Merge branch 'scikit-learn-contrib:master' into master

Author: mvargas33

doc/supervised.rst

@@ -292,6 +292,11 @@ same class are not imposed to be close.
     lfda = LFDA(k=2, dim=2)
     lfda.fit(X, Y)
 
+.. note::
+    LDFA suffers from a problem called “sign indeterminacy”, which means the sign of the ``components`` and the output from transform depend on a random state. This is directly related to the calculation of eigenvectors in the algorithm. The same input ran in different times might lead to different transforms, but both valid.
+    
+    To work around this, fit instances of this class to data once, then keep the instance around to do transformations.
+
 .. topic:: References:
 
     .. [1] Sugiyama. `Dimensionality Reduction of Multimodal Labeled Data by Local

test/metric_learn_test.py

@@ -929,7 +929,7 @@ def test_singleton_class(self):
       X = X[[ind_0[0], ind_1[0], ind_2[0]]]
       y = y[[ind_0[0], ind_1[0], ind_2[0]]]
 
-      A = make_spd_matrix(X.shape[1], X.shape[1])
+      A = make_spd_matrix(n_dim=X.shape[1], random_state=X.shape[1])
       nca = NCA(init=A, max_iter=30, n_components=X.shape[1])
       nca.fit(X, y)
       assert_array_equal(nca.components_, A)
@@ -940,7 +940,7 @@ def test_one_class(self):
       X = self.iris_points[self.iris_labels == 0]
       y = self.iris_labels[self.iris_labels == 0]
 
-      A = make_spd_matrix(X.shape[1], X.shape[1])
+      A = make_spd_matrix(n_dim=X.shape[1], random_state=X.shape[1])
       nca = NCA(init=A, max_iter=30, n_components=X.shape[1])
       nca.fit(X, y)
       assert_array_equal(nca.components_, A)

test/test_mahalanobis_mixin.py

@@ -503,12 +503,12 @@ def test_init_mahalanobis(estimator, build_dataset):
       model.fit(input_data, labels)
 
       # Initialize with a random spd matrix
-      init = make_spd_matrix(X.shape[1], random_state=rng)
+      init = make_spd_matrix(n_dim=X.shape[1], random_state=rng)
       model.set_params(**{param: init})
       model.fit(input_data, labels)
 
       # init.shape[1] must match X.shape[1]
-      init = make_spd_matrix(X.shape[1] + 1, X.shape[1] + 1)
+      init = make_spd_matrix(n_dim=X.shape[1] + 1, random_state=rng)
       model.set_params(**{param: init})
       msg = ('The input dimensionality {} of the given '
              'mahalanobis matrix `{}` must match the '