8000 [MRG] Fix predict_proba not fitted check in SGDClassifier by aniruddhadave · Pull Request #10961 · scikit-learn/scikit-learn · GitHub
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[MRG] Fix predict_proba not fitted check in SGDClassifier #10961

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Merged
merged 6 commits into from
Apr 17, 2018
Merged

[MRG] Fix predict_proba not fitted check in SGDClassifier #10961

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6c22645
Fix Issue 10938
aniruddhadave Apr 12, 2018
d326c69
Add test case for issue #10938
aniruddhadave Apr 14, 2018
c2dcd0d
Issue #10938: Code Cleanup
aniruddhadave Apr 15, 2018
a3f514d
Remove Trailing whitespaces
aniruddhadave Apr 15, 2018
b2be9db
Add test to check error message
aniruddhadave Apr 16, 2018
3b86a08
Tweaks
lesteve Apr 16, 2018
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4 changes: 2 additions & 2 deletions sklearn/linear_model/stochastic_gradient.py
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Original file line number Diff line number Diff line change
Expand Up @@ -802,8 +802,6 @@ def __init__(self, loss="hinge", penalty='l2', alpha=0.0001, l1_ratio=0.15,
average=average, n_iter=n_iter)

def _check_proba(self):
check_is_fitted(self, "t_")

if self.loss not in ("log", "modified_huber"):
raise AttributeError("probability estimates are not available for"
" loss=%r" % self.loss)
Expand Down Expand Up @@ -848,6 +846,8 @@ def predict_proba(self):
return self._predict_proba

def _predict_proba(self, X):
check_is_fitted(self, "t_")

if self.loss == "log":
return self._predict_proba_lr(X)

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24 changes: 24 additions & 0 deletions sklearn/linear_model/tests/test_sgd.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
import pickle
import unittest
import pytest

import numpy as np
import scipy.sparse as sp
Expand Down Expand Up @@ -467,6 +468,29 @@ def test_set_coef_multiclass(self):
# Provided intercept_ does match dataset.
clf = self.factory().fit(X2, Y2, intercept_init=np.zeros((3,)))

def test_sgd_predict_proba_method_access(self):
# Checks that SGDClassifier predict_proba and predict_log_proba methods
# can either be accessed or raise an appropriate error message
# otherwise. See
# https://github.com/scikit-learn/scikit-learn/issues/10938 for more
# details.
for loss in SGDClassifier.loss_functions:
clf = SGDClassifier(loss=loss)
if loss in ('log', 'modified_huber'):
assert hasattr(clf,  6525 9;predict_proba')
assert hasattr(clf, 'predict_log_proba')
else:
message = ("probability estimates are not "
"available for loss={!r}".format(loss))
assert not hasattr(clf, 'predict_proba')
assert not hasattr(clf, 'predict_log_proba')
with pytest.raises(AttributeError,
message=message):
clf.predict_proba
with pytest.raises(AttributeError,
message=message):
clf.predict_log_proba

def test_sgd_proba(self):
# Check SGD.predict_proba

Expand Down
0