def test_linearmodel():
"""Test LinearModel class for computing filters and patterns.
"""
clf = LinearModel()
X = np.random.rand(20, 3)
y = np.arange(20) % 2
clf.fit(X, y)
assert_equal(clf.filters_.shape, (3, ))
assert_equal(clf.patterns_.shape, (3, ))
assert_raises(ValueError, clf.fit, np.random.rand(20, 3, 2), y)
def test_linearmodel():
"""Test LinearModel class for computing filters and patterns.
"""
clf = LinearModel()
X = np.random.rand(20, 3)
y = np.arange(20) % 2
clf.fit(X, y)
assert_equal(clf.filters_.shape, (3,))
assert_equal(clf.patterns_.shape, (3,))
assert_raises(ValueError, clf.fit, np.random.rand(20, 3, 2), y)
def test_get_coef_multiclass_full(n_classes, n_channels, n_times):
"""Test a full example with pattern extraction."""
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
data = np.zeros((10 * n_classes, n_channels, n_times))
# Make only the first channel informative
for ii in range(n_classes):
data[ii * 10:(ii + 1) * 10, 0] = ii
events = np.zeros((len(data), 3), int)
events[:, 0] = np.arange(len(events))
events[:, 2] = data[:, 0, 0]
info = create_info(n_channels, 1000., 'eeg')
epochs = EpochsArray(data, info, events, tmin=0)
clf = make_pipeline(
Scaler(epochs.info),
Vectorizer(),
LinearModel(LogisticRegression(random_state=0, multi_class='ovr')),
)
scorer = 'roc_auc_ovr_weighted'
time_gen = GeneralizingEstimator(clf, scorer, verbose=True)
X = epochs.get_data()
y = epochs.events[:, 2]
n_splits = 3
cv = StratifiedKFold(n_splits=n_splits)
scores = cross_val_multiscore(time_gen, X, y, cv=cv, verbose=True)
want = (n_splits, )
if n_times > 1:
want += (n_times, n_times)
assert scores.shape == want
assert_array_less(0.8, scores)
clf.fit(X, y)
patterns = get_coef(clf, 'patterns_', inverse_transform=True)
assert patterns.shape == (n_classes, n_channels, n_times)
assert_allclose(patterns[:, 1:], 0., atol=1e-7) # no other channels useful
def test_get_coef_inverse_transform(inverse, Scale, kwargs):
"""Test get_coef with and without inverse_transform."""
from sklearn.linear_model import Ridge
from sklearn.pipeline import make_pipeline
lm_regression = LinearModel(Ridge())
X, y, A = _make_data(n_samples=1000, n_features=3, n_targets=1)
# Check with search_light and combination of preprocessing ending with sl:
# slider = SlidingEstimator(make_pipeline(StandardScaler(), lm_regression))
# XXX : line above should work but does not as only last step is
# used in get_coef ...
slider = SlidingEstimator(make_pipeline(lm_regression))
X = np.transpose([X, -X], [1, 2, 0]) # invert X across 2 time samples
clf = make_pipeline(Scale(**kwargs), slider)
clf.fit(X, y)
patterns = get_coef(clf, 'patterns_', inverse)
filters = get_coef(clf, 'filters_', inverse)
assert_array_equal(filters.shape, patterns.shape, X.shape[1:])
# the two time samples get inverted patterns
assert_equal(patterns[0, 0], -patterns[0, 1])
for t in [0, 1]:
filters_t = get_coef(
clf.named_steps['slidingestimator'].estimators_[t], 'filters_',
False)
if Scale is _Noop:
assert_array_equal(filters_t, filters[:, t])
def test_get_coef_multiclass(n_features, n_targets):
"""Test get_coef on multiclass problems."""
# Check patterns with more than 1 regressor
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.pipeline import make_pipeline
X, Y, A = _make_data(n_samples=30000,
n_features=n_features,
n_targets=n_targets)
lm = LinearModel(LinearRegression()).fit(X, Y)
assert_array_equal(lm.filters_.shape, lm.patterns_.shape)
if n_targets == 1:
want_shape = (n_features, )
else:
want_shape = (n_targets, n_features)
assert_array_equal(lm.filters_.shape, want_shape)
if n_features > 1 and n_targets > 1:
assert_array_almost_equal(A, lm.patterns_.T, decimal=2)
lm = LinearModel(Ridge(alpha=0))
clf = make_pipeline(lm)
clf.fit(X, Y)
if n_features > 1 and n_targets > 1:
assert_allclose(A, lm.patterns_.T, atol=2e-2)
coef = get_coef(clf, 'patterns_', inverse_transform=True)
assert_allclose(lm.patterns_, coef, atol=1e-5)
# With epochs, scaler, and vectorizer (typical use case)
X_epo = X.reshape(X.shape + (1, ))
info = create_info(n_features, 1000., 'eeg')
lm = LinearModel(Ridge(alpha=1))
clf = make_pipeline(
Scaler(info, scalings=dict(eeg=1.)), # XXX adding this step breaks
Vectorizer(),
lm,
)
clf.fit(X_epo, Y)
if n_features > 1 and n_targets > 1:
assert_allclose(A, lm.patterns_.T, atol=2e-2)
coef = get_coef(clf, 'patterns_', inverse_transform=True)
lm_patterns_ = lm.patterns_[..., np.newaxis]
assert_allclose(lm_patterns_, coef, atol=1e-5)
# Check can pass fitting parameters
lm.fit(X, Y, sample_weight=np.ones(len(Y)))
def test_linearmodel():
"""Test LinearModel class for computing filters and patterns."""
from sklearn.linear_model import LinearRegression
np.random.seed(42)
clf = LinearModel()
n, n_features = 20, 3
X = np.random.rand(n, n_features)
y = np.arange(n) % 2
clf.fit(X, y)
assert_equal(clf.filters_.shape, (n_features, ))
assert_equal(clf.patterns_.shape, (n_features, ))
pytest.raises(ValueError, clf.fit, np.random.rand(n, n_features, 99), y)
# check multi-target fit
n_targets = 5
clf = LinearModel(LinearRegression())
Y = np.random.rand(n, n_targets)
clf.fit(X, Y)
assert_equal(clf.filters_.shape, (n_targets, n_features))
assert_equal(clf.patterns_.shape, (n_targets, n_features))
pytest.raises(ValueError, clf.fit, X, np.random.rand(n, n_features, 99))
def test_linearmodel():
"""Test LinearModel class for computing filters and patterns."""
from sklearn.linear_model import LinearRegression
np.random.seed(42)
clf = LinearModel()
n, n_features = 20, 3
X = np.random.rand(n, n_features)
y = np.arange(n) % 2
clf.fit(X, y)
assert_equal(clf.filters_.shape, (n_features,))
assert_equal(clf.patterns_.shape, (n_features,))
assert_raises(ValueError, clf.fit, np.random.rand(n, n_features, 99), y)
# check multi-target fit
n_targets = 5
clf = LinearModel(LinearRegression())
Y = np.random.rand(n, n_targets)
clf.fit(X, Y)
assert_equal(clf.filters_.shape, (n_targets, n_features))
assert_equal(clf.patterns_.shape, (n_targets, n_features))
assert_raises(ValueError, clf.fit, X, np.random.rand(n, n_features, 99))
def test_get_coef():
"""Test getting linear coefficients (filters/patterns) from estimators."""
from sklearn.base import TransformerMixin, BaseEstimator
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Ridge, LinearRegression
lm = LinearModel()
assert (is_classifier(lm))
lm = LinearModel(Ridge())
assert (is_regressor(lm))
# Define a classifier, an invertible transformer and an non-invertible one.
class Clf(BaseEstimator):
def fit(self, X, y):
return self
class NoInv(TransformerMixin):
def fit(self, X, y):
return self
def transform(self, X):
return X
class Inv(NoInv):
def inverse_transform(self, X):
return X
X, y, A = _make_data(n_samples=2000, n_features=3, n_targets=1)
# I. Test inverse function
# Check that we retrieve the right number of inverse functions even if
# there are nested pipelines
good_estimators = [
(1, make_pipeline(Inv(), Clf())),
(2, make_pipeline(Inv(), Inv(), Clf())),
(3, make_pipeline(Inv(), make_pipeline(Inv(), Inv()), Clf())),
]
for expected_n, est in good_estimators:
est.fit(X, y)
assert (expected_n == len(_get_inverse_funcs(est)))
bad_estimators = [
Clf(), # no preprocessing
Inv(), # final estimator isn't classifier
make_pipeline(NoInv(), Clf()), # first step isn't invertible
make_pipeline(Inv(), make_pipeline(Inv(), NoInv()),
Clf()), # nested step isn't invertible
]
for est in bad_estimators:
est.fit(X, y)
invs = _get_inverse_funcs(est)
assert_equal(invs, list())
# II. Test get coef for simple estimator and pipelines
for clf in (lm, make_pipeline(StandardScaler(), lm)):
clf.fit(X, y)
# Retrieve final linear model
filters = get_coef(clf, 'filters_', False)
if hasattr(clf, 'steps'):
coefs = clf.steps[-1][-1].model.coef_
else:
coefs = clf.model.coef_
assert_array_equal(filters, coefs[0])
patterns = get_coef(clf, 'patterns_', False)
assert (filters[0] != patterns[0])
n_chans = X.shape[1]
assert_array_equal(filters.shape, patterns.shape, [n_chans, n_chans])
# Inverse transform linear model
filters_inv = get_coef(clf, 'filters_', True)
assert (filters[0] != filters_inv[0])
patterns_inv = get_coef(clf, 'patterns_', True)
assert (patterns[0] != patterns_inv[0])
# Check with search_light and combination of preprocessing ending with sl:
slider = SlidingEstimator(make_pipeline(StandardScaler(), lm))
X = np.transpose([X, -X], [1, 2, 0]) # invert X across 2 time samples
clfs = (make_pipeline(Scaler(None, scalings='mean'), slider), slider)
for clf in clfs:
clf.fit(X, y)
for inverse in (True, False):
patterns = get_coef(clf, 'patterns_', inverse)
filters = get_coef(clf, 'filters_', inverse)
assert_array_equal(filters.shape, patterns.shape, X.shape[1:])
# the two time samples get inverted patterns
assert_equal(patterns[0, 0], -patterns[0, 1])
for t in [0, 1]:
assert_array_equal(get_coef(clf.estimators_[t], 'filters_', False),
filters[:, t])
# Check patterns with more than 1 regressor
for n_features in [1, 5]:
for n_targets in [1, 3]:
X, Y, A = _make_data(n_samples=5000, n_features=5, n_targets=3)
lm = LinearModel(LinearRegression()).fit(X, Y)
assert_array_equal(lm.filters_.shape, lm.patterns_.shape)
assert_array_equal(lm.filters_.shape, [3, 5])
assert_array_almost_equal(A, lm.patterns_.T, decimal=2)
lm = LinearModel(Ridge(alpha=1)).fit(X, Y)
assert_array_almost_equal(A, lm.patterns_.T, decimal=2)
# Check can pass fitting parameters
lm.fit(X, Y, sample_weight=np.ones(len(Y)))
def test_get_coef():
"""Test getting linear coefficients (filters/patterns) from estimators."""
from sklearn.base import TransformerMixin, BaseEstimator
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn import svm
from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV
lm_classification = LinearModel()
assert (is_classifier(lm_classification))
lm_regression = LinearModel(Ridge())
assert (is_regressor(lm_regression))
parameters = {'kernel': ['linear'], 'C': [1, 10]}
lm_gs_classification = LinearModel(
GridSearchCV(svm.SVC(), parameters, cv=2, refit=True, n_jobs=1))
assert (is_classifier(lm_gs_classification))
lm_gs_regression = LinearModel(
GridSearchCV(svm.SVR(), parameters, cv=2, refit=True, n_jobs=1))
assert (is_regressor(lm_gs_regression))
# Define a classifier, an invertible transformer and an non-invertible one.
class Clf(BaseEstimator):
def fit(self, X, y):
return self
class NoInv(TransformerMixin):
def fit(self, X, y):
return self
def transform(self, X):
return X
class Inv(NoInv):
def inverse_transform(self, X):
return X
X, y, A = _make_data(n_samples=1000, n_features=3, n_targets=1)
# I. Test inverse function
# Check that we retrieve the right number of inverse functions even if
# there are nested pipelines
good_estimators = [
(1, make_pipeline(Inv(), Clf())),
(2, make_pipeline(Inv(), Inv(), Clf())),
(3, make_pipeline(Inv(), make_pipeline(Inv(), Inv()), Clf())),
]
for expected_n, est in good_estimators:
est.fit(X, y)
assert (expected_n == len(_get_inverse_funcs(est)))
bad_estimators = [
Clf(), # no preprocessing
Inv(), # final estimator isn't classifier
make_pipeline(NoInv(), Clf()), # first step isn't invertible
make_pipeline(Inv(), make_pipeline(Inv(), NoInv()),
Clf()), # nested step isn't invertible
]
for est in bad_estimators:
est.fit(X, y)
invs = _get_inverse_funcs(est)
assert_equal(invs, list())
# II. Test get coef for classification/regression estimators and pipelines
rng = np.random.RandomState(0)
for clf in (lm_regression, lm_gs_classification,
make_pipeline(StandardScaler(), lm_classification),
make_pipeline(StandardScaler(), lm_gs_regression)):
# generate some categorical/continuous data
# according to the type of estimator.
if is_classifier(clf):
n, n_features = 1000, 3
X = rng.rand(n, n_features)
y = np.arange(n) % 2
else:
X, y, A = _make_data(n_samples=1000, n_features=3, n_targets=1)
y = np.ravel(y)
clf.fit(X, y)
# Retrieve final linear model
filters = get_coef(clf, 'filters_', False)
if hasattr(clf, 'steps'):
if hasattr(clf.steps[-1][-1].model, 'best_estimator_'):
# Linear Model with GridSearchCV
coefs = clf.steps[-1][-1].model.best_estimator_.coef_
else:
# Standard Linear Model
coefs = clf.steps[-1][-1].model.coef_
else:
if hasattr(clf.model, 'best_estimator_'):
# Linear Model with GridSearchCV
coefs = clf.model.best_estimator_.coef_
else:
# Standard Linear Model
coefs = clf.model.coef_
if coefs.ndim == 2 and coefs.shape[0] == 1:
coefs = coefs[0]
assert_array_equal(filters, coefs)
patterns = get_coef(clf, 'patterns_', False)
assert (filters[0] != patterns[0])
n_chans = X.shape[1]
assert_array_equal(filters.shape, patterns.shape, [n_chans, n_chans])
# Inverse transform linear model
filters_inv = get_coef(clf, 'filters_', True)
assert (filters[0] != filters_inv[0])
patterns_inv = get_coef(clf, 'patterns_', True)
assert (patterns[0] != patterns_inv[0])
def test_linearmodel():
"""Test LinearModel class for computing filters and patterns."""
# check categorical target fit in standard linear model
from sklearn.linear_model import LinearRegression
rng = np.random.RandomState(0)
clf = LinearModel()
n, n_features = 20, 3
X = rng.rand(n, n_features)
y = np.arange(n) % 2
clf.fit(X, y)
assert_equal(clf.filters_.shape, (n_features, ))
assert_equal(clf.patterns_.shape, (n_features, ))
with pytest.raises(ValueError):
wrong_X = rng.rand(n, n_features, 99)
clf.fit(wrong_X, y)
# check categorical target fit in standard linear model with GridSearchCV
from sklearn import svm
from sklearn.model_selection import GridSearchCV
parameters = {'kernel': ['linear'], 'C': [1, 10]}
clf = LinearModel(
GridSearchCV(svm.SVC(), parameters, cv=2, refit=True, n_jobs=1))
clf.fit(X, y)
assert_equal(clf.filters_.shape, (n_features, ))
assert_equal(clf.patterns_.shape, (n_features, ))
with pytest.raises(ValueError):
wrong_X = rng.rand(n, n_features, 99)
clf.fit(wrong_X, y)
# check continuous target fit in standard linear model with GridSearchCV
n_targets = 1
Y = rng.rand(n, n_targets)
clf = LinearModel(
GridSearchCV(svm.SVR(), parameters, cv=2, refit=True, n_jobs=1))
clf.fit(X, y)
assert_equal(clf.filters_.shape, (n_features, ))
assert_equal(clf.patterns_.shape, (n_features, ))
with pytest.raises(ValueError):
wrong_y = rng.rand(n, n_features, 99)
clf.fit(X, wrong_y)
# check multi-target fit in standard linear model
n_targets = 5
Y = rng.rand(n, n_targets)
clf = LinearModel(LinearRegression())
clf.fit(X, Y)
assert_equal(clf.filters_.shape, (n_targets, n_features))
assert_equal(clf.patterns_.shape, (n_targets, n_features))
with pytest.raises(ValueError):
wrong_y = rng.rand(n, n_features, 99)
clf.fit(X, wrong_y)
def test_get_coef():
"""Test getting linear coefficients (filters/patterns) from estimators."""
from sklearn.base import TransformerMixin, BaseEstimator
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Ridge, LinearRegression
lm = LinearModel()
assert_true(is_classifier(lm))
lm = LinearModel(Ridge())
assert_true(is_regressor(lm))
# Define a classifier, an invertible transformer and an non-invertible one.
class Clf(BaseEstimator):
def fit(self, X, y):
return self
class NoInv(TransformerMixin):
def fit(self, X, y):
return self
def transform(self, X):
return X
class Inv(NoInv):
def inverse_transform(self, X):
return X
X, y, A = _make_data(n_samples=2000, n_features=3, n_targets=1)
# I. Test inverse function
# Check that we retrieve the right number of inverse functions even if
# there are nested pipelines
good_estimators = [
(1, make_pipeline(Inv(), Clf())),
(2, make_pipeline(Inv(), Inv(), Clf())),
(3, make_pipeline(Inv(), make_pipeline(Inv(), Inv()), Clf())),
]
for expected_n, est in good_estimators:
est.fit(X, y)
assert_true(expected_n == len(_get_inverse_funcs(est)))
bad_estimators = [
Clf(), # no preprocessing
Inv(), # final estimator isn't classifier
make_pipeline(NoInv(), Clf()), # first step isn't invertible
make_pipeline(Inv(), make_pipeline(
Inv(), NoInv()), Clf()), # nested step isn't invertible
]
for est in bad_estimators:
est.fit(X, y)
invs = _get_inverse_funcs(est)
assert_equal(invs, list())
# II. Test get coef for simple estimator and pipelines
for clf in (lm, make_pipeline(StandardScaler(), lm)):
clf.fit(X, y)
# Retrieve final linear model
filters = get_coef(clf, 'filters_', False)
if hasattr(clf, 'steps'):
coefs = clf.steps[-1][-1].model.coef_
else:
coefs = clf.model.coef_
assert_array_equal(filters, coefs[0])
patterns = get_coef(clf, 'patterns_', False)
assert_true(filters[0] != patterns[0])
n_chans = X.shape[1]
assert_array_equal(filters.shape, patterns.shape, [n_chans, n_chans])
# Inverse transform linear model
filters_inv = get_coef(clf, 'filters_', True)
assert_true(filters[0] != filters_inv[0])
patterns_inv = get_coef(clf, 'patterns_', True)
assert_true(patterns[0] != patterns_inv[0])
# Check with search_light and combination of preprocessing ending with sl:
slider = SlidingEstimator(make_pipeline(StandardScaler(), lm))
X = np.transpose([X, -X], [1, 2, 0]) # invert X across 2 time samples
clfs = (make_pipeline(Scaler(None, scalings='mean'), slider), slider)
for clf in clfs:
clf.fit(X, y)
for inverse in (True, False):
patterns = get_coef(clf, 'patterns_', inverse)
filters = get_coef(clf, 'filters_', inverse)
assert_array_equal(filters.shape, patterns.shape,
X.shape[1:])
# the two time samples get inverted patterns
assert_equal(patterns[0, 0], -patterns[0, 1])
for t in [0, 1]:
assert_array_equal(get_coef(clf.estimators_[t], 'filters_', False),
filters[:, t])
# Check patterns with more than 1 regressor
for n_features in [1, 5]:
for n_targets in [1, 3]:
X, Y, A = _make_data(n_samples=5000, n_features=5, n_targets=3)
lm = LinearModel(LinearRegression()).fit(X, Y)
assert_array_equal(lm.filters_.shape, lm.patterns_.shape)
assert_array_equal(lm.filters_.shape, [3, 5])
assert_array_almost_equal(A, lm.patterns_.T, decimal=2)
lm = LinearModel(Ridge(alpha=1)).fit(X, Y)
assert_array_almost_equal(A, lm.patterns_.T, decimal=2)
# Check can pass fitting parameters
lm.fit(X, Y, sample_weight=np.ones(len(Y)))
def test_get_coef():
"""Test getting linear coefficients (filters/patterns) from estimators."""
from sklearn.base import TransformerMixin, BaseEstimator
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn import svm
from sklearn.linear_model import Ridge, LinearRegression
from sklearn.model_selection import GridSearchCV
lm_classification = LinearModel()
assert (is_classifier(lm_classification))
lm_regression = LinearModel(Ridge())
assert (is_regressor(lm_regression))
parameters = {'kernel': ['linear'], 'C': [1, 10]}
lm_gs_classification = LinearModel(
GridSearchCV(svm.SVC(),
parameters,
cv=2,
refit=True,
iid=False,
n_jobs=1))
assert (is_classifier(lm_gs_classification))
lm_gs_regression = LinearModel(
GridSearchCV(svm.SVR(),
parameters,
cv=2,
refit=True,
iid=False,
n_jobs=1))
assert (is_regressor(lm_gs_regression))
# Define a classifier, an invertible transformer and an non-invertible one.
class Clf(BaseEstimator):
def fit(self, X, y):
return self
class NoInv(TransformerMixin):
def fit(self, X, y):
return self
def transform(self, X):
return X
class Inv(NoInv):
def inverse_transform(self, X):
return X
X, y, A = _make_data(n_samples=1000, n_features=3, n_targets=1)
# I. Test inverse function
# Check that we retrieve the right number of inverse functions even if
# there are nested pipelines
good_estimators = [
(1, make_pipeline(Inv(), Clf())),
(2, make_pipeline(Inv(), Inv(), Clf())),
(3, make_pipeline(Inv(), make_pipeline(Inv(), Inv()), Clf())),
]
for expected_n, est in good_estimators:
est.fit(X, y)
assert (expected_n == len(_get_inverse_funcs(est)))
bad_estimators = [
Clf(), # no preprocessing
Inv(), # final estimator isn't classifier
make_pipeline(NoInv(), Clf()), # first step isn't invertible
make_pipeline(Inv(), make_pipeline(Inv(), NoInv()),
Clf()), # nested step isn't invertible
]
for est in bad_estimators:
est.fit(X, y)
invs = _get_inverse_funcs(est)
assert_equal(invs, list())
# II. Test get coef for classification/regression estimators and pipelines
rng = np.random.RandomState(0)
for clf in (lm_regression, lm_gs_classification,
make_pipeline(StandardScaler(), lm_classification),
make_pipeline(StandardScaler(), lm_gs_regression)):
# generate some categorical/continuous data
# according to the type of estimator.
if is_classifier(clf):
n, n_features = 1000, 3
X = rng.rand(n, n_features)
y = np.arange(n) % 2
else:
X, y, A = _make_data(n_samples=1000, n_features=3, n_targets=1)
y = np.ravel(y)
clf.fit(X, y)
# Retrieve final linear model
filters = get_coef(clf, 'filters_', False)
if hasattr(clf, 'steps'):
if hasattr(clf.steps[-1][-1].model, 'best_estimator_'):
# Linear Model with GridSearchCV
coefs = clf.steps[-1][-1].model.best_estimator_.coef_
else:
# Standard Linear Model
coefs = clf.steps[-1][-1].model.coef_
else:
if hasattr(clf.model, 'best_estimator_'):
# Linear Model with GridSearchCV
coefs = clf.model.best_estimator_.coef_
else:
# Standard Linear Model
coefs = clf.model.coef_
if coefs.ndim == 2 and coefs.shape[0] == 1:
coefs = coefs[0]
assert_array_equal(filters, coefs)
patterns = get_coef(clf, 'patterns_', False)
assert (filters[0] != patterns[0])
n_chans = X.shape[1]
assert_array_equal(filters.shape, patterns.shape, [n_chans, n_chans])
# Inverse transform linear model
filters_inv = get_coef(clf, 'filters_', True)
assert (filters[0] != filters_inv[0])
patterns_inv = get_coef(clf, 'patterns_', True)
assert (patterns[0] != patterns_inv[0])
# Check with search_light and combination of preprocessing ending with sl:
slider = SlidingEstimator(make_pipeline(StandardScaler(), lm_regression))
X = np.transpose([X, -X], [1, 2, 0]) # invert X across 2 time samples
clfs = (make_pipeline(Scaler(None, scalings='mean'), slider), slider)
for clf in clfs:
clf.fit(X, y)
for inverse in (True, False):
patterns = get_coef(clf, 'patterns_', inverse)
filters = get_coef(clf, 'filters_', inverse)
assert_array_equal(filters.shape, patterns.shape, X.shape[1:])
# the two time samples get inverted patterns
assert_equal(patterns[0, 0], -patterns[0, 1])
for t in [0, 1]:
assert_array_equal(get_coef(clf.estimators_[t], 'filters_', False),
filters[:, t])
# Check patterns with more than 1 regressor
for n_features in [1, 5]:
for n_targets in [1, 3]:
X, Y, A = _make_data(n_samples=3000, n_features=5, n_targets=3)
lm = LinearModel(LinearRegression()).fit(X, Y)
assert_array_equal(lm.filters_.shape, lm.patterns_.shape)
assert_array_equal(lm.filters_.shape, [3, 5])
assert_array_almost_equal(A, lm.patterns_.T, decimal=2)
lm = LinearModel(Ridge(alpha=1)).fit(X, Y)
assert_array_almost_equal(A, lm.patterns_.T, decimal=2)
# Check can pass fitting parameters
lm.fit(X, Y, sample_weight=np.ones(len(Y)))
def test_get_coef():
"""Test the retrieval of linear coefficients (filters and patterns) from
simple and pipeline estimators.
"""
from sklearn.base import TransformerMixin, BaseEstimator
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
# Define a classifier, an invertible transformer and an non-invertible one.
class Clf(BaseEstimator):
def fit(self, X, y):
return self
class NoInv(TransformerMixin):
def fit(self, X, y):
return self
def transform(self, X):
return X
class Inv(NoInv):
def inverse_transform(self, X):
return X
np.random.RandomState(0)
n_samples, n_features = 20, 3
y = (np.arange(n_samples) % 2) * 2 - 1
w = np.random.randn(n_features, 1)
X = w.dot(y[np.newaxis, :]).T + np.random.randn(n_samples, n_features)
# I. Test inverse function
# Check that we retrieve the right number of inverse functions even if
# there are nested pipelines
good_estimators = [
(1, make_pipeline(Inv(), Clf())),
(2, make_pipeline(Inv(), Inv(), Clf())),
(3, make_pipeline(Inv(), make_pipeline(Inv(), Inv()), Clf())),
]
for expected_n, est in good_estimators:
est.fit(X, y)
assert_true(expected_n == len(_get_inverse_funcs(est)))
bad_estimators = [
Clf(), # no preprocessing
Inv(), # final estimator isn't classifier
make_pipeline(NoInv(), Clf()), # first step isn't invertible
make_pipeline(Inv(), make_pipeline(Inv(), NoInv()),
Clf()), # nested step isn't invertible
]
for est in bad_estimators:
est.fit(X, y)
invs = _get_inverse_funcs(est)
assert_equal(invs, list())
# II. Test get coef for simple estimator and pipelines
for clf in (LinearModel(), make_pipeline(StandardScaler(), LinearModel())):
clf.fit(X, y)
# Retrieve final linear model
filters = get_coef(clf, 'filters_', False)
if hasattr(clf, 'steps'):
coefs = clf.steps[-1][-1].model.coef_
else:
coefs = clf.model.coef_
assert_array_equal(filters, coefs[0])
patterns = get_coef(clf, 'patterns_', False)
assert_true(filters[0] != patterns[0])
n_chans = X.shape[1]
assert_array_equal(filters.shape, patterns.shape, [n_chans, n_chans])
# Inverse transform linear model
filters_inv = get_coef(clf, 'filters_', True)
assert_true(filters[0] != filters_inv[0])
patterns_inv = get_coef(clf, 'patterns_', True)
assert_true(patterns[0] != patterns_inv[0])
# Check patterns values
clf = make_pipeline(StandardScaler(), LinearModel(LinearRegression()))
clf.fit(X, y)
patterns = get_coef(clf, 'patterns_', True)
mean, std = X.mean(0), X.std(0)
X = (X - mean) / std
coef = np.linalg.pinv(X.T.dot(X)).dot(X.T.dot(y))
patterns_manual = np.cov(X.T).dot(coef)
assert_array_almost_equal(patterns, patterns_manual * std + mean)
# Check with search_light and combination of preprocessing ending with sl:
n_samples, n_features, n_times = 20, 3, 5
y = np.arange(n_samples) % 2
X = np.random.rand(n_samples, n_features, n_times)
slider = SlidingEstimator(make_pipeline(StandardScaler(), LinearModel()))
clfs = (make_pipeline(Scaler(None, scalings='mean'), slider), slider)
for clf in clfs:
clf.fit(X, y)
for inverse in (True, False):
patterns = get_coef(clf, 'patterns_', inverse)
filters = get_coef(clf, 'filters_', inverse)
assert_array_equal(filters.shape, patterns.shape,
[n_features, n_times])
for t in [0, 1]:
assert_array_equal(get_coef(clf.estimators_[t], 'filters_', False),
filters[:, t])
