97 lines
2.9 KiB
Python
97 lines
2.9 KiB
Python
|
"""
|
||
|
|
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)
|