Assigned n_basis to SCML when needed for tests. Catch warn when needed as well. (#341)

Author: mvargas33

test/metric_learn_test.py

@@ -95,7 +95,7 @@ def test_big_n_features(self):
                                n_informative=60, n_redundant=0, n_repeated=0,
                                random_state=42)
     X = StandardScaler().fit_transform(X)
-    scml = SCML_Supervised(random_state=42)
+    scml = SCML_Supervised(random_state=42, n_basis=399)
     scml.fit(X, y)
     csep = class_separation(scml.transform(X), y)
     assert csep < 0.7
@@ -106,7 +106,7 @@ def test_big_n_features(self):
                                                          [2, 0], [2, 1]]),
                                                np.array([1, 0, 1, 0])))])
   def test_bad_basis(self, estimator, data):
-    model = estimator(basis='bad_basis')
+    model = estimator(basis='bad_basis', n_basis=33)  # n_basis doesn't matter
     msg = ("`basis` must be one of the options '{}' or an array of shape "
            "(n_basis, n_features)."
            .format("', '".join(model._authorized_basis)))
@@ -238,16 +238,23 @@ def test_lda_toy(self):
   @pytest.mark.parametrize('n_features', [10, 50, 100])
   @pytest.mark.parametrize('n_classes', [5, 10, 15])
   def test_triplet_diffs(self, n_samples, n_features, n_classes):
+    """
+    Test that the correct value of n_basis is being generated with
+    different triplet constraints.
+    """
     X, y = make_classification(n_samples=n_samples, n_classes=n_classes,
                                n_features=n_features, n_informative=n_features,
                                n_redundant=0, n_repeated=0)
     X = StandardScaler().fit_transform(X)
-
-    model = SCML_Supervised()
+    model = SCML_Supervised(n_basis=None)  # Explicit n_basis=None
     constraints = Constraints(y)
     triplets = constraints.generate_knntriplets(X, model.k_genuine,
                                                 model.k_impostor)
-    basis, n_basis = model._generate_bases_dist_diff(triplets, X)
+
+    msg = "As no value for `n_basis` was selected, "
+    with pytest.warns(UserWarning) as raised_warning:
+      basis, n_basis = model._generate_bases_dist_diff(triplets, X)
+    assert msg in str(raised_warning[0].message)
 
     expected_n_basis = n_features * 80
     assert n_basis == expected_n_basis
@@ -257,13 +264,21 @@ def test_triplet_diffs(self, n_samples, n_features, n_classes):
   @pytest.mark.parametrize('n_features', [10, 50, 100])
   @pytest.mark.parametrize('n_classes', [5, 10, 15])
   def test_lda(self, n_samples, n_features, n_classes):
+    """
+    Test that when n_basis=None, the correct n_basis is generated,
+    for SCML_Supervised and different values of n_samples, n_features
+    and n_classes.
+    """
     X, y = make_classification(n_samples=n_samples, n_classes=n_classes,
                                n_features=n_features, n_informative=n_features,
                                n_redundant=0, n_repeated=0)
     X = StandardScaler().fit_transform(X)
 
-    model = SCML_Supervised()
-    basis, n_basis = model._generate_bases_LDA(X, y)
+    msg = "As no value for `n_basis` was selected, "
+    with pytest.warns(UserWarning) as raised_warning:
+      model = SCML_Supervised(n_basis=None)  # Explicit n_basis=None
+      basis, n_basis = model._generate_bases_LDA(X, y)
+    assert msg in str(raised_warning[0].message)
 
     num_eig = min(n_classes - 1, n_features)
     expected_n_basis = min(20 * n_features, n_samples * 2 * num_eig - 1)
@@ -299,7 +314,7 @@ def test_int_inputs_supervised(self, name):
     assert msg == raised_error.value.args[0]
 
   def test_large_output_iter(self):
-    scml = SCML(max_iter=1, output_iter=2)
+    scml = SCML(max_iter=1, output_iter=2, n_basis=33)  # n_basis don't matter
     triplets = np.array([[[0, 1], [2, 1], [0, 0]]])
     msg = ("The value of output_iter must be equal or smaller than"
            " max_iter.")

test/test_mahalanobis_mixin.py

@@ -291,8 +291,8 @@ def test_components_is_2D(estimator, build_dataset):
   model.fit(*remove_y(estimator, input_data, labels))
   assert model.components_.shape == (X.shape[1], X.shape[1])
 
-  # test that it works for 1 feature
-  trunc_data = input_data[..., :1]
+  # test that it works for 1 feature. Use 2nd dimention, to avoid border cases
+  trunc_data = input_data[..., 1:2]
   # we drop duplicates that might have been formed, i.e. of the form
   # aabc or abcc or aabb for quadruplets, and aa for pairs.
 

test/test_sklearn_compat.py

@@ -79,7 +79,10 @@ def test_rca(self):
     check_estimator(Stable_RCA_Supervised())
 
   def test_scml(self):
-    check_estimator(SCML_Supervised())
+    msg = "As no value for `n_basis` was selected, "
+    with pytest.warns(UserWarning) as raised_warning:
+      check_estimator(SCML_Supervised())
+    assert msg in str(raised_warning[0].message)
 
 
 RNG = check_random_state(0)

test/test_triplets_classifiers.py

@@ -1,7 +1,6 @@
 import pytest
 from sklearn.exceptions import NotFittedError
 from sklearn.model_selection import train_test_split
-import metric_learn
 
 from test.test_utils import triplets_learners, ids_triplets_learners
 from metric_learn.sklearn_shims import set_random_state
@@ -21,13 +20,7 @@ def test_predict_only_one_or_minus_one(estimator, build_dataset,
   estimator.set_params(preprocessor=preprocessor)
   set_random_state(estimator)
   triplets_train, triplets_test = train_test_split(input_data)
-  if isinstance(estimator, metric_learn.SCML):
-    msg = "As no value for `n_basis` was selected, "
-    with pytest.warns(UserWarning) as raised_warning:
-      estimator.fit(triplets_train)
-    assert msg in str(raised_warning[0].message)
-  else:
-    estimator.fit(triplets_train)
+  estimator.fit(triplets_train)
   predictions = estimator.predict(triplets_test)
 
   not_valid = [e for e in predictions if e not in [-1, 1]]
@@ -49,13 +42,7 @@ def test_no_zero_prediction(estimator, build_dataset):
   # Dummy fit
   estimator = clone(estimator)
   set_random_state(estimator)
-  if isinstance(estimator, metric_learn.SCML):
-    msg = "As no value for `n_basis` was selected, "
-    with pytest.warns(UserWarning) as raised_warning:
-      estimator.fit(triplets)
-    assert msg in str(raised_warning[0].message)
-  else:
-    estimator.fit(triplets)
+  estimator.fit(triplets)
   # We force the transformation to be identity, to force euclidean distance
   estimator.components_ = np.eye(X.shape[1])
 
@@ -106,13 +93,7 @@ def test_accuracy_toy_example(estimator, build_dataset):
   triplets, _, _, X = build_dataset(with_preprocessor=False)
   estimator = clone(estimator)
   set_random_state(estimator)
-  if isinstance(estimator, metric_learn.SCML):
-    msg = "As no value for `n_basis` was selected, "
-    with pytest.warns(UserWarning) as raised_warning:
-      estimator.fit(triplets)
-    assert msg in str(raised_warning[0].message)
-  else:
-    estimator.fit(triplets)
+  estimator.fit(triplets)
   # We take the two first points and we build 4 regularly spaced points on the
   # line they define, so that it's easy to build triplets of different
   # similarities.

test/test_utils.py

@@ -117,7 +117,7 @@ def build_quadruplets(with_preprocessor=False):
                                 [learner for (learner, _) in
                                  quadruplets_learners]))
 
-triplets_learners = [(SCML(), build_triplets)]
+triplets_learners = [(SCML(n_basis=320), build_triplets)]
 ids_triplets_learners = list(map(lambda x: x.__class__.__name__,
                              [learner for (learner, _) in
                               triplets_learners]))
@@ -140,7 +140,7 @@ def build_quadruplets(with_preprocessor=False):
                (RCA_Supervised(num_chunks=5), build_classification),
                (SDML_Supervised(prior='identity', balance_param=1e-5),
                build_classification),
-               (SCML_Supervised(), build_classification)]
+               (SCML_Supervised(n_basis=80), build_classification)]
 ids_classifiers = list(map(lambda x: x.__class__.__name__,
                            [learner for (learner, _) in
                             classifiers]))