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)
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_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]))
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_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_feature_selection(): # make two feature dicts with two useful features and a bunch of useless # ones, in terms of chi2 d1 = dict([("useless%d" % i, 10) for i in range(20)], useful1=1, useful2=20) d2 = dict([("useless%d" % i, 10) for i in range(20)], useful1=20, useful2=1) for indices in (True, False): v = DictVectorizer().fit([d1, d2]) X = v.transform([d1, d2]) sel = SelectKBest(chi2, k=2).fit(X, [0, 1]) v.restrict(sel.get_support(indices=indices), indices=indices) assert v.get_feature_names() == ["useful1", "useful2"]
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)