'Predict class probabilities for X.
Parameters
X : array-like or sparse matrix, shape = [n_samples, n_features]
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
Raises
AttributeError
If the ``loss`` does not support probabilities.
Returns
p : array of shape = [n_samples]
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute `classes_`.'

def predict_proba(self, X):

    score = self.decision_function(X)
    try:
        return self.loss_._score_to_proba(score)
    except NotFittedError:
        raise
    except AttributeError:
        raise AttributeError(('loss=%r    does    not    support    predict_proba' % self.loss))
