def test_select_fwe_classif(): """ Test whether the relative univariate feature selection gets the correct items in a simple classification problem with the fpr 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 = SelectFwe(f_classif, alpha=0.01) X_r = univariate_filter.fit(X, Y).transform(X) X_r2 = GenericUnivariateSelect(f_classif, 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 (np.sum(np.abs(support - gtruth)) < 2)
def test_select_fwe_classif(): """ Test whether the relative univariate feature selection gets the correct items in a simple classification problem with the fpr 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 = SelectFwe(f_classif, alpha=0.01) X_r = univariate_filter.fit(X, Y).transform(X) X_r2 = GenericUnivariateSelect(f_classif, 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 np.sum(np.abs(support - gtruth)) < 2
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 (support[:5] == 1).all() assert np.sum(support[5:] == 1) < 2
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(support[:5] == 1).all() assert(np.sum(support[5:] == 1) < 2)
'RandomForestRegressor':RandomForestRegressor(), 'RandomizedLasso':RandomizedLasso(), 'RandomizedLogisticRegression':RandomizedLogisticRegression(), 'RandomizedPCA':RandomizedPCA(), 'Ridge':Ridge(), 'RidgeCV':RidgeCV(), 'RidgeClassifier':RidgeClassifier(), 'RidgeClassifierCV':RidgeClassifierCV(), 'RobustScaler':RobustScaler(), 'SGDClassifier':SGDClassifier(), 'SGDRegressor':SGDRegressor(), 'SVC':SVC(), 'SVR':SVR(), 'SelectFdr':SelectFdr(), 'SelectFpr':SelectFpr(), 'SelectFwe':SelectFwe(), 'SelectKBest':SelectKBest(), 'SelectPercentile':SelectPercentile(), 'ShrunkCovariance':ShrunkCovariance(), 'SkewedChi2Sampler':SkewedChi2Sampler(), 'SparsePCA':SparsePCA(), 'SparseRandomProjection':SparseRandomProjection(), 'SpectralBiclustering':SpectralBiclustering(), 'SpectralClustering':SpectralClustering(), 'SpectralCoclustering':SpectralCoclustering(), 'SpectralEmbedding':SpectralEmbedding(), 'StandardScaler':StandardScaler(), 'TSNE':TSNE(), 'TheilSenRegressor':TheilSenRegressor(), 'VBGMM':VBGMM(), 'VarianceThreshold':VarianceThreshold(),}