'Predict class log-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 log-probabilities of the input samples. The order of the
classes corresponds to that in the attribute `classes_`.'

def predict_log_proba(self, X):

    proba = self.predict_proba(X)
    return np.log(proba)
