[MRG] Fix test failures due to updated packages: deprecated pytest.warns(None) syntax + GLasso update in sklearn

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