deeppavlov/models/classifiers/utils.py
def proba2onehot(proba: [list, np.ndarray], confident_threshold: float, classes:  [list, np.ndarray]) -> np.ndarray:
    """
    Convert vectors of probabilities to one-hot representations using confident threshold

    Args:
        proba: samples where each sample is a vector of probabilities to belong with given classes
        confident_threshold: boundary of probability to belong with a class
        classes: array of classes' names

    Returns:
        2d array with one-hot representation of given samples
    """
    return labels2onehot(proba2labels(proba, confident_threshold, classes), classes)
def proba2onehot(proba: [list, np.ndarray], confident_threshold: float, classes:  [list, np.ndarray]) -> np.ndarray:
    return labels2onehot(proba2labels(proba, confident_threshold, classes), classes)
def proba2onehot ( proba : [ list , np . ndarray ] , confident_threshold : float , classes : [ list , np . ndarray ] ) -> np . ndarray : return labels2onehot ( proba2labels ( proba , confident_threshold , classes ) , classes )
Convert vectors of probabilities to one-hot representations using confident threshold

    Args:
        proba: samples where each sample is a vector of probabilities to belong with given classes
        confident_threshold: boundary of probability to belong with a class
        classes: array of classes' names

    Returns:
        2d array with one-hot representation of given samples
Convert vectors of probabilities to one - hot representations using confident threshold