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

Submodules

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

class climb.tool.impl.data_suite.third_party.uq360.algorithms.ensemble_heteroscedastic_regression.ensemble_heteroscedastic_regression.EnsembleHeteroscedasticRegression(model_type=None, config=None, device=None, verbose=True)

Bases: BuiltinUQ

Ensemble Regression assumes an ensemble of models of Gaussian form for the predictive distribution and returns the mean and log variance of the ensemble of Gaussians.

fit(X, y)

Fit the Ensemble of Heteroscedastic Regression models. :param X: array-like of shape (n_samples, n_features).

Features vectors of the training data.

Parameters:

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

Returns:

self

predict(X, return_dists=False)

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). :param X: array-like of shape (n_samples, n_features).

Features vectors of the test points.

Parameters:

return_dists – If True, the predictive distribution for each instance using scipy distributions is returned.

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.

dists: list of predictive distribution as scipy.stats objects with length n_samples.

Only returned when return_dists is True.

Return type:

namedtuple

Module contents