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[WIP] Correct ducktyping of predict proba in GridSearchCV #4909

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[WIP] Correct ducktyping of predict proba in GridSearchCV #4909

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bb0aed4
FIX use best_estimator_ as delegate to avoid incorrect attr checks
raghavrv Jun 30, 2015
9533a81
FIX raise NotFittedError before checking if model supports predict_proba
raghavrv Jun 30, 2015
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12 changes: 6 additions & 6 deletions sklearn/grid_search.py
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Original file line number Diff line number Diff line change
Expand Up @@ -415,7 +415,7 @@ def score(self, X, y=None):
ChangedBehaviorWarning)
return self.scorer_(self.best_estimator_, X, y)

@if_delegate_has_method(delegate='estimator')
@if_delegate_has_method(delegate='best_estimator_')
def predict(self, X):
"""Call predict on the estimator with the best found parameters.

Expand All @@ -431,7 +431,7 @@ def predict(self, X):
"""
return self.best_estimator_.predict(X)

@if_delegate_has_method(delegate='estimator')
@if_delegate_has_method(delegate='best_estimator_')
def predict_proba(self, X):
"""Call predict_proba on the estimator with the best found parameters.

Expand All @@ -447,7 +447,7 @@ def predict_proba(self, X):
"""
return self.best_estimator_.predict_proba(X)

@if_delegate_has_method(delegate='estimator')
@if_delegate_has_method(delegate='best_estimator_')
def predict_log_proba(self, X):
"""Call predict_log_proba on the estimator with the best found parameters.

Expand All @@ -463,7 +463,7 @@ def predict_log_proba(self, X):
"""
return self.best_estimator_.predict_log_proba(X)

@if_delegate_has_method(delegate='estimator')
@if_delegate_has_method(delegate='best_estimator_')
def decision_function(self, X):
"""Call decision_function on the estimator with the best found parameters.

Expand All @@ -479,7 +479,7 @@ def decision_function(self, X):
"""
return self.best_estimator_.decision_function(X)

@if_delegate_has_method(delegate='estimator')
@if_delegate_has_method(delegate='best_estimator_')
def transform(self, X):
"""Call transform on the estimator with the best found parameters.

Expand All @@ -495,7 +495,7 @@ def transform(self, X):
"""
return self.best_estimator_.transform(X)

@if_delegate_has_method(delegate='estimator')
@if_delegate_has_method(delegate='best_estimator_')
def inverse_transform(self, Xt):
"""Call inverse_transform on the estimator with the best found parameters.

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1 change: 1 addition & 0 deletions sklearn/svm/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -571,6 +571,7 @@ def predict(self, X):
# probabilities are not available depending on a setting, introduce two
# estimators.
def _check_proba(self):
check_is_fitted(self, 'support_')
if not self.probability or self.probA_.size == 0 or self.probB_.size == 0:
raise AttributeError("predict_proba is not available when fitted with"
" probability=False")
Expand Down