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

Submodules

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

class climb.tool.impl.data_suite.third_party.uq360.algorithms.heteroscedastic_regression.heteroscedastic_regression.HeteroscedasticRegression(model_type=None, model=None, config=None, device=None, verbose=True)[source]

Bases: BuiltinUQ

Wrapper for heteroscedastic regression. We learn to predict targets given features, assuming that the targets are noisy and that the amount of noise varies between data points. https://en.wikipedia.org/wiki/Heteroscedasticity

fit(X, y)[source]

Fit the Heteroscedastic 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)[source]
predict(X, return_dists=False)[source]

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.

  • 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