[MRG] Add checks for labels when having pairs

metric_learn/_util.py

@@ -137,6 +137,11 @@ def check_input(input_data, y=None, preprocessor=None,
     input_data = check_input_tuples(input_data, context, preprocessor,
                                     args_for_sk_checks, tuple_size)
 
+    # if we have y and the input data are pairs, we need to ensure
+    # the labels are in [-1, 1]:
+    if y is not None and input_data.shape[1] == 2:
+      check_y_valid_values_for_pairs(y)
+
   else:
     raise ValueError("Unknown value {} for type_of_inputs. Valid values are "
                      "'classic' or 'tuples'.".format(type_of_inputs))
@@ -297,6 +302,13 @@ def check_tuple_size(tuples, tuple_size, context):
     raise ValueError(msg_t)
 
 
+def check_y_valid_values_for_pairs(y):
+  """Checks that y values are in [-1, 1]"""
+  if not np.array_equal(np.abs(y), np.ones_like(y)):
+    raise ValueError("When training on pairs, the labels (y) should contain "
+                     "only values in [-1, 1]. Found an incorrect value.")
+
+
 class ArrayIndexer:
 
   def __init__(self, X):

test/test_utils.py

@@ -10,7 +10,8 @@
 from metric_learn._util import (check_input, make_context, preprocess_tuples,
                                 make_name, preprocess_points,
                                 check_collapsed_pairs, validate_vector,
-                                _check_sdp_from_eigen)
+                                _check_sdp_from_eigen,
+                                check_y_valid_values_for_pairs)
 from metric_learn import (ITML, LSML, MMC, RCA, SDML, Covariance, LFDA,
                           LMNN, MLKR, NCA, ITML_Supervised, LSML_Supervised,
                           MMC_Supervised, RCA_Supervised, SDML_Supervised,
@@ -1067,3 +1068,73 @@ def test_check_sdp_from_eigen_positive_err_messages():
   _check_sdp_from_eigen(w, 1.)
   _check_sdp_from_eigen(w, 0.)
   _check_sdp_from_eigen(w, None)
+
+
+@pytest.mark.unit
+@pytest.mark.parametrize('wrong_labels',
+                         [[0.5, 0.6, 0.7, 0.8, 0.9],
+                          np.random.RandomState(42).randn(5),
+                          np.random.RandomState(42).choice([0, 1], size=5)])
+def test_check_y_valid_values_for_pairs(wrong_labels):
+  expected_msg = ("When training on pairs, the labels (y) should contain "
+                  "only values in [-1, 1]. Found an incorrect value.")
+  with pytest.raises(ValueError) as raised_error:
+    check_y_valid_values_for_pairs(wrong_labels)
+  assert str(raised_error.value) == expected_msg
+
+
+@pytest.mark.integration
+@pytest.mark.parametrize('wrong_labels',
+                         [[0.5, 0.6, 0.7, 0.8, 0.9],
+                          np.random.RandomState(42).randn(5),
+                          np.random.RandomState(42).choice([0, 1], size=5)])
+def test_check_input_invalid_tuples_without_preprocessor(wrong_labels):
+  pairs = np.random.RandomState(42).randn(5, 2, 3)
+  expected_msg = ("When training on pairs, the labels (y) should contain "
+                  "only values in [-1, 1]. Found an incorrect value.")
+  with pytest.raises(ValueError) as raised_error:
+    check_input(pairs, wrong_labels, preprocessor=None,
+                type_of_inputs='tuples')
+  assert str(raised_error.value) == expected_msg
+
+
+@pytest.mark.integration
+@pytest.mark.parametrize('wrong_labels',
+                         [[0.5, 0.6, 0.7, 0.8, 0.9],
+                          np.random.RandomState(42).randn(5),
+                          np.random.RandomState(42).choice([0, 1], size=5)])
+def test_check_input_invalid_tuples_with_preprocessor(wrong_labels):
+  n_samples, n_features, n_pairs = 10, 4, 5
+  rng = np.random.RandomState(42)
+  pairs = rng.randint(10, size=(n_pairs, 2))
+  preprocessor = rng.randn(n_samples, n_features)
+  expected_msg = ("When training on pairs, the labels (y) should contain "
+                  "only values in [-1, 1]. Found an incorrect value.")
+  with pytest.raises(ValueError) as raised_error:
+    check_input(pairs, wrong_labels, preprocessor=ArrayIndexer(preprocessor),
+                type_of_inputs='tuples')
+  assert str(raised_error.value) == expected_msg
+
+
+@pytest.mark.integration
+@pytest.mark.parametrize('with_preprocessor', [True, False])
+@pytest.mark.parametrize('estimator, build_dataset', pairs_learners,
+                         ids=ids_pairs_learners)
+def test_check_input_pairs_learners_invalid_y(estimator, build_dataset,
+                                              with_preprocessor):
+  """checks that the only allowed labels for learning pairs are +1 and -1"""
+  input_data, labels, _, X = build_dataset()
+  wrong_labels_list = [labels + 0.5,
+                       np.random.RandomState(42).randn(len(labels)),
+                       np.random.RandomState(42).choice([0, 1],
+                                                        size=len(labels))]
+  model = clone(estimator)
+  set_random_state(model)
+
+  expected_msg = ("When training on pairs, the labels (y) should contain "
+                  "only values in [-1, 1]. Found an incorrect value.")
+
+  for wrong_labels in wrong_labels_list:
+    with pytest.raises(ValueError) as raised_error:
+      model.fit(input_data, wrong_labels)
+  assert str(raised_error.value) == expected_msg