class darts.utils.multioutput.MultiOutputRegressor(estimator, *, n_jobs=None)[source]

Bases: MultiOutputRegressor

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.

Methods

fit(X, y[, sample_weight])

Fit the model to data, separately for each output variable.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

partial_fit(X, y[, sample_weight])

Incrementally fit the model to data, for each output variable.

predict(X)

Predict multi-output variable using model for each target variable.

score(X, y[, sample_weight])

Return the coefficient of determination of the prediction.

set_fit_request(*[, sample_weight])

Request metadata passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

set_partial_fit_request(*[, sample_weight])

Request metadata passed to the partial_fit method.

set_score_request(*[, sample_weight])

Request metadata passed to the score method.
fit(X, y, sample_weight=None, **fit_params)[source]

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.

    New in version 0.23.

Returns

self – Returns a fitted instance.

Return type

object

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

New in version 1.3.

Returns

routing – A MetadataRouter encapsulating routing information.

Return type

MetadataRouter

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

partial_fit(X, y, sample_weight=None, **partial_fit_params)[source]

Incrementally fit the model to data, 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.

  • 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.

  • **partial_fit_params (dict of str -> object) –

    Parameters passed to the estimator.partial_fit method of each sub-estimator.

    Only available if enable_metadata_routing=True. See the User Guide.

    New in version 1.3.

Returns

self – Returns a fitted instance.

Return type

object

predict(X)

Predict multi-output variable using model for each target variable.

Parameters

X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input data.

Returns

y – Multi-output targets predicted across multiple predictors. Note: Separate models are generated for each predictor.

Return type

{array-like, sparse matrix} of shape (n_samples, n_outputs)

score(X, y, sample_weight=None)

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters

  • X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.

  • sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns

score – \(R^2\) of self.predict(X) w.r.t. y.

Return type

float

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_fit_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') → MultiOutputRegressor

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns

self – The updated object.

Return type

object

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance

set_partial_fit_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') → MultiOutputRegressor

Request metadata passed to the partial_fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to partial_fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to partial_fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in partial_fit.

Returns

self – The updated object.

Return type

object

set_score_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') → MultiOutputRegressor

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns

self – The updated object.

Return type

object

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