from sklearn import __version__ as sklearn_version
from sklearn.base import is_classifier
from sklearn.multioutput import MultiOutputRegressor as sk_MultiOutputRegressor
from sklearn.multioutput import _fit_estimator
from sklearn.utils.multiclass import check_classification_targets
from sklearn.utils.validation import has_fit_parameter
if sklearn_version >= "1.4":
# sklearn renamed `_check_fit_params` to `_check_method_params` in v1.4
from sklearn.utils.validation import _check_method_params
else:
from sklearn.utils.validation import _check_fit_params as _check_method_params
if sklearn_version >= "1.3":
# delayed was moved from sklearn.utils.fixes to sklearn.utils.parallel in v1.3
from sklearn.utils.parallel import Parallel, delayed
else:
from joblib import Parallel
from sklearn.utils.fixes import delayed
[docs]class MultiOutputRegressor(sk_MultiOutputRegressor):
"""
:class:`sklearn.utils.multioutput.MultiOutputRegressor` with a modified ``fit()`` method that also slices
validation data correctly. The validation data has to be passed as parameter ``eval_set`` in ``**fit_params``.
"""
[docs] def fit(self, X, y, sample_weight=None, **fit_params):
"""Fit the model to data, separately for each output variable.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets. An indicator matrix turns on multilabel
estimation.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If `None`, then samples are equally weighted.
Only supported if the underlying regressor supports sample
weights.
**fit_params : dict of string -> object
Parameters passed to the ``estimator.fit`` method of each step.
.. versionadded:: 0.23
Returns
-------
self : object
Returns a fitted instance.
"""
if not hasattr(self.estimator, "fit"):
raise ValueError("The base estimator should implement a fit method")
y = self._validate_data(X="no_validation", y=y, multi_output=True)
if is_classifier(self):
check_classification_targets(y)
if y.ndim == 1:
raise ValueError(
"y must have at least two dimensions for "
"multi-output regression but has only one."
)
if sample_weight is not None and not has_fit_parameter(
self.estimator, "sample_weight"
):
raise ValueError("Underlying estimator does not support sample weights.")
fit_params_validated = _check_method_params(X, fit_params)
if "eval_set" in fit_params_validated.keys():
# with validation set
eval_set = fit_params_validated.pop("eval_set")
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_estimator)(
self.estimator,
X,
y[:, i],
sample_weight,
# eval set may be a list (for XGBRegressor), in which case we have to keep it as a list
eval_set=(
[(eval_set[0][0], eval_set[0][1][:, i])]
if isinstance(eval_set, list)
else (eval_set[0], eval_set[1][:, i])
),
**fit_params_validated
)
for i in range(y.shape[1])
)
else:
# without validation set
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_estimator)(
self.estimator, X, y[:, i], sample_weight, **fit_params_validated
)
for i in range(y.shape[1])
)
if hasattr(self.estimators_[0], "n_features_in_"):
self.n_features_in_ = self.estimators_[0].n_features_in_
if hasattr(self.estimators_[0], "feature_names_in_"):
self.feature_names_in_ = self.estimators_[0].feature_names_in_
return self