Uploaded Test files

venv/Lib/site-packages/sklearn/feature_selection/tests/test_chi2.py

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+"""
+Tests for chi2, currently the only feature selection function designed
+specifically to work with sparse matrices.
+"""
+
+import warnings
+
+import numpy as np
+import pytest
+from scipy.sparse import coo_matrix, csr_matrix
+import scipy.stats
+
+from sklearn.feature_selection import SelectKBest, chi2
+from sklearn.feature_selection._univariate_selection import _chisquare
+from sklearn.utils._testing import assert_array_almost_equal
+from sklearn.utils._testing import assert_array_equal
+
+# Feature 0 is highly informative for class 1;
+# feature 1 is the same everywhere;
+# feature 2 is a bit informative for class 2.
+X = [[2, 1, 2],
+     [9, 1, 1],
+     [6, 1, 2],
+     [0, 1, 2]]
+y = [0, 1, 2, 2]
+
+
+def mkchi2(k):
+    """Make k-best chi2 selector"""
+    return SelectKBest(chi2, k=k)
+
+
+def test_chi2():
+    # Test Chi2 feature extraction
+
+    chi2 = mkchi2(k=1).fit(X, y)
+    chi2 = mkchi2(k=1).fit(X, y)
+    assert_array_equal(chi2.get_support(indices=True), [0])
+    assert_array_equal(chi2.transform(X), np.array(X)[:, [0]])
+
+    chi2 = mkchi2(k=2).fit(X, y)
+    assert_array_equal(sorted(chi2.get_support(indices=True)), [0, 2])
+
+    Xsp = csr_matrix(X, dtype=np.float64)
+    chi2 = mkchi2(k=2).fit(Xsp, y)
+    assert_array_equal(sorted(chi2.get_support(indices=True)), [0, 2])
+    Xtrans = chi2.transform(Xsp)
+    assert_array_equal(Xtrans.shape, [Xsp.shape[0], 2])
+
+    # == doesn't work on scipy.sparse matrices
+    Xtrans = Xtrans.toarray()
+    Xtrans2 = mkchi2(k=2).fit_transform(Xsp, y).toarray()
+    assert_array_almost_equal(Xtrans, Xtrans2)
+
+
+def test_chi2_coo():
+    # Check that chi2 works with a COO matrix
+    # (as returned by CountVectorizer, DictVectorizer)
+    Xcoo = coo_matrix(X)
+    mkchi2(k=2).fit_transform(Xcoo, y)
+    # if we got here without an exception, we're safe
+
+
+def test_chi2_negative():
+    # Check for proper error on negative numbers in the input X.
+    X, y = [[0, 1], [-1e-20, 1]], [0, 1]
+    for X in (X, np.array(X), csr_matrix(X)):
+        with pytest.raises(ValueError):
+            chi2(X, y)
+
+
+def test_chi2_unused_feature():
+    # Unused feature should evaluate to NaN
+    # and should issue no runtime warning
+    with warnings.catch_warnings(record=True) as warned:
+        warnings.simplefilter('always')
+        chi, p = chi2([[1, 0], [0, 0]], [1, 0])
+        for w in warned:
+            if 'divide by zero' in repr(w):
+                raise AssertionError('Found unexpected warning %s' % w)
+    assert_array_equal(chi, [1, np.nan])
+    assert_array_equal(p[1], np.nan)
+
+
+def test_chisquare():
+    # Test replacement for scipy.stats.chisquare against the original.
+    obs = np.array([[2., 2.],
+                    [1., 1.]])
+    exp = np.array([[1.5, 1.5],
+                    [1.5, 1.5]])
+    # call SciPy first because our version overwrites obs
+    chi_scp, p_scp = scipy.stats.chisquare(obs, exp)
+    chi_our, p_our = _chisquare(obs, exp)
+
+    assert_array_almost_equal(chi_scp, chi_our)
+    assert_array_almost_equal(p_scp, p_our)