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kmike/scikit-learn | sklearn/utils/__init__.py | 3 | 10094 | """
The :mod:`sklearn.utils` module includes various utilites.
"""
from collections import Sequence
import numpy as np
from scipy.sparse import issparse
import warnings
from .murmurhash import murmurhash3_32
from .validation import (as_float_array, check_arrays, safe_asarray,
assert_all_finite, array2d, atleast2d_or_csc,
atleast2d_or_csr, warn_if_not_float,
check_random_state)
from .class_weight import compute_class_weight
__all__ = ["murmurhash3_32", "as_float_array", "check_arrays", "safe_asarray",
"assert_all_finite", "array2d", "atleast2d_or_csc",
"atleast2d_or_csr", "warn_if_not_float", "check_random_state",
"compute_class_weight"]
# Make sure that DeprecationWarning get printed
warnings.simplefilter("always", DeprecationWarning)
class deprecated(object):
"""Decorator to mark a function or class as deprecated.
Issue a warning when the function is called/the class is instantiated and
adds a warning to the docstring.
The optional extra argument will be appended to the deprecation message
and the docstring. Note: to use this with the default value for extra, put
in an empty of parentheses:
>>> from sklearn.utils import deprecated
>>> deprecated() # doctest: +ELLIPSIS
<sklearn.utils.deprecated object at ...>
>>> @deprecated()
... def some_function(): pass
"""
# Adapted from http://wiki.python.org/moin/PythonDecoratorLibrary,
# but with many changes.
def __init__(self, extra=''):
"""
Parameters
----------
extra: string
to be added to the deprecation messages
"""
self.extra = extra
def __call__(self, obj):
if isinstance(obj, type):
return self._decorate_class(obj)
else:
return self._decorate_fun(obj)
def _decorate_class(self, cls):
msg = "Class %s is deprecated" % cls.__name__
if self.extra:
msg += "; %s" % self.extra
# FIXME: we should probably reset __new__ for full generality
init = cls.__init__
def wrapped(*args, **kwargs):
warnings.warn(msg, category=DeprecationWarning)
return init(*args, **kwargs)
cls.__init__ = wrapped
wrapped.__name__ = '__init__'
wrapped.__doc__ = self._update_doc(init.__doc__)
wrapped.deprecated_original = init
return cls
def _decorate_fun(self, fun):
"""Decorate function fun"""
msg = "Function %s is deprecated" % fun.__name__
if self.extra:
msg += "; %s" % self.extra
def wrapped(*args, **kwargs):
warnings.warn(msg, category=DeprecationWarning)
return fun(*args, **kwargs)
wrapped.__name__ = fun.__name__
wrapped.__dict__ = fun.__dict__
wrapped.__doc__ = self._update_doc(fun.__doc__)
return wrapped
def _update_doc(self, olddoc):
newdoc = "DEPRECATED"
if self.extra:
newdoc = "%s: %s" % (newdoc, self.extra)
if olddoc:
newdoc = "%s\n\n%s" % (newdoc, olddoc)
return newdoc
def safe_mask(X, mask):
"""Return a mask which is safe to use on X.
Parameters
----------
X : {array-like, sparse matrix}
Data on which to apply mask.
mask: array
Mask to be used on X.
Returns
-------
mask
"""
mask = np.asanyarray(mask)
if np.issubdtype(mask.dtype, np.int):
return mask
if hasattr(X, "toarray"):
ind = np.arange(mask.shape[0])
mask = ind[mask]
return mask
def resample(*arrays, **options):
"""Resample arrays or sparse matrices in a consistent way
The default strategy implements one step of the bootstrapping
procedure.
Parameters
----------
`*arrays` : sequence of arrays or scipy.sparse matrices with same shape[0]
replace : boolean, True by default
Implements resampling with replacement. If False, this will implement
(sliced) random permutations.
n_samples : int, None by default
Number of samples to generate. If left to None this is
automatically set to the first dimension of the arrays.
random_state : int or RandomState instance
Control the shuffling for reproducible behavior.
Returns
-------
Sequence of resampled views of the collections. The original arrays are
not impacted.
Examples
--------
It is possible to mix sparse and dense arrays in the same run::
>>> X = [[1., 0.], [2., 1.], [0., 0.]]
>>> y = np.array([0, 1, 2])
>>> from scipy.sparse import coo_matrix
>>> X_sparse = coo_matrix(X)
>>> from sklearn.utils import resample
>>> X, X_sparse, y = resample(X, X_sparse, y, random_state=0)
>>> X
array([[ 1., 0.],
[ 2., 1.],
[ 1., 0.]])
>>> X_sparse # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
<3x2 sparse matrix of type '<... 'numpy.float64'>'
with 4 stored elements in Compressed Sparse Row format>
>>> X_sparse.toarray()
array([[ 1., 0.],
[ 2., 1.],
[ 1., 0.]])
>>> y
array([0, 1, 0])
>>> resample(y, n_samples=2, random_state=0)
array([0, 1])
See also
--------
:class:`sklearn.cross_validation.Bootstrap`
:func:`sklearn.utils.shuffle`
"""
random_state = check_random_state(options.pop('random_state', None))
replace = options.pop('replace', True)
max_n_samples = options.pop('n_samples', None)
if options:
raise ValueError("Unexpected kw arguments: %r" % options.keys())
if len(arrays) == 0:
return None
first = arrays[0]
n_samples = first.shape[0] if hasattr(first, 'shape') else len(first)
if max_n_samples is None:
max_n_samples = n_samples
if max_n_samples > n_samples:
raise ValueError("Cannot sample %d out of arrays with dim %d" % (
max_n_samples, n_samples))
arrays = check_arrays(*arrays, sparse_format='csr')
if replace:
indices = random_state.randint(0, n_samples, size=(max_n_samples,))
else:
indices = np.arange(n_samples)
random_state.shuffle(indices)
indices = indices[:max_n_samples]
resampled_arrays = []
for array in arrays:
array = array[indices]
resampled_arrays.append(array)
if len(resampled_arrays) == 1:
# syntactic sugar for the unit argument case
return resampled_arrays[0]
else:
return resampled_arrays
def shuffle(*arrays, **options):
"""Shuffle arrays or sparse matrices in a consistent way
This is a convenience alias to ``resample(*arrays, replace=False)`` to do
random permutations of the collections.
Parameters
----------
`*arrays` : sequence of arrays or scipy.sparse matrices with same shape[0]
random_state : int or RandomState instance
Control the shuffling for reproducible behavior.
n_samples : int, None by default
Number of samples to generate. If left to None this is
automatically set to the first dimension of the arrays.
Returns
-------
Sequence of shuffled views of the collections. The original arrays are
not impacted.
Examples
--------
It is possible to mix sparse and dense arrays in the same run::
>>> X = [[1., 0.], [2., 1.], [0., 0.]]
>>> y = np.array([0, 1, 2])
>>> from scipy.sparse import coo_matrix
>>> X_sparse = coo_matrix(X)
>>> from sklearn.utils import shuffle
>>> X, X_sparse, y = shuffle(X, X_sparse, y, random_state=0)
>>> X
array([[ 0., 0.],
[ 2., 1.],
[ 1., 0.]])
>>> X_sparse # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
<3x2 sparse matrix of type '<... 'numpy.float64'>'
with 3 stored elements in Compressed Sparse Row format>
>>> X_sparse.toarray()
array([[ 0., 0.],
[ 2., 1.],
[ 1., 0.]])
>>> y
array([2, 1, 0])
>>> shuffle(y, n_samples=2, random_state=0)
array([0, 1])
See also
--------
:func:`sklearn.utils.resample`
"""
options['replace'] = False
return resample(*arrays, **options)
def safe_sqr(X, copy=True):
"""Element wise squaring of array-likes and sparse matrices.
Parameters
----------
X : array like, matrix, sparse matrix
Returns
-------
X ** 2 : element wise square
"""
X = safe_asarray(X)
if issparse(X):
if copy:
X = X.copy()
X.data **= 2
else:
if copy:
X = X ** 2
else:
X **= 2
return X
def gen_even_slices(n, n_packs):
"""Generator to create n_packs slices going up to n.
Examples
--------
>>> from sklearn.utils import gen_even_slices
>>> list(gen_even_slices(10, 1))
[slice(0, 10, None)]
>>> list(gen_even_slices(10, 10)) #doctest: +ELLIPSIS
[slice(0, 1, None), slice(1, 2, None), ..., slice(9, 10, None)]
>>> list(gen_even_slices(10, 5)) #doctest: +ELLIPSIS
[slice(0, 2, None), slice(2, 4, None), ..., slice(8, 10, None)]
>>> list(gen_even_slices(10, 3))
[slice(0, 4, None), slice(4, 7, None), slice(7, 10, None)]
"""
start = 0
for pack_num in range(n_packs):
this_n = n // n_packs
if pack_num < n % n_packs:
this_n += 1
if this_n > 0:
end = start + this_n
yield slice(start, end, None)
start = end
def tosequence(x):
"""Cast iterable x to a Sequence, avoiding a copy if possible."""
if isinstance(x, np.ndarray):
return np.asarray(x)
elif isinstance(x, Sequence):
return x
else:
return list(x)
class ConvergenceWarning(Warning):
"Custom warning to capture convergence problems"
| bsd-3-clause |
mne-tools/mne-tools.github.io | 0.20/_downloads/76822bb92a8465181ec2a7ee96ca8cf4/plot_decoding_csp_timefreq.py | 1 | 6457 | """
============================================================================
Decoding in time-frequency space data using the Common Spatial Pattern (CSP)
============================================================================
The time-frequency decomposition is estimated by iterating over raw data that
has been band-passed at different frequencies. This is used to compute a
covariance matrix over each epoch or a rolling time-window and extract the CSP
filtered signals. A linear discriminant classifier is then applied to these
signals.
"""
# Authors: Laura Gwilliams <laura.gwilliams@nyu.edu>
# Jean-Remi King <jeanremi.king@gmail.com>
# Alex Barachant <alexandre.barachant@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from mne import Epochs, create_info, events_from_annotations
from mne.io import concatenate_raws, read_raw_edf
from mne.datasets import eegbci
from mne.decoding import CSP
from mne.time_frequency import AverageTFR
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import StratifiedKFold, cross_val_score
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import LabelEncoder
###############################################################################
# Set parameters and read data
event_id = dict(hands=2, feet=3) # motor imagery: hands vs feet
subject = 1
runs = [6, 10, 14]
raw_fnames = eegbci.load_data(subject, runs)
raw = concatenate_raws([read_raw_edf(f, preload=True) for f in raw_fnames])
# Extract information from the raw file
sfreq = raw.info['sfreq']
events, _ = events_from_annotations(raw, event_id=dict(T1=2, T2=3))
raw.pick_types(meg=False, eeg=True, stim=False, eog=False, exclude='bads')
# Assemble the classifier using scikit-learn pipeline
clf = make_pipeline(CSP(n_components=4, reg=None, log=True, norm_trace=False),
LinearDiscriminantAnalysis())
n_splits = 5 # how many folds to use for cross-validation
cv = StratifiedKFold(n_splits=n_splits, shuffle=True)
# Classification & Time-frequency parameters
tmin, tmax = -.200, 2.000
n_cycles = 10. # how many complete cycles: used to define window size
min_freq = 5.
max_freq = 25.
n_freqs = 8 # how many frequency bins to use
# Assemble list of frequency range tuples
freqs = np.linspace(min_freq, max_freq, n_freqs) # assemble frequencies
freq_ranges = list(zip(freqs[:-1], freqs[1:])) # make freqs list of tuples
# Infer window spacing from the max freq and number of cycles to avoid gaps
window_spacing = (n_cycles / np.max(freqs) / 2.)
centered_w_times = np.arange(tmin, tmax, window_spacing)[1:]
n_windows = len(centered_w_times)
# Instantiate label encoder
le = LabelEncoder()
###############################################################################
# Loop through frequencies, apply classifier and save scores
# init scores
freq_scores = np.zeros((n_freqs - 1,))
# Loop through each frequency range of interest
for freq, (fmin, fmax) in enumerate(freq_ranges):
# Infer window size based on the frequency being used
w_size = n_cycles / ((fmax + fmin) / 2.) # in seconds
# Apply band-pass filter to isolate the specified frequencies
raw_filter = raw.copy().filter(fmin, fmax, n_jobs=1, fir_design='firwin',
skip_by_annotation='edge')
# Extract epochs from filtered data, padded by window size
epochs = Epochs(raw_filter, events, event_id, tmin - w_size, tmax + w_size,
proj=False, baseline=None, preload=True)
epochs.drop_bad()
y = le.fit_transform(epochs.events[:, 2])
X = epochs.get_data()
# Save mean scores over folds for each frequency and time window
freq_scores[freq] = np.mean(cross_val_score(estimator=clf, X=X, y=y,
scoring='roc_auc', cv=cv,
n_jobs=1), axis=0)
###############################################################################
# Plot frequency results
plt.bar(freqs[:-1], freq_scores, width=np.diff(freqs)[0],
align='edge', edgecolor='black')
plt.xticks(freqs)
plt.ylim([0, 1])
plt.axhline(len(epochs['feet']) / len(epochs), color='k', linestyle='--',
label='chance level')
plt.legend()
plt.xlabel('Frequency (Hz)')
plt.ylabel('Decoding Scores')
plt.title('Frequency Decoding Scores')
###############################################################################
# Loop through frequencies and time, apply classifier and save scores
# init scores
tf_scores = np.zeros((n_freqs - 1, n_windows))
# Loop through each frequency range of interest
for freq, (fmin, fmax) in enumerate(freq_ranges):
# Infer window size based on the frequency being used
w_size = n_cycles / ((fmax + fmin) / 2.) # in seconds
# Apply band-pass filter to isolate the specified frequencies
raw_filter = raw.copy().filter(fmin, fmax, n_jobs=1, fir_design='firwin',
skip_by_annotation='edge')
# Extract epochs from filtered data, padded by window size
epochs = Epochs(raw_filter, events, event_id, tmin - w_size, tmax + w_size,
proj=False, baseline=None, preload=True)
epochs.drop_bad()
y = le.fit_transform(epochs.events[:, 2])
# Roll covariance, csp and lda over time
for t, w_time in enumerate(centered_w_times):
# Center the min and max of the window
w_tmin = w_time - w_size / 2.
w_tmax = w_time + w_size / 2.
# Crop data into time-window of interest
X = epochs.copy().crop(w_tmin, w_tmax).get_data()
# Save mean scores over folds for each frequency and time window
tf_scores[freq, t] = np.mean(cross_val_score(estimator=clf, X=X, y=y,
scoring='roc_auc', cv=cv,
n_jobs=1), axis=0)
###############################################################################
# Plot time-frequency results
# Set up time frequency object
av_tfr = AverageTFR(create_info(['freq'], sfreq), tf_scores[np.newaxis, :],
centered_w_times, freqs[1:], 1)
chance = np.mean(y) # set chance level to white in the plot
av_tfr.plot([0], vmin=chance, title="Time-Frequency Decoding Scores",
cmap=plt.cm.Reds)
| bsd-3-clause |
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