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