.assertEqual(samples.shape, (n, self.n_features)) def test_train(self, params='wmc'): g = mixture.GMM(n_components=self.n_components, covariance_type=self.covariance_type) g.weights_ = self.weights g.means_ = self.means g.covars_ = 20 * self.covars[self.covariance_type] # Create a training set by sampling from the predefined distribution. X = g.sample(n_samples=100) g = self.model(n_components=self.n_components, covariance_type=self.covariance_type, random_state=rng, min_covar=1e-1, n_iter=1, init_params=params) g.fit(X) # Do one training iteration at a time so we can keep track of # the log likelihood to make sure that it increases after each # iteration. trainll = [] for _ in range(5): g.params = params g.init_params = '' g.fit(X) trainll.append(self.score(g, X)) g.n_iter = 10 g.init_params = '' g.params = params g.fit(X) # finish fitting # Note that the log likelihood will sometimes decrease by a # very small amount after it has more or less converged due to # the addition of min_covar to the covariance (to prevent # underflow). This is why the threshold is set to -0.5 # instead of 0. delta_min = np.diff(trainll).min() self.assertTrue( delta_min > self.threshold, "The min nll increase is %f which is lower than the admissible" " threshold of %f, for model %s. The likelihoods are %s." % (delta_min, self.threshold, self.covariance_type, trainll)) def test_train_degenerate(self, params='wmc'): # Train on degenerate data with 0 in some dimensions # Create a training set by sampling from the predefined distribution. X = rng.randn(100, self.n_features) X.T[1:] = 0 g = self.model(n_components=2, covariance_type=self.covariance_type, random_state=rng, min_covar=1e-3, n_iter=5, init_params=params) g.fit(X) trainll = g.score(X) self.assertTrue(np.sum(np.abs(trainll / 100 / X.shape[1])) < 5) def test_train_1d(self, params='wmc'): # Train on 1-D data # Create a training set by sampling from the predefined distribution. X = rng.randn(100, 1) # X.T[1:] = 0 g = self.model(n_components=2, covariance_type=self.covariance_type, random_state=rng, min_covar=1e-7, n_iter=5, init_params=params) g.fit(X) trainll = g.score(X) if isinstance(g, mixture.DPGMM): self.assertTrue(np.sum(np.abs(trainll / 100)) < 5) else: self.assertTrue(np.sum(np.abs(trainll / 100)) < 2) def score(self, g, X): return g.score(X).sum() class TestGMMWithSphericalCovars(unittest.TestCase, GMMTester): covariance_type = 'spherical' model = mixture.GMM setUp = GMMTester._setUp class TestGMMWithDiagonalCovars(unittest.TestCase, GMMTester): covariance_type = 'diag' model = mixture.GMM setUp = GMMTester._setUp class TestGMMWithTiedCovars(unittest.TestCase, GMMTester): covariance_type = 'tied' model = mixture.GMM setUp = GMMTester._setUp class TestGMMWithFullCovars(unittest.TestCase, GMMTester): covariance_type = 'full' model = mixture.GMM setUp = GMMTester._setUp def test_multiple_init(): # Test that multiple inits does not much worse than a single one X = rng.randn(30, 5) X[:10] += 2 g = mixture.GMM(n_components=2, covariance_type='spherical', random_state=rng, min_covar=1e-7, n_iter=5) train1 = g.fit(X).score(X).sum() g.n_init = 5 train2 = g.fit(X).score(X).sum() assert_true(train2 >= train1 - 1.e-2) def test_n_parameters(): # Test that the right number of parameters is estimated n_samples, n_dim, n_components = 7, 5, 2 X = rng.randn(n_samples, n_dim) n_params = {'spherical': 13, 'diag': 21, 'tied': 26, 'full': 41} for cv_type in ['full', 'tied', 'diag', 'spherical']: g = mixture.GMM(n_components=n_components, covariance_type=cv_type, random_state=rng, min_covar=1e-7, n_iter=1) g.fit(X) assert_true(g._n_parameters() == n_params[cv_type]) def test_1d_1component(): # Test all of the covariance_types return the same BIC score for # 1-dimensional, 1 component fits. n_samples, n_dim, n_components = 100, 1, 1 X = rng.randn(n_samples, n_dim) g_full = mixture.GMM(n_components=n_components, covariance_type='full', random_state=rng, min_covar=1e-7, n_iter=1) g_full.fit(X) g_full_bic = g_full.bic(X) for cv_type in ['tied', 'diag', 'spherical']: g = mixture.GMM(n_components=n_components, covariance_type=cv_type, random_state=rng, min_covar=1e-7, n_iter=1) g.fit(X) assert_array_almost_equal(g.bic(X), g_full_bic) def assert_fit_predict_correct(model, X): model2 = copy.deepcopy(model) predictions_1 = model.fit(X).predict(X) predictions_2 = model2.fit_predict(X) assert adjusted_rand_score(predictions_1, predictions_2) == 1.0 def test_fit_predict(): """ test that gmm.fit_predict is equivalent to gmm.fit + gmm.predict """ lrng = np.random.RandomState(101) n_samples, n_dim, n_comps = 100, 2, 2 mu = np.array([[8, 8]]) component_0 = lrng.randn(n_samples, n_dim) component_1 = lrng.randn(n_samples, n_dim) + mu X = np.vstack((component_0, component_1)) for m_constructor in (mixture.GMM, mixture.VBGMM, mixture.DPGMM): model = m_constructor(n_components=n_comps, covariance_type='full', min_covar=1e-7, n_iter=5, random_state=np.random.RandomState(0)) assert_fit_predict_correct(model, X) model = mixture.GMM(n_components=n_comps, n_iter=0) z = model.fit_predict(X) assert np.all(z == 0), "Quick Initialization Failed!" def test_aic(): # Test the aic and bic criteria n_samples, n_dim, n_components = 50, 3, 2 X = rng.randn(n_samples, n_dim) SGH = 0.5 * (X.var() + np.log(2 * np.pi)) # standard gaussian entropy for cv_type in ['full', 'tied', 'diag', 'spherical']: g = mixture.GMM(n_components=n_components, covariance_type=cv_type, random_state=rng, min_covar=1e-7) g.fit(X) aic = 2 * n_samples * SGH * n_dim + 2 * g._n_parameters() bic = (2 * n_samples * SGH * n_dim + np.log(n_samples) * g._n_parameters()) bound = n_dim * 3. / np.sqrt(n_samples) assert_true(np.abs(g.aic(X) - aic) / n_samples < bound) assert_true(np.abs(g.bic(X) - bic) / n_samples < bound) def check_positive_definite_covars(covariance_type): r"""Test that covariance matrices do not become non positive definite Due to the accumulation of round-off errors, the computation of the covariance matrices during the learning phase could lead to non-positive definite covariance matrices. Namely the use of the formula: .. math:: C = (\sum_i w_i x_i x_i^T) - \mu \mu^T instead of: .. math:: C = \sum_i w_i (x_i - \mu)(x_i - \mu)^T while mathematically equivalent, was observed a ``LinAlgError`` exception, when computing a ``GMM`` with full covariance matrices and fixed mean. This function ensures that some later optimization will not introduce the problem again. """ rng = np.random.RandomState(1) # we build a dataset with 2 2d component. The components are unbalanced # (respective weights 0.9 and 0.1) X = rng.randn(100, 2) X[-10:] += (3, 3) # Shift the 10 last points gmm = mixture.GMM(2, params="wc", covariance_type=covariance_type, min_covar=1e-3) # This is a non-regression test for issue #2640. The following call used # to trigger: # numpy.linalg.linalg.LinAlgError: 2-th leading minor not positive definite gmm.fit(X) if covariance_type == "diag" or covariance_type == "spherical": assert_greater(gmm.covars_.min(), 0) else: if covariance_type == "tied": covs = [gmm.covars_] else: covs = gmm.covars_ for c in covs: assert_greater(np.linalg.det(c), 0) def test_positive_definite_covars(): # Check positive definiteness for all covariance types for covariance_type in ["full", "tied", "diag", "spherical"]: yield check_positive_definite_covars, covariance_type def test_verbose_first_level(): # Create sample data X = rng.randn(30, 5) X[:10] += 2 g = mixture.GMM(n_components=2, n_init=2, verbose=1) old_stdout = sys.stdout sys.stdout = StringIO() try: g.fit(X) finally: sys.stdout = old_stdout def test_verbose_second_level(): # Create sample data X = rng.randn(30, 5) X[:10] += 2 g = mixture.GMM(n_components=2, n_init=2, verbose=2) old_stdout = sys.stdout sys.stdout = StringIO() try: g.fit(X) finally: sys.stdout = old_stdout

bsd-3-clause

LohithBlaze/scikit-learn

sklearn/datasets/svmlight_format.py

113

15826

"""This module implements a loader and dumper for the svmlight format This format is a text-based format, with one sample per line. It does not store zero valued features hence is suitable for sparse dataset. The first element of each line can be used to store a target variable to predict. This format is used as the default format for both svmlight and the libsvm command line programs. """ # Authors: Mathieu Blondel <mathieu@mblondel.org> # Lars Buitinck <L.J.Buitinck@uva.nl> # Olivier Grisel <olivier.grisel@ensta.org> # License: BSD 3 clause from contextlib import closing import io import os.path import numpy as np import scipy.sparse as sp from ._svmlight_format import _load_svmlight_file from .. import __version__ from ..externals import six from ..externals.six import u, b from ..externals.six.moves import range, zip from ..utils import check_array from ..utils.fixes import frombuffer_empty def load_svmlight_file(f, n_features=None, dtype=np.float64, multilabel=False, zero_based="auto", query_id=False): """Load datasets in the svmlight / libsvm format into sparse CSR matrix This format is a text-based format, with one sample per line. It does not store zero valued features hence is suitable for sparse dataset. The first element of each line can be used to store a target variable to predict. This format is used as the default format for both svmlight and the libsvm command line programs. Parsing a text based source can be expensive. When working on repeatedly on the same dataset, it is recommended to wrap this loader with joblib.Memory.cache to store a memmapped backup of the CSR results of the first call and benefit from the near instantaneous loading of memmapped structures for the subsequent calls. In case the file contains a pairwise preference constraint (known as "qid" in the svmlight format) these are ignored unless the query_id parameter is set to True. These pairwise preference constraints can be used to constraint the combination of samples when using pairwise loss functions (as is the case in some learning to rank problems) so that only pairs with the same query_id value are considered. This implementation is written in Cython and is reasonably fast. However, a faster API-compatible loader is also available at: https://github.com/mblondel/svmlight-loader Parameters ---------- f : {str, file-like, int} (Path to) a file to load. If a path ends in ".gz" or ".bz2", it will be uncompressed on the fly. If an integer is passed, it is assumed to be a file descriptor. A file-like or file descriptor will not be closed by this function. A file-like object must be opened in binary mode. n_features : int or None The number of features to use. If None, it will be inferred. This argument is useful to load several files that are subsets of a bigger sliced dataset: each subset might not have examples of every feature, hence the inferred shape might vary from one slice to another. multilabel : boolean, optional, default False Samples may have several labels each (see http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html) zero_based : boolean or "auto", optional, default "auto" Whether column indices in f are zero-based (True) or one-based (False). If column indices are one-based, they are transformed to zero-based to match Python/NumPy conventions. If set to "auto", a heuristic check is applied to determine this from the file contents. Both kinds of files occur "in the wild", but they are unfortunately not self-identifying. Using "auto" or True should always be safe. query_id : boolean, default False If True, will return the query_id array for each file. dtype : numpy data type, default np.float64 Data type of dataset to be loaded. This will be the data type of the output numpy arrays ``X`` and ``y``. Returns ------- X: scipy.sparse matrix of shape (n_samples, n_features) y: ndarray of shape (n_samples,), or, in the multilabel a list of tuples of length n_samples. query_id: array of shape (n_samples,) query_id for each sample. Only returned when query_id is set to True. See also -------- load_svmlight_files: similar function for loading multiple files in this format, enforcing the same number of features/columns on all of them. Examples -------- To use joblib.Memory to cache the svmlight file:: from sklearn.externals.joblib import Memory from sklearn.datasets import load_svmlight_file mem = Memory("./mycache") @mem.cache def get_data(): data = load_svmlight_file("mysvmlightfile") return data[0], data[1] X, y = get_data() """ return tuple(load_svmlight_files([f], n_features, dtype, multilabel, zero_based, query_id)) def _gen_open(f): if isinstance(f, int): # file descriptor return io.open(f, "rb", closefd=False) elif not isinstance(f, six.string_types): raise TypeError("expected {str, int, file-like}, got %s" % type(f)) _, ext = os.path.splitext(f) if ext == ".gz": import gzip return gzip.open(f, "rb") elif ext == ".bz2": from bz2 import BZ2File return BZ2File(f, "rb") else: return open(f, "rb") def _open_and_load(f, dtype, multilabel, zero_based, query_id): if hasattr(f, "read"): actual_dtype, data, ind, indptr, labels, query = \ _load_svmlight_file(f, dtype, multilabel, zero_based, query_id) # XXX remove closing when Python 2.7+/3.1+ required else: with closing(_gen_open(f)) as f: actual_dtype, data, ind, indptr, labels, query = \ _load_svmlight_file(f, dtype, multilabel, zero_based, query_id) # convert from array.array, give data the right dtype if not multilabel: labels = frombuffer_empty(labels, np.float64) data = frombuffer_empty(data, actual_dtype) indices = frombuffer_empty(ind, np.intc) indptr = np.frombuffer(indptr, dtype=np.intc) # never empty query = frombuffer_empty(query, np.intc) data = np.asarray(data, dtype=dtype) # no-op for float{32,64} return data, indices, indptr, labels, query def load_svmlight_files(files, n_features=None, dtype=np.float64, multilabel=False, zero_based="auto", query_id=False): """Load dataset from multiple files in SVMlight format This function is equivalent to mapping load_svmlight_file over a list of files, except that the results are concatenated into a single, flat list and the samples vectors are constrained to all have the same number of features. In case the file contains a pairwise preference constraint (known as "qid" in the svmlight format) these are ignored unless the query_id parameter is set to True. These pairwise preference constraints can be used to constraint the combination of samples when using pairwise loss functions (as is the case in some learning to rank problems) so that only pairs with the same query_id value are considered. Parameters ---------- files : iterable over {str, file-like, int} (Paths of) files to load. If a path ends in ".gz" or ".bz2", it will be uncompressed on the fly. If an integer is passed, it is assumed to be a file descriptor. File-likes and file descriptors will not be closed by this function. File-like objects must be opened in binary mode. n_features: int or None The number of features to use. If None, it will be inferred from the maximum column index occurring in any of the files. This can be set to a higher value than the actual number of features in any of the input files, but setting it to a lower value will cause an exception to be raised. multilabel: boolean, optional Samples may have several labels each (see http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html) zero_based: boolean or "auto", optional Whether column indices in f are zero-based (True) or one-based (False). If column indices are one-based, they are transformed to zero-based to match Python/NumPy conventions. If set to "auto", a heuristic check is applied to determine this from the file contents. Both kinds of files occur "in the wild", but they are unfortunately not self-identifying. Using "auto" or True should always be safe. query_id: boolean, defaults to False If True, will return the query_id array for each file. dtype : numpy data type, default np.float64 Data type of dataset to be loaded. This will be the data type of the output numpy arrays ``X`` and ``y``. Returns ------- [X1, y1, ..., Xn, yn] where each (Xi, yi) pair is the result from load_svmlight_file(files[i]). If query_id is set to True, this will return instead [X1, y1, q1, ..., Xn, yn, qn] where (Xi, yi, qi) is the result from load_svmlight_file(files[i]) Notes ----- When fitting a model to a matrix X_train and evaluating it against a matrix X_test, it is essential that X_train and X_test have the same number of features (X_train.shape[1] == X_test.shape[1]). This may not be the case if you load the files individually with load_svmlight_file. See also -------- load_svmlight_file """ r = [_open_and_load(f, dtype, multilabel, bool(zero_based), bool(query_id)) for f in files] if (zero_based is False or zero_based == "auto" and all(np.min(tmp[1]) > 0 for tmp in r)): for ind in r: indices = ind[1] indices -= 1 n_f = max(ind[1].max() for ind in r) + 1 if n_features is None: n_features = n_f elif n_features < n_f: raise ValueError("n_features was set to {}," " but input file contains {} features" .format(n_features, n_f)) result = [] for data, indices, indptr, y, query_values in r: shape = (indptr.shape[0] - 1, n_features) X = sp.csr_matrix((data, indices, indptr), shape) X.sort_indices() result += X, y if query_id: result.append(query_values) return result def _dump_svmlight(X, y, f, multilabel, one_based, comment, query_id): is_sp = int(hasattr(X, "tocsr")) if X.dtype.kind == 'i': value_pattern = u("%d:%d") else: value_pattern = u("%d:%.16g") if y.dtype.kind == 'i': label_pattern = u("%d") else: label_pattern = u("%.16g") line_pattern = u("%s") if query_id is not None: line_pattern += u(" qid:%d") line_pattern += u(" %s\n") if comment: f.write(b("# Generated by dump_svmlight_file from scikit-learn %s\n" % __version__)) f.write(b("# Column indices are %s-based\n" % ["zero", "one"][one_based])) f.write(b("#\n")) f.writelines(b("# %s\n" % line) for line in comment.splitlines()) for i in range(X.shape[0]): if is_sp: span = slice(X.indptr[i], X.indptr[i + 1]) row = zip(X.indices[span], X.data[span]) else: nz = X[i] != 0 row = zip(np.where(nz)[0], X[i, nz]) s = " ".join(value_pattern % (j + one_based, x) for j, x in row) if multilabel: nz_labels = np.where(y[i] != 0)[0] labels_str = ",".join(label_pattern % j for j in nz_labels) else: labels_str = label_pattern % y[i] if query_id is not None: feat = (labels_str, query_id[i], s) else: feat = (labels_str, s) f.write((line_pattern % feat).encode('ascii')) def dump_svmlight_file(X, y, f, zero_based=True, comment=None, query_id=None, multilabel=False): """Dump the dataset in svmlight / libsvm file format. This format is a text-based format, with one sample per line. It does not store zero valued features hence is suitable for sparse dataset. The first element of each line can be used to store a target variable to predict. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] Target values. f : string or file-like in binary mode If string, specifies the path that will contain the data. If file-like, data will be written to f. f should be opened in binary mode. zero_based : boolean, optional Whether column indices should be written zero-based (True) or one-based (False). comment : string, optional Comment to insert at the top of the file. This should be either a Unicode string, which will be encoded as UTF-8, or an ASCII byte string. If a comment is given, then it will be preceded by one that identifies the file as having been dumped by scikit-learn. Note that not all tools grok comments in SVMlight files. query_id : array-like, shape = [n_samples] Array containing pairwise preference constraints (qid in svmlight format). multilabel: boolean, optional Samples may have several labels each (see http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html) """ if comment is not None: # Convert comment string to list of lines in UTF-8. # If a byte string is passed, then check whether it's ASCII; # if a user wants to get fancy, they'll have to decode themselves. # Avoid mention of str and unicode types for Python 3.x compat. if isinstance(comment, bytes): comment.decode("ascii") # just for the exception else: comment = comment.encode("utf-8") if six.b("\0") in comment: raise ValueError("comment string contains NUL byte") y = np.asarray(y) if y.ndim != 1 and not multilabel: raise ValueError("expected y of shape (n_samples,), got %r" % (y.shape,)) Xval = check_array(X, accept_sparse='csr') if Xval.shape[0] != y.shape[0]: raise ValueError("X.shape[0] and y.shape[0] should be the same, got" " %r and %r instead." % (Xval.shape[0], y.shape[0])) # We had some issues with CSR matrices with unsorted indices (e.g. #1501), # so sort them here, but first make sure we don't modify the user's X. # TODO We can do this cheaper; sorted_indices copies the whole matrix. if Xval is X and hasattr(Xval, "sorted_indices"): X = Xval.sorted_indices() else: X = Xval if hasattr(X, "sort_indices"): X.sort_indices() if query_id is not None: query_id = np.asarray(query_id) if query_id.shape[0] != y.shape[0]: raise ValueError("expected query_id of shape (n_samples,), got %r" % (query_id.shape,)) one_based = not zero_based if hasattr(f, "write"): _dump_svmlight(X, y, f, multilabel, one_based, comment, query_id) else: with open(f, "wb") as f: _dump_svmlight(X, y, f, multilabel, one_based, comment, query_id)

bsd-3-clause

jzt5132/scikit-learn

examples/feature_selection/plot_feature_selection.py

248

2827

""" =============================== Univariate Feature Selection =============================== An example showing univariate feature selection. Noisy (non informative) features are added to the iris data and univariate feature selection is applied. For each feature, we plot the p-values for the univariate feature selection and the corresponding weights of an SVM. We can see that univariate feature selection selects the informative features and that these have larger SVM weights. In the total set of features, only the 4 first ones are significant. We can see that they have the highest score with univariate feature selection. The SVM assigns a large weight to one of these features, but also Selects many of the non-informative features. Applying univariate feature selection before the SVM increases the SVM weight attributed to the significant features, and will thus improve classification. """ print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import datasets, svm from sklearn.feature_selection import SelectPercentile, f_classif ############################################################################### # import some data to play with # The iris dataset iris = datasets.load_iris() # Some noisy data not correlated E = np.random.uniform(0, 0.1, size=(len(iris.data), 20)) # Add the noisy data to the informative features X = np.hstack((iris.data, E)) y = iris.target ############################################################################### plt.figure(1) plt.clf() X_indices = np.arange(X.shape[-1]) ############################################################################### # Univariate feature selection with F-test for feature scoring # We use the default selection function: the 10% most significant features selector = SelectPercentile(f_classif, percentile=10) selector.fit(X, y) scores = -np.log10(selector.pvalues_) scores /= scores.max() plt.bar(X_indices - .45, scores, width=.2, label=r'Univariate score ($-Log(p_{value})$)', color='g') ############################################################################### # Compare to the weights of an SVM clf = svm.SVC(kernel='linear') clf.fit(X, y) svm_weights = (clf.coef_ ** 2).sum(axis=0) svm_weights /= svm_weights.max() plt.bar(X_indices - .25, svm_weights, width=.2, label='SVM weight', color='r') clf_selected = svm.SVC(kernel='linear') clf_selected.fit(selector.transform(X), y) svm_weights_selected = (clf_selected.coef_ ** 2).sum(axis=0) svm_weights_selected /= svm_weights_selected.max() plt.bar(X_indices[selector.get_support()] - .05, svm_weights_selected, width=.2, label='SVM weights after selection', color='b') plt.title("Comparing feature selection") plt.xlabel('Feature number') plt.yticks(()) plt.axis('tight') plt.legend(loc='upper right') plt.show()

bsd-3-clause

JSilva90/MITWS

Asynchronous_SFS/new_async.py

1

5082

from __future__ import division import multiprocessing as mp import time import pandas as pd import numpy as np import sys import utils #from sklearn.model_selection import StratifiedKFold from sklearn.ensemble import RandomForestClassifier from ML_test import ML_test from ML_data import ML_instance max_search_level = 50 expansions_per_worker = 1 expansions_per_level = 5 min_share_work = 3 score_lock = 0 work_lock = 1 total_locks = 2 debug = True def starter(work, ml_instance, n_workers): t = time.time() manager = mp.Manager() ##manager creates a new process to handle variables, may not be the best option ##setup for paralelization locks = [] for i in range(total_locks): locks.append(mp.Lock()) global_info = manager.Namespace() ##manager for variables global_info.history = {} ##saves the history global_info.expanded_history = [] global_info.best_scores = {} global_info.worklist = work workers = [] for i in range (n_workers-1): p = mp.Process(target=search, args=(i, locks, ml_instance, global_info)) ##cant pass arguments to evaluator workers.append(p) p.start() search(n_workers-1, locks, ml_instance, global_info) for w in workers: w.join() print "Total time, ", time.time() - t, " tested a total of:", len(global_info.history) tester = ML_test() tester.history = global_info.history tester.save_history(filename="history_async") df = pd.DataFrame() df["expanded"] = global_info.expanded_history df.to_csv("expanded_async.csv", index = False) def update_score(id, subset, score, lock, global_info): size = len(subset) lock.acquire() aux = global_info.best_scores if size not in aux: aux[size] = [] if len(aux[size]) < 8 or aux[size][-1][0] < score : aux[size].append((score, subset)) aux[size] = sorted(aux[size], key=lambda tup: tup[0]) aux[size].reverse() aux[size] = aux[size][:8] global_info.best_scores = aux lock.release() def expand_stage(id, global_info, ml_instance, score_lock, current_size ): t = time.time() print id, " nominated to generate work for current_size ", current_size if current_size == max_search_level: print id , " determined its time to end search" aux = [] for i in range(50): ##append 50 subsets greater than max search level so everyone stops aux.append(range(max_search_level+3)) global_info.worklist = list(aux) return score_lock.acquire() subsets_to_expand = global_info.best_scores[current_size][:expansions_per_level] work = [] hist = global_info.history exp_hist = global_info.expanded_history for s in subsets_to_expand: subset = s[1] exp_hist.append(subset) for i in ml_instance.features: if i not in subset: sub = subset + [i] sub.sort() aux = [str(x) for x in sub] aux = ",".join(aux) if aux not in hist: work.append(sub) ##add subset for next round hist[aux] = True ##add subset to the testing print id, " genereated ", len(work), " new subsets in ", round(time.time() - t, 3), " seconds" global_info.history = hist global_info.expanded_history = exp_hist global_info.worklist = list(work) score_lock.release() def search(id, locks, ml_instance, global_info): tasks = 0 task_time = [] start_t = time.time() current_size = 1 worklist_waste = 0.0 update_waste = 0.0 while (True): ##mutual exclusion lock to get a subset from worklist aux_t = time.time() locks[work_lock].acquire() worklist_waste += (time.time() - aux_t) if global_info.worklist == []: expand_stage(id, global_info, ml_instance, locks[score_lock], current_size) ##acquires both locks to prevent strange stuff on the scores aux = list(global_info.worklist) subset = aux[0] del(aux[0]) global_info.worklist = aux locks[work_lock].release() if tasks % 4 == 0: ##periodicall print print id, " processed ", tasks, " remaining work ", len(aux) if len(subset) > max_search_level: ##search ends break current_size = len(subset) tasks += 1 t = time.time() score = ml_instance.rf_evaluator(subset) #score = ml_instance.svm_evaluator(subset) task_time.append(round(time.time()-t, 2)) t = time.time() update_score(id, subset, score, locks[score_lock], global_info) update_waste += (time.time() -t) print id, " ending processing ", tasks, " in ", round(time.time()-start_t,0), " seconds" print id, " wasted in worklist access ", round(worklist_waste,2), " updating ", round(update_waste,2), " average task time", round(sum(task_time) / len(task_time),2)

gpl-3.0

glouppe/scikit-learn

examples/covariance/plot_lw_vs_oas.py

150

2951

""" ============================= Ledoit-Wolf vs OAS estimation ============================= The usual covariance maximum likelihood estimate can be regularized using shrinkage. Ledoit and Wolf proposed a close formula to compute the asymptotically optimal shrinkage parameter (minimizing a MSE criterion), yielding the Ledoit-Wolf covariance estimate. Chen et al. proposed an improvement of the Ledoit-Wolf shrinkage parameter, the OAS coefficient, whose convergence is significantly better under the assumption that the data are Gaussian. This example, inspired from Chen's publication [1], shows a comparison of the estimated MSE of the LW and OAS methods, using Gaussian distributed data. [1] "Shrinkage Algorithms for MMSE Covariance Estimation" Chen et al., IEEE Trans. on Sign. Proc., Volume 58, Issue 10, October 2010. """ print(__doc__) import numpy as np import matplotlib.pyplot as plt from scipy.linalg import toeplitz, cholesky from sklearn.covariance import LedoitWolf, OAS np.random.seed(0) ############################################################################### n_features = 100 # simulation covariance matrix (AR(1) process) r = 0.1 real_cov = toeplitz(r ** np.arange(n_features)) coloring_matrix = cholesky(real_cov) n_samples_range = np.arange(6, 31, 1) repeat = 100 lw_mse = np.zeros((n_samples_range.size, repeat)) oa_mse = np.zeros((n_samples_range.size, repeat)) lw_shrinkage = np.zeros((n_samples_range.size, repeat)) oa_shrinkage = np.zeros((n_samples_range.size, repeat)) for i, n_samples in enumerate(n_samples_range): for j in range(repeat): X = np.dot( np.random.normal(size=(n_samples, n_features)), coloring_matrix.T) lw = LedoitWolf(store_precision=False, assume_centered=True) lw.fit(X) lw_mse[i, j] = lw.error_norm(real_cov, scaling=False) lw_shrinkage[i, j] = lw.shrinkage_ oa = OAS(store_precision=False, assume_centered=True) oa.fit(X) oa_mse[i, j] = oa.error_norm(real_cov, scaling=False) oa_shrinkage[i, j] = oa.shrinkage_ # plot MSE plt.subplot(2, 1, 1) plt.errorbar(n_samples_range, lw_mse.mean(1), yerr=lw_mse.std(1), label='Ledoit-Wolf', color='navy', lw=2) plt.errorbar(n_samples_range, oa_mse.mean(1), yerr=oa_mse.std(1), label='OAS', color='darkorange', lw=2) plt.ylabel("Squared error") plt.legend(loc="upper right") plt.title("Comparison of covariance estimators") plt.xlim(5, 31) # plot shrinkage coefficient plt.subplot(2, 1, 2) plt.errorbar(n_samples_range, lw_shrinkage.mean(1), yerr=lw_shrinkage.std(1), label='Ledoit-Wolf', color='navy', lw=2) plt.errorbar(n_samples_range, oa_shrinkage.mean(1), yerr=oa_shrinkage.std(1), label='OAS', color='darkorange', lw=2) plt.xlabel("n_samples") plt.ylabel("Shrinkage") plt.legend(loc="lower right") plt.ylim(plt.ylim()[0], 1. + (plt.ylim()[1] - plt.ylim()[0]) / 10.) plt.xlim(5, 31) plt.show()

bsd-3-clause

glouppe/scikit-learn

sklearn/neighbors/tests/test_neighbors.py

22

45330

from itertools import product import pickle import numpy as np from scipy.sparse import (bsr_matrix, coo_matrix, csc_matrix, csr_matrix, dok_matrix, lil_matrix) from sklearn import metrics from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_warns from sklearn.utils.testing import ignore_warnings from sklearn.utils.validation import check_random_state from sklearn.metrics.pairwise import pairwise_distances from sklearn import neighbors, datasets rng = np.random.RandomState(0) # load and shuffle iris dataset iris = datasets.load_iris() perm = rng.permutation(iris.target.size) iris.data = iris.data[perm] iris.target = iris.target[perm] # load and shuffle digits digits = datasets.load_digits() perm = rng.permutation(digits.target.size) digits.data = digits.data[perm] digits.target = digits.target[perm] SPARSE_TYPES = (bsr_matrix, coo_matrix, csc_matrix, csr_matrix, dok_matrix, lil_matrix) SPARSE_OR_DENSE = SPARSE_TYPES + (np.asarray,) ALGORITHMS = ('ball_tree', 'brute', 'kd_tree', 'auto') P = (1, 2, 3, 4, np.inf) # Filter deprecation warnings. neighbors.kneighbors_graph = ignore_warnings(neighbors.kneighbors_graph) neighbors.radius_neighbors_graph = ignore_warnings( neighbors.radius_neighbors_graph) def _weight_func(dist): """ Weight function to replace lambda d: d ** -2. The lambda function is not valid because: if d==0 then 0^-2 is not valid. """ # Dist could be multidimensional, flatten it so all values # can be looped with np.errstate(divide='ignore'): retval = 1. / dist return retval ** 2 def test_unsupervised_kneighbors(n_samples=20, n_features=5, n_query_pts=2, n_neighbors=5): # Test unsupervised neighbors methods X = rng.rand(n_samples, n_features) test = rng.rand(n_query_pts, n_features) for p in P: results_nodist = [] results = [] for algorithm in ALGORITHMS: neigh = neighbors.NearestNeighbors(n_neighbors=n_neighbors, algorithm=algorithm, p=p) neigh.fit(X) results_nodist.append(neigh.kneighbors(test, return_distance=False)) results.append(neigh.kneighbors(test, return_distance=True)) for i in range(len(results) - 1): assert_array_almost_equal(results_nodist[i], results[i][1]) assert_array_almost_equal(results[i][0], results[i + 1][0]) assert_array_almost_equal(results[i][1], results[i + 1][1]) def test_unsupervised_inputs(): # test the types of valid input into NearestNeighbors X = rng.random_sample((10, 3)) nbrs_fid = neighbors.NearestNeighbors(n_neighbors=1) nbrs_fid.fit(X) dist1, ind1 = nbrs_fid.kneighbors(X) nbrs = neighbors.NearestNeighbors(n_neighbors=1) for input in (nbrs_fid, neighbors.BallTree(X), neighbors.KDTree(X)): nbrs.fit(input) dist2, ind2 = nbrs.kneighbors(X) assert_array_almost_equal(dist1, dist2) assert_array_almost_equal(ind1, ind2) def test_precomputed(random_state=42): """Tests unsupervised NearestNeighbors with a distance matrix.""" # Note: smaller samples may result in spurious test success rng = np.random.RandomState(random_state) X = rng.random_sample((10, 4)) Y = rng.random_sample((3, 4)) DXX = metrics.pairwise_distances(X, metric='euclidean') DYX = metrics.pairwise_distances(Y, X, metric='euclidean') for method in ['kneighbors']: # TODO: also test radius_neighbors, but requires different assertion # As a feature matrix (n_samples by n_features) nbrs_X = neighbors.NearestNeighbors(n_neighbors=3) nbrs_X.fit(X) dist_X, ind_X = getattr(nbrs_X, method)(Y) # As a dense distance matrix (n_samples by n_samples) nbrs_D = neighbors.NearestNeighbors(n_neighbors=3, algorithm='brute', metric='precomputed') nbrs_D.fit(DXX) dist_D, ind_D = getattr(nbrs_D, method)(DYX) assert_array_almost_equal(dist_X, dist_D) assert_array_almost_equal(ind_X, ind_D) # Check auto works too nbrs_D = neighbors.NearestNeighbors(n_neighbors=3, algorithm='auto', metric='precomputed') nbrs_D.fit(DXX) dist_D, ind_D = getattr(nbrs_D, method)(DYX) assert_array_almost_equal(dist_X, dist_D) assert_array_almost_equal(ind_X, ind_D) # Check X=None in prediction dist_X, ind_X = getattr(nbrs_X, method)(None) dist_D, ind_D = getattr(nbrs_D, method)(None) assert_array_almost_equal(dist_X, dist_D) assert_array_almost_equal(ind_X, ind_D) # Must raise a ValueError if the matrix is not of correct shape assert_raises(ValueError, getattr(nbrs_D, method), X) target = np.arange(X.shape[0]) for Est in (neighbors.KNeighborsClassifier, neighbors.RadiusNeighborsClassifier, neighbors.KNeighborsRegressor, neighbors.RadiusNeighborsRegressor): print(Est) est = Est(metric='euclidean') est.radius = est.n_neighbors = 1 pred_X = est.fit(X, target).predict(Y) est.metric = 'precomputed' pred_D = est.fit(DXX, target).predict(DYX) assert_array_almost_equal(pred_X, pred_D) def test_precomputed_cross_validation(): # Ensure array is split correctly rng = np.random.RandomState(0) X = rng.rand(20, 2) D = pairwise_distances(X, metric='euclidean') y = rng.randint(3, size=20) for Est in (neighbors.KNeighborsClassifier, neighbors.RadiusNeighborsClassifier, neighbors.KNeighborsRegressor, neighbors.RadiusNeighborsRegressor): metric_score = cross_val_score(Est(), X, y) precomp_score = cross_val_score(Est(metric='precomputed'), D, y) assert_array_equal(metric_score, precomp_score) def test_unsupervised_radius_neighbors(n_samples=20, n_features=5, n_query_pts=2, radius=0.5, random_state=0): # Test unsupervised radius-based query rng = np.random.RandomState(random_state) X = rng.rand(n_samples, n_features) test = rng.rand(n_query_pts, n_features) for p in P: results = [] for algorithm in ALGORITHMS: neigh = neighbors.NearestNeighbors(radius=radius, algorithm=algorithm, p=p) neigh.fit(X) ind1 = neigh.radius_neighbors(test, return_distance=False) # sort the results: this is not done automatically for # radius searches dist, ind = neigh.radius_neighbors(test, return_distance=True) for (d, i, i1) in zip(dist, ind, ind1): j = d.argsort() d[:] = d[j] i[:] = i[j] i1[:] = i1[j] results.append((dist, ind)) assert_array_almost_equal(np.concatenate(list(ind)), np.concatenate(list(ind1))) for i in range(len(results) - 1): assert_array_almost_equal(np.concatenate(list(results[i][0])), np.concatenate(list(results[i + 1][0]))), assert_array_almost_equal(np.concatenate(list(results[i][1])), np.concatenate(list(results[i + 1][1]))) def test_kneighbors_classifier(n_samples=40, n_features=5, n_test_pts=10, n_neighbors=5, random_state=0): # Test k-neighbors classification rng = np.random.RandomState(random_state) X = 2 * rng.rand(n_samples, n_features) - 1 y = ((X ** 2).sum(axis=1) < .5).astype(np.int) y_str = y.astype(str) weight_func = _weight_func for algorithm in ALGORITHMS: for weights in ['uniform', 'distance', weight_func]: knn = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors, weights=weights, algorithm=algorithm) knn.fit(X, y) epsilon = 1e-5 * (2 * rng.rand(1, n_features) - 1) y_pred = knn.predict(X[:n_test_pts] + epsilon) assert_array_equal(y_pred, y[:n_test_pts]) # Test prediction with y_str knn.fit(X, y_str) y_pred = knn.predict(X[:n_test_pts] + epsilon) assert_array_equal(y_pred, y_str[:n_test_pts]) def test_kneighbors_classifier_float_labels(n_samples=40, n_features=5, n_test_pts=10, n_neighbors=5, random_state=0): # Test k-neighbors classification rng = np.random.RandomState(random_state) X = 2 * rng.rand(n_samples, n_features) - 1 y = ((X ** 2).sum(axis=1) < .5).astype(np.int) knn = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors) knn.fit(X, y.astype(np.float)) epsilon = 1e-5 * (2 * rng.rand(1, n_features) - 1) y_pred = knn.predict(X[:n_test_pts] + epsilon) assert_array_equal(y_pred, y[:n_test_pts]) def test_kneighbors_classifier_predict_proba(): # Test KNeighborsClassifier.predict_proba() method X = np.array([[0, 2, 0], [0, 2, 1], [2, 0, 0], [2, 2, 0], [0, 0, 2], [0, 0, 1]]) y = np.array([4, 4, 5, 5, 1, 1]) cls = neighbors.KNeighborsClassifier(n_neighbors=3, p=1) # cityblock dist cls.fit(X, y) y_prob = cls.predict_proba(X) real_prob = np.array([[0, 2. / 3, 1. / 3], [1. / 3, 2. / 3, 0], [1. / 3, 0, 2. / 3], [0, 1. / 3, 2. / 3], [2. / 3, 1. / 3, 0], [2. / 3, 1. / 3, 0]]) assert_array_equal(real_prob, y_prob) # Check that it also works with non integer labels cls.fit(X, y.astype(str)) y_prob = cls.predict_proba(X) assert_array_equal(real_prob, y_prob) # Check that it works with weights='distance' cls = neighbors.KNeighborsClassifier( n_neighbors=2, p=1, weights='distance') cls.fit(X, y) y_prob = cls.predict_proba(np.array([[0, 2, 0], [2, 2, 2]])) real_prob = np.array([[0, 1, 0], [0, 0.4, 0.6]]) assert_array_almost_equal(real_prob, y_prob) def test_radius_neighbors_classifier(n_samples=40, n_features=5, n_test_pts=10, radius=0.5, random_state=0): # Test radius-based classification rng = np.random.RandomState(random_state) X = 2 * rng.rand(n_samples, n_features) - 1 y = ((X ** 2).sum(axis=1) < .5).astype(np.int) y_str = y.astype(str) weight_func = _weight_func for algorithm in ALGORITHMS: for weights in ['uniform', 'distance', weight_func]: neigh = neighbors.RadiusNeighborsClassifier(radius=radius, weights=weights, algorithm=algorithm) neigh.fit(X, y) epsilon = 1e-5 * (2 * rng.rand(1, n_features) - 1) y_pred = neigh.predict(X[:n_test_pts] + epsilon) assert_array_equal(y_pred, y[:n_test_pts]) neigh.fit(X, y_str) y_pred = neigh.predict(X[:n_test_pts] + epsilon) assert_array_equal(y_pred, y_str[:n_test_pts]) def test_radius_neighbors_classifier_when_no_neighbors(): # Test radius-based classifier when no neighbors found. # In this case it should rise an informative exception X = np.array([[1.0, 1.0], [2.0, 2.0]]) y = np.array([1, 2]) radius = 0.1 z1 = np.array([[1.01, 1.01], [2.01, 2.01]]) # no outliers z2 = np.array([[1.01, 1.01], [1.4, 1.4]]) # one outlier weight_func = _weight_func for outlier_label in [0, -1, None]: for algorithm in ALGORITHMS: for weights in ['uniform', 'distance', weight_func]: rnc = neighbors.RadiusNeighborsClassifier clf = rnc(radius=radius, weights=weights, algorithm=algorithm, outlier_label=outlier_label) clf.fit(X, y) assert_array_equal(np.array([1, 2]), clf.predict(z1)) if outlier_label is None: assert_raises(ValueError, clf.predict, z2) elif False: assert_array_equal(np.array([1, outlier_label]), clf.predict(z2)) def test_radius_neighbors_classifier_outlier_labeling(): # Test radius-based classifier when no neighbors found and outliers # are labeled. X = np.array([[1.0, 1.0], [2.0, 2.0]]) y = np.array([1, 2]) radius = 0.1 z1 = np.array([[1.01, 1.01], [2.01, 2.01]]) # no outliers z2 = np.array([[1.01, 1.01], [1.4, 1.4]]) # one outlier correct_labels1 = np.array([1, 2]) correct_labels2 = np.array([1, -1]) weight_func = _weight_func for algorithm in ALGORITHMS: for weights in ['uniform', 'distance', weight_func]: clf = neighbors.RadiusNeighborsClassifier(radius=radius, weights=weights, algorithm=algorithm, outlier_label=-1) clf.fit(X, y) assert_array_equal(correct_labels1, clf.predict(z1)) assert_array_equal(correct_labels2, clf.predict(z2)) def test_radius_neighbors_classifier_zero_distance(): # Test radius-based classifier, when distance to a sample is zero. X = np.array([[1.0, 1.0], [2.0, 2.0]]) y = np.array([1, 2]) radius = 0.1 z1 = np.array([[1.01, 1.01], [2.0, 2.0]]) correct_labels1 = np.array([1, 2]) weight_func = _weight_func for algorithm in ALGORITHMS: for weights in ['uniform', 'distance', weight_func]: clf = neighbors.RadiusNeighborsClassifier(radius=radius, weights=weights, algorithm=algorithm) clf.fit(X, y) assert_array_equal(correct_labels1, clf.predict(z1)) def test_neighbors_regressors_zero_distance(): # Test radius-based regressor, when distance to a sample is zero. X = np.array([[1.0, 1.0], [1.0, 1.0], [2.0, 2.0], [2.5, 2.5]]) y = np.array([1.0, 1.5, 2.0, 0.0]) radius = 0.2 z = np.array([[1.1, 1.1], [2.0, 2.0]]) rnn_correct_labels = np.array([1.25, 2.0]) knn_correct_unif = np.array([1.25, 1.0]) knn_correct_dist = np.array([1.25, 2.0]) for algorithm in ALGORITHMS: # we don't test for weights=_weight_func since user will be expected # to handle zero distances themselves in the function. for weights in ['uniform', 'distance']: rnn = neighbors.RadiusNeighborsRegressor(radius=radius, weights=weights, algorithm=algorithm) rnn.fit(X, y) assert_array_almost_equal(rnn_correct_labels, rnn.predict(z)) for weights, corr_labels in zip(['uniform', 'distance'], [knn_correct_unif, knn_correct_dist]): knn = neighbors.KNeighborsRegressor(n_neighbors=2, weights=weights, algorithm=algorithm) knn.fit(X, y) assert_array_almost_equal(corr_labels, knn.predict(z)) def test_radius_neighbors_boundary_handling(): """Test whether points lying on boundary are handled consistently Also ensures that even with only one query point, an object array is returned rather than a 2d array. """ X = np.array([[1.5], [3.0], [3.01]]) radius = 3.0 for algorithm in ALGORITHMS: nbrs = neighbors.NearestNeighbors(radius=radius, algorithm=algorithm).fit(X) results = nbrs.radius_neighbors([[0.0]], return_distance=False) assert_equal(results.shape, (1,)) assert_equal(results.dtype, object) assert_array_equal(results[0], [0, 1]) def test_RadiusNeighborsClassifier_multioutput(): # Test k-NN classifier on multioutput data rng = check_random_state(0) n_features = 2 n_samples = 40 n_output = 3 X = rng.rand(n_samples, n_features) y = rng.randint(0, 3, (n_samples, n_output)) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) weights = [None, 'uniform', 'distance', _weight_func] for algorithm, weights in product(ALGORITHMS, weights): # Stack single output prediction y_pred_so = [] for o in range(n_output): rnn = neighbors.RadiusNeighborsClassifier(weights=weights, algorithm=algorithm) rnn.fit(X_train, y_train[:, o]) y_pred_so.append(rnn.predict(X_test)) y_pred_so = np.vstack(y_pred_so).T assert_equal(y_pred_so.shape, y_test.shape) # Multioutput prediction rnn_mo = neighbors.RadiusNeighborsClassifier(weights=weights, algorithm=algorithm) rnn_mo.fit(X_train, y_train) y_pred_mo = rnn_mo.predict(X_test) assert_equal(y_pred_mo.shape, y_test.shape) assert_array_almost_equal(y_pred_mo, y_pred_so) def test_kneighbors_classifier_sparse(n_samples=40, n_features=5, n_test_pts=10, n_neighbors=5, random_state=0): # Test k-NN classifier on sparse matrices # Like the above, but with various types of sparse matrices rng = np.random.RandomState(random_state) X = 2 * rng.rand(n_samples, n_features) - 1 X *= X > .2 y = ((X ** 2).sum(axis=1) < .5).astype(np.int) for sparsemat in SPARSE_TYPES: knn = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors, algorithm='auto') knn.fit(sparsemat(X), y) epsilon = 1e-5 * (2 * rng.rand(1, n_features) - 1) for sparsev in SPARSE_TYPES + (np.asarray,): X_eps = sparsev(X[:n_test_pts] + epsilon) y_pred = knn.predict(X_eps) assert_array_equal(y_pred, y[:n_test_pts]) def test_KNeighborsClassifier_multioutput(): # Test k-NN classifier on multioutput data rng = check_random_state(0) n_features = 5 n_samples = 50 n_output = 3 X = rng.rand(n_samples, n_features) y = rng.randint(0, 3, (n_samples, n_output)) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) weights = [None, 'uniform', 'distance', _weight_func] for algorithm, weights in product(ALGORITHMS, weights): # Stack single output prediction y_pred_so = [] y_pred_proba_so = [] for o in range(n_output): knn = neighbors.KNeighborsClassifier(weights=weights, algorithm=algorithm) knn.fit(X_train, y_train[:, o]) y_pred_so.append(knn.predict(X_test)) y_pred_proba_so.append(knn.predict_proba(X_test)) y_pred_so = np.vstack(y_pred_so).T assert_equal(y_pred_so.shape, y_test.shape) assert_equal(len(y_pred_proba_so), n_output) # Multioutput prediction knn_mo = neighbors.KNeighborsClassifier(weights=weights, algorithm=algorithm) knn_mo.fit(X_train, y_train) y_pred_mo = knn_mo.predict(X_test) assert_equal(y_pred_mo.shape, y_test.shape) assert_array_almost_equal(y_pred_mo, y_pred_so) # Check proba y_pred_proba_mo = knn_mo.predict_proba(X_test) assert_equal(len(y_pred_proba_mo), n_output) for proba_mo, proba_so in zip(y_pred_proba_mo, y_pred_proba_so): assert_array_almost_equal(proba_mo, proba_so) def test_kneighbors_regressor(n_samples=40, n_features=5, n_test_pts=10, n_neighbors=3, random_state=0): # Test k-neighbors regression rng = np.random.RandomState(random_state) X = 2 * rng.rand(n_samples, n_features) - 1 y = np.sqrt((X ** 2).sum(1)) y /= y.max() y_target = y[:n_test_pts] weight_func = _weight_func for algorithm in ALGORITHMS: for weights in ['uniform', 'distance', weight_func]: knn = neighbors.KNeighborsRegressor(n_neighbors=n_neighbors, weights=weights, algorithm=algorithm) knn.fit(X, y) epsilon = 1E-5 * (2 * rng.rand(1, n_features) - 1) y_pred = knn.predict(X[:n_test_pts] + epsilon) assert_true(np.all(abs(y_pred - y_target) < 0.3)) def test_KNeighborsRegressor_multioutput_uniform_weight(): # Test k-neighbors in multi-output regression with uniform weight rng = check_random_state(0) n_features = 5 n_samples = 40 n_output = 4 X = rng.rand(n_samples, n_features) y = rng.rand(n_samples, n_output) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) for algorithm, weights in product(ALGORITHMS, [None, 'uniform']): knn = neighbors.KNeighborsRegressor(weights=weights, algorithm=algorithm) knn.fit(X_train, y_train) neigh_idx = knn.kneighbors(X_test, return_distance=False) y_pred_idx = np.array([np.mean(y_train[idx], axis=0) for idx in neigh_idx]) y_pred = knn.predict(X_test) assert_equal(y_pred.shape, y_test.shape) assert_equal(y_pred_idx.shape, y_test.shape) assert_array_almost_equal(y_pred, y_pred_idx) def test_kneighbors_regressor_multioutput(n_samples=40, n_features=5, n_test_pts=10, n_neighbors=3, random_state=0): # Test k-neighbors in multi-output regression rng = np.random.RandomState(random_state) X = 2 * rng.rand(n_samples, n_features) - 1 y = np.sqrt((X ** 2).sum(1)) y /= y.max() y = np.vstack([y, y]).T y_target = y[:n_test_pts] weights = ['uniform', 'distance', _weight_func] for algorithm, weights in product(ALGORITHMS, weights): knn = neighbors.KNeighborsRegressor(n_neighbors=n_neighbors, weights=weights, algorithm=algorithm) knn.fit(X, y) epsilon = 1E-5 * (2 * rng.rand(1, n_features) - 1) y_pred = knn.predict(X[:n_test_pts] + epsilon) assert_equal(y_pred.shape, y_target.shape) assert_true(np.all(np.abs(y_pred - y_target) < 0.3)) def test_radius_neighbors_regressor(n_samples=40, n_features=3, n_test_pts=10, radius=0.5, random_state=0): # Test radius-based neighbors regression rng = np.random.RandomState(random_state) X = 2 * rng.rand(n_samples, n_features) - 1 y = np.sqrt((X ** 2).sum(1)) y /= y.max() y_target = y[:n_test_pts] weight_func = _weight_func for algorithm in ALGORITHMS: for weights in ['uniform', 'distance', weight_func]: neigh = neighbors.RadiusNeighborsRegressor(radius=radius, weights=weights, algorithm=algorithm) neigh.fit(X, y) epsilon = 1E-5 * (2 * rng.rand(1, n_features) - 1) y_pred = neigh.predict(X[:n_test_pts] + epsilon) assert_true(np.all(abs(y_pred - y_target) < radius / 2)) def test_RadiusNeighborsRegressor_multioutput_with_uniform_weight(): # Test radius neighbors in multi-output regression (uniform weight) rng = check_random_state(0) n_features = 5 n_samples = 40 n_output = 4 X = rng.rand(n_samples, n_features) y = rng.rand(n_samples, n_output) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) for algorithm, weights in product(ALGORITHMS, [None, 'uniform']): rnn = neighbors. RadiusNeighborsRegressor(weights=weights, algorithm=algorithm) rnn.fit(X_train, y_train) neigh_idx = rnn.radius_neighbors(X_test, return_distance=False) y_pred_idx = np.array([np.mean(y_train[idx], axis=0) for idx in neigh_idx]) y_pred_idx = np.array(y_pred_idx) y_pred = rnn.predict(X_test) assert_equal(y_pred_idx.shape, y_test.shape) assert_equal(y_pred.shape, y_test.shape) assert_array_almost_equal(y_pred, y_pred_idx) def test_RadiusNeighborsRegressor_multioutput(n_samples=40, n_features=5, n_test_pts=10, n_neighbors=3, random_state=0): # Test k-neighbors in multi-output regression with various weight rng = np.random.RandomState(random_state) X = 2 * rng.rand(n_samples, n_features) - 1 y = np.sqrt((X ** 2).sum(1)) y /= y.max() y = np.vstack([y, y]).T y_target = y[:n_test_pts] weights = ['uniform', 'distance', _weight_func] for algorithm, weights in product(ALGORITHMS, weights): rnn = neighbors.RadiusNeighborsRegressor(n_neighbors=n_neighbors, weights=weights, algorithm=algorithm) rnn.fit(X, y) epsilon = 1E-5 * (2 * rng.rand(1, n_features) - 1) y_pred = rnn.predict(X[:n_test_pts] + epsilon) assert_equal(y_pred.shape, y_target.shape) assert_true(np.all(np.abs(y_pred - y_target) < 0.3)) def test_kneighbors_regressor_sparse(n_samples=40, n_features=5, n_test_pts=10, n_neighbors=5, random_state=0): # Test radius-based regression on sparse matrices # Like the above, but with various types of sparse matrices rng = np.random.RandomState(random_state) X = 2 * rng.rand(n_samples, n_features) - 1 y = ((X ** 2).sum(axis=1) < .25).astype(np.int) for sparsemat in SPARSE_TYPES: knn = neighbors.KNeighborsRegressor(n_neighbors=n_neighbors, algorithm='auto') knn.fit(sparsemat(X), y) for sparsev in SPARSE_OR_DENSE: X2 = sparsev(X) assert_true(np.mean(knn.predict(X2).round() == y) > 0.95) def test_neighbors_iris(): # Sanity checks on the iris dataset # Puts three points of each label in the plane and performs a # nearest neighbor query on points near the decision boundary. for algorithm in ALGORITHMS: clf = neighbors.KNeighborsClassifier(n_neighbors=1, algorithm=algorithm) clf.fit(iris.data, iris.target) assert_array_equal(clf.predict(iris.data), iris.target) clf.set_params(n_neighbors=9, algorithm=algorithm) clf.fit(iris.data, iris.target) assert_true(np.mean(clf.predict(iris.data) == iris.target) > 0.95) rgs = neighbors.KNeighborsRegressor(n_neighbors=5, algorithm=algorithm) rgs.fit(iris.data, iris.target) assert_true(np.mean(rgs.predict(iris.data).round() == iris.target) > 0.95) def test_neighbors_digits(): # Sanity check on the digits dataset # the 'brute' algorithm has been observed to fail if the input # dtype is uint8 due to overflow in distance calculations. X = digits.data.astype('uint8') Y = digits.target (n_samples, n_features) = X.shape train_test_boundary = int(n_samples * 0.8) train = np.arange(0, train_test_boundary) test = np.arange(train_test_boundary, n_samples) (X_train, Y_train, X_test, Y_test) = X[train], Y[train], X[test], Y[test] clf = neighbors.KNeighborsClassifier(n_neighbors=1, algorithm='brute') score_uint8 = clf.fit(X_train, Y_train).score(X_test, Y_test) score_float = clf.fit(X_train.astype(float), Y_train).score( X_test.astype(float), Y_test) assert_equal(score_uint8, score_float) def test_kneighbors_graph(): # Test kneighbors_graph to build the k-Nearest Neighbor graph. X = np.array([[0, 1], [1.01, 1.], [2, 0]]) # n_neighbors = 1 A = neighbors.kneighbors_graph(X, 1, mode='connectivity', include_self=True) assert_array_equal(A.toarray(), np.eye(A.shape[0])) A = neighbors.kneighbors_graph(X, 1, mode='distance') assert_array_almost_equal( A.toarray(), [[0.00, 1.01, 0.], [1.01, 0., 0.], [0.00, 1.40716026, 0.]]) # n_neighbors = 2 A = neighbors.kneighbors_graph(X, 2, mode='connectivity', include_self=True) assert_array_equal( A.toarray(), [[1., 1., 0.], [1., 1., 0.], [0., 1., 1.]]) A = neighbors.kneighbors_graph(X, 2, mode='distance') assert_array_almost_equal( A.toarray(), [[0., 1.01, 2.23606798], [1.01, 0., 1.40716026], [2.23606798, 1.40716026, 0.]]) # n_neighbors = 3 A = neighbors.kneighbors_graph(X, 3, mode='connectivity', include_self=True) assert_array_almost_equal( A.toarray(), [[1, 1, 1], [1, 1, 1], [1, 1, 1]]) def test_kneighbors_graph_sparse(seed=36): # Test kneighbors_graph to build the k-Nearest Neighbor graph # for sparse input. rng = np.random.RandomState(seed) X = rng.randn(10, 10) Xcsr = csr_matrix(X) for n_neighbors in [1, 2, 3]: for mode in ["connectivity", "distance"]: assert_array_almost_equal( neighbors.kneighbors_graph(X, n_neighbors, mode=mode).toarray(), neighbors.kneighbors_graph(Xcsr, n_neighbors, mode=mode).toarray()) def test_radius_neighbors_graph(): # Test radius_neighbors_graph to build the Nearest Neighbor graph. X = np.array([[0, 1], [1.01, 1.], [2, 0]]) A = neighbors.radius_neighbors_graph(X, 1.5, mode='connectivity', include_self=True) assert_array_equal( A.toarray(), [[1., 1., 0.], [1., 1., 1.], [0., 1., 1.]]) A = neighbors.radius_neighbors_graph(X, 1.5, mode='distance') assert_array_almost_equal( A.toarray(), [[0., 1.01, 0.], [1.01, 0., 1.40716026], [0., 1.40716026, 0.]]) def test_radius_neighbors_graph_sparse(seed=36): # Test radius_neighbors_graph to build the Nearest Neighbor graph # for sparse input. rng = np.random.RandomState(seed) X = rng.randn(10, 10) Xcsr = csr_matrix(X) for n_neighbors in [1, 2, 3]: for mode in ["connectivity", "distance"]: assert_array_almost_equal( neighbors.radius_neighbors_graph(X, n_neighbors, mode=mode).toarray(), neighbors.radius_neighbors_graph(Xcsr, n_neighbors, mode=mode).toarray()) def test_neighbors_badargs(): # Test bad argument values: these should all raise ValueErrors assert_raises(ValueError, neighbors.NearestNeighbors, algorithm='blah') X = rng.random_sample((10, 2)) Xsparse = csr_matrix(X) y = np.ones(10) for cls in (neighbors.KNeighborsClassifier, neighbors.RadiusNeighborsClassifier, neighbors.KNeighborsRegressor, neighbors.RadiusNeighborsRegressor): assert_raises(ValueError, cls, weights='blah') assert_raises(ValueError, cls, p=-1) assert_raises(ValueError, cls, algorithm='blah') nbrs = cls(algorithm='ball_tree', metric='haversine') assert_raises(ValueError, nbrs.predict, X) assert_raises(ValueError, ignore_warnings(nbrs.fit), Xsparse, y) nbrs = cls() assert_raises(ValueError, nbrs.fit, np.ones((0, 2)), np.ones(0)) assert_raises(ValueError, nbrs.fit, X[:, :, None], y) nbrs.fit(X, y) assert_raises(ValueError, nbrs.predict, [[]]) if (isinstance(cls, neighbors.KNeighborsClassifier) or isinstance(cls, neighbors.KNeighborsRegressor)): nbrs = cls(n_neighbors=-1) assert_raises(ValueError, nbrs.fit, X, y) nbrs = neighbors.NearestNeighbors().fit(X) assert_raises(ValueError, nbrs.kneighbors_graph, X, mode='blah') assert_raises(ValueError, nbrs.radius_neighbors_graph, X, mode='blah') def test_neighbors_metrics(n_samples=20, n_features=3, n_query_pts=2, n_neighbors=5): # Test computing the neighbors for various metrics # create a symmetric matrix V = rng.rand(n_features, n_features) VI = np.dot(V, V.T) metrics = [('euclidean', {}), ('manhattan', {}), ('minkowski', dict(p=1)), ('minkowski', dict(p=2)), ('minkowski', dict(p=3)), ('minkowski', dict(p=np.inf)), ('chebyshev', {}), ('seuclidean', dict(V=rng.rand(n_features))), ('wminkowski', dict(p=3, w=rng.rand(n_features))), ('mahalanobis', dict(VI=VI))] algorithms = ['brute', 'ball_tree', 'kd_tree'] X = rng.rand(n_samples, n_features) test = rng.rand(n_query_pts, n_features) for metric, metric_params in metrics: results = [] p = metric_params.pop('p', 2) for algorithm in algorithms: # KD tree doesn't support all metrics if (algorithm == 'kd_tree' and metric not in neighbors.KDTree.valid_metrics): assert_raises(ValueError, neighbors.NearestNeighbors, algorithm=algorithm, metric=metric, metric_params=metric_params) continue neigh = neighbors.NearestNeighbors(n_neighbors=n_neighbors, algorithm=algorithm, metric=metric, p=p, metric_params=metric_params) neigh.fit(X) results.append(neigh.kneighbors(test, return_distance=True)) assert_array_almost_equal(results[0][0], results[1][0]) assert_array_almost_equal(results[0][1], results[1][1]) def test_callable_metric(): metric = lambda x1, x2: np.sqrt(np.sum(x1 ** 2 + x2 ** 2)) X = np.random.RandomState(42).rand(20, 2) nbrs1 = neighbors.NearestNeighbors(3, algorithm='auto', metric=metric) nbrs2 = neighbors.NearestNeighbors(3, algorithm='brute', metric=metric) nbrs1.fit(X) nbrs2.fit(X) dist1, ind1 = nbrs1.kneighbors(X) dist2, ind2 = nbrs2.kneighbors(X) assert_array_almost_equal(dist1, dist2) def test_metric_params_interface(): assert_warns(SyntaxWarning, neighbors.KNeighborsClassifier, metric_params={'p': 3}) def test_predict_sparse_ball_kd_tree(): rng = np.random.RandomState(0) X = rng.rand(5, 5) y = rng.randint(0, 2, 5) nbrs1 = neighbors.KNeighborsClassifier(1, algorithm='kd_tree') nbrs2 = neighbors.KNeighborsRegressor(1, algorithm='ball_tree') for model in [nbrs1, nbrs2]: model.fit(X, y) assert_raises(ValueError, model.predict, csr_matrix(X)) def test_non_euclidean_kneighbors(): rng = np.random.RandomState(0) X = rng.rand(5, 5) # Find a reasonable radius. dist_array = pairwise_distances(X).flatten() np.sort(dist_array) radius = dist_array[15] # Test kneighbors_graph for metric in ['manhattan', 'chebyshev']: nbrs_graph = neighbors.kneighbors_graph( X, 3, metric=metric, mode='connectivity', include_self=True).toarray() nbrs1 = neighbors.NearestNeighbors(3, metric=metric).fit(X) assert_array_equal(nbrs_graph, nbrs1.kneighbors_graph(X).toarray()) # Test radiusneighbors_graph for metric in ['manhattan', 'chebyshev']: nbrs_graph = neighbors.radius_neighbors_graph( X, radius, metric=metric, mode='connectivity', include_self=True).toarray() nbrs1 = neighbors.NearestNeighbors(metric=metric, radius=radius).fit(X) assert_array_equal(nbrs_graph, nbrs1.radius_neighbors_graph(X).A) # Raise error when wrong parameters are supplied, X_nbrs = neighbors.NearestNeighbors(3, metric='manhattan') X_nbrs.fit(X) assert_raises(ValueError, neighbors.kneighbors_graph, X_nbrs, 3, metric='euclidean') X_nbrs = neighbors.NearestNeighbors(radius=radius, metric='manhattan') X_nbrs.fit(X) assert_raises(ValueError, neighbors.radius_neighbors_graph, X_nbrs, radius, metric='euclidean') def check_object_arrays(nparray, list_check): for ind, ele in enumerate(nparray): assert_array_equal(ele, list_check[ind]) def test_k_and_radius_neighbors_train_is_not_query(): # Test kneighbors et.al when query is not training data for algorithm in ALGORITHMS: nn = neighbors.NearestNeighbors(n_neighbors=1, algorithm=algorithm) X = [[0], [1]] nn.fit(X) test_data = [[2], [1]] # Test neighbors. dist, ind = nn.kneighbors(test_data) assert_array_equal(dist, [[1], [0]]) assert_array_equal(ind, [[1], [1]]) dist, ind = nn.radius_neighbors([[2], [1]], radius=1.5) check_object_arrays(dist, [[1], [1, 0]]) check_object_arrays(ind, [[1], [0, 1]]) # Test the graph variants. assert_array_equal( nn.kneighbors_graph(test_data).A, [[0., 1.], [0., 1.]]) assert_array_equal( nn.kneighbors_graph([[2], [1]], mode='distance').A, np.array([[0., 1.], [0., 0.]])) rng = nn.radius_neighbors_graph([[2], [1]], radius=1.5) assert_array_equal(rng.A, [[0, 1], [1, 1]]) def test_k_and_radius_neighbors_X_None(): # Test kneighbors et.al when query is None for algorithm in ALGORITHMS: nn = neighbors.NearestNeighbors(n_neighbors=1, algorithm=algorithm) X = [[0], [1]] nn.fit(X) dist, ind = nn.kneighbors() assert_array_equal(dist, [[1], [1]]) assert_array_equal(ind, [[1], [0]]) dist, ind = nn.radius_neighbors(None, radius=1.5) check_object_arrays(dist, [[1], [1]]) check_object_arrays(ind, [[1], [0]]) # Test the graph variants. rng = nn.radius_neighbors_graph(None, radius=1.5) kng = nn.kneighbors_graph(None) for graph in [rng, kng]: assert_array_equal(rng.A, [[0, 1], [1, 0]]) assert_array_equal(rng.data, [1, 1]) assert_array_equal(rng.indices, [1, 0]) X = [[0, 1], [0, 1], [1, 1]] nn = neighbors.NearestNeighbors(n_neighbors=2, algorithm=algorithm) nn.fit(X) assert_array_equal( nn.kneighbors_graph().A, np.array([[0., 1., 1.], [1., 0., 1.], [1., 1., 0]])) def test_k_and_radius_neighbors_duplicates(): # Test behavior of kneighbors when duplicates are present in query for algorithm in ALGORITHMS: nn = neighbors.NearestNeighbors(n_neighbors=1, algorithm=algorithm) nn.fit([[0], [1]]) # Do not do anything special to duplicates. kng = nn.kneighbors_graph([[0], [1]], mode='distance') assert_array_equal( kng.A, np.array([[0., 0.], [0., 0.]])) assert_array_equal(kng.data, [0., 0.]) assert_array_equal(kng.indices, [0, 1]) dist, ind = nn.radius_neighbors([[0], [1]], radius=1.5) check_object_arrays(dist, [[0, 1], [1, 0]]) check_object_arrays(ind, [[0, 1], [0, 1]]) rng = nn.radius_neighbors_graph([[0], [1]], radius=1.5) assert_array_equal(rng.A, np.ones((2, 2))) rng = nn.radius_neighbors_graph([[0], [1]], radius=1.5, mode='distance') assert_array_equal(rng.A, [[0, 1], [1, 0]]) assert_array_equal(rng.indices, [0, 1, 0, 1]) assert_array_equal(rng.data, [0, 1, 1, 0]) # Mask the first duplicates when n_duplicates > n_neighbors. X = np.ones((3, 1)) nn = neighbors.NearestNeighbors(n_neighbors=1) nn.fit(X) dist, ind = nn.kneighbors() assert_array_equal(dist, np.zeros((3, 1))) assert_array_equal(ind, [[1], [0], [1]]) # Test that zeros are explicitly marked in kneighbors_graph. kng = nn.kneighbors_graph(mode='distance') assert_array_equal( kng.A, np.zeros((3, 3))) assert_array_equal(kng.data, np.zeros(3)) assert_array_equal(kng.indices, [1., 0., 1.]) assert_array_equal( nn.kneighbors_graph().A, np.array([[0., 1., 0.], [1., 0., 0.], [0., 1., 0.]])) def test_include_self_neighbors_graph(): # Test include_self parameter in neighbors_graph X = [[2, 3], [4, 5]] kng = neighbors.kneighbors_graph(X, 1, include_self=True).A kng_not_self = neighbors.kneighbors_graph(X, 1, include_self=False).A assert_array_equal(kng, [[1., 0.], [0., 1.]]) assert_array_equal(kng_not_self, [[0., 1.], [1., 0.]]) rng = neighbors.radius_neighbors_graph(X, 5.0, include_self=True).A rng_not_self = neighbors.radius_neighbors_graph( X, 5.0, include_self=False).A assert_array_equal(rng, [[1., 1.], [1., 1.]]) assert_array_equal(rng_not_self, [[0., 1.], [1., 0.]]) def test_kneighbors_parallel(): X, y = datasets.make_classification(n_samples=10, n_features=2, n_redundant=0, random_state=0) X_train, X_test, y_train, y_test = train_test_split(X, y) for algorithm in ALGORITHMS: clf = neighbors.KNeighborsClassifier(n_neighbors=3, algorithm=algorithm) clf.fit(X_train, y_train) y_1 = clf.predict(X_test) dist_1, ind_1 = clf.kneighbors(X_test) A_1 = clf.kneighbors_graph(X_test, mode='distance').toarray() for n_jobs in [-1, 2, 5]: clf.set_params(n_jobs=n_jobs) y = clf.predict(X_test) dist, ind = clf.kneighbors(X_test) A = clf.kneighbors_graph(X_test, mode='distance').toarray() assert_array_equal(y_1, y) assert_array_almost_equal(dist_1, dist) assert_array_equal(ind_1, ind) assert_array_almost_equal(A_1, A) def test_dtype_convert(): classifier = neighbors.KNeighborsClassifier(n_neighbors=1) CLASSES = 15 X = np.eye(CLASSES) y = [ch for ch in 'ABCDEFGHIJKLMNOPQRSTU'[:CLASSES]] result = classifier.fit(X, y).predict(X) assert_array_equal(result, y)

bsd-3-clause

automl/auto-sklearn

autosklearn/pipeline/components/data_preprocessing/rescaling/power_transformer.py

1

1715

from typing import Dict, Optional, Tuple, Union import numpy as np from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import ( # noqa: E501 Rescaling, ) from autosklearn.pipeline.constants import DENSE, INPUT, UNSIGNED_DATA class PowerTransformerComponent(Rescaling, AutoSklearnPreprocessingAlgorithm): def __init__( self, random_state: Optional[Union[int, np.random.RandomState]] = None, ) -> None: from sklearn.preprocessing import PowerTransformer self.preprocessor = PowerTransformer(copy=False) @staticmethod def get_properties( dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None, ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]: return { "shortname": "PowerTransformer", "name": "PowerTransformer", "handles_missing_values": False, "handles_nominal_values": False, "handles_numerical_features": True, "prefers_data_scaled": False, "prefers_data_normalized": False, "handles_regression": True, "handles_classification": True, "handles_multiclass": True, "handles_multilabel": True, "handles_multioutput": True, "is_deterministic": True, # TODO find out of this is right! "handles_sparse": False, "handles_dense": True, "input": (DENSE, UNSIGNED_DATA), "output": (INPUT,), "preferred_dtype": None, }

bsd-3-clause

fx2003/tensorflow-study

TensorFlow实战/models/cognitive_mapping_and_planning/scripts/script_preprocess_annoations_S3DIS.py

14

7024

# Copyright 2016 The TensorFlow Authors All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== import os import glob import numpy as np import logging import cPickle from datasets import nav_env from datasets import factory from src import utils from src import map_utils as mu logging.basicConfig(level=logging.INFO) DATA_DIR = 'data/stanford_building_parser_dataset_raw/' mkdir_if_missing = utils.mkdir_if_missing save_variables = utils.save_variables def _get_semantic_maps(building_name, transform, map_, flip, cats): rooms = get_room_in_building(building_name) maps = [] for cat in cats: maps.append(np.zeros((map_.size[1], map_.size[0]))) for r in rooms: room = load_room(building_name, r, category_list=cats) classes = room['class_id'] for i, cat in enumerate(cats): c_ind = cats.index(cat) ind = [_ for _, c in enumerate(classes) if c == c_ind] if len(ind) > 0: vs = [room['vertexs'][x]*1 for x in ind] vs = np.concatenate(vs, axis=0) if transform: vs = np.array([vs[:,1], vs[:,0], vs[:,2]]).T vs[:,0] = -vs[:,0] vs[:,1] += 4.20 vs[:,0] += 6.20 vs = vs*100. if flip: vs[:,1] = -vs[:,1] maps[i] = maps[i] + \ mu._project_to_map(map_, vs, ignore_points_outside_map=True) return maps def _map_building_name(building_name): b = int(building_name.split('_')[0][4]) out_name = 'Area_{:d}'.format(b) if b == 5: if int(building_name.split('_')[0][5]) == 1: transform = True else: transform = False else: transform = False return out_name, transform def get_categories(): cats = ['beam', 'board', 'bookcase', 'ceiling', 'chair', 'clutter', 'column', 'door', 'floor', 'sofa', 'table', 'wall', 'window'] return cats def _write_map_files(b_in, b_out, transform): cats = get_categories() env = utils.Foo(padding=10, resolution=5, num_point_threshold=2, valid_min=-10, valid_max=200, n_samples_per_face=200) robot = utils.Foo(radius=15, base=10, height=140, sensor_height=120, camera_elevation_degree=-15) building_loader = factory.get_dataset('sbpd') for flip in [False, True]: b = nav_env.Building(b_out, robot, env, flip=flip, building_loader=building_loader) logging.info("building_in: %s, building_out: %s, transform: %d", b_in, b_out, transform) maps = _get_semantic_maps(b_in, transform, b.map, flip, cats) maps = np.transpose(np.array(maps), axes=[1,2,0]) # Load file from the cache. file_name = '{:s}_{:d}_{:d}_{:d}_{:d}_{:d}_{:d}.pkl' file_name = file_name.format(b.building_name, b.map.size[0], b.map.size[1], b.map.origin[0], b.map.origin[1], b.map.resolution, flip) out_file = os.path.join(DATA_DIR, 'processing', 'class-maps', file_name) logging.info('Writing semantic maps to %s.', out_file) save_variables(out_file, [maps, cats], ['maps', 'cats'], overwrite=True) def _transform_area5b(room_dimension): for a in room_dimension.keys(): r = room_dimension[a]*1 r[[0,1,3,4]] = r[[1,0,4,3]] r[[0,3]] = -r[[3,0]] r[[1,4]] += 4.20 r[[0,3]] += 6.20 room_dimension[a] = r return room_dimension def collect_room(building_name, room_name): room_dir = os.path.join(DATA_DIR, 'Stanford3dDataset_v1.2', building_name, room_name, 'Annotations') files = glob.glob1(room_dir, '*.txt') files = sorted(files, key=lambda s: s.lower()) vertexs = []; colors = []; for f in files: file_name = os.path.join(room_dir, f) logging.info(' %s', file_name) a = np.loadtxt(file_name) vertex = a[:,:3]*1. color = a[:,3:]*1 color = color.astype(np.uint8) vertexs.append(vertex) colors.append(color) files = [f.split('.')[0] for f in files] out = {'vertexs': vertexs, 'colors': colors, 'names': files} return out def load_room(building_name, room_name, category_list=None): room = collect_room(building_name, room_name) room['building_name'] = building_name room['room_name'] = room_name instance_id = range(len(room['names'])) room['instance_id'] = instance_id if category_list is not None: name = [r.split('_')[0] for r in room['names']] class_id = [] for n in name: if n in category_list: class_id.append(category_list.index(n)) else: class_id.append(len(category_list)) room['class_id'] = class_id room['category_list'] = category_list return room def get_room_in_building(building_name): building_dir = os.path.join(DATA_DIR, 'Stanford3dDataset_v1.2', building_name) rn = os.listdir(building_dir) rn = [x for x in rn if os.path.isdir(os.path.join(building_dir, x))] rn = sorted(rn, key=lambda s: s.lower()) return rn def write_room_dimensions(b_in, b_out, transform): rooms = get_room_in_building(b_in) room_dimension = {} for r in rooms: room = load_room(b_in, r, category_list=None) vertex = np.concatenate(room['vertexs'], axis=0) room_dimension[r] = np.concatenate((np.min(vertex, axis=0), np.max(vertex, axis=0)), axis=0) if transform == 1: room_dimension = _transform_area5b(room_dimension) out_file = os.path.join(DATA_DIR, 'processing', 'room-dimension', b_out+'.pkl') save_variables(out_file, [room_dimension], ['room_dimension'], overwrite=True) def write_room_dimensions_all(I): mkdir_if_missing(os.path.join(DATA_DIR, 'processing', 'room-dimension')) bs_in = ['Area_1', 'Area_2', 'Area_3', 'Area_4', 'Area_5', 'Area_5', 'Area_6'] bs_out = ['area1', 'area2', 'area3', 'area4', 'area5a', 'area5b', 'area6'] transforms = [0, 0, 0, 0, 0, 1, 0] for i in I: b_in = bs_in[i] b_out = bs_out[i] t = transforms[i] write_room_dimensions(b_in, b_out, t) def write_class_maps_all(I): mkdir_if_missing(os.path.join(DATA_DIR, 'processing', 'class-maps')) bs_in = ['Area_1', 'Area_2', 'Area_3', 'Area_4', 'Area_5', 'Area_5', 'Area_6'] bs_out = ['area1', 'area2', 'area3', 'area4', 'area5a', 'area5b', 'area6'] transforms = [0, 0, 0, 0, 0, 1, 0] for i in I: b_in = bs_in[i] b_out = bs_out[i] t = transforms[i] _write_map_files(b_in, b_out, t) if __name__ == '__main__': write_room_dimensions_all([0, 2, 3, 4, 5, 6]) write_class_maps_all([0, 2, 3, 4, 5, 6])

mit

switowski/invenio

invenio/modules/indexer/tokenizers/BibIndexDOITokenizer.py

12

2004

# -*- coding: utf-8 -*- # # This file is part of Invenio. # Copyright (C) 2013, 2015 CERN. # # Invenio is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License as # published by the Free Software Foundation; either version 2 of the # License, or (at your option) any later version. # # Invenio is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Invenio; if not, write to the Free Software Foundation, Inc., # 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA. from invenio.modules.indexer.tokenizers.BibIndexFilteringTokenizer import BibIndexFilteringTokenizer class BibIndexDOITokenizer(BibIndexFilteringTokenizer): """ Filtering tokenizer which tokenizes DOI tag (0247_a) only if "0247_2" tag is present and its value equals "DOI" and 909C4a tag without any constraints. """ def __init__(self, stemming_language=None, remove_stopwords=False, remove_html_markup=False, remove_latex_markup=False): self.rules = (('0247_a', '2', 'DOI'), ('909C4a', '', '')) def get_tokenizing_function(self, wordtable_type): """Returns proper tokenizing function""" return self.tokenize def tokenize_via_recjson(self, recID): """ Nonmarc version of tokenize function for DOI. Note: with nonmarc we don't need to filter anymore. We just need to take value from record because we use bibfield here. """ rec = get_record(recID) values = rec.get('doi', []) return values def get_nonmarc_tokenizing_function(self, table_type): """ Returns proper tokenizing function for non-marc records. """ return self.tokenize_via_recjson

gpl-2.0

MatthieuCourbariaux/BinaryConnect

svhn.py

3

10981

# Copyright 2015 Matthieu Courbariaux # This file is part of BinaryConnect. # BinaryConnect is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # BinaryConnect is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with BinaryConnect. If not, see <http://www.gnu.org/licenses/>. from __future__ import print_function import sys import os import time import numpy as np np.random.seed(1234) # for reproducibility? # specifying the gpu to use # import theano.sandbox.cuda # theano.sandbox.cuda.use('gpu1') import theano import theano.tensor as T import lasagne import cPickle as pickle import gzip import batch_norm import binary_connect from pylearn2.datasets.svhn import SVHN from pylearn2.utils import serial from collections import OrderedDict if __name__ == "__main__": # Batch Normalization parameters batch_size = 50 print("batch_size = "+str(batch_size)) # alpha is the exponential moving average factor alpha = .1 print("alpha = "+str(alpha)) epsilon = 1e-4 print("epsilon = "+str(epsilon)) # Training parameters num_epochs = 200 print("num_epochs = "+str(num_epochs)) # BinaryConnect binary = True print("binary = "+str(binary)) stochastic = True print("stochastic = "+str(stochastic)) # (-H,+H) are the two binary values # H = "Glorot" H = 1. print("H = "+str(H)) # W_LR_scale = 1. W_LR_scale = "Glorot" # "Glorot" means we are using the coefficients from Glorot's paper print("W_LR_scale = "+str(W_LR_scale)) # Decaying LR LR_start = 0.01 print("LR_start = "+str(LR_start)) LR_fin = 0.000003 print("LR_fin = "+str(LR_fin)) LR_decay = (LR_fin/LR_start)**(1./num_epochs) print("LR_decay = "+str(LR_decay)) # BTW, LR decay might good for the BN moving average... print('Loading SVHN dataset') train_set = SVHN( which_set= 'splitted_train', path= "${SVHN_LOCAL_PATH}", axes= ['b', 'c', 0, 1]) valid_set = SVHN( which_set= 'valid', path= "${SVHN_LOCAL_PATH}", axes= ['b', 'c', 0, 1]) test_set = SVHN( which_set= 'test', path= "${SVHN_LOCAL_PATH}", axes= ['b', 'c', 0, 1]) # bc01 format # print train_set.X.shape train_set.X = np.reshape(train_set.X,(-1,3,32,32)) valid_set.X = np.reshape(valid_set.X,(-1,3,32,32)) test_set.X = np.reshape(test_set.X,(-1,3,32,32)) # for hinge loss (targets are already onehot) train_set.y = np.subtract(np.multiply(2,train_set.y),1.) valid_set.y = np.subtract(np.multiply(2,valid_set.y),1.) test_set.y = np.subtract(np.multiply(2,test_set.y),1.) print('Building the CNN...') # Prepare Theano variables for inputs and targets input = T.tensor4('inputs') target = T.matrix('targets') LR = T.scalar('LR', dtype=theano.config.floatX) cnn = lasagne.layers.InputLayer( shape=(None, 3, 32, 32), input_var=input) # 64C3-64C3-P2 cnn = binary_connect.Conv2DLayer( cnn, binary=binary, stochastic=stochastic, H=H, W_LR_scale=W_LR_scale, num_filters=64, filter_size=(3, 3), pad=1, nonlinearity=lasagne.nonlinearities.identity) cnn = batch_norm.BatchNormLayer( cnn, epsilon=epsilon, alpha=alpha, nonlinearity=lasagne.nonlinearities.rectify) cnn = binary_connect.Conv2DLayer( cnn, binary=binary, stochastic=stochastic, H=H, W_LR_scale=W_LR_scale, num_filters=64, filter_size=(3, 3), pad=1, nonlinearity=lasagne.nonlinearities.identity) cnn = lasagne.layers.MaxPool2DLayer(cnn, pool_size=(2, 2)) cnn = batch_norm.BatchNormLayer( cnn, epsilon=epsilon, alpha=alpha, nonlinearity=lasagne.nonlinearities.rectify) # 128C3-128C3-P2 cnn = binary_connect.Conv2DLayer( cnn, binary=binary, stochastic=stochastic, H=H, W_LR_scale=W_LR_scale, num_filters=128, filter_size=(3, 3), pad=1, nonlinearity=lasagne.nonlinearities.identity) cnn = batch_norm.BatchNormLayer( cnn, epsilon=epsilon, alpha=alpha, nonlinearity=lasagne.nonlinearities.rectify) cnn = binary_connect.Conv2DLayer( cnn, binary=binary, stochastic=stochastic, H=H, W_LR_scale=W_LR_scale, num_filters=128, filter_size=(3, 3), pad=1, nonlinearity=lasagne.nonlinearities.identity) cnn = lasagne.layers.MaxPool2DLayer(cnn, pool_size=(2, 2)) cnn = batch_norm.BatchNormLayer( cnn, epsilon=epsilon, alpha=alpha, nonlinearity=lasagne.nonlinearities.rectify) # 256C3-256C3-P2 cnn = binary_connect.Conv2DLayer( cnn, binary=binary, stochastic=stochastic, H=H, W_LR_scale=W_LR_scale, num_filters=256, filter_size=(3, 3), pad=1, nonlinearity=lasagne.nonlinearities.identity) cnn = batch_norm.BatchNormLayer( cnn, epsilon=epsilon, alpha=alpha, nonlinearity=lasagne.nonlinearities.rectify) cnn = binary_connect.Conv2DLayer( cnn, binary=binary, stochastic=stochastic, H=H, W_LR_scale=W_LR_scale, num_filters=256, filter_size=(3, 3), pad=1, nonlinearity=lasagne.nonlinearities.identity) cnn = lasagne.layers.MaxPool2DLayer(cnn, pool_size=(2, 2)) cnn = batch_norm.BatchNormLayer( cnn, epsilon=epsilon, alpha=alpha, nonlinearity=lasagne.nonlinearities.rectify) # print(cnn.output_shape) # 1024FP-1024FP-10FP cnn = binary_connect.DenseLayer( cnn, binary=binary, stochastic=stochastic, H=H, W_LR_scale=W_LR_scale, nonlinearity=lasagne.nonlinearities.identity, num_units=1024) cnn = batch_norm.BatchNormLayer( cnn, epsilon=epsilon, alpha=alpha, nonlinearity=lasagne.nonlinearities.rectify) cnn = binary_connect.DenseLayer( cnn, binary=binary, stochastic=stochastic, H=H, W_LR_scale=W_LR_scale, nonlinearity=lasagne.nonlinearities.identity, num_units=1024) cnn = batch_norm.BatchNormLayer( cnn, epsilon=epsilon, alpha=alpha, nonlinearity=lasagne.nonlinearities.rectify) cnn = binary_connect.DenseLayer( cnn, binary=binary, stochastic=stochastic, H=H, W_LR_scale=W_LR_scale, nonlinearity=lasagne.nonlinearities.identity, num_units=10) cnn = batch_norm.BatchNormLayer( cnn, epsilon=epsilon, alpha=alpha, nonlinearity=lasagne.nonlinearities.identity) train_output = lasagne.layers.get_output(cnn, deterministic=False) # squared hinge loss loss = T.mean(T.sqr(T.maximum(0.,1.-target*train_output))) if binary: # W updates W = lasagne.layers.get_all_params(cnn, binary=True) W_grads = binary_connect.compute_grads(loss,cnn) updates = lasagne.updates.adam(loss_or_grads=W_grads, params=W, learning_rate=LR) updates = binary_connect.clipping_scaling(updates,cnn) # other parameters updates params = lasagne.layers.get_all_params(cnn, trainable=True, binary=False) updates = OrderedDict(updates.items() + lasagne.updates.adam(loss_or_grads=loss, params=params, learning_rate=LR).items()) else: params = lasagne.layers.get_all_params(cnn, trainable=True) updates = lasagne.updates.adam(loss_or_grads=loss, params=params, learning_rate=LR) test_output = lasagne.layers.get_output(cnn, deterministic=True) test_loss = T.mean(T.sqr(T.maximum(0.,1.-target*test_output))) test_err = T.mean(T.neq(T.argmax(test_output, axis=1), T.argmax(target, axis=1)),dtype=theano.config.floatX) # Compile a function performing a training step on a mini-batch (by giving the updates dictionary) # and returning the corresponding training loss: train_fn = theano.function([input, target, LR], loss, updates=updates) # Compile a second function computing the validation loss and accuracy: val_fn = theano.function([input, target], [test_loss, test_err]) print('Training...') binary_connect.train( train_fn,val_fn, batch_size, LR_start,LR_decay, num_epochs, train_set.X,train_set.y, valid_set.X,valid_set.y, test_set.X,test_set.y) # print("display histogram") # W = lasagne.layers.get_all_layers(mlp)[2].W.get_value() # print(W.shape) # histogram = np.histogram(W,bins=1000,range=(-1.1,1.1)) # np.savetxt(str(dropout_hidden)+str(binary)+str(stochastic)+str(H)+"_hist0.csv", histogram[0], delimiter=",") # np.savetxt(str(dropout_hidden)+str(binary)+str(stochastic)+str(H)+"_hist1.csv", histogram[1], delimiter=",") # Optionally, you could now dump the network weights to a file like this: # np.savez('model.npz', lasagne.layers.get_all_param_values(network))

gpl-2.0

sgenoud/scikit-learn

examples/linear_model/plot_sgd_iris.py

3

2170

""" ======================================== Plot multi-class SGD on the iris dataset ======================================== Plot decision surface of multi-class SGD on iris dataset. The hyperplanes corresponding to the three one-versus-all (OVA) classifiers are represented by the dashed lines. """ print __doc__ import numpy as np import pylab as pl from sklearn import datasets from sklearn.linear_model import SGDClassifier # import some data to play with iris = datasets.load_iris() X = iris.data[:, :2] # we only take the first two features. We could # avoid this ugly slicing by using a two-dim dataset y = iris.target colors = "bry" # shuffle idx = np.arange(X.shape[0]) np.random.seed(13) np.random.shuffle(idx) X = X[idx] y = y[idx] # standardize mean = X.mean(axis=0) std = X.std(axis=0) X = (X - mean) / std h = .02 # step size in the mesh clf = SGDClassifier(alpha=0.001, n_iter=100).fit(X, y) # create a mesh to plot in x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # Plot the decision boundary. For that, we will asign a color to each # point in the mesh [x_min, m_max]x[y_min, y_max]. Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape(xx.shape) cs = pl.contourf(xx, yy, Z, cmap=pl.cm.Paired) pl.axis('tight') # Plot also the training points for i, color in zip(clf.classes_, colors): idx = np.where(y == i) pl.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i], cmap=pl.cm.Paired) pl.title("Decision surface of multi-class SGD") pl.axis('tight') # Plot the three one-against-all classifiers xmin, xmax = pl.xlim() ymin, ymax = pl.ylim() coef = clf.coef_ intercept = clf.intercept_ def plot_hyperplane(c, color): def line(x0): return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1] pl.plot([xmin, xmax], [line(xmin), line(xmax)], ls="--", color=color) for i, color in zip(clf.classes_, colors): plot_hyperplane(i, color) pl.legend() pl.show()

bsd-3-clause

glouppe/scikit-learn

sklearn/cluster/__init__.py

359

1228

""" The :mod:`sklearn.cluster` module gathers popular unsupervised clustering algorithms. """ from .spectral import spectral_clustering, SpectralClustering from .mean_shift_ import (mean_shift, MeanShift, estimate_bandwidth, get_bin_seeds) from .affinity_propagation_ import affinity_propagation, AffinityPropagation from .hierarchical import (ward_tree, AgglomerativeClustering, linkage_tree, FeatureAgglomeration) from .k_means_ import k_means, KMeans, MiniBatchKMeans from .dbscan_ import dbscan, DBSCAN from .bicluster import SpectralBiclustering, SpectralCoclustering from .birch import Birch __all__ = ['AffinityPropagation', 'AgglomerativeClustering', 'Birch', 'DBSCAN', 'KMeans', 'FeatureAgglomeration', 'MeanShift', 'MiniBatchKMeans', 'SpectralClustering', 'affinity_propagation', 'dbscan', 'estimate_bandwidth', 'get_bin_seeds', 'k_means', 'linkage_tree', 'mean_shift', 'spectral_clustering', 'ward_tree', 'SpectralBiclustering', 'SpectralCoclustering']

bsd-3-clause

luo66/scikit-learn

sklearn/linear_model/tests/test_bayes.py

296

1770

# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr> # Fabian Pedregosa <fabian.pedregosa@inria.fr> # # License: BSD 3 clause import numpy as np from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import SkipTest from sklearn.linear_model.bayes import BayesianRidge, ARDRegression from sklearn import datasets from sklearn.utils.testing import assert_array_almost_equal def test_bayesian_on_diabetes(): # Test BayesianRidge on diabetes raise SkipTest("XFailed Test") diabetes = datasets.load_diabetes() X, y = diabetes.data, diabetes.target clf = BayesianRidge(compute_score=True) # Test with more samples than features clf.fit(X, y) # Test that scores are increasing at each iteration assert_array_equal(np.diff(clf.scores_) > 0, True) # Test with more features than samples X = X[:5, :] y = y[:5] clf.fit(X, y) # Test that scores are increasing at each iteration assert_array_equal(np.diff(clf.scores_) > 0, True) def test_toy_bayesian_ridge_object(): # Test BayesianRidge on toy X = np.array([[1], [2], [6], [8], [10]]) Y = np.array([1, 2, 6, 8, 10]) clf = BayesianRidge(compute_score=True) clf.fit(X, Y) # Check that the model could approximately learn the identity function test = [[1], [3], [4]] assert_array_almost_equal(clf.predict(test), [1, 3, 4], 2) def test_toy_ard_object(): # Test BayesianRegression ARD classifier X = np.array([[1], [2], [3]]) Y = np.array([1, 2, 3]) clf = ARDRegression(compute_score=True) clf.fit(X, Y) # Check that the model could approximately learn the identity function test = [[1], [3], [4]] assert_array_almost_equal(clf.predict(test), [1, 3, 4], 2)

bsd-3-clause

elkingtonmcb/h2o-2

bench/BMscripts/glmBench.py

11

9671

#GLM bench import os, sys, time, csv, socket, string sys.path.append('../py/') sys.path.extend(['.','..']) import h2o_cmd, h2o, h2o_hosts, h2o_browse as h2b, h2o_import as h2i, h2o_rf, h2o_jobs csv_header = ('h2o_build','nMachines','nJVMs','Xmx/JVM','dataset','nTrainRows','nTestRows','nCols','trainParseWallTime','nfolds','family','glmBuildTime','testParseWallTime','scoreTime','AUC','AIC','error') files = {'Airlines' : {'train': ('AirlinesTrain1x', 'AirlinesTrain10x', 'AirlinesTrain100x'), 'test' : 'AirlinesTest'}, 'AllBedrooms' : {'train': ('AllBedroomsTrain1x', 'AllBedroomsTrain10x', 'AllBedroomsTrain100x'), 'test' : 'AllBedroomsTest'}, } build = "" debug = False json = "" def doGLM(f, folderPath, family, link, lambda_, alpha, nfolds, y, x, testFilehex, row): debug = False bench = "bench" if debug: print "DOING GLM DEBUG" bench = "bench/debug" date = '-'.join([str(z) for z in list(time.localtime())][0:3]) overallWallStart = time.time() pre = "" if debug: pre = "DEBUG" glmbenchcsv = 'benchmarks/'+build+'/'+pre+'glmbench.csv' if not os.path.exists(glmbenchcsv): output = open(glmbenchcsv,'w') output.write(','.join(csv_header)+'\n') else: output = open(glmbenchcsv,'a') csvWrt = csv.DictWriter(output, fieldnames=csv_header, restval=None, dialect='excel', extrasaction='ignore',delimiter=',') try: java_heap_GB = h2o.nodes[0].java_heap_GB importFolderPath = bench + "/" + folderPath if (f in ['AirlinesTrain1x','AllBedroomsTrain1x', 'AllBedroomsTrain10x', 'AllBedroomsTrain100x']): csvPathname = importFolderPath + "/" + f + '.csv' else: csvPathname = importFolderPath + "/" + f + "/*linked*" hex_key = f + '.hex' hK = folderPath + "Header.csv" headerPathname = importFolderPath + "/" + hK h2i.import_only(bucket='home-0xdiag-datasets', path=headerPathname) headerKey = h2i.find_key(hK) trainParseWallStart = time.time() parseResult = h2i.import_parse(bucket = 'home-0xdiag-datasets', path = csvPathname, schema = 'local', hex_key = hex_key, header = 1, header_from_file = headerKey, separator = 44, timeoutSecs = 7200, retryDelaySecs = 5, pollTimeoutSecs = 7200, doSummary = False ) parseWallTime = time.time() - trainParseWallStart print "Parsing training file took ", parseWallTime ," seconds." inspect_train = h2o.nodes[0].inspect(parseResult['destination_key'], timeoutSecs=7200) inspect_test = h2o.nodes[0].inspect(testFilehex, timeoutSecs=7200) nMachines = 1 if len(h2o_hosts.hosts) is 0 else len(h2o_hosts.hosts) row.update( {'h2o_build' : build, 'nMachines' : nMachines, 'nJVMs' : len(h2o.nodes), 'Xmx/JVM' : java_heap_GB, 'dataset' : f, 'nTrainRows' : inspect_train['num_rows'], 'nTestRows' : inspect_test['num_rows'], 'nCols' : inspect_train['num_cols'], 'trainParseWallTime' : parseWallTime, 'nfolds' : nfolds, 'family' : family, }) params = {'y' : y, 'x' : x, 'family' : family, 'link' : link, 'lambda' : lambda_, 'alpha' : alpha, 'n_folds' : nfolds, 'case_mode' : "n/a", 'destination_key' : "GLM("+f+")", 'expert_settings' : 0, } kwargs = params.copy() glmStart = time.time() glm = h2o_cmd.runGLM(parseResult = parseResult, timeoutSecs = 7200, **kwargs) glmTime = time.time() - glmStart row.update( {'glmBuildTime' : glmTime, #'AverageErrorOver10Folds' : glm['GLMModel']['validations'][0]['err'], }) glmScoreStart = time.time() glmScore = h2o_cmd.runGLMScore(key = testFilehex, model_key = params['destination_key'], timeoutSecs = 1800) scoreTime = time.time() - glmScoreStart cmd = 'bash startloggers.sh ' + json + ' stop_' os.system(cmd) if family == "binomial": row.update( {'scoreTime' : scoreTime, 'AUC' : glmScore['validation']['auc'], 'AIC' : glmScore['validation']['aic'], 'error' : glmScore['validation']['err'], }) else: row.update( {'scoreTime' : scoreTime, 'AIC' : glmScore['validation']['aic'], 'AUC' : 'NA', 'error' : glmScore['validation']['err'], }) csvWrt.writerow(row) finally: output.close() if __name__ == '__main__': dat = sys.argv.pop(-1) debug = sys.argv.pop(-1) build = sys.argv.pop(-1) json = sys.argv[-1].split('/')[-1] h2o.parse_our_args() h2o_hosts.build_cloud_with_hosts() fp = 'Airlines' if 'Air' in dat else 'AllBedrooms' if dat == 'Air1x' : fs = files['Airlines']['train'][0] if dat == 'Air10x' : fs = files['Airlines']['train'][1] if dat == 'Air100x' : fs = files['Airlines']['train'][2] if dat == 'AllB1x' : fs = files['AllBedrooms']['train'][0] if dat == 'AllB10x' : fs = files['AllBedrooms']['train'][1] if dat == 'AllB100x' : fs = files['AllBedrooms']['train'][2] bench = "bench" debug = False if debug: bench = "bench/debug" if fp == 'Airlines': airlinesTestParseStart = time.time() hK = "AirlinesHeader.csv" headerPathname = bench+"/Airlines" + "/" + hK h2i.import_only(bucket='home-0xdiag-datasets', path=headerPathname) headerKey = h2i.find_key(hK) testFile = h2i.import_parse(bucket='home-0xdiag-datasets', path=bench +'/Airlines/AirlinesTest.csv', schema='local', hex_key="atest.hex", header=1, header_from_file=headerKey, separator=44, doSummary=False, timeoutSecs=7200,retryDelaySecs=5, pollTimeoutSecs=7200) elapsedAirlinesTestParse = time.time() - airlinesTestParseStart row = {'testParseWallTime' : elapsedAirlinesTestParse} x = "Year,Month,DayofMonth,DayOfWeek,DepTime,ArrTime,UniqueCarrier,Origin,Dest,Distance" doGLM(fs, 'Airlines', 'binomial', 'logit', 1E-5, 0.5, 10, 'IsDepDelayed', x, testFile['destination_key'], row) if fp == 'AllBedrooms': allBedroomsTestParseStart = time.time() x = 'areaname,state,metro,count1,count2,count3,count4,count5,count6,count7,count8,count9,count10,count11,count12,count13,count14,count15,count16,count17,count18,count19,count20,count21,count22,count23,count24,count25,count26,count27,count28,count29,count30,count31,count32,count33,count34,count35,count36,count37,count38,count39,count40,count41,count42,count43,count44,count45,count46,count47,count48,count49,count50,count51,count52,count53,count54,count55,count56,count57,count58,count59,count60,count61,count62,count63,count64,count65,count66,count67,count68,count69,count70,count71,count72,count73,count74,count75,count76,count77,count78,count79,count80,count81,count82,count83,count84,count85,count86,count87,count88,count89,count90,count91,count92,count93,count94,count95,count96,count97,count98,count99' hK = "AllBedroomsHeader.csv" headerPathname = bench+"/AllBedrooms" + "/" + hK h2i.import_only(bucket='home-0xdiag-datasets', path=headerPathname) headerKey = h2i.find_key(hK) testFile = h2i.import_parse(bucket='home-0xdiag-datasets', path=bench+'/AllBedrooms/AllBedroomsTest.csv', schema='local', hex_key="allBtest.hex", header=1, header_from_file=headerKey, separator=44, doSummary=False, timeoutSecs=7200,retryDelaySecs=5, pollTimeoutSecs=7200) elapsedAllBedroomsTestParse = time.time() - allBedroomsTestParseStart row = {'testParseWallTime' : elapsedAllBedroomsTestParse} doGLM(fs, 'AllBedrooms', 'gaussian', 'identity', 1E-4, 0.75, 10, 'medrent',x, testFile['destination_key'],row) h2o.tear_down_cloud()

apache-2.0

codeworldprodigy/lab2

lib/jinja2/jinja2/visitor.py

1402

3316

# -*- coding: utf-8 -*- """ jinja2.visitor ~~~~~~~~~~~~~~ This module implements a visitor for the nodes. :copyright: (c) 2010 by the Jinja Team. :license: BSD. """ from jinja2.nodes import Node class NodeVisitor(object): """Walks the abstract syntax tree and call visitor functions for every node found. The visitor functions may return values which will be forwarded by the `visit` method. Per default the visitor functions for the nodes are ``'visit_'`` + class name of the node. So a `TryFinally` node visit function would be `visit_TryFinally`. This behavior can be changed by overriding the `get_visitor` function. If no visitor function exists for a node (return value `None`) the `generic_visit` visitor is used instead. """ def get_visitor(self, node): """Return the visitor function for this node or `None` if no visitor exists for this node. In that case the generic visit function is used instead. """ method = 'visit_' + node.__class__.__name__ return getattr(self, method, None) def visit(self, node, *args, **kwargs): """Visit a node.""" f = self.get_visitor(node) if f is not None: return f(node, *args, **kwargs) return self.generic_visit(node, *args, **kwargs) def generic_visit(self, node, *args, **kwargs): """Called if no explicit visitor function exists for a node.""" for node in node.iter_child_nodes(): self.visit(node, *args, **kwargs) class NodeTransformer(NodeVisitor): """Walks the abstract syntax tree and allows modifications of nodes. The `NodeTransformer` will walk the AST and use the return value of the visitor functions to replace or remove the old node. If the return value of the visitor function is `None` the node will be removed from the previous location otherwise it's replaced with the return value. The return value may be the original node in which case no replacement takes place. """ def generic_visit(self, node, *args, **kwargs): for field, old_value in node.iter_fields(): if isinstance(old_value, list): new_values = [] for value in old_value: if isinstance(value, Node): value = self.visit(value, *args, **kwargs) if value is None: continue elif not isinstance(value, Node): new_values.extend(value) continue new_values.append(value) old_value[:] = new_values elif isinstance(old_value, Node): new_node = self.visit(old_value, *args, **kwargs) if new_node is None: delattr(node, field) else: setattr(node, field, new_node) return node def visit_list(self, node, *args, **kwargs): """As transformers may return lists in some places this method can be used to enforce a list as return value. """ rv = self.visit(node, *args, **kwargs) if not isinstance(rv, list): rv = [rv] return rv

apache-2.0

davidhstocker/Graphyne

Smoketest.py

1

217442

#!/usr/bin/env python3 """ Smoketest.py: Regression testing utility for Graphyne. Multiprocessing wrapper for Smokest, allowing multiple simultaneous tests against different persistence types. """ from tkinter.test.runtktests import this_dir_path from graphyne.DatabaseDrivers.DriverTermplate import linkTypes __author__ = 'David Stocker' __copyright__ = 'Copyright 2016, David Stocker' __license__ = 'MIT' __version__ = '1.0.0' __maintainer__ = 'David Stocker' __email__ = 'mrdave991@gmail.com' __status__ = 'Production' from xml.dom import minidom from time import ctime from os.path import expanduser import copy import os import codecs import time import decimal import queue import sys import argparse #from os.path import expanduser import graphyne.Graph as Graph import graphyne.Fileutils as Fileutils import graphyne.Exceptions as Exceptions responseQueue = queue.Queue() entityList = [] api = None global testImplicit testImplicit = True #Globals #graphDir = expanduser("~") #graphDir = os.getcwd() graphDir = os.path.dirname(os.path.abspath(__file__)) testDirPath = os.path.join("Config", "Test") configDirPath = os.path.join("utils", "Config") resultFile = None moduleName = 'Smoketest' logType = Graph.logTypes.CONTENT logLevel = Graph.logLevel class DBError(ValueError): pass def testMetaMemeProperty(): method = moduleName + '.' + 'testMetaMemeProperty' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, "MetaMeme_Properties.atest") readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: n = n+1 stringArray = str.split(eachReadLine) testArgumentMap = {stringArray[1] : stringArray[2]} Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, metameme %s" %(n, stringArray[0])]) #colums after 2 can me repeated in pairs. 4/3 and 6/5 can also contain argument/vlaue pairs try: testArgumentMap[str(stringArray[3])] = str(stringArray[4]) except: pass try: testArgumentMap[str(stringArray[5])] = str(stringArray[6]) except: pass try: testArgumentMap[str(stringArray[7])] = str(stringArray[8]) except: pass try: testArgumentMap[str(stringArray[9])] = str(stringArray[10]) except: pass try: testArgumentMap[str(stringArray[11])] = str(stringArray[12]) except: pass removeMe = 'XXX' try: del testArgumentMap[removeMe] except: pass allTrue = True errata = [] try: mmToTest = Graph.templateRepository.templates[stringArray[0]] props = mmToTest.properties Graph.logQ.put( [logType , logLevel.DEBUG , method , "testing metameme %s, props = %s" %(mmToTest.path.fullTemplatePath, props)]) for testKey in testArgumentMap.keys(): testType = testArgumentMap[testKey] Graph.logQ.put( [logType , logLevel.DEBUG , method , "testKey = %s, testType = %s" %(testKey, testType)]) #ToDo: Fix Me. We should not be using temp properties anymore try: prop = mmToTest.getProperty(testKey) Graph.logQ.put( [logType , logLevel.DEBUG , method , "prop = %s" %(prop)]) splitName = testKey.rpartition('.') if (prop is not None) and (prop.name.find(splitName[2]) < 0): Graph.logQ.put( [logType , logLevel.DEBUG , method , "property %s and test property %s don't match" %(prop.name, testKey)]) allTrue = False else: Graph.logQ.put( [logType , logLevel.DEBUG , method , "property %s and test property %s match" %(prop.name, testKey)]) if prop is not None: if prop.propertyType != testType: Graph.logQ.put( [logType , logLevel.WARNING , method , "property %s type %s and testType %s do not match" %(prop.name, prop.propertyType, testType)]) allTrue = False else: Graph.logQ.put( [logType , logLevel.DEBUG , method , "property %s type %s and testType %s match" %(prop.name, prop.propertyType, testType)]) else: Graph.logQ.put( [logType , logLevel.WARNING , method , "property %s is invalid" %(testKey)]) except Exception as e: Graph.logQ.put( [logType , logLevel.ERROR , method , "Error pulling testkey %s from %s's properties. Traceback = %s" %(testKey, mmToTest.path.fullTemplatePath, e)]) allTrue = False if allTrue == False: Graph.logQ.put( [logType , logLevel.DEBUG , method , "testkey %s has no match" %(testKey)]) except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(allTrue) expectedResult = stringArray[13] results = [n, testcase, allTrueResult, expectedResult, copy.deepcopy(errata)] resultSet.append(results) del errata Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testMetaMemeSingleton(): method = moduleName + '.' + 'testMetaMemeSingleton' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, "MetaMeme_Singleton.atest") readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, metameme %s" %(n, stringArray[0])]) expectedTestResult = False if stringArray[1] == 'TRUE': expectedTestResult = True Graph.logQ.put( [logType , logLevel.DEBUG , method , 'Metameme %s is expected to be a singleton == %s' %(stringArray[0], expectedTestResult)]) testResult = False try: mmToTest = Graph.templateRepository.resolveTemplateAbsolutely(stringArray[0]) if mmToTest.isSingleton == True: Graph.logQ.put( [logType , logLevel.DEBUG , method , 'Metameme %s is a singleton' %(stringArray[0])]) testResult = True else: Graph.logQ.put( [logType , logLevel.DEBUG , method , 'Metameme %s is not a singleton' %(stringArray[0])]) except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = stringArray[1] results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testMetaMemeSwitch(): method = moduleName + '.' + 'testMetaMemeSwitch' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, "MetaMeme_Switch.atest") readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, metameme %s" %(n, stringArray[0])]) expectedTestResult = False if stringArray[1] == 'TRUE': expectedTestResult = True Graph.logQ.put( [logType , logLevel.DEBUG , method , 'Metameme %s is expected to be a singleton == %s' %(stringArray[0], expectedTestResult)]) testResult = False try: mmToTest = Graph.templateRepository.resolveTemplateAbsolutely(stringArray[0]) if mmToTest.isSwitch == True: Graph.logQ.put( [logType , logLevel.DEBUG , method , 'Metameme %s is a switch' %(stringArray[0])]) testResult = True else: Graph.logQ.put( [logType , logLevel.DEBUG , method , 'Metameme %s is not a switch' %(stringArray[0])]) except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = stringArray[1] results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testMetaMemeEnhancements(): method = moduleName + '.' + 'testMetaMemeEnhancements' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, "MetaMeme_Enhances.atest") readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) testArgumentList = [] Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, metameme %s" %(n, stringArray[0])]) #columns 1&2 may contain data if stringArray[1] != 'XXX': testArgumentList.append(stringArray[1]) if stringArray[2] != 'XXX': testArgumentList.append(stringArray[2]) allTrue = False try: mmToTest = Graph.templateRepository.resolveTemplateAbsolutely(stringArray[0]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "testing metameme %s, enhancements = %s" %(mmToTest.path.fullTemplatePath, mmToTest.enhances)]) for testArgument in testArgumentList: #Hack alert! If we have no enhancements in the testcase, the result should be false. # Hence we initialize to false, but if we actually have test cases, we re-initialize to True allTrue = True for testArgument in testArgumentList: amIextended = Graph.templateRepository.resolveTemplate(mmToTest.path, testArgument) Graph.logQ.put( [logType , logLevel.DEBUG , method , "checking to see if %s, enhances %s" %(mmToTest.path.fullTemplatePath, amIextended.path.fullTemplatePath)]) #iterate over the enhancement list and see if we have a match testResult = False for enhancement in mmToTest.enhances: Graph.logQ.put( [logType , logLevel.DEBUG , method , "testing enhancement %s against %s" %(enhancement, amIextended.path.fullTemplatePath)]) try: enhancedMetaMeme = Graph.templateRepository.resolveTemplate(mmToTest.path, enhancement) if enhancedMetaMeme.path.fullTemplatePath == amIextended.path.fullTemplatePath: testResult = True Graph.logQ.put( [logType , logLevel.DEBUG , method , "enhancement %s == %s" %(enhancement, amIextended.path.fullTemplatePath)]) else: Graph.logQ.put( [logType , logLevel.DEBUG , method , "enhancement %s != %s" %(enhancement, amIextended.path.fullTemplatePath)]) except: Graph.logQ.put( [logType , logLevel.DEBUG , method , "tested metameme %s extends metameme %s, but is not in the repository." %(enhancement, mmToTest.path.fullTemplatePath)]) if testResult == False: allTrue = False if allTrue == False: Graph.logQ.put( [logType , logLevel.DEBUG , method , "tested metameme %s does not have sought tested enhancement %s" %(mmToTest.path.fullTemplatePath, amIextended.path.fullTemplatePath)]) except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(allTrue) expectedResult = stringArray[3] results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testMemeValidity(): method = moduleName + '.' + 'testMemeValidity' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, "Meme_Validity.atest") readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 memeValid = False for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, metameme %s" %(n, stringArray[0])]) expectedTestResult = False if stringArray[1] == 'TRUE': expectedTestResult = True try: memeToTest = Graph.templateRepository.resolveTemplateAbsolutely(stringArray[0]) memeValidReport = memeToTest.validate([]) memeValid = memeValidReport[0] if expectedTestResult != memeValid: Graph.logQ.put( [logType , logLevel.DEBUG , method , "testkey %s has an unexpected validity status" %(memeToTest.path.fullTemplatePath)]) except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(memeValid) expectedResult = stringArray[1] results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testMemeSingleton(): method = moduleName + '.' + 'testMemeSingleton' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, "Meme_Singleton.atest") readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, metameme %s" %(n, stringArray[0])]) expectedTestResult = False if stringArray[1] == 'TRUE': expectedTestResult = True testResult = False try: mmToTest = Graph.templateRepository.templates[stringArray[0]] if expectedTestResult == mmToTest.isSingleton: if mmToTest.entityUUID is not None: testResult = True else: Graph.logQ.put( [logType , logLevel.DEBUG , method , "meme %s has no deployed entity" %(stringArray[0])]) else: Graph.logQ.put( [logType , logLevel.DEBUG , method , "meme %s has an unexpected singleton status" %(stringArray[0])]) except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = stringArray[1] results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testEntityPhase1(phaseName = 'testEntityPhase1', fName = "Entity_Phase1.atest"): ''' Create the entity from the meme and add it to the entity repo. Retrieve the entity. Check to see if it has the properties it is supposed to, if the type is correct and if the value is correct. Entity Phase 5 also uses this function ''' method = moduleName + '.' + phaseName Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, fName) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.INFO , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) testResult = False try: entityID = Graph.api.createEntityFromMeme(stringArray[0]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "Entity UUID = %s" %(entityID)]) propTypeCorrect = False propValueCorrect = False Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) hasProp = Graph.api.getEntityHasProperty(entityID, stringArray[1]) if hasProp == False: Graph.logQ.put( [logType , logLevel.DEBUG , method , "entity from meme %s does not have property %s" %(entityID, stringArray[1])]) else: propType = Graph.api.getEntityPropertyType(entityID, stringArray[1]) if stringArray[2] == propType: propTypeCorrect = True else: Graph.logQ.put( [logType , logLevel.DEBUG , method , "property %s in entity from meme %s is wrong type. Expected %s. Got %s" %(stringArray[1], entityID, stringArray[2], propType)]) propValue = Graph.api.getEntityPropertyValue(entityID, stringArray[1]) if propType == 'Boolean': expValue = False if stringArray[3].lower() == "true": expValue = True if propValue == expValue: propValueCorrect = True elif propType == 'Decimal': expValue = decimal.Decimal(stringArray[3]) if propValue == expValue: propValueCorrect = True elif propType == 'Integer': expValue = int(stringArray[3]) if propValue == expValue: propValueCorrect = True else: if propValue == stringArray[3]: propValueCorrect = True if propValueCorrect == False: Graph.logQ.put( [logType , logLevel.DEBUG , method , "property %s in entity from meme %s is wrong value. Expected %s. Got %s" %(stringArray[1], stringArray[0], stringArray[3], propValue)]) if (propValueCorrect == True) and (propTypeCorrect == True) and (hasProp == True): testResult = True except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = stringArray[4] results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testEntityPhase1_1(phaseName = 'testEntityPhase1_1', fName = "Entity_Phase1.atest"): ''' a repeat of testEntityPhase1, but using the Python script interface instead of going directly against Graph.api Tests the following script commands: createEntityFromMeme getEntityHasProperty getEntityPropertyType getEntityPropertyValue ''' method = moduleName + '.' + phaseName Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, fName) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.INFO , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) testResult = False try: #entityID = Graph.api.createEntityFromMeme(stringArray[0]) entityID = api.createEntityFromMeme(stringArray[0]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "Entity UUID = %s" %(entityID)]) propTypeCorrect = False propValueCorrect = False Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) #hasProp = Graph.api.getEntityHasProperty(entityID, stringArray[1]) hasProp = api.getEntityHasProperty(entityID, stringArray[1]) if hasProp == False: Graph.logQ.put( [logType , logLevel.DEBUG , method , "entity from meme %s does not have property %s" %(entityID, stringArray[1])]) else: #propType = Graph.api.getEntityPropertyType(entityID, stringArray[1]) propType = api.getEntityPropertyType(entityID, stringArray[1]) if stringArray[2] == propType: propTypeCorrect = True else: Graph.logQ.put( [logType , logLevel.DEBUG , method , "property %s in entity from meme %s is wrong type. Expected %s. Got %s" %(stringArray[1], entityID, stringArray[2], propType)]) #propValue = Graph.api.getEntityPropertyValue(entityID, stringArray[1]) propValue = api.getEntityPropertyValue(entityID, stringArray[1]) if propType == 'Boolean': expValue = False if stringArray[3].lower() == "true": expValue = True if propValue == expValue: propValueCorrect = True elif propType == 'Decimal': expValue = decimal.Decimal(stringArray[3]) if propValue == expValue: propValueCorrect = True elif propType == 'Integer': expValue = int(stringArray[3]) if propValue == expValue: propValueCorrect = True else: if propValue == stringArray[3]: propValueCorrect = True if propValueCorrect == False: Graph.logQ.put( [logType , logLevel.DEBUG , method , "property %s in entity from meme %s is wrong value. Expected %s. Got %s" %(stringArray[1], stringArray[0], stringArray[3], propValue)]) if (propValueCorrect == True) and (propTypeCorrect == True) and (hasProp == True): testResult = True except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = stringArray[4] results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testEntityPhase2(testPhase = 'testEntityPhase2', fileName = 'Entity_Phase2.atest'): ''' Change the values of the various properties. Can we change the value to the desired value and are constraints working? ''' method = moduleName + '.' + testPhase Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] testFileName = os.path.join(testDirPath, fileName) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) testResult = False try: entityID = Graph.api.createEntityFromMeme(stringArray[0]) Graph.api.setEntityPropertyValue(entityID, stringArray[1], stringArray[2]) getter = Graph.api.getEntityPropertyValue(entityID, stringArray[1]) propType = Graph.api.getEntityPropertyType(entityID, stringArray[1]) #reformat the expected result from unicode string to that which is expected in the property expectedResult = None if propType == "String": expectedResult = stringArray[2] elif propType == "Integer": expectedResult = int(stringArray[2]) elif propType == "Decimal": expectedResult = decimal.Decimal(stringArray[2]) else: expectedResult = False if str.lower(stringArray[2]) == 'true': expectedResult = True #now compare getter to the reformatted stringArray[2] and see if we have successfully altered the property if getter == expectedResult: testResult = True except Exceptions.ScriptError as e: #Some test cases violate restriction constraints and will raise an exception. # This works as intended testResult = False except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = stringArray[3] results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testEntityPhase2_1( testPhase = 'testEntityPhase2_1', fileName = 'Entity_Phase2.atest'): ''' a repeat of testEntityPhase2, but using the Python script interface instead of going directly against Graph.api Tests the following script commands: setEntityPropertyValue getEntityPropertyValue getEntityPropertyType ''' method = moduleName + '.' + testPhase Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] testFileName = os.path.join(testDirPath, fileName) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) testResult = False try: entityID = api.createEntityFromMeme(stringArray[0]) api.setEntityPropertyValue(entityID, stringArray[1], stringArray[2]) getter = api.getEntityPropertyValue(entityID, stringArray[1]) propType = api.getEntityPropertyType(entityID, stringArray[1]) #reformat the expected result from unicode string to that which is expected in the property expectedResult = None if propType == "String": expectedResult = stringArray[2] elif propType == "Integer": expectedResult = int(stringArray[2]) elif propType == "Decimal": expectedResult = decimal.Decimal(stringArray[2]) else: expectedResult = False if str.lower(stringArray[2]) == 'true': expectedResult = True #now compare getter to the reformatted stringArray[2] and see if we have successfully altered the property if getter == expectedResult: testResult = True except Exceptions.ScriptError: #Some test cases violate restriction constraints and will raise an exception. # This works as intended testResult = False except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = stringArray[3] results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testEntityPhase3(): ''' Add and remove properties. Remove custom properties. Tests the following script commands: addEntityDecimalProperty addEntityIntegerProperty addEntityStringProperty addEntityBooleanProperty removeAllCustomPropertiesFromEntity removeEntityProperty Step 1. add a prop and test its existence and value Step 2. remove that custom prop and check to make sure it is gone (getHasProperty == False) Step 3. add the prop again, test its existence and then use removeAllCustomPropertiesFromEntity to remove it''' method = moduleName + '.' + 'testEntityPhase3' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] testFileName = os.path.join(testDirPath, "Entity_Phase3.atest") readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) testResult = False step1Result = False step2Result = False step3Result = False try: entityID = Graph.api.createEntityFromMeme(stringArray[0]) #step 1 if stringArray[2] == "String": Graph.api.addEntityStringProperty(entityID, stringArray[1], stringArray[3]) expectedResult = stringArray[3] elif stringArray[2] == "Integer": Graph.api.addEntityIntegerProperty(entityID, stringArray[1], stringArray[3]) expectedResult = int(stringArray[3]) elif stringArray[2] == "Decimal": Graph.api.addEntityDecimalProperty(entityID, stringArray[1], stringArray[3]) expectedResult = decimal.Decimal(stringArray[3]) else: Graph.api.addEntityBooleanProperty(entityID, stringArray[1], stringArray[3]) expectedResult = False if str.lower(stringArray[3]) == 'true': expectedResult = True getter = Graph.api.getEntityPropertyValue(entityID, stringArray[1]) #now compare getter to the reformatted stringArray[2] and see if we have successfully altered the property if getter == expectedResult: step1Result = True #step 2 Graph.api.removeEntityProperty(entityID, stringArray[1]) getter = Graph.api.getEntityHasProperty(entityID, stringArray[1]) if getter == False: step2Result = True #step 3 if stringArray[2] == "String": Graph.api.addEntityStringProperty(entityID, stringArray[1], stringArray[3]) elif stringArray[2] == "Integer": Graph.api.addEntityIntegerProperty(entityID, stringArray[1], stringArray[3]) elif stringArray[2] == "Decimal": Graph.api.addEntityDecimalProperty(entityID, stringArray[1], stringArray[3]) else: Graph.api.addEntityBooleanProperty(entityID, stringArray[1], stringArray[3]) Graph.api.removeAllCustomPropertiesFromEntity(entityID) getter = Graph.api.getEntityHasProperty(entityID, stringArray[1]) if getter == False: step3Result = True except Exceptions.ScriptError as e: #Some test cases violate restriction constraints and will raise an exception. # This works as intended testResult = False except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) if (step1Result == True) and (step2Result == True) and (step3Result == True): testResult = True testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = "True" results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testEntityPhase3_1(): ''' a repeat of testEntityPhase3, but using the Python script interface instead of going directly against Graph.api Tests the following script commands: addEntityDecimalProperty addEntityIntegerProperty addEntityStringProperty addEntityBooleanProperty removeAllCustomPropertiesFromEntity removeEntityProperty ''' method = moduleName + '.' + 'testEntityPhase3_1' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] testFileName = os.path.join(testDirPath, "Entity_Phase3.atest") readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) testResult = False step1Result = False step2Result = False step3Result = False try: entityID = api.createEntityFromMeme(stringArray[0]) #step 1 if stringArray[2] == "String": #Graph.api.addEntityStringProperty(entityID, stringArray[1], stringArray[3]) api.addEntityStringProperty(entityID, stringArray[1], stringArray[3]) expectedResult = stringArray[3] elif stringArray[2] == "Integer": #Graph.api.addEntityIntegerProperty(entityID, stringArray[1], stringArray[3]) api.addEntityIntegerProperty(entityID, stringArray[1], stringArray[3]) expectedResult = int(stringArray[3]) elif stringArray[2] == "Decimal": #Graph.api.addEntityDecimalProperty(entityID, stringArray[1], stringArray[3]) api.addEntityDecimalProperty(entityID, stringArray[1], stringArray[3]) expectedResult = decimal.Decimal(stringArray[3]) else: Graph.api.addEntityBooleanProperty(entityID, stringArray[1], stringArray[3]) expectedResult = False if str.lower(stringArray[3]) == 'true': expectedResult = True #getter = Graph.api.getEntityPropertyValue(entityID, stringArray[1]) getter = api.getEntityPropertyValue(entityID, stringArray[1]) #now compare getter to the reformatted stringArray[2] and see if we have successfully altered the property if getter == expectedResult: step1Result = True #step 2 #Graph.api.removeEntityProperty(entityID, stringArray[1]) #getter = Graph.api.getEntityHasProperty(entityID, stringArray[1]) api.removeEntityProperty(entityID, stringArray[1]) getter = api.getEntityHasProperty(entityID, stringArray[1]) if getter == False: step2Result = True #step 3 if stringArray[2] == "String": #Graph.api.addEntityStringProperty(entityID, stringArray[1], stringArray[3]) api.addEntityStringProperty(entityID, stringArray[1], stringArray[3]) elif stringArray[2] == "Integer": #Graph.api.addEntityIntegerProperty(entityID, stringArray[1], stringArray[3]) api.addEntityIntegerProperty(entityID, stringArray[1], stringArray[3]) elif stringArray[2] == "Decimal": #Graph.api.addEntityDecimalProperty(entityID, stringArray[1], stringArray[3]) api.addEntityIntegerProperty(entityID, stringArray[1], stringArray[3]) else: #Graph.api.addEntityBooleanProperty(entityID, stringArray[1], stringArray[3]) api.addEntityBooleanProperty(entityID, stringArray[1], stringArray[3]) #Graph.api.removeAllCustomPropertiesFromEntity(entityID) #getter = Graph.api.getEntityHasProperty(entityID, stringArray[1]) api.removeAllCustomPropertiesFromEntity(entityID) getter = api.getEntityHasProperty(entityID, stringArray[1]) if getter == False: step3Result = True except Exceptions.ScriptError: #Some test cases violate restriction constraints and will raise an exception. # This works as intended testResult = False except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) if (step1Result == True) and (step2Result == True) and (step3Result == True): testResult = True testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = "True" results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testEntityPhase4(): ''' Revert the entity to original condition. Tests the following script commands: revertEntityPropertyValues Step 1. change a standard value Step 2. use revertEntityPropertyValues to return it to stock''' method = moduleName + '.' + 'testEntityPhase4' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] testFileName = os.path.join(testDirPath, "Entity_Phase4.atest") readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) testResult = False try: entityID = Graph.api.createEntityFromMeme(stringArray[0]) baseValue = Graph.api.getEntityPropertyValue(entityID, stringArray[1]) Graph.api.setEntityPropertyValue(entityID, stringArray[1], stringArray[2]) getter = Graph.api.getEntityPropertyValue(entityID, stringArray[1]) propType = Graph.api.getEntityPropertyType(entityID, stringArray[1]) #reformat the expected result from unicode string to that which is expected in the property expectedResult = None if propType == "String": expectedResult = stringArray[2] elif propType == "Integer": expectedResult = int(stringArray[2]) elif propType == "Decimal": expectedResult = decimal.Decimal(stringArray[2]) else: expectedResult = False if str.lower(stringArray[2]) == 'true': expectedResult = True #now compare getter to the reformatted stringArray[2] and see if we have successfully altered the property if getter == expectedResult: Graph.api.revertEntityPropertyValues(entityID, False) getter = Graph.api.getEntityPropertyValue(entityID, stringArray[1]) if getter == baseValue: testResult = True except Exceptions.ScriptError: #Some test cases violate restriction constraints and will raise an exception. # This works as intended testResult = False except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = "True" results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testEntityPhase4_1(): ''' a repeat of testEntityPhase3, but using the Python script interface instead of going directly against Graph.api ''' method = moduleName + '.' + 'testEntityPhase4.1' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] testFileName = os.path.join(testDirPath, "Entity_Phase4.atest") readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) testResult = False try: entityID = api.createEntityFromMeme(stringArray[0]) baseValue = api.getEntityPropertyValue(entityID, stringArray[1]) api.setEntityPropertyValue(entityID, stringArray[1], stringArray[2]) getter = api.getEntityPropertyValue(entityID, stringArray[1]) propType = api.getEntityPropertyType(entityID, stringArray[1]) #reformat the expected result from unicode string to that which is expected in the property expectedResult = None if propType == "String": expectedResult = stringArray[2] elif propType == "Integer": expectedResult = int(stringArray[2]) elif propType == "Decimal": expectedResult = decimal.Decimal(stringArray[2]) else: expectedResult = False if str.lower(stringArray[2]) == 'true': expectedResult = True #now compare getter to the reformatted stringArray[2] and see if we have successfully altered the property if getter == expectedResult: api.revertEntityPropertyValues(entityID, False) getter = api.getEntityPropertyValue(entityID, stringArray[1]) if getter == baseValue: testResult = True except Exceptions.ScriptError: #Some test cases violate restriction constraints and will raise an exception. # This works as intended testResult = False except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = "True" results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testRevertEntity(): ''' a repeat of the testEntityPhase4 tests, but using revertEntity''' method = moduleName + '.' + 'testRevertEntity' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] testFileName = os.path.join(testDirPath, "Entity_Phase4.atest") readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 #First, re-run the 4 tests with revertEntity() for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) testResult = True try: entityID = api.createEntityFromMeme(stringArray[0]) baseValue = api.getEntityPropertyValue(entityID, stringArray[1]) api.setEntityPropertyValue(entityID, stringArray[1], stringArray[2]) getter = api.getEntityPropertyValue(entityID, stringArray[1]) propType = api.getEntityPropertyType(entityID, stringArray[1]) #reformat the expected result from unicode string to that which is expected in the property expectedResult = None if propType == "String": expectedResult = stringArray[2] elif propType == "Integer": expectedResult = int(stringArray[2]) elif propType == "Decimal": expectedResult = decimal.Decimal(stringArray[2]) else: expectedResult = False if str.lower(stringArray[2]) == 'true': expectedResult = True #now compare getter to the reformatted stringArray[2] and see if we have successfully altered the property if getter == expectedResult: api.revertEntity(entityID, False) getter = api.getEntityPropertyValue(entityID, stringArray[1]) if getter != baseValue: testResult = False except Exceptions.ScriptError: #Some test cases violate restriction constraints and will raise an exception. # This works as intended testResult = False except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = "True" results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) #Second, test with a custom property with revertEntity() for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) testResult = True try: entityID = api.createEntityFromMeme(stringArray[0]) #Create a property named after the current n count and give it the n value currValue = "%s" %n Graph.api.addEntityIntegerProperty(entityID, currValue, currValue) getter = Graph.api.getEntityHasProperty(entityID, currValue) if getter != True: testResult = False Graph.api.revertEntity(entityID, currValue) getter = Graph.api.getEntityHasProperty(entityID, currValue) if getter == True: testResult = False except Exceptions.ScriptError: #Some test cases violate restriction constraints and will raise an exception. # This works as intended testResult = False except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = "True" results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) #Lastly, rerun test 4 and then add a property and test revertEntity() for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) testResult = True try: entityID = api.createEntityFromMeme(stringArray[0]) baseValue = api.getEntityPropertyValue(entityID, stringArray[1]) api.setEntityPropertyValue(entityID, stringArray[1], stringArray[2]) getter = api.getEntityPropertyValue(entityID, stringArray[1]) propType = api.getEntityPropertyType(entityID, stringArray[1]) #reformat the expected result from unicode string to that which is expected in the property expectedResult = None if propType == "String": expectedResult = stringArray[2] elif propType == "Integer": expectedResult = int(stringArray[2]) elif propType == "Decimal": expectedResult = decimal.Decimal(stringArray[2]) else: expectedResult = False if str.lower(stringArray[2]) == 'true': expectedResult = True #Create a property named after the current n count and give it the n value currValue = "%s" %n Graph.api.addEntityIntegerProperty(entityID, currValue, currValue) getter = Graph.api.getEntityHasProperty(entityID, currValue) if getter != True: testResult = False #now compare getter to the reformatted stringArray[2] and see if we have successfully altered the property if getter == expectedResult: api.revertEntity(entityID, False) getter = api.getEntityPropertyValue(entityID, stringArray[1]) if getter != baseValue: testResult = False #Make sure the custom property is gone Graph.api.revertEntity(entityID, currValue) getter = Graph.api.getEntityHasProperty(entityID, currValue) if getter == True: testResult = False except Exceptions.ScriptError: #Some test cases violate restriction constraints and will raise an exception. # This works as intended testResult = False except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = "True" results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testEntityPhase6(): ''' Check and see if the meme is a singleton Tests getMemeIsSingleton Tests getEntityFromMeme in singleton context Strategy - If the meme is a singleton, then it should have had an entity created already 1 - Is the meme a singleton? 2a - If not, then entity.uuid should be non-existent 2b - If so, then entity.uuid should have a UUID 3b - create an entiity 4b - is the UUID the same as before? It should be ''' method = moduleName + '.' + 'testEntityPhase6' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] testFileName = os.path.join(testDirPath, "Entity_Phase6.atest") readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) expectedTestResult = False if stringArray[1] == 'TRUE': expectedTestResult = True testResult = False mSingletonFlagCorrect = False mEntityUUIDCorrect = False eSingletonFlagCorrect = False eSameUUIDasInMeme = False try: isSingleton = Graph.api.getIsMemeSingleton(stringArray[0]) if expectedTestResult == isSingleton: mSingletonFlagCorrect = True meme = Graph.templateRepository.resolveTemplateAbsolutely(stringArray[0]) oldEntityID = None #Is the meme a singleton? if isSingleton == False: #2a - If not, then entity.uuid should be non-existent try: if meme.entityUUID is None: mEntityUUIDCorrect = True except: mEntityUUIDCorrect = True else: #2b - If so, then entity.uuid should have a UUID if meme.entityUUID is not None: mEntityUUIDCorrect = True oldEntityID = meme.entityUUID entityID = Graph.api.createEntityFromMeme(stringArray[0]) entityIsSingleton = Graph.api.getIsEntitySingleton(entityID) if isSingleton == False: if entityIsSingleton == False: eSingletonFlagCorrect = True eSameUUIDasInMeme = True else: if (entityIsSingleton == True) and (entityID == oldEntityID): eSingletonFlagCorrect = True eSameUUIDasInMeme = True #now compare getter to the reformatted stringArray[2] and see if we have successfully altered the property if (mSingletonFlagCorrect == True) and (mEntityUUIDCorrect == True) and (eSingletonFlagCorrect == True) and (eSameUUIDasInMeme == True): testResult = True except Exceptions.ScriptError: #Some test cases violate restriction constraints and will raise an exception. # This works as intended testResult = False except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = "True" results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testEntityPhase6_1(): ''' Repeat 6 using python script interface. Tests the following script functions: getIsEntitySingleton getIsMemeSingleton ''' method = moduleName + '.' + 'testEntityPhase6.1' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] testFileName = os.path.join(testDirPath, "Entity_Phase6.atest") readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.DEBUG , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) expectedTestResult = False if stringArray[1] == 'TRUE': expectedTestResult = True testResult = False mSingletonFlagCorrect = False mEntityUUIDCorrect = False eSingletonFlagCorrect = False eSameUUIDasInMeme = False try: isSingleton = api.getIsMemeSingleton(stringArray[0]) if expectedTestResult == isSingleton: mSingletonFlagCorrect = True meme = Graph.templateRepository.resolveTemplateAbsolutely(stringArray[0]) oldEntityID = None #Is the meme a singleton? if isSingleton == False: #2a - If not, then entity.uuid should be non-existent try: if meme.entityUUID is None: mEntityUUIDCorrect = True except: mEntityUUIDCorrect = True else: #2b - If so, then entity.uuid should have a UUID if meme.entityUUID is not None: mEntityUUIDCorrect = True oldEntityID = meme.entityUUID entityID = api.createEntityFromMeme(stringArray[0]) entityIsSingleton = api.getIsEntitySingleton(entityID) if isSingleton == False: if entityIsSingleton == False: eSingletonFlagCorrect = True eSameUUIDasInMeme = True else: if (entityIsSingleton == True) and (entityID == oldEntityID): eSingletonFlagCorrect = True eSameUUIDasInMeme = True #now compare getter to the reformatted stringArray[2] and see if we have successfully altered the property if (mSingletonFlagCorrect == True) and (mEntityUUIDCorrect == True) and (eSingletonFlagCorrect == True) and (eSameUUIDasInMeme == True): testResult = True except Exceptions.ScriptError: #Some test cases violate restriction constraints and will raise an exception. # This works as intended testResult = False except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = "True" results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testEntityPhase7(phaseName = 'testEntityPhase7', fName = "Entity_Phase7.atest"): ''' Create entities from the meme in the first two colums. Add a link between the two at the location on entity in from column 3. Check and see if each is a counterpart as seen from the other using the addresses in columns 4&5 (CheckPath & Backpath) & the filter. The filter must be the same as the type of link (or None) The check location must be the same as the added loation. ''' method = moduleName + '.' + phaseName Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] lresultSet = [] del lresultSet[:] #try: testFileName = os.path.join(testDirPath, fName) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.INFO , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) testResult = False try: entityID0 = Graph.api.createEntityFromMeme(stringArray[0]) entityID1 = Graph.api.createEntityFromMeme(stringArray[1]) #Attach entityID1 at the mount point specified in stringArray[2] if stringArray[2] != "X": mountPoints = api.getLinkCounterpartsByType(entityID0, stringArray[2], 0) unusedMountPointsOverview = {} for mountPoint in mountPoints: try: mpMemeType = api.getEntityMemeType(mountPoint) unusedMountPointsOverview[mountPoint] = mpMemeType except Exception as e: #errorMessage = "debugHelperMemeType warning in Smoketest.testEntityPhase7. Traceback = %s" %e #Graph.logQ.put( [logType , logLevel.WARNING , method , errorMessage]) raise e for mountPoint in mountPoints: api.addEntityLink(mountPoint, entityID1, {}, int(stringArray[5])) else: api.addEntityLink(entityID0, entityID1, {}, int(stringArray[5])) backTrackCorrect = False linkType = None if stringArray[6] != "X": linkType = int(stringArray[6]) #see if we can get from entityID0 to entityID1 via stringArray[3] addLocationCorrect = False addLocationList = api.getLinkCounterpartsByType(entityID0, stringArray[3], linkType) if len(addLocationList) > 0: addLocationCorrect = True #see if we can get from entityID1 to entityID0 via stringArray[4] backTrackCorrect = False backTrackLocationList = api.getLinkCounterpartsByType(entityID1, stringArray[4], linkType) if len(backTrackLocationList) > 0: backTrackCorrect = True if (backTrackCorrect == True) and (addLocationCorrect == True): testResult = True except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = stringArray[7] results = [n, testcase, allTrueResult, expectedResult, errata] lresultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return lresultSet def testLinkCounterpartsByMetaMemeType(phaseName = 'LinkCounterpartsByMetaMemeType', fName = "LinkCounterpartsByMetaMemeType.atest"): ''' Repeat Phase 7, but traversing with metameme paths, instead of meme paths. LinkCounterpartsByMetaMemeType.atest differs from TestEntityPhase7.atest only in that cols D and E use metameme paths. Create entities from the meme in the first two colums. Add a link between the two at the location on entity in from column 3. Check and see if each is a counterpart as seen from the other using the addresses in columns 4&5 (CheckPath & Backpath) & the filter. The filter must be the same as the type of link (or None) The check location must be the same as the added loation. ''' method = moduleName + '.' + phaseName Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] lresultSet = [] del lresultSet[:] #try: testFileName = os.path.join(testDirPath, fName) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.INFO , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) testResult = False try: entityID0 = Graph.api.createEntityFromMeme(stringArray[0]) entityID1 = Graph.api.createEntityFromMeme(stringArray[1]) #Attach entityID1 at the mount point specified in stringArray[2] if stringArray[2] != "X": mountPoints = api.getLinkCounterpartsByType(entityID0, stringArray[2], 0) unusedMountPointsOverview = {} for mountPoint in mountPoints: try: mpMemeType = api.getEntityMemeType(mountPoint) unusedMountPointsOverview[mountPoint] = mpMemeType except Exception as e: #errorMessage = "debugHelperMemeType warning in Smoketest.testEntityPhase7. Traceback = %s" %e #Graph.logQ.put( [logType , logLevel.WARNING , method , errorMessage]) raise e for mountPoint in mountPoints: api.addEntityLink(mountPoint, entityID1, {}, int(stringArray[5])) else: api.addEntityLink(entityID0, entityID1, {}, int(stringArray[5])) backTrackCorrect = False linkType = None if stringArray[6] != "X": linkType = int(stringArray[6]) #see if we can get from entityID0 to entityID1 via stringArray[3] addLocationCorrect = False addLocationList = api.getLinkCounterpartsByMetaMemeType(entityID0, stringArray[3], linkType) if len(addLocationList) > 0: addLocationCorrect = True #see if we can get from entityID1 to entityID0 via stringArray[4] backTrackCorrect = False backTrackLocationList = api.getLinkCounterpartsByMetaMemeType(entityID1, stringArray[4], linkType) if len(backTrackLocationList) > 0: backTrackCorrect = True if (backTrackCorrect == True) and (addLocationCorrect == True): testResult = True except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = stringArray[7] results = [n, testcase, allTrueResult, expectedResult, errata] lresultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return lresultSet def testEntityPhase9(phaseName = 'testEntityPhase9', fName = "Entity_Phase9.atest"): ''' A modified phase 7 test with entity link removal after testing. Add a link between the two at the location on entity in from column 3. Check and see if each is a counterpart as seen from the other using the addresses in columns 4&5 (CheckPath & Backpath) & the filter. The filter must be the same as the type of link (or None) The check location must be the same as the added loation. (So far, so good. this is the same as in phase 7) added: Now remove the link Check again to make sure that the link no longer exists ''' method = moduleName + '.' + phaseName Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, fName) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.INFO , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) part1TestResult = False testResult = False try: entityID0 = Graph.api.createEntityFromMeme(stringArray[0]) entityID1 = Graph.api.createEntityFromMeme(stringArray[1]) #Attach entityID1 at the mount point specified in stringArray[2] rememberMe = {} mountPoints = api.getLinkCounterpartsByType(entityID0, stringArray[2], 0) for mountPoint in mountPoints: api.addEntityLink(mountPoint, entityID1, {}, int(stringArray[5])) rememberMe[mountPoint] = entityID1 backTrackCorrect = False linkType = None if stringArray[6] != "X": linkType = int(stringArray[6]) addLocationCorrect = False addLocationList = api.getLinkCounterpartsByType(entityID0, stringArray[3], linkType) if len(addLocationList) > 0: addLocationCorrect = True #see if we can get from entityID1 to entityID0 via stringArray[4] backTrackCorrect = False backTrackLocationList = api.getLinkCounterpartsByType(entityID1, stringArray[4], linkType) if len(backTrackLocationList) > 0: backTrackCorrect = True if (backTrackCorrect == True) and (addLocationCorrect == True): part1TestResult = True #Time for phase 2 #Now remove that added member. This is why we kept track of that added member; to speed up removal for mountPoint in rememberMe.keys(): api.removeEntityLink(mountPoint, entityID1) secondAddLocationCorrect = False addLocationList = api.getLinkCounterpartsByType(entityID0, stringArray[3], linkType) if len(addLocationList) == 0: secondAddLocationCorrect = True if (part1TestResult == True) and (secondAddLocationCorrect == True): testResult = True except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = stringArray[7] results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testEntityPhase10(phaseName = 'testEntityPhase10', fName = "Entity_Phase10.atest"): """ Create two entities from the meme in the first two colums. Both will should have the same singleton in their association (link) networks Try to traverse from one to the other This tests the 'singleton bridge' with respect to souble and triple wildcards """ method = moduleName + '.' + phaseName Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, fName) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.INFO , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) testResult = False try: entityID0 = Graph.api.createEntityFromMeme(stringArray[0]) trackLocationList = api.getLinkCounterpartsByType(entityID0, stringArray[2], None) if len(trackLocationList) > 0: testResult = True except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = stringArray[3] results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testTraverseParams(phaseName = 'testTraverseParams', fName = "TraverseWithParams.atest"): """ Create a TraverseParameters.A and TraverseParameters.B. Attach them and assign values to the edges (links). Then fpor each test case: 1 -Try to select A (with or without params, depending on the test case) 2 -Try to navigate to B (with or without node/traverse params, depending on the test case 3 -Compare our cuccessful reaching of B with the expected outcome. """ method = moduleName + '.' + phaseName Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, fName) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = eachReadLine.split(' | ') Graph.logQ.put( [logType , logLevel.INFO , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) if n == 40: unusedCatch = True testResult = False try: entityID0 = Graph.api.createEntityFromMeme("TraverseParameters.A") entityID1 = Graph.api.createEntityFromMeme("TraverseParameters.B") Graph.api.addEntityLink(entityID0, entityID1, {'a':4}, 0) if n == 70: unusedCatchMe = True traversePath = stringArray[0].strip() trackLocationList = api.getLinkCounterpartsByType(entityID0, traversePath, None) if len(trackLocationList) > 0: testResult = True except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = stringArray[1].strip() results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testNumericValue(filename): #NumericValue.atest method = moduleName + '.' + 'testNumericValue' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, filename) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 unicodeReadLine = str(eachReadLine) stringArray = str.split(unicodeReadLine) testArgumentMap = {} testResult = False try: entityIDList = api.getEntitiesByMemeType(stringArray[0]) for entityIDListEntry in entityIDList: entityID = entityIDListEntry numberListS = api.evaluateEntity(entityID, testArgumentMap) numberList = [] for numberString in numberListS: dec = decimal.Decimal(numberString) numberList.append(dec) argAsDecimal = decimal.Decimal(stringArray[1]) if argAsDecimal in numberList: testResult = True except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = stringArray[2] results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testImplicitMeme(phaseName = 'testImplicitMeme', fName = "ImplicitMeme.atest"): ''' Create entities from the meme in the first two colums. Add a link between the two at the location on entity in from column 3, if it is not direct. Otherwise diorectly to entity 0 Check and see if each is a counterpart as seen from the other using the addresses in columns 4&5 (CheckPath & Backpath) & the filter. The filter must be the same as the type of link (or None) The check location must be the same as the added loation. ''' method = moduleName + '.' + phaseName Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, fName) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.INFO , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) #debug #print ("Starting testcase %s, meme %s" %(n, stringArray[0])) #if n == 30: # pass #/debug testResult = False try: try: entityID0 = Graph.api.createEntityFromMeme(stringArray[0]) except Exception as e: raise DBError(stringArray[0]) try: entityID1 = Graph.api.createEntityFromMeme(stringArray[1]) except Exception as e: raise DBError(stringArray[1]) #Attach entityID1 at the mount point specified in stringArray[2] if (stringArray[2] != '**DIRECT**'): mountPoints = api.getLinkCounterpartsByType(entityID0, stringArray[2], 0) for mountPoint in mountPoints: api.addEntityLink(mountPoint, entityID1) else: #If we have a **DIRECT** mount, then attach entity 1 to entity 0 api.addEntityLink(entityID0, entityID1) backTrackCorrect = False linkType = None #see if we can get from entityID0 to entityID1 via stringArray[3] addLocationCorrect = False addLocationList = api.getLinkCounterpartsByType(entityID0, stringArray[3], linkType) if len(addLocationList) > 0: addLocationCorrect = True #see if we can get from entityID1 to entityID0 via stringArray[4] backTrackCorrect = False backTrackLocationList = api.getLinkCounterpartsByType(entityID1, stringArray[4], linkType) if len(backTrackLocationList) > 0: backTrackCorrect = True if (backTrackCorrect == True) and (addLocationCorrect == True): testResult = True except DBError as e: errorMsg = ('Database Error! Check to see if the Database has been started and that meme %s is in the appropriate table.' % (e) ) errata.append(errorMsg) except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[2]) allTrueResult = str(testResult) expectedResult = stringArray[5] results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testCondition(filename): method = moduleName + '.' + 'testCondition' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, filename) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 unicodeReadLine = str(eachReadLine) stringArray = str.split(unicodeReadLine) entityIDList = api.getEntitiesByMemeType(stringArray[0]) for entityIDListEntry in entityIDList: testArgumentMap = {stringArray[2] : stringArray[1]} try: testArgumentMap[stringArray[4]] = stringArray[3] except: pass try: testArgumentMap[stringArray[6]] = stringArray[5] except: pass try: del testArgumentMap['XXX'] except: pass testResult = False try: entityIDList = api.getEntitiesByMemeType(stringArray[0]) for entityIDListEntry in entityIDList: entityID = entityIDListEntry testResult = api.evaluateEntity(entityID, testArgumentMap) except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = stringArray[7] results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testAACondition(filename): method = moduleName + '.' + 'testAACondition' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, filename) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 unicodeReadLine = str(eachReadLine) stringArray = str.split(unicodeReadLine) testArgumentMap = {} subjectID = api.createEntityFromMeme(stringArray[1]) objectID = None try: objectID = Graph.api.createEntityFromMeme(stringArray[2]) except: pass if objectID is None: objectID = subjectID try: del testArgumentMap['XXX'] except: pass testResult = False try: entityIDList = api.getEntitiesByMemeType(stringArray[0]) for entityIDListEntry in entityIDList: cEntityID = entityIDListEntry testResult = api.evaluateEntity(cEntityID, testArgumentMap, None, subjectID, objectID) except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = stringArray[3] results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testSourceCreateMeme(filename): method = moduleName + '.' + 'testSourceCreateMeme' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, filename) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 #Phase 1 - explicit Metameme and Meme declaration for eachReadLine in allLines: errata = [] n = n+1 unicodeReadLine = str(eachReadLine) stringArray = str.split(unicodeReadLine) metamemePath = stringArray[0] modulePath = stringArray[1] memeName = stringArray[2] operationResult = {} testResult = False try: operationResult = api.sourceMemeCreate(memeName, modulePath, metamemePath) except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) operationResult = {"memeID" : "%s.%s" %(modulePath, memeName), "ValidationResults" : [False, errorMsg]} errata.append(errorMsg) testcase = str(operationResult["memeID"]) validation = operationResult["ValidationResults"] if validation[0] == True: testResult = True else: testResult = False errata = validation[1] allTrueResult = str(testResult) expectedResult = stringArray[3] results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) #Phase 2 - Default Metameme, default module testResult = False memeName = "DefaultMetamemeMeme" try: operationResult = api.sourceMemeCreate(memeName) except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) operationResult = {"memeID" : "%s.%s" %("Graphyne", memeName), "ValidationResults" : [False, errorMsg]} errata.append(errorMsg) testcase = str(operationResult["memeID"]) validation = operationResult["ValidationResults"] if validation[0] == True: testResult = True else: testResult = False errata = validation[1] allTrueResult = str(testResult) expectedResult = "True" results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) #Phase 3 - Default Metameme, custom module testResult = False try: operationResult = api.sourceMemeCreate(memeName, "CustomModule") except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) operationResult = {"memeID" : "%s.%s" %("Graphyne", memeName), "ValidationResults" : [False, errorMsg]} errata.append(errorMsg) testcase = str(operationResult["memeID"]) validation = operationResult["ValidationResults"] if validation[0] == True: testResult = True else: testResult = False errata = validation[1] allTrueResult = str(testResult) expectedResult = "True" results = [n, testcase, allTrueResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testSourceProperty(filename): method = moduleName + '.' + 'testSourceProperty' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, filename) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 unicodeReadLine = str(eachReadLine) stringArray = str.split(unicodeReadLine) metamemePath = stringArray[0] modulePath = stringArray[1] memeName = stringArray[2] propName = stringArray[3] propValueStr = stringArray[4] operationResult = {} testResult = "False" try: sourceMeme = api.sourceMemeCreate(memeName, modulePath, metamemePath) operationResult = api.sourceMemePropertySet(sourceMeme["memeID"], propName, propValueStr) except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) operationResult = {"memeID" : "%s.%s" %(modulePath, memeName), "ValidationResults" : [False, errorMsg]} errata.append(errorMsg) testcase = "%s with property %s, %s" %(testResult[0], propName, propValueStr) validation = operationResult["ValidationResults"] if validation[0] == True: testResult = str(True) else: testResult = str(False) errata = validation[1] expectedResult = stringArray[5] results = [n, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testSourcePropertyRemove(filename): method = moduleName + '.' + 'testSourcePropertyRemove' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, filename) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 unicodeReadLine = str(eachReadLine) stringArray = str.split(unicodeReadLine) metamemePath = stringArray[0] modulePath = "%s_remove" %stringArray[1] memeName = stringArray[2] propName = stringArray[3] propValueStr = stringArray[4] sourceMeme = [] testResult = str(False) try: sourceMeme = api.sourceMemeCreate(memeName, modulePath, metamemePath) unusedAddProp = api.sourceMemePropertySet(sourceMeme["memeID"], propName, propValueStr) operationResult = api.sourceMemePropertyRemove(sourceMeme["memeID"], propName) #list: [u'SourceProperty1_remove.L', [True, []]] validation = operationResult["ValidationResults"] if validation[0] == True: testResult = str(True) else: testResult = str(False) errata = validation[1] except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) operationResult = {"memeID" : "%s.%s" %(modulePath, memeName), "ValidationResults" : [False, errorMsg]} errata.append(errorMsg) testcase = "%s with property %s, %s removed" %(sourceMeme["memeID"], propName, propValueStr) expectedResult = stringArray[5] results = [n, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testSourceMember(filename): method = moduleName + '.' + 'testSourceMember' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, filename) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: #e.g. (Examples.M, SourceMember3, M, Examples.L, SourceMember3, L, 2, False) errata = [] n = n+1 unicodeReadLine = str(eachReadLine) stringArray = str.split(unicodeReadLine) metamemePath = stringArray[0] modulePath = stringArray[1] memeName = stringArray[2] memberMetamemePath = stringArray[3] memberModulePath = stringArray[4] memberMemeName = stringArray[5] occurrence = stringArray[6] sourceMeme = [''] sourceMemberMeme = [''] testResult = str(False) try: sourceMeme = api.sourceMemeCreate(memeName, modulePath, metamemePath) sourceMemberMeme = api.sourceMemeCreate(memberMemeName, memberModulePath, memberMetamemePath) operationResult = api.sourceMemeMemberAdd(sourceMeme["memeID"], sourceMemberMeme["memeID"], occurrence) validation = operationResult["ValidationResults"] if validation[0] == True: testResult = str(True) else: testResult = str(False) errata = validation[1] except Exception as e: errorMsg = ('Error in testcase testSourceMember! Traceback = %s' % (e) ) api.writeError(errorMsg) errata.append(errorMsg) testcase = "%s has member %s" %(sourceMeme["memeID"], sourceMemberMeme["memeID"]) expectedResult = stringArray[7] results = [n, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testSourceMemberRemove(filename): method = moduleName + '.' + 'testSourceMemberRemove' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, filename) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 unicodeReadLine = str(eachReadLine) stringArray = str.split(unicodeReadLine) metamemePath = stringArray[0] modulePath = "%s_remove" %stringArray[1] memeName = stringArray[2] memberMetamemePath = stringArray[3] memberModulePath = "%s_remove" %stringArray[4] memberMemeName = stringArray[5] occurrence = stringArray[6] sourceMeme = [''] sourceMemberMeme = [''] testResult = str(False) try: sourceMeme = api.sourceMemeCreate(memeName, modulePath, metamemePath) sourceMemberMeme = api.sourceMemeCreate(memberMemeName, memberModulePath, memberMetamemePath) unusedAdd = api.sourceMemeMemberAdd(sourceMeme["memeID"], sourceMemberMeme["memeID"], occurrence) operationResult = api.sourceMemeMemberRemove(sourceMeme["memeID"], sourceMemberMeme["memeID"]) validation = operationResult["ValidationResults"] if validation[0] == True: testResult = str(True) else: testResult = str(False) errata = validation[1] except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) operationResult = {"memeID" : "%s.%s" %(modulePath, memeName), "ValidationResults" : [False, errorMsg]} errata.append(errorMsg) testcase = "%s has member %s" %(sourceMeme["memeID"], sourceMemberMeme["memeID"]) expectedResult = "True" results = [n, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testSourceEnhancement(filename): method = moduleName + '.' + 'testSourceEnhancement' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, filename) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 unicodeReadLine = str(eachReadLine) stringArray = str.split(unicodeReadLine) metamemePath = stringArray[0] modulePath = stringArray[1] memeName = stringArray[2] enhancedMetamemePath = stringArray[3] enhancedModulePath = stringArray[4] enhancedMemeName = stringArray[5] sourceMeme = [''] sourceMemberMeme = [''] testResult = str(False) try: sourceMeme = api.sourceMemeCreate(memeName, modulePath, metamemePath) sourceMemberMeme = api.sourceMemeCreate(enhancedMemeName, enhancedModulePath, enhancedMetamemePath) operationResult = api.sourceMemeEnhancementAdd(sourceMeme["memeID"], sourceMemberMeme["memeID"]) validation = operationResult["ValidationResults"] if validation[0] == True: testResult = str(True) else: testResult = str(False) errata = validation[1] except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) operationResult = {"memeID" : "%s.%s" %(modulePath, memeName), "ValidationResults" : [False, errorMsg]} errata.append(errorMsg) testcase = "%s enhancing %s" %(sourceMeme["memeID"], sourceMemberMeme["memeID"]) expectedResult = stringArray[6] results = [n, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) #Part 2 - Create two generic memes and use one to enhance the other # Create the two memes # Add a property to each # Create entities from the two memes # Check to ensure that they have the peoper properties # Use one meme to enhanece the other. # Create a new entity. # Test that it has all properties part2AllTrue = True # Create the two memes enhancingMeme = api.sourceMemeCreate("Enhancing") enhancedMeme = api.sourceMemeCreate("Enhanced") testcase = "Generic enhancing Generic" try: # Add a property to each api.sourceMemePropertySet(enhancingMeme["memeID"], "A", "A") api.sourceMemePropertySet(enhancedMeme["memeID"], "B", "B") # Create entities from the two memes entityA = api.createEntityFromMeme(enhancingMeme["memeID"]) entityB = api.createEntityFromMeme(enhancedMeme["memeID"]) # Check to ensure that they have the peoper properties entityAhasA = Graph.api.getEntityHasProperty(entityA, "A") entityBhasA = Graph.api.getEntityHasProperty(entityB, "A") entityAhasB = Graph.api.getEntityHasProperty(entityA, "B") entityBhasB = Graph.api.getEntityHasProperty(entityB, "B") if entityAhasA == False: part2AllTrue = False if entityBhasA == True: part2AllTrue = False if entityAhasB == True: part2AllTrue = False if entityBhasB == False: part2AllTrue = False # Use one meme to enhanece the other. unusedReturn = api.sourceMemeEnhancementAdd(enhancingMeme["memeID"], enhancedMeme["memeID"]) # Test that it has all properties entityAB = api.createEntityFromMeme(enhancedMeme["memeID"]) entityABhasA = Graph.api.getEntityHasProperty(entityAB, "A") entityABhasB = Graph.api.getEntityHasProperty(entityAB, "B") if entityABhasA == False: part2AllTrue = False if entityABhasB == False: part2AllTrue = False part2AllTrue = str(part2AllTrue) results = [n, testcase, part2AllTrue, "True", []] resultSet.append(results) except Exception as e: results = [n, testcase, "False", "True", []] resultSet.append(results) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testSourceEnhancementRemove(filename): method = moduleName + '.' + 'testSourceEnhancementRemove' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, filename) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 unicodeReadLine = str(eachReadLine) stringArray = str.split(unicodeReadLine) metamemePath = stringArray[0] modulePath = "%s_remove" %stringArray[1] memeName = stringArray[2] enhancedMetamemePath = stringArray[3] enhancedModulePath = "%s_remove" %stringArray[4] enhancedMemeName = stringArray[5] sourceMeme = [''] sourceMemberMeme = [''] testResult = str(False) try: sourceMeme = api.sourceMemeCreate(memeName, modulePath, metamemePath) sourceMemberMeme = api.sourceMemeCreate(enhancedMemeName, enhancedModulePath, enhancedMetamemePath) unusedAddEnhancement = api.sourceMemeEnhancementAdd(sourceMeme["memeID"], sourceMemberMeme["memeID"]) operationResult = api.sourceMemeEnhancementRemove(sourceMeme["memeID"], sourceMemberMeme["memeID"]) validation = operationResult["ValidationResults"] if validation[0] == True: testResult = str(True) else: testResult = str(False) errata = validation[1] except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) operationResult = {"memeID" : "%s.%s" %(modulePath, memeName), "ValidationResults" : [False, errorMsg]} errata.append(errorMsg) testcase = "%s enhancing %s" %(sourceMeme["memeID"], sourceMemberMeme["memeID"]) expectedResult = "True" results = [n, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) #Part 2 - Create two generic memes and use one to enhance the other # Create the two memes # Add a property to each # Create entities from the two memes # Check to ensure that they have the peoper properties # Use one meme to enhanece the other. # Create a new entity. # Test that it has all properties # Remove the enhancement # Create a new entity and test that the enhancing property is not there part2AllTrue = True # Create the two memes enhancingMeme = api.sourceMemeCreate("Enhancing") enhancedMeme = api.sourceMemeCreate("Enhanced") testcase = "Generic enhancing Generic" try: # Add a property to each api.sourceMemePropertySet(enhancingMeme["memeID"], "A", "A") api.sourceMemePropertySet(enhancedMeme["memeID"], "B", "B") # Create entities from the two memes entityA = api.createEntityFromMeme(enhancingMeme["memeID"]) entityB = api.createEntityFromMeme(enhancedMeme["memeID"]) # Check to ensure that they have the peoper properties entityAhasA = Graph.api.getEntityHasProperty(entityA, "A") entityBhasA = Graph.api.getEntityHasProperty(entityB, "A") entityAhasB = Graph.api.getEntityHasProperty(entityA, "B") entityBhasB = Graph.api.getEntityHasProperty(entityB, "B") if entityAhasA == False: part2AllTrue = False if entityBhasA == True: part2AllTrue = False if entityAhasB == True: part2AllTrue = False if entityBhasB == False: part2AllTrue = False # Use one meme to enhanece the other. unusedReturn = api.sourceMemeEnhancementAdd(enhancingMeme["memeID"], enhancedMeme["memeID"]) # Test that it has all properties entityAB = api.createEntityFromMeme(enhancedMeme["memeID"]) entityABhasA = Graph.api.getEntityHasProperty(entityAB, "A") entityABhasB = Graph.api.getEntityHasProperty(entityAB, "B") if entityABhasA == False: part2AllTrue = False if entityABhasB == False: part2AllTrue = False # Remove the enhancement unusedReturn = api.sourceMemeEnhancementRemove(enhancingMeme["memeID"], enhancedMeme["memeID"]) # Create a new entity and test that the enhancing property is not there entityABRemoved = api.createEntityFromMeme(enhancedMeme["memeID"]) entityABRemovedHasA = Graph.api.getEntityHasProperty(entityABRemoved, "A") entityABRemovedHasB = Graph.api.getEntityHasProperty(entityABRemoved, "B") if entityABRemovedHasA == True: part2AllTrue = False if entityABRemovedHasB == False: part2AllTrue = False part2AllTrue = str(part2AllTrue) results = [n, testcase, part2AllTrue, "True", []] resultSet.append(results) except Exception as e: results = [n, testcase, "False", "True", []] resultSet.append(results) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testSourceSingletonSet(filename): method = moduleName + '.' + 'testSourceEnhancementRemove' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] resultSet = [] #try: testFileName = os.path.join(testDirPath, filename) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 unicodeReadLine = str(eachReadLine) stringArray = str.split(unicodeReadLine) metamemePath = stringArray[0] modulePath = "%s_singleton" %stringArray[1] memeName = stringArray[2] sourceMeme = [''] testResult = str(False) afterSingleton = False afterRemoval = False operationResult = {} try: sourceMeme = api.sourceMemeCreate(memeName, modulePath, metamemePath) setAsSingleton = api.sourceMemeSetSingleton(sourceMeme["memeID"], True) afterSingleton = api.getIsMemeSingleton(sourceMeme["memeID"]) if afterSingleton == False: verboseResults = setAsSingleton["ValidationResults"] errata.append(verboseResults[1]) setAsNonSingleton = api.sourceMemeSetSingleton(sourceMeme["memeID"], False) afterRemoval = api.getIsMemeSingleton(sourceMeme["memeID"]) if afterRemoval == True: verboseResults = setAsNonSingleton["ValidationResults"] errata.append(verboseResults[1]) operationResult = {"memeID" : sourceMeme["memeID"], "ValidationResults" : [True, []]} except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) operationResult = {"memeID" : "%s.%s" %(modulePath, memeName), "ValidationResults" : [False, errorMsg]} errata.append(errorMsg) if (afterSingleton == True) and (afterRemoval == False): testResult = str(True) testcase = str(operationResult["memeID"]) expectedResult = "True" results = [n, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testGeneric(): """ Greate a generic meme; one of type Graphyne.Generic. """ method = moduleName + '.' + 'testGeneric' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) resultSet = [] errata = [] testResult = False expectedResult = "True" try: testEntityID = api.createEntity() memeType = api.getEntityMemeType(testEntityID) if memeType == "Graphyne.Generic": operationResult = {"memeID" : "Graphyne.Generic", "ValidationResults" : [True, []]} testResult = "True" else: errorMsg = ('Generic Entity Has meme type = %s' % (memeType) ) operationResult = {"memeID" : "Graphyne.Generic", "ValidationResults" : [True, []]} except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) operationResult = {"memeID" : "Graphyne.Generic", "ValidationResults" : [False, errorMsg]} errata.append(errorMsg) testcase = str(operationResult["memeID"]) results = [1, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(1)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testDeleteEntity(): """ Test Entity Removal. Create 5 entities of type Graphyne.Generic. Chain them together: E1 >> E2 >> E3 >> E4 >> E5 Check that they are functional Traverse from E1 to E5 Traverse from E5 to E1 Delete E3 We should not be able to traverse form E1 to E5 We should not be able to traverse form E5 to E1 We should not be able to traverse from E2 to E3 We should not be able to traverse from E3 to E2 We should not be able to traverse from E4 to E3 We should not be able to traverse from E3 to E4 We should be able to traverse from E1 to E2 We should be able to traverse from E2 to E1 We should be able to traverse from E4 to E5 We should be able to traverse from E5 to E4 We should not be able to aquire E3 via getEntity() """ method = moduleName + '.' + 'testDeleteEntity' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) resultSet = [] errata = [] testResult = "True" expectedResult = "True" errorMsg = "" #Create 5 entities of type Graphyne.Generic. Chain them together: E1 >> E2 >> E3 >> E4 >> E5 try: testEntityID1 = api.createEntity() testEntityID2 = api.createEntity() testEntityID3 = api.createEntity() testEntityID4 = api.createEntity() testEntityID5 = api.createEntity() api.addEntityLink(testEntityID1, testEntityID2) api.addEntityLink(testEntityID2, testEntityID3) api.addEntityLink(testEntityID3, testEntityID4) api.addEntityLink(testEntityID4, testEntityID5) except Exception as e: testResult = "False" errorMsg = ('Error creating entities! Traceback = %s' % (e) ) errata.append(errorMsg) #Navitate to end of chain and back try: uuid15 = api.getLinkCounterpartsByType(testEntityID1, "Graphyne.Generic::Graphyne.Generic::Graphyne.Generic::Graphyne.Generic") uuid11 = api.getLinkCounterpartsByType(uuid15[0], "Graphyne.Generic::Graphyne.Generic::Graphyne.Generic::Graphyne.Generic") if (uuid15[0] != testEntityID5) or (uuid11[0] != testEntityID1): testResult = "False" errorMsg = ('%sShould be able to navigate full chain and back before deleting middle entity, but could not!\n') except Exception as e: testResult = "False" errorMsg = ('Error deleting Entity! Traceback = %s' % (e) ) errata.append(errorMsg) #Delete E3 try: api.destroyEntity(testEntityID3) except Exception as e: testResult = "False" errorMsg = ('Error deleting Entity! Traceback = %s' % (e) ) errata.append(errorMsg) #E3 should no longer be there try: e3 = api.getEntity(testEntityID3) if e3 is not None: testResult = "False" errorMsg = ('Deleted entity still present!') errata.append(errorMsg) except Exceptions.NoSuchEntityError as e: #We expect a NoSuchEntityError here pass except Exception as e: #But we ONLY expect a NoSuchEntityError exception. Anything else is a problem testResult = "False" errorMsg = ('Unexpected Error while checking for previously deleted entity! Traceback = %s' % (e) ) errata.append(errorMsg) #But E4 should remain try: e4 = api.getEntity(testEntityID4) if e4 is None: testResult = "False" errorMsg = ('Entity that should not be deleted was!') errata.append(errorMsg) except Exception as e: testResult = "False" errorMsg = ('Error while checking to see if entity that was not supposed to be deleted is still present! Traceback = %s' % (e) ) errata.append(errorMsg) #Post delete navigation try: #First hops should work uuid22 = api.getLinkCounterpartsByType(testEntityID1, "Graphyne.Generic") uuid24 = api.getLinkCounterpartsByType(testEntityID5, "Graphyne.Generic") if (len(uuid22) == 0) or (len(uuid24) == 0) : testResult = "False" errorMsg = ('%sShould be able to navigate between undeleted entities, but can not!\n' %errorMsg) except Exception as e: testResult = "False" errorMsg = ('%sProblem in spost delete navigation between undeleted entities. Traceback = %s' %(errorMsg, e)) try: #This should not uuid25 = api.getLinkCounterpartsByType(testEntityID1, "Graphyne.Generic::Graphyne.Generic::Graphyne.Generic::Graphyne.Generic") uuid21 = api.getLinkCounterpartsByType(testEntityID5, "Graphyne.Generic::Graphyne.Generic::Graphyne.Generic::Graphyne.Generic") if (len(uuid25) > 0) or (len(uuid21) > 0) : testResult = "False" errorMsg = ('%sShould not be able to navigate full chain and back, but did!\n' %errorMsg) except: pass try: #neither should this nearestNeighbors = api.getLinkCounterpartsByType(testEntityID2, "*") if (testEntityID1 not in nearestNeighbors) or (testEntityID4 in nearestNeighbors) : testResult = "False" errorMsg = ('%sShould not be able to navigate full chain and back, but did!\n' %errorMsg) except: pass testcase = "Deletion" results = [1, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(1)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testSubatomicLinks(): """ Test creating and traversing subatomic links Create 3 entities of type Graphyne.Generic. """ method = moduleName + '.' + 'testSubatomicLinks' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) resultSet = [] errata = [] testResult = "True" expectedResult = "True" errorMsg = "" #Create 5 entities of type Graphyne.Generic and get the Examples.MemeA4 singleton as well. #Chain them together: E1 >> E2 >> E3 >> E4 >> Examples.MemeA4 << E5 try: testEntityID1 = api.createEntity() testEntityID2 = api.createEntity() testEntityID3 = api.createEntity() api.addEntityLink(testEntityID1, testEntityID2) #Atomic api.addEntityLink(testEntityID2, testEntityID3, {}, 1) #Subatomic except Exception as e: testResult = "False" errorMsg = ('Error creating entities! Traceback = %s' % (e) ) errata.append(errorMsg) #Atomic Navigation try: uuid12 = api.getLinkCounterpartsByType(testEntityID2, "Graphyne.Generic", 0) if len(uuid12) != 1: testResult = "False" errorMsg = ('%sError in getLinkCounterpartsByType() chile checking for Atomic links. Memberlist should return exactly one entry. Actually returned %s members!\n' %len(uuid12)) elif uuid12[0] != testEntityID1: testResult = "False" errorMsg = ('%sError in getLinkCounterpartsByType() chile checking for Atomic links. Wrong cluster sibling returned.!\n') except Exception as e: testResult = "False" errorMsg = ('Error traversing atomic link! Traceback = %s' % (e) ) errata.append(errorMsg) #SubAtomic Navigation try: uuid23 = api.getLinkCounterpartsByType(testEntityID2, "Graphyne.Generic", 1) if len(uuid23) != 1: testResult = "False" errorMsg = ('%sError in getLinkCounterpartsByType() chile checking for SubAtomic links. Memberlist should return exactly one entry. Actually returned %s members!\n' %len(uuid12)) elif uuid23[0] != testEntityID3: testResult = "False" errorMsg = ('%sError in getLinkCounterpartsByType() chile checking for SubAtomic links. Wrong cluster sibling returned.!\n') except Exception as e: testResult = "False" errorMsg = ('Error traversing subatomic link Traceback = %s' % (e) ) errata.append(errorMsg) #Universal Navigation try: uuidBoth = api.getLinkCounterpartsByType(testEntityID2, "Graphyne.Generic") if len(uuidBoth) != 2: testResult = "False" errorMsg = ('%sError in getLinkCounterpartsByType() chile checking for SubAtomic links. Memberlist should return exactly two entries. Actually returned %s members!\n' %len(uuid12)) except Exception as e: testResult = "False" errorMsg = ('Error traversing link! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = "Subatomic Links" results = [1, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(1)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testGetClusterMembers(): """ Test Getting Clister Members. Create 6 entities of type Graphyne.Generic. Chain four of them together: E1 >> E2 >> E3 >> E4 Connect E4 to a singleton, Examples.MemeA4 Connect E5 to Examples.MemeA4 Connect E3 to E6 via a subatomic link Check that we can traverse from E1 to E5. Get the cluseter member list of E3 with linktype = None. It should include E2, E3, E4, E6 Get the cluseter member list of E3 with linktype = 0. It should include E2, E3, E4 Get the cluseter member list of E3 with linktype = 1. It should include E6 Get the cluseter member list of E5. It should be empty memeStructure = script.getClusterMembers(conditionContainer, 1, False) """ method = moduleName + '.' + 'testGetClusterMembers' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) resultSet = [] errata = [] testResult = "True" expectedResult = "True" errorMsg = "" #Create 5 entities of type Graphyne.Generic and get the Examples.MemeA4 singleton as well. #Chain them together: E1 >> E2 >> E3 >> E4 >> Examples.MemeA4 << E5 try: testEntityID1 = api.createEntity() testEntityID2 = api.createEntity() testEntityID3 = api.createEntity() testEntityID4 = api.createEntity() testEntityID5 = api.createEntity() testEntityID6 = api.createEntity() theSingleton = Graph.api.createEntityFromMeme("Examples.MemeA4") api.addEntityLink(testEntityID1, testEntityID2) api.addEntityLink(testEntityID2, testEntityID3) api.addEntityLink(testEntityID3, testEntityID4) api.addEntityLink(testEntityID3, testEntityID6, {}, 1) api.addEntityLink(testEntityID4, theSingleton) api.addEntityLink(testEntityID5, theSingleton) except Exception as e: testResult = "False" errorMsg = ('Error creating entities! Traceback = %s' % (e) ) errata.append(errorMsg) #Navitate to end of chain and back try: uuid15 = api.getLinkCounterpartsByType(testEntityID1, "Graphyne.Generic::Graphyne.Generic::Graphyne.Generic::Examples.MemeA4::Graphyne.Generic") uuid11 = api.getLinkCounterpartsByType(uuid15[0], "Examples.MemeA4::Graphyne.Generic::Graphyne.Generic::Graphyne.Generic::Graphyne.Generic") if (uuid15[0] != testEntityID5) or (uuid11[0] != testEntityID1): testResult = "False" errorMsg = ('%sShould be able to navigate full chain and back before measuring cluster membership, but could not!\n') except Exception as e: testResult = "False" errorMsg = ('Error measuring cluster membership! Traceback = %s' % (e) ) errata.append(errorMsg) #From E3, atomic try: entityListRaw = api.getClusterMembers(testEntityID3) entityList1 = [] for entityUUID in entityListRaw: entityList1.append(entityUUID) if testEntityID1 not in entityList1: testResult = "False" errorMsg = ('%E1 should be in atomic link cluster of E3, but is not!\n' %errorMsg) if testEntityID2 not in entityList1: testResult = "False" errorMsg = ('%E2 should be in atomic link cluster of E3, but is not!\n' %errorMsg) if testEntityID4 not in entityList1: testResult = "False" errorMsg = ('%E4 should be in atomic link cluster of E3, but is not!\n' %errorMsg) if theSingleton not in entityList1: testResult = "False" errorMsg = ('%Examples.MemeA4 should be in atomic link cluster of E3, but is not!\n' %errorMsg) if len(entityList1) != 4: testResult = "False" errorMsg = ('%E3 should have 3 siblings in its atomic link cluster, but it has %s!\n' %(errorMsg, len(entityList1))) except Exception as e: testResult = "False" errorMsg = ('Getting atomic cluster of E3! Traceback = %s' % (e) ) errata.append(errorMsg) #From E3, subatomic try: entityList2 = api.getClusterMembers(testEntityID3, 1) if testEntityID6 not in entityList2: testResult = "False" errorMsg = ('%E6 should be in subatomic link cluster of E3, but is not!\n' %errorMsg) if len(entityList2) != 1: testResult = "False" errorMsg = ('%E3 should have 1 sibling in its atomic link cluster, but it has %s!\n' %(errorMsg, len(entityList1))) except Exception as e: testResult = "False" errorMsg = ('Getting atomic cluster of E3! Traceback = %s' % (e) ) errata.append(errorMsg) #From E5, atomic try: entityList3 = api.getClusterMembers(testEntityID5) if theSingleton not in entityList3: testResult = "False" errorMsg = ('%Examples.MemeA4 should be in atomic link cluster of E3, but is not!\n' %errorMsg) if len(entityList3) != 1: testResult = "False" errorMsg = ('%E5 should have 0 siblings in its atomic link cluster, but it has %s!\n' %(errorMsg, len(entityList1))) except Exception as e: testResult = "False" errorMsg = ('Getting atomic cluster of E5! Traceback = %s' % (e) ) errata.append(errorMsg) #From E5, subatomic try: entityList4 = api.getClusterMembers(testEntityID5) if len(entityList4) != 1: testResult = "False" errorMsg = ('%E5 should have 1 sibling in its atomic link cluster, but it has %s!\n' %(errorMsg, len(entityList1))) except Exception as e: testResult = "False" errorMsg = ('Getting atomic cluster of E5! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = "getClusterMembers()" results = [1, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(1)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testGetHasCounterpartsByType(phaseName = 'getHasCounterpartsByType', fName = "Entity_Phase7.atest"): ''' Basically a repeat of Phase 7, but with getHasCounterpartsByType() Create entities from the meme in the first two colums. Add a link between the two at the location on entity in from column 3. Check and see if each is a counterpart as seen from the other using the addresses in columns 4&5 (CheckPath & Backpath) & the filter. The filter must be the same as the type of link (or None) The check location must be the same as the added loation. ''' method = moduleName + '.' + phaseName Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) results = [] lresultSet = [] del lresultSet[:] #try: testFileName = os.path.join(testDirPath, fName) readLoc = codecs.open(testFileName, "r", "utf-8") allLines = readLoc.readlines() readLoc.close n = 0 for eachReadLine in allLines: errata = [] n = n+1 stringArray = str.split(eachReadLine) Graph.logQ.put( [logType , logLevel.INFO , method , "Starting testcase %s, meme %s" %(n, stringArray[0])]) testResult = False try: entityID0 = Graph.api.createEntityFromMeme(stringArray[0]) entityID1 = Graph.api.createEntityFromMeme(stringArray[1]) entityID2 = Graph.api.createEntityFromMeme(stringArray[1]) #Attach entityID1 at the mount point specified in stringArray[2] if stringArray[2] != "X": mountPoints = api.getLinkCounterpartsByType(entityID0, stringArray[2], 0) unusedMountPointsOverview = {} for mountPoint in mountPoints: try: mpMemeType = api.getEntityMemeType(mountPoint) unusedMountPointsOverview[mountPoint] = mpMemeType except Exception as e: #errorMessage = "debugHelperMemeType warning in Smoketest.testEntityPhase7. Traceback = %s" %e #Graph.logQ.put( [logType , logLevel.WARNING , method , errorMessage]) raise e for mountPoint in mountPoints: api.addEntityLink(mountPoint, entityID1, {}, int(stringArray[5])) else: api.addEntityLink(entityID0, entityID1, {}, int(stringArray[5])) backTrackCorrect = False linkType = None if stringArray[6] != "X": linkType = int(stringArray[6]) backTrackCorrect = False linkType = None if stringArray[6] != "X": linkType = int(stringArray[6]) #see if we can get from entityID0 to entityID1 via stringArray[3] addLocationCorrect = api.getHasCounterpartsByType(entityID0, stringArray[3], linkType) #see if we can get from entityID1 to entityID0 via stringArray[4] backTrackCorrect = api.getHasCounterpartsByType(entityID1, stringArray[4], linkType) #see if we can get from entityID2 to entityID0 via stringArray[4] e3Attached = api.getHasCounterpartsByType(entityID2, stringArray[4], linkType) if (backTrackCorrect == True) and (addLocationCorrect == True) and (e3Attached == False): testResult = True except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = str(stringArray[0]) allTrueResult = str(testResult) expectedResult = stringArray[7] results = [n, testcase, allTrueResult, expectedResult, errata] lresultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(n)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return lresultSet def testGetEntityMetaMemeType(): """ Greate a generic meme; one of type Graphyne.Generic. Ensure that it's metameme is Graphyne.GenericMetaMeme """ method = moduleName + '.' + 'testGetEntityMetaMemeType' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) resultSet = [] errata = [] testResult = False expectedResult = "True" try: testEntityID = api.createEntity() metaMemeType = api.getEntityMetaMemeType(testEntityID) if metaMemeType == "Graphyne.GenericMetaMeme": operationResult = {"metamemeID" : "Graphyne.GenericMetaMeme", "ValidationResults" : [True, []]} testResult = "True" else: errorMsg = ('Generic Entity Has metameme type = %s' % (metaMemeType) ) operationResult = {"metamemeID" : "Graphyne.GenericMetaMeme", "ValidationResults" : [True, []]} except Exception as e: errorMsg = ('Error! Traceback = %s' % (e) ) operationResult = {"metamemeID" : "Graphyne.GenericMetaMeme", "ValidationResults" : [False, errorMsg]} errata.append(errorMsg) testcase = str(operationResult["metamemeID"]) results = [1, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(1)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testInstallExecutor(): """ Greate a generic meme; one of type Graphyne.Generic. Ensure that it's metameme is Graphyne.GenericMetaMeme """ method = moduleName + '.' + 'testInstallExecutor' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) resultSet = [] errata = [] testResult = "True" errorMsg = "" expectedResult = "True" try: testEntityID = api.createEntity() e2MemeType = api.getEntityMemeType(testEntityID) from Config.Test.TestRepository import InstallPyExecTest as testMod testExec = testMod.TestClass(e2MemeType) api.installPythonExecutor(testEntityID, testExec) #The execute() method of testMod.TestClass hould return the memeID when returnVal1 = api.evaluateEntity(testEntityID) if returnVal1 != e2MemeType: testResult = "False" errorMsg = ("%sCalling TestClass.execute() should return %s, but %s was returned instead!\n" %(errorMsg, e2MemeType, returnVal1)) else: operationResult = {"metamemeID" : "Graphyne.GenericMetaMeme", "ValidationResults" : [True, errorMsg]} if testResult == "True": returnVal2 = api.evaluateEntity(testEntityID, {"returnMe" : "Hello World"}) if returnVal2 != "Hello World": testResult = "False" errorMsg = ("%sCalling TestClass.execute() with 'returnMe' in runtime parameter keys should return 'Hello World', but %s was returned instead!\n" %(errorMsg, returnVal2)) else: operationResult = {"metamemeID" : "Graphyne.GenericMetaMeme", "ValidationResults" : [True, []]} if testResult == "True": try: unusedReturnVal3 = api.evaluateEntity(testEntityID, {"thisWontReturnAnything" : "Hello World"}) testResult = "False" errorMsg = ("%sCalling TestClass.execute() 'thisWontReturnAnything' in runtime parameter keys should return a keyError exception, but %s was returned instead!\n" %(errorMsg, returnVal2)) except Exceptions.EventScriptFailure as e: #We should have this result operationResult = {"metamemeID" : "Graphyne.GenericMetaMeme", "ValidationResults" : [True, errorMsg]} except Exception as e: testResult = "False" errorMsg = ('Error! Traceback = %s' % (e) ) operationResult = {"metamemeID" : "Graphyne.GenericMetaMeme", "ValidationResults" : [False, errorMsg]} errata.append(errorMsg) testcase = str(operationResult["metamemeID"]) results = [1, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(1)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testGetCluster(): """ Test Getting Cluster Dictionary. Create 6 entities of type Graphyne.Generic. Chain four of them together: E1 >> E2 >> E3 >> E4 Connect E4 to a singleton, Examples.MemeA4 Connect E5 to Examples.MemeA4 Connect E3 to E6 via a subatomic link Check that we can traverse from E1 to E5. Get the cluster member list of E3 with linktype = None. It should include E2, E3, E4, E6 Get the cluster member list of E3 with linktype = 0. It should include E2, E3, E4 Get the cluster member list of E3 with linktype = 1. It should include E6 Get the cluster member list of E5. It should be empty memeStructure = script.getClusterMembers(conditionContainer, 1, False) """ method = moduleName + '.' + 'testGetClusterMembers' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) resultSet = [] errata = [] testResult = "True" expectedResult = "True" errorMsg = "" #Create 5 entities of type Graphyne.Generic and get the Examples.MemeA4 singleton as well. #Chain them together: E1 >> E2 >> E3 >> E4 >> Examples.MemeA4 << E5 try: testEntityID1 = api.createEntity() testEntityID2 = api.createEntity() testEntityID3 = api.createEntity() testEntityID4 = api.createEntity() testEntityID5 = api.createEntity() testEntityID6 = api.createEntity() theSingleton = Graph.api.createEntityFromMeme("Examples.MemeA4") api.addEntityLink(testEntityID1, testEntityID2) api.addEntityLink(testEntityID2, testEntityID3) api.addEntityLink(testEntityID3, testEntityID4) api.addEntityLink(testEntityID3, testEntityID6, {}, 1) api.addEntityLink(testEntityID4, theSingleton) api.addEntityLink(testEntityID5, theSingleton) except Exception as e: testResult = "False" errorMsg = ('Error creating entities! Traceback = %s' % (e) ) errata.append(errorMsg) #Navitate to end of chain and back try: uuid15 = api.getLinkCounterpartsByType(testEntityID1, ">>Graphyne.Generic>>Graphyne.Generic>>Graphyne.Generic>>Examples.MemeA4<<Graphyne.Generic", None, True) uuid11 = api.getLinkCounterpartsByType(uuid15[0], "Examples.MemeA4<<Graphyne.Generic<<Graphyne.Generic<<Graphyne.Generic<<Graphyne.Generic", None, True) if (testEntityID5 not in uuid15) or (testEntityID1 not in uuid11): testResult = "False" errorMsg = ('%sShould be able to navigate full chain and back before measuring cluster membership, but could not!\n') except Exception as e: testResult = "False" errorMsg = ('Error measuring cluster membership! Traceback = %s' % (e) ) errata.append(errorMsg) #From E3, atomic try: entityListRaw = api.getCluster(testEntityID3) entityList1 = [] for entityNode in entityListRaw["nodes"]: entityList1.append(entityNode['id']) if str(testEntityID1) not in entityList1: testResult = "False" errorMsg = ('%E1 should be in atomic link cluster of E3, but is not!\n' %errorMsg) if str(testEntityID2) not in entityList1: testResult = "False" errorMsg = ('%E2 should be in atomic link cluster of E3, but is not!\n' %errorMsg) if str(testEntityID4) not in entityList1: testResult = "False" errorMsg = ('%E4 should be in atomic link cluster of E3, but is not!\n' %errorMsg) if str(theSingleton) not in entityList1: testResult = "False" errorMsg = ('%Examples.MemeA4 should be in atomic link cluster of E3, but is not!\n' %errorMsg) if len(entityList1) != 5: testResult = "False" errorMsg = ('%E3 should have 5 members in its atomic link cluster - itself, 3 generics and the singleton - in its atomic link cluster, but it has %s members!\n' %(errorMsg, len(entityList1))) except Exception as e: testResult = "False" errorMsg = ('Getting atomic cluster of E3! Traceback = %s' % (e) ) errata.append(errorMsg) #From E3, subatomic try: entityListRaw = api.getCluster(testEntityID3, 1) entityList2 = [] for entityNode in entityListRaw["nodes"]: entityList2.append(entityNode['id']) if str(testEntityID6) not in entityList2: testResult = "False" errorMsg = ('%E6 should be in subatomic link cluster of E3, but is not!\n' %errorMsg) if len(entityList2) != 2: testResult = "False" errorMsg = ('%E3 should have 2 members in its subatomic link cluster - itself and 1 sibling, but it has %s members!\n' %(errorMsg, len(entityList1))) except Exception as e: testResult = "False" errorMsg = ('Getting atomic cluster of E3! Traceback = %s' % (e) ) errata.append(errorMsg) #From E5, atomic try: entityListRaw = api.getCluster(testEntityID5) entityList3 = [] for entityNode in entityListRaw["nodes"]: entityList3.append(entityNode['id']) if str(theSingleton) not in entityList3: testResult = "False" errorMsg = ('%Examples.MemeA4 should be in atomic link cluster of E5, but is not!\n' %errorMsg) if len(entityList3) != 2: testResult = "False" errorMsg = ('%E5 should 2 members in its atomic link cluster, itself and the Examples.MemeA4 singleton, but the cluster has %s members!\n' %(errorMsg, len(entityList1))) except Exception as e: testResult = "False" errorMsg = ('Getting atomic cluster of E5! Traceback = %s' % (e) ) errata.append(errorMsg) #From E5, subatomic try: entityListRaw = api.getCluster(testEntityID5, 1) entityList4 = [] for entityNode in entityListRaw["nodes"]: entityList4.append(entityNode['id']) if len(entityList4) != 1: testResult = "False" errorMsg = ('%E5 should be alone in its atomic link cluster, but the cluster has %s members!\n' %(errorMsg, len(entityList1))) except Exception as e: testResult = "False" errorMsg = ('Getting atomic cluster of E5! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = "getClusterMembers()" results = [1, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(1)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testGetTraverseReport(): """ Test Getting Traverse Report Dictionary. Create 4 entities of type Graphyne.Generic. (as in the cluser test) Chain 3 of them together: E1 >> E2 >> E3 Connect E4 to a singleton, Examples.MemeA4 Connect E4 to Examples.MemeA4 Now get the traverse report from E1 to E4. The traverse report step for E1 should contain E2 and only E2. The traverse report step for E2 should contain E1 and E3. The traverse report step for E3 should contain E2 and Examples.MemeA4. The traverse report step for E3 should contain Examples.MemeA4 and only Examples.MemeA4. memeStructure = script.getTraversePathReport(conditionContainer, 1, False) """ method = moduleName + '.' + 'testGetTraverseReport' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) resultSet = [] errata = [] testResult = "True" expectedResult = "True" errorMsg = "" #Create 5 entities of type Graphyne.Generic and get the Examples.MemeA4 singleton as well. #Chain them together: E1 >> E2 >> E3 >> E4 >> Examples.MemeA4 << E5 try: testEntityID1 = api.createEntity() testEntityID2 = api.createEntity() testEntityID3 = api.createEntity() testEntityID4 = api.createEntity() theSingleton = Graph.api.createEntityFromMeme("Examples.MemeA4") api.addEntityLink(testEntityID1, testEntityID2) api.addEntityLink(testEntityID2, testEntityID3) api.addEntityLink(testEntityID3, theSingleton) api.addEntityLink(testEntityID4, theSingleton) except Exception as e: testResult = "False" errorMsg = ('Error creating entities! Traceback = %s' % (e) ) errata.append(errorMsg) traverseStringByMeme = ">>Graphyne.Generic>>Graphyne.Generic>>Examples.MemeA4<<Graphyne.Generic" # Reference for debugging assistance unusedExpectedReport = {testEntityID1 : {"meme" : "Graphyne.Generic", "metameme" : "Graphyne.GenericMetaMeme", "members" : {testEntityID2 : {"meme" : "Graphyne.Generic", "metameme" : "Graphyne.GenericMetaMeme"}}}, testEntityID2 : {"meme" : "Graphyne.Generic", "metameme" : "Graphyne.GenericMetaMeme", "members" : {testEntityID1 : {"meme" : "Graphyne.Generic", "metameme" : "Graphyne.GenericMetaMeme"}, testEntityID3 : {"meme" : "Graphyne.Generic", "metameme" : "Graphyne.GenericMetaMeme"}}}, testEntityID3 : {"meme" : "Graphyne.Generic", "metameme" : "Graphyne.GenericMetaMeme", "members" : {testEntityID2 : {"meme" : "Graphyne.Generic", "metameme" : "Graphyne.GenericMetaMeme"}, theSingleton : {"meme" : "Examples.MemeA4", "metameme" : "Examples.A"}}}, testEntityID4 : {"meme" : "Graphyne.Generic", "metameme" : "Graphyne.GenericMetaMeme", "members" : {theSingleton : {"meme" : "Examples.MemeA4", "metameme" : "Examples.A"}}} } #Navitate to end of chain and back try: uuid14 = api.getLinkCounterpartsByType(testEntityID1, traverseStringByMeme, None, True) uuid41 = api.getLinkCounterpartsByType(uuid14[0], "Examples.MemeA4<<Graphyne.Generic<<Graphyne.Generic<<Graphyne.Generic", None, True) if (testEntityID4 not in uuid14) or (testEntityID1 not in uuid41): testResult = "False" errorMsg = ('%sShould be able to navigate full chain and back before measuring cluster membership, but could not!\n') except Exception as e: testResult = "False" errorMsg = ('Error reporting on traverse path! Traceback = %s' % (e) ) errata.append(errorMsg) #From E1, atomic, Meme traverse # def getTraverseReport(self, entityUUID, traversePath, isMeme = True, linkType = None, returnUniqueValuesOnly = True): try: reportRaw = api.getTraverseReport(testEntityID1, traverseStringByMeme) except Exception as e: testResult = "False" errorMsg = ('Getting atomic cluster of E3! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = "getTraverseReport()" reportNodes = reportRaw["nodes"] reportLinks = reportRaw["links"] e1ID = str(testEntityID1) e2ID = str(testEntityID2) e3ID = str(testEntityID3) e4ID = str(testEntityID4) eSID = str(theSingleton) traverseNodeKeyList = [] for reportNode in reportNodes: traverseNodeKeyList.append(reportNode["id"]) if e1ID not in traverseNodeKeyList: testResult = False if e2ID not in traverseNodeKeyList: testResult = False if e3ID not in traverseNodeKeyList: testResult = False if e4ID not in traverseNodeKeyList: testResult = False if eSID not in traverseNodeKeyList: testResult = False tempTraverse12Found = False #Entity 1 shoukd be the parent of 2, but not be connected to 3 or 4 tempTraverse23Found = False tempTraverse3SFound = False #The singleton should be connected to 3 and 4, but not to 1 or 2. tempTraverseS4Found = False for traverseLink in reportLinks: if (traverseLink["source"] == e1ID) and (traverseLink["target"] == e2ID): tempTraverse12Found = True if (traverseLink["source"] == e2ID) and (traverseLink["target"] == e3ID): tempTraverse23Found = True if (traverseLink["source"] == e3ID) and (traverseLink["target"] == eSID): tempTraverse3SFound = True if (traverseLink["source"] == e4ID) and (traverseLink["target"] == eSID): tempTraverseS4Found = True # 1 >> 2, but not 1 << 2 # 2 >> 3 and 1 >> 2, but not 2 << 3 # 3 >> S and S << 4, but not 3 << S or S >> 4 if (traverseLink["source"] == e2ID) and (traverseLink["target"] == e1ID): testResult = False if (traverseLink["source"] == e3ID) and (traverseLink["target"] == e2ID): testResult = False #wrong link if (traverseLink["source"] == eSID) and (traverseLink["target"] == e3ID): testResult = False #wrong link if (traverseLink["source"] == eSID) and (traverseLink["target"] == e4ID): testResult = False if (traverseLink["source"] == eSID) and (traverseLink["target"] == e4ID): testResult = False if (traverseLink["source"] == eSID) and (traverseLink["target"] == e1ID): testResult = False if (traverseLink["source"] == e1ID) and (traverseLink["target"] == e3ID): testResult = False if (tempTraverse12Found == False) or\ (tempTraverse23Found == False) or\ (tempTraverse3SFound == False) or\ (tempTraverseS4Found == False): testResult = False results = [1, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(1)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testPropertyChangeEvent(): """ Create an entity from PropertyChangeEvent.PropChangeTest. It starts with: propA = 11 ( has an event script. Returns a hash "<oldVal> <newVal>" ) propB = xyz ( has an event script. Returns the entiry UUID) propC = abc ( no SES) 1 - Alter its prop A to an allowed value. Verify the value of the return. 2 - Alter its prop A a second time (to an allowed value) and verify. 3 - Alter prop B and check that the returned UUID is correct. 4 - Alter prop C to a disallowed value. Verify that return is None """ method = moduleName + '.' + 'testPropertyChangeEvent' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) resultSet = [] errata = [] testResult = "True" expectedResult = "True" errorMsg = "" #Create 5 entities of type Graphyne.Generic and get the Examples.MemeA4 singleton as well. #Chain them together: E1 >> E2 >> E3 >> E4 >> Examples.MemeA4 << E5 try: theEntity = Graph.api.createEntityFromMeme("PropertyChangeEvent.PropChangeTest") except Exception as e: testResult = "False" errorMsg = ('Error creating entities! Traceback = %s' % (e) ) errata.append(errorMsg) #Alter its prop A to an allowed value. Verify the value of the return. try: expectedReturnValue = "11 12" returnValue = api.setEntityPropertyValue(theEntity, "propA", 12) if returnValue != expectedReturnValue: testResult = "False" errorMsg = ('%sSetting the value of propA from 11 to 12 should return "%s" in the return value of the property change event. "%s returned" !\n' %(errorMsg, expectedReturnValue, returnValue)) except Exception as e: testResult = "False" errorMsg = ('Error setting value of propA! Traceback = %s' % (e) ) errata.append(errorMsg) #Alter its prop A a second time (to an allowed value) and verify. try: expectedReturnValue = "12 15" returnValue = api.setEntityPropertyValue(theEntity, "propA", 15) if returnValue != expectedReturnValue: testResult = "False" errorMsg = ('%sSetting the value of propA from 12 to 15 should return "%s" in the return value of the property change event. "%s returned" !\n' %(errorMsg, expectedReturnValue, returnValue)) except Exception as e: testResult = "False" errorMsg = ('Error setting value of propA! Traceback = %s' % (e) ) errata.append(errorMsg) #Alter prop B and check that the returned UUID is correct. try: returnValue = api.setEntityPropertyValue(theEntity, "propB", 'abc') if returnValue != str(theEntity): testResult = "False" errorMsg = ('%sSetting the value of propB should return "%s" in the return value of the property change event. "%s returned" !\n' %(errorMsg, theEntity, returnValue)) except Exception as e: testResult = "False" errorMsg = ('Error setting value of propA! Traceback = %s' % (e) ) errata.append(errorMsg) #Alter prop C to a disallowed value. Verify that return is None try: returnValue = api.setEntityPropertyValue(theEntity, "propC", 'xyz') if returnValue != None: testResult = "False" errorMsg = ('%sSetting the value of propB should return "%s" in the return value of the property change event. "%s returned" !\n' %(errorMsg, None, returnValue)) except Exception as e: testResult = "False" errorMsg = ('Error setting value of propA! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = "propertyChangeEvent()" results = [1, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(1)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testLinkEvent(): """ Create two entities from LinkEvent.LinkChangeTest. Greate three generic entities 1 - Link the a LinkEvent.LinkChangeTest entitiy with a generic one, with LinkChangeTest as the source 2 - Break the link 3 - Link the two with LinkChangeTest as the target 4 - Link the two generics 5 - Break the link Create two generic entities 6 - Link the twoLinkEvent.LinkChangeTest entities 7 - Break the link """ method = moduleName + '.' + 'testLinkEvent' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) resultSet = [] errata = [] testResult = "True" expectedResult = "True" errorMsg = "" #Create two entities from LinkEvent.LinkChangeTest. #Greate three generic entities try: linkChangeTest0 = Graph.api.createEntityFromMeme("LinkEvent.LinkChangeTest") linkChangeTest1 = Graph.api.createEntityFromMeme("LinkEvent.LinkChangeTest") genEntity0 = Graph.api.createEntity() genEntity1 = Graph.api.createEntity() genEntity2 = Graph.api.createEntity() except Exception as e: testResult = "False" errorMsg = ('Error creating entities! Traceback = %s' % (e) ) errata.append(errorMsg) #1 - Link the a LinkEvent.LinkChangeTest entitiy with a generic one, with LinkChangeTest as the source try: expectedReturnValue10 = "Added %s as link source for %s" %(linkChangeTest0, genEntity0) returnArray = api.addEntityLink(linkChangeTest0, genEntity0) if returnArray[0] != expectedReturnValue10: testResult = "False" errorMsg = '%sAdding link to LinkEvent.LinkChangeTest should return "%s" in the return value [0] of the link added event. "%s returned" !\n' %(errorMsg, expectedReturnValue10, returnArray[0]) if returnArray[1] is not None: testResult = "False" errorMsg = '%sAdding link to LinkEvent.LinkChangeTest should return "%s" in the return value [0] of the link added event. "%s returned" !\n' %(errorMsg, None, returnArray[1]) except Exception as e: testResult = "False" errorMsg = ('Error adding link! Traceback = %s' % (e) ) errata.append(errorMsg) #2 - Break the link try: expectedReturnValue20 = "Removed %s as link source for %s" %(linkChangeTest0, genEntity0) returnArray = api.removeEntityLink(linkChangeTest0, genEntity0) if returnArray[0] != expectedReturnValue20: testResult = "False" errorMsg = '%sRemoving link to LinkEvent.LinkChangeTest should return "%s" in the return value [0] of the link added event. "%s returned" !\n' %(errorMsg, expectedReturnValue20, returnArray[0]) if returnArray[1] is not None: testResult = "False" errorMsg = '%sRemoving link to LinkEvent.LinkChangeTest should return "%s" in the return value [0] of the link added event. "%s returned" !\n' %(errorMsg, None, returnArray[1]) except Exception as e: testResult = "False" errorMsg = ('Error removing link! Traceback = %s' % (e) ) errata.append(errorMsg) #3 - Link the two with LinkChangeTest as the target try: expectedReturnValue30 = "Added %s as link target for %s" %(linkChangeTest0, genEntity0) returnArray = api.addEntityLink(genEntity0, linkChangeTest0) if returnArray[1] != expectedReturnValue30: testResult = "False" errorMsg = '%sAdding link to LinkEvent.LinkChangeTest should return "%s" in the return value [0] of the link added event. "%s returned" !\n' %(errorMsg, expectedReturnValue30, returnArray[0]) if returnArray[0] is not None: testResult = "False" errorMsg = '%sAdding link to LinkEvent.LinkChangeTest should return "%s" in the return value [0] of the link added event. "%s returned" !\n' %(errorMsg, None, returnArray[1]) except Exception as e: testResult = "False" errorMsg = ('Error adding link! Traceback = %s' % (e) ) errata.append(errorMsg) #4 - Link two generics try: returnArray = api.addEntityLink(genEntity1, genEntity2) if returnArray[0] is not None: testResult = "False" errorMsg = '%sAdding link to generic entity should return "%s" in the return value [0] of the link added event. "%s returned" !\n' %(errorMsg, None, returnArray[0]) if returnArray[1] is not None: testResult = "False" errorMsg = '%sAdding link to generic entity should return "%s" in the return value [0] of the link added event. "%s returned" !\n' %(errorMsg, None, returnArray[1]) except Exception as e: testResult = "False" errorMsg = ('Error adding link! Traceback = %s' % (e) ) errata.append(errorMsg) #5 - Break the link try: returnArray = api.removeEntityLink(genEntity1, genEntity1) if returnArray[0] is not None: testResult = "False" errorMsg = '%sRemoving link from generic entity should return "%s" in the return value [0] of the link added event. "%s returned" !\n' %(errorMsg, None, returnArray[0]) if returnArray[1] is not None: testResult = "False" errorMsg = '%sRemoving link from generic entity should return "%s" in the return value [0] of the link added event. "%s returned" !\n' %(errorMsg, None, returnArray[1]) except Exception as e: testResult = "False" errorMsg = ('Error removing link! Traceback = %s' % (e) ) errata.append(errorMsg) #6 - Link the twoLinkEvent.LinkChangeTest entities try: expectedReturnValue60 = "Added %s as link source for %s" %(linkChangeTest0, linkChangeTest1) expectedReturnValue61 = "Added %s as link target for %s" %(linkChangeTest1, linkChangeTest0) returnArray = api.addEntityLink(linkChangeTest0, linkChangeTest1) if returnArray[0] != expectedReturnValue60: testResult = "False" errorMsg = '%sAdding link to LinkEvent.LinkChangeTest should return "%s" in the return value [0] of the link added event. "%s returned" !\n' %(errorMsg, expectedReturnValue60, returnArray[0]) if returnArray[1] != expectedReturnValue61: testResult = "False" errorMsg = '%sAdding link to LinkEvent.LinkChangeTest should return "%s" in the return value [0] of the link added event. "%s returned" !\n' %(errorMsg, expectedReturnValue61, returnArray[1]) except Exception as e: testResult = "False" errorMsg = ('Error adding link! Traceback = %s' % (e) ) errata.append(errorMsg) #6 - remove the link try: expectedReturnValue70 = "Removed %s as link source for %s" %(linkChangeTest0, linkChangeTest1) expectedReturnValue71 = "Removed %s as link target for %s" %(linkChangeTest1, linkChangeTest0) returnArray = api.removeEntityLink(linkChangeTest0, linkChangeTest1) if returnArray[0] != expectedReturnValue70: testResult = "False" errorMsg = '%sRemoving link from LinkEvent.LinkChangeTest should return "%s" in the return value [0] of the link added event. "%s returned" !\n' %(errorMsg, expectedReturnValue70, returnArray[0]) if returnArray[1] != expectedReturnValue71: testResult = "False" errorMsg = '%sRemoving link from LinkEvent.LinkChangeTest should return "%s" in the return value [0] of the link added event. "%s returned" !\n' %(errorMsg, expectedReturnValue71, returnArray[1]) except Exception as e: testResult = "False" errorMsg = ('Error removing link! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = "testLinkEvent()" results = [1, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(1)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testBrokenEvents(): """ This method tests the SES event handling of broken scripts The first series of tests (execute) runs with a SES script that: 1- causes an uncaught KeyError exception 2- causes the same KeyError exception, but catches and actively raises it (as an exception) 3 - The SES script class has no execute() method The second series of tests (propertyChanged) runs with a SES script that: 1- causes an uncaught KeyError exception 2- causes the same KeyError exception, but catches and actively raises it (as an exception) The third series of tests (linkAdd) runs with a SES script that: 1- causes an uncaught KeyError exception 2- causes the same KeyError exception, but catches and actively raises it (as an exception) The fourth series of tests (linkRemove) runs with a SES script that: 1- causes an uncaught KeyError exception 2- causes the same KeyError exception, but catches and actively raises it (as an exception) """ method = moduleName + '.' + 'testBrokenEvents' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) resultSet = [] errata = [] testResult = "True" expectedResult = "True" errorMsg = "" #Create two entities from LinkEvent.LinkChangeTest. #Greate three generic entities try: entity0 = Graph.api.createEntityFromMeme("EventFailure.BrokenLinkChangeTest") entity1 = Graph.api.createEntityFromMeme("EventFailure.ThrowsLinkChangeTest") entity2 = Graph.api.createEntityFromMeme("EventFailure.MalformedEvent") except Exception as e: testResult = "False" errorMsg = ('Error creating entities! Traceback = %s' % (e) ) errata.append(errorMsg) #execute for all. try: unusedReturnvalue = api.evaluateEntity(entity0) #yes, in this testcase, valid tests throw exceptions testResult = "False" errorMsg = ('Error. execute event for EventFailure.BrokenLinkChangeTest should raise an Exceptions.ScriptError exception, but did not!') errata.append(errorMsg) except Exceptions.EventScriptFailure as e: pass except Exception as e: testResult = "False" erorMessage = ('Error. execute event for EventFailure.BrokenLinkChangeTest should raise an Exceptions.ScriptError exception, but did not!') fullerror = sys.exc_info() errorMsg = str(fullerror[1]) tb = sys.exc_info()[2] erorMessage = "%s Traceback = %s %s" %(erorMessage, errorMsg, tb) errata.append(erorMessage) try: unusedReturnvalue = api.evaluateEntity(entity1) testResult = "False" errorMsg = ('Error. execute event for EventFailure.ThrowsLinkChangeTest should raise an exception, but did not!') errata.append(errorMsg) except Exceptions.EventScriptFailure as e: pass except Exception as e: testResult = "False" erorMessage = ('Error. execute event for EventFailure.BrokenLinkChangeTest should raise an Exceptions.ScriptError exception, but did not!') fullerror = sys.exc_info() errorMsg = str(fullerror[1]) tb = sys.exc_info()[2] erorMessage = "%s Traceback = %s %s" %(erorMessage, errorMsg, tb) errata.append(erorMessage) try: unusedReturnvalue = api.evaluateEntity(entity2) testResult = "False" errorMsg = ('Error. execute event for EventFailure.MalformedEvent should raise an exception, but did not!') errata.append(errorMsg) except Exceptions.EventScriptFailure as e: pass except Exception as e: testResult = "False" erorMessage = ('Error. execute event for EventFailure.BrokenLinkChangeTest should raise an Exceptions.ScriptError exception, but did not!') fullerror = sys.exc_info() errorMsg = str(fullerror[1]) tb = sys.exc_info()[2] erorMessage = "%s Traceback = %s %s" %(erorMessage, errorMsg, tb) errata.append(erorMessage) #propertyChanged. try: unusedReturnvalue = api.setEntityPropertyValue(entity0, "propB", "abc") #yes, in this testcase, valid tests throw exceptions testResult = "False" errorMsg = ('Error. propertyChanged event for EventFailure.BrokenLinkChangeTest should raise an exception, but did not!') errata.append(errorMsg) except Exceptions.EventScriptFailure as e: pass except Exception as e: testResult = "False" erorMessage = ('Error. execute event for EventFailure.BrokenLinkChangeTest should raise an Exceptions.ScriptError exception, but did not!') fullerror = sys.exc_info() errorMsg = str(fullerror[1]) tb = sys.exc_info()[2] erorMessage = "%s Traceback = %s %s" %(erorMessage, errorMsg, tb) errata.append(erorMessage) try: unusedReturnvalue = api.setEntityPropertyValue(entity1, "propB", "abc") testResult = "False" errorMsg = ('Error. propertyChanged event for EventFailure.ThrowsLinkChangeTest should raise an exception, but did not!') errata.append(errorMsg) except Exceptions.EventScriptFailure as e: pass except Exception as e: testResult = "False" erorMessage = ('Error. execute event for EventFailure.BrokenLinkChangeTest should raise an Exceptions.ScriptError exception, but did not!') fullerror = sys.exc_info() errorMsg = str(fullerror[1]) tb = sys.exc_info()[2] erorMessage = "%s Traceback = %s %s" %(erorMessage, errorMsg, tb) errata.append(erorMessage) #linkAdd try: unusedReturnvalue = api.addEntityLink(entity0, entity1) #yes, in this testcase, valid tests throw exceptions testResult = "False" errorMsg = ('Error. linkAdd event for EventFailure.BrokenLinkChangeTest should raise an exception, but did not!') errata.append(errorMsg) except Exceptions.EventScriptFailure as e: pass except Exception as e: testResult = "False" erorMessage = ('Error. execute event for EventFailure.BrokenLinkChangeTest should raise an Exceptions.ScriptError exception, but did not!') fullerror = sys.exc_info() errorMsg = str(fullerror[1]) tb = sys.exc_info()[2] erorMessage = "%s Traceback = %s %s" %(erorMessage, errorMsg, tb) errata.append(erorMessage) #linkRemove try: unusedReturnvalue = api.removeEntityLink(entity0, entity1) #yes, in this testcase, valid tests throw exceptions testResult = "False" errorMsg = ('Error. linkRemove event for EventFailure.BrokenLinkChangeTest should raise an exception, but did not!') errata.append(errorMsg) except Exceptions.EventScriptFailure as e: pass except Exception as e: testResult = "False" erorMessage = ('Error. execute event for EventFailure.BrokenLinkChangeTest should raise an Exceptions.ScriptError exception, but did not!') fullerror = sys.exc_info() errorMsg = str(fullerror[1]) erorMessage = "%s Traceback = %s" %(erorMessage, errorMsg) tb = sys.exc_info()[2] #raise Exceptions.EventScriptFailure(errorMsg).with_traceback(tb) errata.append(erorMessage) testcase = "testLinkEvent()" results = [1, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(1)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testInitializeEvent(): """ Create one entity from EventInitRemove.InitRemoveEventTest. Greate three generic entities 1 - Check that it has an AProp property and its value is 'Hello' The meme has no proeprties, but the initialize event script adds the AProp property """ method = moduleName + '.' + 'testInitializeEvent' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) resultSet = [] errata = [] testResult = "True" expectedResult = "True" errorMsg = "" #Create two entities from LinkEvent.LinkChangeTest. #Greate three generic entities try: theEntity = Graph.api.createEntityFromMeme("EventInitRemove.InitRemoveEventTest") except Exception as e: testResult = "False" errorMsg = ('Error creating entity! Traceback = %s' % (e) ) errata.append(errorMsg) #1 - Link the a LinkEvent.LinkChangeTest entitiy with a generic one, with LinkChangeTest as the source try: retrunValue = api.getEntityPropertyValue(theEntity, "AProp") if retrunValue != "Hello": testResult = "False" errorMsg = 'The initialize event script, EventInitRemove.OnInitialize, should add a property called AProp to EventInitRemove.InitRemoveEventTest and its value should be "Hello". It is actually "%s" !\n' %(retrunValue) except Exception as e: testResult = "False" errorMsg = ('Error in initialze event script! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = "testInitializeEvent()" results = [1, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(1)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testRemoveEvent(): """ Locate the EventInitRemove.InitRemoveEventTest entity created in testInitializeEvent(). (it should be singular) Delete it 1 - Check that delete script return value is 'Hello World' The meme has no proeprties, but the initialize event script adds the AProp property """ method = moduleName + '.' + 'testInitializeEvent' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) resultSet = [] errata = [] testResult = "True" expectedResult = "True" errorMsg = "" #Create two entities from LinkEvent.LinkChangeTest. #Greate three generic entities try: theEntities = Graph.api.getEntitiesByMemeType("EventInitRemove.InitRemoveEventTest") if len(theEntities) != 1: testResult = "False" errorMsg = 'One EventInitRemove.InitRemoveEventTest entity was created in the graph, during testInitializeEvent(). There can be only one! There are actually %s ' %(len(theEntities)) else: theEntity = theEntities[0] except Exception as e: testResult = "False" errorMsg = ('Error locating entity! Traceback = %s' % (e) ) errata.append(errorMsg) #1 - Link the a LinkEvent.LinkChangeTest entitiy with a generic one, with LinkChangeTest as the source try: destroyReturn = api.destroyEntity(theEntity) if destroyReturn != "Hello World": testResult = "False" errorMsg = 'The terminate event script, EventInitRemove.OnDelete, should return "Hello World". It actually returned "%s" !\n' %(destroyReturn) except Exception as e: testResult = "False" errorMsg = ('Error in terminate event script! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = "testRemoveEvent()" results = [1, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(1)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet def testAtomicSubatomic(): """ Test atomic/subatomic links defined in memes. """ method = moduleName + '.' + 'testAtomicSubatomic' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) resultSet = [] errata = [] testResult = "True" expectedResult = "True" errorMsg = "" #The testcase entities try: parentMeme1 = Graph.api.createEntityFromMeme("AtomicSubatomic.ParentMeme1") #Both shild entites have subatomic links parentMeme2 = Graph.api.createEntityFromMeme("AtomicSubatomic.ParentMeme2") #One child has an atomic link, the other subatomic parentMeme3 = Graph.api.createEntityFromMeme("AtomicSubatomic.ParentMeme3") #Both shild entites have atomic links except Exception as e: testResult = "False" errorMsg = ('Error creating test entities! Traceback = %s' % (e) ) errata.append(errorMsg) try: pm1aChildren = api.getLinkCounterpartsByMetaMemeType(parentMeme1, "AtomicSubatomic.ChildMM", linkTypes.ATOMIC) pm1sChildren = api.getLinkCounterpartsByMetaMemeType(parentMeme1, "AtomicSubatomic.ChildMM", linkTypes.SUBATOMIC) if len(pm1sChildren) < 2: testResult = "False" errorMsg = "Meme AtomicSubatomic.ParentMeme1 should have two subatomic children. It actually has %s\n" %(len(pm1sChildren)) if len(pm1aChildren) > 0: testResult = "False" errorMsg = "Meme AtomicSubatomic.ParentMeme1 should have no atomic children. It actually has %s\n" %(len(pm1aChildren)) except Exception as e: testResult = "False" errorMsg = ('Error when searching for children of AtomicSubatomic.ParentMeme1! Traceback = %s' % (e) ) errata.append(errorMsg) try: pm2aChildren = api.getLinkCounterpartsByMetaMemeType(parentMeme2, "AtomicSubatomic.ChildMM", linkTypes.ATOMIC) pm2sChildren = api.getLinkCounterpartsByMetaMemeType(parentMeme2, "AtomicSubatomic.ChildMM", linkTypes.SUBATOMIC) if len(pm2sChildren) != 1: testResult = "False" errorMsg = "Meme AtomicSubatomic.ParentMeme2 should have one subatomic child. It actually has %s\n" %(len(pm2sChildren)) if len(pm2aChildren) != 1: testResult = "False" errorMsg = "Meme AtomicSubatomic.ParentMeme2 should have one atomic child. It actually has %s\n" %(len(pm2aChildren)) except Exception as e: testResult = "False" errorMsg = ('Error when searching for children of AtomicSubatomic.ParentMeme2! Traceback = %s' % (e) ) errata.append(errorMsg) try: pm3aChildren = api.getLinkCounterpartsByMetaMemeType(parentMeme3, "AtomicSubatomic.ChildMM", linkTypes.ATOMIC) pm3sChildren = api.getLinkCounterpartsByMetaMemeType(parentMeme3, "AtomicSubatomic.ChildMM", linkTypes.SUBATOMIC) if len(pm3sChildren) > 0: testResult = "False" errorMsg = "Meme AtomicSubatomic.ParentMeme1 should have no subatomic children. It actually has %s\n" %(len(pm3sChildren)) if len(pm3aChildren) < 2: testResult = "False" errorMsg = "Meme AtomicSubatomic.ParentMeme1 should have two atomic children. It actually has %s\n" %(len(pm3aChildren)) except Exception as e: testResult = "False" errorMsg = ('Error when searching for children of AtomicSubatomic.ParentMeme3! Traceback = %s' % (e) ) errata.append(errorMsg) testcase = "testAtomicSubatomic()" results = [1, testcase, testResult, expectedResult, errata] resultSet.append(results) Graph.logQ.put( [logType , logLevel.INFO , method , "Finished testcase %s" %(1)]) Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) return resultSet ###################### #End Test Block ##################### def getResultPercentage(resultSet): #results = [n, testcase, allTrueResult, expectedResult, errata] totalTests = len(resultSet) if totalTests == 0: return 0 else: partialResult = 0 if totalTests > 0: for test in resultSet: try: if test[2].upper() == test[3].upper(): partialResult = partialResult + 1 except Exception as e: print(e) pp = partialResult/totalTests resultPercentage = pp * 100 return int(resultPercentage) def publishResults(testReports, css, fileName, titleText): #testReport = {"resultSet" : resultSet, "validationTime" : validationTime, "persistence" : persistence.__name__} #resultSet = [u"Condition (Remote Child)", copy.deepcopy(testSetData), testSetPercentage]) "Every report repeats exactly the same result sets, so we need only count onece" testCaseCount = 0 exampleTestReport = testReports[0] exampleResultSet = exampleTestReport["resultSet"] for testScenario in exampleResultSet: testCaseCount = testCaseCount + len(testScenario[2]) #Totals for time and number of test cases numReports = len(testReports) totalTCCount = testCaseCount * numReports totalTCTime = 0.0 for countedTestReport in testReports: totalTCTime = totalTCTime + countedTestReport["validationTime"] # Create the minidom document doc = minidom.Document() # Create the <html> base element html = doc.createElement("html") # Create the <head> element head = doc.createElement("head") style = doc.createElement("style") defaultCSS = doc.createTextNode(css) style.appendChild(defaultCSS) title = doc.createElement("title") titleTextNode = doc.createTextNode(titleText) title.appendChild(titleTextNode) head.appendChild(style) head.appendChild(title) body = doc.createElement("body") h1 = doc.createElement("h1") h1Text = doc.createTextNode(titleText) h1.appendChild(h1Text) body.appendChild(h1) h2 = doc.createElement("h2") h2Text = doc.createTextNode("%s regression tests over %s persistence types in in %.1f seconds: %s" %(totalTCCount, numReports, totalTCTime, ctime())) h2.appendChild(h2Text) body.appendChild(h2) h3 = doc.createElement("h2") h3Text = doc.createTextNode("Entity Count at start of tests: %s" %(exampleTestReport["entityCount"])) h3.appendChild(h3Text) body.appendChild(h3) """ The Master table wraps all the result sets. masterTableHeader contains all of the overview blocks masterTableBody contains all of the detail elements """ masterTable = doc.createElement("table") masterTableHeader = doc.createElement("table") masterTableBody = doc.createElement("table") for testReport in testReports: masterTableHeaderRow = doc.createElement("tr") masterTableBodyRow = doc.createElement("tr") localValTime = testReport["validationTime"] localPersistenceName = testReport["persistence"] resultSet = testReport["resultSet"] profileName = testReport["profileName"] #Module Overview numberOfColumns = 1 numberOfModules = len(resultSet) if numberOfModules > 6: numberOfColumns = 2 if numberOfModules > 12: numberOfColumns = 3 if numberOfModules > 18: numberOfColumns = 4 if numberOfModules > 24: numberOfColumns = 5 rowsPerColumn = numberOfModules//numberOfColumns + 1 listPosition = 0 icTable = doc.createElement("table") icTableHead= doc.createElement("thead") icTableHeadText = doc.createTextNode("%s, %s: %.1f seconds" %(profileName, localPersistenceName, localValTime) ) icTableHead.appendChild(icTableHeadText) icTableHead.setAttribute("class", "tableheader") icTable.appendChild(icTableHead) icTableFoot= doc.createElement("tfoot") icTableFootText = doc.createTextNode("Problem test case sets are detailed in tables below" ) icTableFoot.appendChild(icTableFootText) icTable.appendChild(icTableFoot) icTableRow = doc.createElement("tr") for unusedI in range(0, numberOfColumns): bigCell = doc.createElement("td") nestedTable = doc.createElement("table") #Header headers = ["", "Tests", "Valid"] nestedTableHeaderRow = doc.createElement("tr") for headerElement in headers: nestedCell = doc.createElement("th") nestedCellText = doc.createTextNode("%s" %headerElement) nestedCell.appendChild(nestedCellText) nestedTableHeaderRow.appendChild(nestedCell) #nestedTableHeaderRow.setAttribute("class", "tableHeaderRow") nestedTable.appendChild(nestedTableHeaderRow) for dummyJ in range(0, rowsPerColumn): currPos = listPosition listPosition = listPosition + 1 if listPosition <= numberOfModules: try: moduleReport = resultSet[currPos] #Write Data Row To Table row = doc.createElement("tr") #Module Name is first cell cell = doc.createElement("td") cellText = doc.createTextNode("%s" %moduleReport[0]) hyperlinkNode = doc.createElement("a") hyperlinkNode.setAttribute("href", "#%s%s" %(moduleReport[0], localPersistenceName)) hyperlinkNode.appendChild(cellText) cell.appendChild(hyperlinkNode) if moduleReport[1] < 100: row.setAttribute("class", "badOverviewRow") else: row.setAttribute("class", "goodOverviewRow") row.appendChild(cell) rowData = [len(moduleReport[2]), "%s %%" %moduleReport[1]] for dataEntry in rowData: percentCell = doc.createElement("td") percentCellText = doc.createTextNode("%s" %dataEntry) percentCell.appendChild(percentCellText) row.appendChild(percentCell) nestedTable.appendChild(row) except: pass else: row = doc.createElement("tr") cell = doc.createElement("td") cellText = doc.createTextNode("") cell.appendChild(cellText) row.appendChild(cellText) nestedTable.appendChild(row) nestedTable.setAttribute("class", "subdivision") bigCell.appendChild(nestedTable) icTableRow.appendChild(bigCell) icTableDiv = doc.createElement("div") icTableDiv.setAttribute("class", "vAlignment") icTableDiv.appendChild(icTableRow) icTable.appendChild(icTableDiv) #Add some blank spave before icTable frontSpacer = doc.createElement("div") frontSpacer.setAttribute("class", "vBlankSpace") frontSpacer.appendChild(icTable) masterTableDiv = doc.createElement("div") masterTableDiv.setAttribute("class", "vAlignment") masterTableDiv.appendChild(frontSpacer) masterTableHeaderRow.appendChild(masterTableDiv) masterTableHeader.appendChild(masterTableHeaderRow) #Individual Data Sets for testSet in resultSet: #first, build up the "outer" table header, which has the header idHash = "%s%s" %(testSet[0], localPersistenceName) oTable = doc.createElement("table") oTable.setAttribute("style", "border-style:solid") tableHeader= doc.createElement("thead") tableHeaderText = doc.createTextNode("%s (%s)" %(testSet[0], localPersistenceName) ) tableAnchor = doc.createElement("a") tableAnchor.setAttribute("id", idHash) tableAnchor.appendChild(tableHeaderText) tableHeader.appendChild(tableAnchor) tableHeader.setAttribute("class", "tableheader") oTable.appendChild(tableHeader) oTableRow = doc.createElement("tr") oTableContainer = doc.createElement("td") #Inner Table table = doc.createElement("table") headers = ["#", "Test Case", "Result", "Expected Result", "Notes"] tableHeaderRow = doc.createElement("tr") for headerEntry in headers: cell = doc.createElement("th") cellText = doc.createTextNode("%s" %headerEntry) cell.appendChild(cellText) cell.setAttribute("class", "tableHeaderRow") tableHeaderRow.appendChild(cell) table.appendChild(tableHeaderRow) for fullTestRow in testSet[2]: #fullTestRow = [n, testcase, allTrueResult, expectedResult, errata] test = [fullTestRow[0], fullTestRow[1], fullTestRow[2], fullTestRow[3]] tableRow = doc.createElement("tr") for dataEntry in test: cell = doc.createElement("td") cellText = doc.createTextNode("%s" %dataEntry) cell.appendChild(cellText) cell.setAttribute("class", "detailsCell") tableRow.appendChild(cell) try: if test[2].upper() != test[3].upper(): #then mark the whole row as red tableRow.setAttribute("class", "badDRow") else: tableRow.setAttribute("class", "goodDRow") except: cell = doc.createElement("td") cellText = doc.createTextNode("Please check Testcase code: actual test result = %s, expected = %s" %(test[2], test[3])) cell.appendChild(cellText) cell.setAttribute("class", "detailsCell") tableRow.appendChild(cell) tableRow.setAttribute("class", "badDRow") errataCell = doc.createElement("td") if type(fullTestRow[4]) == type([]): filteredErrata = Graph.filterListDuplicates(fullTestRow[4]) for bulletpointElement in filteredErrata: paragraph = doc.createElement("p") pText = doc.createTextNode("%s" %bulletpointElement) paragraph.appendChild(pText) errataCell.appendChild(paragraph) tableRow.appendChild(cell) else: filteredErrata = Graph.filterListDuplicates(fullTestRow[4]) paragraph = doc.createElement("p") pText = doc.createTextNode("%s" %filteredErrata) paragraph.appendChild(pText) #rowValidityCell.appendChild(paragraph) errataCell.appendChild(paragraph) tableRow.appendChild(errataCell) table.appendChild(tableRow) oTableContainer.appendChild(table) oTableRow.appendChild(oTableContainer) oTable.appendChild(oTableRow) #Add some blank spave before any tables tableSpacer = doc.createElement("div") tableSpacer.setAttribute("class", "vBlankSpace") tableSpacer.appendChild(oTable) masterTableDivL = doc.createElement("div") masterTableDivL.setAttribute("class", "vAlignment") masterTableDivL.appendChild(tableSpacer) masterTableBodyRow.appendChild(masterTableDivL) masterTableBody.appendChild(masterTableBodyRow) masterTable.appendChild(masterTableHeader) masterTable.appendChild(masterTableBody) body.appendChild(masterTable) html.appendChild(head) html.appendChild(body) doc.appendChild(html) fileStream = doc.toprettyxml(indent = " ") logRoot = expanduser("~") logDir = os.path.join(logRoot, "Graphyne") if not os.path.exists(logDir): os.makedirs(logDir) resultFileLoc = os.path.join(logDir, fileName) fileObject = open(resultFileLoc, "w", encoding="utf-8") #fileObject.write(Fileutils.smart_str(fileStream)) fileObject.write(fileStream) fileObject.close() def usage(): print(__doc__) def runTests(css): global testImplicit method = moduleName + '.' + 'main' Graph.logQ.put( [logType , logLevel.DEBUG , method , "entering"]) #Make sure that we have a script facade available global api api = Graph.api.getAPI() # A line to prevent pydev from complaining about unused variables dummyIgnoreThis = str(api) # a helper item for debugging whther or not a particular entity is in the repo debugHelperIDs = api.getAllEntities() for debugHelperID in debugHelperIDs: try: debugHelperMemeType = api.getEntityMemeType(debugHelperID) entityList.append([str(debugHelperID), debugHelperMemeType]) except Exception as unusedE: #This exception is normally left as a pass. If you need to debug the preceeding code, then uncomment the block below. # The exception is called 'unusedE', so that Pydev will ignore the unused variable #errorMessage = "debugHelperMemeType warning in Smoketest.Runtests. Traceback = %s" %unusedE #Graph.logQ.put( [logType , logLevel.WARNING , method , errorMessage]) pass #test resultSet = [] print("Meta Meme Properties") testSetData = testMetaMemeProperty() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Meta Meme Properties", testSetPercentage, copy.deepcopy(testSetData)]) print("Meta Meme Singleton") testSetData = testMetaMemeSingleton() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Meta Meme Singleton", testSetPercentage, copy.deepcopy(testSetData)]) print("Meta Meme Switch") testSetData = testMetaMemeSwitch() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Meta Meme Switch", testSetPercentage, copy.deepcopy(testSetData)]) print("Meta Meme Enhancements") testSetData = testMetaMemeEnhancements() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Meta Meme Enhancements", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testMemeValidity() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Meme Validity", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testEntityPhase1() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 1", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testEntityPhase1_1() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 1.1", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testEntityPhase2() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 2", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testEntityPhase2_1() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 2.1", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testEntityPhase3() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 3", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testEntityPhase3_1() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 3.1", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testEntityPhase4() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 4", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testEntityPhase4_1() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 4.1", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testEntityPhase1('testEntityPhase5', 'Entity_Phase5.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 5", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testEntityPhase1_1('testEntityPhase5.1', 'Entity_Phase5.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 5.1", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testEntityPhase6() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 6", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testEntityPhase6_1() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 6.1", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testEntityPhase7() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 7", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testLinkCounterpartsByMetaMemeType() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 7.1", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testEntityPhase2('testEntityPhase8', 'Entity_Phase8.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 8", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testEntityPhase2_1('testEntityPhase8_1', 'Entity_Phase8.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 8.1", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testEntityPhase9() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 9", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testEntityPhase10() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 10", testSetPercentage, copy.deepcopy(testSetData)]) #Repeats 7, but with directional references testSetData = testEntityPhase7('testEntityPhase11', "Entity_Phase11.atest") testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Phase 11", testSetPercentage, copy.deepcopy(testSetData)]) #Repeats 7, but with directionasl references filters testSetData = testTraverseParams() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Traverse Params", testSetPercentage, copy.deepcopy(testSetData)]) #NumericValue.atest testSetData = testNumericValue('NumericValue.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["NumericValue", testSetPercentage, copy.deepcopy(testSetData)]) if (testImplicit == True): print("Implicit Memes") testSetData = testImplicitMeme() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Implicit Meme", testSetPercentage, copy.deepcopy(testSetData)]) else: print("No Persistence: Skipping Implicit Memes") print("Conditions") testSetData = testCondition('ConditionSimple.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Condition (Simple)", testSetPercentage, copy.deepcopy(testSetData)]) #ConditionSet.atest testSetData = testCondition('ConditionSet.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Condition (Set)", testSetPercentage, copy.deepcopy(testSetData)]) # Script Conditions testSetData = testCondition('ConditionScript.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Condition (Script)", testSetPercentage, copy.deepcopy(testSetData)]) #Child conditions in remote packages testSetData = testCondition('ConditionRemotePackage.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Condition (Remote Child)", testSetPercentage, copy.deepcopy(testSetData)]) #String and Numeric Conditions with Agent Attributes testSetData = testAACondition('ConditionAA.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Condition (Agent Attributes)", testSetPercentage, copy.deepcopy(testSetData)]) #String and Numeric Conditions with Multi Agent Attributes testSetData = testAACondition('ConditionMAA.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Condition (Multi Agent Attributes)", testSetPercentage, copy.deepcopy(testSetData)]) #Creating source metamemes via the script facade testSetData = testSourceCreateMeme('SourceCreateMeme.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Editor Meme Creation", testSetPercentage, copy.deepcopy(testSetData)]) #Set a source meme property via the script facade testSetData = testSourceProperty('SourceProperty.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Editor Meme Property Set", testSetPercentage, copy.deepcopy(testSetData)]) #Delete a source meme property via the script facade testSetData = testSourcePropertyRemove('SourceProperty.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Editor Meme Property Remove", testSetPercentage, copy.deepcopy(testSetData)]) #Add a member meme via the script facade testSetData = testSourceMember('SourceMember.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Editor Member Meme Add", testSetPercentage, copy.deepcopy(testSetData)]) #Remove a member meme via the script facade testSetData = testSourceMemberRemove('SourceMember.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Editor Member Meme Remove", testSetPercentage, copy.deepcopy(testSetData)]) #Add an enhancement via the script facade testSetData = testSourceEnhancement('SourceEnhancement.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Editor Enhancement Add", testSetPercentage, copy.deepcopy(testSetData)]) #Remove an enhancement via the script facade testSetData = testSourceEnhancementRemove('SourceEnhancement.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Editor Enhancement Remove", testSetPercentage, copy.deepcopy(testSetData)]) #Set the singleton flag via the script facade testSetData = testSourceSingletonSet('SourceCreateMeme.atest') testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Editor Singleton Setting", testSetPercentage, copy.deepcopy(testSetData)]) #Create a Generic entity and check to see that it's meme is Graphyne.Generic testSetData = testGeneric() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Generic Entity", testSetPercentage, copy.deepcopy(testSetData)]) #Test Entity Deletion testSetData = testDeleteEntity() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Entity Deletion", testSetPercentage, copy.deepcopy(testSetData)]) #Atomic and subatomic links testSetData = testSubatomicLinks() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Subatomic Links", testSetPercentage, copy.deepcopy(testSetData)]) #getting the cluster member list testSetData = testGetClusterMembers() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Cluster Member List", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testGetHasCounterpartsByType() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Has Counterparts by Type", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testGetEntityMetaMemeType() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["API method testGetEntityMetaMemeType", testSetPercentage, copy.deepcopy(testSetData)]) testSetData = testInstallExecutor() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["API method testInstallExecutor", testSetPercentage, copy.deepcopy(testSetData)]) #getting the cluster dictionary testSetData = testGetCluster() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Cluster", testSetPercentage, copy.deepcopy(testSetData)]) #testRevertEntity testSetData = testRevertEntity() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["API Method revertEntity", testSetPercentage, copy.deepcopy(testSetData)]) #testPropertyChangeEvent testSetData = testPropertyChangeEvent() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Property Change Event", testSetPercentage, copy.deepcopy(testSetData)]) #testLinkEvent testSetData = testLinkEvent() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Link Event", testSetPercentage, copy.deepcopy(testSetData)]) #testBrokenEvents testSetData = testBrokenEvents() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Broken Event", testSetPercentage, copy.deepcopy(testSetData)]) #testLinkEvent testSetData = testInitializeEvent() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Initialize Event", testSetPercentage, copy.deepcopy(testSetData)]) #testBrokenEvents testSetData = testRemoveEvent() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Remove Event", testSetPercentage, copy.deepcopy(testSetData)]) #testAtomicSubatomic testSetData = testAtomicSubatomic() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Atomic and Subatomic", testSetPercentage, copy.deepcopy(testSetData)]) #testGetTraverseReport testSetData = testGetTraverseReport() testSetPercentage = getResultPercentage(testSetData) resultSet.append(["Traverse Report", testSetPercentage, copy.deepcopy(testSetData)]) #endTime = time.time() #validationTime = endTime - startTime #publishResults(resultSet, validationTime, css) return resultSet #Graph.logQ.put( [logType , logLevel.DEBUG , method , "exiting"]) def smokeTestSet(persistence, lLevel, css, profileName, persistenceArg = None, persistenceType = None, resetDatabase = False, createTestDatabase = False, scaleFactor = 0): ''' repoLocations = a list of all of the filesystem location that that compose the repository. useDeaultSchema. I True, then load the 'default schema' of Graphyne persistenceType = The type of database used by the persistence engine. This is used to determine which flavor of SQL syntax to use. Enumeration of Possible values: Default to None, which is no persistence "sqlite" - Sqlite3 "mssql" - Miscrosoft SQL Server "hana" - SAP Hana persistenceArg = the Module/class supplied to host the entityRepository and LinkRepository. If default, then use the Graphyne.DatabaseDrivers.NonPersistent module. Enumeration of possible values: None - May only be used in conjunction with "sqlite" as persistenceType and will throw an InconsistentPersistenceArchitecture otherwise "none" - no persistence. May only be used in conjunction with "sqlite" as persistenceType and will throw an InconsistentPersistenceArchitecture otherwise "memory" - Use SQLite in in-memory mode (connection = ":memory:") "<valid filename with .sqlite as extension>" - Use SQLite, with that file as the database "<filename with .sqlite as extension, but no file>" - Use SQLite and create that file to use as the DB file "<anything else>" - Presume that it is a pyodbc connection string and throw a InconsistentPersistenceArchitecture exception if the dbtype is "sqlite". createTestDatabase = a flag for creating regression test data. This flag is only to be used for regression testing the graph and even then, only if the test database does not already exist. scaleFactor = Scale factor (S). Given N non-singleton memes, N*S "ballast" entities will be created in the DB before starting the test suite. This allows us to use larger datasets to test scalability (at least with regards to entity repository size) *If persistenceType is None (no persistence, then this is ignored and won't throw any InconsistentPersistenceArchitecture exceptions) ''' global testImplicit print(("\nStarting Graphyne Smoke Test: %s") %(persistence.__name__)) print(("...%s: Engine Start") %(persistence.__name__)) #Only test implicit memes in the case that we are using persistence if persistenceType is None: testImplicit = False #Don't validate the repo when we are performance testing if scaleFactor < 1: validateOnLoad = True else: validateOnLoad = False time.sleep(10.0) installFilePath = os.path.dirname(__file__) testRepo = os.path.join(installFilePath, "Config", "Test", "TestRepository") #mainAngRepo = os.path.join(os.environ['ANGELA_HOME'], "RMLRepository") try: Graph.startLogger(lLevel) Graph.startDB([testRepo], persistenceType, persistenceArg, True, resetDatabase, True, validateOnLoad) except Exception as e: print(("Graph not started. Traceback = %s" %e)) raise e print(("...Engine Started: %s") %persistence.__name__) time.sleep(30.0) print(("...%s: Engine Started") %(persistence.__name__)) #If scaleFactor > 0, then we are also testing performance if (scaleFactor > 0): print("Performance Test: ...Creating Content") for unusedj in range(1, scaleFactor): for moduleID in Graph.templateRepository.modules.keys(): if moduleID != "BrokenExamples": #The module BrokenExamples contaons mmemes that are deliberately malformed. Don't beother with these module = Graph.templateRepository.modules[moduleID] for listing in module: template = Graph.templateRepository.resolveTemplateAbsolutely(listing[1]) if template.className == "Meme": if template.isSingleton != True: try: unusedEntityID = Graph.api.createEntityFromMeme(template.path.fullTemplatePath) except Exception as e: pass print("Performance Test: Finished Creating Content") # /Scale Factor' entityCount = Graph.countEntities() startTime = time.time() try: resultSet = runTests(css) except Exception as e: print(("test run problem. Traceback = %s" %e)) raise e endTime = time.time() validationTime = endTime - startTime testReport = {"resultSet" : resultSet, "validationTime" : validationTime, "persistence" : persistence.__name__, "profileName" : profileName, "entityCount" : entityCount} #publishResults(resultSet, validationTime, css) print(("...%s: Test run finished. Waiting 30 seconds for log thread to catch up before starting shutdown") %(persistence.__name__)) time.sleep(30.0) print(("...%s: Engine Stop (%s)") %(persistence.__name__, profileName)) Graph.stopLogger() print(("...%s: Engine Stopped (%s)") %(persistence.__name__, profileName)) return testReport if __name__ == "__main__": print("\nStarting Graphyne Smoke Test") parser = argparse.ArgumentParser(description="Graphyne Smoke Test") parser.add_argument("-l", "--logl", type=str, help="|String| Graphyne's log level during the validation run. \n Options are (in increasing order of verbosity) 'warning', 'info' and 'debug'. \n Default is 'warning'") parser.add_argument("-r", "--resetdb", type=str, help="|String| Reset the esisting persistence DB This defaults to true and is only ever relevant when Graphyne is using relational database persistence.") parser.add_argument("-d", "--dbtype", type=str, help="|String| The database type to be used. If --dbtype is a relational database, it will also determine which flavor of SQL syntax to use.\n Possible options are 'none', 'sqlite', 'mssql' and 'hana'. \n Default is 'none'") parser.add_argument("-c", "--dbtcon", type=str, help="|String| The database connection string (if a relational DB) or filename (if SQLite).\n 'none' - no persistence. This is the default value\n 'memory' - Use SQLite in in-memory mode (connection = ':memory:') None persistence defaults to memory id SQlite is used\n '<valid filename>' - Use SQLite, with that file as the database\n <filename with .sqlite as extension, but no file> - Use SQLite and create that file to use as the DB file\n <anything else> - Presume that it is a pyodbc connection string") args = parser.parse_args() lLevel = Graph.logLevel.WARNING if args.logl: if args.logl == "info": lLevel = Graph.logLevel.INFO print("\n -- log level = 'info'") elif args.logl == "debug": lLevel = Graph.logLevel.DEBUG print("\n -- log level = 'debug'") elif args.logl == "warning": pass else: print("Invalid log level %s! Permitted valies of --logl are 'warning', 'info' and 'debug'!" %args.logl) sys.exit() persistenceType = None if args.dbtype: if (args.dbtype is None) or (args.dbtype == 'none'): pass elif (args.dbtype == 'sqlite') or (args.dbtype == 'mssql') or (args.dbtype == 'hana'): persistenceType = args.dbtype print("\n -- using persistence type %s" %args.dbtype) else: print("Invalid persistence type %s! Permitted valies of --dbtype are 'none', 'sqlite', 'mssql' and 'hana'!" %args.logl) sys.exit() dbConnectionString = None if args.dbtcon: if (args.dbtcon is None) or (args.dbtcon == 'none'): if persistenceType is None: print("\n -- Using in-memory persistence (no connection required)") elif persistenceType == 'sqlite': dbConnectionString = 'memory' print("\n -- Using sqlite persistence with connection = :memory:") else: print("\n -- Persistence type %s requires a valid database connection. Please provide a --dbtcon argument!" %persistenceType) sys.exit() elif args.dbtcon == 'memory': if persistenceType is None: #memory is a valid alternative to none with no persistence print("\n -- Using in-memory persistence (no connection required)") elif persistenceType == 'sqlite': dbConnectionString = args.dbtcon print("\n -- Using sqlite persistence with connection = :memory:") else: print("\n -- Persistence type %s requires a valid database connection. Please provide a --dbtcon argument!" %persistenceType) sys.exit() else: dbConnectionString = args.dbtcon if persistenceType == 'sqlite': if dbConnectionString.endswith(".sqlite"): print("\n -- Using sqlite persistence with file %s" %dbConnectionString) else: print("\n -- Using sqlite persistence with invalid filename %s. It must end with the .sqlite extension" %dbConnectionString) sys.exit() else: print("\n -- Using persistence type %s with connection = %s" %(args.dbtype, args.dbtcon)) resetDatabase = True if args.resetdb: if args.logl.lower() == "false": resetDatabase = False print((" ...params: log level = %s, db driver = %s, connection string = %s" %(lLevel, persistenceType, dbConnectionString))) testReport = None css = Fileutils.defaultCSS() try: if persistenceType is None: from graphyne.DatabaseDrivers import NonPersistent as persistenceModule1 testReport = smokeTestSet(persistenceModule1, lLevel, css, "No-Persistence", dbConnectionString, persistenceType, resetDatabase, True) elif ((persistenceType == "sqlite") and (dbConnectionString== "memory")): from graphyne.DatabaseDrivers import RelationalDatabase as persistenceModule2 testReport = smokeTestSet(persistenceModule2, lLevel, css, "sqllite", dbConnectionString, persistenceType, resetDatabase, True) elif persistenceType == "sqlite": from graphyne.DatabaseDrivers import RelationalDatabase as persistenceModule4 testReport = smokeTestSet(persistenceModule4, lLevel, css, "sqllite", dbConnectionString, persistenceType, resetDatabase) else: from graphyne.DatabaseDrivers import RelationalDatabase as persistenceModul3 testReport = smokeTestSet(persistenceModul3, lLevel, css, persistenceType, dbConnectionString, persistenceType, resetDatabase) except Exception as e: from graphyne.DatabaseDrivers import RelationalDatabase as persistenceModul32 testReport = smokeTestSet(persistenceModul32, lLevel, css, persistenceType, dbConnectionString, persistenceType, resetDatabase) titleText = "Graphyne Smoke Test Suite - Results" publishResults([testReport], css, "GraphyneTestResult.html", titleText)

apache-2.0

mlperf/training_results_v0.5

v0.5.0/google/cloud_v2.8/gnmt-tpuv2-8/code/gnmt/model/t2t/tensor2tensor/layers/transformer_layers.py

3

13829

# coding=utf-8 # Copyright 2018 The Tensor2Tensor Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Commonly re-used transformer layers.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensor2tensor.layers import common_attention from tensor2tensor.layers import common_layers from tensor2tensor.utils import expert_utils from tensor2tensor.utils import mlperf_log import tensorflow as tf def transformer_prepare_encoder(inputs, target_space, hparams, features=None): """Prepare one shard of the model for the encoder. Args: inputs: a Tensor. target_space: a Tensor. hparams: run hyperparameters features: optionally pass the entire features dictionary as well. This is needed now for "packed" datasets. Returns: encoder_input: a Tensor, bottom of encoder stack encoder_self_attention_bias: a bias tensor for use in encoder self-attention encoder_decoder_attention_bias: a bias tensor for use in encoder-decoder attention """ ishape_static = inputs.shape.as_list() encoder_input = inputs if features and "inputs_segmentation" in features: # Packed dataset. Keep the examples from seeing each other. inputs_segmentation = features["inputs_segmentation"] inputs_position = features["inputs_position"] targets_segmentation = features["targets_segmentation"] encoder_self_attention_bias = common_attention.attention_bias_same_segment( inputs_segmentation, inputs_segmentation) encoder_decoder_attention_bias = ( common_attention.attention_bias_same_segment(targets_segmentation, inputs_segmentation)) else: # Usual case - not a packed dataset. encoder_padding = common_attention.embedding_to_padding(encoder_input) ignore_padding = common_attention.attention_bias_ignore_padding( encoder_padding) encoder_self_attention_bias = ignore_padding encoder_decoder_attention_bias = ignore_padding inputs_position = None if hparams.proximity_bias: encoder_self_attention_bias += common_attention.attention_bias_proximal( common_layers.shape_list(inputs)[1]) if hparams.get("use_target_space_embedding", True): # Append target_space_id embedding to inputs. emb_target_space = common_layers.embedding( target_space, 32, ishape_static[-1], name="target_space_embedding", dtype=tf.bfloat16 if hparams.activation_dtype == "bfloat16" else tf.float32) emb_target_space = tf.reshape(emb_target_space, [1, 1, -1]) encoder_input += emb_target_space if hparams.pos == "timing": if inputs_position is not None: encoder_input = common_attention.add_timing_signal_1d_given_position( encoder_input, inputs_position) else: encoder_input = common_attention.add_timing_signal_1d(encoder_input) elif hparams.pos == "emb": encoder_input = common_attention.add_positional_embedding( encoder_input, hparams.max_length, "inputs_positional_embedding", inputs_position) if hparams.activation_dtype == "bfloat16": encoder_self_attention_bias = tf.cast(encoder_self_attention_bias, tf.bfloat16) encoder_decoder_attention_bias = tf.cast(encoder_decoder_attention_bias, tf.bfloat16) return (encoder_input, encoder_self_attention_bias, encoder_decoder_attention_bias) def transformer_encoder(encoder_input, encoder_self_attention_bias, hparams, name="encoder", nonpadding=None, save_weights_to=None, make_image_summary=True, losses=None): """A stack of transformer layers. Args: encoder_input: a Tensor encoder_self_attention_bias: bias Tensor for self-attention (see common_attention.attention_bias()) hparams: hyperparameters for model name: a string nonpadding: optional Tensor with shape [batch_size, encoder_length] indicating what positions are not padding. This must either be passed in, which we do for "packed" datasets, or inferred from encoder_self_attention_bias. The knowledge about padding is used for pad_remover(efficiency) and to mask out padding in convolutional layers. save_weights_to: an optional dictionary to capture attention weights for visualization; the weights tensor will be appended there under a string key created from the variable scope (including name). make_image_summary: Whether to make an attention image summary. losses: optional list onto which to append extra training losses Returns: y: a Tensors """ x = encoder_input attention_dropout_broadcast_dims = ( common_layers.comma_separated_string_to_integer_list( getattr(hparams, "attention_dropout_broadcast_dims", ""))) mlperf_log.transformer_print( key=mlperf_log.MODEL_HP_NUM_HIDDEN_LAYERS, value=hparams.num_encoder_layers or hparams.num_hidden_layers) mlperf_log.transformer_print( key=mlperf_log.MODEL_HP_ATTENTION_DROPOUT, value=hparams.attention_dropout) mlperf_log.transformer_print( key=mlperf_log.MODEL_HP_ATTENTION_DENSE, value={ "use_bias": "false", "num_heads": hparams.num_heads, "hidden_size": hparams.hidden_size }) with tf.variable_scope(name): if nonpadding is not None: padding = 1.0 - nonpadding else: padding = common_attention.attention_bias_to_padding( encoder_self_attention_bias) nonpadding = 1.0 - padding pad_remover = None if hparams.use_pad_remover and not common_layers.is_xla_compiled(): pad_remover = expert_utils.PadRemover(padding) for layer in range(hparams.num_encoder_layers or hparams.num_hidden_layers): with tf.variable_scope("layer_%d" % layer): with tf.variable_scope("self_attention"): y = common_attention.multihead_attention( common_layers.layer_preprocess(x, hparams), None, encoder_self_attention_bias, hparams.attention_key_channels or hparams.hidden_size, hparams.attention_value_channels or hparams.hidden_size, hparams.hidden_size, hparams.num_heads, hparams.attention_dropout, attention_type=hparams.self_attention_type, max_relative_position=hparams.max_relative_position, heads_share_relative_embedding=( hparams.heads_share_relative_embedding), add_relative_to_values=hparams.add_relative_to_values, save_weights_to=save_weights_to, make_image_summary=make_image_summary, dropout_broadcast_dims=attention_dropout_broadcast_dims, max_length=hparams.get("max_length"), vars_3d=hparams.get("attention_variables_3d")) x = common_layers.layer_postprocess(x, y, hparams) with tf.variable_scope("ffn"): y = transformer_ffn_layer( common_layers.layer_preprocess(x, hparams), hparams, pad_remover, conv_padding="SAME", nonpadding_mask=nonpadding, losses=losses) x = common_layers.layer_postprocess(x, y, hparams) # if normalization is done in layer_preprocess, then it should also be done # on the output, since the output can grow very large, being the sum of # a whole stack of unnormalized layer outputs. mlperf_log.transformer_print( key=mlperf_log.MODEL_HP_NORM, value={"hidden_size": hparams.hidden_size}) return common_layers.layer_preprocess(x, hparams) def transformer_ffn_layer(x, hparams, pad_remover=None, conv_padding="LEFT", nonpadding_mask=None, losses=None, cache=None, decode_loop_step=None, readout_filter_size=0): """Feed-forward layer in the transformer. Args: x: a Tensor of shape [batch_size, length, hparams.hidden_size] hparams: hyperparameters for model pad_remover: an expert_utils.PadRemover object tracking the padding positions. If provided, when using convolutional settings, the padding is removed before applying the convolution, and restored afterward. This can give a significant speedup. conv_padding: a string - either "LEFT" or "SAME". nonpadding_mask: an optional Tensor with shape [batch_size, length]. needed for convolutional layers with "SAME" padding. Contains 1.0 in positions corresponding to nonpadding. losses: optional list onto which to append extra training losses cache: dict, containing tensors which are the results of previous attentions, used for fast decoding. decode_loop_step: An integer, step number of the decoding loop. Only used for inference on TPU. readout_filter_size: if it's greater than 0, then it will be used instead of filter_size Returns: a Tensor of shape [batch_size, length, hparams.hidden_size] Raises: ValueError: If losses arg is None, but layer generates extra losses. """ ffn_layer = hparams.ffn_layer relu_dropout_broadcast_dims = ( common_layers.comma_separated_string_to_integer_list( getattr(hparams, "relu_dropout_broadcast_dims", ""))) if ffn_layer == "conv_hidden_relu": # Backwards compatibility ffn_layer = "dense_relu_dense" if ffn_layer == "dense_relu_dense": # In simple convolution mode, use `pad_remover` to speed up processing. mlperf_log.transformer_print( key=mlperf_log.MODEL_HP_FFN_FILTER_DENSE, value={ "filter_size": hparams.filter_size, "use_bias": "True", "activation": mlperf_log.RELU }) mlperf_log.transformer_print( key=mlperf_log.MODEL_HP_FFN_OUTPUT_DENSE, value={ "hidden_size": hparams.hidden_size, "use_bias": "True", }) mlperf_log.transformer_print( key=mlperf_log.MODEL_HP_RELU_DROPOUT, value=hparams.relu_dropout) if pad_remover: original_shape = common_layers.shape_list(x) # Collapse `x` across examples, and remove padding positions. x = tf.reshape(x, tf.concat([[-1], original_shape[2:]], axis=0)) x = tf.expand_dims(pad_remover.remove(x), axis=0) conv_output = common_layers.dense_relu_dense( x, hparams.filter_size, hparams.hidden_size, dropout=hparams.relu_dropout, dropout_broadcast_dims=relu_dropout_broadcast_dims) if pad_remover: # Restore `conv_output` to the original shape of `x`, including padding. conv_output = tf.reshape( pad_remover.restore(tf.squeeze(conv_output, axis=0)), original_shape) return conv_output elif ffn_layer == "conv_relu_conv": return common_layers.conv_relu_conv( x, readout_filter_size or hparams.filter_size, hparams.hidden_size, first_kernel_size=hparams.conv_first_kernel, second_kernel_size=1, padding=conv_padding, nonpadding_mask=nonpadding_mask, dropout=hparams.relu_dropout, cache=cache, decode_loop_step=decode_loop_step) elif ffn_layer == "parameter_attention": return common_attention.parameter_attention( x, hparams.parameter_attention_key_channels or hparams.hidden_size, hparams.parameter_attention_value_channels or hparams.hidden_size, hparams.hidden_size, readout_filter_size or hparams.filter_size, hparams.num_heads, hparams.attention_dropout) elif ffn_layer == "conv_hidden_relu_with_sepconv": return common_layers.conv_hidden_relu( x, readout_filter_size or hparams.filter_size, hparams.hidden_size, kernel_size=(3, 1), second_kernel_size=(31, 1), padding="LEFT", dropout=hparams.relu_dropout) elif ffn_layer == "sru": return common_layers.sru(x) elif ffn_layer == "local_moe_tpu": overhead = ( hparams.moe_overhead_train if hparams.mode == tf.estimator.ModeKeys.TRAIN else hparams.moe_overhead_eval) ret, loss = expert_utils.local_moe_tpu( x, hparams.filter_size // 2, hparams.hidden_size, hparams.moe_num_experts, overhead=overhead, loss_coef=hparams.moe_loss_coef) elif ffn_layer == "local_moe": overhead = ( hparams.moe_overhead_train if hparams.mode == tf.estimator.ModeKeys.TRAIN else hparams.moe_overhead_eval) ret, loss = expert_utils.local_moe( x, True, expert_utils.ffn_expert_fn(hparams.hidden_size, [hparams.filter_size], hparams.hidden_size), hparams.moe_num_experts, k=hparams.moe_k, hparams=hparams) losses.append(loss) return ret else: assert ffn_layer == "none" return x

apache-2.0

tomsilver/nupic

tests/swarming/nupic/swarming/experiments/input_predicted_field/description.py

1

14270

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupicengine/frameworks/opf/expGenerator/ExpGenerator.pyc' """ from nupic.frameworks.opf.expdescriptionapi import ExperimentDescriptionAPI from nupic.frameworks.opf.expdescriptionhelpers import ( updateConfigFromSubConfig, applyValueGettersToContainer ) from nupic.frameworks.opf.clamodelcallbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opfutils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opftaskdriver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # config = { # Type of model that the rest of these parameters apply to. 'model': "CLA", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'consumption', 'sum'), ], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalMultiStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # 'encoders': {'field1': {'fieldname': 'field1', 'n':100, # 'name': 'field1', 'type': 'AdaptiveScalarEncoder', # 'w': 21}} # 'encoders': { 'consumption': { 'clipInput': True, 'fieldname': u'consumption', 'n': 100, 'name': u'consumption', 'type': 'AdaptiveScalarEncoder', 'w': 21}, 'address': { 'fieldname': u'address', 'n': 300, 'name': u'address', 'type': 'SDRCategoryEncoder', 'w': 21}, 'gym': { 'fieldname': u'gym', 'n': 100, 'name': u'gym', 'type': 'SDRCategoryEncoder', 'w': 21}, 'timestamp_dayOfWeek': { 'dayOfWeek': (7, 3), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, 'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 8), 'type': 'DateEncoder'}, '_classifierInput': { 'name': u'_classifierInput', 'fieldname': u'consumption', 'classifierOnly': True, 'type': 'AdaptiveScalarEncoder', 'clipInput': True, 'n': 100, 'w': 21}, }, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : { u'days': 0, u'hours': 0}, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TP) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2*potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TP and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TP is enabled or disabled; # TP is necessary for making temporal predictions, such as predicting # the next inputs. Without TP, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tpEnable' : True, 'tpParams': { # TP diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/TP.py and TP10X*.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TP) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 20, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TP # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TP # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TP how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'CLAClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'clVerbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'anomalyParams': { u'anomalyCacheRecords': None, u'autoDetectThreshold': None, u'autoDetectWaitRecords': None}, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupic/frameworks/opf/jsonschema/stream_def.json. # 'dataset' : { u'info': u'test_hotgym', u'streams': [ { u'columns': [u'*'], u'info': u'test data', u'source': u'file://swarming/test_data.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset 'iterationCount' : -1, # A dictionary containing all the supplementary parameters for inference "inferenceArgs":{u'predictedField': u'consumption', u'predictionSteps': [1]}, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption', metric='multiStep', inferenceElement='multiStepBestPredictions', params={'window': 1000, 'steps': [1], 'errorMetric': 'altMAPE'}), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*'], } ################################################################################ ################################################################################ descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)

gpl-3.0

LohithBlaze/scikit-learn

benchmarks/bench_plot_svd.py

322

2899

"""Benchmarks of Singular Value Decomposition (Exact and Approximate) The data is mostly low rank but is a fat infinite tail. """ import gc from time import time import numpy as np from collections import defaultdict from scipy.linalg import svd from sklearn.utils.extmath import randomized_svd from sklearn.datasets.samples_generator import make_low_rank_matrix def compute_bench(samples_range, features_range, n_iter=3, rank=50): it = 0 results = defaultdict(lambda: []) max_it = len(samples_range) * len(features_range) for n_samples in samples_range: for n_features in features_range: it += 1 print('====================') print('Iteration %03d of %03d' % (it, max_it)) print('====================') X = make_low_rank_matrix(n_samples, n_features, effective_rank=rank, tail_strength=0.2) gc.collect() print("benchmarking scipy svd: ") tstart = time() svd(X, full_matrices=False) results['scipy svd'].append(time() - tstart) gc.collect() print("benchmarking scikit-learn randomized_svd: n_iter=0") tstart = time() randomized_svd(X, rank, n_iter=0) results['scikit-learn randomized_svd (n_iter=0)'].append( time() - tstart) gc.collect() print("benchmarking scikit-learn randomized_svd: n_iter=%d " % n_iter) tstart = time() randomized_svd(X, rank, n_iter=n_iter) results['scikit-learn randomized_svd (n_iter=%d)' % n_iter].append(time() - tstart) return results if __name__ == '__main__': from mpl_toolkits.mplot3d import axes3d # register the 3d projection import matplotlib.pyplot as plt samples_range = np.linspace(2, 1000, 4).astype(np.int) features_range = np.linspace(2, 1000, 4).astype(np.int) results = compute_bench(samples_range, features_range) label = 'scikit-learn singular value decomposition benchmark results' fig = plt.figure(label) ax = fig.gca(projection='3d') for c, (label, timings) in zip('rbg', sorted(results.iteritems())): X, Y = np.meshgrid(samples_range, features_range) Z = np.asarray(timings).reshape(samples_range.shape[0], features_range.shape[0]) # plot the actual surface ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3, color=c) # dummy point plot to stick the legend to since surface plot do not # support legends (yet?) ax.plot([1], [1], [1], color=c, label=label) ax.set_xlabel('n_samples') ax.set_ylabel('n_features') ax.set_zlabel('Time (s)') ax.legend() plt.show()

bsd-3-clause

jzt5132/scikit-learn

sklearn/linear_model/bayes.py

219

15248

""" Various bayesian regression """ from __future__ import print_function # Authors: V. Michel, F. Pedregosa, A. Gramfort # License: BSD 3 clause from math import log import numpy as np from scipy import linalg from .base import LinearModel from ..base import RegressorMixin from ..utils.extmath import fast_logdet, pinvh from ..utils import check_X_y ############################################################################### # BayesianRidge regression class BayesianRidge(LinearModel, RegressorMixin): """Bayesian ridge regression Fit a Bayesian ridge model and optimize the regularization parameters lambda (precision of the weights) and alpha (precision of the noise). Read more in the :ref:`User Guide <bayesian_regression>`. Parameters ---------- n_iter : int, optional Maximum number of iterations. Default is 300. tol : float, optional Stop the algorithm if w has converged. Default is 1.e-3. alpha_1 : float, optional Hyper-parameter : shape parameter for the Gamma distribution prior over the alpha parameter. Default is 1.e-6 alpha_2 : float, optional Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the alpha parameter. Default is 1.e-6. lambda_1 : float, optional Hyper-parameter : shape parameter for the Gamma distribution prior over the lambda parameter. Default is 1.e-6. lambda_2 : float, optional Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the lambda parameter. Default is 1.e-6 compute_score : boolean, optional If True, compute the objective function at each step of the model. Default is False fit_intercept : boolean, optional whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). Default is True. normalize : boolean, optional, default False If True, the regressors X will be normalized before regression. copy_X : boolean, optional, default True If True, X will be copied; else, it may be overwritten. verbose : boolean, optional, default False Verbose mode when fitting the model. Attributes ---------- coef_ : array, shape = (n_features) Coefficients of the regression model (mean of distribution) alpha_ : float estimated precision of the noise. lambda_ : array, shape = (n_features) estimated precisions of the weights. scores_ : float if computed, value of the objective function (to be maximized) Examples -------- >>> from sklearn import linear_model >>> clf = linear_model.BayesianRidge() >>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2]) ... # doctest: +NORMALIZE_WHITESPACE BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=False, copy_X=True, fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300, normalize=False, tol=0.001, verbose=False) >>> clf.predict([[1, 1]]) array([ 1.]) Notes ----- See examples/linear_model/plot_bayesian_ridge.py for an example. """ def __init__(self, n_iter=300, tol=1.e-3, alpha_1=1.e-6, alpha_2=1.e-6, lambda_1=1.e-6, lambda_2=1.e-6, compute_score=False, fit_intercept=True, normalize=False, copy_X=True, verbose=False): self.n_iter = n_iter self.tol = tol self.alpha_1 = alpha_1 self.alpha_2 = alpha_2 self.lambda_1 = lambda_1 self.lambda_2 = lambda_2 self.compute_score = compute_score self.fit_intercept = fit_intercept self.normalize = normalize self.copy_X = copy_X self.verbose = verbose def fit(self, X, y): """Fit the model Parameters ---------- X : numpy array of shape [n_samples,n_features] Training data y : numpy array of shape [n_samples] Target values Returns ------- self : returns an instance of self. """ X, y = check_X_y(X, y, dtype=np.float64, y_numeric=True) X, y, X_mean, y_mean, X_std = self._center_data( X, y, self.fit_intercept, self.normalize, self.copy_X) n_samples, n_features = X.shape ### Initialization of the values of the parameters alpha_ = 1. / np.var(y) lambda_ = 1. verbose = self.verbose lambda_1 = self.lambda_1 lambda_2 = self.lambda_2 alpha_1 = self.alpha_1 alpha_2 = self.alpha_2 self.scores_ = list() coef_old_ = None XT_y = np.dot(X.T, y) U, S, Vh = linalg.svd(X, full_matrices=False) eigen_vals_ = S ** 2 ### Convergence loop of the bayesian ridge regression for iter_ in range(self.n_iter): ### Compute mu and sigma # sigma_ = lambda_ / alpha_ * np.eye(n_features) + np.dot(X.T, X) # coef_ = sigma_^-1 * XT * y if n_samples > n_features: coef_ = np.dot(Vh.T, Vh / (eigen_vals_ + lambda_ / alpha_)[:, None]) coef_ = np.dot(coef_, XT_y) if self.compute_score: logdet_sigma_ = - np.sum( np.log(lambda_ + alpha_ * eigen_vals_)) else: coef_ = np.dot(X.T, np.dot( U / (eigen_vals_ + lambda_ / alpha_)[None, :], U.T)) coef_ = np.dot(coef_, y) if self.compute_score: logdet_sigma_ = lambda_ * np.ones(n_features) logdet_sigma_[:n_samples] += alpha_ * eigen_vals_ logdet_sigma_ = - np.sum(np.log(logdet_sigma_)) ### Update alpha and lambda rmse_ = np.sum((y - np.dot(X, coef_)) ** 2) gamma_ = (np.sum((alpha_ * eigen_vals_) / (lambda_ + alpha_ * eigen_vals_))) lambda_ = ((gamma_ + 2 * lambda_1) / (np.sum(coef_ ** 2) + 2 * lambda_2)) alpha_ = ((n_samples - gamma_ + 2 * alpha_1) / (rmse_ + 2 * alpha_2)) ### Compute the objective function if self.compute_score: s = lambda_1 * log(lambda_) - lambda_2 * lambda_ s += alpha_1 * log(alpha_) - alpha_2 * alpha_ s += 0.5 * (n_features * log(lambda_) + n_samples * log(alpha_) - alpha_ * rmse_ - (lambda_ * np.sum(coef_ ** 2)) - logdet_sigma_ - n_samples * log(2 * np.pi)) self.scores_.append(s) ### Check for convergence if iter_ != 0 and np.sum(np.abs(coef_old_ - coef_)) < self.tol: if verbose: print("Convergence after ", str(iter_), " iterations") break coef_old_ = np.copy(coef_) self.alpha_ = alpha_ self.lambda_ = lambda_ self.coef_ = coef_ self._set_intercept(X_mean, y_mean, X_std) return self ############################################################################### # ARD (Automatic Relevance Determination) regression class ARDRegression(LinearModel, RegressorMixin): """Bayesian ARD regression. Fit the weights of a regression model, using an ARD prior. The weights of the regression model are assumed to be in Gaussian distributions. Also estimate the parameters lambda (precisions of the distributions of the weights) and alpha (precision of the distribution of the noise). The estimation is done by an iterative procedures (Evidence Maximization) Read more in the :ref:`User Guide <bayesian_regression>`. Parameters ---------- n_iter : int, optional Maximum number of iterations. Default is 300 tol : float, optional Stop the algorithm if w has converged. Default is 1.e-3. alpha_1 : float, optional Hyper-parameter : shape parameter for the Gamma distribution prior over the alpha parameter. Default is 1.e-6. alpha_2 : float, optional Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the alpha parameter. Default is 1.e-6. lambda_1 : float, optional Hyper-parameter : shape parameter for the Gamma distribution prior over the lambda parameter. Default is 1.e-6. lambda_2 : float, optional Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the lambda parameter. Default is 1.e-6. compute_score : boolean, optional If True, compute the objective function at each step of the model. Default is False. threshold_lambda : float, optional threshold for removing (pruning) weights with high precision from the computation. Default is 1.e+4. fit_intercept : boolean, optional whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). Default is True. normalize : boolean, optional, default False If True, the regressors X will be normalized before regression. copy_X : boolean, optional, default True. If True, X will be copied; else, it may be overwritten. verbose : boolean, optional, default False Verbose mode when fitting the model. Attributes ---------- coef_ : array, shape = (n_features) Coefficients of the regression model (mean of distribution) alpha_ : float estimated precision of the noise. lambda_ : array, shape = (n_features) estimated precisions of the weights. sigma_ : array, shape = (n_features, n_features) estimated variance-covariance matrix of the weights scores_ : float if computed, value of the objective function (to be maximized) Examples -------- >>> from sklearn import linear_model >>> clf = linear_model.ARDRegression() >>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2]) ... # doctest: +NORMALIZE_WHITESPACE ARDRegression(alpha_1=1e-06, alpha_2=1e-06, compute_score=False, copy_X=True, fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300, normalize=False, threshold_lambda=10000.0, tol=0.001, verbose=False) >>> clf.predict([[1, 1]]) array([ 1.]) Notes -------- See examples/linear_model/plot_ard.py for an example. """ def __init__(self, n_iter=300, tol=1.e-3, alpha_1=1.e-6, alpha_2=1.e-6, lambda_1=1.e-6, lambda_2=1.e-6, compute_score=False, threshold_lambda=1.e+4, fit_intercept=True, normalize=False, copy_X=True, verbose=False): self.n_iter = n_iter self.tol = tol self.fit_intercept = fit_intercept self.normalize = normalize self.alpha_1 = alpha_1 self.alpha_2 = alpha_2 self.lambda_1 = lambda_1 self.lambda_2 = lambda_2 self.compute_score = compute_score self.threshold_lambda = threshold_lambda self.copy_X = copy_X self.verbose = verbose def fit(self, X, y): """Fit the ARDRegression model according to the given training data and parameters. Iterative procedure to maximize the evidence Parameters ---------- X : array-like, shape = [n_samples, n_features] Training vector, where n_samples in the number of samples and n_features is the number of features. y : array, shape = [n_samples] Target values (integers) Returns ------- self : returns an instance of self. """ X, y = check_X_y(X, y, dtype=np.float64, y_numeric=True) n_samples, n_features = X.shape coef_ = np.zeros(n_features) X, y, X_mean, y_mean, X_std = self._center_data( X, y, self.fit_intercept, self.normalize, self.copy_X) ### Launch the convergence loop keep_lambda = np.ones(n_features, dtype=bool) lambda_1 = self.lambda_1 lambda_2 = self.lambda_2 alpha_1 = self.alpha_1 alpha_2 = self.alpha_2 verbose = self.verbose ### Initialization of the values of the parameters alpha_ = 1. / np.var(y) lambda_ = np.ones(n_features) self.scores_ = list() coef_old_ = None ### Iterative procedure of ARDRegression for iter_ in range(self.n_iter): ### Compute mu and sigma (using Woodbury matrix identity) sigma_ = pinvh(np.eye(n_samples) / alpha_ + np.dot(X[:, keep_lambda] * np.reshape(1. / lambda_[keep_lambda], [1, -1]), X[:, keep_lambda].T)) sigma_ = np.dot(sigma_, X[:, keep_lambda] * np.reshape(1. / lambda_[keep_lambda], [1, -1])) sigma_ = - np.dot(np.reshape(1. / lambda_[keep_lambda], [-1, 1]) * X[:, keep_lambda].T, sigma_) sigma_.flat[::(sigma_.shape[1] + 1)] += 1. / lambda_[keep_lambda] coef_[keep_lambda] = alpha_ * np.dot( sigma_, np.dot(X[:, keep_lambda].T, y)) ### Update alpha and lambda rmse_ = np.sum((y - np.dot(X, coef_)) ** 2) gamma_ = 1. - lambda_[keep_lambda] * np.diag(sigma_) lambda_[keep_lambda] = ((gamma_ + 2. * lambda_1) / ((coef_[keep_lambda]) ** 2 + 2. * lambda_2)) alpha_ = ((n_samples - gamma_.sum() + 2. * alpha_1) / (rmse_ + 2. * alpha_2)) ### Prune the weights with a precision over a threshold keep_lambda = lambda_ < self.threshold_lambda coef_[~keep_lambda] = 0 ### Compute the objective function if self.compute_score: s = (lambda_1 * np.log(lambda_) - lambda_2 * lambda_).sum() s += alpha_1 * log(alpha_) - alpha_2 * alpha_ s += 0.5 * (fast_logdet(sigma_) + n_samples * log(alpha_) + np.sum(np.log(lambda_))) s -= 0.5 * (alpha_ * rmse_ + (lambda_ * coef_ ** 2).sum()) self.scores_.append(s) ### Check for convergence if iter_ > 0 and np.sum(np.abs(coef_old_ - coef_)) < self.tol: if verbose: print("Converged after %s iterations" % iter_) break coef_old_ = np.copy(coef_) self.coef_ = coef_ self.alpha_ = alpha_ self.sigma_ = sigma_ self.lambda_ = lambda_ self._set_intercept(X_mean, y_mean, X_std) return self

bsd-3-clause

MohammedWasim/scikit-learn

sklearn/linear_model/bayes.py

219

15248

""" Various bayesian regression """ from __future__ import print_function # Authors: V. Michel, F. Pedregosa, A. Gramfort # License: BSD 3 clause from math import log import numpy as np from scipy import linalg from .base import LinearModel from ..base import RegressorMixin from ..utils.extmath import fast_logdet, pinvh from ..utils import check_X_y ############################################################################### # BayesianRidge regression class BayesianRidge(LinearModel, RegressorMixin): """Bayesian ridge regression Fit a Bayesian ridge model and optimize the regularization parameters lambda (precision of the weights) and alpha (precision of the noise). Read more in the :ref:`User Guide <bayesian_regression>`. Parameters ---------- n_iter : int, optional Maximum number of iterations. Default is 300. tol : float, optional Stop the algorithm if w has converged. Default is 1.e-3. alpha_1 : float, optional Hyper-parameter : shape parameter for the Gamma distribution prior over the alpha parameter. Default is 1.e-6 alpha_2 : float, optional Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the alpha parameter. Default is 1.e-6. lambda_1 : float, optional Hyper-parameter : shape parameter for the Gamma distribution prior over the lambda parameter. Default is 1.e-6. lambda_2 : float, optional Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the lambda parameter. Default is 1.e-6 compute_score : boolean, optional If True, compute the objective function at each step of the model. Default is False fit_intercept : boolean, optional whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). Default is True. normalize : boolean, optional, default False If True, the regressors X will be normalized before regression. copy_X : boolean, optional, default True If True, X will be copied; else, it may be overwritten. verbose : boolean, optional, default False Verbose mode when fitting the model. Attributes ---------- coef_ : array, shape = (n_features) Coefficients of the regression model (mean of distribution) alpha_ : float estimated precision of the noise. lambda_ : array, shape = (n_features) estimated precisions of the weights. scores_ : float if computed, value of the objective function (to be maximized) Examples -------- >>> from sklearn import linear_model >>> clf = linear_model.BayesianRidge() >>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2]) ... # doctest: +NORMALIZE_WHITESPACE BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=False, copy_X=True, fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300, normalize=False, tol=0.001, verbose=False) >>> clf.predict([[1, 1]]) array([ 1.]) Notes ----- See examples/linear_model/plot_bayesian_ridge.py for an example. """ def __init__(self, n_iter=300, tol=1.e-3, alpha_1=1.e-6, alpha_2=1.e-6, lambda_1=1.e-6, lambda_2=1.e-6, compute_score=False, fit_intercept=True, normalize=False, copy_X=True, verbose=False): self.n_iter = n_iter self.tol = tol self.alpha_1 = alpha_1 self.alpha_2 = alpha_2 self.lambda_1 = lambda_1 self.lambda_2 = lambda_2 self.compute_score = compute_score self.fit_intercept = fit_intercept self.normalize = normalize self.copy_X = copy_X self.verbose = verbose def fit(self, X, y): """Fit the model Parameters ---------- X : numpy array of shape [n_samples,n_features] Training data y : numpy array of shape [n_samples] Target values Returns ------- self : returns an instance of self. """ X, y = check_X_y(X, y, dtype=np.float64, y_numeric=True) X, y, X_mean, y_mean, X_std = self._center_data( X, y, self.fit_intercept, self.normalize, self.copy_X) n_samples, n_features = X.shape ### Initialization of the values of the parameters alpha_ = 1. / np.var(y) lambda_ = 1. verbose = self.verbose lambda_1 = self.lambda_1 lambda_2 = self.lambda_2 alpha_1 = self.alpha_1 alpha_2 = self.alpha_2 self.scores_ = list() coef_old_ = None XT_y = np.dot(X.T, y) U, S, Vh = linalg.svd(X, full_matrices=False) eigen_vals_ = S ** 2 ### Convergence loop of the bayesian ridge regression for iter_ in range(self.n_iter): ### Compute mu and sigma # sigma_ = lambda_ / alpha_ * np.eye(n_features) + np.dot(X.T, X) # coef_ = sigma_^-1 * XT * y if n_samples > n_features: coef_ = np.dot(Vh.T, Vh / (eigen_vals_ + lambda_ / alpha_)[:, None]) coef_ = np.dot(coef_, XT_y) if self.compute_score: logdet_sigma_ = - np.sum( np.log(lambda_ + alpha_ * eigen_vals_)) else: coef_ = np.dot(X.T, np.dot( U / (eigen_vals_ + lambda_ / alpha_)[None, :], U.T)) coef_ = np.dot(coef_, y) if self.compute_score: logdet_sigma_ = lambda_ * np.ones(n_features) logdet_sigma_[:n_samples] += alpha_ * eigen_vals_ logdet_sigma_ = - np.sum(np.log(logdet_sigma_)) ### Update alpha and lambda rmse_ = np.sum((y - np.dot(X, coef_)) ** 2) gamma_ = (np.sum((alpha_ * eigen_vals_) / (lambda_ + alpha_ * eigen_vals_))) lambda_ = ((gamma_ + 2 * lambda_1) / (np.sum(coef_ ** 2) + 2 * lambda_2)) alpha_ = ((n_samples - gamma_ + 2 * alpha_1) / (rmse_ + 2 * alpha_2)) ### Compute the objective function if self.compute_score: s = lambda_1 * log(lambda_) - lambda_2 * lambda_ s += alpha_1 * log(alpha_) - alpha_2 * alpha_ s += 0.5 * (n_features * log(lambda_) + n_samples * log(alpha_) - alpha_ * rmse_ - (lambda_ * np.sum(coef_ ** 2)) - logdet_sigma_ - n_samples * log(2 * np.pi)) self.scores_.append(s) ### Check for convergence if iter_ != 0 and np.sum(np.abs(coef_old_ - coef_)) < self.tol: if verbose: print("Convergence after ", str(iter_), " iterations") break coef_old_ = np.copy(coef_) self.alpha_ = alpha_ self.lambda_ = lambda_ self.coef_ = coef_ self._set_intercept(X_mean, y_mean, X_std) return self ############################################################################### # ARD (Automatic Relevance Determination) regression class ARDRegression(LinearModel, RegressorMixin): """Bayesian ARD regression. Fit the weights of a regression model, using an ARD prior. The weights of the regression model are assumed to be in Gaussian distributions. Also estimate the parameters lambda (precisions of the distributions of the weights) and alpha (precision of the distribution of the noise). The estimation is done by an iterative procedures (Evidence Maximization) Read more in the :ref:`User Guide <bayesian_regression>`. Parameters ---------- n_iter : int, optional Maximum number of iterations. Default is 300 tol : float, optional Stop the algorithm if w has converged. Default is 1.e-3. alpha_1 : float, optional Hyper-parameter : shape parameter for the Gamma distribution prior over the alpha parameter. Default is 1.e-6. alpha_2 : float, optional Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the alpha parameter. Default is 1.e-6. lambda_1 : float, optional Hyper-parameter : shape parameter for the Gamma distribution prior over the lambda parameter. Default is 1.e-6. lambda_2 : float, optional Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the lambda parameter. Default is 1.e-6. compute_score : boolean, optional If True, compute the objective function at each step of the model. Default is False. threshold_lambda : float, optional threshold for removing (pruning) weights with high precision from the computation. Default is 1.e+4. fit_intercept : boolean, optional whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). Default is True. normalize : boolean, optional, default False If True, the regressors X will be normalized before regression. copy_X : boolean, optional, default True. If True, X will be copied; else, it may be overwritten. verbose : boolean, optional, default False Verbose mode when fitting the model. Attributes ---------- coef_ : array, shape = (n_features) Coefficients of the regression model (mean of distribution) alpha_ : float estimated precision of the noise. lambda_ : array, shape = (n_features) estimated precisions of the weights. sigma_ : array, shape = (n_features, n_features) estimated variance-covariance matrix of the weights scores_ : float if computed, value of the objective function (to be maximized) Examples -------- >>> from sklearn import linear_model >>> clf = linear_model.ARDRegression() >>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2]) ... # doctest: +NORMALIZE_WHITESPACE ARDRegression(alpha_1=1e-06, alpha_2=1e-06, compute_score=False, copy_X=True, fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300, normalize=False, threshold_lambda=10000.0, tol=0.001, verbose=False) >>> clf.predict([[1, 1]]) array([ 1.]) Notes -------- See examples/linear_model/plot_ard.py for an example. """ def __init__(self, n_iter=300, tol=1.e-3, alpha_1=1.e-6, alpha_2=1.e-6, lambda_1=1.e-6, lambda_2=1.e-6, compute_score=False, threshold_lambda=1.e+4, fit_intercept=True, normalize=False, copy_X=True, verbose=False): self.n_iter = n_iter self.tol = tol self.fit_intercept = fit_intercept self.normalize = normalize self.alpha_1 = alpha_1 self.alpha_2 = alpha_2 self.lambda_1 = lambda_1 self.lambda_2 = lambda_2 self.compute_score = compute_score self.threshold_lambda = threshold_lambda self.copy_X = copy_X self.verbose = verbose def fit(self, X, y): """Fit the ARDRegression model according to the given training data and parameters. Iterative procedure to maximize the evidence Parameters ---------- X : array-like, shape = [n_samples, n_features] Training vector, where n_samples in the number of samples and n_features is the number of features. y : array, shape = [n_samples] Target values (integers) Returns ------- self : returns an instance of self. """ X, y = check_X_y(X, y, dtype=np.float64, y_numeric=True) n_samples, n_features = X.shape coef_ = np.zeros(n_features) X, y, X_mean, y_mean, X_std = self._center_data( X, y, self.fit_intercept, self.normalize, self.copy_X) ### Launch the convergence loop keep_lambda = np.ones(n_features, dtype=bool) lambda_1 = self.lambda_1 lambda_2 = self.lambda_2 alpha_1 = self.alpha_1 alpha_2 = self.alpha_2 verbose = self.verbose ### Initialization of the values of the parameters alpha_ = 1. / np.var(y) lambda_ = np.ones(n_features) self.scores_ = list() coef_old_ = None ### Iterative procedure of ARDRegression for iter_ in range(self.n_iter): ### Compute mu and sigma (using Woodbury matrix identity) sigma_ = pinvh(np.eye(n_samples) / alpha_ + np.dot(X[:, keep_lambda] * np.reshape(1. / lambda_[keep_lambda], [1, -1]), X[:, keep_lambda].T)) sigma_ = np.dot(sigma_, X[:, keep_lambda] * np.reshape(1. / lambda_[keep_lambda], [1, -1])) sigma_ = - np.dot(np.reshape(1. / lambda_[keep_lambda], [-1, 1]) * X[:, keep_lambda].T, sigma_) sigma_.flat[::(sigma_.shape[1] + 1)] += 1. / lambda_[keep_lambda] coef_[keep_lambda] = alpha_ * np.dot( sigma_, np.dot(X[:, keep_lambda].T, y)) ### Update alpha and lambda rmse_ = np.sum((y - np.dot(X, coef_)) ** 2) gamma_ = 1. - lambda_[keep_lambda] * np.diag(sigma_) lambda_[keep_lambda] = ((gamma_ + 2. * lambda_1) / ((coef_[keep_lambda]) ** 2 + 2. * lambda_2)) alpha_ = ((n_samples - gamma_.sum() + 2. * alpha_1) / (rmse_ + 2. * alpha_2)) ### Prune the weights with a precision over a threshold keep_lambda = lambda_ < self.threshold_lambda coef_[~keep_lambda] = 0 ### Compute the objective function if self.compute_score: s = (lambda_1 * np.log(lambda_) - lambda_2 * lambda_).sum() s += alpha_1 * log(alpha_) - alpha_2 * alpha_ s += 0.5 * (fast_logdet(sigma_) + n_samples * log(alpha_) + np.sum(np.log(lambda_))) s -= 0.5 * (alpha_ * rmse_ + (lambda_ * coef_ ** 2).sum()) self.scores_.append(s) ### Check for convergence if iter_ > 0 and np.sum(np.abs(coef_old_ - coef_)) < self.tol: if verbose: print("Converged after %s iterations" % iter_) break coef_old_ = np.copy(coef_) self.coef_ = coef_ self.alpha_ = alpha_ self.sigma_ = sigma_ self.lambda_ = lambda_ self._set_intercept(X_mean, y_mean, X_std) return self

bsd-3-clause

MohammedWasim/scikit-learn

examples/mixture/plot_gmm_sin.py

247

2747

""" ================================= Gaussian Mixture Model Sine Curve ================================= This example highlights the advantages of the Dirichlet Process: complexity control and dealing with sparse data. The dataset is formed by 100 points loosely spaced following a noisy sine curve. The fit by the GMM class, using the expectation-maximization algorithm to fit a mixture of 10 Gaussian components, finds too-small components and very little structure. The fits by the Dirichlet process, however, show that the model can either learn a global structure for the data (small alpha) or easily interpolate to finding relevant local structure (large alpha), never falling into the problems shown by the GMM class. """ import itertools import numpy as np from scipy import linalg import matplotlib.pyplot as plt import matplotlib as mpl from sklearn import mixture from sklearn.externals.six.moves import xrange # Number of samples per component n_samples = 100 # Generate random sample following a sine curve np.random.seed(0) X = np.zeros((n_samples, 2)) step = 4 * np.pi / n_samples for i in xrange(X.shape[0]): x = i * step - 6 X[i, 0] = x + np.random.normal(0, 0.1) X[i, 1] = 3 * (np.sin(x) + np.random.normal(0, .2)) color_iter = itertools.cycle(['r', 'g', 'b', 'c', 'm']) for i, (clf, title) in enumerate([ (mixture.GMM(n_components=10, covariance_type='full', n_iter=100), "Expectation-maximization"), (mixture.DPGMM(n_components=10, covariance_type='full', alpha=0.01, n_iter=100), "Dirichlet Process,alpha=0.01"), (mixture.DPGMM(n_components=10, covariance_type='diag', alpha=100., n_iter=100), "Dirichlet Process,alpha=100.")]): clf.fit(X) splot = plt.subplot(3, 1, 1 + i) Y_ = clf.predict(X) for i, (mean, covar, color) in enumerate(zip( clf.means_, clf._get_covars(), color_iter)): v, w = linalg.eigh(covar) u = w[0] / linalg.norm(w[0]) # as the DP will not use every component it has access to # unless it needs it, we shouldn't plot the redundant # components. if not np.any(Y_ == i): continue plt.scatter(X[Y_ == i, 0], X[Y_ == i, 1], .8, color=color) # Plot an ellipse to show the Gaussian component angle = np.arctan(u[1] / u[0]) angle = 180 * angle / np.pi # convert to degrees ell = mpl.patches.Ellipse(mean, v[0], v[1], 180 + angle, color=color) ell.set_clip_box(splot.bbox) ell.set_alpha(0.5) splot.add_artist(ell) plt.xlim(-6, 4 * np.pi - 6) plt.ylim(-5, 5) plt.title(title) plt.xticks(()) plt.yticks(()) plt.show()

bsd-3-clause

jzt5132/scikit-learn

sklearn/utils/tests/test_testing.py

106

4210

import warnings import unittest import sys from nose.tools import assert_raises from sklearn.utils.testing import ( _assert_less, _assert_greater, assert_less_equal, assert_greater_equal, assert_warns, assert_no_warnings, assert_equal, set_random_state, assert_raise_message) from sklearn.tree import DecisionTreeClassifier from sklearn.discriminant_analysis import LinearDiscriminantAnalysis try: from nose.tools import assert_less def test_assert_less(): # Check that the nose implementation of assert_less gives the # same thing as the scikit's assert_less(0, 1) _assert_less(0, 1) assert_raises(AssertionError, assert_less, 1, 0) assert_raises(AssertionError, _assert_less, 1, 0) except ImportError: pass try: from nose.tools import assert_greater def test_assert_greater(): # Check that the nose implementation of assert_less gives the # same thing as the scikit's assert_greater(1, 0) _assert_greater(1, 0) assert_raises(AssertionError, assert_greater, 0, 1) assert_raises(AssertionError, _assert_greater, 0, 1) except ImportError: pass def test_assert_less_equal(): assert_less_equal(0, 1) assert_less_equal(1, 1) assert_raises(AssertionError, assert_less_equal, 1, 0) def test_assert_greater_equal(): assert_greater_equal(1, 0) assert_greater_equal(1, 1) assert_raises(AssertionError, assert_greater_equal, 0, 1) def test_set_random_state(): lda = LinearDiscriminantAnalysis() tree = DecisionTreeClassifier() # Linear Discriminant Analysis doesn't have random state: smoke test set_random_state(lda, 3) set_random_state(tree, 3) assert_equal(tree.random_state, 3) def test_assert_raise_message(): def _raise_ValueError(message): raise ValueError(message) def _no_raise(): pass assert_raise_message(ValueError, "test", _raise_ValueError, "test") assert_raises(AssertionError, assert_raise_message, ValueError, "something else", _raise_ValueError, "test") assert_raises(ValueError, assert_raise_message, TypeError, "something else", _raise_ValueError, "test") assert_raises(AssertionError, assert_raise_message, ValueError, "test", _no_raise) # multiple exceptions in a tuple assert_raises(AssertionError, assert_raise_message, (ValueError, AttributeError), "test", _no_raise) # This class is inspired from numpy 1.7 with an alteration to check # the reset warning filters after calls to assert_warns. # This assert_warns behavior is specific to scikit-learn because #`clean_warning_registry()` is called internally by assert_warns # and clears all previous filters. class TestWarns(unittest.TestCase): def test_warn(self): def f(): warnings.warn("yo") return 3 # Test that assert_warns is not impacted by externally set # filters and is reset internally. # This is because `clean_warning_registry()` is called internally by # assert_warns and clears all previous filters. warnings.simplefilter("ignore", UserWarning) assert_equal(assert_warns(UserWarning, f), 3) # Test that the warning registry is empty after assert_warns assert_equal(sys.modules['warnings'].filters, []) assert_raises(AssertionError, assert_no_warnings, f) assert_equal(assert_no_warnings(lambda x: x, 1), 1) def test_warn_wrong_warning(self): def f(): warnings.warn("yo", DeprecationWarning) failed = False filters = sys.modules['warnings'].filters[:] try: try: # Should raise an AssertionError assert_warns(UserWarning, f) failed = True except AssertionError: pass finally: sys.modules['warnings'].filters = filters if failed: raise AssertionError("wrong warning caught by assert_warn")

bsd-3-clause

jzt5132/scikit-learn

sklearn/neighbors/base.py

71

31147

"""Base and mixin classes for nearest neighbors""" # Authors: Jake Vanderplas <vanderplas@astro.washington.edu> # Fabian Pedregosa <fabian.pedregosa@inria.fr> # Alexandre Gramfort <alexandre.gramfort@inria.fr> # Sparseness support by Lars Buitinck <L.J.Buitinck@uva.nl> # Multi-output support by Arnaud Joly <a.joly@ulg.ac.be> # # License: BSD 3 clause (C) INRIA, University of Amsterdam import warnings from abc import ABCMeta, abstractmethod import numpy as np from scipy.sparse import csr_matrix, issparse from .ball_tree import BallTree from .kd_tree import KDTree from ..base import BaseEstimator from ..metrics import pairwise_distances from ..metrics.pairwise import PAIRWISE_DISTANCE_FUNCTIONS from ..utils import check_X_y, check_array, _get_n_jobs, gen_even_slices from ..utils.fixes import argpartition from ..utils.validation import DataConversionWarning from ..utils.validation import NotFittedError from ..externals import six from ..externals.joblib import Parallel, delayed VALID_METRICS = dict(ball_tree=BallTree.valid_metrics, kd_tree=KDTree.valid_metrics, # The following list comes from the # sklearn.metrics.pairwise doc string brute=(list(PAIRWISE_DISTANCE_FUNCTIONS.keys()) + ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'cosine', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule', 'wminkowski'])) VALID_METRICS_SPARSE = dict(ball_tree=[], kd_tree=[], brute=PAIRWISE_DISTANCE_FUNCTIONS.keys()) class NeighborsWarning(UserWarning): pass # Make sure that NeighborsWarning are displayed more than once warnings.simplefilter("always", NeighborsWarning) def _check_weights(weights): """Check to make sure weights are valid""" if weights in (None, 'uniform', 'distance'): return weights elif callable(weights): return weights else: raise ValueError("weights not recognized: should be 'uniform', " "'distance', or a callable function") def _get_weights(dist, weights): """Get the weights from an array of distances and a parameter ``weights`` Parameters =========== dist: ndarray The input distances weights: {'uniform', 'distance' or a callable} The kind of weighting used Returns ======== weights_arr: array of the same shape as ``dist`` if ``weights == 'uniform'``, then returns None """ if weights in (None, 'uniform'): return None elif weights == 'distance': # if user attempts to classify a point that was zero distance from one # or more training points, those training points are weighted as 1.0 # and the other points as 0.0 if dist.dtype is np.dtype(object): for point_dist_i, point_dist in enumerate(dist): # check if point_dist is iterable # (ex: RadiusNeighborClassifier.predict may set an element of # dist to 1e-6 to represent an 'outlier') if hasattr(point_dist, '__contains__') and 0. in point_dist: dist[point_dist_i] = point_dist == 0. else: dist[point_dist_i] = 1. / point_dist else: with np.errstate(divide='ignore'): dist = 1. / dist inf_mask = np.isinf(dist) inf_row = np.any(inf_mask, axis=1) dist[inf_row] = inf_mask[inf_row] return dist elif callable(weights): return weights(dist) else: raise ValueError("weights not recognized: should be 'uniform', " "'distance', or a callable function") class NeighborsBase(six.with_metaclass(ABCMeta, BaseEstimator)): """Base class for nearest neighbors estimators.""" @abstractmethod def __init__(self): pass def _init_params(self, n_neighbors=None, radius=None, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, n_jobs=1, **kwargs): if kwargs: warnings.warn("Passing additional arguments to the metric " "function as **kwargs is deprecated " "and will no longer be supported in 0.18. " "Use metric_params instead.", DeprecationWarning, stacklevel=3) if metric_params is None: metric_params = {} metric_params.update(kwargs) self.n_neighbors = n_neighbors self.radius = radius self.algorithm = algorithm self.leaf_size = leaf_size self.metric = metric self.metric_params = metric_params self.p = p self.n_jobs = n_jobs if algorithm not in ['auto', 'brute', 'kd_tree', 'ball_tree']: raise ValueError("unrecognized algorithm: '%s'" % algorithm) if algorithm == 'auto': if metric == 'precomputed': alg_check = 'brute' else: alg_check = 'ball_tree' else: alg_check = algorithm if callable(metric): if algorithm == 'kd_tree': # callable metric is only valid for brute force and ball_tree raise ValueError( "kd_tree algorithm does not support callable metric '%s'" % metric) elif metric not in VALID_METRICS[alg_check]: raise ValueError("Metric '%s' not valid for algorithm '%s'" % (metric, algorithm)) if self.metric_params is not None and 'p' in self.metric_params: warnings.warn("Parameter p is found in metric_params. " "The corresponding parameter from __init__ " "is ignored.", SyntaxWarning, stacklevel=3) effective_p = metric_params['p'] else: effective_p = self.p if self.metric in ['wminkowski', 'minkowski'] and effective_p < 1: raise ValueError("p must be greater than one for minkowski metric") self._fit_X = None self._tree = None self._fit_method = None def _fit(self, X): if self.metric_params is None: self.effective_metric_params_ = {} else: self.effective_metric_params_ = self.metric_params.copy() effective_p = self.effective_metric_params_.get('p', self.p) if self.metric in ['wminkowski', 'minkowski']: self.effective_metric_params_['p'] = effective_p self.effective_metric_ = self.metric # For minkowski distance, use more efficient methods where available if self.metric == 'minkowski': p = self.effective_metric_params_.pop('p', 2) if p < 1: raise ValueError("p must be greater than one " "for minkowski metric") elif p == 1: self.effective_metric_ = 'manhattan' elif p == 2: self.effective_metric_ = 'euclidean' elif p == np.inf: self.effective_metric_ = 'chebyshev' else: self.effective_metric_params_['p'] = p if isinstance(X, NeighborsBase): self._fit_X = X._fit_X self._tree = X._tree self._fit_method = X._fit_method return self elif isinstance(X, BallTree): self._fit_X = X.data self._tree = X self._fit_method = 'ball_tree' return self elif isinstance(X, KDTree): self._fit_X = X.data self._tree = X self._fit_method = 'kd_tree' return self X = check_array(X, accept_sparse='csr') n_samples = X.shape[0] if n_samples == 0: raise ValueError("n_samples must be greater than 0") if issparse(X): if self.algorithm not in ('auto', 'brute'): warnings.warn("cannot use tree with sparse input: " "using brute force") if self.effective_metric_ not in VALID_METRICS_SPARSE['brute']: raise ValueError("metric '%s' not valid for sparse input" % self.effective_metric_) self._fit_X = X.copy() self._tree = None self._fit_method = 'brute' return self self._fit_method = self.algorithm self._fit_X = X if self._fit_method == 'auto': # A tree approach is better for small number of neighbors, # and KDTree is generally faster when available if ((self.n_neighbors is None or self.n_neighbors < self._fit_X.shape[0] // 2) and self.metric != 'precomputed'): if self.effective_metric_ in VALID_METRICS['kd_tree']: self._fit_method = 'kd_tree' else: self._fit_method = 'ball_tree' else: self._fit_method = 'brute' if self._fit_method == 'ball_tree': self._tree = BallTree(X, self.leaf_size, metric=self.effective_metric_, **self.effective_metric_params_) elif self._fit_method == 'kd_tree': self._tree = KDTree(X, self.leaf_size, metric=self.effective_metric_, **self.effective_metric_params_) elif self._fit_method == 'brute': self._tree = None else: raise ValueError("algorithm = '%s' not recognized" % self.algorithm) if self.n_neighbors is not None: if self.n_neighbors <= 0: raise ValueError( "Expected n_neighbors > 0. Got %d" % self.n_neighbors ) return self @property def _pairwise(self): # For cross-validation routines to split data correctly return self.metric == 'precomputed' class KNeighborsMixin(object): """Mixin for k-neighbors searches""" def kneighbors(self, X=None, n_neighbors=None, return_distance=True): """Finds the K-neighbors of a point. Returns indices of and distances to the neighbors of each point. Parameters ---------- X : array-like, shape (n_query, n_features), \ or (n_query, n_indexed) if metric == 'precomputed' The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor. n_neighbors : int Number of neighbors to get (default is the value passed to the constructor). return_distance : boolean, optional. Defaults to True. If False, distances will not be returned Returns ------- dist : array Array representing the lengths to points, only present if return_distance=True ind : array Indices of the nearest points in the population matrix. Examples -------- In the following example, we construct a NeighborsClassifier class from an array representing our data set and ask who's the closest point to [1,1,1] >>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(n_neighbors=1) >>> neigh.fit(samples) # doctest: +ELLIPSIS NearestNeighbors(algorithm='auto', leaf_size=30, ...) >>> print(neigh.kneighbors([[1., 1., 1.]])) # doctest: +ELLIPSIS (array([[ 0.5]]), array([[2]]...)) As you can see, it returns [[0.5]], and [[2]], which means that the element is at distance 0.5 and is the third element of samples (indexes start at 0). You can also query for multiple points: >>> X = [[0., 1., 0.], [1., 0., 1.]] >>> neigh.kneighbors(X, return_distance=False) # doctest: +ELLIPSIS array([[1], [2]]...) """ if self._fit_method is None: raise NotFittedError("Must fit neighbors before querying.") if n_neighbors is None: n_neighbors = self.n_neighbors if X is not None: query_is_train = False X = check_array(X, accept_sparse='csr') else: query_is_train = True X = self._fit_X # Include an extra neighbor to account for the sample itself being # returned, which is removed later n_neighbors += 1 train_size = self._fit_X.shape[0] if n_neighbors > train_size: raise ValueError( "Expected n_neighbors <= n_samples, " " but n_samples = %d, n_neighbors = %d" % (train_size, n_neighbors) ) n_samples, _ = X.shape sample_range = np.arange(n_samples)[:, None] n_jobs = _get_n_jobs(self.n_jobs) if self._fit_method == 'brute': # for efficiency, use squared euclidean distances if self.effective_metric_ == 'euclidean': dist = pairwise_distances(X, self._fit_X, 'euclidean', n_jobs=n_jobs, squared=True) else: dist = pairwise_distances( X, self._fit_X, self.effective_metric_, n_jobs=n_jobs, **self.effective_metric_params_) neigh_ind = argpartition(dist, n_neighbors - 1, axis=1) neigh_ind = neigh_ind[:, :n_neighbors] # argpartition doesn't guarantee sorted order, so we sort again neigh_ind = neigh_ind[ sample_range, np.argsort(dist[sample_range, neigh_ind])] if return_distance: if self.effective_metric_ == 'euclidean': result = np.sqrt(dist[sample_range, neigh_ind]), neigh_ind else: result = dist[sample_range, neigh_ind], neigh_ind else: result = neigh_ind elif self._fit_method in ['ball_tree', 'kd_tree']: if issparse(X): raise ValueError( "%s does not work with sparse matrices. Densify the data, " "or set algorithm='brute'" % self._fit_method) result = Parallel(n_jobs, backend='threading')( delayed(self._tree.query, check_pickle=False)( X[s], n_neighbors, return_distance) for s in gen_even_slices(X.shape[0], n_jobs) ) if return_distance: dist, neigh_ind = tuple(zip(*result)) result = np.vstack(dist), np.vstack(neigh_ind) else: result = np.vstack(result) else: raise ValueError("internal: _fit_method not recognized") if not query_is_train: return result else: # If the query data is the same as the indexed data, we would like # to ignore the first nearest neighbor of every sample, i.e # the sample itself. if return_distance: dist, neigh_ind = result else: neigh_ind = result sample_mask = neigh_ind != sample_range # Corner case: When the number of duplicates are more # than the number of neighbors, the first NN will not # be the sample, but a duplicate. # In that case mask the first duplicate. dup_gr_nbrs = np.all(sample_mask, axis=1) sample_mask[:, 0][dup_gr_nbrs] = False neigh_ind = np.reshape( neigh_ind[sample_mask], (n_samples, n_neighbors - 1)) if return_distance: dist = np.reshape( dist[sample_mask], (n_samples, n_neighbors - 1)) return dist, neigh_ind return neigh_ind def kneighbors_graph(self, X=None, n_neighbors=None, mode='connectivity'): """Computes the (weighted) graph of k-Neighbors for points in X Parameters ---------- X : array-like, shape (n_query, n_features), \ or (n_query, n_indexed) if metric == 'precomputed' The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor. n_neighbors : int Number of neighbors for each sample. (default is value passed to the constructor). mode : {'connectivity', 'distance'}, optional Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, in 'distance' the edges are Euclidean distance between points. Returns ------- A : sparse matrix in CSR format, shape = [n_samples, n_samples_fit] n_samples_fit is the number of samples in the fitted data A[i, j] is assigned the weight of edge that connects i to j. Examples -------- >>> X = [[0], [3], [1]] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(n_neighbors=2) >>> neigh.fit(X) # doctest: +ELLIPSIS NearestNeighbors(algorithm='auto', leaf_size=30, ...) >>> A = neigh.kneighbors_graph(X) >>> A.toarray() array([[ 1., 0., 1.], [ 0., 1., 1.], [ 1., 0., 1.]]) See also -------- NearestNeighbors.radius_neighbors_graph """ if n_neighbors is None: n_neighbors = self.n_neighbors # kneighbors does the None handling. if X is not None: X = check_array(X, accept_sparse='csr') n_samples1 = X.shape[0] else: n_samples1 = self._fit_X.shape[0] n_samples2 = self._fit_X.shape[0] n_nonzero = n_samples1 * n_neighbors A_indptr = np.arange(0, n_nonzero + 1, n_neighbors) # construct CSR matrix representation of the k-NN graph if mode == 'connectivity': A_data = np.ones(n_samples1 * n_neighbors) A_ind = self.kneighbors(X, n_neighbors, return_distance=False) elif mode == 'distance': A_data, A_ind = self.kneighbors( X, n_neighbors, return_distance=True) A_data = np.ravel(A_data) else: raise ValueError( 'Unsupported mode, must be one of "connectivity" ' 'or "distance" but got "%s" instead' % mode) kneighbors_graph = csr_matrix((A_data, A_ind.ravel(), A_indptr), shape=(n_samples1, n_samples2)) return kneighbors_graph class RadiusNeighborsMixin(object): """Mixin for radius-based neighbors searches""" def radius_neighbors(self, X=None, radius=None, return_distance=True): """Finds the neighbors within a given radius of a point or points. Return the indices and distances of each point from the dataset lying in a ball with size ``radius`` around the points of the query array. Points lying on the boundary are included in the results. The result points are *not* necessarily sorted by distance to their query point. Parameters ---------- X : array-like, (n_samples, n_features), optional The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor. radius : float Limiting distance of neighbors to return. (default is the value passed to the constructor). return_distance : boolean, optional. Defaults to True. If False, distances will not be returned Returns ------- dist : array, shape (n_samples,) of arrays Array representing the distances to each point, only present if return_distance=True. The distance values are computed according to the ``metric`` constructor parameter. ind : array, shape (n_samples,) of arrays An array of arrays of indices of the approximate nearest points from the population matrix that lie within a ball of size ``radius`` around the query points. Examples -------- In the following example, we construct a NeighborsClassifier class from an array representing our data set and ask who's the closest point to [1, 1, 1]: >>> import numpy as np >>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(radius=1.6) >>> neigh.fit(samples) # doctest: +ELLIPSIS NearestNeighbors(algorithm='auto', leaf_size=30, ...) >>> rng = neigh.radius_neighbors([[1., 1., 1.]]) >>> print(np.asarray(rng[0][0])) # doctest: +ELLIPSIS [ 1.5 0.5] >>> print(np.asarray(rng[1][0])) # doctest: +ELLIPSIS [1 2] The first array returned contains the distances to all points which are closer than 1.6, while the second array returned contains their indices. In general, multiple points can be queried at the same time. Notes ----- Because the number of neighbors of each point is not necessarily equal, the results for multiple query points cannot be fit in a standard data array. For efficiency, `radius_neighbors` returns arrays of objects, where each object is a 1D array of indices or distances. """ if self._fit_method is None: raise NotFittedError("Must fit neighbors before querying.") if X is not None: query_is_train = False X = check_array(X, accept_sparse='csr') else: query_is_train = True X = self._fit_X if radius is None: radius = self.radius n_samples = X.shape[0] if self._fit_method == 'brute': # for efficiency, use squared euclidean distances if self.effective_metric_ == 'euclidean': dist = pairwise_distances(X, self._fit_X, 'euclidean', squared=True) radius *= radius else: dist = pairwise_distances(X, self._fit_X, self.effective_metric_, **self.effective_metric_params_) neigh_ind_list = [np.where(d <= radius)[0] for d in dist] # See https://github.com/numpy/numpy/issues/5456 # if you want to understand why this is initialized this way. neigh_ind = np.empty(n_samples, dtype='object') neigh_ind[:] = neigh_ind_list if return_distance: dist_array = np.empty(n_samples, dtype='object') if self.effective_metric_ == 'euclidean': dist_list = [np.sqrt(d[neigh_ind[i]]) for i, d in enumerate(dist)] else: dist_list = [d[neigh_ind[i]] for i, d in enumerate(dist)] dist_array[:] = dist_list results = dist_array, neigh_ind else: results = neigh_ind elif self._fit_method in ['ball_tree', 'kd_tree']: if issparse(X): raise ValueError( "%s does not work with sparse matrices. Densify the data, " "or set algorithm='brute'" % self._fit_method) results = self._tree.query_radius(X, radius, return_distance=return_distance) if return_distance: results = results[::-1] else: raise ValueError("internal: _fit_method not recognized") if not query_is_train: return results else: # If the query data is the same as the indexed data, we would like # to ignore the first nearest neighbor of every sample, i.e # the sample itself. if return_distance: dist, neigh_ind = results else: neigh_ind = results for ind, ind_neighbor in enumerate(neigh_ind): mask = ind_neighbor != ind neigh_ind[ind] = ind_neighbor[mask] if return_distance: dist[ind] = dist[ind][mask] if return_distance: return dist, neigh_ind return neigh_ind def radius_neighbors_graph(self, X=None, radius=None, mode='connectivity'): """Computes the (weighted) graph of Neighbors for points in X Neighborhoods are restricted the points at a distance lower than radius. Parameters ---------- X : array-like, shape = [n_samples, n_features], optional The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor. radius : float Radius of neighborhoods. (default is the value passed to the constructor). mode : {'connectivity', 'distance'}, optional Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, in 'distance' the edges are Euclidean distance between points. Returns ------- A : sparse matrix in CSR format, shape = [n_samples, n_samples] A[i, j] is assigned the weight of edge that connects i to j. Examples -------- >>> X = [[0], [3], [1]] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(radius=1.5) >>> neigh.fit(X) # doctest: +ELLIPSIS NearestNeighbors(algorithm='auto', leaf_size=30, ...) >>> A = neigh.radius_neighbors_graph(X) >>> A.toarray() array([[ 1., 0., 1.], [ 0., 1., 0.], [ 1., 0., 1.]]) See also -------- kneighbors_graph """ if X is not None: X = check_array(X, accept_sparse=['csr', 'csc', 'coo']) n_samples2 = self._fit_X.shape[0] if radius is None: radius = self.radius # construct CSR matrix representation of the NN graph if mode == 'connectivity': A_ind = self.radius_neighbors(X, radius, return_distance=False) A_data = None elif mode == 'distance': dist, A_ind = self.radius_neighbors(X, radius, return_distance=True) A_data = np.concatenate(list(dist)) else: raise ValueError( 'Unsupported mode, must be one of "connectivity", ' 'or "distance" but got %s instead' % mode) n_samples1 = A_ind.shape[0] n_neighbors = np.array([len(a) for a in A_ind]) A_ind = np.concatenate(list(A_ind)) if A_data is None: A_data = np.ones(len(A_ind)) A_indptr = np.concatenate((np.zeros(1, dtype=int), np.cumsum(n_neighbors))) return csr_matrix((A_data, A_ind, A_indptr), shape=(n_samples1, n_samples2)) class SupervisedFloatMixin(object): def fit(self, X, y): """Fit the model using X as training data and y as target values Parameters ---------- X : {array-like, sparse matrix, BallTree, KDTree} Training data. If array or matrix, shape [n_samples, n_features], or [n_samples, n_samples] if metric='precomputed'. y : {array-like, sparse matrix} Target values, array of float values, shape = [n_samples] or [n_samples, n_outputs] """ if not isinstance(X, (KDTree, BallTree)): X, y = check_X_y(X, y, "csr", multi_output=True) self._y = y return self._fit(X) class SupervisedIntegerMixin(object): def fit(self, X, y): """Fit the model using X as training data and y as target values Parameters ---------- X : {array-like, sparse matrix, BallTree, KDTree} Training data. If array or matrix, shape [n_samples, n_features], or [n_samples, n_samples] if metric='precomputed'. y : {array-like, sparse matrix} Target values of shape = [n_samples] or [n_samples, n_outputs] """ if not isinstance(X, (KDTree, BallTree)): X, y = check_X_y(X, y, "csr", multi_output=True) if y.ndim == 1 or y.ndim == 2 and y.shape[1] == 1: if y.ndim != 1: warnings.warn("A column-vector y was passed when a 1d array " "was expected. Please change the shape of y to " "(n_samples, ), for example using ravel().", DataConversionWarning, stacklevel=2) self.outputs_2d_ = False y = y.reshape((-1, 1)) else: self.outputs_2d_ = True self.classes_ = [] self._y = np.empty(y.shape, dtype=np.int) for k in range(self._y.shape[1]): classes, self._y[:, k] = np.unique(y[:, k], return_inverse=True) self.classes_.append(classes) if not self.outputs_2d_: self.classes_ = self.classes_[0] self._y = self._y.ravel() return self._fit(X) class UnsupervisedMixin(object): def fit(self, X, y=None): """Fit the model using X as training data Parameters ---------- X : {array-like, sparse matrix, BallTree, KDTree} Training data. If array or matrix, shape [n_samples, n_features], or [n_samples, n_samples] if metric='precomputed'. """ return self._fit(X)

bsd-3-clause

Vict0rSch/deep_learning

keras/feedforward/feedforward_keras_mnist.py

1

2623

import time import numpy as np from matplotlib import pyplot as plt from keras.utils import np_utils import keras.callbacks as cb from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation from keras.optimizers import RMSprop from keras.datasets import mnist class LossHistory(cb.Callback): def on_train_begin(self, logs={}): self.losses = [] def on_batch_end(self, batch, logs={}): batch_loss = logs.get('loss') self.losses.append(batch_loss) def load_data(): print 'Loading data...' (X_train, y_train), (X_test, y_test) = mnist.load_data() X_train = X_train.astype('float32') X_test = X_test.astype('float32') X_train /= 255 X_test /= 255 y_train = np_utils.to_categorical(y_train, 10) y_test = np_utils.to_categorical(y_test, 10) X_train = np.reshape(X_train, (60000, 784)) X_test = np.reshape(X_test, (10000, 784)) print 'Data loaded.' return [X_train, X_test, y_train, y_test] def init_model(): start_time = time.time() print 'Compiling Model ... ' model = Sequential() model.add(Dense(500, input_dim=784)) model.add(Activation('relu')) model.add(Dropout(0.4)) model.add(Dense(300)) model.add(Activation('relu')) model.add(Dropout(0.4)) model.add(Dense(10)) model.add(Activation('softmax')) rms = RMSprop() model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=['accuracy']) print 'Model compield in {0} seconds'.format(time.time() - start_time) return model def run_network(data=None, model=None, epochs=20, batch=256): try: start_time = time.time() if data is None: X_train, X_test, y_train, y_test = load_data() else: X_train, X_test, y_train, y_test = data if model is None: model = init_model() history = LossHistory() print 'Training model...' model.fit(X_train, y_train, nb_epoch=epochs, batch_size=batch, callbacks=[history], validation_data=(X_test, y_test), verbose=2) print "Training duration : {0}".format(time.time() - start_time) score = model.evaluate(X_test, y_test, batch_size=16) print "Network's test score [loss, accuracy]: {0}".format(score) return model, history.losses except KeyboardInterrupt: print ' KeyboardInterrupt' return model, history.losses def plot_losses(losses): fig = plt.figure() ax = fig.add_subplot(111) ax.plot(losses) ax.set_title('Loss per batch') fig.show()

gpl-2.0

luo66/scikit-learn

examples/applications/plot_outlier_detection_housing.py

241

5577

""" ==================================== Outlier detection on a real data set ==================================== This example illustrates the need for robust covariance estimation on a real data set. It is useful both for outlier detection and for a better understanding of the data structure. We selected two sets of two variables from the Boston housing data set as an illustration of what kind of analysis can be done with several outlier detection tools. For the purpose of visualization, we are working with two-dimensional examples, but one should be aware that things are not so trivial in high-dimension, as it will be pointed out. In both examples below, the main result is that the empirical covariance estimate, as a non-robust one, is highly influenced by the heterogeneous structure of the observations. Although the robust covariance estimate is able to focus on the main mode of the data distribution, it sticks to the assumption that the data should be Gaussian distributed, yielding some biased estimation of the data structure, but yet accurate to some extent. The One-Class SVM algorithm First example ------------- The first example illustrates how robust covariance estimation can help concentrating on a relevant cluster when another one exists. Here, many observations are confounded into one and break down the empirical covariance estimation. Of course, some screening tools would have pointed out the presence of two clusters (Support Vector Machines, Gaussian Mixture Models, univariate outlier detection, ...). But had it been a high-dimensional example, none of these could be applied that easily. Second example -------------- The second example shows the ability of the Minimum Covariance Determinant robust estimator of covariance to concentrate on the main mode of the data distribution: the location seems to be well estimated, although the covariance is hard to estimate due to the banana-shaped distribution. Anyway, we can get rid of some outlying observations. The One-Class SVM is able to capture the real data structure, but the difficulty is to adjust its kernel bandwidth parameter so as to obtain a good compromise between the shape of the data scatter matrix and the risk of over-fitting the data. """ print(__doc__) # Author: Virgile Fritsch <virgile.fritsch@inria.fr> # License: BSD 3 clause import numpy as np from sklearn.covariance import EllipticEnvelope from sklearn.svm import OneClassSVM import matplotlib.pyplot as plt import matplotlib.font_manager from sklearn.datasets import load_boston # Get data X1 = load_boston()['data'][:, [8, 10]] # two clusters X2 = load_boston()['data'][:, [5, 12]] # "banana"-shaped # Define "classifiers" to be used classifiers = { "Empirical Covariance": EllipticEnvelope(support_fraction=1., contamination=0.261), "Robust Covariance (Minimum Covariance Determinant)": EllipticEnvelope(contamination=0.261), "OCSVM": OneClassSVM(nu=0.261, gamma=0.05)} colors = ['m', 'g', 'b'] legend1 = {} legend2 = {} # Learn a frontier for outlier detection with several classifiers xx1, yy1 = np.meshgrid(np.linspace(-8, 28, 500), np.linspace(3, 40, 500)) xx2, yy2 = np.meshgrid(np.linspace(3, 10, 500), np.linspace(-5, 45, 500)) for i, (clf_name, clf) in enumerate(classifiers.items()): plt.figure(1) clf.fit(X1) Z1 = clf.decision_function(np.c_[xx1.ravel(), yy1.ravel()]) Z1 = Z1.reshape(xx1.shape) legend1[clf_name] = plt.contour( xx1, yy1, Z1, levels=[0], linewidths=2, colors=colors[i]) plt.figure(2) clf.fit(X2) Z2 = clf.decision_function(np.c_[xx2.ravel(), yy2.ravel()]) Z2 = Z2.reshape(xx2.shape) legend2[clf_name] = plt.contour( xx2, yy2, Z2, levels=[0], linewidths=2, colors=colors[i]) legend1_values_list = list( legend1.values() ) legend1_keys_list = list( legend1.keys() ) # Plot the results (= shape of the data points cloud) plt.figure(1) # two clusters plt.title("Outlier detection on a real data set (boston housing)") plt.scatter(X1[:, 0], X1[:, 1], color='black') bbox_args = dict(boxstyle="round", fc="0.8") arrow_args = dict(arrowstyle="->") plt.annotate("several confounded points", xy=(24, 19), xycoords="data", textcoords="data", xytext=(13, 10), bbox=bbox_args, arrowprops=arrow_args) plt.xlim((xx1.min(), xx1.max())) plt.ylim((yy1.min(), yy1.max())) plt.legend((legend1_values_list[0].collections[0], legend1_values_list[1].collections[0], legend1_values_list[2].collections[0]), (legend1_keys_list[0], legend1_keys_list[1], legend1_keys_list[2]), loc="upper center", prop=matplotlib.font_manager.FontProperties(size=12)) plt.ylabel("accessibility to radial highways") plt.xlabel("pupil-teacher ratio by town") legend2_values_list = list( legend2.values() ) legend2_keys_list = list( legend2.keys() ) plt.figure(2) # "banana" shape plt.title("Outlier detection on a real data set (boston housing)") plt.scatter(X2[:, 0], X2[:, 1], color='black') plt.xlim((xx2.min(), xx2.max())) plt.ylim((yy2.min(), yy2.max())) plt.legend((legend2_values_list[0].collections[0], legend2_values_list[1].collections[0], legend2_values_list[2].collections[0]), (legend2_values_list[0], legend2_values_list[1], legend2_values_list[2]), loc="upper center", prop=matplotlib.font_manager.FontProperties(size=12)) plt.ylabel("% lower status of the population") plt.xlabel("average number of rooms per dwelling") plt.show()

bsd-3-clause

sgenoud/scikit-learn

sklearn/setup.py

1

2735

import os from os.path import join import warnings def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration from numpy.distutils.system_info import get_info, BlasNotFoundError import numpy libraries = [] if os.name == 'posix': libraries.append('m') config = Configuration('sklearn', parent_package, top_path) config.add_subpackage('__check_build') config.add_subpackage('svm') config.add_subpackage('datasets') config.add_subpackage('datasets/tests') config.add_subpackage('feature_extraction') config.add_subpackage('feature_extraction/tests') config.add_subpackage('cluster') config.add_subpackage('cluster/tests') config.add_subpackage('covariance') config.add_subpackage('covariance/tests') config.add_subpackage('decomposition') config.add_subpackage('decomposition/tests') config.add_subpackage("ensemble") config.add_subpackage("ensemble/tests") config.add_subpackage('feature_selection') config.add_subpackage('feature_selection/tests') config.add_subpackage('utils') config.add_subpackage('utils/tests') config.add_subpackage('externals') config.add_subpackage('mixture') config.add_subpackage('mixture/tests') config.add_subpackage('gaussian_process') config.add_subpackage('gaussian_process/tests') config.add_subpackage('neighbors') config.add_subpackage('manifold') config.add_subpackage('metrics') config.add_subpackage('semi_supervised') config.add_subpackage("tree") config.add_subpackage("tree/tests") config.add_subpackage('metrics/tests') config.add_subpackage('metrics/tests') config.add_subpackage('metrics/cluster') config.add_subpackage('metrics/cluster/tests') # add cython extension module for hmm config.add_extension( '_hmmc', sources=['_hmmc.c'], include_dirs=[numpy.get_include()], libraries=libraries, ) # some libs needs cblas, fortran-compiled BLAS will not be sufficient blas_info = get_info('blas_opt', 0) if (not blas_info) or ( ('NO_ATLAS_INFO', 1) in blas_info.get('define_macros', [])): config.add_library('cblas', sources=[join('src', 'cblas', '*.c')]) warnings.warn(BlasNotFoundError.__doc__) # the following packages depend on cblas, so they have to be build # after the above. config.add_subpackage('linear_model') config.add_subpackage('utils') # add the test directory config.add_subpackage('tests') return config if __name__ == '__main__': from numpy.distutils.core import setup setup(**configuration(top_path='').todict())

bsd-3-clause

LohithBlaze/scikit-learn

examples/linear_model/plot_multi_task_lasso_support.py

248

2211

#!/usr/bin/env python """ ============================================= Joint feature selection with multi-task Lasso ============================================= The multi-task lasso allows to fit multiple regression problems jointly enforcing the selected features to be the same across tasks. This example simulates sequential measurements, each task is a time instant, and the relevant features vary in amplitude over time while being the same. The multi-task lasso imposes that features that are selected at one time point are select for all time point. This makes feature selection by the Lasso more stable. """ print(__doc__) # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr> # License: BSD 3 clause import matplotlib.pyplot as plt import numpy as np from sklearn.linear_model import MultiTaskLasso, Lasso rng = np.random.RandomState(42) # Generate some 2D coefficients with sine waves with random frequency and phase n_samples, n_features, n_tasks = 100, 30, 40 n_relevant_features = 5 coef = np.zeros((n_tasks, n_features)) times = np.linspace(0, 2 * np.pi, n_tasks) for k in range(n_relevant_features): coef[:, k] = np.sin((1. + rng.randn(1)) * times + 3 * rng.randn(1)) X = rng.randn(n_samples, n_features) Y = np.dot(X, coef.T) + rng.randn(n_samples, n_tasks) coef_lasso_ = np.array([Lasso(alpha=0.5).fit(X, y).coef_ for y in Y.T]) coef_multi_task_lasso_ = MultiTaskLasso(alpha=1.).fit(X, Y).coef_ ############################################################################### # Plot support and time series fig = plt.figure(figsize=(8, 5)) plt.subplot(1, 2, 1) plt.spy(coef_lasso_) plt.xlabel('Feature') plt.ylabel('Time (or Task)') plt.text(10, 5, 'Lasso') plt.subplot(1, 2, 2) plt.spy(coef_multi_task_lasso_) plt.xlabel('Feature') plt.ylabel('Time (or Task)') plt.text(10, 5, 'MultiTaskLasso') fig.suptitle('Coefficient non-zero location') feature_to_plot = 0 plt.figure() plt.plot(coef[:, feature_to_plot], 'k', label='Ground truth') plt.plot(coef_lasso_[:, feature_to_plot], 'g', label='Lasso') plt.plot(coef_multi_task_lasso_[:, feature_to_plot], 'r', label='MultiTaskLasso') plt.legend(loc='upper center') plt.axis('tight') plt.ylim([-1.1, 1.1]) plt.show()

bsd-3-clause

glouppe/scikit-learn

sklearn/utils/tests/test_validation.py

54

18600

"""Tests for input validation functions""" import warnings from tempfile import NamedTemporaryFile from itertools import product import numpy as np from numpy.testing import assert_array_equal import scipy.sparse as sp from nose.tools import assert_raises, assert_true, assert_false, assert_equal from sklearn.utils.testing import assert_raises_regexp from sklearn.utils.testing import assert_no_warnings from sklearn.utils.testing import assert_warns_message from sklearn.utils.testing import assert_warns from sklearn.utils.testing import ignore_warnings from sklearn.utils import as_float_array, check_array, check_symmetric from sklearn.utils import check_X_y from sklearn.utils.mocking import MockDataFrame from sklearn.utils.estimator_checks import NotAnArray from sklearn.random_projection import sparse_random_matrix from sklearn.linear_model import ARDRegression from sklearn.neighbors import KNeighborsClassifier from sklearn.ensemble import RandomForestRegressor from sklearn.svm import SVR from sklearn.datasets import make_blobs from sklearn.utils.validation import ( has_fit_parameter, check_is_fitted, check_consistent_length, ) from sklearn.exceptions import NotFittedError from sklearn.exceptions import DataConversionWarning from sklearn.utils.testing import assert_raise_message def test_as_float_array(): # Test function for as_float_array X = np.ones((3, 10), dtype=np.int32) X = X + np.arange(10, dtype=np.int32) # Checks that the return type is ok X2 = as_float_array(X, copy=False) np.testing.assert_equal(X2.dtype, np.float32) # Another test X = X.astype(np.int64) X2 = as_float_array(X, copy=True) # Checking that the array wasn't overwritten assert_true(as_float_array(X, False) is not X) # Checking that the new type is ok np.testing.assert_equal(X2.dtype, np.float64) # Here, X is of the right type, it shouldn't be modified X = np.ones((3, 2), dtype=np.float32) assert_true(as_float_array(X, copy=False) is X) # Test that if X is fortran ordered it stays X = np.asfortranarray(X) assert_true(np.isfortran(as_float_array(X, copy=True))) # Test the copy parameter with some matrices matrices = [ np.matrix(np.arange(5)), sp.csc_matrix(np.arange(5)).toarray(), sparse_random_matrix(10, 10, density=0.10).toarray() ] for M in matrices: N = as_float_array(M, copy=True) N[0, 0] = np.nan assert_false(np.isnan(M).any()) def test_np_matrix(): # Confirm that input validation code does not return np.matrix X = np.arange(12).reshape(3, 4) assert_false(isinstance(as_float_array(X), np.matrix)) assert_false(isinstance(as_float_array(np.matrix(X)), np.matrix)) assert_false(isinstance(as_float_array(sp.csc_matrix(X)), np.matrix)) def test_memmap(): # Confirm that input validation code doesn't copy memory mapped arrays asflt = lambda x: as_float_array(x, copy=False) with NamedTemporaryFile(prefix='sklearn-test') as tmp: M = np.memmap(tmp, shape=(10, 10), dtype=np.float32) M[:] = 0 for f in (check_array, np.asarray, asflt): X = f(M) X[:] = 1 assert_array_equal(X.ravel(), M.ravel()) X[:] = 0 def test_ordering(): # Check that ordering is enforced correctly by validation utilities. # We need to check each validation utility, because a 'copy' without # 'order=K' will kill the ordering. X = np.ones((10, 5)) for A in X, X.T: for copy in (True, False): B = check_array(A, order='C', copy=copy) assert_true(B.flags['C_CONTIGUOUS']) B = check_array(A, order='F', copy=copy) assert_true(B.flags['F_CONTIGUOUS']) if copy: assert_false(A is B) X = sp.csr_matrix(X) X.data = X.data[::-1] assert_false(X.data.flags['C_CONTIGUOUS']) @ignore_warnings def test_check_array(): # accept_sparse == None # raise error on sparse inputs X = [[1, 2], [3, 4]] X_csr = sp.csr_matrix(X) assert_raises(TypeError, check_array, X_csr) # ensure_2d assert_warns(DeprecationWarning, check_array, [0, 1, 2]) X_array = check_array([0, 1, 2]) assert_equal(X_array.ndim, 2) X_array = check_array([0, 1, 2], ensure_2d=False) assert_equal(X_array.ndim, 1) # don't allow ndim > 3 X_ndim = np.arange(8).reshape(2, 2, 2) assert_raises(ValueError, check_array, X_ndim) check_array(X_ndim, allow_nd=True) # doesn't raise # force_all_finite X_inf = np.arange(4).reshape(2, 2).astype(np.float) X_inf[0, 0] = np.inf assert_raises(ValueError, check_array, X_inf) check_array(X_inf, force_all_finite=False) # no raise # nan check X_nan = np.arange(4).reshape(2, 2).astype(np.float) X_nan[0, 0] = np.nan assert_raises(ValueError, check_array, X_nan) check_array(X_inf, force_all_finite=False) # no raise # dtype and order enforcement. X_C = np.arange(4).reshape(2, 2).copy("C") X_F = X_C.copy("F") X_int = X_C.astype(np.int) X_float = X_C.astype(np.float) Xs = [X_C, X_F, X_int, X_float] dtypes = [np.int32, np.int, np.float, np.float32, None, np.bool, object] orders = ['C', 'F', None] copys = [True, False] for X, dtype, order, copy in product(Xs, dtypes, orders, copys): X_checked = check_array(X, dtype=dtype, order=order, copy=copy) if dtype is not None: assert_equal(X_checked.dtype, dtype) else: assert_equal(X_checked.dtype, X.dtype) if order == 'C': assert_true(X_checked.flags['C_CONTIGUOUS']) assert_false(X_checked.flags['F_CONTIGUOUS']) elif order == 'F': assert_true(X_checked.flags['F_CONTIGUOUS']) assert_false(X_checked.flags['C_CONTIGUOUS']) if copy: assert_false(X is X_checked) else: # doesn't copy if it was already good if (X.dtype == X_checked.dtype and X_checked.flags['C_CONTIGUOUS'] == X.flags['C_CONTIGUOUS'] and X_checked.flags['F_CONTIGUOUS'] == X.flags['F_CONTIGUOUS']): assert_true(X is X_checked) # allowed sparse != None X_csc = sp.csc_matrix(X_C) X_coo = X_csc.tocoo() X_dok = X_csc.todok() X_int = X_csc.astype(np.int) X_float = X_csc.astype(np.float) Xs = [X_csc, X_coo, X_dok, X_int, X_float] accept_sparses = [['csr', 'coo'], ['coo', 'dok']] for X, dtype, accept_sparse, copy in product(Xs, dtypes, accept_sparses, copys): with warnings.catch_warnings(record=True) as w: X_checked = check_array(X, dtype=dtype, accept_sparse=accept_sparse, copy=copy) if (dtype is object or sp.isspmatrix_dok(X)) and len(w): message = str(w[0].message) messages = ["object dtype is not supported by sparse matrices", "Can't check dok sparse matrix for nan or inf."] assert_true(message in messages) else: assert_equal(len(w), 0) if dtype is not None: assert_equal(X_checked.dtype, dtype) else: assert_equal(X_checked.dtype, X.dtype) if X.format in accept_sparse: # no change if allowed assert_equal(X.format, X_checked.format) else: # got converted assert_equal(X_checked.format, accept_sparse[0]) if copy: assert_false(X is X_checked) else: # doesn't copy if it was already good if (X.dtype == X_checked.dtype and X.format == X_checked.format): assert_true(X is X_checked) # other input formats # convert lists to arrays X_dense = check_array([[1, 2], [3, 4]]) assert_true(isinstance(X_dense, np.ndarray)) # raise on too deep lists assert_raises(ValueError, check_array, X_ndim.tolist()) check_array(X_ndim.tolist(), allow_nd=True) # doesn't raise # convert weird stuff to arrays X_no_array = NotAnArray(X_dense) result = check_array(X_no_array) assert_true(isinstance(result, np.ndarray)) def test_check_array_pandas_dtype_object_conversion(): # test that data-frame like objects with dtype object # get converted X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.object) X_df = MockDataFrame(X) assert_equal(check_array(X_df).dtype.kind, "f") assert_equal(check_array(X_df, ensure_2d=False).dtype.kind, "f") # smoke-test against dataframes with column named "dtype" X_df.dtype = "Hans" assert_equal(check_array(X_df, ensure_2d=False).dtype.kind, "f") def test_check_array_dtype_stability(): # test that lists with ints don't get converted to floats X = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] assert_equal(check_array(X).dtype.kind, "i") assert_equal(check_array(X, ensure_2d=False).dtype.kind, "i") def test_check_array_dtype_warning(): X_int_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] X_float64 = np.asarray(X_int_list, dtype=np.float64) X_float32 = np.asarray(X_int_list, dtype=np.float32) X_int64 = np.asarray(X_int_list, dtype=np.int64) X_csr_float64 = sp.csr_matrix(X_float64) X_csr_float32 = sp.csr_matrix(X_float32) X_csc_float32 = sp.csc_matrix(X_float32) X_csc_int32 = sp.csc_matrix(X_int64, dtype=np.int32) y = [0, 0, 1] integer_data = [X_int64, X_csc_int32] float64_data = [X_float64, X_csr_float64] float32_data = [X_float32, X_csr_float32, X_csc_float32] for X in integer_data: X_checked = assert_no_warnings(check_array, X, dtype=np.float64, accept_sparse=True) assert_equal(X_checked.dtype, np.float64) X_checked = assert_warns(DataConversionWarning, check_array, X, dtype=np.float64, accept_sparse=True, warn_on_dtype=True) assert_equal(X_checked.dtype, np.float64) # Check that the warning message includes the name of the Estimator X_checked = assert_warns_message(DataConversionWarning, 'SomeEstimator', check_array, X, dtype=[np.float64, np.float32], accept_sparse=True, warn_on_dtype=True, estimator='SomeEstimator') assert_equal(X_checked.dtype, np.float64) X_checked, y_checked = assert_warns_message( DataConversionWarning, 'KNeighborsClassifier', check_X_y, X, y, dtype=np.float64, accept_sparse=True, warn_on_dtype=True, estimator=KNeighborsClassifier()) assert_equal(X_checked.dtype, np.float64) for X in float64_data: X_checked = assert_no_warnings(check_array, X, dtype=np.float64, accept_sparse=True, warn_on_dtype=True) assert_equal(X_checked.dtype, np.float64) X_checked = assert_no_warnings(check_array, X, dtype=np.float64, accept_sparse=True, warn_on_dtype=False) assert_equal(X_checked.dtype, np.float64) for X in float32_data: X_checked = assert_no_warnings(check_array, X, dtype=[np.float64, np.float32], accept_sparse=True) assert_equal(X_checked.dtype, np.float32) assert_true(X_checked is X) X_checked = assert_no_warnings(check_array, X, dtype=[np.float64, np.float32], accept_sparse=['csr', 'dok'], copy=True) assert_equal(X_checked.dtype, np.float32) assert_false(X_checked is X) X_checked = assert_no_warnings(check_array, X_csc_float32, dtype=[np.float64, np.float32], accept_sparse=['csr', 'dok'], copy=False) assert_equal(X_checked.dtype, np.float32) assert_false(X_checked is X_csc_float32) assert_equal(X_checked.format, 'csr') def test_check_array_min_samples_and_features_messages(): # empty list is considered 2D by default: msg = "0 feature(s) (shape=(1, 0)) while a minimum of 1 is required." assert_raise_message(ValueError, msg, check_array, [[]]) # If considered a 1D collection when ensure_2d=False, then the minimum # number of samples will break: msg = "0 sample(s) (shape=(0,)) while a minimum of 1 is required." assert_raise_message(ValueError, msg, check_array, [], ensure_2d=False) # Invalid edge case when checking the default minimum sample of a scalar msg = "Singleton array array(42) cannot be considered a valid collection." assert_raise_message(TypeError, msg, check_array, 42, ensure_2d=False) # But this works if the input data is forced to look like a 2 array with # one sample and one feature: X_checked = assert_warns(DeprecationWarning, check_array, [42], ensure_2d=True) assert_array_equal(np.array([[42]]), X_checked) # Simulate a model that would need at least 2 samples to be well defined X = np.ones((1, 10)) y = np.ones(1) msg = "1 sample(s) (shape=(1, 10)) while a minimum of 2 is required." assert_raise_message(ValueError, msg, check_X_y, X, y, ensure_min_samples=2) # The same message is raised if the data has 2 dimensions even if this is # not mandatory assert_raise_message(ValueError, msg, check_X_y, X, y, ensure_min_samples=2, ensure_2d=False) # Simulate a model that would require at least 3 features (e.g. SelectKBest # with k=3) X = np.ones((10, 2)) y = np.ones(2) msg = "2 feature(s) (shape=(10, 2)) while a minimum of 3 is required." assert_raise_message(ValueError, msg, check_X_y, X, y, ensure_min_features=3) # Only the feature check is enabled whenever the number of dimensions is 2 # even if allow_nd is enabled: assert_raise_message(ValueError, msg, check_X_y, X, y, ensure_min_features=3, allow_nd=True) # Simulate a case where a pipeline stage as trimmed all the features of a # 2D dataset. X = np.empty(0).reshape(10, 0) y = np.ones(10) msg = "0 feature(s) (shape=(10, 0)) while a minimum of 1 is required." assert_raise_message(ValueError, msg, check_X_y, X, y) # nd-data is not checked for any minimum number of features by default: X = np.ones((10, 0, 28, 28)) y = np.ones(10) X_checked, y_checked = check_X_y(X, y, allow_nd=True) assert_array_equal(X, X_checked) assert_array_equal(y, y_checked) def test_has_fit_parameter(): assert_false(has_fit_parameter(KNeighborsClassifier, "sample_weight")) assert_true(has_fit_parameter(RandomForestRegressor, "sample_weight")) assert_true(has_fit_parameter(SVR, "sample_weight")) assert_true(has_fit_parameter(SVR(), "sample_weight")) def test_check_symmetric(): arr_sym = np.array([[0, 1], [1, 2]]) arr_bad = np.ones(2) arr_asym = np.array([[0, 2], [0, 2]]) test_arrays = {'dense': arr_asym, 'dok': sp.dok_matrix(arr_asym), 'csr': sp.csr_matrix(arr_asym), 'csc': sp.csc_matrix(arr_asym), 'coo': sp.coo_matrix(arr_asym), 'lil': sp.lil_matrix(arr_asym), 'bsr': sp.bsr_matrix(arr_asym)} # check error for bad inputs assert_raises(ValueError, check_symmetric, arr_bad) # check that asymmetric arrays are properly symmetrized for arr_format, arr in test_arrays.items(): # Check for warnings and errors assert_warns(UserWarning, check_symmetric, arr) assert_raises(ValueError, check_symmetric, arr, raise_exception=True) output = check_symmetric(arr, raise_warning=False) if sp.issparse(output): assert_equal(output.format, arr_format) assert_array_equal(output.toarray(), arr_sym) else: assert_array_equal(output, arr_sym) def test_check_is_fitted(): # Check is ValueError raised when non estimator instance passed assert_raises(ValueError, check_is_fitted, ARDRegression, "coef_") assert_raises(TypeError, check_is_fitted, "SVR", "support_") ard = ARDRegression() svr = SVR() try: assert_raises(NotFittedError, check_is_fitted, ard, "coef_") assert_raises(NotFittedError, check_is_fitted, svr, "support_") except ValueError: assert False, "check_is_fitted failed with ValueError" # NotFittedError is a subclass of both ValueError and AttributeError try: check_is_fitted(ard, "coef_", "Random message %(name)s, %(name)s") except ValueError as e: assert_equal(str(e), "Random message ARDRegression, ARDRegression") try: check_is_fitted(svr, "support_", "Another message %(name)s, %(name)s") except AttributeError as e: assert_equal(str(e), "Another message SVR, SVR") ard.fit(*make_blobs()) svr.fit(*make_blobs()) assert_equal(None, check_is_fitted(ard, "coef_")) assert_equal(None, check_is_fitted(svr, "support_")) def test_check_consistent_length(): check_consistent_length([1], [2], [3], [4], [5]) check_consistent_length([[1, 2], [[1, 2]]], [1, 2], ['a', 'b']) check_consistent_length([1], (2,), np.array([3]), sp.csr_matrix((1, 2))) assert_raises_regexp(ValueError, 'inconsistent numbers of samples', check_consistent_length, [1, 2], [1]) assert_raises_regexp(TypeError, 'got <\w+ \'int\'>', check_consistent_length, [1, 2], 1) assert_raises_regexp(TypeError, 'got <\w+ \'object\'>', check_consistent_length, [1, 2], object()) assert_raises(TypeError, check_consistent_length, [1, 2], np.array(1)) # Despite ensembles having __len__ they must raise TypeError assert_raises_regexp(TypeError, 'estimator', check_consistent_length, [1, 2], RandomForestRegressor()) # XXX: We should have a test with a string, but what is correct behaviour?

bsd-3-clause

RensaProject/nodebox_linguistics_extended

nodebox_linguistics_extended/parser/nltk_lite/__init__.py

10

2228

# Natural Language Toolkit (NLTK-Lite) # # Copyright (C) 2001-2006 University of Pennsylvania # Authors: Steven Bird <sb@csse.unimelb.edu.au> # Edward Loper <edloper@gradient.cis.upenn.edu> # URL: <http://nltk.sf.net> # For license information, see LICENSE.TXT """ NLTK-Lite is a collection of lightweight NLP modules designed for maximum simplicity and efficiency. NLTK-Lite only covers the simple variants of standard data structures and tasks. It makes extensive use of iterators so that large tasks generate output as early as possible. Key differences from NLTK are as follows: - tokens are represented as strings, tuples, or trees - all tokenizers are iterators - less object orientation NLTK-Lite is primarily intended to facilitate teaching NLP to students having limited programming experience. The focus is on teaching Python together with the help of NLP recipes, instead of teaching students to use a large set of specialized classes. @version: 0.7a2 """ ##////////////////////////////////////////////////////// ## Metadata ##////////////////////////////////////////////////////// # Version. For each new release, the version number should be updated # here and in the Epydoc comment (above). __version__ = "0.7a2" # Copyright notice __copyright__ = """\ Copyright (C) 2001-2006 University of Pennsylvania. Distributed and Licensed under provisions of the GNU Public License, which is included by reference. """ __license__ = "GNU Public License" # Description of the toolkit, keywords, and the project's primary URL. __longdescr__ = """\ The Natural Langauge Toolkit (NLTK-Lite) is a Python package for processing natural language text. It was developed as a simpler, lightweight version of NLTK. NLTK-Lite requires Python 2.4 or higher.""" __keywords__ = ['NLP', 'CL', 'natural language processing', 'computational linguistics', 'parsing', 'tagging', 'tokenizing', 'syntax', 'linguistics', 'language', 'natural language'] __url__ = "http://nltk.sf.net/" # Maintainer, contributors, etc. __maintainer__ = "Steven Bird" __maintainer_email__ = "sb@csse.unimelb.edu.au" __author__ = __maintainer__ __author_email__ = __maintainer_email__

gpl-2.0

lukeiwanski/tensorflow

tensorflow/contrib/losses/python/metric_learning/metric_loss_ops_test.py

40

20535

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for triplet_semihard_loss.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.contrib.losses.python import metric_learning as metric_loss_ops from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn from tensorflow.python.platform import test try: # pylint: disable=g-import-not-at-top from sklearn import datasets from sklearn import metrics HAS_SKLEARN = True except ImportError: HAS_SKLEARN = False def pairwise_distance_np(feature, squared=False): """Computes the pairwise distance matrix in numpy. Args: feature: 2-D numpy array of size [number of data, feature dimension] squared: Boolean. If true, output is the pairwise squared euclidean distance matrix; else, output is the pairwise euclidean distance matrix. Returns: pairwise_distances: 2-D numpy array of size [number of data, number of data]. """ triu = np.triu_indices(feature.shape[0], 1) upper_tri_pdists = np.linalg.norm(feature[triu[1]] - feature[triu[0]], axis=1) if squared: upper_tri_pdists **= 2. num_data = feature.shape[0] pairwise_distances = np.zeros((num_data, num_data)) pairwise_distances[np.triu_indices(num_data, 1)] = upper_tri_pdists # Make symmetrical. pairwise_distances = pairwise_distances + pairwise_distances.T - np.diag( pairwise_distances.diagonal()) return pairwise_distances class ContrastiveLossTest(test.TestCase): def testContrastive(self): with self.test_session(): num_data = 10 feat_dim = 6 margin = 1.0 embeddings_anchor = np.random.rand(num_data, feat_dim).astype(np.float32) embeddings_positive = np.random.rand(num_data, feat_dim).astype( np.float32) labels = np.random.randint(0, 2, size=(num_data,)).astype(np.float32) # Compute the loss in NP dist = np.sqrt( np.sum(np.square(embeddings_anchor - embeddings_positive), axis=1)) loss_np = np.mean( labels * np.square(dist) + (1.0 - labels) * np.square(np.maximum(margin - dist, 0.0))) # Compute the loss with TF loss_tf = metric_loss_ops.contrastive_loss( labels=ops.convert_to_tensor(labels), embeddings_anchor=ops.convert_to_tensor(embeddings_anchor), embeddings_positive=ops.convert_to_tensor(embeddings_positive), margin=margin) loss_tf = loss_tf.eval() self.assertAllClose(loss_np, loss_tf) class TripletSemiHardLossTest(test.TestCase): def testTripletSemiHard(self): with self.test_session(): num_data = 10 feat_dim = 6 margin = 1.0 num_classes = 4 embedding = np.random.rand(num_data, feat_dim).astype(np.float32) labels = np.random.randint( 0, num_classes, size=(num_data)).astype(np.float32) # Reshape labels to compute adjacency matrix. labels_reshaped = np.reshape(labels, (labels.shape[0], 1)) # Compute the loss in NP. adjacency = np.equal(labels_reshaped, labels_reshaped.T) pdist_matrix = pairwise_distance_np(embedding, squared=True) loss_np = 0.0 num_positives = 0.0 for i in range(num_data): for j in range(num_data): if adjacency[i][j] > 0.0 and i != j: num_positives += 1.0 pos_distance = pdist_matrix[i][j] neg_distances = [] for k in range(num_data): if adjacency[i][k] == 0: neg_distances.append(pdist_matrix[i][k]) # Sort by distance. neg_distances.sort() chosen_neg_distance = neg_distances[0] for l in range(len(neg_distances)): chosen_neg_distance = neg_distances[l] if chosen_neg_distance > pos_distance: break loss_np += np.maximum( 0.0, margin - chosen_neg_distance + pos_distance) loss_np /= num_positives # Compute the loss in TF. loss_tf = metric_loss_ops.triplet_semihard_loss( labels=ops.convert_to_tensor(labels), embeddings=ops.convert_to_tensor(embedding), margin=margin) loss_tf = loss_tf.eval() self.assertAllClose(loss_np, loss_tf) class LiftedStructLossTest(test.TestCase): def testLiftedStruct(self): with self.test_session(): num_data = 10 feat_dim = 6 margin = 1.0 num_classes = 4 embedding = np.random.rand(num_data, feat_dim).astype(np.float32) labels = np.random.randint( 0, num_classes, size=(num_data)).astype(np.float32) # Reshape labels to compute adjacency matrix. labels_reshaped = np.reshape(labels, (labels.shape[0], 1)) # Compute the loss in NP adjacency = np.equal(labels_reshaped, labels_reshaped.T) pdist_matrix = pairwise_distance_np(embedding) loss_np = 0.0 num_constraints = 0.0 for i in range(num_data): for j in range(num_data): if adjacency[i][j] > 0.0 and i != j: d_pos = pdist_matrix[i][j] negs = [] for k in range(num_data): if not adjacency[i][k]: negs.append(margin - pdist_matrix[i][k]) for l in range(num_data): if not adjacency[j][l]: negs.append(margin - pdist_matrix[j][l]) negs = np.array(negs) max_elem = np.max(negs) negs -= max_elem negs = np.exp(negs) soft_maximum = np.log(np.sum(negs)) + max_elem num_constraints += 1.0 this_loss = max(soft_maximum + d_pos, 0) loss_np += this_loss * this_loss loss_np = loss_np / num_constraints / 2.0 # Compute the loss in TF loss_tf = metric_loss_ops.lifted_struct_loss( labels=ops.convert_to_tensor(labels), embeddings=ops.convert_to_tensor(embedding), margin=margin) loss_tf = loss_tf.eval() self.assertAllClose(loss_np, loss_tf) def convert_to_list_of_sparse_tensor(np_matrix): list_of_sparse_tensors = [] nrows, ncols = np_matrix.shape for i in range(nrows): sp_indices = [] for j in range(ncols): if np_matrix[i][j] == 1: sp_indices.append([j]) num_non_zeros = len(sp_indices) list_of_sparse_tensors.append(sparse_tensor.SparseTensor( indices=np.array(sp_indices), values=np.ones((num_non_zeros,)), dense_shape=np.array([ncols,]))) return list_of_sparse_tensors class NpairsLossTest(test.TestCase): def testNpairs(self): with self.test_session(): num_data = 15 feat_dim = 6 num_classes = 5 reg_lambda = 0.02 embeddings_anchor = np.random.rand(num_data, feat_dim).astype(np.float32) embeddings_positive = np.random.rand(num_data, feat_dim).astype( np.float32) labels = np.random.randint( 0, num_classes, size=(num_data)).astype(np.float32) # Reshape labels to compute adjacency matrix. labels_reshaped = np.reshape(labels, (labels.shape[0], 1)) # Compute the loss in NP reg_term = np.mean(np.sum(np.square(embeddings_anchor), 1)) reg_term += np.mean(np.sum(np.square(embeddings_positive), 1)) reg_term *= 0.25 * reg_lambda similarity_matrix = np.matmul(embeddings_anchor, embeddings_positive.T) labels_remapped = np.equal( labels_reshaped, labels_reshaped.T).astype(np.float32) labels_remapped /= np.sum(labels_remapped, axis=1, keepdims=True) xent_loss = math_ops.reduce_mean(nn.softmax_cross_entropy_with_logits( logits=ops.convert_to_tensor(similarity_matrix), labels=ops.convert_to_tensor(labels_remapped))).eval() loss_np = xent_loss + reg_term # Compute the loss in TF loss_tf = metric_loss_ops.npairs_loss( labels=ops.convert_to_tensor(labels), embeddings_anchor=ops.convert_to_tensor(embeddings_anchor), embeddings_positive=ops.convert_to_tensor(embeddings_positive), reg_lambda=reg_lambda) loss_tf = loss_tf.eval() self.assertAllClose(loss_np, loss_tf) class NpairsLossMultiLabelTest(test.TestCase): def testNpairsMultiLabelLossWithSingleLabelEqualsNpairsLoss(self): with self.test_session(): num_data = 15 feat_dim = 6 reg_lambda = 0.02 embeddings_anchor = np.random.rand(num_data, feat_dim).astype(np.float32) embeddings_positive = np.random.rand(num_data, feat_dim).astype( np.float32) labels = np.arange(num_data) labels = np.reshape(labels, -1) # Compute vanila npairs loss. loss_npairs = metric_loss_ops.npairs_loss( labels=ops.convert_to_tensor(labels), embeddings_anchor=ops.convert_to_tensor(embeddings_anchor), embeddings_positive=ops.convert_to_tensor(embeddings_positive), reg_lambda=reg_lambda).eval() # Compute npairs multilabel loss. labels_one_hot = np.identity(num_data) loss_npairs_multilabel = metric_loss_ops.npairs_loss_multilabel( sparse_labels=convert_to_list_of_sparse_tensor(labels_one_hot), embeddings_anchor=ops.convert_to_tensor(embeddings_anchor), embeddings_positive=ops.convert_to_tensor(embeddings_positive), reg_lambda=reg_lambda).eval() self.assertAllClose(loss_npairs, loss_npairs_multilabel) def testNpairsMultiLabel(self): with self.test_session(): num_data = 15 feat_dim = 6 num_classes = 10 reg_lambda = 0.02 embeddings_anchor = np.random.rand(num_data, feat_dim).astype(np.float32) embeddings_positive = np.random.rand(num_data, feat_dim).astype( np.float32) labels = np.random.randint(0, 2, (num_data, num_classes)) # set entire column to one so that each row has at least one bit set. labels[:, -1] = 1 # Compute the loss in NP reg_term = np.mean(np.sum(np.square(embeddings_anchor), 1)) reg_term += np.mean(np.sum(np.square(embeddings_positive), 1)) reg_term *= 0.25 * reg_lambda similarity_matrix = np.matmul(embeddings_anchor, embeddings_positive.T) labels_remapped = np.dot(labels, labels.T).astype(np.float) labels_remapped /= np.sum(labels_remapped, 1, keepdims=True) xent_loss = math_ops.reduce_mean(nn.softmax_cross_entropy_with_logits( logits=ops.convert_to_tensor(similarity_matrix), labels=ops.convert_to_tensor(labels_remapped))).eval() loss_np = xent_loss + reg_term # Compute the loss in TF loss_tf = metric_loss_ops.npairs_loss_multilabel( sparse_labels=convert_to_list_of_sparse_tensor(labels), embeddings_anchor=ops.convert_to_tensor(embeddings_anchor), embeddings_positive=ops.convert_to_tensor(embeddings_positive), reg_lambda=reg_lambda) loss_tf = loss_tf.eval() self.assertAllClose(loss_np, loss_tf) def compute_ground_truth_cluster_score(feat, y): y_unique = np.unique(y) score_gt_np = 0.0 for c in y_unique: feat_subset = feat[y == c, :] pdist_subset = pairwise_distance_np(feat_subset) score_gt_np += -1.0 * np.min(np.sum(pdist_subset, axis=0)) score_gt_np = score_gt_np.astype(np.float32) return score_gt_np def compute_cluster_loss_numpy(feat, y, margin_multiplier=1.0, enable_pam_finetuning=True): if enable_pam_finetuning: facility = ForwardGreedyFacility( n_clusters=np.unique(y).size).pam_augmented_fit(feat, y, margin_multiplier) else: facility = ForwardGreedyFacility( n_clusters=np.unique(y).size).loss_augmented_fit(feat, y, margin_multiplier) score_augmented = facility.score_aug_ score_gt = compute_ground_truth_cluster_score(feat, y) return np.maximum(np.float32(0.0), score_augmented - score_gt) class ForwardGreedyFacility(object): def __init__(self, n_clusters=8): self.n_clusters = n_clusters self.center_ics_ = None def _check_init_args(self): # Check n_clusters. if (self.n_clusters is None or self.n_clusters <= 0 or not isinstance(self.n_clusters, int)): raise ValueError('n_clusters has to be nonnegative integer.') def loss_augmented_fit(self, feat, y, loss_mult): """Fit K-Medoids to the provided data.""" self._check_init_args() # Check that the array is good and attempt to convert it to # Numpy array if possible. feat = self._check_array(feat) # Apply distance metric to get the distance matrix. pdists = pairwise_distance_np(feat) num_data = feat.shape[0] candidate_ids = list(range(num_data)) candidate_scores = np.zeros(num_data,) subset = [] k = 0 while k < self.n_clusters: candidate_scores = [] for i in candidate_ids: # push i to subset. subset.append(i) marginal_cost = -1.0 * np.sum(np.min(pdists[:, subset], axis=1)) loss = 1.0 - metrics.normalized_mutual_info_score( y, self._get_cluster_ics(pdists, subset)) candidate_scores.append(marginal_cost + loss_mult * loss) # remove i from subset. subset.pop() # push i_star to subset. i_star = candidate_ids[np.argmax(candidate_scores)] subset.append(i_star) # remove i_star from candidate indices. candidate_ids.remove(i_star) k += 1 # Expose labels_ which are the assignments of # the training data to clusters. self.labels_ = self._get_cluster_ics(pdists, subset) # Expose cluster centers, i.e. medoids. self.cluster_centers_ = feat.take(subset, axis=0) # Expose indices of chosen cluster centers. self.center_ics_ = subset # Expose the score = -\sum_{i \in V} min_{j \in S} || x_i - x_j || self.score_ = np.float32(-1.0) * self._get_facility_distance(pdists, subset) self.score_aug_ = self.score_ + loss_mult * ( 1.0 - metrics.normalized_mutual_info_score( y, self._get_cluster_ics(pdists, subset))) self.score_aug_ = self.score_aug_.astype(np.float32) # Expose the chosen cluster indices. self.subset_ = subset return self def _augmented_update_medoid_ics_in_place(self, pdists, y_gt, cluster_ics, medoid_ics, loss_mult): for cluster_idx in range(self.n_clusters): # y_pred = self._get_cluster_ics(D, medoid_ics) # Don't prematurely do the assignment step. # Do this after we've updated all cluster medoids. y_pred = cluster_ics if sum(y_pred == cluster_idx) == 0: # Cluster is empty. continue curr_score = ( -1.0 * np.sum( pdists[medoid_ics[cluster_idx], y_pred == cluster_idx]) + loss_mult * (1.0 - metrics.normalized_mutual_info_score( y_gt, y_pred))) pdist_in = pdists[y_pred == cluster_idx, :] pdist_in = pdist_in[:, y_pred == cluster_idx] all_scores_fac = np.sum(-1.0 * pdist_in, axis=1) all_scores_loss = [] for i in range(y_pred.size): if y_pred[i] != cluster_idx: continue # remove this cluster's current centroid medoid_ics_i = medoid_ics[:cluster_idx] + medoid_ics[cluster_idx + 1:] # add this new candidate to the centroid list medoid_ics_i += [i] y_pred_i = self._get_cluster_ics(pdists, medoid_ics_i) all_scores_loss.append(loss_mult * ( 1.0 - metrics.normalized_mutual_info_score(y_gt, y_pred_i))) all_scores = all_scores_fac + all_scores_loss max_score_idx = np.argmax(all_scores) max_score = all_scores[max_score_idx] if max_score > curr_score: medoid_ics[cluster_idx] = np.where( y_pred == cluster_idx)[0][max_score_idx] def pam_augmented_fit(self, feat, y, loss_mult): pam_max_iter = 5 self._check_init_args() feat = self._check_array(feat) pdists = pairwise_distance_np(feat) self.loss_augmented_fit(feat, y, loss_mult) print('PAM -1 (before PAM): score: %f, score_aug: %f' % ( self.score_, self.score_aug_)) # Initialize from loss augmented facility location subset = self.center_ics_ for iter_ in range(pam_max_iter): # update the cluster assignment cluster_ics = self._get_cluster_ics(pdists, subset) # update the medoid for each clusters self._augmented_update_medoid_ics_in_place(pdists, y, cluster_ics, subset, loss_mult) self.score_ = np.float32(-1.0) * self._get_facility_distance( pdists, subset) self.score_aug_ = self.score_ + loss_mult * ( 1.0 - metrics.normalized_mutual_info_score( y, self._get_cluster_ics(pdists, subset))) self.score_aug_ = self.score_aug_.astype(np.float32) print('PAM iter: %d, score: %f, score_aug: %f' % (iter_, self.score_, self.score_aug_)) self.center_ics_ = subset self.labels_ = cluster_ics return self def _check_array(self, feat): # Check that the number of clusters is less than or equal to # the number of samples if self.n_clusters > feat.shape[0]: raise ValueError('The number of medoids ' + '({}) '.format( self.n_clusters) + 'must be larger than the number ' + 'of samples ({})'.format(feat.shape[0])) return feat def _get_cluster_ics(self, pdists, subset): """Returns cluster indices for pdist and current medoid indices.""" # Assign data points to clusters based on # which cluster assignment yields # the smallest distance` cluster_ics = np.argmin(pdists[subset, :], axis=0) return cluster_ics def _get_facility_distance(self, pdists, subset): return np.sum(np.min(pdists[subset, :], axis=0)) class ClusterLossTest(test.TestCase): def _genClusters(self, n_samples, n_clusters): blobs = datasets.make_blobs( n_samples=n_samples, centers=n_clusters) embedding, labels = blobs embedding = (embedding - embedding.mean(axis=0)) / embedding.std(axis=0) embedding = embedding.astype(np.float32) return embedding, labels def testClusteringLossPAMOff(self): if not HAS_SKLEARN: return with self.test_session(): margin_multiplier = 10.0 embeddings, labels = self._genClusters(n_samples=128, n_clusters=64) loss_np = compute_cluster_loss_numpy( embeddings, labels, margin_multiplier, enable_pam_finetuning=False) loss_tf = metric_loss_ops.cluster_loss( labels=ops.convert_to_tensor(labels), embeddings=ops.convert_to_tensor(embeddings), margin_multiplier=margin_multiplier, enable_pam_finetuning=False) loss_tf = loss_tf.eval() self.assertAllClose(loss_np, loss_tf) def testClusteringLossPAMOn(self): if not HAS_SKLEARN: return with self.test_session(): margin_multiplier = 10.0 embeddings, labels = self._genClusters(n_samples=128, n_clusters=64) loss_np = compute_cluster_loss_numpy( embeddings, labels, margin_multiplier, enable_pam_finetuning=True) loss_tf = metric_loss_ops.cluster_loss( labels=ops.convert_to_tensor(labels), embeddings=ops.convert_to_tensor(embeddings), margin_multiplier=margin_multiplier, enable_pam_finetuning=True) loss_tf = loss_tf.eval() self.assertAllClose(loss_np, loss_tf) if __name__ == '__main__': test.main()

apache-2.0

MohammedWasim/scikit-learn

sklearn/feature_selection/tests/test_feature_select.py

102

22297

""" Todo: cross-check the F-value with stats model """ from __future__ import division import itertools import warnings import numpy as np from scipy import stats, sparse from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_not_in from sklearn.utils.testing import assert_less from sklearn.utils.testing import assert_warns from sklearn.utils.testing import ignore_warnings from sklearn.utils.testing import assert_warns_message from sklearn.utils.testing import assert_greater from sklearn.utils.testing import assert_greater_equal from sklearn.utils import safe_mask from sklearn.datasets.samples_generator import (make_classification, make_regression) from sklearn.feature_selection import (chi2, f_classif, f_oneway, f_regression, SelectPercentile, SelectKBest, SelectFpr, SelectFdr, SelectFwe, GenericUnivariateSelect) ############################################################################## # Test the score functions def test_f_oneway_vs_scipy_stats(): # Test that our f_oneway gives the same result as scipy.stats rng = np.random.RandomState(0) X1 = rng.randn(10, 3) X2 = 1 + rng.randn(10, 3) f, pv = stats.f_oneway(X1, X2) f2, pv2 = f_oneway(X1, X2) assert_true(np.allclose(f, f2)) assert_true(np.allclose(pv, pv2)) def test_f_oneway_ints(): # Smoke test f_oneway on integers: that it does raise casting errors # with recent numpys rng = np.random.RandomState(0) X = rng.randint(10, size=(10, 10)) y = np.arange(10) fint, pint = f_oneway(X, y) # test that is gives the same result as with float f, p = f_oneway(X.astype(np.float), y) assert_array_almost_equal(f, fint, decimal=4) assert_array_almost_equal(p, pint, decimal=4) def test_f_classif(): # Test whether the F test yields meaningful results # on a simple simulated classification problem X, y = make_classification(n_samples=200, n_features=20, n_informative=3, n_redundant=2, n_repeated=0, n_classes=8, n_clusters_per_class=1, flip_y=0.0, class_sep=10, shuffle=False, random_state=0) F, pv = f_classif(X, y) F_sparse, pv_sparse = f_classif(sparse.csr_matrix(X), y) assert_true((F > 0).all()) assert_true((pv > 0).all()) assert_true((pv < 1).all()) assert_true((pv[:5] < 0.05).all()) assert_true((pv[5:] > 1.e-4).all()) assert_array_almost_equal(F_sparse, F) assert_array_almost_equal(pv_sparse, pv) def test_f_regression(): # Test whether the F test yields meaningful results # on a simple simulated regression problem X, y = make_regression(n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0) F, pv = f_regression(X, y) assert_true((F > 0).all()) assert_true((pv > 0).all()) assert_true((pv < 1).all()) assert_true((pv[:5] < 0.05).all()) assert_true((pv[5:] > 1.e-4).all()) # again without centering, compare with sparse F, pv = f_regression(X, y, center=False) F_sparse, pv_sparse = f_regression(sparse.csr_matrix(X), y, center=False) assert_array_almost_equal(F_sparse, F) assert_array_almost_equal(pv_sparse, pv) def test_f_regression_input_dtype(): # Test whether f_regression returns the same value # for any numeric data_type rng = np.random.RandomState(0) X = rng.rand(10, 20) y = np.arange(10).astype(np.int) F1, pv1 = f_regression(X, y) F2, pv2 = f_regression(X, y.astype(np.float)) assert_array_almost_equal(F1, F2, 5) assert_array_almost_equal(pv1, pv2, 5) def test_f_regression_center(): # Test whether f_regression preserves dof according to 'center' argument # We use two centered variates so we have a simple relationship between # F-score with variates centering and F-score without variates centering. # Create toy example X = np.arange(-5, 6).reshape(-1, 1) # X has zero mean n_samples = X.size Y = np.ones(n_samples) Y[::2] *= -1. Y[0] = 0. # have Y mean being null F1, _ = f_regression(X, Y, center=True) F2, _ = f_regression(X, Y, center=False) assert_array_almost_equal(F1 * (n_samples - 1.) / (n_samples - 2.), F2) assert_almost_equal(F2[0], 0.232558139) # value from statsmodels OLS def test_f_classif_multi_class(): # Test whether the F test yields meaningful results # on a simple simulated classification problem X, y = make_classification(n_samples=200, n_features=20, n_informative=3, n_redundant=2, n_repeated=0, n_classes=8, n_clusters_per_class=1, flip_y=0.0, class_sep=10, shuffle=False, random_state=0) F, pv = f_classif(X, y) assert_true((F > 0).all()) assert_true((pv > 0).all()) assert_true((pv < 1).all()) assert_true((pv[:5] < 0.05).all()) assert_true((pv[5:] > 1.e-4).all()) def test_select_percentile_classif(): # Test whether the relative univariate feature selection # gets the correct items in a simple classification problem # with the percentile heuristic X, y = make_classification(n_samples=200, n_features=20, n_informative=3, n_redundant=2, n_repeated=0, n_classes=8, n_clusters_per_class=1, flip_y=0.0, class_sep=10, shuffle=False, random_state=0) univariate_filter = SelectPercentile(f_classif, percentile=25) X_r = univariate_filter.fit(X, y).transform(X) X_r2 = GenericUnivariateSelect(f_classif, mode='percentile', param=25).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() gtruth = np.zeros(20) gtruth[:5] = 1 assert_array_equal(support, gtruth) def test_select_percentile_classif_sparse(): # Test whether the relative univariate feature selection # gets the correct items in a simple classification problem # with the percentile heuristic X, y = make_classification(n_samples=200, n_features=20, n_informative=3, n_redundant=2, n_repeated=0, n_classes=8, n_clusters_per_class=1, flip_y=0.0, class_sep=10, shuffle=False, random_state=0) X = sparse.csr_matrix(X) univariate_filter = SelectPercentile(f_classif, percentile=25) X_r = univariate_filter.fit(X, y).transform(X) X_r2 = GenericUnivariateSelect(f_classif, mode='percentile', param=25).fit(X, y).transform(X) assert_array_equal(X_r.toarray(), X_r2.toarray()) support = univariate_filter.get_support() gtruth = np.zeros(20) gtruth[:5] = 1 assert_array_equal(support, gtruth) X_r2inv = univariate_filter.inverse_transform(X_r2) assert_true(sparse.issparse(X_r2inv)) support_mask = safe_mask(X_r2inv, support) assert_equal(X_r2inv.shape, X.shape) assert_array_equal(X_r2inv[:, support_mask].toarray(), X_r.toarray()) # Check other columns are empty assert_equal(X_r2inv.getnnz(), X_r.getnnz()) ############################################################################## # Test univariate selection in classification settings def test_select_kbest_classif(): # Test whether the relative univariate feature selection # gets the correct items in a simple classification problem # with the k best heuristic X, y = make_classification(n_samples=200, n_features=20, n_informative=3, n_redundant=2, n_repeated=0, n_classes=8, n_clusters_per_class=1, flip_y=0.0, class_sep=10, shuffle=False, random_state=0) univariate_filter = SelectKBest(f_classif, k=5) X_r = univariate_filter.fit(X, y).transform(X) X_r2 = GenericUnivariateSelect( f_classif, mode='k_best', param=5).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() gtruth = np.zeros(20) gtruth[:5] = 1 assert_array_equal(support, gtruth) def test_select_kbest_all(): # Test whether k="all" correctly returns all features. X, y = make_classification(n_samples=20, n_features=10, shuffle=False, random_state=0) univariate_filter = SelectKBest(f_classif, k='all') X_r = univariate_filter.fit(X, y).transform(X) assert_array_equal(X, X_r) def test_select_kbest_zero(): # Test whether k=0 correctly returns no features. X, y = make_classification(n_samples=20, n_features=10, shuffle=False, random_state=0) univariate_filter = SelectKBest(f_classif, k=0) univariate_filter.fit(X, y) support = univariate_filter.get_support() gtruth = np.zeros(10, dtype=bool) assert_array_equal(support, gtruth) X_selected = assert_warns_message(UserWarning, 'No features were selected', univariate_filter.transform, X) assert_equal(X_selected.shape, (20, 0)) def test_select_heuristics_classif(): # Test whether the relative univariate feature selection # gets the correct items in a simple classification problem # with the fdr, fwe and fpr heuristics X, y = make_classification(n_samples=200, n_features=20, n_informative=3, n_redundant=2, n_repeated=0, n_classes=8, n_clusters_per_class=1, flip_y=0.0, class_sep=10, shuffle=False, random_state=0) univariate_filter = SelectFwe(f_classif, alpha=0.01) X_r = univariate_filter.fit(X, y).transform(X) gtruth = np.zeros(20) gtruth[:5] = 1 for mode in ['fdr', 'fpr', 'fwe']: X_r2 = GenericUnivariateSelect( f_classif, mode=mode, param=0.01).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() assert_array_almost_equal(support, gtruth) ############################################################################## # Test univariate selection in regression settings def assert_best_scores_kept(score_filter): scores = score_filter.scores_ support = score_filter.get_support() assert_array_equal(np.sort(scores[support]), np.sort(scores)[-support.sum():]) def test_select_percentile_regression(): # Test whether the relative univariate feature selection # gets the correct items in a simple regression problem # with the percentile heuristic X, y = make_regression(n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0) univariate_filter = SelectPercentile(f_regression, percentile=25) X_r = univariate_filter.fit(X, y).transform(X) assert_best_scores_kept(univariate_filter) X_r2 = GenericUnivariateSelect( f_regression, mode='percentile', param=25).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() gtruth = np.zeros(20) gtruth[:5] = 1 assert_array_equal(support, gtruth) X_2 = X.copy() X_2[:, np.logical_not(support)] = 0 assert_array_equal(X_2, univariate_filter.inverse_transform(X_r)) # Check inverse_transform respects dtype assert_array_equal(X_2.astype(bool), univariate_filter.inverse_transform(X_r.astype(bool))) def test_select_percentile_regression_full(): # Test whether the relative univariate feature selection # selects all features when '100%' is asked. X, y = make_regression(n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0) univariate_filter = SelectPercentile(f_regression, percentile=100) X_r = univariate_filter.fit(X, y).transform(X) assert_best_scores_kept(univariate_filter) X_r2 = GenericUnivariateSelect( f_regression, mode='percentile', param=100).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() gtruth = np.ones(20) assert_array_equal(support, gtruth) def test_invalid_percentile(): X, y = make_regression(n_samples=10, n_features=20, n_informative=2, shuffle=False, random_state=0) assert_raises(ValueError, SelectPercentile(percentile=-1).fit, X, y) assert_raises(ValueError, SelectPercentile(percentile=101).fit, X, y) assert_raises(ValueError, GenericUnivariateSelect(mode='percentile', param=-1).fit, X, y) assert_raises(ValueError, GenericUnivariateSelect(mode='percentile', param=101).fit, X, y) def test_select_kbest_regression(): # Test whether the relative univariate feature selection # gets the correct items in a simple regression problem # with the k best heuristic X, y = make_regression(n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0, noise=10) univariate_filter = SelectKBest(f_regression, k=5) X_r = univariate_filter.fit(X, y).transform(X) assert_best_scores_kept(univariate_filter) X_r2 = GenericUnivariateSelect( f_regression, mode='k_best', param=5).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() gtruth = np.zeros(20) gtruth[:5] = 1 assert_array_equal(support, gtruth) def test_select_heuristics_regression(): # Test whether the relative univariate feature selection # gets the correct items in a simple regression problem # with the fpr, fdr or fwe heuristics X, y = make_regression(n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0, noise=10) univariate_filter = SelectFpr(f_regression, alpha=0.01) X_r = univariate_filter.fit(X, y).transform(X) gtruth = np.zeros(20) gtruth[:5] = 1 for mode in ['fdr', 'fpr', 'fwe']: X_r2 = GenericUnivariateSelect( f_regression, mode=mode, param=0.01).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() assert_array_equal(support[:5], np.ones((5, ), dtype=np.bool)) assert_less(np.sum(support[5:] == 1), 3) def test_select_fdr_regression(): # Test that fdr heuristic actually has low FDR. def single_fdr(alpha, n_informative, random_state): X, y = make_regression(n_samples=150, n_features=20, n_informative=n_informative, shuffle=False, random_state=random_state, noise=10) with warnings.catch_warnings(record=True): # Warnings can be raised when no features are selected # (low alpha or very noisy data) univariate_filter = SelectFdr(f_regression, alpha=alpha) X_r = univariate_filter.fit(X, y).transform(X) X_r2 = GenericUnivariateSelect( f_regression, mode='fdr', param=alpha).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() num_false_positives = np.sum(support[n_informative:] == 1) num_true_positives = np.sum(support[:n_informative] == 1) if num_false_positives == 0: return 0. false_discovery_rate = (num_false_positives / (num_true_positives + num_false_positives)) return false_discovery_rate for alpha in [0.001, 0.01, 0.1]: for n_informative in [1, 5, 10]: # As per Benjamini-Hochberg, the expected false discovery rate # should be lower than alpha: # FDR = E(FP / (TP + FP)) <= alpha false_discovery_rate = np.mean([single_fdr(alpha, n_informative, random_state) for random_state in range(30)]) assert_greater_equal(alpha, false_discovery_rate) # Make sure that the empirical false discovery rate increases # with alpha: if false_discovery_rate != 0: assert_greater(false_discovery_rate, alpha / 10) def test_select_fwe_regression(): # Test whether the relative univariate feature selection # gets the correct items in a simple regression problem # with the fwe heuristic X, y = make_regression(n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0) univariate_filter = SelectFwe(f_regression, alpha=0.01) X_r = univariate_filter.fit(X, y).transform(X) X_r2 = GenericUnivariateSelect( f_regression, mode='fwe', param=0.01).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() gtruth = np.zeros(20) gtruth[:5] = 1 assert_array_equal(support[:5], np.ones((5, ), dtype=np.bool)) assert_less(np.sum(support[5:] == 1), 2) def test_selectkbest_tiebreaking(): # Test whether SelectKBest actually selects k features in case of ties. # Prior to 0.11, SelectKBest would return more features than requested. Xs = [[0, 1, 1], [0, 0, 1], [1, 0, 0], [1, 1, 0]] y = [1] dummy_score = lambda X, y: (X[0], X[0]) for X in Xs: sel = SelectKBest(dummy_score, k=1) X1 = ignore_warnings(sel.fit_transform)([X], y) assert_equal(X1.shape[1], 1) assert_best_scores_kept(sel) sel = SelectKBest(dummy_score, k=2) X2 = ignore_warnings(sel.fit_transform)([X], y) assert_equal(X2.shape[1], 2) assert_best_scores_kept(sel) def test_selectpercentile_tiebreaking(): # Test if SelectPercentile selects the right n_features in case of ties. Xs = [[0, 1, 1], [0, 0, 1], [1, 0, 0], [1, 1, 0]] y = [1] dummy_score = lambda X, y: (X[0], X[0]) for X in Xs: sel = SelectPercentile(dummy_score, percentile=34) X1 = ignore_warnings(sel.fit_transform)([X], y) assert_equal(X1.shape[1], 1) assert_best_scores_kept(sel) sel = SelectPercentile(dummy_score, percentile=67) X2 = ignore_warnings(sel.fit_transform)([X], y) assert_equal(X2.shape[1], 2) assert_best_scores_kept(sel) def test_tied_pvalues(): # Test whether k-best and percentiles work with tied pvalues from chi2. # chi2 will return the same p-values for the following features, but it # will return different scores. X0 = np.array([[10000, 9999, 9998], [1, 1, 1]]) y = [0, 1] for perm in itertools.permutations((0, 1, 2)): X = X0[:, perm] Xt = SelectKBest(chi2, k=2).fit_transform(X, y) assert_equal(Xt.shape, (2, 2)) assert_not_in(9998, Xt) Xt = SelectPercentile(chi2, percentile=67).fit_transform(X, y) assert_equal(Xt.shape, (2, 2)) assert_not_in(9998, Xt) def test_tied_scores(): # Test for stable sorting in k-best with tied scores. X_train = np.array([[0, 0, 0], [1, 1, 1]]) y_train = [0, 1] for n_features in [1, 2, 3]: sel = SelectKBest(chi2, k=n_features).fit(X_train, y_train) X_test = sel.transform([[0, 1, 2]]) assert_array_equal(X_test[0], np.arange(3)[-n_features:]) def test_nans(): # Assert that SelectKBest and SelectPercentile can handle NaNs. # First feature has zero variance to confuse f_classif (ANOVA) and # make it return a NaN. X = [[0, 1, 0], [0, -1, -1], [0, .5, .5]] y = [1, 0, 1] for select in (SelectKBest(f_classif, 2), SelectPercentile(f_classif, percentile=67)): ignore_warnings(select.fit)(X, y) assert_array_equal(select.get_support(indices=True), np.array([1, 2])) def test_score_func_error(): X = [[0, 1, 0], [0, -1, -1], [0, .5, .5]] y = [1, 0, 1] for SelectFeatures in [SelectKBest, SelectPercentile, SelectFwe, SelectFdr, SelectFpr, GenericUnivariateSelect]: assert_raises(TypeError, SelectFeatures(score_func=10).fit, X, y) def test_invalid_k(): X = [[0, 1, 0], [0, -1, -1], [0, .5, .5]] y = [1, 0, 1] assert_raises(ValueError, SelectKBest(k=-1).fit, X, y) assert_raises(ValueError, SelectKBest(k=4).fit, X, y) assert_raises(ValueError, GenericUnivariateSelect(mode='k_best', param=-1).fit, X, y) assert_raises(ValueError, GenericUnivariateSelect(mode='k_best', param=4).fit, X, y) def test_f_classif_constant_feature(): # Test that f_classif warns if a feature is constant throughout. X, y = make_classification(n_samples=10, n_features=5) X[:, 0] = 2.0 assert_warns(UserWarning, f_classif, X, y) def test_no_feature_selected(): rng = np.random.RandomState(0) # Generate random uncorrelated data: a strict univariate test should # rejects all the features X = rng.rand(40, 10) y = rng.randint(0, 4, size=40) strict_selectors = [ SelectFwe(alpha=0.01).fit(X, y), SelectFdr(alpha=0.01).fit(X, y), SelectFpr(alpha=0.01).fit(X, y), SelectPercentile(percentile=0).fit(X, y), SelectKBest(k=0).fit(X, y), ] for selector in strict_selectors: assert_array_equal(selector.get_support(), np.zeros(10)) X_selected = assert_warns_message( UserWarning, 'No features were selected', selector.transform, X) assert_equal(X_selected.shape, (40, 0))

bsd-3-clause

jfconavarrete/kaggle-facebook

src/data/pre_process_k_means.py

1

1226

import pandas as pd import numpy as np import seaborn as sb import matplotlib.pyplot as plt import cv2 from sklearn import metrics from sklearn.cluster import MiniBatchKMeans, KMeans from sklearn.decomposition import PCA from sklearn.preprocessing import scale from sklearn import cross_validation as cv from sklearn import svm from sklearn import ensemble from sklearn import linear_model def main(): train = pd.read_csv('../../data/raw/train.csv') print train.shape uniq = train['place_id'].nunique() print uniq col_headers = list(train.columns.values) print col_headers train[col_headers[1:-1]] = train[col_headers[1:-1]].apply(lambda x: (x - x.min()) / (x.max() - x.min())) train['accuracy'] = 1 - train['accuracy'] train_X_norm = train.values[:,:-1] print train_X_norm.shape K = uniq clusters = range(0,K) batch_size = 500 n_init = 10 train_X_norm = train_X_norm.astype(np.float32) print train_X_norm.dtype print train_X_norm.shape # define criteria and apply kmeans() criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0) ret, label, center = cv2.kmeans(train_X_norm, K, criteria, n_init, cv2.KMEANS_RANDOM_CENTERS) print center.shape if __name__ == '__main__': main()

mit

tomsilver/nupic

examples/opf/experiments/spatial_classification/category_1/description.py

17

1557

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- ## This file defines parameters for a prediction experiment. import os from nupic.frameworks.opf.expdescriptionhelpers import importBaseDescription # the sub-experiment configuration config = \ { 'dataSource': 'file://' + os.path.join(os.path.dirname(__file__), '../datasets/category_1.csv'), 'errorMetric': 'avg_err', 'modelParams': { 'sensorParams': { 'verbosity': 0}, 'clParams': { 'clVerbosity': 0, }, } } mod = importBaseDescription('../base/description.py', config) locals().update(mod.__dict__)

gpl-3.0

MohammedWasim/scikit-learn

examples/covariance/plot_lw_vs_oas.py

247

2903

""" ============================= Ledoit-Wolf vs OAS estimation ============================= The usual covariance maximum likelihood estimate can be regularized using shrinkage. Ledoit and Wolf proposed a close formula to compute the asymptotically optimal shrinkage parameter (minimizing a MSE criterion), yielding the Ledoit-Wolf covariance estimate. Chen et al. proposed an improvement of the Ledoit-Wolf shrinkage parameter, the OAS coefficient, whose convergence is significantly better under the assumption that the data are Gaussian. This example, inspired from Chen's publication [1], shows a comparison of the estimated MSE of the LW and OAS methods, using Gaussian distributed data. [1] "Shrinkage Algorithms for MMSE Covariance Estimation" Chen et al., IEEE Trans. on Sign. Proc., Volume 58, Issue 10, October 2010. """ print(__doc__) import numpy as np import matplotlib.pyplot as plt from scipy.linalg import toeplitz, cholesky from sklearn.covariance import LedoitWolf, OAS np.random.seed(0) ############################################################################### n_features = 100 # simulation covariance matrix (AR(1) process) r = 0.1 real_cov = toeplitz(r ** np.arange(n_features)) coloring_matrix = cholesky(real_cov) n_samples_range = np.arange(6, 31, 1) repeat = 100 lw_mse = np.zeros((n_samples_range.size, repeat)) oa_mse = np.zeros((n_samples_range.size, repeat)) lw_shrinkage = np.zeros((n_samples_range.size, repeat)) oa_shrinkage = np.zeros((n_samples_range.size, repeat)) for i, n_samples in enumerate(n_samples_range): for j in range(repeat): X = np.dot( np.random.normal(size=(n_samples, n_features)), coloring_matrix.T) lw = LedoitWolf(store_precision=False, assume_centered=True) lw.fit(X) lw_mse[i, j] = lw.error_norm(real_cov, scaling=False) lw_shrinkage[i, j] = lw.shrinkage_ oa = OAS(store_precision=False, assume_centered=True) oa.fit(X) oa_mse[i, j] = oa.error_norm(real_cov, scaling=False) oa_shrinkage[i, j] = oa.shrinkage_ # plot MSE plt.subplot(2, 1, 1) plt.errorbar(n_samples_range, lw_mse.mean(1), yerr=lw_mse.std(1), label='Ledoit-Wolf', color='g') plt.errorbar(n_samples_range, oa_mse.mean(1), yerr=oa_mse.std(1), label='OAS', color='r') plt.ylabel("Squared error") plt.legend(loc="upper right") plt.title("Comparison of covariance estimators") plt.xlim(5, 31) # plot shrinkage coefficient plt.subplot(2, 1, 2) plt.errorbar(n_samples_range, lw_shrinkage.mean(1), yerr=lw_shrinkage.std(1), label='Ledoit-Wolf', color='g') plt.errorbar(n_samples_range, oa_shrinkage.mean(1), yerr=oa_shrinkage.std(1), label='OAS', color='r') plt.xlabel("n_samples") plt.ylabel("Shrinkage") plt.legend(loc="lower right") plt.ylim(plt.ylim()[0], 1. + (plt.ylim()[1] - plt.ylim()[0]) / 10.) plt.xlim(5, 31) plt.show()

bsd-3-clause

LohithBlaze/scikit-learn

examples/linear_model/plot_polynomial_interpolation.py

250

1895

#!/usr/bin/env python """ ======================== Polynomial interpolation ======================== This example demonstrates how to approximate a function with a polynomial of degree n_degree by using ridge regression. Concretely, from n_samples 1d points, it suffices to build the Vandermonde matrix, which is n_samples x n_degree+1 and has the following form: [[1, x_1, x_1 ** 2, x_1 ** 3, ...], [1, x_2, x_2 ** 2, x_2 ** 3, ...], ...] Intuitively, this matrix can be interpreted as a matrix of pseudo features (the points raised to some power). The matrix is akin to (but different from) the matrix induced by a polynomial kernel. This example shows that you can do non-linear regression with a linear model, using a pipeline to add non-linear features. Kernel methods extend this idea and can induce very high (even infinite) dimensional feature spaces. """ print(__doc__) # Author: Mathieu Blondel # Jake Vanderplas # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import Ridge from sklearn.preprocessing import PolynomialFeatures from sklearn.pipeline import make_pipeline def f(x): """ function to approximate by polynomial interpolation""" return x * np.sin(x) # generate points used to plot x_plot = np.linspace(0, 10, 100) # generate points and keep a subset of them x = np.linspace(0, 10, 100) rng = np.random.RandomState(0) rng.shuffle(x) x = np.sort(x[:20]) y = f(x) # create matrix versions of these arrays X = x[:, np.newaxis] X_plot = x_plot[:, np.newaxis] plt.plot(x_plot, f(x_plot), label="ground truth") plt.scatter(x, y, label="training points") for degree in [3, 4, 5]: model = make_pipeline(PolynomialFeatures(degree), Ridge()) model.fit(X, y) y_plot = model.predict(X_plot) plt.plot(x_plot, y_plot, label="degree %d" % degree) plt.legend(loc='lower left') plt.show()

bsd-3-clause

jzt5132/scikit-learn

sklearn/utils/tests/test_shortest_path.py

292

2841

from collections import defaultdict import numpy as np from numpy.testing import assert_array_almost_equal from sklearn.utils.graph import (graph_shortest_path, single_source_shortest_path_length) def floyd_warshall_slow(graph, directed=False): N = graph.shape[0] #set nonzero entries to infinity graph[np.where(graph == 0)] = np.inf #set diagonal to zero graph.flat[::N + 1] = 0 if not directed: graph = np.minimum(graph, graph.T) for k in range(N): for i in range(N): for j in range(N): graph[i, j] = min(graph[i, j], graph[i, k] + graph[k, j]) graph[np.where(np.isinf(graph))] = 0 return graph def generate_graph(N=20): #sparse grid of distances rng = np.random.RandomState(0) dist_matrix = rng.random_sample((N, N)) #make symmetric: distances are not direction-dependent dist_matrix = dist_matrix + dist_matrix.T #make graph sparse i = (rng.randint(N, size=N * N // 2), rng.randint(N, size=N * N // 2)) dist_matrix[i] = 0 #set diagonal to zero dist_matrix.flat[::N + 1] = 0 return dist_matrix def test_floyd_warshall(): dist_matrix = generate_graph(20) for directed in (True, False): graph_FW = graph_shortest_path(dist_matrix, directed, 'FW') graph_py = floyd_warshall_slow(dist_matrix.copy(), directed) assert_array_almost_equal(graph_FW, graph_py) def test_dijkstra(): dist_matrix = generate_graph(20) for directed in (True, False): graph_D = graph_shortest_path(dist_matrix, directed, 'D') graph_py = floyd_warshall_slow(dist_matrix.copy(), directed) assert_array_almost_equal(graph_D, graph_py) def test_shortest_path(): dist_matrix = generate_graph(20) # We compare path length and not costs (-> set distances to 0 or 1) dist_matrix[dist_matrix != 0] = 1 for directed in (True, False): if not directed: dist_matrix = np.minimum(dist_matrix, dist_matrix.T) graph_py = floyd_warshall_slow(dist_matrix.copy(), directed) for i in range(dist_matrix.shape[0]): # Non-reachable nodes have distance 0 in graph_py dist_dict = defaultdict(int) dist_dict.update(single_source_shortest_path_length(dist_matrix, i)) for j in range(graph_py[i].shape[0]): assert_array_almost_equal(dist_dict[j], graph_py[i, j]) def test_dijkstra_bug_fix(): X = np.array([[0., 0., 4.], [1., 0., 2.], [0., 5., 0.]]) dist_FW = graph_shortest_path(X, directed=False, method='FW') dist_D = graph_shortest_path(X, directed=False, method='D') assert_array_almost_equal(dist_D, dist_FW)

bsd-3-clause

jzt5132/scikit-learn

examples/bicluster/plot_spectral_coclustering.py

274

1736

""" ============================================== A demo of the Spectral Co-Clustering algorithm ============================================== This example demonstrates how to generate a dataset and bicluster it using the the Spectral Co-Clustering algorithm. The dataset is generated using the ``make_biclusters`` function, which creates a matrix of small values and implants bicluster with large values. The rows and columns are then shuffled and passed to the Spectral Co-Clustering algorithm. Rearranging the shuffled matrix to make biclusters contiguous shows how accurately the algorithm found the biclusters. """ print(__doc__) # Author: Kemal Eren <kemal@kemaleren.com> # License: BSD 3 clause import numpy as np from matplotlib import pyplot as plt from sklearn.datasets import make_biclusters from sklearn.datasets import samples_generator as sg from sklearn.cluster.bicluster import SpectralCoclustering from sklearn.metrics import consensus_score data, rows, columns = make_biclusters( shape=(300, 300), n_clusters=5, noise=5, shuffle=False, random_state=0) plt.matshow(data, cmap=plt.cm.Blues) plt.title("Original dataset") data, row_idx, col_idx = sg._shuffle(data, random_state=0) plt.matshow(data, cmap=plt.cm.Blues) plt.title("Shuffled dataset") model = SpectralCoclustering(n_clusters=5, random_state=0) model.fit(data) score = consensus_score(model.biclusters_, (rows[:, row_idx], columns[:, col_idx])) print("consensus score: {:.3f}".format(score)) fit_data = data[np.argsort(model.row_labels_)] fit_data = fit_data[:, np.argsort(model.column_labels_)] plt.matshow(fit_data, cmap=plt.cm.Blues) plt.title("After biclustering; rearranged to show biclusters") plt.show()

bsd-3-clause

automl/auto-sklearn

test/test_metalearning/pyMetaLearn/test_metalearning_configuration.py

1

1879

import logging import os import autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner as metalearner # noqa: E501 import autosklearn.pipeline.classification from autosklearn.metalearning.metalearning.meta_base import MetaBase import unittest logging.basicConfig() class MetalearningConfiguration(unittest.TestCase): def test_metalearning_cs_size(self): self.cwd = os.getcwd() data_dir = os.path.dirname(__file__) data_dir = os.path.join(data_dir, "test_meta_base_data") os.chdir(data_dir) # Total: 176, categorical: 3, numerical: 7, string: 7 total = 179 num_numerical = 6 num_string = 11 num_categorical = 3 for feat_type, cs_size in [ ({"A": "numerical"}, total - num_string - num_categorical), ({"A": "categorical"}, total - num_string - num_numerical), ({"A": "string"}, total - num_categorical - num_numerical), ({"A": "numerical", "B": "categorical"}, total - num_string), ({"A": "numerical", "B": "string"}, total - num_categorical), ({"A": "categorical", "B": "string"}, total - num_numerical), ({"A": "categorical", "B": "string", "C": "numerical"}, total), ]: pipeline = autosklearn.pipeline.classification.SimpleClassificationPipeline( feat_type=feat_type ) self.cs = pipeline.get_hyperparameter_search_space(feat_type=feat_type) self.logger = logging.getLogger() meta_base = MetaBase(self.cs, data_dir, logger=self.logger) self.meta_optimizer = metalearner.MetaLearningOptimizer( "233", self.cs, meta_base, logger=self.logger ) self.assertEqual( len(self.meta_optimizer.configuration_space), cs_size, feat_type )

bsd-3-clause

sgenoud/scikit-learn

sklearn/cluster/tests/test_k_means.py

3

18449

"""Testing for K-means""" import numpy as np import warnings from scipy import sparse as sp from numpy.testing import assert_equal from numpy.testing import assert_array_equal from numpy.testing import assert_array_almost_equal from nose import SkipTest from nose.tools import assert_almost_equal from nose.tools import assert_raises from nose.tools import assert_true from sklearn.utils.testing import assert_greater from sklearn.utils.testing import assert_less from sklearn.utils.fixes import unique from sklearn.metrics.cluster import v_measure_score from sklearn.cluster import KMeans from sklearn.cluster import MiniBatchKMeans from sklearn.cluster.k_means_ import _labels_inertia from sklearn.cluster.k_means_ import _mini_batch_step from sklearn.cluster._k_means import csr_row_norm_l2 from sklearn.datasets.samples_generator import make_blobs # non centered, sparse centers to check the centers = np.array([ [0.0, 5.0, 0.0, 0.0, 0.0], [1.0, 1.0, 4.0, 0.0, 0.0], [1.0, 0.0, 0.0, 5.0, 1.0], ]) n_samples = 100 n_clusters, n_features = centers.shape X, true_labels = make_blobs(n_samples=n_samples, centers=centers, cluster_std=1., random_state=42) X_csr = sp.csr_matrix(X) def test_square_norms(): x_squared_norms = (X ** 2).sum(axis=1) x_squared_norms_from_csr = csr_row_norm_l2(X_csr) assert_array_almost_equal(x_squared_norms, x_squared_norms_from_csr, 5) def test_kmeans_dtype(): rnd = np.random.RandomState(0) X = rnd.normal(size=(40, 2)) X = (X * 10).astype(np.uint8) km = KMeans(n_init=1).fit(X) with warnings.catch_warnings(record=True) as w: assert_array_equal(km.labels_, km.predict(X)) assert_equal(len(w), 1) def test_labels_assignement_and_inertia(): # pure numpy implementation as easily auditable reference gold # implementation rng = np.random.RandomState(42) noisy_centers = centers + rng.normal(size=centers.shape) labels_gold = - np.ones(n_samples, dtype=np.int) mindist = np.empty(n_samples) mindist.fill(np.infty) for center_id in range(n_clusters): dist = np.sum((X - noisy_centers[center_id]) ** 2, axis=1) labels_gold[dist < mindist] = center_id mindist = np.minimum(dist, mindist) inertia_gold = mindist.sum() assert_true((mindist >= 0.0).all()) assert_true((labels_gold != -1).all()) # perform label assignement using the dense array input x_squared_norms = (X ** 2).sum(axis=1) labels_array, inertia_array = _labels_inertia( X, x_squared_norms, noisy_centers) assert_array_almost_equal(inertia_array, inertia_gold) assert_array_equal(labels_array, labels_gold) # perform label assignement using the sparse CSR input x_squared_norms_from_csr = csr_row_norm_l2(X_csr) labels_csr, inertia_csr = _labels_inertia( X_csr, x_squared_norms_from_csr, noisy_centers) assert_array_almost_equal(inertia_csr, inertia_gold) assert_array_equal(labels_csr, labels_gold) def test_minibatch_update_consistency(): """Check that dense and sparse minibatch update give the same results""" rng = np.random.RandomState(42) old_centers = centers + rng.normal(size=centers.shape) new_centers = old_centers.copy() new_centers_csr = old_centers.copy() counts = np.zeros(new_centers.shape[0], dtype=np.int32) counts_csr = np.zeros(new_centers.shape[0], dtype=np.int32) x_squared_norms = (X ** 2).sum(axis=1) x_squared_norms_csr = csr_row_norm_l2(X_csr, squared=True) buffer = np.zeros(centers.shape[1], dtype=np.double) buffer_csr = np.zeros(centers.shape[1], dtype=np.double) # extract a small minibatch X_mb = X[:10] X_mb_csr = X_csr[:10] x_mb_squared_norms = x_squared_norms[:10] x_mb_squared_norms_csr = x_squared_norms_csr[:10] # step 1: compute the dense minibatch update old_inertia, incremental_diff = _mini_batch_step( X_mb, x_mb_squared_norms, new_centers, counts, buffer, 1) assert_greater(old_inertia, 0.0) # compute the new inertia on the same batch to check that it decreased labels, new_inertia = _labels_inertia( X_mb, x_mb_squared_norms, new_centers) assert_greater(new_inertia, 0.0) assert_less(new_inertia, old_inertia) # check that the incremental difference computation is matching the # final observed value effective_diff = np.sum((new_centers - old_centers) ** 2) assert_almost_equal(incremental_diff, effective_diff) # step 2: compute the sparse minibatch update old_inertia_csr, incremental_diff_csr = _mini_batch_step( X_mb_csr, x_mb_squared_norms_csr, new_centers_csr, counts_csr, buffer_csr, 1) assert_greater(old_inertia_csr, 0.0) # compute the new inertia on the same batch to check that it decreased labels_csr, new_inertia_csr = _labels_inertia( X_mb_csr, x_mb_squared_norms_csr, new_centers_csr) assert_greater(new_inertia_csr, 0.0) assert_less(new_inertia_csr, old_inertia_csr) # check that the incremental difference computation is matching the # final observed value effective_diff = np.sum((new_centers_csr - old_centers) ** 2) assert_almost_equal(incremental_diff_csr, effective_diff) # step 3: check that sparse and dense updates lead to the same results assert_array_equal(labels, labels_csr) assert_array_almost_equal(new_centers, new_centers_csr) assert_almost_equal(incremental_diff, incremental_diff_csr) assert_almost_equal(old_inertia, old_inertia_csr) assert_almost_equal(new_inertia, new_inertia_csr) def _check_fitted_model(km): # check that the number of clusters centers and distinct labels match # the expectation centers = km.cluster_centers_ assert_equal(centers.shape, (n_clusters, n_features)) labels = km.labels_ assert_equal(np.unique(labels).shape[0], n_clusters) # check that the labels assignements are perfect (up to a permutation) assert_equal(v_measure_score(true_labels, labels), 1.0) assert_greater(km.inertia_, 0.0) # check error on dataset being too small assert_raises(ValueError, km.fit, [[0., 1.]]) def test_k_means_plus_plus_init(): k_means = KMeans(init="k-means++", n_clusters=n_clusters, random_state=42).fit(X) _check_fitted_model(k_means) def test_k_means_new_centers(): # Explore the part of the code where a new center is reassigned X = np.array([[0, 0, 1, 1], [0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 1, 0, 0]]) labels = [0, 1, 2, 1, 1, 2] bad_centers = np.array([[+0, 1, 0, 0], [.2, 0, .2, .2], [+0, 0, 0, 0]]) km = KMeans(n_clusters=3, init=bad_centers, n_init=1, max_iter=10, random_state=1) for this_X in (X, sp.coo_matrix(X)): km.fit(this_X) this_labels = km.labels_ # Reorder the labels so that the first instance is in cluster 0, # the second in cluster 1, ... this_labels = unique(this_labels, return_index=True)[1][this_labels] np.testing.assert_array_equal(this_labels, labels) def _get_mac_os_version(): import platform mac_version, _, _ = platform.mac_ver() if mac_version: # turn something like '10.7.3' into '10.7' return '.'.join(mac_version.split('.')[:2]) def test_k_means_plus_plus_init_2_jobs(): if _get_mac_os_version() == '10.7': raise SkipTest('Multi-process bug in Mac OS X Lion (see issue #636)') k_means = KMeans(init="k-means++", n_clusters=n_clusters, n_jobs=2, random_state=42).fit(X) _check_fitted_model(k_means) def test_k_means_plus_plus_init_sparse(): k_means = KMeans(init="k-means++", n_clusters=n_clusters, random_state=42) k_means.fit(X_csr) _check_fitted_model(k_means) def test_k_means_random_init(): k_means = KMeans(init="random", n_clusters=n_clusters, random_state=42) k_means.fit(X) _check_fitted_model(k_means) def test_k_means_random_init_sparse(): k_means = KMeans(init="random", n_clusters=n_clusters, random_state=42) k_means.fit(X_csr) _check_fitted_model(k_means) def test_k_means_plus_plus_init_not_precomputed(): k_means = KMeans(init="k-means++", n_clusters=n_clusters, random_state=42, precompute_distances=False).fit(X) _check_fitted_model(k_means) def test_k_means_random_init_not_precomputed(): k_means = KMeans(init="random", n_clusters=n_clusters, random_state=42, precompute_distances=False).fit(X) _check_fitted_model(k_means) def test_k_means_perfect_init(): k_means = KMeans(init=centers.copy(), n_clusters=n_clusters, random_state=42, n_init=1) k_means.fit(X) _check_fitted_model(k_means) def test_mb_k_means_plus_plus_init_dense_array(): mb_k_means = MiniBatchKMeans(init="k-means++", n_clusters=n_clusters, random_state=42) mb_k_means.fit(X) _check_fitted_model(mb_k_means) def test_mb_k_means_plus_plus_init_sparse_matrix(): mb_k_means = MiniBatchKMeans(init="k-means++", n_clusters=n_clusters, random_state=42) mb_k_means.fit(X_csr) _check_fitted_model(mb_k_means) def test_minibatch_init_with_large_k(): mb_k_means = MiniBatchKMeans(init='k-means++', init_size=10, n_clusters=20) # Check that a warning is raised, as the number clusters is larger # than the init_size with warnings.catch_warnings(record=True) as warn_queue: mb_k_means.fit(X) assert_equal(len(warn_queue), 1) def test_minibatch_k_means_random_init_dense_array(): # increase n_init to make random init stable enough mb_k_means = MiniBatchKMeans(init="random", n_clusters=n_clusters, random_state=42, n_init=10).fit(X) _check_fitted_model(mb_k_means) def test_minibatch_k_means_random_init_sparse_csr(): # increase n_init to make random init stable enough mb_k_means = MiniBatchKMeans(init="random", n_clusters=n_clusters, random_state=42, n_init=10).fit(X_csr) _check_fitted_model(mb_k_means) def test_minibatch_k_means_perfect_init_dense_array(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, random_state=42).fit(X) _check_fitted_model(mb_k_means) def test_minibatch_k_means_perfect_init_sparse_csr(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, random_state=42).fit(X_csr) _check_fitted_model(mb_k_means) def test_sparse_mb_k_means_callable_init(): def test_init(X, k, random_state): return centers mb_k_means = MiniBatchKMeans(init=test_init, random_state=42).fit(X_csr) _check_fitted_model(mb_k_means) def test_mini_batch_k_means_random_init_partial_fit(): km = MiniBatchKMeans(n_clusters=n_clusters, init="random", random_state=42) # use the partial_fit API for online learning for X_minibatch in np.array_split(X, 10): km.partial_fit(X_minibatch) # compute the labeling on the complete dataset labels = km.predict(X) assert_equal(v_measure_score(true_labels, labels), 1.0) def test_minibatch_default_init_size(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, batch_size=10, random_state=42).fit(X) assert_equal(mb_k_means.init_size_, 3 * mb_k_means.batch_size) _check_fitted_model(mb_k_means) def test_minibatch_set_init_size(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, init_size=666, random_state=42).fit(X) assert_equal(mb_k_means.init_size, 666) assert_equal(mb_k_means.init_size_, n_samples) _check_fitted_model(mb_k_means) def test_k_means_invalid_init(): k_means = KMeans(init="invalid", n_init=1, n_clusters=n_clusters) assert_raises(ValueError, k_means.fit, X) def test_mini_match_k_means_invalid_init(): k_means = MiniBatchKMeans(init="invalid", n_init=1, n_clusters=n_clusters) assert_raises(ValueError, k_means.fit, X) def test_k_means_copyx(): """Check if copy_x=False returns nearly equal X after de-centering.""" my_X = X.copy() k_means = KMeans(copy_x=False, n_clusters=n_clusters, random_state=42) k_means.fit(my_X) _check_fitted_model(k_means) # check if my_X is centered assert_array_almost_equal(my_X, X) def test_k_means_non_collapsed(): """Check k_means with a bad initialization does not yield a singleton Starting with bad centers that are quickly ignored should not result in a repositioning of the centers to the center of mass that would lead to collapsed centers which in turns make the clustering dependent of the numerical unstabilities. """ my_X = np.array([[1.1, 1.1], [0.9, 1.1], [1.1, 0.9], [0.9, 1.1]]) array_init = np.array([[1.0, 1.0], [5.0, 5.0], [-5.0, -5.0]]) k_means = KMeans(init=array_init, n_clusters=3, random_state=42, n_init=1) k_means.fit(my_X) # centers must not been collapsed assert_equal(len(np.unique(k_means.labels_)), 3) centers = k_means.cluster_centers_ assert_true(np.linalg.norm(centers[0] - centers[1]) >= 0.1) assert_true(np.linalg.norm(centers[0] - centers[2]) >= 0.1) assert_true(np.linalg.norm(centers[1] - centers[2]) >= 0.1) def test_predict(): k_means = KMeans(n_clusters=n_clusters, random_state=42) k_means.fit(X) # sanity check: predict centroid labels pred = k_means.predict(k_means.cluster_centers_) assert_array_equal(pred, np.arange(n_clusters)) # sanity check: re-predict labeling for training set samples pred = k_means.predict(X) assert_array_equal(pred, k_means.labels_) # re-predict labels for training set using fit_predict pred = k_means.fit_predict(X) assert_array_equal(pred, k_means.labels_) def test_score(): km1 = KMeans(n_clusters=n_clusters, max_iter=1, random_state=42) s1 = km1.fit(X).score(X) km2 = KMeans(n_clusters=n_clusters, max_iter=10, random_state=42) s2 = km2.fit(X).score(X) assert_greater(s2, s1) def test_predict_minibatch_dense_input(): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, random_state=40).fit(X) # sanity check: predict centroid labels pred = mb_k_means.predict(mb_k_means.cluster_centers_) assert_array_equal(pred, np.arange(n_clusters)) # sanity check: re-predict labeling for training set samples pred = mb_k_means.predict(X) assert_array_equal(mb_k_means.predict(X), mb_k_means.labels_) def test_predict_minibatch_kmeanspp_init_sparse_input(): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, init='k-means++', n_init=10).fit(X_csr) # sanity check: re-predict labeling for training set samples assert_array_equal(mb_k_means.predict(X_csr), mb_k_means.labels_) # sanity check: predict centroid labels pred = mb_k_means.predict(mb_k_means.cluster_centers_) assert_array_equal(pred, np.arange(n_clusters)) # check that models trained on sparse input also works for dense input at # predict time assert_array_equal(mb_k_means.predict(X), mb_k_means.labels_) def test_predict_minibatch_random_init_sparse_input(): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, init='random', n_init=10).fit(X_csr) # sanity check: re-predict labeling for training set samples assert_array_equal(mb_k_means.predict(X_csr), mb_k_means.labels_) # sanity check: predict centroid labels pred = mb_k_means.predict(mb_k_means.cluster_centers_) assert_array_equal(pred, np.arange(n_clusters)) # check that models trained on sparse input also works for dense input at # predict time assert_array_equal(mb_k_means.predict(X), mb_k_means.labels_) def test_input_dtypes(): X_list = [[0, 0], [10, 10], [12, 9], [-1, 1], [2, 0], [8, 10]] X_int = np.array(X_list, dtype=np.int32) X_int_csr = sp.csr_matrix(X_int) init_int = X_int[:2] fitted_models = [ KMeans(n_clusters=2).fit(X_list), KMeans(n_clusters=2).fit(X_int), KMeans(n_clusters=2, init=init_int, n_init=1).fit(X_list), KMeans(n_clusters=2, init=init_int, n_init=1).fit(X_int), # mini batch kmeans is very unstable on such a small dataset hence # we use many inits MiniBatchKMeans(n_clusters=2, n_init=10, batch_size=2).fit(X_list), MiniBatchKMeans(n_clusters=2, n_init=10, batch_size=2).fit(X_int), MiniBatchKMeans(n_clusters=2, n_init=10, batch_size=2).fit(X_int_csr), MiniBatchKMeans(n_clusters=2, batch_size=2, init=init_int).fit(X_list), MiniBatchKMeans(n_clusters=2, batch_size=2, init=init_int).fit(X_int), MiniBatchKMeans(n_clusters=2, batch_size=2, init=init_int).fit(X_int_csr), ] expected_labels = [0, 1, 1, 0, 0, 1] scores = np.array([v_measure_score(expected_labels, km.labels_) for km in fitted_models]) assert_array_equal(scores, np.ones(scores.shape[0])) def test_transform(): k_means = KMeans(n_clusters=n_clusters) k_means.fit(X) X_new = k_means.transform(k_means.cluster_centers_) for c in range(n_clusters): assert_equal(X_new[c, c], 0) for c2 in range(n_clusters): if c != c2: assert_greater(X_new[c, c2], 0) def test_n_init(): """Check that increasing the number of init increases the quality""" n_runs = 5 n_init_range = [1, 5, 10] inertia = np.zeros((len(n_init_range), n_runs)) for i, n_init in enumerate(n_init_range): for j in range(n_runs): km = KMeans(n_clusters=n_clusters, init="random", n_init=n_init, random_state=j).fit(X) inertia[i, j] = km.inertia_ inertia = inertia.mean(axis=1) failure_msg = ("Inertia %r should be decreasing" " when n_init is increasing.") % list(inertia) for i in range(len(n_init_range) - 1): assert_true(inertia[i] >= inertia[i + 1], failure_msg)

bsd-3-clause