GitHub PullRequestReviewCommentEvent at 2014-08-02 22:14:08
Repository: scikit-learn
Actor: agramfort
Payload: {"action":"created","comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/15731642","id":15731642,"diff_hunk":"@@ -562,8 +565,46 @@ def _log_reg_scoring_path(X, y, train, test, pos_class=None, Cs=10,\n     return coefs, Cs, np.array(scores)\n \n \n-class LogisticRegression(BaseLibLinear, LinearClassifierMixin,\n-                         _LearntSelectorMixin, SparseCoefMixin):\n+class _LogRegMixin(LinearClassifierMixin):\n+    def predict_proba(self, X):\n+        \"\"\"Probability estimates.\n+\n+        The returned estimates for all classes are ordered by the\n+        label of classes.\n+\n+        Parameters\n+        ----------\n+        X : array-like, shape = [n_samples, n_features]\n+\n+        Returns\n+        -------\n+        T : array-like, shape = [n_samples, n_classes]\n+            Returns the probability of the sample for each class in the model,\n+            where classes are ordered as they are in ``self.classes_``.\n+        \"\"\"\n+        return self._predict_proba_lr(X)\n+\n+    def predict_log_proba(self, X):\n+        \"\"\"Log of probability estimates.\n+\n+        The returned estimates for all classes are ordered by the\n+        label of classes.\n+\n+        Parameters\n+        ----------\n+        X : array-like, shape = [n_samples, n_features]\n+\n+        Returns\n+        -------\n+        T : array-like, shape = [n_samples, 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