climb.tool.impl.data_suite.third_party.uq360.algorithms.quantile_regression package

Submodules

climb.tool.impl.data_suite.third_party.uq360.algorithms.quantile_regression.quantile_regression module

class climb.tool.impl.data_suite.third_party.uq360.algorithms.quantile_regression.quantile_regression.QuantileRegression(model_type='gbr', config=None)

Bases: BuiltinUQ

Quantile Regression uses quantile loss and learns two separate models for the upper and lower quantile to obtain the prediction intervals.

fit(X, y)

Fit the Quantile Regression model.

Parameters:

• X – array-like of shape (n_samples, n_features). Features vectors of the training data.

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

Returns:

self

get_params(deep=True)

predict(X)

Obtain predictions for the test points.

In addition to the mean and lower/upper bounds, also returns epistemic uncertainty (return_epistemic=True) and full predictive distribution (return_dists=True).

Parameters:

X – array-like of shape (n_samples, n_features). Features vectors of the test points.

Returns:

A namedtupe that holds

y_mean: ndarray of shape (n_samples, [n_output_dims])

Mean of predictive distribution of the test points.

y_lower: ndarray of shape (n_samples, [n_output_dims])

Lower quantile of predictive distribution of the test points.

y_upper: ndarray of shape (n_samples, [n_output_dims])

Upper quantile of predictive distribution of the test points.

Return type:

namedtuple

Module contents