[MRG + 2] Fixed parameter setting in SelectFromModel (scikit-learn#7764)

* Fixed cloning ``estimator`` again when calling fit a second time in SelectFromModel * fix link in whatsnew

Author: amueller

doc/whats_new.rst

@@ -141,6 +141,9 @@ Bug fixes
  functions were not accepting multi-label targets. :issue:`7676`
  by `Mohammed Affan`_
 
+ - Fixed setting parameters when calling ``fit`` multiple times on
+ :class:`feature_selection.SelectFromModel`. :issue:`7756` by `Andreas Müller`_
+
  - Fixes issue in ``partial_fit`` method of
  :class:`multiclass.OneVsRestClassifier` when number of classes used in
  ``partial_fit`` was less than the total number of classes in the

sklearn/feature_selection/from_model.py

@@ -232,8 +232,7 @@ def fit(self, X, y=None, **fit_params):
  if self.prefit:
  raise NotFittedError(
  "Since 'prefit=True', call transform directly")
- if not hasattr(self, "estimator_"):
- self.estimator_ = clone(self.estimator)
+ self.estimator_ = clone(self.estimator)
  self.estimator_.fit(X, y, **fit_params)
  return self
 

sklearn/feature_selection/tests/test_from_model.py

@@ -2,6 +2,7 @@
 import scipy.sparse as sp
 
 from sklearn.utils.testing import assert_true
+from sklearn.utils.testing import assert_equal
 from sklearn.utils.testing import assert_less
 from sklearn.utils.testing import assert_greater
 from sklearn.utils.testing import assert_array_almost_equal
@@ -144,14 +145,13 @@ def test_partial_fit():
  assert_array_equal(X_transform, transformer.transform(data))
 
 
-def test_warm_start():
- est = PassiveAggressiveClassifier(warm_start=True, random_state=0)
+def test_calling_fit_reinitializes():
+ est = LinearSVC(random_state=0)
  transformer = SelectFromModel(estimator=est)
  transformer.fit(data, y)
- old_model = transformer.estimator_
+ transformer.set_params(estimator__C=100)
  transformer.fit(data, y)
- new_model = transformer.estimator_
- assert_true(old_model is new_model)
+ assert_equal(transformer.estimator_.C, 100)
 
 
 def test_prefit():