[MRG + 2] ENH add sample_weight support to Pipeline.score (scikit-lea…

…rn#7723)

* add sample_weight support to Pipeline.score

* TST check that pipe.score raises TypeError for unsorrported arguments

* TST check error message for invalid score_params in pipeline.score

* TST check that pipe.fit raises TypeError when argument is not supported

* only support sample_weight argument

* whats_new: mention sample_weight in Pipeline.score

doc/whats_new.rst

@@ -60,6 +60,10 @@ Enhancements
  - Added ``norm_order`` parameter to :class:`feature_selection.SelectFromModel`
  to enable selection of the norm order when ``coef_`` is more than 1D
 
+ - Added ``sample_weight`` parameter to :meth:`pipeline.Pipeline.score`.
+ (`#7723 <https://github.com/scikit-learn/scikit-learn/pull/7723>`_)
+ by `Mikhail Korobov`_.
+
 Bug fixes
 .........
 
@@ -4675,7 +4679,7 @@ David Huard, Dave Morrill, Ed Schofield, Travis Oliphant, Pearu Peterson.
 
 .. _Yannick Schwartz: https://team.inria.fr/parietal/schwarty/
 
-.. _Mikhail Korobov: http:https://kmike.ru/pages/about/
+.. _Mikhail Korobov: https:https://github.com/kmike
 
 .. _Kyle Kastner: http:https://kastnerkyle.github.io
 

sklearn/pipeline.py

@@ -481,7 +481,7 @@ def _inverse_transform(self, X):
  return Xt
 
  @if_delegate_has_method(delegate='_final_estimator')
- def score(self, X, y=None):
+ def score(self, X, y=None, sample_weight=None):
  """Apply transforms, and score with the final estimator
 
  Parameters
@@ -494,6 +494,10 @@ def score(self, X, y=None):
  Targets used for scoring. Must fulfill label requirements for all
  steps of the pipeline.
 
+ sample_weight : array-like, default=None
+ If not None, this argument is passed as ``sample_weight`` keyword
+ argument to the ``score`` method of the final estimator.
+
  Returns
  -------
  score : float
@@ -502,7 +506,10 @@ def score(self, X, y=None):
  for name, transform in self.steps[:-1]:
  if transform is not None:
  Xt = transform.transform(Xt)
- return self.steps[-1][-1].score(Xt, y)
+ score_params = {}
+ if sample_weight is not None:
+ score_params['sample_weight'] = sample_weight
+ return self.steps[-1][-1].score(Xt, y, **score_params)
 
  @property
  def classes_(self):

sklearn/tests/test_pipeline.py

@@ -120,6 +120,11 @@ def fit_predict(self, X, y, should_succeed=False):
  self.fit(X, y, should_succeed=should_succeed)
  return self.predict(X)
 
+ def score(self, X, y=None, sample_weight=None):
+ if sample_weight is not None:
+ X = X * sample_weight
+ return np.sum(X)
+
 
 def test_pipeline_init():
  # Test the various init parameters of the pipeline.
@@ -212,6 +217,37 @@ def test_pipeline_fit_params():
  # and transformer params should not be changed
  assert_true(pipe.named_steps['transf'].a is None)
  assert_true(pipe.named_steps['transf'].b is None)
+ # invalid parameters should raise an error message
+ assert_raise_message(
+ TypeError,
+ "fit() got an unexpected keyword argument 'bad'",
+ pipe.fit, None, None, clf__bad=True
+ )
+
+
+def test_pipeline_sample_weight_supported():
+ # Pipeline should pass sample_weight
+ X = np.array([[1, 2]])
+ pipe = Pipeline([('transf', Transf()), ('clf', FitParamT())])
+ pipe.fit(X, y=None)
+ assert_equal(pipe.score(X), 3)
+ assert_equal(pipe.score(X, y=None), 3)
+ assert_equal(pipe.score(X, y=None, sample_weight=None), 3)
+ assert_equal(pipe.score(X, sample_weight=np.array([2, 3])), 8)
+
+
+def test_pipeline_sample_weight_unsupported():
+ # When sample_weight is None it shouldn't be passed
+ X = np.array([[1, 2]])
+ pipe = Pipeline([('transf', Transf()), ('clf', Mult())])
+ pipe.fit(X, y=None)
+ assert_equal(pipe.score(X), 3)
+ assert_equal(pipe.score(X, sample_weight=None), 3)
+ assert_raise_message(
+ TypeError,
+ "score() got an unexpected keyword argument 'sample_weight'",
+ pipe.score, X, sample_weight=np.array([2, 3])
+ )
 
 
 def test_pipeline_raise_set_params_error():