[MRG] fix quadruplets decision_function (#217)

* fix quadruplets decision_function

* Address #217 (comment)

* fix: I put the column at the wrong side, now it does some subsampling

* Fix number of samples

* let's try again with 30 samples

* Use less chunks

Author: wdevazelhes

metric_learn/base_metric.py

@@ -618,6 +618,9 @@ def decision_function(self, quadruplets):
     decision_function : `numpy.ndarray` of floats, shape=(n_constraints,)
       Metric differences.
     """
+    quadruplets = check_input(quadruplets, type_of_inputs='tuples',
+                              preprocessor=self.preprocessor_,
+                              estimator=self, tuple_size=self._tuple_size)
     return (self.score_pairs(quadruplets[:, 2:]) -
             self.score_pairs(quadruplets[:, :2]))
 

test/test_sklearn_compat.py

@@ -105,6 +105,70 @@ def stable_init(self, n_components=None, pca_comps=None,
 
 # ---------------------- Test scikit-learn compatibility ----------------------
 
+def generate_array_like(input_data, labels=None):
+  """Helper function to generate array-like variants of numpy datasets,
+  for testing purposes."""
+  list_data = input_data.tolist()
+  input_data_changed = [input_data, list_data, tuple(list_data)]
+  if input_data.ndim >= 2:
+    input_data_changed.append(tuple(tuple(x) for x in list_data))
+  if input_data.ndim >= 3:
+    input_data_changed.append(tuple(tuple(tuple(x) for x in y) for y in
+                                    list_data))
+  if input_data.ndim == 2:
+    pd = pytest.importorskip('pandas')
+    input_data_changed.append(pd.DataFrame(input_data))
+  if labels is not None:
+    labels_changed = [labels, list(labels), tuple(labels)]
+  else:
+    labels_changed = [labels]
+  return input_data_changed, labels_changed
+
+
+@pytest.mark.integration
+@pytest.mark.parametrize('with_preprocessor', [True, False])
+@pytest.mark.parametrize('estimator, build_dataset', metric_learners,
+                         ids=ids_metric_learners)
+def test_array_like_inputs(estimator, build_dataset, with_preprocessor):
+  """Test that metric-learners can have as input (of all functions that are
+  applied on data) any array-like object."""
+  input_data, labels, preprocessor, X = build_dataset(with_preprocessor)
+
+  # we subsample the data for the test to be more efficient
+  input_data, _, labels, _ = train_test_split(input_data, labels,
+                                              train_size=20)
+  X = X[:10]
+
+  estimator = clone(estimator)
+  estimator.set_params(preprocessor=preprocessor)
+  set_random_state(estimator)
+  input_variants, label_variants = generate_array_like(input_data, labels)
+  for input_variant in input_variants:
+    for label_variant in label_variants:
+      estimator.fit(*remove_y_quadruplets(estimator, input_variant,
+                                          label_variant))
+    if hasattr(estimator, "predict"):
+      estimator.predict(input_variant)
+    if hasattr(estimator, "predict_proba"):
+      estimator.predict_proba(input_variant)  # anticipation in case some
+      # time we have that, or if ppl want to contribute with new algorithms
+      # it will be checked automatically
+    if hasattr(estimator, "decision_function"):
+      estimator.decision_function(input_variant)
+    if hasattr(estimator, "score"):
+      for label_variant in label_variants:
+        estimator.score(*remove_y_quadruplets(estimator, input_variant,
+                                              label_variant))
+
+  X_variants, _ = generate_array_like(X)
+  for X_variant in X_variants:
+    estimator.transform(X_variant)
+
+  pairs = np.array([[X[0], X[1]], [X[0], X[2]]])
+  pairs_variants, _ = generate_array_like(pairs)
+  for pairs_variant in pairs_variants:
+    estimator.score_pairs(pairs_variant)
+
 
 @pytest.mark.parametrize('with_preprocessor', [True, False])
 @pytest.mark.parametrize('estimator, build_dataset', pairs_learners,

test/test_utils.py

@@ -118,7 +118,7 @@ def build_quadruplets(with_preprocessor=False):
                (ITML_Supervised(max_iter=5), build_classification),
                (LSML_Supervised(), build_classification),
                (MMC_Supervised(max_iter=5), build_classification),
-               (RCA_Supervised(num_chunks=10), build_classification),
+               (RCA_Supervised(num_chunks=5), build_classification),
                (SDML_Supervised(prior='identity', balance_param=1e-5),
                build_classification)]
 ids_classifiers = list(map(lambda x: x.__class__.__name__,