[MRG] Fix test failures due to updated packages: deprecated pytest.wa…

…rns(None) syntax + GLasso update in sklearn (#357)

* Fix GLasso import for SDML for newer sklearn versions

* fix import and argument issue

* also fix deprecated pytest.warns(None) syntex

* fix flake8

metric_learn/sdml.py

@@ -6,7 +6,13 @@
 import numpy as np
 from sklearn.base import TransformerMixin
 from scipy.linalg import pinvh
-from sklearn.covariance import graphical_lasso
+try:
+  from sklearn.covariance._graph_lasso import (
+    _graphical_lasso as graphical_lasso
+  )
+except ImportError:
+  from sklearn.covariance import graphical_lasso
+
 from sklearn.exceptions import ConvergenceWarning
 
 from .base_metric import MahalanobisMixin, _PairsClassifierMixin
@@ -79,9 +85,9 @@ def _fit(self, pairs, y):
                                 msg=self.verbose,
                                 Theta0=theta0, Sigma0=sigma0)
       else:
-        _, M = graphical_lasso(emp_cov, alpha=self.sparsity_param,
-                               verbose=self.verbose,
-                               cov_init=sigma0)
+        _, M, *_ = graphical_lasso(emp_cov, alpha=self.sparsity_param,
+                                   verbose=self.verbose,
+                                   cov_init=sigma0)
       raised_error = None
       w_mahalanobis, _ = np.linalg.eigh(M)
       not_spd = any(w_mahalanobis < 0.)

test/metric_learn_test.py

@@ -1,3 +1,4 @@
+import warnings
 import unittest
 import re
 import pytest
@@ -734,12 +735,12 @@ def test_raises_no_warning_installed_skggm(self):
     pairs = np.array([[[-10., 0.], [10., 0.]], [[0., -55.], [0., -60]]])
     y_pairs = [1, -1]
     X, y = make_classification(random_state=42)
-    with pytest.warns(None) as records:
+    with warnings.catch_warnings(record=True) as records:
       sdml = SDML(prior='covariance')
       sdml.fit(pairs, y_pairs)
     for record in records:
       assert record.category is not ConvergenceWarning
-    with pytest.warns(None) as records:
+    with warnings.catch_warnings(record=True) as records:
       sdml_supervised = SDML_Supervised(prior='identity', balance_param=1e-5)
       sdml_supervised.fit(X, y)
     for record in records:
@@ -999,7 +1000,7 @@ def test_rank_deficient_returns_warning(self):
            'for instance using `sklearn.decomposition.PCA` as a '
            'preprocessing step.')
 
-    with pytest.warns(None) as raised_warnings:
+    with warnings.catch_warnings(record=True) as raised_warnings:
       rca.fit(X, y)
     assert any(str(w.message) == msg for w in raised_warnings)
 
@@ -1034,7 +1035,7 @@ def test_bad_parameters(self):
            'Increase the number or size of the chunks to correct '
            'this problem.'
            )
-    with pytest.warns(None) as raised_warning:
+    with warnings.catch_warnings(record=True) as raised_warning:
       rca.fit(X, y)
     assert any(str(w.message) == msg for w in raised_warning)
 

test/test_base_metric.py

@@ -1,4 +1,5 @@
 from numpy.core.numeric import array_equal
+import warnings
 import pytest
 import re
 import unittest
@@ -226,15 +227,15 @@ def test_get_metric_works_does_not_raise(estimator, build_dataset):
                                        (X[0][None], X[1][None])]
 
   for u, v in list_test_get_metric_doesnt_raise:
-    with pytest.warns(None) as record:
+    with warnings.catch_warnings(record=True) as record:
       metric(u, v)
     assert len(record) == 0
 
   # Test that the scalar case works
   model.components_ = np.array([3.1])
   metric = model.get_metric()
   for u, v in [(5, 6.7), ([5], [6.7]), ([[5]], [[6.7]])]:
-    with pytest.warns(None) as record:
+    with warnings.catch_warnings(record=True) as record:
       metric(u, v)
     assert len(record) == 0
 

test/test_pairs_classifiers.py

@@ -1,5 +1,6 @@
 from functools import partial
 
+import warnings
 import pytest
 from numpy.testing import assert_array_equal
 from scipy.spatial.distance import euclidean
@@ -136,7 +137,7 @@ def test_threshold_different_scores_is_finite(estimator, build_dataset,
   estimator.set_params(preprocessor=preprocessor)
   set_random_state(estimator)
   estimator.fit(input_data, labels)
-  with pytest.warns(None) as record:
+  with warnings.catch_warnings(record=True) as record:
     estimator.calibrate_threshold(input_data, labels, **kwargs)
   assert len(record) == 0
 
@@ -383,7 +384,7 @@ def test_calibrate_threshold_valid_parameters(valid_args):
   pairs, y = rng.randn(20, 2, 5), rng.choice([-1, 1], size=20)
   pairs_learner = IdentityPairsClassifier()
   pairs_learner.fit(pairs, y)
-  with pytest.warns(None) as record:
+  with warnings.catch_warnings(record=True) as record:
     pairs_learner.calibrate_threshold(pairs, y, **valid_args)
   assert len(record) == 0
 
@@ -518,7 +519,7 @@ def test_validate_calibration_params_valid_parameters(
   # test that no warning message is returned if valid arguments are given to
   # _validate_calibration_params for all pairs metric learners, as well as
   # a mocking example, and the class itself
-  with pytest.warns(None) as record:
+  with warnings.catch_warnings(record=True) as record:
     estimator._validate_calibration_params(**valid_args)
   assert len(record) == 0
 

test/test_utils.py

@@ -1,3 +1,4 @@
+import warnings
 import pytest
 from scipy.linalg import eigh, pinvh
 from collections import namedtuple
@@ -353,7 +354,7 @@ def test_check_tuples_valid_tuple_size(tuple_size):
   checks that checking the number of tuples (pairs, quadruplets, etc) raises
   no warning if there is the right number of points in a tuple.
   """
-  with pytest.warns(None) as record:
+  with warnings.catch_warnings(record=True) as record:
     check_input(tuples_prep(), type_of_inputs='tuples',
                 preprocessor=mock_preprocessor, tuple_size=tuple_size)
     check_input(tuples_no_prep(), type_of_inputs='tuples', preprocessor=None,
@@ -378,7 +379,7 @@ def test_check_tuples_valid_tuple_size(tuple_size):
                                     [[2.6, 2.3], [3.4, 5.0]]])])
 def test_check_tuples_valid_with_preprocessor(tuples):
   """Test that valid inputs when using a preprocessor raises no warning"""
-  with pytest.warns(None) as record:
+  with warnings.catch_warnings(record=True) as record:
     check_input(tuples, type_of_inputs='tuples',
                 preprocessor=mock_preprocessor)
   assert len(record) == 0
@@ -399,7 +400,7 @@ def test_check_tuples_valid_with_preprocessor(tuples):
                            ((3, 1), (4, 4), (29, 4)))])
 def test_check_tuples_valid_without_preprocessor(tuples):
   """Test that valid inputs when using no preprocessor raises no warning"""
-  with pytest.warns(None) as record:
+  with warnings.catch_warnings(record=True) as record:
     check_input(tuples, type_of_inputs='tuples', preprocessor=None)
   assert len(record) == 0
 
@@ -408,12 +409,12 @@ def test_check_tuples_behaviour_auto_dtype():
   """Checks that check_tuples allows by default every type if using a
   preprocessor, and numeric types if using no preprocessor"""
   tuples_prep = [['img1.png', 'img2.png'], ['img3.png', 'img5.png']]
-  with pytest.warns(None) as record:
+  with warnings.catch_warnings(record=True) as record:
     check_input(tuples_prep, type_of_inputs='tuples',
                 preprocessor=mock_preprocessor)
   assert len(record) == 0
 
-  with pytest.warns(None) as record:
+  with warnings.catch_warnings(record=True) as record:
       check_input(tuples_no_prep(), type_of_inputs='tuples')  # numeric type
   assert len(record) == 0
 
@@ -549,7 +550,7 @@ def test_check_classic_invalid_dtype_not_convertible(preprocessor, points):
                                     [2.6, 2.3]])])
 def test_check_classic_valid_with_preprocessor(points):
   """Test that valid inputs when using a preprocessor raises no warning"""
-  with pytest.warns(None) as record:
+  with warnings.catch_warnings(record=True) as record:
     check_input(points, type_of_inputs='classic',
                 preprocessor=mock_preprocessor)
   assert len(record) == 0
@@ -570,7 +571,7 @@ def test_check_classic_valid_with_preprocessor(points):
                            (3, 1, 4, 4, 29, 4))])
 def test_check_classic_valid_without_preprocessor(points):
   """Test that valid inputs when using no preprocessor raises no warning"""
-  with pytest.warns(None) as record:
+  with warnings.catch_warnings(record=True) as record:
     check_input(points, type_of_inputs='classic', preprocessor=None)
   assert len(record) == 0
 
@@ -585,12 +586,12 @@ def test_check_classic_behaviour_auto_dtype():
   """Checks that check_input (for points) allows by default every type if
   using a preprocessor, and numeric types if using no preprocessor"""
   points_prep = ['img1.png', 'img2.png', 'img3.png', 'img5.png']
-  with pytest.warns(None) as record:
+  with warnings.catch_warnings(record=True) as record:
     check_input(points_prep, type_of_inputs='classic',
                 preprocessor=mock_preprocessor)
   assert len(record) == 0
 
-  with pytest.warns(None) as record:
+  with warnings.catch_warnings(record=True) as record:
       check_input(points_no_prep(), type_of_inputs='classic')  # numeric type
   assert len(record) == 0