Merge branch 'master' of https://github.com/scikit-learn-contrib/metr…

…ic-learn into score-deprecation

README.rst

@@ -1,4 +1,4 @@
-|Travis-CI Build Status| |License| |PyPI version| |Code coverage|
+|GitHub Actions Build Status| |License| |PyPI version| |Code coverage|
 
 metric-learn: Metric Learning in Python
 =======================================
@@ -67,8 +67,8 @@ Bibtex entry::
 
 .. _sphinx documentation: http://contrib.scikit-learn.org/metric-learn/
 
-.. |Travis-CI Build Status| image:: https://api.travis-ci.org/scikit-learn-contrib/metric-learn.svg?branch=master
-   :target: https://travis-ci.org/scikit-learn-contrib/metric-learn
+.. |GitHub Actions Build Status| image:: https://github.com/scikit-learn-contrib/metric-learn/workflows/CI/badge.svg
+   :target: https://github.com/scikit-learn-contrib/metric-learn/actions?query=event%3Apush+branch%3Amaster
 .. |License| image:: http://img.shields.io/:license-mit-blue.svg?style=flat
    :target: http://badges.mit-license.org
 .. |PyPI version| image:: https://badge.fury.io/py/metric-learn.svg

test/metric_learn_test.py

@@ -79,12 +79,17 @@ def test_singular_returns_pseudo_inverse(self):
 class TestSCML(object):
   @pytest.mark.parametrize('basis', ('lda', 'triplet_diffs'))
   def test_iris(self, basis):
+    """
+    SCML applied to Iris dataset should give better results when
+    computing class separation.
+    """
     X, y = load_iris(return_X_y=True)
+    before = class_separation(X, y)
     scml = SCML_Supervised(basis=basis, n_basis=85, k_genuine=7, k_impostor=5,
                            random_state=42)
     scml.fit(X, y)
-    csep = class_separation(scml.transform(X), y)
-    assert csep < 0.24
+    after = class_separation(scml.transform(X), y)
+    assert before > after + 0.03  # It's better by a margin of 0.03
 
   def test_big_n_features(self):
     X, y = make_classification(n_samples=100, n_classes=3, n_features=60,