sklearn_utilities.proba package

class sklearn_utilities.proba.ComposeVarEstimator(estimator: TEstimator, estimator_var: TEstimatorVar = DummyRegressorVar())

Bases: EstimatorWrapperBase[TEstimator], Generic[TEstimator, TEstimatorVar]

Compose an estimator with a variance estimator.

estimator: TEstimator

fit(X: TX, y: TY, **fit_params: Any) → Self

predict(X: TX, return_std: Literal[False] = False, **predict_params: Any) → TY

predict(X: TX, return_std: Literal[True], **predict_params: Any) → tuple[TY, TY]

predict_var(X: TX, **predict_params: Any) → TY

set_predict_request(*, return_std: bool | None | str = '$UNCHANGED$') → ComposeVarEstimator

Request metadata passed to the predict 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 predict 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 predict.

• 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:

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

Returns:

self – The updated object.

Return type:

object

class sklearn_utilities.proba.DummyRegressorVar(*, strategy: Literal['mean', 'median', 'quantile', 'constant', 'mean'] = 'mean', constant: float | None | ArrayLike | int = None, quantile: float | None = None, allow_nan: bool = True)

Bases: DummyRegressor

DummyRegressor with 1.0 variance.

fit(X: ArrayLike, y: ArrayLike, sample_weight: ArrayLike | None = None) → Self

Fit the random regressor.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Training data.

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

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

Returns:

self – Fitted estimator.

Return type:

object

predict(X: ArrayLike, return_std: bool = False) → ndarray | tuple[ndarray, ndarray]

Perform classification on test vectors X.

Parameters:

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

• return_std (bool, default=False) –

  Whether to return the standard deviation of posterior prediction. All zeros in this case.

  New in version 0.20.

Returns:

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

• y_std (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Standard deviation of predictive distribution of query points.

predict_var(X: _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes]) → _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes]

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → DummyRegressorVar

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_predict_request(*, return_std: bool | None | str = '$UNCHANGED$') → DummyRegressorVar

Request metadata passed to the predict 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 predict 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 predict.

• 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:

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

Returns:

self – The updated object.

Return type:

object

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → DummyRegressorVar

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

class sklearn_utilities.proba.PipelineVar(steps, *, memory=None, verbose=False)

Bases: Pipeline

Pipeline that supports predict_var method

predict_var(X: Any, **predict_params: Any) → Any

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → PipelineVar

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

class sklearn_utilities.proba.StandardScalerVar(*, copy: bool = True, with_mean: bool = True, with_std: bool = True, var_type: Literal['std', 'var'] = 'std')

Bases: StandardScaler

inverse_transform(X: Any, copy: bool | None = None, return_std: bool = False) → Any

Scale back the data to the original representation.

Parameters:

• X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The data used to scale along the features axis.

• copy (bool, default=None) – Copy the input X or not.

Returns:

X_tr – Transformed array.

Return type:

{ndarray, sparse matrix} of shape (n_samples, n_features)

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → StandardScalerVar

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_inverse_transform_request(*, copy: bool | None | str = '$UNCHANGED$', return_std: bool | None | str = '$UNCHANGED$') → StandardScalerVar

Request metadata passed to the inverse_transform 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 inverse_transform 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 inverse_transform.

• 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:

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

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

Returns:

self – The updated object.

Return type:

object

set_partial_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → StandardScalerVar

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_transform_request(*, copy: bool | None | str = '$UNCHANGED$') → StandardScalerVar

Request metadata passed to the transform 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 transform 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 transform.

• 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:

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

Returns:

self – The updated object.

Return type:

object

with_mean: bool

class sklearn_utilities.proba.TransformedTargetEstimatorVar(estimator: TEstimator, *, transformer: TTransformer = IdTransformer(), inverse_transform_separately: bool = False)

Bases: EstimatorWrapperBase[TEstimator], Generic[TEstimator, TTransformer]

TransformTargetRegressor with std/var support.

estimator: TEstimator

fit(X: TX, y: TY, **fit_params: Any) → Self

predict(X: TX, **predict_params: Any) → TY | tuple[TY, TY]

predict_var(X: TX, **predict_params: Any) → TY

Submodules

sklearn_utilities.proba.compose_var module

class sklearn_utilities.proba.compose_var.ComposeVarEstimator(estimator: TEstimator, estimator_var: TEstimatorVar = DummyRegressorVar())

Bases: EstimatorWrapperBase[TEstimator], Generic[TEstimator, TEstimatorVar]

Compose an estimator with a variance estimator.

estimator: TEstimator

fit(X: TX, y: TY, **fit_params: Any) → Self

predict(X: TX, return_std: Literal[False] = False, **predict_params: Any) → TY

predict(X: TX, return_std: Literal[True], **predict_params: Any) → tuple[TY, TY]

predict_var(X: TX, **predict_params: Any) → TY

set_predict_request(*, return_std: bool | None | str = '$UNCHANGED$') → ComposeVarEstimator

Request metadata passed to the predict 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 predict 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 predict.

• 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:

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

Returns:

self – The updated object.

Return type:

object

sklearn_utilities.proba.dummy_regressor module

class sklearn_utilities.proba.dummy_regressor.DummyRegressorVar(*, strategy: Literal['mean', 'median', 'quantile', 'constant', 'mean'] = 'mean', constant: float | None | ArrayLike | int = None, quantile: float | None = None, allow_nan: bool = True)

Bases: DummyRegressor

DummyRegressor with 1.0 variance.

fit(X: ArrayLike, y: ArrayLike, sample_weight: ArrayLike | None = None) → Self

Fit the random regressor.

Parameters:

• X (array-like of shape (n_samples, n_features)) – Training data.

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

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

Returns:

self – Fitted estimator.

Return type:

object

predict(X: ArrayLike, return_std: bool = False) → ndarray | tuple[ndarray, ndarray]

Perform classification on test vectors X.

Parameters:

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

• return_std (bool, default=False) –

  Whether to return the standard deviation of posterior prediction. All zeros in this case.

  New in version 0.20.

Returns:

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

• y_std (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Standard deviation of predictive distribution of query points.

predict_var(X: _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes]) → _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes]

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → DummyRegressorVar

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_predict_request(*, return_std: bool | None | str = '$UNCHANGED$') → DummyRegressorVar

Request metadata passed to the predict 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 predict 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 predict.

• 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:

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

Returns:

self – The updated object.

Return type:

object

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → DummyRegressorVar

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

sklearn_utilities.proba.pipeline_var module

class sklearn_utilities.proba.pipeline_var.PipelineVar(steps, *, memory=None, verbose=False)

Bases: Pipeline

Pipeline that supports predict_var method

predict_var(X: Any, **predict_params: Any) → Any

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → PipelineVar

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

sklearn_utilities.proba.standard_scaler_var module

class sklearn_utilities.proba.standard_scaler_var.StandardScalerVar(*, copy: bool = True, with_mean: bool = True, with_std: bool = True, var_type: Literal['std', 'var'] = 'std')

Bases: StandardScaler

inverse_transform(X: Any, copy: bool | None = None, return_std: bool = False) → Any

Scale back the data to the original representation.

Parameters:

• X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The data used to scale along the features axis.

• copy (bool, default=None) – Copy the input X or not.

Returns:

X_tr – Transformed array.

Return type:

{ndarray, sparse matrix} of shape (n_samples, n_features)

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → StandardScalerVar

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_inverse_transform_request(*, copy: bool | None | str = '$UNCHANGED$', return_std: bool | None | str = '$UNCHANGED$') → StandardScalerVar

Request metadata passed to the inverse_transform 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 inverse_transform 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 inverse_transform.

• 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:

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

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

Returns:

self – The updated object.

Return type:

object

set_partial_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → StandardScalerVar

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_transform_request(*, copy: bool | None | str = '$UNCHANGED$') → StandardScalerVar

Request metadata passed to the transform 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 transform 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 transform.

• 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:

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

Returns:

self – The updated object.

Return type:

object

with_mean: bool

sklearn_utilities.proba.transformed_target_estimator module

class sklearn_utilities.proba.transformed_target_estimator.TransformedTargetEstimatorVar(estimator: TEstimator, *, transformer: TTransformer = IdTransformer(), inverse_transform_separately: bool = False)

Bases: EstimatorWrapperBase[TEstimator], Generic[TEstimator, TTransformer]

TransformTargetRegressor with std/var support.

estimator: TEstimator

fit(X: TX, y: TY, **fit_params: Any) → Self

predict(X: TX, **predict_params: Any) → TY | tuple[TY, TY]

predict_var(X: TX, **predict_params: Any) → TY