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# Author: Jean-Remi King, <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
from numpy.testing import assert_array_equal, assert_equal
import pytest
from mne.utils import requires_version
from mne.decoding.search_light import SlidingEstimator, GeneralizingEstimator
from mne.decoding.transformer import Vectorizer
def make_data():
"""Make data."""
n_epochs, n_chan, n_time = 50, 32, 10
X = np.random.rand(n_epochs, n_chan, n_time)
y = np.arange(n_epochs) % 2
for ii in range(n_time):
coef = np.random.randn(n_chan)
X[y == 0, :, ii] += coef
X[y == 1, :, ii] -= coef
return X, y
@requires_version('sklearn', '0.17')
def test_search_light():
"""Test SlidingEstimator."""
from sklearn.linear_model import Ridge, LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.metrics import roc_auc_score, make_scorer
with pytest.warns(None): # NumPy module import
from sklearn.ensemble import BaggingClassifier
from sklearn.base import is_classifier
logreg = LogisticRegression(solver='liblinear', multi_class='ovr',
random_state=0)
X, y = make_data()
n_epochs, _, n_time = X.shape
# init
pytest.raises(ValueError, SlidingEstimator, 'foo')
sl = SlidingEstimator(Ridge())
assert (not is_classifier(sl))
sl = SlidingEstimator(LogisticRegression(solver='liblinear'))
assert (is_classifier(sl))
# fit
assert_equal(sl.__repr__()[:18], '<SlidingEstimator(')
sl.fit(X, y)
assert_equal(sl.__repr__()[-28:], ', fitted with 10 estimators>')
pytest.raises(ValueError, sl.fit, X[1:], y)
pytest.raises(ValueError, sl.fit, X[:, :, 0], y)
sl.fit(X, y, sample_weight=np.ones_like(y))
# transforms
pytest.raises(ValueError, sl.predict, X[:, :, :2])
y_pred = sl.predict(X)
assert (y_pred.dtype == int)
assert_array_equal(y_pred.shape, [n_epochs, n_time])
y_proba = sl.predict_proba(X)
assert (y_proba.dtype == float)
assert_array_equal(y_proba.shape, [n_epochs, n_time, 2])
# score
score = sl.score(X, y)
assert_array_equal(score.shape, [n_time])
assert (np.sum(np.abs(score)) != 0)
assert (score.dtype == float)
sl = SlidingEstimator(logreg)
assert_equal(sl.scoring, None)
# Scoring method
for scoring in ['foo', 999]:
sl = SlidingEstimator(logreg, scoring=scoring)
sl.fit(X, y)
pytest.raises((ValueError, TypeError), sl.score, X, y)
# Check sklearn's roc_auc fix: scikit-learn/scikit-learn#6874
# -- 3 class problem
sl = SlidingEstimator(logreg, scoring='roc_auc')
y = np.arange(len(X)) % 3
sl.fit(X, y)
pytest.raises(ValueError, sl.score, X, y)
# -- 2 class problem not in [0, 1]
y = np.arange(len(X)) % 2 + 1
sl.fit(X, y)
score = sl.score(X, y)
assert_array_equal(score, [roc_auc_score(y - 1, _y_pred - 1)
for _y_pred in sl.decision_function(X).T])
y = np.arange(len(X)) % 2
# Cannot pass a metric as a scoring parameter
sl1 = SlidingEstimator(logreg, scoring=roc_auc_score)
sl1.fit(X, y)
pytest.raises(ValueError, sl1.score, X, y)
# Now use string as scoring
sl1 = SlidingEstimator(logreg, scoring='roc_auc')
sl1.fit(X, y)
rng = np.random.RandomState(0)
X = rng.randn(*X.shape) # randomize X to avoid AUCs in [0, 1]
score_sl = sl1.score(X, y)
assert_array_equal(score_sl.shape, [n_time])
assert (score_sl.dtype == float)
# Check that scoring was applied adequately
scoring = make_scorer(roc_auc_score, needs_threshold=True)
score_manual = [scoring(est, x, y) for est, x in zip(
sl1.estimators_, X.transpose(2, 0, 1))]
assert_array_equal(score_manual, score_sl)
# n_jobs
sl = SlidingEstimator(logreg, n_jobs=1, scoring='roc_auc')
score_1job = sl.fit(X, y).score(X, y)
sl.n_jobs = 2
score_njobs = sl.fit(X, y).score(X, y)
assert_array_equal(score_1job, score_njobs)
sl.predict(X)
# n_jobs > n_estimators
sl.fit(X[..., [0]], y)
sl.predict(X[..., [0]])
# pipeline
class _LogRegTransformer(LogisticRegression):
# XXX needs transformer in pipeline to get first proba only
def __init__(self):
super(_LogRegTransformer, self).__init__()
self.multi_class = 'auto'
self.random_state = 0
self.solver = 'liblinear'
def transform(self, X):
return super(_LogRegTransformer, self).predict_proba(X)[..., 1]
pipe = make_pipeline(SlidingEstimator(_LogRegTransformer()),
logreg)
pipe.fit(X, y)
pipe.predict(X)
# n-dimensional feature space
X = np.random.rand(10, 3, 4, 2)
y = np.arange(10) % 2
y_preds = list()
for n_jobs in [1, 2]:
pipe = SlidingEstimator(
make_pipeline(Vectorizer(), logreg), n_jobs=n_jobs)
y_preds.append(pipe.fit(X, y).predict(X))
features_shape = pipe.estimators_[0].steps[0][1].features_shape_
assert_array_equal(features_shape, [3, 4])
assert_array_equal(y_preds[0], y_preds[1])
# Bagging classifiers
X = np.random.rand(10, 3, 4)
for n_jobs in (1, 2):
pipe = SlidingEstimator(BaggingClassifier(None, 2), n_jobs=n_jobs)
pipe.fit(X, y)
pipe.score(X, y)
assert (isinstance(pipe.estimators_[0], BaggingClassifier))
@requires_version('sklearn', '0.17')
def test_generalization_light():
"""Test GeneralizingEstimator."""
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
logreg = LogisticRegression(solver='liblinear', multi_class='ovr',
random_state=0)
X, y = make_data()
n_epochs, _, n_time = X.shape
# fit
gl = GeneralizingEstimator(logreg)
assert_equal(repr(gl)[:23], '<GeneralizingEstimator(')
gl.fit(X, y)
gl.fit(X, y, sample_weight=np.ones_like(y))
assert_equal(gl.__repr__()[-28:], ', fitted with 10 estimators>')
# transforms
y_pred = gl.predict(X)
assert_array_equal(y_pred.shape, [n_epochs, n_time, n_time])
assert (y_pred.dtype == int)
y_proba = gl.predict_proba(X)
assert (y_proba.dtype == float)
assert_array_equal(y_proba.shape, [n_epochs, n_time, n_time, 2])
# transform to different datasize
y_pred = gl.predict(X[:, :, :2])
assert_array_equal(y_pred.shape, [n_epochs, n_time, 2])
# score
score = gl.score(X[:, :, :3], y)
assert_array_equal(score.shape, [n_time, 3])
assert (np.sum(np.abs(score)) != 0)
assert (score.dtype == float)
gl = GeneralizingEstimator(logreg, scoring='roc_auc')
gl.fit(X, y)
score = gl.score(X, y)
auc = roc_auc_score(y, gl.estimators_[0].predict_proba(X[..., 0])[..., 1])
assert_equal(score[0, 0], auc)
for scoring in ['foo', 999]:
gl = GeneralizingEstimator(logreg, scoring=scoring)
gl.fit(X, y)
pytest.raises((ValueError, TypeError), gl.score, X, y)
# Check sklearn's roc_auc fix: scikit-learn/scikit-learn#6874
# -- 3 class problem
gl = GeneralizingEstimator(logreg, scoring='roc_auc')
y = np.arange(len(X)) % 3
gl.fit(X, y)
pytest.raises(ValueError, gl.score, X, y)
# -- 2 class problem not in [0, 1]
y = np.arange(len(X)) % 2 + 1
gl.fit(X, y)
score = gl.score(X, y)
manual_score = [[roc_auc_score(y - 1, _y_pred) for _y_pred in _y_preds]
for _y_preds in gl.decision_function(X).transpose(1, 2, 0)]
assert_array_equal(score, manual_score)
# n_jobs
gl = GeneralizingEstimator(logreg, n_jobs=2)
gl.fit(X, y)
y_pred = gl.predict(X)
assert_array_equal(y_pred.shape, [n_epochs, n_time, n_time])
score = gl.score(X, y)
assert_array_equal(score.shape, [n_time, n_time])
# n_jobs > n_estimators
gl.fit(X[..., [0]], y)
gl.predict(X[..., [0]])
# n-dimensional feature space
X = np.random.rand(10, 3, 4, 2)
y = np.arange(10) % 2
y_preds = list()
for n_jobs in [1, 2]:
pipe = GeneralizingEstimator(
make_pipeline(Vectorizer(), logreg), n_jobs=n_jobs)
y_preds.append(pipe.fit(X, y).predict(X))
features_shape = pipe.estimators_[0].steps[0][1].features_shape_
assert_array_equal(features_shape, [3, 4])
assert_array_equal(y_preds[0], y_preds[1])
@requires_version('sklearn', '0.17')
def test_cross_val_predict():
"""Test cross_val_predict with predict_proba."""
from sklearn.linear_model import LinearRegression
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.base import BaseEstimator, clone
from sklearn.model_selection import cross_val_predict
rng = np.random.RandomState(42)
X = rng.randn(10, 1, 3)
y = rng.randint(0, 2, 10)
estimator = SlidingEstimator(LinearRegression())
cross_val_predict(estimator, X, y, cv=2)
class Classifier(BaseEstimator):
"""Moch class that does not have classes_ attribute."""
def __init__(self):
self.base_estimator = LinearDiscriminantAnalysis()
def fit(self, X, y):
self.estimator_ = clone(self.base_estimator).fit(X, y)
return self
def predict_proba(self, X):
return self.estimator_.predict_proba(X)
with pytest.raises(AttributeError, match="classes_ attribute"):
estimator = SlidingEstimator(Classifier())
cross_val_predict(estimator, X, y, method='predict_proba', cv=2)
estimator = SlidingEstimator(LinearDiscriminantAnalysis())
cross_val_predict(estimator, X, y, method='predict_proba', cv=2)
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