Function _mean_standard_deviation

modAL/dropout.py:297–318

Calculates the mean of the per class calculated standard deviations. 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

(proba: list)

def _mean_standard_deviation(proba: list) -> np.ndarray:
    """
        Calculates the mean of the per class calculated standard deviations.

        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 mean standard deviation of the dropout cycles over all classes. 
    """

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

    standard_deviation_class_vise = np.std(proba_stacked, axis=-1)
    mean_standard_deviation = np.mean(standard_deviation_class_vise, where=~np.isnan(
        standard_deviation_class_vise), axis=-1)

    return mean_standard_deviation


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