remove some more deprecation tests

amueller · amueller · commit cd48d6d43ce3 · 2018-10-01T18:14:49.000-04:00
diff --git a/sklearn/decomposition/tests/test_sparse_pca.py b/sklearn/decomposition/tests/test_sparse_pca.py
@@ -78,13 +78,6 @@ def test_fit_transform(norm_comp):
     spca_lasso.fit(Y)
     assert_array_almost_equal(spca_lasso.components_, spca_lars.components_)
 
-    # Test that deprecated ridge_alpha parameter throws warning
-    warning_msg = "The ridge_alpha parameter on transform()"
-    assert_warns_message(DeprecationWarning, warning_msg, spca_lars.transform,
-                         Y, ridge_alpha=0.01)
-    assert_warns_message(DeprecationWarning, warning_msg, spca_lars.transform,
-                         Y, ridge_alpha=None)
-
 
 @pytest.mark.filterwarnings("ignore:normalize_components")
 @pytest.mark.parametrize("norm_comp", [False, True])
diff --git a/sklearn/discriminant_analysis.py b/sklearn/discriminant_analysis.py
@@ -567,9 +567,6 @@ class QuadraticDiscriminantAnalysis(BaseEstimator, ClassifierMixin):
 
         .. versionadded:: 0.17
 
-    store_covariances : boolean
-        Deprecated, use `store_covariance`.
-
     Attributes
     ----------
     covariance_ : list of array-like, shape = [n_features, n_features]
@@ -602,8 +599,7 @@ class QuadraticDiscriminantAnalysis(BaseEstimator, ClassifierMixin):
     >>> clf.fit(X, y)
     ... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
     QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,
-                                  store_covariance=False,
-                                  store_covariances=None, tol=0.0001)
+                                  store_covariance=False, tol=0.0001)
     >>> print(clf.predict([[-0.8, -1]]))
     [1]
 
@@ -653,8 +649,7 @@ def fit(self, X, y):
             self.priors_ = self.priors
 
         cov = None
-        store_covariance = self.store_covariance or self.store_covariances
-
+        store_covariance = self.store_covariance
         if store_covariance:
             cov = []
         means = []
diff --git a/sklearn/feature_extraction/tests/test_feature_hasher.py b/sklearn/feature_extraction/tests/test_feature_hasher.py
@@ -33,7 +33,7 @@ def test_feature_hasher_strings():
 
         it = (x for x in raw_X)                 # iterable
 
-        h = FeatureHasher(n_features, non_negative=True, input_type="string")
+        h = FeatureHasher(n_features, input_type="string")
         X = h.transform(it)
 
         assert_equal(X.shape[0], len(raw_X))
@@ -120,11 +120,11 @@ def test_hasher_alternate_sign():
                        input_type='string').fit_transform(X)
     assert Xt.data.min() < 0 and Xt.data.max() > 0
 
-    Xt = FeatureHasher(alternate_sign=True, non_negative=True,
+    Xt = FeatureHasher(alternate_sign=True, 
                        input_type='string').fit_transform(X)
     assert Xt.data.min() > 0
 
-    Xt = FeatureHasher(alternate_sign=False, non_negative=True,
+    Xt = FeatureHasher(alternate_sign=False, 
                        input_type='string').fit_transform(X)
     assert Xt.data.min() > 0
     Xt_2 = FeatureHasher(alternate_sign=False, non_negative=False,
@@ -144,11 +144,11 @@ def test_hash_collisions():
     # with an opposite sign and cancel out
     assert abs(Xt.data[0]) < len(X[0])
 
-    Xt = FeatureHasher(alternate_sign=True, non_negative=True,
+    Xt = FeatureHasher(alternate_sign=True, 
                        n_features=1, input_type='string').fit_transform(X)
     assert abs(Xt.data[0]) < len(X[0])
 
-    Xt = FeatureHasher(alternate_sign=False, non_negative=True,
+    Xt = FeatureHasher(alternate_sign=False, 
                        n_features=1, input_type='string').fit_transform(X)
     assert Xt.data[0] == len(X[0])
 
@@ -159,12 +159,12 @@ def test_hasher_negative():
     Xt = FeatureHasher(alternate_sign=False, non_negative=False,
                        input_type="pair").fit_transform(X)
     assert_true(Xt.data.min() < 0 and Xt.data.max() > 0)
-    Xt = FeatureHasher(alternate_sign=False, non_negative=True,
+    Xt = FeatureHasher(alternate_sign=False, 
                        input_type="pair").fit_transform(X)
     assert_true(Xt.data.min() > 0)
     Xt = FeatureHasher(alternate_sign=True, non_negative=False,
                        input_type="pair").fit_transform(X)
     assert_true(Xt.data.min() < 0 and Xt.data.max() > 0)
-    Xt = FeatureHasher(alternate_sign=True, non_negative=True,
+    Xt = FeatureHasher(alternate_sign=True, 
                        input_type="pair").fit_transform(X)
     assert_true(Xt.data.min() > 0)
diff --git a/sklearn/metrics/tests/test_pairwise.py b/sklearn/metrics/tests/test_pairwise.py
@@ -82,10 +82,6 @@ def test_pairwise_distances():
     assert_equal(S.shape[0], X.shape[0])
     assert_equal(S.shape[1], Y.shape[0])
     assert_array_almost_equal(S, S2)
-    # Using size_threshold argument should raise
-    # a deprecation warning
-    assert_warns(DeprecationWarning,
-                 manhattan_distances, X, Y, size_threshold=10)
     # Test cosine as a string metric versus cosine callable
     # The string "cosine" uses sklearn.metric,
     # while the function cosine is scipy.spatial
