markdown stringlengths 0 37k | code stringlengths 1 33.3k | path stringlengths 8 215 | repo_name stringlengths 6 77 | license stringclasses 15
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View Image | # Show image
plt.imshow(image_enhanced, cmap='gray'), plt.axis("off")
plt.show() | machine-learning/enhance_contrast_of_greyscale_image.ipynb | tpin3694/tpin3694.github.io | mit |
If you don't have the image viewing tool ds9, you should install it - it's very useful astronomical software. You can download it (later!) from this webpage.
We can also display the image in the notebook: | plt.imshow(viz.scale_image(im, scale='log', max_cut=40), cmap='gray', origin='lower');
plt.savefig("figures/cluster_image.png") | examples/XrayImage/FirstLook.ipynb | enoordeh/StatisticalMethods | gpl-2.0 |
Exercise
What is going on in this image?
Make a list of everything that is interesting about this image with your neighbor, and we'll discuss the features you identify in about 5 minutes time.
Just to prove that images really are arrays of numbers: | im[350:359,350:359]
index = np.unravel_index(im.argmax(), im.shape)
print("image dimensions:",im.shape)
print("location of maximum pixel value:",index)
print("maximum pixel value: ",im[index]) | examples/XrayImage/FirstLook.ipynb | enoordeh/StatisticalMethods | gpl-2.0 |
A full adder has three single bit inputs, and returns the sum and the carry. The sum is the exclusive or of the 3 bits, the carry is 1 if any two of the inputs bits are 1. Here is a schematic of a full adder circuit (from logisim).
<img src="images/full_adder_logisim.png" width="500"/>
We start by defining a magma comb... | @m.circuit.combinational
def full_adder(A: m.Bit, B: m.Bit, C: m.Bit) -> (m.Bit, m.Bit):
return A ^ B ^ C, A & B | B & C | C & A # sum, carry | notebooks/tutorial/coreir/FullAdder.ipynb | phanrahan/magmathon | mit |
We can test our combinational function to verify that our implementation behaves as expected fault.
We'll use the fault.PythonTester which will simulate the circuit using magma's Python simulator. | import fault
tester = fault.PythonTester(full_adder)
assert tester(1, 0, 0) == (1, 0), "Failed"
assert tester(0, 1, 0) == (1, 0), "Failed"
assert tester(1, 1, 0) == (0, 1), "Failed"
assert tester(1, 0, 1) == (0, 1), "Failed"
assert tester(1, 1, 1) == (1, 1), "Failed"
print("Success!") | notebooks/tutorial/coreir/FullAdder.ipynb | phanrahan/magmathon | mit |
combinational functions are polymorphic over Python and magma types. If the function is called with magma values, it will produce a circuit instance, wire up the inputs, and return references to the outputs. Otherwise, it will invoke the function in Python. For example, we can use the Python function to verify the ... | assert tester(1, 0, 0) == full_adder(1, 0, 0), "Failed"
assert tester(0, 1, 0) == full_adder(0, 1, 0), "Failed"
assert tester(1, 1, 0) == full_adder(1, 1, 0), "Failed"
assert tester(1, 0, 1) == full_adder(1, 0, 1), "Failed"
assert tester(1, 1, 1) == full_adder(1, 1, 1), "Failed"
print("Success!") | notebooks/tutorial/coreir/FullAdder.ipynb | phanrahan/magmathon | mit |
Circuits
Now that we have an implementation of full_adder as a combinational function,
we'll use it to construct a magma Circuit.
A Circuit in magma corresponds to a module in verilog.
This example shows using the combinational function inside a circuit definition, as opposed to using the Python implementation shown ... | class FullAdder(m.Circuit):
io = m.IO(I0=m.In(m.Bit),
I1=m.In(m.Bit),
CIN=m.In(m.Bit),
O=m.Out(m.Bit),
COUT=m.Out(m.Bit))
O, COUT = full_adder(io.I0, io.I1, io.CIN)
io.O @= O
io.COUT @= COUT | notebooks/tutorial/coreir/FullAdder.ipynb | phanrahan/magmathon | mit |
First, notice that the FullAdder is a subclass of Circuit. All magma circuits are classes in python.
Second, the function IO creates the interface to the circuit.
The arguments toIO are keyword arguments.
The key is the name of the argument in the circuit, and the value is its type.
In this circuit, all the inputs a... | print(repr(FullAdder)) | notebooks/tutorial/coreir/FullAdder.ipynb | phanrahan/magmathon | mit |
We see that it has created an instance of the full_adder combinational function and wired up the interface.
We can also inspect the contents of the full_adder circuit definition. Notice that it has lowered the Python operators into a structural representation of the primitive logicoperations. | print(repr(full_adder.circuit_definition)) | notebooks/tutorial/coreir/FullAdder.ipynb | phanrahan/magmathon | mit |
We can also inspect the code generated by the m.circuit.combinational decorator by looking in the .magma directory for a file named .magma/full_adder.py. When using m.circuit.combinational, magma will generate a file matching the name of the decorated function. You'll notice that the generated code introduces an extr... | with open(".magma/full_adder.py") as f:
print(f.read()) | notebooks/tutorial/coreir/FullAdder.ipynb | phanrahan/magmathon | mit |
In the code above, a mux is imported and named phi. If the combinational circuit contains any if-then-else constructs, they will be transformed into muxes.
Note also the m.wire function. m.wire(O0, io.I0) is equivalent to io.O0 @= O0.
Staged testing with Fault
fault is a python package for testing magma circuits. By d... | import logging
logging.basicConfig(level=logging.INFO)
import fault | notebooks/tutorial/coreir/FullAdder.ipynb | phanrahan/magmathon | mit |
Earlier in the notebook, we showed an example using fault.PythonTester to simulate a circuit. This uses an interactive programming model where test actions are immediately dispatched to the underlying simulator (which is why we can perform assertions on the simulation values in Python.
fault also provides a staged met... | tester = fault.Tester(FullAdder) | notebooks/tutorial/coreir/FullAdder.ipynb | phanrahan/magmathon | mit |
An instance of a Tester has an attribute .circuit that enables the user to record test actions. For example, inputs to a circuit can be poked by setting the attribute corresponding to the input port name. | tester.circuit.I0 = 1
tester.circuit.I1 = 1
tester.circuit.CIN = 1 | notebooks/tutorial/coreir/FullAdder.ipynb | phanrahan/magmathon | mit |
fault's default Tester provides the semantics of a cycle accurate simulator, so, unlike verilog, pokes do not create events that trigger computation. Instead, these poke values are staged, and the propogation of their effect occurs when the user calls the eval action. | tester.eval() | notebooks/tutorial/coreir/FullAdder.ipynb | phanrahan/magmathon | mit |
To assert that the output of the circuit is equal to a value, we use the expect method that are defined on the attributes corresponding to circuit output ports | tester.circuit.O.expect(1)
tester.circuit.COUT.expect(1) | notebooks/tutorial/coreir/FullAdder.ipynb | phanrahan/magmathon | mit |
Because fault is a staged programming environment, the above actions are not executed until we have advanced to the next stage. In the first stage, the user records test actions (e.g. poke, eval, expect). In the second stage, the test is compiled and run using a target runtime. Here's examples of running the test us... | # compile_and_run throws an exception if the test fails
tester.compile_and_run("verilator") | notebooks/tutorial/coreir/FullAdder.ipynb | phanrahan/magmathon | mit |
The tester also provides the same convenient __call__ interface we saw before. | O, COUT = tester(1, 0, 0)
tester.expect(O, 1)
tester.expect(COUT, 0)
tester.compile_and_run("verilator") | notebooks/tutorial/coreir/FullAdder.ipynb | phanrahan/magmathon | mit |
Generate Verilog
Magma's default compiler will generate verilog using CoreIR | m.compile("build/FullAdder", FullAdder, inline=True)
%cat build/FullAdder.v | notebooks/tutorial/coreir/FullAdder.ipynb | phanrahan/magmathon | mit |
Generate CoreIR
We can also inspect the intermediate CoreIR used in the generation process. | %cat build/FullAdder.json | notebooks/tutorial/coreir/FullAdder.ipynb | phanrahan/magmathon | mit |
Here's an example of running a CoreIR pass on the intermediate representation. | !coreir -i build/FullAdder.json -p instancecount | notebooks/tutorial/coreir/FullAdder.ipynb | phanrahan/magmathon | mit |
При увеличении порога мы делаем меньше ошибок FP и больше ошибок FN, поэтому одна из кривых растет, а вторая - падает. По такому графику можно подобрать оптимальное значение порога, при котором precision и recall будут приемлемы. Если такого порога не нашлось, нужно обучать другой алгоритм.
Оговоримся, что приемлемые... | ############### Programming assignment: problem 1 ###############
T = 0.65
for _actual, _predicted in zip([actual_1, actual_10, actual_11],
[predicted_1, predicted_10, predicted_11]):
print('Precision: %s' % precision_score(_actual, _predicted > T))
print('Recall: %s\n' % recall_scor... | Coursera/Machine-learning-data-analysis/Course 2/Week_02/MetricsPA.ipynb | ALEXKIRNAS/DataScience | mit |
F1-метрика в двух последних случаях, когда одна из парных метрик равна 1, значительно меньше, чем в первом, сбалансированном случае.
<font color="green" size=5>Programming assignment: problem 2. </font> На precision и recall влияют и характер вектора вероятностей, и установленный порог.
Для тех же пар (actual, predict... | ############### Programming assignment: problem 2 ###############
ks = np.zeros(3)
idexes = np.empty(3)
for threshold in np.arange(11):
T = threshold * 0.1
for actual, predicted, idx in zip([actual_1, actual_10, actual_11],
[predicted_1 > T, predicted_10 > T, predicted_11 ... | Coursera/Machine-learning-data-analysis/Course 2/Week_02/MetricsPA.ipynb | ALEXKIRNAS/DataScience | mit |
Как и предыдущие метрики, log_loss хорошо различает идеальный, типичный и плохой случаи. Но обратите внимание, что интерпретировать величину достаточно сложно: метрика не достигает нуля никогда и не имеет верхней границы. Поэтому даже для идеального алгоритма, если смотреть только на одно значение log_loss, невозможно ... | ############### Programming assignment: problem 3 ##############
ans = []
def modified_log(actual, predicted):
return - np.sum(0.3 * actual * np.log(predicted) + 0.7 * (1 - actual) * np.log(1 - predicted)) / len(actual)
for _actual, _predicted in zip([actual_0, actual_1, actual_2, actual_0r, actual_1r, actual_10, ... | Coursera/Machine-learning-data-analysis/Course 2/Week_02/MetricsPA.ipynb | ALEXKIRNAS/DataScience | mit |
Чем больше объектов в выборке, тем более гладкой выглядит кривая (хотя на самом деле она все равно ступенчатая).
Как и ожидалось, кривые всех идеальных алгоритмов проходят через левый верхний угол. На первом графике также показана типичная ROC-кривая (обычно на практике они не доходят до "идеального" угла).
AUC рискую... | ############### Programming assignment: problem 4 ###############
ans = []
for actual, predicted in zip([actual_0, actual_1, actual_2, actual_0r, actual_1r, actual_10, actual_11],
[predicted_0, predicted_1, predicted_2, predicted_0r, predicted_1r, predicted_10, predicted_11]):
fpr, tpr... | Coursera/Machine-learning-data-analysis/Course 2/Week_02/MetricsPA.ipynb | ALEXKIRNAS/DataScience | mit |
Exercise 1.
a.
$\Omega$ will be all the possible combinations we have for 150 object two have two diffent values. For example (0, 0, ..., 0), (1, 0, ..., 0), (0, 1, ..., 0), ... (1, 1, ..., 0), ... (1, 1, ..., 1). This sample space has size of $2^{150}$. The random variable $X(\omega)$ will be the number of defective o... | p = 1. / 365
1 - np.sum(binomial(p, 23 * (22) / 2, 0)) | UQ/assignment_3/Assignment 3.ipynb | LorenzoBi/courses | mit |
Quick Start | # import data
data_fc, data_p_value = kinact.get_example_data()
# import prior knowledge
adj_matrix = kinact.get_kinase_targets()
print data_fc.head()
print
print data_p_value.head()
# Perform ksea using the Mean method
score, p_value = kinact.ksea.ksea_mean(data_fc=data_fc['5min'].dropna(),
... | doc/KSEA_example.ipynb | saezlab/kinact | gpl-3.0 |
1. Loading the data
In order to perform the described kinase enrichment analysis, we load the data into a Pandas DataFrame. Here, we use the data from <em>de Graaf et al., 2014</em> for demonstration of KSEA. The data is available as supplemental material to the article online under http://mcponline.org/content/13/9/24... | # Read data
data_raw = pd.read_csv('../kinact/data/deGraaf_2014_jurkat.csv', sep=',', header=0)
# Filter for those p-sites that were matched ambiguously
data_reduced = data_raw[~data_raw['Proteins'].str.contains(';')]
# Create identifier for each phosphorylation site, e.g. P06239_S59 for the Serine 59 in the protein ... | doc/KSEA_example.ipynb | saezlab/kinact | gpl-3.0 |
2. Import prior-knowledge kinase-substrate relationships from PhosphoSitePlus
In the following example, we use the data from the PhosphoSitePlus database, which can be downloaded here: http://www.phosphosite.org/staticDownloads.action.
Consider, that the downloaded file contains a disclaimer at the top of the file, wh... | # Read data
ks_rel = pd.read_csv('../kinact/data/PhosphoSitePlus.txt', sep='\t')
# The data from the PhosphoSitePlus database is not provided as comma-separated value file (csv),
# but instead, a tab = \t delimits the individual cells
# Restrict the data on interactions in the organism of interest
ks_rel_human = ks_... | doc/KSEA_example.ipynb | saezlab/kinact | gpl-3.0 |
3. KSEA
3.1 Quick start for KSEA
Together with this tutorial, we will provide an implementation of KSEA as custom Python functions. Examplary, the use of the function for the dataset by de Graaf et al. could look like this. | score, p_value = kinact.ksea.ksea_delta(data_fc=data_fc['5min'],
p_values=data_p_value['5min'],
interactions=adj_matrix,
)
print pd.DataFrame({'score': score, 'p_value': p_value}).head()
# Calculate the KSEA scores for all data with the kse... | doc/KSEA_example.ipynb | saezlab/kinact | gpl-3.0 |
In de Graaf et al., they associated (amongst others) the Casein kinase II alpha (CSNK2A1) with higher activity after prolonged stimulation with prostaglandin E2. Here, we plot the activity scores of CSNK2A1 for all three methods of KSEA, which are in good agreement. | kinase='CSNK2A1'
df_plot = pd.DataFrame({'mean': activity_mean.loc[kinase],
'delta': activity_delta.loc[kinase],
'mean_alt': activity_mean_alt.loc[kinase]})
df_plot['time [min]'] = [5, 10, 20, 30, 60]
df_plot = pd.melt(df_plot, id_vars='time [min]', var_name='method', val... | doc/KSEA_example.ipynb | saezlab/kinact | gpl-3.0 |
3.2. KSEA in detail
In the following, we show in detail the computations that are carried out inside the provided functions. Let us concentrate on a single condition (60 minutes after stimulation with prostaglandin E2) and a single kinase (CDK1). | data_condition = data_fc['60min'].copy()
p_values = data_p_value['60min']
kinase = 'CDK1'
substrates = adj_matrix[kinase].replace(0, np.nan).dropna().index
detected_p_sites = data_fc.index
intersect = list(set(substrates).intersection(detected_p_sites)) | doc/KSEA_example.ipynb | saezlab/kinact | gpl-3.0 |
3.2.1. Mean method | mS = data_condition.loc[intersect].mean()
mP = data_fc.values.mean()
m = len(intersect)
delta = data_fc.values.std()
z_score = (mS - mP) * np.sqrt(m) * 1/delta
from scipy.stats import norm
p_value_mean = norm.sf(abs(z_score))
print mS, p_value_mean | doc/KSEA_example.ipynb | saezlab/kinact | gpl-3.0 |
3.2.2. Alternative Mean method | cut_off = -np.log10(0.05)
set_alt = data_condition.loc[intersect].where(p_values.loc[intersect] > cut_off).dropna()
mS_alt = set_alt.mean()
z_score_alt = (mS_alt - mP) * np.sqrt(len(set_alt)) * 1/delta
p_value_mean_alt = norm.sf(abs(z_score_alt))
print mS_alt, p_value_mean_alt | doc/KSEA_example.ipynb | saezlab/kinact | gpl-3.0 |
3.2.3. Delta Method | cut_off = -np.log10(0.05)
score_delta = len(data_condition.loc[intersect].where((data_condition.loc[intersect] > 0) &
(p_values.loc[intersect] > cut_off)).dropna()) -\
len(data_condition.loc[intersect].where((data_condition.loc[intersect] < 0) &
... | doc/KSEA_example.ipynb | saezlab/kinact | gpl-3.0 |
1. Basic Linear Model | LM = keras.model.Sequential([Dense(Num_Classes, input_shape=(784,))])
LM.compile(optimizer=SGD(lr=0.01), loss='mse')
# LM.compile(optimizer=RMSprop(lr=0.01), loss='mse')
| FAI_old/lesson2/L2HW.ipynb | WNoxchi/Kaukasos | mit |
2. 1-Layer Neural Network
3. Finetuned VGG16 | import os, sys
sys.path.insert(os.path.join(1, '../utils/'))
import Vgg16 | FAI_old/lesson2/L2HW.ipynb | WNoxchi/Kaukasos | mit |
Setting up the notebook's environment
Install AI Platform Pipelines client library
For AI Platform Pipelines (Unified), which is in the Experimental stage, you need to download and install the AI Platform client library on top of the KFP and TFX SDKs that were installed as part of the initial environment setup. | AIP_CLIENT_WHEEL = "aiplatform_pipelines_client-0.1.0.caip20201123-py3-none-any.whl"
AIP_CLIENT_WHEEL_GCS_LOCATION = (
f"gs://cloud-aiplatform-pipelines/releases/20201123/{AIP_CLIENT_WHEEL}"
)
!gsutil cp {AIP_CLIENT_WHEEL_GCS_LOCATION} {AIP_CLIENT_WHEEL}
%pip install {AIP_CLIENT_WHEEL} | notebooks/community/analytics-componetized-patterns/retail/recommendation-system/bqml-scann/ann02_run_pipeline.ipynb | GoogleCloudPlatform/bigquery-notebooks | apache-2.0 |
Import notebook dependencies | import logging
import tensorflow as tf
import tfx
from aiplatform.pipelines import client
from tfx.orchestration.beam.beam_dag_runner import BeamDagRunner
print("TFX Version: ", tfx.__version__) | notebooks/community/analytics-componetized-patterns/retail/recommendation-system/bqml-scann/ann02_run_pipeline.ipynb | GoogleCloudPlatform/bigquery-notebooks | apache-2.0 |
Configure GCP environment
If you're on AI Platform Notebooks, authenticate with Google Cloud before running the next section, by running
sh
gcloud auth login
in the Terminal window (which you can open via File > New in the menu). You only need to do this once per notebook instance.
Set the following constants to the v... | PROJECT_ID = "jk-mlops-dev" # <---CHANGE THIS
PROJECT_NUMBER = "895222332033" # <---CHANGE THIS
API_KEY = "AIzaSyBS_RiaK3liaVthTUD91XuPDKIbiwDFlV8" # <---CHANGE THIS
USER = "user" # <---CHANGE THIS
BUCKET_NAME = "jk-ann-staging" # <---CHANGE THIS
VPC_NAME = "default" # <---CHANGE THIS IF USING A DIFFERENT VPC
RE... | notebooks/community/analytics-componetized-patterns/retail/recommendation-system/bqml-scann/ann02_run_pipeline.ipynb | GoogleCloudPlatform/bigquery-notebooks | apache-2.0 |
Defining custom components
In this section of the notebook you define a set of custom TFX components that encapsulate BQ, BQML and ANN Service calls. The components are TFX Custom Python function components.
Each component is created as a separate Python module. You also create a couple of helper modules that encapsul... | component_folder = "bq_components"
if tf.io.gfile.exists(component_folder):
print("Removing older file")
tf.io.gfile.rmtree(component_folder)
print("Creating component folder")
tf.io.gfile.mkdir(component_folder)
%cd {component_folder} | notebooks/community/analytics-componetized-patterns/retail/recommendation-system/bqml-scann/ann02_run_pipeline.ipynb | GoogleCloudPlatform/bigquery-notebooks | apache-2.0 |
Creating a TFX pipeline
The pipeline automates the process of preparing item embeddings (in BigQuery), training a Matrix Factorization model (in BQML), and creating and deploying an ANN Service index.
The pipeline has a simple sequential flow. The pipeline accepts a set of runtime parameters that define GCP environment... | import os
from compute_pmi import compute_pmi
from create_index import create_index
from deploy_index import deploy_index
from export_embeddings import export_embeddings
from extract_embeddings import extract_embeddings
from tfx.orchestration.kubeflow.v2 import kubeflow_v2_dag_runner
# Only required for local run.
fro... | notebooks/community/analytics-componetized-patterns/retail/recommendation-system/bqml-scann/ann02_run_pipeline.ipynb | GoogleCloudPlatform/bigquery-notebooks | apache-2.0 |
Testing the pipeline locally
You will first run the pipeline locally using the Beam runner.
Clean the metadata and artifacts from the previous runs | pipeline_root = f"/tmp/{PIPELINE_NAME}"
local_mlmd_folder = "/tmp/mlmd"
if tf.io.gfile.exists(pipeline_root):
print("Removing previous artifacts...")
tf.io.gfile.rmtree(pipeline_root)
if tf.io.gfile.exists(local_mlmd_folder):
print("Removing local mlmd SQLite...")
tf.io.gfile.rmtree(local_mlmd_folder)
... | notebooks/community/analytics-componetized-patterns/retail/recommendation-system/bqml-scann/ann02_run_pipeline.ipynb | GoogleCloudPlatform/bigquery-notebooks | apache-2.0 |
Set pipeline parameters and create the pipeline | bq_dataset_name = "song_embeddings"
index_display_name = "Song embeddings"
deployed_index_id_prefix = "deployed_song_embeddings_"
min_item_frequency = 15
max_group_size = 100
dimensions = 50
embeddings_gcs_location = f"gs://{BUCKET_NAME}/embeddings"
metadata_connection_config = sqlite_metadata_connection_config(
o... | notebooks/community/analytics-componetized-patterns/retail/recommendation-system/bqml-scann/ann02_run_pipeline.ipynb | GoogleCloudPlatform/bigquery-notebooks | apache-2.0 |
Inspect produced metadata
During the execution of the pipeline, the inputs and outputs of each component have been tracked in ML Metadata. | from ml_metadata import metadata_store
from ml_metadata.proto import metadata_store_pb2
connection_config = metadata_store_pb2.ConnectionConfig()
connection_config.sqlite.filename_uri = os.path.join(
local_mlmd_folder, "metadata.sqlite"
)
connection_config.sqlite.connection_mode = 3 # READWRITE_OPENCREATE
store =... | notebooks/community/analytics-componetized-patterns/retail/recommendation-system/bqml-scann/ann02_run_pipeline.ipynb | GoogleCloudPlatform/bigquery-notebooks | apache-2.0 |
Set the the parameters for AIPP execution and create the pipeline | metadata_connection_config = None
pipeline_root = PIPELINE_ROOT
pipeline = ann_pipeline(
pipeline_name=PIPELINE_NAME,
pipeline_root=pipeline_root,
metadata_connection_config=metadata_connection_config,
project_id=PROJECT_ID,
project_number=PROJECT_NUMBER,
region=REGION,
vpc_name=VPC_NAME,
... | notebooks/community/analytics-componetized-patterns/retail/recommendation-system/bqml-scann/ann02_run_pipeline.ipynb | GoogleCloudPlatform/bigquery-notebooks | apache-2.0 |
Compile the pipeline | config = kubeflow_v2_dag_runner.KubeflowV2DagRunnerConfig(
project_id=PROJECT_ID,
display_name=PIPELINE_NAME,
default_image="gcr.io/{}/caip-tfx-custom:{}".format(PROJECT_ID, USER),
)
runner = kubeflow_v2_dag_runner.KubeflowV2DagRunner(
config=config, output_filename="pipeline.json"
)
runner.compile(pipe... | notebooks/community/analytics-componetized-patterns/retail/recommendation-system/bqml-scann/ann02_run_pipeline.ipynb | GoogleCloudPlatform/bigquery-notebooks | apache-2.0 |
Submit the pipeline run | aipp_client.create_run_from_job_spec("pipeline.json") | notebooks/community/analytics-componetized-patterns/retail/recommendation-system/bqml-scann/ann02_run_pipeline.ipynb | GoogleCloudPlatform/bigquery-notebooks | apache-2.0 |
creates in memory an object with the name "ObjectCreator". | %%HTML
<p style="color:red;font-size: 150%;">This object (the class) is itself capable of creating objects (the instances), and this is why it's a class.</p> | content/posts/meditations/Python_objects.ipynb | dm-wyncode/zipped-code | mit |
But still, it's an object, and therefore:
you can assign it to a variable | object_creator_class = ObjectCreator
print(object_creator_class) | content/posts/meditations/Python_objects.ipynb | dm-wyncode/zipped-code | mit |
you can copy it | from copy import copy
ObjectCreatorCopy = copy(ObjectCreator)
print(ObjectCreatorCopy)
print("copy ObjectCreatorCopy is not ObjectCreator: ", ObjectCreatorCopy is not ObjectCreator)
print("variable object_creator_class is ObjectCreator: ", object_creator_class is ObjectCreator) | content/posts/meditations/Python_objects.ipynb | dm-wyncode/zipped-code | mit |
you can add attributes to it | print("ObjectCreator has an attribute 'new_attribute': ", hasattr(ObjectCreator, 'new_attribute'))
ObjectCreator.new_attribute = 'foo' # you can add attributes to a class
print("ObjectCreator has an attribute 'new_attribute': ", hasattr(ObjectCreator, 'new_attribute'))
print("attribute 'new_attribute': ", ObjectCreat... | content/posts/meditations/Python_objects.ipynb | dm-wyncode/zipped-code | mit |
you can pass it as a function parameter | def echo(o):
print(o)
# you can pass a class as a parameter
print("return value of passing Object Creator to {}: ".format(echo), echo(ObjectCreator))
%%HTML
<p style="color:red;font-size: 150%;">Since classes are objects, you can create them on the fly, like any object.</p>
def get_class_by(name):
class Fo... | content/posts/meditations/Python_objects.ipynb | dm-wyncode/zipped-code | mit |
But it's not so dynamic, since you still have to write the whole class yourself.
Since classes are objects, they must be generated by something.
When you use the class keyword, Python creates this object automatically. But as with most things in Python, it gives you a way to do it manually.
Remember the function type? ... | print(type(1))
print(type("1"))
print(type(int))
print(type(ObjectCreator))
print(type(type)) | content/posts/meditations/Python_objects.ipynb | dm-wyncode/zipped-code | mit |
Well, type has a completely different ability, it can also create classes on the fly. type can take the description of a class as parameters, and return a class. | classes = Foo, Bar = [type(name, (), {}) for name in ('Foo', 'Bar')]
for class_ in classes:
pprint(class_) | content/posts/meditations/Python_objects.ipynb | dm-wyncode/zipped-code | mit |
type accepts a dictionary to define the attributes of the class. So: | classes_with_attributes = Foo, Bar = [type(name, (), namespace)
for name, namespace
in zip(
('Foo', 'Bar'),
(
... | content/posts/meditations/Python_objects.ipynb | dm-wyncode/zipped-code | mit |
Eventually you'll want to add methods to your class. Just define a function with the proper signature and assign it as an attribute. | def an_added_function(self):
return "I am an added function."
Foo.added = an_added_function
foo = Foo()
print(foo.added()) | content/posts/meditations/Python_objects.ipynb | dm-wyncode/zipped-code | mit |
You see where we are going: in Python, classes are objects, and you can create a class on the fly, dynamically. | %%HTML
<p style="color:red;font-size: 150%;">[Creating a class on the fly, dynamically] is what Python does when you use the keyword class, and it does so by using a metaclass.</p>
%%HTML
<p style="color:red;font-size: 150%;">Metaclasses are the 'stuff' that creates classes.</p> | content/posts/meditations/Python_objects.ipynb | dm-wyncode/zipped-code | mit |
You define classes in order to create objects, right?
But we learned that Python classes are objects. | %%HTML
<p style="color:red;font-size: 150%;">Well, metaclasses are what create these objects. They are the classes' classes.</p>
%%HTML
<p style="color:red;font-size: 150%;">Everything, and I mean everything, is an object in Python. That includes ints, strings, functions and classes. All of them are objects. And all... | content/posts/meditations/Python_objects.ipynb | dm-wyncode/zipped-code | mit |
Changing to blog post entitled Python 3 OOP Part 5—Metaclasses
object, which inherits from nothing.
reminds me of Eastern teachings of 'sunyata':
emptiness, voidness, openness, nonexistence, thusness, etc.
```python
a = 5
type(a)
<class 'int'>
a.class
<class 'int'>
a.class.bases
(<class 'object'>,)
object.bases
() ... | class MyType(type):
pass
class MySpecialClass(metaclass=MyType):
pass
msp = MySpecialClass()
type(msp)
type(MySpecialClass)
type(MyType) | content/posts/meditations/Python_objects.ipynb | dm-wyncode/zipped-code | mit |
Metaclasses are a very advanced topic in Python, but they have many practical uses. For example, by means of a custom metaclass you may log any time a class is instanced, which can be important for applications that shall keep a low memory usage or have to monitor it. | %%HTML
<p style="color:red;font-size: 150%;">"Build a class"? This is a task for metaclasses. The following implementation comes from Python 3 Patterns, Recipes and Idioms.</p>
class Singleton(type):
instance = None
def __call__(cls, *args, **kwargs):
if not cls.instance:
cls.instance = su... | content/posts/meditations/Python_objects.ipynb | dm-wyncode/zipped-code | mit |
The constructor mechanism in Python is on the contrary very important, and it is implemented by two methods, instead of just one: new() and init(). | %%HTML
<p style="color:red;font-size: 150%;">The tasks of the two methods are very clear and distinct: __new__() shall perform actions needed when creating a new instance while __init__ deals with object initialization.</p>
class MyClass:
def __new__(cls, *args, **kwargs):
obj = super().__new__(cls, *args... | content/posts/meditations/Python_objects.ipynb | dm-wyncode/zipped-code | mit |
Subclassing int
Object creation is behaviour. For most classes it is enough to provide a different __init__ method, but for immutable classes one often have to provide a different __new__ method.
In this subsection, as preparation for enumerated integers, we will start to code a subclass of int that behave like bool. ... | class MyBool(int):
def __repr__(self):
return 'MyBool.' + ['False', 'True'][self]
t = MyBool(1)
t
bool(2) == 1
MyBool(2) == 1
%%HTML
<p style="color:red;font-size: 150%;">In many classes we use __init__ to mutate the newly constructed object, typically by storing or otherwise using the arguments to __i... | content/posts/meditations/Python_objects.ipynb | dm-wyncode/zipped-code | mit |
The solution to the problem is to use new. Here we will show that it works, and later we will explain elsewhere exactly what happens. | bool.__doc__
class NewBool(int):
def __new__(cls, value):
# bool
return int.__new__(cls, bool(value))
y = NewBool(56)
y == 1 | content/posts/meditations/Python_objects.ipynb | dm-wyncode/zipped-code | mit |
<b>Question 4 Do multiple languages influent the reviews of apps?</b> | multi_language = app.loc[app['multiple languages'] == 'Y']
sin_language = app.loc[app['multiple languages'] == 'N']
multi_language['overall rating'].plot(kind = "density")
sin_language['overall rating'].plot(kind = "density")
plt.xlabel('Overall Rating')
plt.legend(labels = ['multiple languages','single language'], loc... | notebooks/Multiple Languages Effects Analysis (Q4).ipynb | jpzhangvincent/MobileAppMarketAnalysis | mit |
<p>First, the data set is splitted into two parts, one is app with multiple languages and another is app with single language. Then the density plots for the two subsets are made and from the plots we can see that the overall rating of apps with multiple languages is generally higher than the overall rating of apps wit... | import scipy.stats
multi_language = list(multi_language['overall rating'])
sin_language = list(sin_language['overall rating'])
multiple = []
single = []
for each in multi_language:
if each > 0:
multiple.append(each)
for each in sin_language:
if each > 0:
single.append(each)
print(np.mean(mult... | notebooks/Multiple Languages Effects Analysis (Q4).ipynb | jpzhangvincent/MobileAppMarketAnalysis | mit |
<p>I perform t test here. We have two samples here, one is apps with multiple languages and another is apps with single language. So I want to test whether the mean overall rating for these two samples are different.</p>
<p>The null hypothesis is mean overall rating for apps with multiple languages and mean overall ra... | scipy.stats.f_oneway(multiple, single) | notebooks/Multiple Languages Effects Analysis (Q4).ipynb | jpzhangvincent/MobileAppMarketAnalysis | mit |
<p>I also perform one-way ANOVA test here.</p>
<p>The null hypothesis is mean overall rating for apps with multiple languages and mean overall rating for apps with single language are the same and the alternative hypothesis is that the mean overall rating for these two samples are not the same.</p>
<p>From the result... | scipy.stats.kruskal(multiple, single) | notebooks/Multiple Languages Effects Analysis (Q4).ipynb | jpzhangvincent/MobileAppMarketAnalysis | mit |
<span>
Let's parse
</span> | from hit.process.processor import ATTMatrixHitProcessor
from hit.process.processor import ATTPlainHitProcessor
plainProcessor = ATTPlainHitProcessor()
matProcessor = ATTMatrixHitProcessor() | notebooks/Hit Processor.ipynb | Centre-Alt-Rendiment-Esportiu/att | gpl-3.0 |
<span>
Parse a Hit with Plain Processor
</span> | plainHit = plainProcessor.parse_hit("hit: {0:25 1549:4 2757:4 1392:4 2264:7 1764:7 1942:5 2984:5 r}")
print plainHit | notebooks/Hit Processor.ipynb | Centre-Alt-Rendiment-Esportiu/att | gpl-3.0 |
<span>
Compute diffs:
</span> | plainDiffs = plainProcessor.hit_diffs(plainHit["sensor_timings"])
print plainDiffs | notebooks/Hit Processor.ipynb | Centre-Alt-Rendiment-Esportiu/att | gpl-3.0 |
<span>
Parse a Hit with Matrix Processor
</span> | matHit = matProcessor.parse_hit("hit: {0:25 1549:4 2757:4 1392:4 2264:7 1764:7 1942:5 2984:5 r}")
print matHit | notebooks/Hit Processor.ipynb | Centre-Alt-Rendiment-Esportiu/att | gpl-3.0 |
<span>
Compute diffs:
</span> | matDiffs = matProcessor.hit_diffs((matHit["sensor_timings"]))
print matDiffs
matDiffs | notebooks/Hit Processor.ipynb | Centre-Alt-Rendiment-Esportiu/att | gpl-3.0 |
Tensor multiplication with transpose in numpy and einsum | w = np.arange(6).reshape(2,3).astype(np.float32)
x = np.ones((1,3), dtype=np.float32)
print("w:\n", w)
print("x:\n", x)
y = np.matmul(w, np.transpose(x))
print("y:\n", y)
y = einsum('ij,kj->ik', w, x)
print("y:\n", y) | versions/2022/tools/python/einsum_demo.ipynb | roatienza/Deep-Learning-Experiments | mit |
Properties of square matrices in numpy and einsum
We demonstrate diagonal. | w = np.arange(9).reshape(3,3).astype(np.float32)
d = np.diag(w)
print("w:\n", w)
print("d:\n", d)
d = einsum('ii->i', w)
print("d:\n", d) | versions/2022/tools/python/einsum_demo.ipynb | roatienza/Deep-Learning-Experiments | mit |
Trace. | t = np.trace(w)
print("t:\n", t)
t = einsum('ii->', w)
print("t:\n", t) | versions/2022/tools/python/einsum_demo.ipynb | roatienza/Deep-Learning-Experiments | mit |
Sum along an axis. | s = np.sum(w, axis=0)
print("s:\n", s)
s = einsum('ij->j', w)
print("s:\n", s) | versions/2022/tools/python/einsum_demo.ipynb | roatienza/Deep-Learning-Experiments | mit |
Let us demonstrate tensor transpose. We can also use w.T to transpose w in numpy. | t = np.transpose(w)
print("t:\n", t)
t = einsum("ij->ji", w)
print("t:\n", t) | versions/2022/tools/python/einsum_demo.ipynb | roatienza/Deep-Learning-Experiments | mit |
Dot, inner and outer products in numpy and einsum. | a = np.ones((3,), dtype=np.float32)
b = np.ones((3,), dtype=np.float32) * 2
print("a:\n", a)
print("b:\n", b)
d = np.dot(a,b)
print("d:\n", d)
d = einsum("i,i->", a, b)
print("d:\n", d)
i = np.inner(a, b)
print("i:\n", i)
i = einsum("i,i->", a, b)
print("i:\n", i)
o = np.outer(a,b)
print("o:\n", o)
o = einsum("i,j-... | versions/2022/tools/python/einsum_demo.ipynb | roatienza/Deep-Learning-Experiments | mit |
Inheritance
Inheritance is an OOP practice where a certain class(called subclass/child class) inherits the properties namely data and behaviour of another class(called superclass/parent class). Let us see through an example. | # BITSian class
class BITSian():
def __init__(self, name, id_no, hostel):
self.name = name
self.id_no = id_no
self.hostel = hostel
def get_name(self):
return self.name
def get_id(self):
return self.id_no
def get_hostel(self):
return self.hos... | Week 4/Lecture_9_Inheritance_Overloading_Overidding.ipynb | bpgc-cte/python2017 | mit |
While writing code you must always make sure that you keep it as concise as possible and avoid any sort of repitition. Now, we can clearly see the commonalitites between BITSian and IITian classes.
It would be natural to assume that every college student whether from BITS or IIT or pretty much any other institution in ... | class CollegeStudent():
def __init__(self, name, id_no):
self.name = name
self.id_no = id_no
def get_name(self):
return self.name
def get_id(self):
return self.id_no
# BITSian class
class BITSian(CollegeStudent):
def __init__(self, name, id_no, hostel):
... | Week 4/Lecture_9_Inheritance_Overloading_Overidding.ipynb | bpgc-cte/python2017 | mit |
So, the class definition is as such : class SubClassName(SuperClassName):
Using super()
The main usage of super() in Python is to refer to parent classes without naming them expicitly. This becomes really useful in multiple inheritance where you won't have to worry about parent class name. | class Student():
def __init__(self, name):
self.name = name
def get_name(self):
return self.name
class CollegeStudent(Student):
def __init__(self, name, id_no):
super().__init__(name)
self.id_no = id_no
def get_id(self):
return self.id_no
# BIT... | Week 4/Lecture_9_Inheritance_Overloading_Overidding.ipynb | bpgc-cte/python2017 | mit |
You may come across the following constructor call for a superclass on the net : super(self.__class__, self).__init__(). Please do not do this. It can lead to infinite recursion.
Go through this link for more clarification : Understanding Python Super with init methods
Method Overidding
This is a phenomenon where a sub... | class Student():
def __init__(self, name):
self.name = name
def get_name(self):
return "Student : " + self.name
class CollegeStudent(Student):
def __init__(self, name, id_no):
super().__init__(name)
self.id_no = id_no
def get_id(self):
return self.i... | Week 4/Lecture_9_Inheritance_Overloading_Overidding.ipynb | bpgc-cte/python2017 | mit |
In my experience it's more convenient to build the model with a log-softmax output using nn.LogSoftmax or F.log_softmax (documentation). Then you can get the actual probabilites by taking the exponential torch.exp(output). With a log-softmax output, you want to use the negative log likelihood loss, nn.NLLLoss (document... | ## Solution
# Build a feed-forward network
model = nn.Sequential(nn.Linear(784, 128),
nn.ReLU(),
nn.Linear(128, 64),
nn.ReLU(),
nn.Linear(64, 10),
nn.LogSoftmax(dim=1))
# Define the loss
criterion = nn.NLLLos... | DEEP LEARNING/Pytorch from scratch/MLP/Part 3 - Training Neural Networks (Solution).ipynb | Diyago/Machine-Learning-scripts | apache-2.0 |
Autograd
Now that we know how to calculate a loss, how do we use it to perform backpropagation? Torch provides a module, autograd, for automatically calculating the gradients of tensors. We can use it to calculate the gradients of all our parameters with respect to the loss. Autograd works by keeping track of operation... | x = torch.randn(2,2, requires_grad=True)
print(x)
y = x**2
print(y) | DEEP LEARNING/Pytorch from scratch/MLP/Part 3 - Training Neural Networks (Solution).ipynb | Diyago/Machine-Learning-scripts | apache-2.0 |
These gradients calculations are particularly useful for neural networks. For training we need the gradients of the weights with respect to the cost. With PyTorch, we run data forward through the network to calculate the loss, then, go backwards to calculate the gradients with respect to the loss. Once we have the grad... | # Build a feed-forward network
model = nn.Sequential(nn.Linear(784, 128),
nn.ReLU(),
nn.Linear(128, 64),
nn.ReLU(),
nn.Linear(64, 10),
nn.LogSoftmax(dim=1))
criterion = nn.NLLLoss()
images, labels = next(iter(... | DEEP LEARNING/Pytorch from scratch/MLP/Part 3 - Training Neural Networks (Solution).ipynb | Diyago/Machine-Learning-scripts | apache-2.0 |
Training the network!
There's one last piece we need to start training, an optimizer that we'll use to update the weights with the gradients. We get these from PyTorch's optim package. For example we can use stochastic gradient descent with optim.SGD. You can see how to define an optimizer below. | from torch import optim
# Optimizers require the parameters to optimize and a learning rate
optimizer = optim.SGD(model.parameters(), lr=0.01) | DEEP LEARNING/Pytorch from scratch/MLP/Part 3 - Training Neural Networks (Solution).ipynb | Diyago/Machine-Learning-scripts | apache-2.0 |
Now we know how to use all the individual parts so it's time to see how they work together. Let's consider just one learning step before looping through all the data. The general process with PyTorch:
Make a forward pass through the network
Use the network output to calculate the loss
Perform a backward pass through ... | print('Initial weights - ', model[0].weight)
images, labels = next(iter(trainloader))
images.resize_(64, 784)
# Clear the gradients, do this because gradients are accumulated
optimizer.zero_grad()
# Forward pass, then backward pass, then update weights
output = model(images)
loss = criterion(output, labels)
loss.bac... | DEEP LEARNING/Pytorch from scratch/MLP/Part 3 - Training Neural Networks (Solution).ipynb | Diyago/Machine-Learning-scripts | apache-2.0 |
Training for real
Now we'll put this algorithm into a loop so we can go through all the images. Some nomenclature, one pass through the entire dataset is called an epoch. So here we're going to loop through trainloader to get our training batches. For each batch, we'll doing a training pass where we calculate the loss,... | model = nn.Sequential(nn.Linear(784, 128),
nn.ReLU(),
nn.Linear(128, 64),
nn.ReLU(),
nn.Linear(64, 10),
nn.LogSoftmax(dim=1))
criterion = nn.NLLLoss()
optimizer = optim.SGD(model.parameters(), lr=0.003)
epoch... | DEEP LEARNING/Pytorch from scratch/MLP/Part 3 - Training Neural Networks (Solution).ipynb | Diyago/Machine-Learning-scripts | apache-2.0 |
With the network trained, we can check out it's predictions. | %matplotlib inline
import helper
images, labels = next(iter(trainloader))
img = images[0].view(1, 784)
# Turn off gradients to speed up this part
with torch.no_grad():
logps = model(img)
# Output of the network are log-probabilities, need to take exponential for probabilities
ps = torch.exp(logps)
helper.view_cl... | DEEP LEARNING/Pytorch from scratch/MLP/Part 3 - Training Neural Networks (Solution).ipynb | Diyago/Machine-Learning-scripts | apache-2.0 |
Build LSI Model | model_tfidf = models.TfidfModel(corpus)
corpus_tfidf = model_tfidf[corpus] | Gensim - Word2Vec.ipynb | banyh/ShareIPythonNotebook | gpl-3.0 |
LsiModel的參數
num_topics=200: 設定SVD分解後要保留的維度
id2word: 提供corpus的字典，方便將id轉換為word
chunksize=20000: 在記憶體中一次處理的量，值越大則占用記憶體越多，處理速度也越快
decay=1.0: 因為資料會切成chunk來計算，所以會分成新舊資料，當新的chunk進來時，decay是舊chunk的加權，如果設小於1.0的值，則舊的資料會慢慢「遺忘」
distributed=False: 是否開啟分散式計算，每個core會分到一塊chunk
onepass=True: 設為False強制使用multi-pass stochastic algoritm
po... | model_lsi = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=200)
corpus_lsi = model_lsi[corpus_tfidf]
# 計算V的方法，可以作為document vector
docvec_lsi = gensim.matutils.corpus2dense(corpus_lsi, len(model_lsi.projection.s)).T / model_lsi.projection.s
# word vector直接用U的column vector
wordsim_lsi = similarities.Matri... | Gensim - Word2Vec.ipynb | banyh/ShareIPythonNotebook | gpl-3.0 |
Build Word2Vec Model
Word2Vec的參數
sentences: 用來訓練的list of list of words，但不是必要的，因為可以先建好model，再慢慢丟資料訓練
size=100: vector的維度
alpha=0.025: 初始的學習速度
window=5: context window的大小
min_count=5: 出現次數小於min_count的單字直接忽略
max_vocab_size: 限制vocabulary的大小，如果單字太多，就忽略最少見的單字，預設為無限制
sample=0.001: subsampling，隨機刪除機率小於0.001的單字，兼具擴大context win... | all_text = [doc.split() for doc in documents]
model_w2v = models.Word2Vec(size=200, sg=1)
%timeit model_w2v.build_vocab(all_text)
%timeit model_w2v.train(all_text)
model_w2v.most_similar_cosmul(['deep','learning']) | Gensim - Word2Vec.ipynb | banyh/ShareIPythonNotebook | gpl-3.0 |
Build Doc2Vec Model
Doc2Vec的參數
documents=None: 用來訓練的document，可以是list of TaggedDocument，或TaggedDocument generator
size=300: vector的維度
alpha=0.025: 初始的學習速度
window=8: context window的大小
min_count=5: 出現次數小於min_count的單字直接忽略
max_vocab_size=None: 限制vocabulary的大小，如果單字太多，就忽略最少見的單字，預設為無限制
sample=0: subsampling，隨機刪除機率小於sample的單字，... | from gensim.models.doc2vec import Doc2Vec, TaggedDocument
class PatentDocGenerator(object):
def __init__(self, filename):
self.filename = filename
def __iter__(self):
f = codecs.open(self.filename, 'r', 'UTF-8')
for line in f:
text, appnum = docs_out(line)
... | Gensim - Word2Vec.ipynb | banyh/ShareIPythonNotebook | gpl-3.0 |
Build Doc2Vec Model from 2013 USPTO Patents | from gensim.models.doc2vec import Doc2Vec, TaggedDocument
class PatentDocGenerator(object):
def __init__(self, filename):
self.filename = filename
def __iter__(self):
f = codecs.open(self.filename, 'r', 'UTF-8')
for line in f:
text, appnum = docs_out(line)
... | Gensim - Word2Vec.ipynb | banyh/ShareIPythonNotebook | gpl-3.0 |
To start we'll need some basic libraries. First numpy will be needed for basic array manipulation. Since we will be visualising the results we will need matplotlib and seaborn. Finally we will need umap for doing the dimension reduction itself. | !pip install numpy matplotlib seaborn umap-learn | notebooks/AnimatingUMAP.ipynb | lmcinnes/umap | bsd-3-clause |
To start let's load everything we'll need | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib import animation
from IPython.display import HTML
import seaborn as sns
import itertools
sns.set(style='white', rc={'figure.figsize':(14, 12), 'animation.html': 'html5'})
# Igno... | notebooks/AnimatingUMAP.ipynb | lmcinnes/umap | bsd-3-clause |
To try this out we'll needs a reasonably small dataset (so embedding runs don't take too long since we'll be doing a lot of them). For ease of reproducibility for everyone else I'll use the digits dataset from sklearn. If you want to try other datasets just drop them in here -- COIL20 might be interesting, or you might... | digits = load_digits()
data = digits.data
data | notebooks/AnimatingUMAP.ipynb | lmcinnes/umap | bsd-3-clause |
We need to move the points in between the embeddings given by different parameter values. There are potentially fancy ways to do this (Something using rotation and reflection to get an initial alignment might be interesting), but we'll use straighforward linear interpolation between the two embeddings. To do this we'll... | def tween(e1, e2, n_frames=20):
for i in range(5):
yield e1
for i in range(n_frames):
alpha = i / float(n_frames - 1)
yield (1 - alpha) * e1 + alpha * e2
for i in range(5):
yield(e2)
return | notebooks/AnimatingUMAP.ipynb | lmcinnes/umap | bsd-3-clause |
Now that we can fill in intermediate frame we just need to generate all the embeddings. We'll create a function that can take an argument and set of parameter values and then generate all the embeddings including the in-between frames. | def generate_frame_data(data, arg_name='n_neighbors', arg_list=[]):
result = []
es = []
for arg in arg_list:
kwargs = {arg_name:arg}
if len(es) > 0:
es.append(UMAP(init=es[-1], negative_sample_rate=3, **kwargs).fit_transform(data))
else:
es.append(UMAP(negativ... | notebooks/AnimatingUMAP.ipynb | lmcinnes/umap | bsd-3-clause |
Next we just need to create a function to actually generate the animation given a list of embeddings (one for each frame). This is really just a matter of workign through the details of how matplotlib generates animations -- I would refer you again to Jake's tutorial if you are interested in the detailed mechanics of t... | def create_animation(frame_data, arg_name='n_neighbors', arg_list=[]):
fig, ax = plt.subplots()
all_data = np.vstack(frame_data)
frame_bounds = (all_data[:, 0].min() * 1.1,
all_data[:, 0].max() * 1.1,
all_data[:, 1].min() * 1.1,
all_data[:, 1].ma... | notebooks/AnimatingUMAP.ipynb | lmcinnes/umap | bsd-3-clause |
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