Function _variation_ratios

modAL/dropout.py:345–363

Calculates the variation ratios 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

(proba: list)

def _variation_ratios(proba: list) -> np.ndarray:
    """
        Calculates the variation ratios 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 variation ratios of the dropout cycles. 
    """
    proba_stacked = np.stack(proba, axis=len(proba[0].shape))

    # Calculate the variation ratios over the mean of dropout cycles
    valuesDCMean = np.mean(proba_stacked, axis=-1)
    return 1 - np.amax(valuesDCMean, initial=0, where=~np.isnan(valuesDCMean), axis=-1)


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