def _make_predict_args(estimator, *args, **kwargs):
if is_forecaster(estimator):
fh = 1
return (fh,)
elif is_classifier(estimator):
X, y = make_classification_problem(*args, **kwargs)
return (X,)
elif is_regressor(estimator):
X, y = make_regression_problem(*args, **kwargs)
return (X,)
else:
raise ValueError(f"Estimator type: {type(estimator)} not supported")
def _make_fit_args(estimator, random_state=None, **kwargs):
if is_forecaster(estimator):
y = make_forecasting_problem(random_state=random_state, **kwargs)
fh = 1
return y, fh
elif is_classifier(estimator):
return make_classification_problem(random_state=random_state, **kwargs)
elif is_regressor(estimator):
return make_regression_problem(random_state=random_state, **kwargs)
elif is_series_as_features_transformer(estimator):
return make_classification_problem(random_state=random_state, **kwargs)
elif is_single_series_transformer(estimator):
y = make_forecasting_problem(random_state=random_state, **kwargs)
return (y,)
else:
raise ValueError(f"Estimator type: {type(estimator)} not supported")