TST/FIX Make model selection tests warning free!

- Use data that will converge for the multioutput case - Use atleast 3 samples per class to conform to 3fold cv - Add the elided ignore warnings line - Use the iris dataset to prevent non-convergence of sag solver

Author: raghavrv

sklearn/model_selection/tests/test_search.py

@@ -246,7 +246,6 @@ def test_grid_search_labels():
 
  non_label_cvs = [StratifiedKFold(), StratifiedShuffleSplit()]
  for cv in non_label_cvs:
- print(cv)
  gs = GridSearchCV(clf, grid, cv=cv)
  # Should not raise an error
  gs.fit(X, y)

sklearn/model_selection/tests/test_split.py

@@ -216,6 +216,7 @@ def test_kfold_valueerrors():
  # though all the classes are not necessarily represented at on each
  # side of the split at each split
  with warnings.catch_warnings():
+ warnings.simplefilter("ignore")
  check_cv_coverage(skf_3, X2, y, labels=None, expected_n_iter=3)
 
  # Error when number of folds is <= 1

sklearn/model_selection/tests/test_validation.py

@@ -125,7 +125,9 @@ def _is_training_data(self, X):
 # check_consistent_length
 X = np.ones((10, 2))
 X_sparse = coo_matrix(X)
-y = np.arange(10) // 2
+y = np.array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4])
+# The number of samples per class needs to be > n_folds, for StratifiedKFold(3)
+y2 = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 3])
 
 
 def test_cross_val_score():
@@ -134,38 +136,38 @@ def test_cross_val_score():
  for a in range(-10, 10):
  clf.a = a
  # Smoke test
- scores = cross_val_score(clf, X, y)
- assert_array_equal(scores, clf.score(X, y))
+ scores = cross_val_score(clf, X, y2)
+ assert_array_equal(scores, clf.score(X, y2))
 
  # test with multioutput y
- scores = cross_val_score(clf, X_sparse, X)
- assert_array_equal(scores, clf.score(X_sparse, X))
+ multioutput_y = np.column_stack([y2, y2[::-1]])
+ scores = cross_val_score(clf, X_sparse, multioutput_y)
+ assert_array_equal(scores, clf.score(X_sparse, multioutput_y))
 
- scores = cross_val_score(clf, X_sparse, y)
- assert_array_equal(scores, clf.score(X_sparse, y))
+ scores = cross_val_score(clf, X_sparse, y2)
+ assert_array_equal(scores, clf.score(X_sparse, y2))
 
  # test with multioutput y
- scores = cross_val_score(clf, X_sparse, X)
- assert_array_equal(scores, clf.score(X_sparse, X))
+ scores = cross_val_score(clf, X_sparse, multioutput_y)
+ assert_array_equal(scores, clf.score(X_sparse, multioutput_y))
 
  # test with X and y as list
  list_check = lambda x: isinstance(x, list)
  clf = CheckingClassifier(check_X=list_check)
- scores = cross_val_score(clf, X.tolist(), y.tolist())
+ scores = cross_val_score(clf, X.tolist(), y2.tolist())
 
  clf = CheckingClassifier(check_y=list_check)
- scores = cross_val_score(clf, X, y.tolist())
+ scores = cross_val_score(clf, X, y2.tolist())
 
- assert_raises(ValueError, cross_val_score, clf, X, y,
- scoring="sklearn")
+ assert_raises(ValueError, cross_val_score, clf, X, y2, scoring="sklearn")
 
  # test with 3d X and
  X_3d = X[:, :, np.newaxis]
  clf = MockClassifier(allow_nd=True)
- scores = cross_val_score(clf, X_3d, y)
+ scores = cross_val_score(clf, X_3d, y2)
 
  clf = MockClassifier(allow_nd=False)
- assert_raises(ValueError, cross_val_score, clf, X_3d, y)
+ assert_raises(ValueError, cross_val_score, clf, X_3d, y2)
 
 
 def test_cross_val_score_predict_labels():
@@ -197,7 +199,8 @@ def test_cross_val_score_pandas():
  pass
  for TargetType, InputFeatureType in types:
  # X dataframe, y series
- X_df, y_ser = InputFeatureType(X), TargetType(y)
+ # 3 fold cross val is used so we need atleast 3 samples per class
+ X_df, y_ser = InputFeatureType(X), TargetType(y2)
  check_df = lambda x: isinstance(x, InputFeatureType)
  check_series = lambda x: isinstance(x, TargetType)
  clf = CheckingClassifier(check_X=check_df, check_y=check_series)
@@ -476,21 +479,27 @@ def split(self, X, y=None, labels=None):
 
 
 def test_cross_val_predict_input_types():
- clf = Ridge()
+ iris = load_iris()
+ X, y = iris.data, iris.target
+ X_sparse = coo_matrix(X)
+ multioutput_y = np.column_stack([y, y[::-1]])
+
+ clf = Ridge(fit_intercept=False, random_state=0)
+ # 3 fold cv is used --> atleast 3 samples per class
  # Smoke test
  predictions = cross_val_predict(clf, X, y)
- assert_equal(predictions.shape, (10,))
+ assert_equal(predictions.shape, (150,))
 
  # test with multioutput y
- predictions = cross_val_predict(clf, X_sparse, X)
- assert_equal(predictions.shape, (10, 2))
+ predictions = cross_val_predict(clf, X_sparse, multioutput_y)
+ assert_equal(predictions.shape, (150, 2))
 
  predictions = cross_val_predict(clf, X_sparse, y)
- assert_array_equal(predictions.shape, (10,))
+ assert_array_equal(predictions.shape, (150,))
 
  # test with multioutput y
- predictions = cross_val_predict(clf, X_sparse, X)
- assert_array_equal(predictions.shape, (10, 2))
+ predictions = cross_val_predict(clf, X_sparse, multioutput_y)
+ assert_array_equal(predictions.shape, (150, 2))
 
  # test with X and y as list
  list_check = lambda x: isinstance(x, list)
@@ -505,7 +514,7 @@ def test_cross_val_predict_input_types():
  check_3d = lambda x: x.ndim == 3
  clf = CheckingClassifier(check_X=check_3d)
  predictions = cross_val_predict(clf, X_3d, y)
- assert_array_equal(predictions.shape, (10,))
+ assert_array_equal(predictions.shape, (150,))
 
 
 def test_cross_val_predict_pandas():
@@ -518,7 +527,7 @@ def test_cross_val_predict_pandas():
  pass
  for TargetType, InputFeatureType in types:
  # X dataframe, y series
- X_df, y_ser = InputFeatureType(X), TargetType(y)
+ X_df, y_ser = InputFeatureType(X), TargetType(y2)
  check_df = lambda x: isinstance(x, InputFeatureType)
  check_series = lambda x: isinstance(x, TargetType)
  clf = CheckingClassifier(check_X=check_df, check_y=check_series)