'Returns the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
each label set be correctly predicted.
Parameters
X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples,) or (n_samples, n_outputs)
True labels for X.
sample_weight : array-like, shape = (n_samples,), optional
Sample weights.
Returns
score : float
Mean accuracy of self.predict(X) wrt. y.'

def score(self, X, y, sample_weight=None):

    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
