'Compute log probabilities of possible outcomes for samples in X.
The model need to have probability information computed at training
time: fit with attribute `probability` set to True.
Parameters
X : array-like, shape (n_samples, n_features)
For kernel="precomputed", the expected shape of X is
[n_samples_test, n_samples_train]
Returns
T : array-like, shape (n_samples, n_classes)
Returns the log-probabilities of the sample for each class in
the model. The columns correspond to the classes in sorted
order, as they appear in the attribute `classes_`.
Notes
The probability model is created using cross validation, so
the results can be slightly different than those obtained by
predict. Also, it will produce meaningless results on very small
datasets.'

@property
def predict_log_proba(self):

    self._check_proba()
    return self._predict_log_proba
