MAINT: address comments from review #152 (review)

Author: William de Vazelhes

metric_learn/base_metric.py

@@ -241,47 +241,6 @@ def transform(self, X):
     return X_checked.dot(self.transformer_.T)
 
   def get_metric(self):
-    """Returns a function that takes as input two 1D arrays and outputs the
-    learned metric score on these two points.
-
-    This function will be independent from the metric learner that learned it
-    (it will not be modified if the initial metric learner is modified),
-    and it can be directly plugged into the `metric` argument of
-    scikit-learn's estimators.
-
-    Returns
-    -------
-    metric_fun : function
-      The function described above.
-
-    Examples
-    --------
-    .. doctest::
-
-      >>> from metric_learn import NCA
-      >>> from sklearn.datasets import make_classification
-      >>> from sklearn.neighbors import KNeighborsClassifier
-      >>> nca = NCA()
-      >>> X, y = make_classification()
-      >>> nca.fit(X, y)
-      >>> knn = KNeighborsClassifier(metric=nca.get_metric())
-      >>> knn.fit(X, y) # doctest: +NORMALIZE_WHITESPACE
-      KNeighborsClassifier(algorithm='auto', leaf_size=30,
-        metric=<function MahalanobisMixin.get_metric.<locals>.metric_fun
-                at 0x...>,
-        metric_params=None, n_jobs=None, n_neighbors=5, p=2,
-        weights='uniform')
-
-    See Also
-    --------
-    score_pairs : a method that returns the metric score between several pairs
-      of points. Unlike `get_metric`, this is a method of the metric learner
-      and therefore can change if the metric learner changes. Besides, it can
-      use the metric learner's preprocessor, and works on concatenated arrays.
-
-    :ref:`mahalanobis_distances` : The section of the project documentation
-      that describes Mahalanobis Distances.
-    """
     transformer_T = self.transformer_.T.copy()
 
     def metric_fun(u, v):
@@ -304,12 +263,14 @@ def metric_fun(u, v):
       return euclidean(u.dot(transformer_T), v.dot(transformer_T))
     return metric_fun
 
+  get_metric.__doc__ = BaseMetricLearner.get_metric.__doc__
+
   def metric(self):
     # TODO: remove this method in version 0.6.0
     warnings.warn(("`metric` is deprecated since version 0.5.0 and will be "
                    "removed in 0.6.0. Use `get_mahalanobis_matrix` instead."),
                   DeprecationWarning)
-    return self.transformer_.T.dot(self.transformer_)
+    return self.get_mahalanobis_matrix()
 
   def get_mahalanobis_matrix(self):
     """Returns a copy of the Mahalanobis matrix learned by the metric learner.
@@ -319,7 +280,7 @@ def get_mahalanobis_matrix(self):
     M : `numpy.ndarray`, shape=(n_components, n_features)
       The copy of the learned Mahalanobis matrix.
     """
-    return self.transformer_.T.dot(self.transformer_).copy()
+    return self.transformer_.T.dot(self.transformer_)
 
 
 class _PairsClassifierMixin(BaseMetricLearner):

test/test_mahalanobis_mixin.py

@@ -3,7 +3,7 @@
 import pytest
 import numpy as np
 from numpy.testing import assert_array_almost_equal, assert_allclose
-from scipy.spatial.distance import pdist, squareform, euclidean
+from scipy.spatial.distance import pdist, squareform, mahalanobis
 from sklearn import clone
 from sklearn.cluster import DBSCAN
 from sklearn.utils import check_random_state
@@ -172,10 +172,10 @@ def test_embed_is_linear(estimator, build_dataset):
 
 @pytest.mark.parametrize('estimator, build_dataset', metric_learners,
                          ids=ids_metric_learners)
-def test_get_metric_equivalent_to_transform_and_euclidean(estimator,
-                                                          build_dataset):
-  """Tests that the get_metric method of mahalanobis metric learners is the
-  euclidean distance in the transformed space
+def test_get_metric_equivalent_to_explicit_mahalanobis(estimator,
+                                                       build_dataset):
+  """Tests that using the get_metric method of mahalanobis metric learners is
+  equivalent to explicitely calling scipy's mahalanobis metric
   """
   rng = np.random.RandomState(42)
   input_data, labels, _, X = build_dataset()
@@ -185,8 +185,9 @@ def test_get_metric_equivalent_to_transform_and_euclidean(estimator,
   metric = model.get_metric()
   n_features = X.shape[1]
   a, b = (rng.randn(n_features), rng.randn(n_features))
-  euc_dist = euclidean(model.transform(a[None]), model.transform(b[None]))
-  assert_allclose(metric(a, b), euc_dist, rtol=1e-15)
+  expected_dist = mahalanobis(a[None], b[None],
+                              VI=model.get_mahalanobis_matrix())
+  assert_allclose(metric(a, b), expected_dist, rtol=1e-15)
 
 
 @pytest.mark.parametrize('estimator, build_dataset', metric_learners,