projektAI/venv/Lib/site-packages/sklearn/feature_selection/tests/test_feature_select.py

"""
Todo: cross-check the F-value with stats model
"""
import itertools
import warnings
import numpy as np
from scipy import stats, sparse

import pytest

from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_warns
from sklearn.utils._testing import ignore_warnings
from sklearn.utils._testing import assert_warns_message
from sklearn.utils import safe_mask

from sklearn.datasets import make_classification, make_regression
from sklearn.feature_selection import (
    chi2, f_classif, f_oneway, f_regression, mutual_info_classif,
    mutual_info_regression, SelectPercentile, SelectKBest, SelectFpr,
    SelectFdr, SelectFwe, GenericUnivariateSelect)


##############################################################################
# Test the score functions

def test_f_oneway_vs_scipy_stats():
    # Test that our f_oneway gives the same result as scipy.stats
    rng = np.random.RandomState(0)
    X1 = rng.randn(10, 3)
    X2 = 1 + rng.randn(10, 3)
    f, pv = stats.f_oneway(X1, X2)
    f2, pv2 = f_oneway(X1, X2)
    assert np.allclose(f, f2)
    assert np.allclose(pv, pv2)


def test_f_oneway_ints():
    # Smoke test f_oneway on integers: that it does raise casting errors
    # with recent numpys
    rng = np.random.RandomState(0)
    X = rng.randint(10, size=(10, 10))
    y = np.arange(10)
    fint, pint = f_oneway(X, y)

    # test that is gives the same result as with float
    f, p = f_oneway(X.astype(float), y)
    assert_array_almost_equal(f, fint, decimal=4)
    assert_array_almost_equal(p, pint, decimal=4)


def test_f_classif():
    # Test whether the F test yields meaningful results
    # on a simple simulated classification problem
    X, y = make_classification(n_samples=200, n_features=20,
                               n_informative=3, n_redundant=2,
                               n_repeated=0, n_classes=8,
                               n_clusters_per_class=1, flip_y=0.0,
                               class_sep=10, shuffle=False, random_state=0)

    F, pv = f_classif(X, y)
    F_sparse, pv_sparse = f_classif(sparse.csr_matrix(X), y)
    assert (F > 0).all()
    assert (pv > 0).all()
    assert (pv < 1).all()
    assert (pv[:5] < 0.05).all()
    assert (pv[5:] > 1.e-4).all()
    assert_array_almost_equal(F_sparse, F)
    assert_array_almost_equal(pv_sparse, pv)


def test_f_regression():
    # Test whether the F test yields meaningful results
    # on a simple simulated regression problem
    X, y = make_regression(n_samples=200, n_features=20, n_informative=5,
                           shuffle=False, random_state=0)

    F, pv = f_regression(X, y)
    assert (F > 0).all()
    assert (pv > 0).all()
    assert (pv < 1).all()
    assert (pv[:5] < 0.05).all()
    assert (pv[5:] > 1.e-4).all()

    # with centering, compare with sparse
    F, pv = f_regression(X, y, center=True)
    F_sparse, pv_sparse = f_regression(sparse.csr_matrix(X), y, center=True)
    assert_array_almost_equal(F_sparse, F)
    assert_array_almost_equal(pv_sparse, pv)

    # again without centering, compare with sparse
    F, pv = f_regression(X, y, center=False)
    F_sparse, pv_sparse = f_regression(sparse.csr_matrix(X), y, center=False)
    assert_array_almost_equal(F_sparse, F)
    assert_array_almost_equal(pv_sparse, pv)


def test_f_regression_input_dtype():
    # Test whether f_regression returns the same value
    # for any numeric data_type
    rng = np.random.RandomState(0)
    X = rng.rand(10, 20)
    y = np.arange(10).astype(int)

    F1, pv1 = f_regression(X, y)
    F2, pv2 = f_regression(X, y.astype(float))
    assert_array_almost_equal(F1, F2, 5)
    assert_array_almost_equal(pv1, pv2, 5)


def test_f_regression_center():
    # Test whether f_regression preserves dof according to 'center' argument
    # We use two centered variates so we have a simple relationship between
    # F-score with variates centering and F-score without variates centering.
    # Create toy example
    X = np.arange(-5, 6).reshape(-1, 1)  # X has zero mean
    n_samples = X.size
    Y = np.ones(n_samples)
    Y[::2] *= -1.
    Y[0] = 0.  # have Y mean being null

    F1, _ = f_regression(X, Y, center=True)
    F2, _ = f_regression(X, Y, center=False)
    assert_array_almost_equal(F1 * (n_samples - 1.) / (n_samples - 2.), F2)
    assert_almost_equal(F2[0], 0.232558139)  # value from statsmodels OLS


def test_f_classif_multi_class():
    # Test whether the F test yields meaningful results
    # on a simple simulated classification problem
    X, y = make_classification(n_samples=200, n_features=20,
                               n_informative=3, n_redundant=2,
                               n_repeated=0, n_classes=8,
                               n_clusters_per_class=1, flip_y=0.0,
                               class_sep=10, shuffle=False, random_state=0)

    F, pv = f_classif(X, y)
    assert (F > 0).all()
    assert (pv > 0).all()
    assert (pv < 1).all()
    assert (pv[:5] < 0.05).all()
    assert (pv[5:] > 1.e-4).all()


def test_select_percentile_classif():
    # Test whether the relative univariate feature selection
    # gets the correct items in a simple classification problem
    # with the percentile heuristic
    X, y = make_classification(n_samples=200, n_features=20,
                               n_informative=3, n_redundant=2,
                               n_repeated=0, n_classes=8,
                               n_clusters_per_class=1, flip_y=0.0,
                               class_sep=10, shuffle=False, random_state=0)

    univariate_filter = SelectPercentile(f_classif, percentile=25)
    X_r = univariate_filter.fit(X, y).transform(X)
    X_r2 = GenericUnivariateSelect(f_classif, mode='percentile',
                                   param=25).fit(X, y).transform(X)
    assert_array_equal(X_r, X_r2)
    support = univariate_filter.get_support()
    gtruth = np.zeros(20)
    gtruth[:5] = 1
    assert_array_equal(support, gtruth)


def test_select_percentile_classif_sparse():
    # Test whether the relative univariate feature selection
    # gets the correct items in a simple classification problem
    # with the percentile heuristic
    X, y = make_classification(n_samples=200, n_features=20,
                               n_informative=3, n_redundant=2,
                               n_repeated=0, n_classes=8,
                               n_clusters_per_class=1, flip_y=0.0,
                               class_sep=10, shuffle=False, random_state=0)
    X = sparse.csr_matrix(X)
    univariate_filter = SelectPercentile(f_classif, percentile=25)
    X_r = univariate_filter.fit(X, y).transform(X)
    X_r2 = GenericUnivariateSelect(f_classif, mode='percentile',
                                   param=25).fit(X, y).transform(X)
    assert_array_equal(X_r.toarray(), X_r2.toarray())
    support = univariate_filter.get_support()
    gtruth = np.zeros(20)
    gtruth[:5] = 1
    assert_array_equal(support, gtruth)

    X_r2inv = univariate_filter.inverse_transform(X_r2)
    assert sparse.issparse(X_r2inv)
    support_mask = safe_mask(X_r2inv, support)
    assert X_r2inv.shape == X.shape
    assert_array_equal(X_r2inv[:, support_mask].toarray(), X_r.toarray())
    # Check other columns are empty
    assert X_r2inv.getnnz() == X_r.getnnz()


##############################################################################
# Test univariate selection in classification settings

def test_select_kbest_classif():
    # Test whether the relative univariate feature selection
    # gets the correct items in a simple classification problem
    # with the k best heuristic
    X, y = make_classification(n_samples=200, n_features=20,
                               n_informative=3, n_redundant=2,
                               n_repeated=0, n_classes=8,
                               n_clusters_per_class=1, flip_y=0.0,
                               class_sep=10, shuffle=False, random_state=0)

    univariate_filter = SelectKBest(f_classif, k=5)
    X_r = univariate_filter.fit(X, y).transform(X)
    X_r2 = GenericUnivariateSelect(
        f_classif, mode='k_best', param=5).fit(X, y).transform(X)
    assert_array_equal(X_r, X_r2)
    support = univariate_filter.get_support()
    gtruth = np.zeros(20)
    gtruth[:5] = 1
    assert_array_equal(support, gtruth)


def test_select_kbest_all():
    # Test whether k="all" correctly returns all features.
    X, y = make_classification(n_samples=20, n_features=10,
                               shuffle=False, random_state=0)

    univariate_filter = SelectKBest(f_classif, k='all')
    X_r = univariate_filter.fit(X, y).transform(X)
    assert_array_equal(X, X_r)


def test_select_kbest_zero():
    # Test whether k=0 correctly returns no features.
    X, y = make_classification(n_samples=20, n_features=10,
                               shuffle=False, random_state=0)

    univariate_filter = SelectKBest(f_classif, k=0)
    univariate_filter.fit(X, y)
    support = univariate_filter.get_support()
    gtruth = np.zeros(10, dtype=bool)
    assert_array_equal(support, gtruth)
    X_selected = assert_warns_message(UserWarning, 'No features were selected',
                                      univariate_filter.transform, X)
    assert X_selected.shape == (20, 0)


def test_select_heuristics_classif():
    # Test whether the relative univariate feature selection
    # gets the correct items in a simple classification problem
    # with the fdr, fwe and fpr heuristics
    X, y = make_classification(n_samples=200, n_features=20,
                               n_informative=3, n_redundant=2,
                               n_repeated=0, n_classes=8,
                               n_clusters_per_class=1, flip_y=0.0,
                               class_sep=10, shuffle=False, random_state=0)

    univariate_filter = SelectFwe(f_classif, alpha=0.01)
    X_r = univariate_filter.fit(X, y).transform(X)
    gtruth = np.zeros(20)
    gtruth[:5] = 1
    for mode in ['fdr', 'fpr', 'fwe']:
        X_r2 = GenericUnivariateSelect(
            f_classif, mode=mode, param=0.01).fit(X, y).transform(X)
        assert_array_equal(X_r, X_r2)
        support = univariate_filter.get_support()
        assert_array_almost_equal(support, gtruth)


##############################################################################
# Test univariate selection in regression settings


def assert_best_scores_kept(score_filter):
    scores = score_filter.scores_
    support = score_filter.get_support()
    assert_array_almost_equal(np.sort(scores[support]),
                              np.sort(scores)[-support.sum():])


def test_select_percentile_regression():
    # Test whether the relative univariate feature selection
    # gets the correct items in a simple regression problem
    # with the percentile heuristic
    X, y = make_regression(n_samples=200, n_features=20,
                           n_informative=5, shuffle=False, random_state=0)

    univariate_filter = SelectPercentile(f_regression, percentile=25)
    X_r = univariate_filter.fit(X, y).transform(X)
    assert_best_scores_kept(univariate_filter)
    X_r2 = GenericUnivariateSelect(
        f_regression, mode='percentile', param=25).fit(X, y).transform(X)
    assert_array_equal(X_r, X_r2)
    support = univariate_filter.get_support()
    gtruth = np.zeros(20)
    gtruth[:5] = 1
    assert_array_equal(support, gtruth)
    X_2 = X.copy()
    X_2[:, np.logical_not(support)] = 0
    assert_array_equal(X_2, univariate_filter.inverse_transform(X_r))
    # Check inverse_transform respects dtype
    assert_array_equal(X_2.astype(bool),
                       univariate_filter.inverse_transform(X_r.astype(bool)))


def test_select_percentile_regression_full():
    # Test whether the relative univariate feature selection
    # selects all features when '100%' is asked.
    X, y = make_regression(n_samples=200, n_features=20,
                           n_informative=5, shuffle=False, random_state=0)

    univariate_filter = SelectPercentile(f_regression, percentile=100)
    X_r = univariate_filter.fit(X, y).transform(X)
    assert_best_scores_kept(univariate_filter)
    X_r2 = GenericUnivariateSelect(
        f_regression, mode='percentile', param=100).fit(X, y).transform(X)
    assert_array_equal(X_r, X_r2)
    support = univariate_filter.get_support()
    gtruth = np.ones(20)
    assert_array_equal(support, gtruth)


def test_invalid_percentile():
    X, y = make_regression(n_samples=10, n_features=20,
                           n_informative=2, shuffle=False, random_state=0)

    with pytest.raises(ValueError):
        SelectPercentile(percentile=-1).fit(X, y)
    with pytest.raises(ValueError):
        SelectPercentile(percentile=101).fit(X, y)
    with pytest.raises(ValueError):
        GenericUnivariateSelect(mode='percentile', param=-1).fit(X, y)
    with pytest.raises(ValueError):
        GenericUnivariateSelect(mode='percentile', param=101).fit(X, y)


def test_select_kbest_regression():
    # Test whether the relative univariate feature selection
    # gets the correct items in a simple regression problem
    # with the k best heuristic
    X, y = make_regression(n_samples=200, n_features=20, n_informative=5,
                           shuffle=False, random_state=0, noise=10)

    univariate_filter = SelectKBest(f_regression, k=5)
    X_r = univariate_filter.fit(X, y).transform(X)
    assert_best_scores_kept(univariate_filter)
    X_r2 = GenericUnivariateSelect(
        f_regression, mode='k_best', param=5).fit(X, y).transform(X)
    assert_array_equal(X_r, X_r2)
    support = univariate_filter.get_support()
    gtruth = np.zeros(20)
    gtruth[:5] = 1
    assert_array_equal(support, gtruth)


def test_select_heuristics_regression():
    # Test whether the relative univariate feature selection
    # gets the correct items in a simple regression problem
    # with the fpr, fdr or fwe heuristics
    X, y = make_regression(n_samples=200, n_features=20, n_informative=5,
                           shuffle=False, random_state=0, noise=10)

    univariate_filter = SelectFpr(f_regression, alpha=0.01)
    X_r = univariate_filter.fit(X, y).transform(X)
    gtruth = np.zeros(20)
    gtruth[:5] = 1
    for mode in ['fdr', 'fpr', 'fwe']:
        X_r2 = GenericUnivariateSelect(
            f_regression, mode=mode, param=0.01).fit(X, y).transform(X)
        assert_array_equal(X_r, X_r2)
        support = univariate_filter.get_support()
        assert_array_equal(support[:5], np.ones((5, ), dtype=bool))
        assert np.sum(support[5:] == 1) < 3


def test_boundary_case_ch2():
    # Test boundary case, and always aim to select 1 feature.
    X = np.array([[10, 20], [20, 20], [20, 30]])
    y = np.array([[1], [0], [0]])
    scores, pvalues = chi2(X, y)
    assert_array_almost_equal(scores, np.array([4., 0.71428571]))
    assert_array_almost_equal(pvalues, np.array([0.04550026, 0.39802472]))

    filter_fdr = SelectFdr(chi2, alpha=0.1)
    filter_fdr.fit(X, y)
    support_fdr = filter_fdr.get_support()
    assert_array_equal(support_fdr, np.array([True, False]))

    filter_kbest = SelectKBest(chi2, k=1)
    filter_kbest.fit(X, y)
    support_kbest = filter_kbest.get_support()
    assert_array_equal(support_kbest, np.array([True, False]))

    filter_percentile = SelectPercentile(chi2, percentile=50)
    filter_percentile.fit(X, y)
    support_percentile = filter_percentile.get_support()
    assert_array_equal(support_percentile, np.array([True, False]))

    filter_fpr = SelectFpr(chi2, alpha=0.1)
    filter_fpr.fit(X, y)
    support_fpr = filter_fpr.get_support()
    assert_array_equal(support_fpr, np.array([True, False]))

    filter_fwe = SelectFwe(chi2, alpha=0.1)
    filter_fwe.fit(X, y)
    support_fwe = filter_fwe.get_support()
    assert_array_equal(support_fwe, np.array([True, False]))


@pytest.mark.parametrize("alpha", [0.001, 0.01, 0.1])
@pytest.mark.parametrize("n_informative", [1, 5, 10])
def test_select_fdr_regression(alpha, n_informative):
    # Test that fdr heuristic actually has low FDR.
    def single_fdr(alpha, n_informative, random_state):
        X, y = make_regression(n_samples=150, n_features=20,
                               n_informative=n_informative, shuffle=False,
                               random_state=random_state, noise=10)

        with warnings.catch_warnings(record=True):
            # Warnings can be raised when no features are selected
            # (low alpha or very noisy data)
            univariate_filter = SelectFdr(f_regression, alpha=alpha)
            X_r = univariate_filter.fit(X, y).transform(X)
            X_r2 = GenericUnivariateSelect(
                f_regression, mode='fdr', param=alpha).fit(X, y).transform(X)

        assert_array_equal(X_r, X_r2)
        support = univariate_filter.get_support()
        num_false_positives = np.sum(support[n_informative:] == 1)
        num_true_positives = np.sum(support[:n_informative] == 1)

        if num_false_positives == 0:
            return 0.
        false_discovery_rate = (num_false_positives /
                                (num_true_positives + num_false_positives))
        return false_discovery_rate

    # As per Benjamini-Hochberg, the expected false discovery rate
    # should be lower than alpha:
    # FDR = E(FP / (TP + FP)) <= alpha
    false_discovery_rate = np.mean([single_fdr(alpha, n_informative,
                                               random_state) for
                                    random_state in range(100)])
    assert alpha >= false_discovery_rate

    # Make sure that the empirical false discovery rate increases
    # with alpha:
    if false_discovery_rate != 0:
        assert false_discovery_rate > alpha / 10


def test_select_fwe_regression():
    # Test whether the relative univariate feature selection
    # gets the correct items in a simple regression problem
    # with the fwe heuristic
    X, y = make_regression(n_samples=200, n_features=20,
                           n_informative=5, shuffle=False, random_state=0)

    univariate_filter = SelectFwe(f_regression, alpha=0.01)
    X_r = univariate_filter.fit(X, y).transform(X)
    X_r2 = GenericUnivariateSelect(
        f_regression, mode='fwe', param=0.01).fit(X, y).transform(X)
    assert_array_equal(X_r, X_r2)
    support = univariate_filter.get_support()
    gtruth = np.zeros(20)
    gtruth[:5] = 1
    assert_array_equal(support[:5], np.ones((5, ), dtype=bool))
    assert np.sum(support[5:] == 1) < 2


def test_selectkbest_tiebreaking():
    # Test whether SelectKBest actually selects k features in case of ties.
    # Prior to 0.11, SelectKBest would return more features than requested.
    Xs = [[0, 1, 1], [0, 0, 1], [1, 0, 0], [1, 1, 0]]
    y = [1]
    dummy_score = lambda X, y: (X[0], X[0])
    for X in Xs:
        sel = SelectKBest(dummy_score, k=1)
        X1 = ignore_warnings(sel.fit_transform)([X], y)
        assert X1.shape[1] == 1
        assert_best_scores_kept(sel)

        sel = SelectKBest(dummy_score, k=2)
        X2 = ignore_warnings(sel.fit_transform)([X], y)
        assert X2.shape[1] == 2
        assert_best_scores_kept(sel)


def test_selectpercentile_tiebreaking():
    # Test if SelectPercentile selects the right n_features in case of ties.
    Xs = [[0, 1, 1], [0, 0, 1], [1, 0, 0], [1, 1, 0]]
    y = [1]
    dummy_score = lambda X, y: (X[0], X[0])
    for X in Xs:
        sel = SelectPercentile(dummy_score, percentile=34)
        X1 = ignore_warnings(sel.fit_transform)([X], y)
        assert X1.shape[1] == 1
        assert_best_scores_kept(sel)

        sel = SelectPercentile(dummy_score, percentile=67)
        X2 = ignore_warnings(sel.fit_transform)([X], y)
        assert X2.shape[1] == 2
        assert_best_scores_kept(sel)


def test_tied_pvalues():
    # Test whether k-best and percentiles work with tied pvalues from chi2.
    # chi2 will return the same p-values for the following features, but it
    # will return different scores.
    X0 = np.array([[10000, 9999, 9998], [1, 1, 1]])
    y = [0, 1]

    for perm in itertools.permutations((0, 1, 2)):
        X = X0[:, perm]
        Xt = SelectKBest(chi2, k=2).fit_transform(X, y)
        assert Xt.shape == (2, 2)
        assert 9998 not in Xt

        Xt = SelectPercentile(chi2, percentile=67).fit_transform(X, y)
        assert Xt.shape == (2, 2)
        assert 9998 not in Xt


def test_scorefunc_multilabel():
    # Test whether k-best and percentiles works with multilabels with chi2.

    X = np.array([[10000, 9999, 0], [100, 9999, 0], [1000, 99, 0]])
    y = [[1, 1], [0, 1], [1, 0]]

    Xt = SelectKBest(chi2, k=2).fit_transform(X, y)
    assert Xt.shape == (3, 2)
    assert 0 not in Xt

    Xt = SelectPercentile(chi2, percentile=67).fit_transform(X, y)
    assert Xt.shape == (3, 2)
    assert 0 not in Xt


def test_tied_scores():
    # Test for stable sorting in k-best with tied scores.
    X_train = np.array([[0, 0, 0], [1, 1, 1]])
    y_train = [0, 1]

    for n_features in [1, 2, 3]:
        sel = SelectKBest(chi2, k=n_features).fit(X_train, y_train)
        X_test = sel.transform([[0, 1, 2]])
        assert_array_equal(X_test[0], np.arange(3)[-n_features:])


def test_nans():
    # Assert that SelectKBest and SelectPercentile can handle NaNs.
    # First feature has zero variance to confuse f_classif (ANOVA) and
    # make it return a NaN.
    X = [[0, 1, 0], [0, -1, -1], [0, .5, .5]]
    y = [1, 0, 1]

    for select in (SelectKBest(f_classif, k=2),
                   SelectPercentile(f_classif, percentile=67)):
        ignore_warnings(select.fit)(X, y)
        assert_array_equal(select.get_support(indices=True), np.array([1, 2]))


def test_score_func_error():
    X = [[0, 1, 0], [0, -1, -1], [0, .5, .5]]
    y = [1, 0, 1]

    for SelectFeatures in [SelectKBest, SelectPercentile, SelectFwe,
                           SelectFdr, SelectFpr, GenericUnivariateSelect]:
        with pytest.raises(TypeError):
            SelectFeatures(score_func=10).fit(X, y)


def test_invalid_k():
    X = [[0, 1, 0], [0, -1, -1], [0, .5, .5]]
    y = [1, 0, 1]

    with pytest.raises(ValueError):
        SelectKBest(k=-1).fit(X, y)
    with pytest.raises(ValueError):
        SelectKBest(k=4).fit(X, y)
    with pytest.raises(ValueError):
        GenericUnivariateSelect(mode='k_best', param=-1).fit(X, y)
    with pytest.raises(ValueError):
        GenericUnivariateSelect(mode='k_best', param=4).fit(X, y)


def test_f_classif_constant_feature():
    # Test that f_classif warns if a feature is constant throughout.

    X, y = make_classification(n_samples=10, n_features=5)
    X[:, 0] = 2.0
    assert_warns(UserWarning, f_classif, X, y)


def test_no_feature_selected():
    rng = np.random.RandomState(0)

    # Generate random uncorrelated data: a strict univariate test should
    # rejects all the features
    X = rng.rand(40, 10)
    y = rng.randint(0, 4, size=40)
    strict_selectors = [
        SelectFwe(alpha=0.01).fit(X, y),
        SelectFdr(alpha=0.01).fit(X, y),
        SelectFpr(alpha=0.01).fit(X, y),
        SelectPercentile(percentile=0).fit(X, y),
        SelectKBest(k=0).fit(X, y),
    ]
    for selector in strict_selectors:
        assert_array_equal(selector.get_support(), np.zeros(10))
        X_selected = assert_warns_message(
            UserWarning, 'No features were selected', selector.transform, X)
        assert X_selected.shape == (40, 0)


def test_mutual_info_classif():
    X, y = make_classification(n_samples=100, n_features=5,
                               n_informative=1, n_redundant=1,
                               n_repeated=0, n_classes=2,
                               n_clusters_per_class=1, flip_y=0.0,
                               class_sep=10, shuffle=False, random_state=0)

    # Test in KBest mode.
    univariate_filter = SelectKBest(mutual_info_classif, k=2)
    X_r = univariate_filter.fit(X, y).transform(X)
    X_r2 = GenericUnivariateSelect(
        mutual_info_classif, mode='k_best', param=2).fit(X, y).transform(X)
    assert_array_equal(X_r, X_r2)
    support = univariate_filter.get_support()
    gtruth = np.zeros(5)
    gtruth[:2] = 1
    assert_array_equal(support, gtruth)

    # Test in Percentile mode.
    univariate_filter = SelectPercentile(mutual_info_classif, percentile=40)
    X_r = univariate_filter.fit(X, y).transform(X)
    X_r2 = GenericUnivariateSelect(
        mutual_info_classif, mode='percentile', param=40).fit(X, y).transform(X)
    assert_array_equal(X_r, X_r2)
    support = univariate_filter.get_support()
    gtruth = np.zeros(5)
    gtruth[:2] = 1
    assert_array_equal(support, gtruth)


def test_mutual_info_regression():
    X, y = make_regression(n_samples=100, n_features=10, n_informative=2,
                           shuffle=False, random_state=0, noise=10)

    # Test in KBest mode.
    univariate_filter = SelectKBest(mutual_info_regression, k=2)
    X_r = univariate_filter.fit(X, y).transform(X)
    assert_best_scores_kept(univariate_filter)
    X_r2 = GenericUnivariateSelect(
        mutual_info_regression, mode='k_best', param=2).fit(X, y).transform(X)
    assert_array_equal(X_r, X_r2)
    support = univariate_filter.get_support()
    gtruth = np.zeros(10)
    gtruth[:2] = 1
    assert_array_equal(support, gtruth)

    # Test in Percentile mode.
    univariate_filter = SelectPercentile(mutual_info_regression, percentile=20)
    X_r = univariate_filter.fit(X, y).transform(X)
    X_r2 = GenericUnivariateSelect(mutual_info_regression, mode='percentile',
                                   param=20).fit(X, y).transform(X)
    assert_array_equal(X_r, X_r2)
    support = univariate_filter.get_support()
    gtruth = np.zeros(10)
    gtruth[:2] = 1
    assert_array_equal(support, gtruth)