Treat warnings as errors in tests

CHANGELOG

@@ -13,6 +13,7 @@ The rules for CHANGELOG file:
 
 Unreleased
 ----------
+- Fix a couple of ``DeprecationWarnings`` and ``UserWarnings`` (#280)
 - Fix PCovC scaling (#270)
 - Refactor of reconstruction measures(#275)
 - Code cleanup of Base classes (#264)

pyproject.toml

@@ -83,13 +83,16 @@ include = [
 output = 'tests/coverage.xml'
 
 [tool.pytest.ini_options]
-testpaths = ["tests"]
 addopts = [
     "--cov",
     "--cov-append",
     "--cov-report=",
     "--import-mode=append",
 ]
+# filterwarnings = [
+#     "error",
+# ]
+testpaths = ["tests"]
 
 [tool.ruff]
 exclude = ["docs/src/examples/", "src/torchpme/_version.py"]

src/skmatter/_selection.py

@@ -1019,7 +1019,7 @@ def _update_hausdorff(self, X, y, last_selected):
             )
 
         # update in-place the Hausdorff distance list
-        np.minimum(self.hausdorff_, new_dist, self.hausdorff_)
+        np.minimum(self.hausdorff_, new_dist, out=self.hausdorff_)
 
     def _update_post_selection(self, X, y, last_selected):
         """
@@ -1163,7 +1163,7 @@ def _update_hausdorff(self, X, y, last_selected):
         )
 
         # update in-place the Hausdorff distance list
-        np.minimum(self.hausdorff_, new_dist, self.hausdorff_)
+        np.minimum(self.hausdorff_, new_dist, out=self.hausdorff_)
 
     def _update_post_selection(self, X, y, last_selected):
         """Saves the most recent selections, increments the counter, and, recomputes

src/skmatter/decomposition/_kpcov.py

@@ -12,7 +12,7 @@
 from sklearn.decomposition._pca import _infer_dimension
 from sklearn.utils import check_random_state
 from sklearn.utils._arpack import _init_arpack_v0
-from sklearn.utils.extmath import randomized_svd, stable_cumsum, svd_flip
+from sklearn.utils.extmath import randomized_svd, svd_flip
 from sklearn.utils.validation import check_is_fitted
 from sklearn.utils.validation import validate_data
 from sklearn.metrics.pairwise import pairwise_kernels
@@ -258,7 +258,7 @@ def _decompose_full(self, mat):
             # side='right' ensures that number of features selected
             # their variance is always greater than self.n_components_ float
             # passed. More discussion in issue: #15669
-            ratio_cumsum = stable_cumsum(explained_variance_ratio_)
+            ratio_cumsum = np.cumulative_sum(explained_variance_ratio_)
             self.n_components_ = (
                 np.searchsorted(ratio_cumsum, self.n_components_, side="right") + 1
             )

src/skmatter/decomposition/_pcov.py

@@ -14,7 +14,7 @@
 from sklearn.linear_model._base import LinearModel
 from sklearn.utils import check_random_state
 from sklearn.utils._arpack import _init_arpack_v0
-from sklearn.utils.extmath import randomized_svd, stable_cumsum, svd_flip
+from sklearn.utils.extmath import randomized_svd, svd_flip
 from sklearn.utils.validation import check_is_fitted
 
 from skmatter.utils import pcovr_covariance, pcovr_kernel
@@ -288,7 +288,7 @@ def _decompose_full(self, mat):
             # side='right' ensures that number of features selected
             # their variance is always greater than self.n_components_ float
             # passed. More discussion in issue: #15669
-            ratio_cumsum = stable_cumsum(explained_variance_ratio_)
+            ratio_cumsum = np.cumulative_sum(explained_variance_ratio_)
             self.n_components_ = (
                 np.searchsorted(ratio_cumsum, self.n_components_, side="right") + 1
             )

src/skmatter/sample_selection/_voronoi_fps.py

@@ -312,7 +312,7 @@ def _update_post_selection(self, X, y, last_selected):
 
             updated_points = np.where(self.new_dist_ < self.hausdorff_)[0]
             np.minimum(
-                self.hausdorff_, self.new_dist_, self.hausdorff_, casting="unsafe"
+                self.hausdorff_, self.new_dist_, out=self.hausdorff_, casting="unsafe"
             )
         else:
             updated_points = np.array([])

tests/test_kernel_pcovc.py

@@ -10,6 +10,7 @@
 from sklearn.preprocessing import StandardScaler
 from sklearn.linear_model import LogisticRegression, RidgeClassifier
 from sklearn.metrics.pairwise import pairwise_kernels
+import pytest
 
 from skmatter.decomposition import KernelPCovC
 
@@ -337,6 +338,7 @@ def test_scale_z_parameter(self):
 
         kpcovc_unscaled = self.model(scale_z=False)
         kpcovc_unscaled.fit(self.X, self.Y)
+
         assert not np.allclose(kpcovc_scaled.pkt_, kpcovc_unscaled.pkt_)
 
     def test_z_scaling(self):
@@ -345,11 +347,7 @@ def test_z_scaling(self):
         if it is.
         """
         kpcovc = self.model(n_components=2, scale_z=True)
-
-        with warnings.catch_warnings():
-            kpcovc.fit(self.X, self.Y)
-            warnings.simplefilter("error")
-            self.assertEqual(1 + 1, 2)
+        kpcovc.fit(self.X, self.Y)
 
         kpcovc = self.model(n_components=2, scale_z=False, z_mean_tol=0, z_var_tol=0)
 

tests/test_pcovc.py

@@ -10,6 +10,7 @@
 from sklearn.naive_bayes import GaussianNB
 from sklearn.preprocessing import StandardScaler
 from sklearn.utils.validation import check_X_y
+import pytest
 
 from skmatter.decomposition import PCovC
 
@@ -186,9 +187,10 @@ def test_select_sample_space(self):
         n_samples = 2
 
         # select range where there are at least 2 classes in Y
-        pcovc.fit(self.X[49 : 49 + n_samples], self.Y[49 : 49 + n_samples])
+        with pytest.warns(match="class does not automatically center data"):
+            pcovc.fit(self.X[49 : 49 + n_samples], self.Y[49 : 49 + n_samples])
 
-        self.assertTrue(pcovc.space_ == "sample")
+        assert pcovc.space_ == "sample"
 
     def test_bad_space(self):
         """
@@ -397,25 +399,20 @@ def test_centering(self):
         """
         pcovc = self.model(n_components=2, tol=1e-12)
         X = self.X.copy() + np.random.uniform(-1, 1, self.X.shape[1])
-        with warnings.catch_warnings(record=True) as w:
+        m = (
+            "This class does not automatically center data, and your data mean is "
+            "greater than the supplied tolerance."
+        )
+        with pytest.warns(match=m):
             pcovc.fit(X, self.Y)
-            self.assertEqual(
-                str(w[0].message),
-                "This class does not automatically center data, and your data "
-                "mean is greater than the supplied tolerance.",
-            )
 
     def test_z_scaling(self):
         """
         Check that PCovC raises a warning if Z is not of scale, and does not
         if it is.
         """
         pcovc = self.model(n_components=2, scale_z=True)
-
-        with warnings.catch_warnings():
-            pcovc.fit(self.X, self.Y)
-            warnings.simplefilter("error")
-            self.assertEqual(1 + 1, 2)
+        pcovc.fit(self.X, self.Y)
 
         pcovc = self.model(n_components=2, scale_z=False, z_mean_tol=0, z_var_tol=0)
 
@@ -577,9 +574,11 @@ def test_incompatible_classifier(self):
     def test_none_classifier(self):
         pcovc = PCovC(mixing=0.5, classifier=None)
 
-        pcovc.fit(self.X, self.Y)
-        self.assertTrue(pcovc.classifier is None)
-        self.assertTrue(pcovc.classifier_ is not None)
+        with pytest.warns(match="class does not automatically scale Z"):
+            pcovc.fit(self.X, self.Y)
+
+        assert pcovc.classifier is None
+        assert pcovc.classifier_ is not None
 
     def test_incompatible_coef_shape(self):
         cl_multi = LogisticRegression()
@@ -617,6 +616,7 @@ def test_scale_z_parameter(self):
 
         pcovc_unscaled = self.model(scale_z=False)
         pcovc_unscaled.fit(self.X, self.Y)
+
         assert not np.allclose(
             pcovc_scaled.singular_values_, pcovc_unscaled.singular_values_
         )

tests/test_pcovr.py

@@ -1,5 +1,4 @@
 import unittest
-import warnings
 
 import numpy as np
 from sklearn import exceptions
@@ -9,6 +8,7 @@
 from sklearn.linear_model import Ridge
 from sklearn.preprocessing import StandardScaler
 from sklearn.utils.validation import check_X_y
+import pytest
 
 from skmatter.decomposition import PCovR
 
@@ -167,9 +167,11 @@ def test_select_sample_space(self):
         pcovr = self.model(n_components=2, tol=1e-12)
 
         n_samples = self.X.shape[1] - 1
-        pcovr.fit(self.X[:n_samples], self.Y[:n_samples])
 
-        self.assertTrue(pcovr.space_ == "sample")
+        with pytest.warns(match="class does not automatically center data"):
+            pcovr.fit(self.X[:n_samples], self.Y[:n_samples])
+
+        assert pcovr.space_ == "sample"
 
     def test_bad_space(self):
         """
@@ -280,13 +282,11 @@ def test_good_n_components(self):
 
     def test_bad_n_components(self):
         """Check that PCovR will not work with any prohibited values of n_components."""
-        with self.assertRaises(ValueError) as cm:
-            pcovr = self.model(n_components="mle", svd_solver="full")
-            pcovr.fit(self.X[:2], self.Y[:2])
-        self.assertEqual(
-            str(cm.exception),
-            "n_components='mle' is only supported if n_samples >= n_features",
-        )
+        pcovr = self.model(n_components="mle", svd_solver="full")
+        m = "n_components='mle' is only supported if n_samples >= n_features"
+        with pytest.raises(ValueError, match=m):
+            with pytest.warns(match="class does not automatically center data"):
+                pcovr.fit(self.X[:2], self.Y[:2])
 
         with self.subTest(type="negative_ncomponents"):
             with self.assertRaises(ValueError) as cm:
@@ -376,13 +376,12 @@ def test_centering(self):
         """
         pcovr = self.model(n_components=2, tol=1e-12)
         X = self.X.copy() + np.random.uniform(-1, 1, self.X.shape[1])
-        with warnings.catch_warnings(record=True) as w:
+        m = (
+            "This class does not automatically center data, and your data mean is "
+            "greater than the supplied tolerance."
+        )
+        with pytest.warns(match=m):
             pcovr.fit(X, self.Y)
-            self.assertEqual(
-                str(w[0].message),
-                "This class does not automatically center data, and your data mean is "
-                "greater than the supplied tolerance.",
-            )
 
     def test_T_shape(self):
         """Check that PCovR returns a latent space projection consistent with the shape