Function classifier_uncertainty

modAL/uncertainty.py:63–83

Classification uncertainty of the classifier for the provided samples. Args: classifier: The classifier for which the uncertainty is to be measured. X: The samples for which the uncertainty of classification is to be measured. **predict_proba_kwargs: Keyword argumen

(classifier: BaseEstimator, X: modALinput, **predict_proba_kwargs)

def classifier_uncertainty(classifier: BaseEstimator, X: modALinput, **predict_proba_kwargs) -> np.ndarray:
    """
    Classification uncertainty of the classifier for the provided samples.

    Args:
        classifier: The classifier for which the uncertainty is to be measured.
        X: The samples for which the uncertainty of classification is to be measured.
        **predict_proba_kwargs: Keyword arguments to be passed for the :meth:`predict_proba` of the classifier.

    Returns:
        Classifier uncertainty, which is 1 - P(prediction is correct).
    """
    # calculate uncertainty for each point provided
    try:
        classwise_uncertainty = classifier.predict_proba(X, **predict_proba_kwargs)
    except NotFittedError:
        return np.ones(shape=(X.shape[0], ))

    # for each point, select the maximum uncertainty
    uncertainty = 1 - np.max(classwise_uncertainty, axis=1)
    return uncertainty


def classifier_margin(classifier: BaseEstimator, X: modALinput, **predict_proba_kwargs) -> np.ndarray: