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

Submodules

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

class climb.tool.impl.data_suite.third_party.uq360.algorithms.actively_learned_model.actively_learned_model.ActivelyLearnedModel(config=None, device=None, verbose=True, online=True)

Bases: BuiltinUQ

ActivelyLearnedModel assumes an existing BuiltinUQ model, and implements an active learning training of this model. This code is supporting Pestourie et al. “Active learning of deep surrogates for PDEs: application to metasurface design.” npj Computational Materials 6.1 (2020): 1-7.

fit()

Fit the actively learned model, by increasing the dataset efficiently. NB: it does not take a dataset as argument, because it is building one during training. :returns: self

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). :param 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