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

Submodules

climb.tool.impl.data_suite.third_party.uq360.algorithms.variational_bayesian_neural_networks.bnn module

class climb.tool.impl.data_suite.third_party.uq360.algorithms.variational_bayesian_neural_networks.bnn.BnnClassification(config, prior='Gaussian', device=None)[source]

Bases: BuiltinUQ

Variationally trained BNNs with Gaussian and Horseshoe [6] priors for classification.

fit(X=None, y=None, train_loader=None)[source]

Fits BNN regression model.

Parameters:

  • X – array-like of shape (n_samples, n_features) or (n_samples, n_classes). Features vectors of the training data or the probability scores from the base model. Ignored if train_loader is not None.

  • y – array-like of shape (n_samples,) or (n_samples, n_targets) Target values Ignored if train_loader is not None.

  • train_loader – pytorch train_loader object.

Returns:

self

get_params(deep=True)[source]
predict(X, mc_samples=100)[source]

Obtain calibrated predictions for the test points.

Parameters:

  • X – array-like of shape (n_samples, n_features) or (n_samples, n_classes). Features vectors of the training data or the probability scores from the base model.

  • mc_samples – Number of Monte-Carlo samples.

Returns:

A namedtupe that holds

y_pred: ndarray of shape (n_samples,)

Predicted labels of the test points.

y_prob: ndarray of shape (n_samples, n_classes)

Predicted probability scores of the classes.

y_prob_var: ndarray of shape (n_samples,)

Variance of the prediction on the test points.

y_prob_samples: ndarray of shape (mc_samples, n_samples, n_classes)

Samples from the predictive distribution.

Return type:

namedtuple

class climb.tool.impl.data_suite.third_party.uq360.algorithms.variational_bayesian_neural_networks.bnn.BnnRegression(config, prior='Gaussian')[source]

Bases: BuiltinUQ

Variationally trained BNNs with Gaussian and Horseshoe [6] priors for regression.

References

fit(X, y)[source]

Fit the BNN 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, mc_samples=100, return_dists=False, return_epistemic=True, return_epistemic_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.

  • mc_samples – Number of Monte-Carlo samples.

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

  • return_epistemic – if True, the epistemic upper and lower bounds are returned.

  • return_epistemic_dists – If True, the epistemic 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.

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

Lower quantile of epistemic component of the predictive distribution of the test points. Only returned when return_epistemic is True.

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

Upper quantile of epistemic component of the predictive distribution of the test points. Only returned when return_epistemic is True.

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