From 59db7f02f76c5481a2f7d02289c6afc834d847e8 Mon Sep 17 00:00:00 2001 From: Fabian Pedregosa <fabian.pedregosa@inria.fr> Date: Fri, 25 Jun 2010 17:59:01 -0500 Subject: [PATCH] Initialize with a fixed seed tests in feature_select --- .../tests/test_feature_select.py | 29 ++++++++++--------- 1 file changed, 16 insertions(+), 13 deletions(-) diff --git a/scikits/learn/feature_selection/tests/test_feature_select.py b/scikits/learn/feature_selection/tests/test_feature_select.py index 53177ad38e..6341785462 100644 --- a/scikits/learn/feature_selection/tests/test_feature_select.py +++ b/scikits/learn/feature_selection/tests/test_feature_select.py @@ -9,6 +9,7 @@ from numpy.testing import assert_array_equal, \ assert_raises import scikits.learn.datasets.samples_generator as sg +seed = np.random.RandomState(0) def test_F_test_classif(): """ @@ -16,7 +17,7 @@ def test_F_test_classif(): on a simple simulated classification problem """ X, Y = sg.test_dataset_classif(n_samples=50, n_features=20, k=5, - seed=None) + seed=seed) F, pv = us.f_classif(X, Y) assert(F>0).all() assert(pv>0).all() @@ -29,8 +30,9 @@ def test_F_test_reg(): Test whether the F test yields meaningful results on a simple simulated regression problem """ + np.random.seed(0) X, Y = sg.test_dataset_classif(n_samples=50, n_features=20, k=5, - seed=None) + seed=seed) F, pv = us.f_regression(X, Y) assert(F>0).all() assert(pv>0).all() @@ -44,13 +46,13 @@ def test_F_test_multi_class(): on a simple simulated classification problem """ X, Y = sg.test_dataset_classif(n_samples=50, n_features=20, k=5, - seed=None,param=[1,1,1]) + seed=seed,param=[1,1,1]) F, pv = us.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() + assert(pv[5:]>1.e-5).all() def test_univ_fs_percentile_classif(): """ @@ -58,8 +60,9 @@ def test_univ_fs_percentile_classif(): gets the correct items in a simple classification problem with the percentile heuristic """ + np.random.seed(0) X, Y = sg.test_dataset_classif(n_samples=50, n_features=20, k=5, - seed=None) + seed=seed) univariate_filter = us.SelectPercentile(us.f_classif) X_r = univariate_filter.fit(X, Y).transform(X, percentile=25) support = univariate_filter.support(percentile=25) @@ -74,7 +77,7 @@ def test_univ_fs_kbest_classif(): with the k best heuristic """ X, Y = sg.test_dataset_classif(n_samples=50, n_features=20, k=5, - seed=None) + seed=seed) univariate_filter = us.SelectKBest(us.f_classif) X_r = univariate_filter.fit(X, Y).transform(X, k=5) support = univariate_filter.support(k=5) @@ -89,7 +92,7 @@ def test_univ_fs_fpr_classif(): with the fpr heuristic """ X, Y = sg.test_dataset_classif(n_samples=50, n_features=20, k=5, - seed=None) + seed=seed) univariate_filter = us.SelectFpr(us.f_classif) X_r = univariate_filter.fit(X, Y).transform(X, alpha=0.0001) support = univariate_filter.support(alpha=0.0001) @@ -119,7 +122,7 @@ def test_univ_fs_fwe_classif(): with the fpr heuristic """ X, Y = sg.test_dataset_classif(n_samples=50, n_features=20, k=5, - seed=None) + seed=seed) univariate_filter = us.SelectFwe(us.f_classif) X_r = univariate_filter.fit(X, Y).transform(X, alpha=0.01) support = univariate_filter.support(alpha=0.01) @@ -140,7 +143,7 @@ def test_univ_fs_percentile_regression(): with the percentile heuristic """ X, Y = sg.test_dataset_reg(n_samples=50, n_features=20, k=5, - seed=None) + seed=seed) univariate_filter = us.SelectPercentile(us.f_regression) X_r = univariate_filter.fit(X, Y).transform(X, percentile=25) support = univariate_filter.support(percentile=25) @@ -154,7 +157,7 @@ def test_univ_fs_full_percentile_regression(): selects all features when '100%' is asked. """ X, Y = sg.test_dataset_reg(n_samples=50, n_features=20, k=5, - seed=None) + seed=seed) univariate_filter = us.SelectPercentile(us.f_regression) X_r = univariate_filter.fit(X, Y).transform(X, percentile=100) support = univariate_filter.support(percentile=100) @@ -168,7 +171,7 @@ def test_univ_fs_kbest_regression(): with the k best heuristic """ X, Y = sg.test_dataset_reg(n_samples=50, n_features=20, k=5, - seed=None) + seed=seed) univariate_filter = us.SelectKBest(us.f_regression) X_r = univariate_filter.fit(X, Y).transform(X, k=5) support = univariate_filter.support(k=5) @@ -183,7 +186,7 @@ def test_univ_fs_fpr_regression(): with the fpr heuristic """ X, Y = sg.test_dataset_reg(n_samples=50, n_features=20, k=5, - seed=None) + seed=seed) univariate_filter = us.SelectFpr(us.f_regression) X_r = univariate_filter.fit(X, Y).transform(X, alpha=0.01) support = univariate_filter.support(alpha=0.01) @@ -214,7 +217,7 @@ def test_univ_fs_fwe_regression(): with the fpr heuristic """ X, Y = sg.test_dataset_reg(n_samples=50, n_features=20, k=5, - seed=None) + seed=seed) univariate_filter = us.SelectFwe(us.f_regression) X_r = univariate_filter.fit(X, Y).transform(X, alpha=0.01) support = univariate_filter.support(alpha=0.01) -- GitLab