330 lines
11 KiB
Python
330 lines
11 KiB
Python
"""
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Testing Recursive feature elimination
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"""
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import numpy as np
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from numpy.testing import assert_array_almost_equal, assert_array_equal
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from scipy import sparse
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from sklearn.feature_selection.rfe import RFE, RFECV
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from sklearn.datasets import load_iris, make_friedman1
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from sklearn.metrics import zero_one_loss
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from sklearn.svm import SVC, SVR
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.model_selection import cross_val_score
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from sklearn.utils import check_random_state
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from sklearn.utils.testing import ignore_warnings
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from sklearn.utils.testing import assert_greater, assert_equal, assert_true
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from sklearn.metrics import make_scorer
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from sklearn.metrics import get_scorer
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class MockClassifier(object):
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"""
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Dummy classifier to test recursive feature elimination
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"""
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def __init__(self, foo_param=0):
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self.foo_param = foo_param
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def fit(self, X, Y):
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assert_true(len(X) == len(Y))
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self.coef_ = np.ones(X.shape[1], dtype=np.float64)
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return self
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def predict(self, T):
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return T.shape[0]
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predict_proba = predict
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decision_function = predict
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transform = predict
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def score(self, X=None, Y=None):
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if self.foo_param > 1:
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score = 1.
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else:
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score = 0.
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return score
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def get_params(self, deep=True):
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return {'foo_param': self.foo_param}
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def set_params(self, **params):
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return self
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def test_rfe_features_importance():
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generator = check_random_state(0)
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iris = load_iris()
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X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
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y = iris.target
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clf = RandomForestClassifier(n_estimators=20,
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random_state=generator, max_depth=2)
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rfe = RFE(estimator=clf, n_features_to_select=4, step=0.1)
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rfe.fit(X, y)
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assert_equal(len(rfe.ranking_), X.shape[1])
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clf_svc = SVC(kernel="linear")
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rfe_svc = RFE(estimator=clf_svc, n_features_to_select=4, step=0.1)
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rfe_svc.fit(X, y)
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# Check if the supports are equal
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assert_array_equal(rfe.get_support(), rfe_svc.get_support())
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def test_rfe():
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generator = check_random_state(0)
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iris = load_iris()
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X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
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X_sparse = sparse.csr_matrix(X)
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y = iris.target
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# dense model
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clf = SVC(kernel="linear")
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rfe = RFE(estimator=clf, n_features_to_select=4, step=0.1)
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rfe.fit(X, y)
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X_r = rfe.transform(X)
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clf.fit(X_r, y)
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assert_equal(len(rfe.ranking_), X.shape[1])
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# sparse model
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clf_sparse = SVC(kernel="linear")
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rfe_sparse = RFE(estimator=clf_sparse, n_features_to_select=4, step=0.1)
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rfe_sparse.fit(X_sparse, y)
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X_r_sparse = rfe_sparse.transform(X_sparse)
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assert_equal(X_r.shape, iris.data.shape)
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assert_array_almost_equal(X_r[:10], iris.data[:10])
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assert_array_almost_equal(rfe.predict(X), clf.predict(iris.data))
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assert_equal(rfe.score(X, y), clf.score(iris.data, iris.target))
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assert_array_almost_equal(X_r, X_r_sparse.toarray())
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def test_rfe_mockclassifier():
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generator = check_random_state(0)
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iris = load_iris()
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X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
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y = iris.target
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# dense model
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clf = MockClassifier()
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rfe = RFE(estimator=clf, n_features_to_select=4, step=0.1)
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rfe.fit(X, y)
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X_r = rfe.transform(X)
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clf.fit(X_r, y)
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assert_equal(len(rfe.ranking_), X.shape[1])
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assert_equal(X_r.shape, iris.data.shape)
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def test_rfecv():
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generator = check_random_state(0)
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iris = load_iris()
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X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
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y = list(iris.target) # regression test: list should be supported
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# Test using the score function
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rfecv = RFECV(estimator=SVC(kernel="linear"), step=1, cv=5)
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rfecv.fit(X, y)
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# non-regression test for missing worst feature:
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assert_equal(len(rfecv.grid_scores_), X.shape[1])
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assert_equal(len(rfecv.ranking_), X.shape[1])
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X_r = rfecv.transform(X)
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# All the noisy variable were filtered out
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assert_array_equal(X_r, iris.data)
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# same in sparse
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rfecv_sparse = RFECV(estimator=SVC(kernel="linear"), step=1, cv=5)
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X_sparse = sparse.csr_matrix(X)
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rfecv_sparse.fit(X_sparse, y)
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X_r_sparse = rfecv_sparse.transform(X_sparse)
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assert_array_equal(X_r_sparse.toarray(), iris.data)
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# Test using a customized loss function
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scoring = make_scorer(zero_one_loss, greater_is_better=False)
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rfecv = RFECV(estimator=SVC(kernel="linear"), step=1, cv=5,
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scoring=scoring)
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ignore_warnings(rfecv.fit)(X, y)
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X_r = rfecv.transform(X)
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assert_array_equal(X_r, iris.data)
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# Test using a scorer
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scorer = get_scorer('accuracy')
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rfecv = RFECV(estimator=SVC(kernel="linear"), step=1, cv=5,
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scoring=scorer)
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rfecv.fit(X, y)
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X_r = rfecv.transform(X)
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assert_array_equal(X_r, iris.data)
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# Test fix on grid_scores
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def test_scorer(estimator, X, y):
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return 1.0
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rfecv = RFECV(estimator=SVC(kernel="linear"), step=1, cv=5,
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scoring=test_scorer)
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rfecv.fit(X, y)
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assert_array_equal(rfecv.grid_scores_, np.ones(len(rfecv.grid_scores_)))
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# Same as the first two tests, but with step=2
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rfecv = RFECV(estimator=SVC(kernel="linear"), step=2, cv=5)
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rfecv.fit(X, y)
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assert_equal(len(rfecv.grid_scores_), 6)
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assert_equal(len(rfecv.ranking_), X.shape[1])
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X_r = rfecv.transform(X)
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assert_array_equal(X_r, iris.data)
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rfecv_sparse = RFECV(estimator=SVC(kernel="linear"), step=2, cv=5)
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X_sparse = sparse.csr_matrix(X)
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rfecv_sparse.fit(X_sparse, y)
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X_r_sparse = rfecv_sparse.transform(X_sparse)
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assert_array_equal(X_r_sparse.toarray(), iris.data)
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# Verifying that steps < 1 don't blow up.
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rfecv_sparse = RFECV(estimator=SVC(kernel="linear"), step=.2, cv=5)
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X_sparse = sparse.csr_matrix(X)
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rfecv_sparse.fit(X_sparse, y)
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X_r_sparse = rfecv_sparse.transform(X_sparse)
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assert_array_equal(X_r_sparse.toarray(), iris.data)
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def test_rfecv_mockclassifier():
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generator = check_random_state(0)
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iris = load_iris()
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X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
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y = list(iris.target) # regression test: list should be supported
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# Test using the score function
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rfecv = RFECV(estimator=MockClassifier(), step=1, cv=5)
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rfecv.fit(X, y)
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# non-regression test for missing worst feature:
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assert_equal(len(rfecv.grid_scores_), X.shape[1])
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assert_equal(len(rfecv.ranking_), X.shape[1])
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def test_rfecv_verbose_output():
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# Check verbose=1 is producing an output.
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from sklearn.externals.six.moves import cStringIO as StringIO
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import sys
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sys.stdout = StringIO()
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generator = check_random_state(0)
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iris = load_iris()
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X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
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y = list(iris.target)
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rfecv = RFECV(estimator=SVC(kernel="linear"), step=1, cv=5, verbose=1)
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rfecv.fit(X, y)
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verbose_output = sys.stdout
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verbose_output.seek(0)
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assert_greater(len(verbose_output.readline()), 0)
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def test_rfe_estimator_tags():
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rfe = RFE(SVC(kernel='linear'))
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assert_equal(rfe._estimator_type, "classifier")
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# make sure that cross-validation is stratified
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iris = load_iris()
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score = cross_val_score(rfe, iris.data, iris.target)
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assert_greater(score.min(), .7)
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def test_rfe_min_step():
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n_features = 10
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X, y = make_friedman1(n_samples=50, n_features=n_features, random_state=0)
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n_samples, n_features = X.shape
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estimator = SVR(kernel="linear")
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# Test when floor(step * n_features) <= 0
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selector = RFE(estimator, step=0.01)
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sel = selector.fit(X, y)
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assert_equal(sel.support_.sum(), n_features // 2)
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# Test when step is between (0,1) and floor(step * n_features) > 0
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selector = RFE(estimator, step=0.20)
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sel = selector.fit(X, y)
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assert_equal(sel.support_.sum(), n_features // 2)
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# Test when step is an integer
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selector = RFE(estimator, step=5)
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sel = selector.fit(X, y)
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assert_equal(sel.support_.sum(), n_features // 2)
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def test_number_of_subsets_of_features():
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# In RFE, 'number_of_subsets_of_features'
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# = the number of iterations in '_fit'
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# = max(ranking_)
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# = 1 + (n_features + step - n_features_to_select - 1) // step
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# After optimization #4534, this number
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# = 1 + np.ceil((n_features - n_features_to_select) / float(step))
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# This test case is to test their equivalence, refer to #4534 and #3824
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def formula1(n_features, n_features_to_select, step):
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return 1 + ((n_features + step - n_features_to_select - 1) // step)
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def formula2(n_features, n_features_to_select, step):
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return 1 + np.ceil((n_features - n_features_to_select) / float(step))
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# RFE
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# Case 1, n_features - n_features_to_select is divisible by step
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# Case 2, n_features - n_features_to_select is not divisible by step
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n_features_list = [11, 11]
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n_features_to_select_list = [3, 3]
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step_list = [2, 3]
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for n_features, n_features_to_select, step in zip(
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n_features_list, n_features_to_select_list, step_list):
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generator = check_random_state(43)
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X = generator.normal(size=(100, n_features))
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y = generator.rand(100).round()
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rfe = RFE(estimator=SVC(kernel="linear"),
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n_features_to_select=n_features_to_select, step=step)
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rfe.fit(X, y)
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# this number also equals to the maximum of ranking_
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assert_equal(np.max(rfe.ranking_),
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formula1(n_features, n_features_to_select, step))
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assert_equal(np.max(rfe.ranking_),
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formula2(n_features, n_features_to_select, step))
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# In RFECV, 'fit' calls 'RFE._fit'
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# 'number_of_subsets_of_features' of RFE
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# = the size of 'grid_scores' of RFECV
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# = the number of iterations of the for loop before optimization #4534
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# RFECV, n_features_to_select = 1
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# Case 1, n_features - 1 is divisible by step
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# Case 2, n_features - 1 is not divisible by step
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n_features_to_select = 1
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n_features_list = [11, 10]
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step_list = [2, 2]
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for n_features, step in zip(n_features_list, step_list):
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generator = check_random_state(43)
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X = generator.normal(size=(100, n_features))
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y = generator.rand(100).round()
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rfecv = RFECV(estimator=SVC(kernel="linear"), step=step, cv=5)
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rfecv.fit(X, y)
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assert_equal(rfecv.grid_scores_.shape[0],
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formula1(n_features, n_features_to_select, step))
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assert_equal(rfecv.grid_scores_.shape[0],
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formula2(n_features, n_features_to_select, step))
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def test_rfe_cv_n_jobs():
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generator = check_random_state(0)
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iris = load_iris()
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X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
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y = iris.target
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rfecv = RFECV(estimator=SVC(kernel='linear'))
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rfecv.fit(X, y)
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rfecv_ranking = rfecv.ranking_
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rfecv_grid_scores = rfecv.grid_scores_
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rfecv.set_params(n_jobs=2)
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rfecv.fit(X, y)
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assert_array_almost_equal(rfecv.ranking_, rfecv_ranking)
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assert_array_almost_equal(rfecv.grid_scores_, rfecv_grid_scores)
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