Uploaded Test files

venv/Lib/site-packages/sklearn/utils/tests/test_fixes.py

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+# Authors: Gael Varoquaux <gael.varoquaux@normalesup.org>
+#          Justin Vincent
+#          Lars Buitinck
+# License: BSD 3 clause
+
+import math
+
+import numpy as np
+import pytest
+import scipy.stats
+
+from sklearn.utils._testing import assert_array_equal
+
+from sklearn.utils.fixes import _joblib_parallel_args
+from sklearn.utils.fixes import _object_dtype_isnan
+from sklearn.utils.fixes import loguniform
+from sklearn.utils.fixes import MaskedArray
+
+
+@pytest.mark.parametrize('joblib_version', ('0.11', '0.12.0'))
+def test_joblib_parallel_args(monkeypatch, joblib_version):
+    import joblib
+    monkeypatch.setattr(joblib, '__version__', joblib_version)
+
+    if joblib_version == '0.12.0':
+        # arguments are simply passed through
+        assert _joblib_parallel_args(prefer='threads') == {'prefer': 'threads'}
+        assert _joblib_parallel_args(prefer='processes', require=None) == {
+                    'prefer': 'processes', 'require': None}
+        assert _joblib_parallel_args(non_existing=1) == {'non_existing': 1}
+    elif joblib_version == '0.11':
+        # arguments are mapped to the corresponding backend
+        assert _joblib_parallel_args(prefer='threads') == {
+                    'backend': 'threading'}
+        assert _joblib_parallel_args(prefer='processes') == {
+                    'backend': 'multiprocessing'}
+        with pytest.raises(ValueError):
+            _joblib_parallel_args(prefer='invalid')
+        assert _joblib_parallel_args(
+                prefer='processes', require='sharedmem') == {
+                    'backend': 'threading'}
+        with pytest.raises(ValueError):
+            _joblib_parallel_args(require='invalid')
+        with pytest.raises(NotImplementedError):
+            _joblib_parallel_args(verbose=True)
+    else:
+        raise ValueError
+
+
+@pytest.mark.parametrize("dtype, val", ([object, 1],
+                                        [object, "a"],
+                                        [float, 1]))
+def test_object_dtype_isnan(dtype, val):
+    X = np.array([[val, np.nan],
+                  [np.nan, val]], dtype=dtype)
+
+    expected_mask = np.array([[False, True],
+                              [True, False]])
+
+    mask = _object_dtype_isnan(X)
+
+    assert_array_equal(mask, expected_mask)
+
+
+@pytest.mark.parametrize("low,high,base",
+                         [(-1, 0, 10), (0, 2, np.exp(1)), (-1, 1, 2)])
+def test_loguniform(low, high, base):
+    rv = loguniform(base ** low, base ** high)
+    assert isinstance(rv, scipy.stats._distn_infrastructure.rv_frozen)
+    rvs = rv.rvs(size=2000, random_state=0)
+
+    # Test the basics; right bounds, right size
+    assert (base ** low <= rvs).all() and (rvs <= base ** high).all()
+    assert len(rvs) == 2000
+
+    # Test that it's actually (fairly) uniform
+    log_rvs = np.array([math.log(x, base) for x in rvs])
+    counts, _ = np.histogram(log_rvs)
+    assert counts.mean() == 200
+    assert np.abs(counts - counts.mean()).max() <= 40
+
+    # Test that random_state works
+    assert (
+        loguniform(base ** low, base ** high).rvs(random_state=0)
+        == loguniform(base ** low, base ** high).rvs(random_state=0)
+    )
+
+
+def test_masked_array_deprecated():  # TODO: remove in 0.25
+    with pytest.warns(FutureWarning, match='is deprecated'):
+        MaskedArray()