TST fix platform sensitive test: test_float_precision (#32035)

ogrisel · jeremiedbb · web-flow · commit c7866e6ff7d5 · 2025-09-02T15:55:41.000+02:00
Co-authored-by: Jérémie du Boisberranger &lt;jeremie@probabl.ai&gt;
diff --git a/sklearn/cluster/tests/test_k_means.py b/sklearn/cluster/tests/test_k_means.py
@@ -7,6 +7,7 @@
 import numpy as np
 import pytest
 from scipy import sparse as sp
+from threadpoolctl import threadpool_info
 
 from sklearn.base import clone
 from sklearn.cluster import KMeans, MiniBatchKMeans, k_means, kmeans_plusplus
@@ -744,7 +745,7 @@ def test_transform(Estimator, global_random_seed):
     # In particular, diagonal must be 0
     assert_array_equal(Xt.diagonal(), np.zeros(n_clusters))
 
-    # Transorfming X should return the pairwise distances between X and the
+    # Transforming X should return the pairwise distances between X and the
     # centers
     Xt = km.transform(X)
     assert_allclose(Xt, pairwise_distances(X, km.cluster_centers_))
@@ -794,6 +795,13 @@ def test_k_means_function(global_random_seed):
     ids=data_containers_ids,
 )
 @pytest.mark.parametrize("Estimator", [KMeans, MiniBatchKMeans])
+@pytest.mark.skipif(
+    not any(i for i in threadpool_info() if i["user_api"] == "blas"),
+    reason=(
+        "Fails for some global_random_seed on Atlas which cannot be detected by "
+        "threadpoolctl."
+    ),
+)
 def test_float_precision(Estimator, input_data, global_random_seed):
     # Check that the results are the same for single and double precision.
     km = Estimator(n_init=1, random_state=global_random_seed)
@@ -822,10 +830,11 @@ def test_float_precision(Estimator, input_data, global_random_seed):
 
     # compare arrays with low precision since the difference between 32 and
     # 64 bit comes from an accumulation of rounding errors.
-    assert_allclose(inertia[np.float32], inertia[np.float64], rtol=1e-4)
-    assert_allclose(Xt[np.float32], Xt[np.float64], atol=Xt[np.float64].max() * 1e-4)
+    rtol = 1e-4
+    assert_allclose(inertia[np.float32], inertia[np.float64], rtol=rtol)
+    assert_allclose(Xt[np.float32], Xt[np.float64], atol=Xt[np.float64].max() * rtol)
     assert_allclose(
-        centers[np.float32], centers[np.float64], atol=centers[np.float64].max() * 1e-4
+        centers[np.float32], centers[np.float64], atol=centers[np.float64].max() * rtol
     )
     assert_array_equal(labels[np.float32], labels[np.float64])
 
