TST/COSMIT remove nose call boilerplate

amueller · amueller · commit a8626b36a6d2 · 2015-05-28T14:54:01.000-04:00
diff --git a/sklearn/cluster/tests/test_hierarchical.py b/sklearn/cluster/tests/test_hierarchical.py
@@ -496,6 +496,7 @@ def test_n_components():
     for linkage_func in _TREE_BUILDERS.values():
         assert_equal(ignore_warnings(linkage_func)(X, connectivity)[1], 5)
 
+
 def test_agg_n_clusters():
     # Test that an error is raised when n_clusters <= 0
 
@@ -506,7 +507,3 @@ def test_agg_n_clusters():
         msg = ("n_clusters should be an integer greater than 0."
                " %s was provided." % str(agc.n_clusters))
         assert_raise_message(ValueError, msg, agc.fit, X)
-
-if __name__ == '__main__':
-    import nose
-    nose.run(argv=['', __file__])
diff --git a/sklearn/decomposition/tests/test_fastica.py b/sklearn/decomposition/tests/test_fastica.py
@@ -234,8 +234,3 @@ def test_inverse_transform():
             # reversibility test in non-reduction case
             if n_components == X.shape[1]:
                 assert_array_almost_equal(X, X2)
-
-
-if __name__ == '__main__':
-    import nose
-    nose.run(argv=['', __file__])
diff --git a/sklearn/decomposition/tests/test_kernel_pca.py b/sklearn/decomposition/tests/test_kernel_pca.py
@@ -207,8 +207,3 @@ def test_nested_circles():
     # The data is perfectly linearly separable in that space
     train_score = Perceptron().fit(X_kpca, y).score(X_kpca, y)
     assert_equal(train_score, 1.0)
-
-
-if __name__ == '__main__':
-    import nose
-    nose.run(argv=['', __file__])
diff --git a/sklearn/decomposition/tests/test_nmf.py b/sklearn/decomposition/tests/test_nmf.py
@@ -164,8 +164,3 @@ def test_sparse_transform():
     A_tr = model.transform(A)
     # This solver seems pretty inconsistent
     assert_array_almost_equal(A_fit_tr, A_tr, decimal=2)
-
-
-if __name__ == '__main__':
-    import nose
-    nose.run(argv=['', __file__])
diff --git a/sklearn/decomposition/tests/test_pca.py b/sklearn/decomposition/tests/test_pca.py
@@ -328,8 +328,3 @@ def test_pca_score3():
         ll[k] = pca.score(Xt)
 
     assert_true(ll.argmax() == 1)
-
-
-if __name__ == '__main__':
-    import nose
-    nose.run(argv=['', __file__])
diff --git a/sklearn/ensemble/tests/test_bagging.py b/sklearn/ensemble/tests/test_bagging.py
@@ -662,8 +662,3 @@ def test_oob_score_removed_on_warm_start():
     clf.fit(X, y)
 
     assert_raises(AttributeError, getattr, clf, "oob_score_")
-
-
-if __name__ == "__main__":
-    import nose
-    nose.runmodule()
diff --git a/sklearn/ensemble/tests/test_forest.py b/sklearn/ensemble/tests/test_forest.py
@@ -329,7 +329,7 @@ def test_parallel():
         yield check_parallel, name, iris.data, iris.target
 
     for name in FOREST_REGRESSORS:
-        yield check_parallel, name,  boston.data, boston.target
+        yield check_parallel, name, boston.data, boston.target
 
 
 def check_pickle(name, X, y):
@@ -352,7 +352,7 @@ def test_pickle():
         yield check_pickle, name, iris.data[::2], iris.target[::2]
 
     for name in FOREST_REGRESSORS:
-        yield check_pickle, name,  boston.data[::2], boston.target[::2]
+        yield check_pickle, name, boston.data[::2], boston.target[::2]
 
 
 def check_multioutput(name):
@@ -782,7 +782,7 @@ def check_class_weights(name):
 
     # Check that sample_weight and class_weight are multiplicative
     clf1 = ForestClassifier(random_state=0)
-    clf1.fit(iris.data, iris.target, sample_weight**2)
+    clf1.fit(iris.data, iris.target, sample_weight ** 2)
     clf2 = ForestClassifier(class_weight=class_weight, random_state=0)
     clf2.fit(iris.data, iris.target, sample_weight)
     assert_almost_equal(clf1.feature_importances_, clf2.feature_importances_)
@@ -973,8 +973,3 @@ def test_warm_start_oob():
         yield check_warm_start_oob, name
     for name in FOREST_REGRESSORS:
         yield check_warm_start_oob, name
-
-
-if __name__ == "__main__":
-    import nose
-    nose.runmodule()
diff --git a/sklearn/ensemble/tests/test_gradient_boosting.py b/sklearn/ensemble/tests/test_gradient_boosting.py
@@ -1012,8 +1012,3 @@ def test_non_uniform_weights_toy_edge_case_clf():
         gb = GradientBoostingClassifier(n_estimators=5)
         gb.fit(X, y, sample_weight=sample_weight)
         assert_array_equal(gb.predict([[1, 0]]), [1])
-
-
-if __name__ == "__main__":
-    import nose
-    nose.runmodule()
diff --git a/sklearn/ensemble/tests/test_weight_boosting.py b/sklearn/ensemble/tests/test_weight_boosting.py
@@ -419,8 +419,3 @@ def fit(self, X, y, sample_weight=None):
 
         assert all([(t == csc_matrix or t == csr_matrix)
                    for t in types])
-
-
-if __name__ == "__main__":
-    import nose
-    nose.runmodule()
diff --git a/sklearn/feature_extraction/tests/test_image.py b/sklearn/feature_extraction/tests/test_image.py
@@ -288,8 +288,3 @@ def test_width_patch():
     x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
     assert_raises(ValueError, extract_patches_2d, x, (4, 1))
     assert_raises(ValueError, extract_patches_2d, x, (1, 4))
-
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -604,8 +604,3 @@ def test_sparse_dense_descent_paths():
         _, coefs, _ = path(X, y, fit_intercept=False)
         _, sparse_coefs, _ = path(csr, y, fit_intercept=False)
         assert_array_almost_equal(coefs, sparse_coefs)
-
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
diff --git a/sklearn/linear_model/tests/test_least_angle.py b/sklearn/linear_model/tests/test_least_angle.py
@@ -306,7 +306,6 @@ def test_lasso_lars_vs_lasso_cd_ill_conditioned():
     y = y.squeeze()
     lars_alphas, _, lars_coef = linear_model.lars_path(X, y, method='lasso')
 
-
     _, lasso_coef2, _ = linear_model.lasso_path(X, y,
                                                 alphas=lars_alphas,
                                                 tol=1e-6,
@@ -457,8 +456,3 @@ def test_lars_path_readonly_data():
             shutil.rmtree(temp_folder)
         except shutil.WindowsError:
             warnings.warn("Could not delete temporary folder %s" % temp_folder)
-
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
diff --git a/sklearn/linear_model/tests/test_ransac.py b/sklearn/linear_model/tests/test_ransac.py
@@ -340,7 +340,3 @@ def test_ransac_dynamic_max_trials():
     ransac_estimator = RANSACRegressor(base_estimator, min_samples=2,
                                        stop_probability=1.1)
     assert_raises(ValueError, ransac_estimator.fit, X, y)
-
-
-if __name__ == "__main__":
-    np.testing.run_module_suite()
diff --git a/sklearn/manifold/tests/test_isomap.py b/sklearn/manifold/tests/test_isomap.py
@@ -111,8 +111,3 @@ def test_pipeline():
          ('clf', neighbors.KNeighborsClassifier())])
     clf.fit(X, y)
     assert_less(.9, clf.score(X, y))
-
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
diff --git a/sklearn/manifold/tests/test_locally_linear.py b/sklearn/manifold/tests/test_locally_linear.py
@@ -121,8 +121,3 @@ def test_singular_matrix():
     f = ignore_warnings
     assert_raises(ValueError, f(manifold.locally_linear_embedding),
                   M, 2, 1, method='standard', eigen_solver='arpack')
-
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
diff --git a/sklearn/mixture/tests/test_dpgmm.py b/sklearn/mixture/tests/test_dpgmm.py
@@ -1,8 +1,6 @@
 import unittest
 import sys
 
-import nose
-
 import numpy as np
 
 from sklearn.mixture import DPGMM, VBGMM
@@ -157,7 +155,3 @@ class TestVBGMMWithTiedCovars(unittest.TestCase, VBGMMTester):
 class TestVBGMMWithFullCovars(unittest.TestCase, VBGMMTester):
     covariance_type = 'full'
     setUp = GMMTester._setUp
-
-
-if __name__ == '__main__':
-    nose.runmodule()
diff --git a/sklearn/mixture/tests/test_gmm.py b/sklearn/mixture/tests/test_gmm.py
@@ -471,8 +471,3 @@ def test_verbose_second_level():
         g.fit(X)
     finally:
         sys.stdout = old_stdout
-
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
diff --git a/sklearn/neighbors/tests/test_ball_tree.py b/sklearn/neighbors/tests/test_ball_tree.py
@@ -309,8 +309,3 @@ def test_query_haversine():
 
     assert_array_almost_equal(dist1, dist2)
     assert_array_almost_equal(ind1, ind2)
-
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
diff --git a/sklearn/neighbors/tests/test_dist_metrics.py b/sklearn/neighbors/tests/test_dist_metrics.py
@@ -145,8 +145,3 @@ def test_pyfunc_metric():
 
     assert_array_almost_equal(D1, D2)
     assert_array_almost_equal(D1_pkl, D2_pkl)
-
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
diff --git a/sklearn/neighbors/tests/test_kd_tree.py b/sklearn/neighbors/tests/test_kd_tree.py
@@ -235,8 +235,3 @@ def test_simultaneous_sort(n_rows=10, n_pts=201):
 
     assert_array_almost_equal(dist, dist2)
     assert_array_almost_equal(ind, ind2)
-
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
diff --git a/sklearn/neighbors/tests/test_kde.py b/sklearn/neighbors/tests/test_kde.py
@@ -139,8 +139,3 @@ def test_kde_pipeline_gridsearch():
     search = GridSearchCV(pipe1, param_grid=params, cv=5)
     search.fit(X)
     assert_equal(search.best_params_['kerneldensity__bandwidth'], .1)
-
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
diff --git a/sklearn/neighbors/tests/test_nearest_centroid.py b/sklearn/neighbors/tests/test_nearest_centroid.py
@@ -134,8 +134,3 @@ def test_manhattan_metric():
     clf.fit(X_csr, y)
     assert_array_equal(clf.centroids_, dense_centroid)
     assert_array_equal(dense_centroid, [[-1, -1], [1, 1]])
-
-
-if __name__ == "__main__":
-    import nose
-    nose.runmodule()
diff --git a/sklearn/neighbors/tests/test_neighbors.py b/sklearn/neighbors/tests/test_neighbors.py
@@ -1082,8 +1082,3 @@ def test_include_self_neighbors_graph():
         X, 5.0, include_self=False).A
     assert_array_equal(rng, [[1., 1.], [1., 1.]])
     assert_array_equal(rng_not_self, [[0., 1.], [1., 0.]])
-
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
diff --git a/sklearn/svm/tests/test_svm.py b/sklearn/svm/tests/test_svm.py
@@ -308,7 +308,7 @@ def test_decision_function():
     # kernel binary:
     clf = svm.SVC(kernel='rbf', gamma=1)
     clf.fit(X, Y)
-    
+
     rbfs = rbf_kernel(X, clf.support_vectors_, gamma=clf.gamma)
     dec = np.dot(rbfs, clf.dual_coef_.T) + clf.intercept_
     assert_array_almost_equal(dec.ravel(), clf.decision_function(X))
@@ -330,7 +330,7 @@ def test_svr_decision_function():
 
     # rbf kernel
     reg = svm.SVR(kernel='rbf', gamma=1).fit(X, y)
-    
+
     rbfs = rbf_kernel(X, reg.support_vectors_, gamma=reg.gamma)
     dec = np.dot(rbfs, reg.dual_coef_.T) + reg.intercept_
     assert_array_almost_equal(dec.ravel(), reg.decision_function(X).ravel())
@@ -813,8 +813,3 @@ def test_lsvc_intercept_scaling_zero():
     lsvc = svm.LinearSVC(fit_intercept=False)
     lsvc.fit(X, Y)
     assert_equal(lsvc.intercept_, 0.)
-
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
diff --git a/sklearn/tests/test_dummy.py b/sklearn/tests/test_dummy.py
@@ -597,8 +597,3 @@ def test_dummy_regressor_sample_weight(n_samples=10):
     est = DummyRegressor(strategy="quantile", quantile=.95).fit(X, y,
                                                                 sample_weight)
     assert_equal(est.constant_, _weighted_percentile(y, sample_weight, 95.))
-
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
diff --git a/sklearn/tests/test_isotonic.py b/sklearn/tests/test_isotonic.py
@@ -346,8 +346,3 @@ def test_isotonic_zero_weight_loop():
 
     # This will hang in failure case.
     regression.fit(x, y, sample_weight=w)
-
-
-if __name__ == "__main__":
-    import nose
-    nose.run(argv=['', __file__])
diff --git a/sklearn/tests/test_multiclass.py b/sklearn/tests/test_multiclass.py
@@ -616,8 +616,3 @@ def test_deprecated():
             assert_almost_equal(predict_func(estimators_, classes_or_lb,
                                              codebook, X_test),
                                 meta_est.predict(X_test))
-
-
-if __name__ == "__main__":
-    import nose
-    nose.runmodule()
diff --git a/sklearn/tree/tests/test_export.py b/sklearn/tree/tests/test_export.py
@@ -223,8 +223,3 @@ def test_graphviz_errors():
     # Check class_names error
     out = StringIO()
     assert_raises(IndexError, export_graphviz, clf, out, class_names=[])
-
-
-if __name__ == "__main__":
-    import nose
-    nose.runmodule()
diff --git a/sklearn/utils/tests/test_extmath.py b/sklearn/utils/tests/test_extmath.py
@@ -465,8 +465,3 @@ def test_vector_sign_flip():
     assert_array_equal(max_abs_rows, max_rows)
     signs = np.sign(data[range(data.shape[0]), max_abs_rows])
     assert_array_equal(data, data_flipped * signs[:, np.newaxis])
-
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
diff --git a/sklearn/utils/tests/test_multiclass.py b/sklearn/utils/tests/test_multiclass.py
@@ -392,8 +392,3 @@ def test_class_distribution():
         assert_array_almost_equal(classes_sp[k], classes_expected[k])
         assert_array_almost_equal(n_classes_sp[k], n_classes_expected[k])
         assert_array_almost_equal(class_prior_sp[k], class_prior_expected[k])
-
-
-if __name__ == "__main__":
-    import nose
-    nose.runmodule()
diff --git a/sklearn/utils/tests/test_random.py b/sklearn/utils/tests/test_random.py
@@ -180,8 +180,3 @@ def test_random_choice_csc_errors():
     class_probabilites = [np.array([0.5, 0.6]), np.array([0.6, 0.1, 0.3])]
     assert_raises(ValueError, random_choice_csc, 4, classes,
                   class_probabilites, 1)
-
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
diff --git a/sklearn/utils/tests/test_shortest_path.py b/sklearn/utils/tests/test_shortest_path.py
@@ -93,8 +93,3 @@ def test_dijkstra_bug_fix():
     dist_FW = graph_shortest_path(X, directed=False, method='FW')
     dist_D = graph_shortest_path(X, directed=False, method='D')
     assert_array_almost_equal(dist_D, dist_FW)
-
-
-if __name__ == '__main__':
-    import nose
-    nose.runmodule()
