[MRG+2] adding multilabel support for score_func (scikit-learn#7676)

* added multilabel support for score function

* added test for multilabel score function

* updated whats_new.rst

* updated whats_new.rst with working link

doc/whats_new.rst

@@ -95,7 +95,11 @@ Bug fixes
  `#6497 <https://github.com/scikit-learn/scikit-learn/pull/6497>`_
  by `Sebastian Säger`_
 
-
+ - Fixes issue in :ref:`univariate_feature_selection` where score 
+ functions were not accepting multi-label targets.(`#7676
+ <https://github.com/scikit-learn/scikit-learn/pull/7676>`_)
+ by `Mohammed Affan`_
+
 .. _changes_0_18:
 
 Version 0.18

sklearn/feature_selection/tests/test_feature_select.py

@@ -519,6 +519,21 @@ def test_tied_pvalues():
  assert_not_in(9998, Xt)
 
 
+def test_scorefunc_multilabel():
+ # Test whether k-best and percentiles works with multilabels with chi2.
+
+ X = np.array([[10000, 9999, 0], [100, 9999, 0], [1000, 99, 0]])
+ y = [[1, 1], [0, 1], [1, 0]]
+
+ Xt = SelectKBest(chi2, k=2).fit_transform(X, y)
+ assert_equal(Xt.shape, (3, 2))
+ assert_not_in(0, Xt)
+
+ Xt = SelectPercentile(chi2, percentile=67).fit_transform(X, y)
+ assert_equal(Xt.shape, (3, 2))
+ assert_not_in(0, Xt)
+
+
 def test_tied_scores():
  # Test for stable sorting in k-best with tied scores.
  X_train = np.array([[0, 0, 0], [1, 1, 1]])

sklearn/feature_selection/univariate_selection.py

@@ -319,7 +319,7 @@ def fit(self, X, y):
  self : object
  Returns self.
  """
- X, y = check_X_y(X, y, ['csr', 'csc'])
+ X, y = check_X_y(X, y, ['csr', 'csc'], multi_output=True)
 
  if not callable(self.score_func):
  raise TypeError("The score function should be a callable, %s (%s) "