[MRG+1] RFE can raise NotFittedError (#9283)

* RFE can raise NotFittedError

* boom boom

* dont change tests

* tests pass

* remove two extra lines

sklearn/feature_selection/rfe.py

@@ -9,6 +9,7 @@
 import numpy as np
 from ..utils import check_X_y, safe_sqr
 from ..utils.metaestimators import if_delegate_has_method
+from ..utils.validation import check_is_fitted
 from ..base import BaseEstimator
 from ..base import MetaEstimatorMixin
 from ..base import clone
@@ -233,6 +234,7 @@ class RFE(BaseEstimator, MetaEstimatorMixin, SelectorMixin):
         y : array of shape [n_samples]
             The predicted target values.
         """
+        check_is_fitted(self, 'estimator_')
         return self.estimator_.predict(self.transform(X))
 
     @if_delegate_has_method(delegate='estimator')
@@ -248,21 +250,26 @@ class RFE(BaseEstimator, MetaEstimatorMixin, SelectorMixin):
         y : array of shape [n_samples]
             The target values.
         """
+        check_is_fitted(self, 'estimator_')
         return self.estimator_.score(self.transform(X), y)
 
     def _get_support_mask(self):
+        check_is_fitted(self, 'support_')
         return self.support_
 
     @if_delegate_has_method(delegate='estimator')
     def decision_function(self, X):
+        check_is_fitted(self, 'estimator_')
         return self.estimator_.decision_function(self.transform(X))
 
     @if_delegate_has_method(delegate='estimator')
     def predict_proba(self, X):
+        check_is_fitted(self, 'estimator_')
         return self.estimator_.predict_proba(self.transform(X))
 
     @if_delegate_has_method(delegate='estimator')
     def predict_log_proba(self, X):
+        check_is_fitted(self, 'estimator_')
         return self.estimator_.predict_log_proba(self.transform(X))
 
 

sklearn/tests/test_metaestimators.py

@@ -7,11 +7,14 @@ import numpy as np
 from sklearn.base import BaseEstimator
 from sklearn.externals.six import iterkeys
 from sklearn.datasets import make_classification
+
 from sklearn.utils.testing import assert_true, assert_false, assert_raises
+from sklearn.utils.validation import check_is_fitted
 from sklearn.pipeline import Pipeline
 from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
 from sklearn.feature_selection import RFE, RFECV
 from sklearn.ensemble import BaggingClassifier
+from sklearn.exceptions import NotFittedError
 
 
 class DelegatorData(object):
@@ -64,8 +67,7 @@ def test_metaestimator_delegation():
             return True
 
         def _check_fit(self):
-            if not hasattr(self, 'coef_'):
-                raise RuntimeError('Estimator is not fit')
+            check_is_fitted(self, 'coef_')
 
         @hides
         def inverse_transform(self, X, *args, **kwargs):
@@ -116,8 +118,8 @@ def test_metaestimator_delegation():
             assert_true(hasattr(delegator, method),
                         msg="%s does not have method %r when its delegate does"
                             % (delegator_data.name, method))
-            # delegation before fit raises an exception
-            assert_raises(Exception, getattr(delegator, method),
+            # delegation before fit raises a NotFittedError
+            assert_raises(NotFittedError, getattr(delegator, method),
                           delegator_data.fit_args[0])
 
         delegator.fit(*delegator_data.fit_args)