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# Author: Mainak Jas <mainak@neuro.hut.fi>
# Romain Trachel <trachelr@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import pytest
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_allclose, assert_equal)
from mne import io, read_events, Epochs, pick_types
from mne.decoding import (Scaler, FilterEstimator, PSDEstimator, Vectorizer,
UnsupervisedSpatialFilter, TemporalFilter)
from mne.defaults import DEFAULTS
from mne.utils import requires_version, run_tests_if_main, check_version
tmin, tmax = -0.2, 0.5
event_id = dict(aud_l=1, vis_l=3)
start, stop = 0, 8
data_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data')
raw_fname = op.join(data_dir, 'test_raw.fif')
event_name = op.join(data_dir, 'test-eve.fif')
def test_scaler():
"""Test methods of Scaler."""
raw = io.read_raw_fif(raw_fname)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
epochs_data = epochs.get_data()
y = epochs.events[:, -1]
methods = (None, dict(mag=5, grad=10, eeg=20), 'mean', 'median')
infos = (epochs.info, epochs.info, None, None)
epochs_data_t = epochs_data.transpose([1, 0, 2])
for method, info in zip(methods, infos):
if method == 'median' and not check_version('sklearn', '0.17'):
pytest.raises(ValueError, Scaler, info, method)
continue
if method == 'mean' and not check_version('sklearn', ''):
pytest.raises(ImportError, Scaler, info, method)
continue
scaler = Scaler(info, method)
X = scaler.fit_transform(epochs_data, y)
assert_equal(X.shape, epochs_data.shape)
if method is None or isinstance(method, dict):
sd = DEFAULTS['scalings'] if method is None else method
stds = np.zeros(len(picks))
for key in ('mag', 'grad'):
stds[pick_types(epochs.info, meg=key)] = 1. / sd[key]
stds[pick_types(epochs.info, meg=False, eeg=True)] = 1. / sd['eeg']
means = np.zeros(len(epochs.ch_names))
elif method == 'mean':
stds = np.array([np.std(ch_data) for ch_data in epochs_data_t])
means = np.array([np.mean(ch_data) for ch_data in epochs_data_t])
else: # median
percs = np.array([np.percentile(ch_data, [25, 50, 75])
for ch_data in epochs_data_t])
stds = percs[:, 2] - percs[:, 0]
means = percs[:, 1]
assert_allclose(X * stds[:, np.newaxis] + means[:, np.newaxis],
epochs_data, rtol=1e-12, atol=1e-20, err_msg=method)
X2 = scaler.fit(epochs_data, y).transform(epochs_data)
assert_array_equal(X, X2)
# inverse_transform
Xi = scaler.inverse_transform(X)
assert_array_almost_equal(epochs_data, Xi)
# Test init exception
pytest.raises(ValueError, Scaler, None, None)
pytest.raises(ValueError, scaler.fit, epochs, y)
pytest.raises(ValueError, scaler.transform, epochs)
epochs_bad = Epochs(raw, events, event_id, 0, 0.01,
picks=np.arange(len(raw.ch_names))) # non-data chs
scaler = Scaler(epochs_bad.info, None)
pytest.raises(ValueError, scaler.fit, epochs_bad.get_data(), y)
def test_filterestimator():
"""Test methods of FilterEstimator."""
raw = io.read_raw_fif(raw_fname)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
epochs_data = epochs.get_data()
# Add tests for different combinations of l_freq and h_freq
filt = FilterEstimator(epochs.info, l_freq=40, h_freq=80)
y = epochs.events[:, -1]
X = filt.fit_transform(epochs_data, y)
assert (X.shape == epochs_data.shape)
assert_array_equal(filt.fit(epochs_data, y).transform(epochs_data), X)
filt = FilterEstimator(epochs.info, l_freq=None, h_freq=40,
filter_length='auto',
l_trans_bandwidth='auto', h_trans_bandwidth='auto')
y = epochs.events[:, -1]
X = filt.fit_transform(epochs_data, y)
filt = FilterEstimator(epochs.info, l_freq=1, h_freq=1)
y = epochs.events[:, -1]
with pytest.warns(RuntimeWarning, match='longer than the signal'):
pytest.raises(ValueError, filt.fit_transform, epochs_data, y)
filt = FilterEstimator(epochs.info, l_freq=40, h_freq=None,
filter_length='auto',
l_trans_bandwidth='auto', h_trans_bandwidth='auto')
X = filt.fit_transform(epochs_data, y)
# Test init exception
pytest.raises(ValueError, filt.fit, epochs, y)
pytest.raises(ValueError, filt.transform, epochs)
def test_psdestimator():
"""Test methods of PSDEstimator."""
raw = io.read_raw_fif(raw_fname)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
epochs_data = epochs.get_data()
psd = PSDEstimator(2 * np.pi, 0, np.inf)
y = epochs.events[:, -1]
X = psd.fit_transform(epochs_data, y)
assert (X.shape[0] == epochs_data.shape[0])
assert_array_equal(psd.fit(epochs_data, y).transform(epochs_data), X)
# Test init exception
pytest.raises(ValueError, psd.fit, epochs, y)
pytest.raises(ValueError, psd.transform, epochs)
def test_vectorizer():
"""Test Vectorizer."""
data = np.random.rand(150, 18, 6)
vect = Vectorizer()
result = vect.fit_transform(data)
assert_equal(result.ndim, 2)
# check inverse_trasnform
orig_data = vect.inverse_transform(result)
assert_equal(orig_data.ndim, 3)
assert_array_equal(orig_data, data)
assert_array_equal(vect.inverse_transform(result[1:]), data[1:])
# check with different shape
assert_equal(vect.fit_transform(np.random.rand(150, 18, 6, 3)).shape,
(150, 324))
assert_equal(vect.fit_transform(data[1:]).shape, (149, 108))
# check if raised errors are working correctly
vect.fit(np.random.rand(105, 12, 3))
pytest.raises(ValueError, vect.transform, np.random.rand(105, 12, 3, 1))
pytest.raises(ValueError, vect.inverse_transform,
np.random.rand(102, 12, 12))
@requires_version('sklearn', '0.16')
def test_unsupervised_spatial_filter():
"""Test unsupervised spatial filter."""
from sklearn.decomposition import PCA
from sklearn.kernel_ridge import KernelRidge
raw = io.read_raw_fif(raw_fname)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
preload=True, baseline=None, verbose=False)
# Test estimator
pytest.raises(ValueError, UnsupervisedSpatialFilter, KernelRidge(2))
# Test fit
X = epochs.get_data()
n_components = 4
usf = UnsupervisedSpatialFilter(PCA(n_components))
usf.fit(X)
usf1 = UnsupervisedSpatialFilter(PCA(n_components))
# test transform
assert_equal(usf.transform(X).ndim, 3)
# test fit_transform
assert_array_almost_equal(usf.transform(X), usf1.fit_transform(X))
assert_equal(usf.transform(X).shape[1], n_components)
assert_array_almost_equal(usf.inverse_transform(usf.transform(X)), X)
# Test with average param
usf = UnsupervisedSpatialFilter(PCA(4), average=True)
usf.fit_transform(X)
pytest.raises(ValueError, UnsupervisedSpatialFilter, PCA(4), 2)
def test_temporal_filter():
"""Test methods of TemporalFilter."""
X = np.random.rand(5, 5, 1200)
# Test init test
values = (('10hz', None, 100., 'auto'), (5., '10hz', 100., 'auto'),
(10., 20., 5., 'auto'), (None, None, 100., '5hz'))
for low, high, sf, ltrans in values:
filt = TemporalFilter(low, high, sf, ltrans, fir_design='firwin')
pytest.raises(ValueError, filt.fit_transform, X)
# Add tests for different combinations of l_freq and h_freq
for low, high in ((5., 15.), (None, 15.), (5., None)):
filt = TemporalFilter(low, high, sfreq=100., fir_design='firwin')
Xt = filt.fit_transform(X)
assert_array_equal(filt.fit_transform(X), Xt)
assert (X.shape == Xt.shape)
# Test fit and transform numpy type check
with pytest.warns(RuntimeWarning, match='longer than the signal'):
pytest.raises(TypeError, filt.transform, [1, 2])
# Test with 2 dimensional data array
X = np.random.rand(101, 500)
filt = TemporalFilter(l_freq=25., h_freq=50., sfreq=1000.,
filter_length=150, fir_design='firwin2')
assert_equal(filt.fit_transform(X).shape, X.shape)
run_tests_if_main()
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