MAINT Clean-up utils.__init__: move tests into corresponding test files

sklearn/utils/tests/test_mask.py

@@ -0,0 +1,19 @@
+import pytest
+
+from sklearn.utils._mask import safe_mask
+from sklearn.utils.fixes import CSR_CONTAINERS
+from sklearn.utils.validation import check_random_state
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_safe_mask(csr_container):
+    random_state = check_random_state(0)
+    X = random_state.rand(5, 4)
+    X_csr = csr_container(X)
+    mask = [False, False, True, True, True]
+
+    mask = safe_mask(X, mask)
+    assert X[mask].shape[0] == 3
+
+    mask = safe_mask(X_csr, mask)
+    assert X_csr[mask].shape[0] == 3

sklearn/utils/tests/test_missing.py

@@ -0,0 +1,27 @@
+import numpy as np
+import pytest
+
+from sklearn.utils._missing import is_scalar_nan
+
+
+@pytest.mark.parametrize(
+    "value, result",
+    [
+        (float("nan"), True),
+        (np.nan, True),
+        (float(np.nan), True),
+        (np.float32(np.nan), True),
+        (np.float64(np.nan), True),
+        (0, False),
+        (0.0, False),
+        (None, False),
+        ("", False),
+        ("nan", False),
+        ([np.nan], False),
+        (9867966753463435747313673, False),  # Python int that overflows with C type
+    ],
+)
+def test_is_scalar_nan(value, result):
+    assert is_scalar_nan(value) is result
+    # make sure that we are returning a Python bool
+    assert isinstance(is_scalar_nan(value), bool)

sklearn/utils/tests/test_utils.py

@@ -1,153 +1,7 @@
-import warnings
-
 import joblib
-import numpy as np
 import pytest
 
-from sklearn.utils import (
-    check_random_state,
-    column_or_1d,
-    deprecated,
-    parallel_backend,
-    register_parallel_backend,
-    safe_mask,
-    tosequence,
-)
-from sklearn.utils._missing import is_scalar_nan
-from sklearn.utils._testing import assert_array_equal
-from sklearn.utils.fixes import CSR_CONTAINERS
-from sklearn.utils.validation import _is_polars_df
-
-
-def test_make_rng():
-    # Check the check_random_state utility function behavior
-    assert check_random_state(None) is np.random.mtrand._rand
-    assert check_random_state(np.random) is np.random.mtrand._rand
-
-    rng_42 = np.random.RandomState(42)
-    assert check_random_state(42).randint(100) == rng_42.randint(100)
-
-    rng_42 = np.random.RandomState(42)
-    assert check_random_state(rng_42) is rng_42
-
-    rng_42 = np.random.RandomState(42)
-    assert check_random_state(43).randint(100) != rng_42.randint(100)
-
-    with pytest.raises(ValueError):
-        check_random_state("some invalid seed")
-
-
-def test_deprecated():
-    # Test whether the deprecated decorator issues appropriate warnings
-    # Copied almost verbatim from https://docs.python.org/library/warnings.html
-
-    # First a function...
-    with warnings.catch_warnings(record=True) as w:
-        warnings.simplefilter("always")
-
-        @deprecated()
-        def ham():
-            return "spam"
-
-        spam = ham()
-
-        assert spam == "spam"  # function must remain usable
-
-        assert len(w) == 1
-        assert issubclass(w[0].category, FutureWarning)
-        assert "deprecated" in str(w[0].message).lower()
-
-    # ... then a class.
-    with warnings.catch_warnings(record=True) as w:
-        warnings.simplefilter("always")
-
-        @deprecated("don't use this")
-        class Ham:
-            SPAM = 1
-
-        ham = Ham()
-
-        assert hasattr(ham, "SPAM")
-
-        assert len(w) == 1
-        assert issubclass(w[0].category, FutureWarning)
-        assert "deprecated" in str(w[0].message).lower()
-
-
-@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
-def test_safe_mask(csr_container):
-    random_state = check_random_state(0)
-    X = random_state.rand(5, 4)
-    X_csr = csr_container(X)
-    mask = [False, False, True, True, True]
-
-    mask = safe_mask(X, mask)
-    assert X[mask].shape[0] == 3
-
-    mask = safe_mask(X_csr, mask)
-    assert X_csr[mask].shape[0] == 3
-
-
-def test_column_or_1d():
-    EXAMPLES = [
-        ("binary", ["spam", "egg", "spam"]),
-        ("binary", [0, 1, 0, 1]),
-        ("continuous", np.arange(10) / 20.0),
-        ("multiclass", [1, 2, 3]),
-        ("multiclass", [0, 1, 2, 2, 0]),
-        ("multiclass", [[1], [2], [3]]),
-        ("multilabel-indicator", [[0, 1, 0], [0, 0, 1]]),
-        ("multiclass-multioutput", [[1, 2, 3]]),
-        ("multiclass-multioutput", [[1, 1], [2, 2], [3, 1]]),
-        ("multiclass-multioutput", [[5, 1], [4, 2], [3, 1]]),
-        ("multiclass-multioutput", [[1, 2, 3]]),
-        ("continuous-multioutput", np.arange(30).reshape((-1, 3))),
-    ]
-
-    for y_type, y in EXAMPLES:
-        if y_type in ["binary", "multiclass", "continuous"]:
-            assert_array_equal(column_or_1d(y), np.ravel(y))
-        else:
-            with pytest.raises(ValueError):
-                column_or_1d(y)
-
-
-@pytest.mark.parametrize(
-    "value, result",
-    [
-        (float("nan"), True),
-        (np.nan, True),
-        (float(np.nan), True),
-        (np.float32(np.nan), True),
-        (np.float64(np.nan), True),
-        (0, False),
-        (0.0, False),
-        (None, False),
-        ("", False),
-        ("nan", False),
-        ([np.nan], False),
-        (9867966753463435747313673, False),  # Python int that overflows with C type
-    ],
-)
-def test_is_scalar_nan(value, result):
-    assert is_scalar_nan(value) is result
-    # make sure that we are returning a Python bool
-    assert isinstance(is_scalar_nan(value), bool)
-
-
-def dummy_func():
-    pass
-
-
-def test__is_polars_df():
-    """Check that _is_polars_df return False for non-dataframe objects."""
-
-    class LooksLikePolars:
-        def __init__(self):
-            self.columns = ["a", "b"]
-            self.schema = ["a", "b"]
-
-    assert not _is_polars_df(LooksLikePolars())
+from sklearn.utils import parallel_backend, register_parallel_backend, tosequence
 
 
 # TODO(1.7): remove

sklearn/utils/tests/test_validation.py

@@ -80,11 +80,31 @@
     check_is_fitted,
     check_memory,
     check_non_negative,
+    check_random_state,
     check_scalar,
+    column_or_1d,
     has_fit_parameter,
 )
 
 
+def test_make_rng():
+    # Check the check_random_state utility function behavior
+    assert check_random_state(None) is np.random.mtrand._rand
+    assert check_random_state(np.random) is np.random.mtrand._rand
+
+    rng_42 = np.random.RandomState(42)
+    assert check_random_state(42).randint(100) == rng_42.randint(100)
+
+    rng_42 = np.random.RandomState(42)
+    assert check_random_state(rng_42) is rng_42
+
+    rng_42 = np.random.RandomState(42)
+    assert check_random_state(43).randint(100) != rng_42.randint(100)
+
+    with pytest.raises(ValueError):
+        check_random_state("some invalid seed")
+
+
 def test_as_float_array():
     # Test function for as_float_array
     X = np.ones((3, 10), dtype=np.int32)
@@ -2061,3 +2081,38 @@ def test_to_object_array(sequence):
     assert isinstance(out, np.ndarray)
     assert out.dtype.kind == "O"
     assert out.ndim == 1
+
+
+def test_column_or_1d():
+    EXAMPLES = [
+        ("binary", ["spam", "egg", "spam"]),
+        ("binary", [0, 1, 0, 1]),
+        ("continuous", np.arange(10) / 20.0),
+        ("multiclass", [1, 2, 3]),
+        ("multiclass", [0, 1, 2, 2, 0]),
+        ("multiclass", [[1], [2], [3]]),
+        ("multilabel-indicator", [[0, 1, 0], [0, 0, 1]]),
+        ("multiclass-multioutput", [[1, 2, 3]]),
+        ("multiclass-multioutput", [[1, 1], [2, 2], [3, 1]]),
+        ("multiclass-multioutput", [[5, 1], [4, 2], [3, 1]]),
+        ("multiclass-multioutput", [[1, 2, 3]]),
+        ("continuous-multioutput", np.arange(30).reshape((-1, 3))),
+    ]
+
+    for y_type, y in EXAMPLES:
+        if y_type in ["binary", "multiclass", "continuous"]:
+            assert_array_equal(column_or_1d(y), np.ravel(y))
+        else:
+            with pytest.raises(ValueError):
+                column_or_1d(y)
+
+
+def test__is_polars_df():
+    """Check that _is_polars_df return False for non-dataframe objects."""
+
+    class LooksLikePolars:
+        def __init__(self):
+            self.columns = ["a", "b"]
+            self.schema = ["a", "b"]
+
+    assert not _is_polars_df(LooksLikePolars())