def _make_fit_args(estimator, **kwargs):
if isinstance(estimator, BaseForecaster):
# we need to handle the TransformedTargetForecaster separately
if isinstance(estimator, _SeriesToSeriesTransformer):
y = _make_series(**kwargs)
else:
y = make_forecasting_problem(**kwargs)
fh = 1
X = None
return y, X, fh
elif isinstance(estimator, BaseSeriesAnnotator):
X = make_annotation_problem(**kwargs)
return (X,)
elif isinstance(estimator, BaseClassifier):
return make_classification_problem(**kwargs)
elif isinstance(estimator, BaseRegressor):
return make_regression_problem(**kwargs)
elif isinstance(
estimator, (_SeriesToPrimitivesTransformer, _SeriesToSeriesTransformer)
):
X = _make_series(**kwargs)
return (X,)
elif isinstance(estimator, (_PanelToTabularTransformer, _PanelToPanelTransformer)):
return make_classification_problem(**kwargs)
elif isinstance(estimator, BaseClusterer):
return (make_clustering_problem(**kwargs),)
else:
raise ValueError(_get_err_msg(estimator))
def _make_fit_args(estimator, **kwargs):
if isinstance(estimator, BaseForecaster):
y = make_forecasting_problem(**kwargs)
fh = 1
X = None
return y, X, fh
elif isinstance(estimator, BaseClassifier):
return make_classification_problem(**kwargs)
elif isinstance(estimator, BaseRegressor):
return make_regression_problem(**kwargs)
elif isinstance(
estimator,
(_SeriesToPrimitivesTransformer, _SeriesToSeriesTransformer)):
X = _make_series(**kwargs)
return (X, )
elif isinstance(
estimator,
(
_PanelToTabularTransformer,
_PanelToPanelTransformer,
),
):
return make_classification_problem(**kwargs)
else:
raise ValueError(_get_err_msg(estimator))
def test_make_regression_problem(n_instances, n_columns, n_timepoints, return_numpy):
X, y = make_regression_problem(
n_instances=n_instances,
n_columns=n_columns,
n_timepoints=n_timepoints,
return_numpy=return_numpy,
)
# check dimensions of generated data
_check_X_y(X, y, n_instances, n_columns, n_timepoints, check_numpy=return_numpy)
def _make_fit_args(estimator, **kwargs):
if isinstance(estimator, BaseForecaster):
# we need to handle the TransformedTargetForecaster separately
if isinstance(estimator, _SeriesToSeriesTransformer):
y = _make_series(**kwargs)
else:
# create matching n_columns input, if n_columns not passed
# e.g., to give bivariate y to strictly multivariate forecaster
if "n_columns" not in kwargs.keys():
n_columns = _get_n_columns(
estimator.get_tag(tag_name="scitype:y",
raise_error=False))[0]
y = make_forecasting_problem(n_columns=n_columns, **kwargs)
else:
y = make_forecasting_problem(**kwargs)
fh = 1
X = None
return y, X, fh
elif isinstance(estimator, BaseSeriesAnnotator):
X = make_annotation_problem(**kwargs)
return (X, )
elif isinstance(estimator, BaseClassifier):
return make_classification_problem(**kwargs)
elif isinstance(estimator, BaseRegressor):
return make_regression_problem(**kwargs)
elif isinstance(
estimator,
(_SeriesToPrimitivesTransformer, _SeriesToSeriesTransformer)):
X = _make_series(**kwargs)
return (X, )
elif isinstance(estimator,
(_PanelToTabularTransformer, _PanelToPanelTransformer)):
return make_classification_problem(**kwargs)
elif isinstance(estimator,
BaseTransformer) and estimator.get_tag("requires_y"):
return make_classification_problem(**kwargs)
elif isinstance(estimator, BaseTransformer):
X = _make_series(**kwargs)
return (X, )
elif isinstance(estimator, BaseClusterer):
return (make_clustering_problem(**kwargs), )
elif isinstance(estimator, BasePairwiseTransformer):
return None, None
elif isinstance(estimator, BasePairwiseTransformerPanel):
return None, None
elif isinstance(estimator, BaseAligner):
X = [
_make_series(n_columns=2, **kwargs),
_make_series(n_columns=2, **kwargs)
]
return (X, )
else:
raise ValueError(_get_err_msg(estimator))
