class darts.utils.multioutput.MultiOutputClassifier(estimator, eval_set_name=None, eval_weight_name=None, output_chunk_length=None, **kwargs)[source]

Bases: MultiOutputMixin, MultiOutputClassifier

sklearn.utils.multioutput.MultiOutputClassifier 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.

Attributes

supports_sample_weight

Whether model supports sample weight for training.

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[, classes, sample_weight])

Incrementally fit a separate model for each class output.

predict(X)

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

predict_proba(X)

Return prediction probabilities for each class of each output.

score(X, y)

Return the mean accuracy on the given test data and labels.

set_fit_request(*[, sample_weight])

Configure whether metadata should be requested to be passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

set_partial_fit_request(*[, classes, ...])

Configure whether metadata should be requested to be passed to the partial_fit 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, n_outputs), 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, classes=None, sample_weight=None, **partial_fit_params)

Incrementally fit a separate model for each class output.

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.

  • classes (list of ndarray of shape (n_outputs,), default=None) – Each array is unique classes for one output in str/int. Can be obtained via [np.unique(y[:, i]) for i in range(y.shape[1])], where y is the target matrix of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes.

  • 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)

predict_proba(X)[source]

Return prediction probabilities for each class of each output.

This method will raise a ValueError if any of the estimators do not have predict_proba.

Parameters

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

Returns

p – The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

Changed in version 0.19: This function now returns a list of arrays where the length of the list is n_outputs, and each array is (n_samples, n_classes) for that particular output.

Return type

array of shape (n_samples, n_classes), or a list of n_outputs such arrays if n_outputs > 1.

score(X, y)[source]

Return the mean accuracy on the given test data and labels.

Parameters

  • X (array-like of shape (n_samples, n_features)) – Test samples.

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

Returns

scores – Mean accuracy of predicted target versus true target.

Return type

float

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

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

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

Configure whether metadata should be requested to be passed to the partial_fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

Parameters

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

  • 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

property supports_sample_weight: bool

Whether model supports sample weight for training.

Return type

bool

class darts.utils.multioutput.MultiOutputMixin(estimator, eval_set_name=None, eval_weight_name=None, output_chunk_length=None, **kwargs)[source]

Bases: object

Mixin for sklearn.utils.multioutput._MultiOutputEstimator 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.

Attributes

supports_sample_weight

Whether model supports sample weight for training.

Methods

fit(X, y[, sample_weight])

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

property supports_sample_weight: bool

Whether model supports sample weight for training.

Return type

bool

class darts.utils.multioutput.MultiOutputRegressor(estimator, eval_set_name=None, eval_weight_name=None, output_chunk_length=None, **kwargs)[source]

Bases: MultiOutputMixin, MultiOutputRegressor

sklearn.utils.multioutput.MultiOutputClassifier 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.

Attributes

supports_sample_weight

Whether model supports sample weight for training.

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 coefficient of determination on test data.

set_fit_request(*[, sample_weight])

Configure whether metadata should be requested to be passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

set_partial_fit_request(*[, sample_weight])

Configure whether metadata should be requested to be passed to the partial_fit method.

set_score_request(*[, sample_weight])

Configure whether metadata should be requested to be passed to the score method.
fit(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, n_outputs), 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 coefficient of determination on test data.

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

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

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

Configure whether metadata should be requested to be passed to the partial_fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

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

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

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

property supports_sample_weight: bool

Whether model supports sample weight for training.

Return type

bool

darts.utils.multioutput.get_multioutput_estimator_cls(model_type)[source]
Return type

type[MultiOutputMixin]

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