Function mc_dropout_max_variationRatios

modAL/dropout.py:168–213

Mc-Dropout maximum variation ratios query strategy. Returns the indexes of the instances with the largest variation ratios over multiple dropout cycles and the corresponding metric. Based on the equations of: Deep Bayesian Active Learning with Image Da

(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_max_variationRatios(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 maximum variation ratios query strategy. Returns the indexes of the instances 
        with the largest variation ratios over multiple dropout cycles
        and the corresponding metric.

        Based on the equations of: 
            Deep Bayesian Active Learning with Image Data. 
            (Yarin Gal, Riashat Islam, and Zoubin Ghahramani. 2017.)

        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)

    # get variation ratios values for predictions
    variationRatios = _variation_ratios(predictions)

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

    return shuffled_argmax(variationRatios, n_instances=n_instances)


def get_predictions(classifier: BaseEstimator, X: modALinput, dropout_layer_indexes: list = [],