skopt.learning.GradientBoostingQuantileRegressor#

class skopt.learning.GradientBoostingQuantileRegressor(quantiles=None, base_estimator=None, n_jobs=1, random_state=None)[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 coefficient of determination on test data.

set_params(**params)

Set the parameters of this estimator.

set_predict_request(*[, return_quantiles, ...])

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

set_score_request(*[, sample_weight])

Configure whether metadata should be requested to be passed to the score method.
__init__(quantiles=None, base_estimator=None, n_jobs=1, random_state=None)[source]#
fit(X, y)[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()#

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)#

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]#

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)#

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:
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)#

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#

Configure whether metadata should be requested to be passed to the predict 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 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.

Added in version 1.3.

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#

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.

Added in version 1.3.

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.