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():
# 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():
"""Test Chi2 feature extraction"""
chi = sklearn.feature_selection.chi2(X, y)
print chi
chi2 = mkchi2(k=1).fit(X, y)
chi2 = mkchi2(k=1).fit(X, y)
print chi2.get_support(indices=True), [0]
print chi2.transform(X), np.array(X)[:, [0]]
chi2 = mkchi2(k=2).fit(X, y)
print sorted(chi2.get_support(indices=True)), [0, 2]
Xsp = csr_matrix(X, dtype=np.float)
chi2 = mkchi2(k=2).fit(Xsp, y)
print sorted(chi2.get_support(indices=True)), [0, 2]
Xtrans = chi2.transform(Xsp)
print 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_equal(Xtrans, Xtrans2)
def test_chi2():
"""Test Chi2 feature extraction"""
chi = sklearn.feature_selection.chi2(X, y)
print chi
chi2 = mkchi2(k=1).fit(X, y)
chi2 = mkchi2(k=1).fit(X, y)
print chi2.get_support(indices=True), [0]
print chi2.transform(X), np.array(X)[:, [0]]
chi2 = mkchi2(k=2).fit(X, y)
print sorted(chi2.get_support(indices=True)), [0, 2]
Xsp = csr_matrix(X, dtype=np.float)
chi2 = mkchi2(k=2).fit(Xsp, y)
print sorted(chi2.get_support(indices=True)), [0, 2]
Xtrans = chi2.transform(Xsp)
print 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_equal(Xtrans, Xtrans2)
#print scaled_features
features_train, features_test, labels_train, labels_test = \
train_test_split(scaled_features, labels, test_size=0.1, random_state=42)
# Manually tried several k values, Number of top features to select, for Chi-squared the k=10 was returning best
# results for different methods and clasifiers.
chi2 = SelectKBest(chi2, 10)
features_train = chi2.fit_transform(features_train, labels_train)
features_test = chi2.transform(features_test)
# keep selected feature names
# i+1 because we still have poi as the first name in the feature_list, while the actual features matrix does not
features_list_new = [features_list[i+1] for i in chi2.get_support(indices=True)]
features_list = ["poi"] + features_list_new
print "chi2 selected features_list = "
pprint (features_list)
# I will apply featureFormat to new feature_list with 10 best members and extraxt
# new labels/features to use them for the same varity of clasifiers and compare their scores.
data = featureFormat(my_dataset, features_list)
labels, features = targetFeatureSplit(data)
features_train, features_test, labels_train, labels_test = \
train_test_split(features, labels, test_size=0.1, random_state=42)
clf = GaussianNB()