CI Disable network when SciPy requires it (scikit-learn#25743)

sklearn/conftest.py

@@ -2,6 +2,8 @@
 from functools import wraps
 import platform
 import sys
+from contextlib import suppress
+from unittest import SkipTest
 
 import pytest
 import numpy as np
@@ -11,6 +13,7 @@
 from sklearn.utils import _IS_32BIT
 from sklearn.utils._openmp_helpers import _openmp_effective_n_threads
 from sklearn._min_dependencies import PYTEST_MIN_VERSION
+from sklearn.utils.fixes import sp_version
 from sklearn.utils.fixes import parse_version
 from sklearn.datasets import fetch_20newsgroups
 from sklearn.datasets import fetch_20newsgroups_vectorized
@@ -28,6 +31,28 @@
         "at least pytest >= {} installed.".format(PYTEST_MIN_VERSION)
     )
 
+scipy_datasets_require_network = sp_version >= parse_version("1.10")
+
+
+def raccoon_face_or_skip():
+    # SciPy >= 1.10 requires network to access to get data
+    if scipy_datasets_require_network:
+        run_network_tests = environ.get("SKLEARN_SKIP_NETWORK_TESTS", "1") == "0"
+        if not run_network_tests:
+            raise SkipTest("test is enabled when SKLEARN_SKIP_NETWORK_TESTS=0")
+
+        try:
+            import pooch  # noqa
+        except ImportError:
+            raise SkipTest("test requires pooch to be installed")
+
+        from scipy.datasets import face
+    else:
+        from scipy.misc import face
+
+    return face(gray=True)
+
+
 dataset_fetchers = {
     "fetch_20newsgroups_fxt": fetch_20newsgroups,
     "fetch_20newsgroups_vectorized_fxt": fetch_20newsgroups_vectorized,
@@ -38,6 +63,9 @@
     "fetch_rcv1_fxt": fetch_rcv1,
 }
 
+if scipy_datasets_require_network:
+    dataset_fetchers["raccoon_face_fxt"] = raccoon_face_or_skip
+
 _SKIP32_MARK = pytest.mark.skipif(
     environ.get("SKLEARN_RUN_FLOAT32_TESTS", "0") != "1",
     reason="Set SKLEARN_RUN_FLOAT32_TESTS=1 to run float32 dtype tests",
@@ -75,6 +103,7 @@ def wrapped(*args, **kwargs):
 fetch_kddcup99_fxt = _fetch_fixture(fetch_kddcup99)
 fetch_olivetti_faces_fxt = _fetch_fixture(fetch_olivetti_faces)
 fetch_rcv1_fxt = _fetch_fixture(fetch_rcv1)
+raccoon_face_fxt = pytest.fixture(raccoon_face_or_skip)
 
 
 def pytest_collection_modifyitems(config, items):
@@ -115,7 +144,8 @@ def pytest_collection_modifyitems(config, items):
     worker_id = environ.get("PYTEST_XDIST_WORKER", "gw0")
     if worker_id == "gw0" and run_network_tests:
         for name in datasets_to_download:
-            dataset_fetchers[name]()
+            with suppress(SkipTest):
+                dataset_fetchers[name]()
 
     for item in items:
         # Known failure on with GradientBoostingClassifier on ARM64

sklearn/feature_extraction/tests/test_image.py

@@ -7,7 +7,6 @@
 from scipy.sparse.csgraph import connected_components
 import pytest
 
-from sklearn.utils.fixes import sp_version, parse_version
 from sklearn.feature_extraction.image import (
     img_to_graph,
     grid_to_graph,
@@ -18,17 +17,6 @@
 )
 
 
-@pytest.fixture(scope="module")
-def raccoon_face():
-    if sp_version.release >= parse_version("1.10").release:
-        pytest.importorskip("pooch")
-        from scipy.datasets import face
-    else:
-        from scipy.misc import face
-
-    return face(gray=True)
-
-
 def test_img_to_graph():
     x, y = np.mgrid[:4, :4] - 10
     grad_x = img_to_graph(x)
@@ -93,8 +81,8 @@ def test_grid_to_graph():
     assert A.dtype == np.float64
 
 
-def test_connect_regions(raccoon_face):
-    face = raccoon_face.copy()
+def test_connect_regions(raccoon_face_fxt):
+    face = raccoon_face_fxt
     # subsample by 4 to reduce run time
     face = face[::4, ::4]
     for thr in (50, 150):
@@ -103,8 +91,8 @@ def test_connect_regions(raccoon_face):
         assert ndimage.label(mask)[1] == connected_components(graph)[0]
 
 
-def test_connect_regions_with_grid(raccoon_face):
-    face = raccoon_face.copy()
+def test_connect_regions_with_grid(raccoon_face_fxt):
+    face = raccoon_face_fxt
 
     # subsample by 4 to reduce run time
     face = face[::4, ::4]
@@ -118,33 +106,27 @@ def test_connect_regions_with_grid(raccoon_face):
     assert ndimage.label(mask)[1] == connected_components(graph)[0]
 
 
-def _downsampled_face():
-    if sp_version.release >= parse_version("1.10").release:
-        pytest.importorskip("pooch")
-        from scipy.datasets import face as raccoon_face
-    else:
-        from scipy.misc import face as raccoon_face
-
-    face = raccoon_face(gray=True)
-    face = face.astype(np.float32)
+@pytest.fixture
+def downsampled_face(raccoon_face_fxt):
+    face = raccoon_face_fxt
     face = face[::2, ::2] + face[1::2, ::2] + face[::2, 1::2] + face[1::2, 1::2]
     face = face[::2, ::2] + face[1::2, ::2] + face[::2, 1::2] + face[1::2, 1::2]
     face = face.astype(np.float32)
     face /= 16.0
     return face
 
 
-def _orange_face(face=None):
-    face = _downsampled_face() if face is None else face
+@pytest.fixture
+def orange_face(downsampled_face):
+    face = downsampled_face
     face_color = np.zeros(face.shape + (3,))
     face_color[:, :, 0] = 256 - face
     face_color[:, :, 1] = 256 - face / 2
     face_color[:, :, 2] = 256 - face / 4
     return face_color
 
 
-def _make_images(face=None):
-    face = _downsampled_face() if face is None else face
+def _make_images(face):
     # make a collection of faces
     images = np.zeros((3,) + face.shape)
     images[0] = face
@@ -153,12 +135,12 @@ def _make_images(face=None):
     return images
 
 
-downsampled_face = _downsampled_face()
-orange_face = _orange_face(downsampled_face)
-face_collection = _make_images(downsampled_face)
+@pytest.fixture
+def downsampled_face_collection(downsampled_face):
+    return _make_images(downsampled_face)
 
 
-def test_extract_patches_all():
+def test_extract_patches_all(downsampled_face):
     face = downsampled_face
     i_h, i_w = face.shape
     p_h, p_w = 16, 16
@@ -167,7 +149,7 @@ def test_extract_patches_all():
     assert patches.shape == (expected_n_patches, p_h, p_w)
 
 
-def test_extract_patches_all_color():
+def test_extract_patches_all_color(orange_face):
     face = orange_face
     i_h, i_w = face.shape[:2]
     p_h, p_w = 16, 16
@@ -176,7 +158,7 @@ def test_extract_patches_all_color():
     assert patches.shape == (expected_n_patches, p_h, p_w, 3)
 
 
-def test_extract_patches_all_rect():
+def test_extract_patches_all_rect(downsampled_face):
     face = downsampled_face
     face = face[:, 32:97]
     i_h, i_w = face.shape
@@ -187,7 +169,7 @@ def test_extract_patches_all_rect():
     assert patches.shape == (expected_n_patches, p_h, p_w)
 
 
-def test_extract_patches_max_patches():
+def test_extract_patches_max_patches(downsampled_face):
     face = downsampled_face
     i_h, i_w = face.shape
     p_h, p_w = 16, 16
@@ -205,15 +187,15 @@ def test_extract_patches_max_patches():
         extract_patches_2d(face, (p_h, p_w), max_patches=-1.0)
 
 
-def test_extract_patch_same_size_image():
+def test_extract_patch_same_size_image(downsampled_face):
     face = downsampled_face
     # Request patches of the same size as image
     # Should return just the single patch a.k.a. the image
     patches = extract_patches_2d(face, face.shape, max_patches=2)
     assert patches.shape[0] == 1
 
 
-def test_extract_patches_less_than_max_patches():
+def test_extract_patches_less_than_max_patches(downsampled_face):
     face = downsampled_face
     i_h, i_w = face.shape
     p_h, p_w = 3 * i_h // 4, 3 * i_w // 4
@@ -224,7 +206,7 @@ def test_extract_patches_less_than_max_patches():
     assert patches.shape == (expected_n_patches, p_h, p_w)
 
 
-def test_reconstruct_patches_perfect():
+def test_reconstruct_patches_perfect(downsampled_face):
     face = downsampled_face
     p_h, p_w = 16, 16
 
@@ -233,7 +215,7 @@ def test_reconstruct_patches_perfect():
     np.testing.assert_array_almost_equal(face, face_reconstructed)
 
 
-def test_reconstruct_patches_perfect_color():
+def test_reconstruct_patches_perfect_color(orange_face):
     face = orange_face
     p_h, p_w = 16, 16
 
@@ -242,14 +224,14 @@ def test_reconstruct_patches_perfect_color():
     np.testing.assert_array_almost_equal(face, face_reconstructed)
 
 
-def test_patch_extractor_fit():
-    faces = face_collection
+def test_patch_extractor_fit(downsampled_face_collection):
+    faces = downsampled_face_collection
     extr = PatchExtractor(patch_size=(8, 8), max_patches=100, random_state=0)
     assert extr == extr.fit(faces)
 
 
-def test_patch_extractor_max_patches():
-    faces = face_collection
+def test_patch_extractor_max_patches(downsampled_face_collection):
+    faces = downsampled_face_collection
     i_h, i_w = faces.shape[1:3]
     p_h, p_w = 8, 8
 
@@ -272,15 +254,15 @@ def test_patch_extractor_max_patches():
     assert patches.shape == (expected_n_patches, p_h, p_w)
 
 
-def test_patch_extractor_max_patches_default():
-    faces = face_collection
+def test_patch_extractor_max_patches_default(downsampled_face_collection):
+    faces = downsampled_face_collection
     extr = PatchExtractor(max_patches=100, random_state=0)
     patches = extr.transform(faces)
     assert patches.shape == (len(faces) * 100, 19, 25)
 
 
-def test_patch_extractor_all_patches():
-    faces = face_collection
+def test_patch_extractor_all_patches(downsampled_face_collection):
+    faces = downsampled_face_collection
     i_h, i_w = faces.shape[1:3]
     p_h, p_w = 8, 8
     expected_n_patches = len(faces) * (i_h - p_h + 1) * (i_w - p_w + 1)
@@ -289,7 +271,7 @@ def test_patch_extractor_all_patches():
     assert patches.shape == (expected_n_patches, p_h, p_w)
 
 
-def test_patch_extractor_color():
+def test_patch_extractor_color(orange_face):
     faces = _make_images(orange_face)
     i_h, i_w = faces.shape[1:3]
     p_h, p_w = 8, 8
@@ -347,7 +329,7 @@ def test_extract_patches_strided():
         ).all()
 
 
-def test_extract_patches_square():
+def test_extract_patches_square(downsampled_face):
     # test same patch size for all dimensions
     face = downsampled_face
     i_h, i_w = face.shape