Function _entropy

modAL/dropout.py:321–342

Calculates the entropy per class over dropout cycles As it is explicitly formulated in: Deep Bayesian Active Learning with Image Data. (Yarin Gal, Riashat Islam, and Zoubin Ghahramani. 2017.) Args: proba: list with the predictions ove

(proba: list)

def _entropy(proba: list) -> np.ndarray:
    """
        Calculates the entropy per class over dropout cycles

        As it is explicitly formulated in: 
            Deep Bayesian Active Learning with Image Data. 
            (Yarin Gal, Riashat Islam, and Zoubin Ghahramani. 2017.)

        Args: 
            proba: list with the predictions over the dropout cycles
            mask: mask to detect the padded classes (must be of same shape as elements in proba)
        Return: 
            Returns the entropy of the dropout cycles over all classes. 
    """

    proba_stacked = np.stack(proba, axis=len(proba[0].shape))

    # calculate entropy per class and sum along dropout cycles
    entropy_classes = entropy_sum(proba_stacked, axis=-1)
    entropy = np.mean(entropy_classes, where=~
                      np.isnan(entropy_classes), axis=-1)
    return entropy


def _variation_ratios(proba: list) -> np.ndarray: