Function mc_dropout_bald

modAL/dropout.py:19–68

Mc-Dropout bald query strategy. Returns the indexes of the instances with the largest BALD (Bayesian Active Learning by Disagreement) score calculated through the dropout cycles and the corresponding bald score. Based on the work of: Deep Bayesian Act

(classifier: BaseEstimator, X: modALinput, n_instances: int = 1,
                    random_tie_break: bool = False, dropout_layer_indexes: list = [],
                    num_cycles: int = 50, sample_per_forward_pass: int = 1000,
                    logits_adaptor: Callable[[
                        torch.tensor, modALinput], torch.tensor] = default_logits_adaptor,
                    **mc_dropout_kwargs,)

def mc_dropout_bald(classifier: BaseEstimator, X: modALinput, n_instances: int = 1,
                    random_tie_break: bool = False, dropout_layer_indexes: list = [],
                    num_cycles: int = 50, sample_per_forward_pass: int = 1000,
                    logits_adaptor: Callable[[
                        torch.tensor, modALinput], torch.tensor] = default_logits_adaptor,
                    **mc_dropout_kwargs,) -> np.ndarray:
    """
        Mc-Dropout bald query strategy. Returns the indexes of the instances with the largest BALD 
        (Bayesian Active Learning by Disagreement) score calculated through the dropout cycles
        and the corresponding bald score. 

        Based on the work of: 
            Deep Bayesian Active Learning with Image Data.
            (Yarin Gal, Riashat Islam, and Zoubin Ghahramani. 2017.)
            Dropout as a Bayesian Approximation: Representing Model Uncer- tainty in Deep Learning.
            (Yarin Gal and Zoubin Ghahramani. 2016.)
            Bayesian Active Learning for Classification and Preference Learning.
            (NeilHoulsby,FerencHusza ́r,ZoubinGhahramani,andMa ́te ́Lengyel. 2011.) 

        Args:
            classifier: The classifier for which the labels are to be queried.
            X: The pool of samples to query from.
            n_instances: Number of samples to be queried.
            random_tie_break: If True, shuffles utility scores to randomize the order. This
                can be used to break the tie when the highest utility score is not unique.
            dropout_layer_indexes: Indexes of the dropout layers which should be activated
                Choose indices from : list(torch_model.modules())
            num_cycles: Number of forward passes with activated dropout
            sample_per_forward_pass: max. sample number for each forward pass. 
                The allocated RAM does mainly depend on this.
                Small number --> small RAM allocation
            logits_adaptor: Callable which can be used to adapt the output of a forward pass 
                to the required vector format for the vectorised metric functions 
            **uncertainty_measure_kwargs: Keyword arguments to be passed for the uncertainty
                measure function.

        Returns:
            The indices of the instances from X chosen to be labelled;
            The mc-dropout metric of the chosen instances; 
    """
    predictions = get_predictions(
        classifier, X, dropout_layer_indexes, num_cycles, sample_per_forward_pass, logits_adaptor)
    # calculate BALD (Bayesian active learning divergence))

    bald_scores = _bald_divergence(predictions)

    if not random_tie_break:
        return multi_argmax(bald_scores, n_instances=n_instances)

    return shuffled_argmax(bald_scores, n_instances=n_instances)


def mc_dropout_mean_st(classifier: BaseEstimator, X: modALinput, n_instances: int = 1,