def predict_proba(self, X: np.ndarray, batch_size: int = 1000
) -> np.ndarray:
new_X = np.ones((X.shape[0], 1))
probas = super(MyDummyClassifier, self).predict_proba(new_X)
probas = convert_multioutput_multiclass_to_multilabel(probas).astype(
np.float32)
return probas
def predict_proba(self, X):
if self.estimator is None:
raise NotImplementedError()
probas = self.estimator.predict_proba(X)
probas = convert_multioutput_multiclass_to_multilabel(probas)
return probas
def predict_proba(self, X, batch_size=1000):
new_X = np.ones((X.shape[0], 1))
probas = super(MyDummyClassifier, self).predict_proba(new_X)
probas = convert_multioutput_multiclass_to_multilabel(probas).astype(
np.float32)
return probas
def predict_proba(self, X):
if self.estimator is None:
raise NotImplementedError()
probas = self.estimator.predict_proba(X)
probas = convert_multioutput_multiclass_to_multilabel(probas)
return probas
