[WIP] Add classes attribute and test for CalibratedClassifierCV

metric_learn/base_metric.py

@@ -298,12 +298,18 @@ class _PairsClassifierMixin(BaseMetricLearner):
   """
   Attributes
   ----------
+  classes_ : `list`
+      The possible labels of the pairs the metric learner can fit on.
+      `classes_ = [-1, 1]`, where -1 means points in a pair are dissimilar
+      (negative label), and 1 means they are similar (positive label).
+
   threshold_ : `float`
       If the distance metric between two points is lower than this threshold,
       points will be classified as similar, otherwise they will be
       classified as dissimilar.
   """
 
+  classes_ = [-1, 1]
   _tuple_size = 2  # number of points in a tuple, 2 for pairs
 
   def predict(self, pairs):

metric_learn/itml.py

@@ -150,6 +150,11 @@ class ITML(_BaseITML, _PairsClassifierMixin):
       The linear transformation ``L`` deduced from the learned Mahalanobis
       metric (See function `transformer_from_metric`.)
 
+  classes_ : `list`
+      The possible labels of the pairs `ITML` can fit on. `classes_ = [-1, 1]`,
+      where -1 means points in a pair are dissimilar (negative label), and 1
+      means they are similar (positive label).
+
   threshold_ : `float`
       If the distance metric between two points is lower than this threshold,
       points will be classified as similar, otherwise they will be

metric_learn/mmc.py

@@ -29,7 +29,16 @@
 
 
 class _BaseMMC(MahalanobisMixin):
-  """Mahalanobis Metric for Clustering (MMC)"""
+  """Mahalanobis Metric for Clustering (MMC)
+
+  Attributes
+  ----------
+
+  classes_ : `list`
+      The possible labels of the pairs `MMC` can fit on. `classes_ = [-1, 1]`,
+      where -1 means points in a pair are dissimilar (negative label), and 1
+      means they are similar (positive label).
+  """
 
   _tuple_size = 2  # constraints are pairs
 

metric_learn/sdml.py

@@ -143,6 +143,11 @@ class SDML(_BaseSDML, _PairsClassifierMixin):
       The linear transformation ``L`` deduced from the learned Mahalanobis
       metric (See function `transformer_from_metric`.)
 
+  classes_ : `list`
+      The possible labels of the pairs `SDML` can fit on. `classes_ = [-1, 1]`,
+      where -1 means points in a pair are dissimilar (negative label), and 1
+      means they are similar (positive label).
+
   threshold_ : `float`
       If the distance metric between two points is lower than this threshold,
       points will be classified as similar, otherwise they will be

test/test_sklearn_compat.py

@@ -1,5 +1,6 @@
 import pytest
 import unittest
+from sklearn.calibration import CalibratedClassifierCV
 from sklearn.utils.estimator_checks import check_estimator
 from sklearn.base import TransformerMixin
 from sklearn.pipeline import make_pipeline
@@ -18,9 +19,9 @@
 from sklearn.metrics.scorer import get_scorer
 from sklearn.utils.testing import _get_args
 from test.test_utils import (metric_learners, ids_metric_learners,
-                             mock_preprocessor, tuples_learners,
-                             ids_tuples_learners, pairs_learners,
-                             ids_pairs_learners, remove_y_quadruplets,
+                             mock_preprocessor, pairs_learners,
+                             ids_pairs_learners, tuples_learners,
+                             ids_tuples_learners, remove_y_quadruplets,
                              quadruplets_learners)
 
 
@@ -106,6 +107,31 @@ def stable_init(self, num_dims=None, pca_comps=None,
 # ---------------------- Test scikit-learn compatibility ----------------------
 
 
+@pytest.mark.parametrize('with_preprocessor',
+                         [True,
+                          # TODO: uncomment the below line as soon as
+                          # https://github.com/scikit-learn/scikit-learn/
+                          # issues/13077 is solved:
+                          # False,
+                          ])
+@pytest.mark.parametrize('estimator, build_dataset', pairs_learners,
+                         ids=ids_pairs_learners)
+def test_calibrated_classifier_CV(estimator, build_dataset,
+                                  with_preprocessor):
+  """Tests that metric-learn tuples estimators' work with scikit-learn's
+  CalibratedClassifierCV.
+  """
+  input_data, labels, preprocessor, _ = build_dataset(with_preprocessor)
+  estimator = clone(estimator)
+  estimator.set_params(preprocessor=preprocessor)
+  set_random_state(estimator)
+  calibrated_clf = CalibratedClassifierCV(estimator)
+
+  # test fit and predict_proba
+  calibrated_clf.fit(input_data, labels)
+  calibrated_clf.predict_proba(input_data)
+
+
 @pytest.mark.parametrize('with_preprocessor', [True, False])
 @pytest.mark.parametrize('estimator, build_dataset', pairs_learners,
                          ids=ids_pairs_learners)