deephyper.skopt.utils.GradientBoostingQuantileRegressor#

class deephyper.skopt.utils.GradientBoostingQuantileRegressor(*args: Any, **kwargs: Any)[source]#

Bases: BaseEstimator, RegressorMixin

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:

  • quantiles (array-like) – Quantiles to predict. By default the 16, 50 and 84% quantiles are predicted.

  • base_estimator (GradientBoostingRegressor 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_jobs (int, 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_state (int, RandomState instance, or None (default)) – Set random state to something other than None for reproducible results.

Methods

fit

Fit one regressor for each quantile.

predict

Predict.
__call__(*args: Any, **kwargs: Any) → Any#

Call self as a function.

fit(X, y)[source]#

Fit one regressor for each quantile.

Parameters:

  • X (array-like, shape=(n_samples, n_features)) – Training vectors, where n_samples is the number of samples and n_features is the number of features.

  • y (array-like, shape=(n_samples,)) – Target values (real numbers in regression)

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:

X (array-like, shape=(n_samples, n_features)) – where n_samples is the number of samples and n_features is the number of features.