From b22eb2ac9cfe6328fc6920042b243c346e6f678f Mon Sep 17 00:00:00 2001
From: Lars Buitinck <L.J.Buitinck@uva.nl>
Date: Mon, 30 Apr 2012 14:31:38 +0200
Subject: [PATCH] =?UTF-8?q?BUG=20chi=C2=B2=20feature=20selection=20didn't?=
=?UTF-8?q?=20work=20for=20COO=20matrices?=
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Reported via StackOverflow, http://stackoverflow.com/q/10373317/166749
---
sklearn/feature_selection/tests/test_chi2.py | 20 ++++++++++++++-----
.../feature_selection/univariate_selection.py | 6 +++---
2 files changed, 18 insertions(+), 8 deletions(-)
diff --git a/sklearn/feature_selection/tests/test_chi2.py b/sklearn/feature_selection/tests/test_chi2.py
index a163b6baf2..1fa66e7fdd 100644
--- a/sklearn/feature_selection/tests/test_chi2.py
+++ b/sklearn/feature_selection/tests/test_chi2.py
@@ -5,17 +5,17 @@ specifically to work with sparse matrices.
import numpy as np
from numpy.testing import assert_equal
-from scipy.sparse import csr_matrix
+from scipy.sparse import coo_matrix, csr_matrix
from .. import SelectKBest, chi2
# 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]])
+X = [[2, 1, 2],
+ [9, 1, 1],
+ [6, 1, 2],
+ [0, 1, 2]]
y = [0, 1, 2, 2]
@@ -45,3 +45,13 @@ def test_chi2():
Xtrans = Xtrans.toarray()
Xtrans2 = mkchi2(k=2).fit_transform(Xsp, y).toarray()
assert_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
diff --git a/sklearn/feature_selection/univariate_selection.py b/sklearn/feature_selection/univariate_selection.py
index e19fbd891e..1e82a731ee 100644
--- a/sklearn/feature_selection/univariate_selection.py
+++ b/sklearn/feature_selection/univariate_selection.py
@@ -13,7 +13,7 @@ from scipy.sparse import issparse
from ..base import BaseEstimator, TransformerMixin
from ..preprocessing import LabelBinarizer
-from ..utils import array2d, safe_asarray, deprecated, as_float_array
+from ..utils import array2d, atleast2d_or_csr, deprecated, as_float_array
from ..utils.extmath import safe_sparse_dot
######################################################################
@@ -145,7 +145,7 @@ def chi2(X, y):
# XXX: we might want to do some of the following in logspace instead for
# numerical stability.
- X = safe_asarray(X)
+ X = atleast2d_or_csr(X)
Y = LabelBinarizer().fit_transform(y)
if Y.shape[1] == 1:
Y = np.append(1 - Y, Y, axis=1)
@@ -266,7 +266,7 @@ class _AbstractUnivariateFilter(BaseEstimator, TransformerMixin):
"""
Transform a new matrix using the selected features
"""
- return safe_asarray(X)[:, self.get_support(indices=issparse(X))]
+ return atleast2d_or_csr(X)[:, self.get_support(indices=issparse(X))]
def inverse_transform(self, X):
"""
--
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