`skopt.learning`.GradientBoostingQuantileRegressor#

class skopt.learning.GradientBoostingQuantileRegressor(quantiles=None, base_estimator=None, n_jobs=1, random_state=None)[source][source]#

Predict several quantiles with one estimator.

This is a wrapper around GradientBoostingRegressor’s quantile regression that allows you to predict several quantiles in one go.

Parameters:

quantilesarray-like: Quantiles to predict. By default the 16, 50 and 84% quantiles are predicted.
base_estimatorGradientBoostingRegressor instance or None (default): Quantile regressor used to make predictions. Only instances of GradientBoostingRegressor are supported. Use this to change the hyper-parameters of the estimator.
n_jobsint, default=1: The number of jobs to run in parallel for fit. If -1, then the number of jobs is set to the number of cores.
random_stateint, RandomState instance, or None (default): Set random state to something other than None for reproducible results.

Methods

`fit`(X, y)	Fit one regressor for each quantile.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X[, return_std, return_quantiles])	Predict.
`score`(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
`set_params`(**params)	Set the parameters of this estimator.
`set_predict_request`(*[, return_quantiles, ...])	Request metadata passed to the `predict` method.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.

__init__(quantiles=None, base_estimator=None, n_jobs=1, random_state=None)[source][source]#

fit(X, y)[source][source]#

Fit one regressor for each quantile.

Parameters:

Xarray-like, shape=(n_samples, n_features): Training vectors, where n_samples is the number of samples and n_features is the number of features.
yarray-like, shape=(n_samples,): Target values (real numbers in regression)

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

predict(X, return_std=False, return_quantiles=False)[source][source]#

Predict.

Predict X at every quantile if return_std is set to False. If return_std is set to True, then return the mean and the predicted standard deviation, which is approximated as the (0.84th quantile - 0.16th quantile) divided by 2.0

Parameters:

Xarray-like, shape=(n_samples, n_features): where n_samples is the number of samples and n_features is the number of features.

score(X, y, sample_weight=None)[source]#

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:

Xarray-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.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True values for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: $R^2$ of self.predict(X) w.r.t. y.

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_params(**params)[source]#

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:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

set_predict_request(*, return_quantiles: bool | None | str = '$UNCHANGED$', return_std: bool | None | str = '$UNCHANGED$') → GradientBoostingQuantileRegressor[source]#

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_quantilesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for return_quantiles parameter in predict.
return_stdstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for return_std parameter in predict.

Returns:

selfobject: The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → GradientBoostingQuantileRegressor[source]#

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_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.

skopt.learning.GradientBoostingQuantileRegressor#

`skopt.learning`.GradientBoostingQuantileRegressor#