deeppavlov/models/classifiers/utils.py
def labels2onehot(labels: [List[str], List[List[str]], np.ndarray], classes:  [list, np.ndarray]) -> np.ndarray:
    """
    Convert labels to one-hot vectors for multi-class multi-label classification

    Args:
        labels: list of samples where each sample is a class or a list of classes which sample belongs with
        classes: array of classes' names

    Returns:
        2d array with one-hot representation of given samples
    """
    n_classes = len(classes)
    y = []
    for sample in labels:
        curr = np.zeros(n_classes)
        if isinstance(sample, list):
            for intent in sample:
                if intent not in classes:
                    log.warning('Unknown intent {} detected. Assigning no class'.format(intent))
                else:
                    curr[np.where(np.array(classes) == intent)[0]] = 1
        else:
            curr[np.where(np.array(classes) == sample)[0]] = 1
        y.append(curr)
    y = np.asarray(y)
    return y
def labels2onehot(labels: [List[str], List[List[str]], np.ndarray], classes:  [list, np.ndarray]) -> np.ndarray:
    """
    Convert labels to one-hot vectors for multi-class multi-label classification

    Args:
        labels: list of samples where each sample is a class or a list of classes which sample belongs with
        classes: array of classes' names

    Returns:
        2d array with one-hot representation of given samples
    """
    n_classes = len(classes)
    y = []
    for sample in labels:
        curr = np.zeros(n_classes)
        if isinstance(sample, list):
            for intent in sample:
                if intent not in classes:
                    log.warning('Unknown intent {} detected. Assigning no class'.format(intent))
                else:
                    curr[np.where(np.array(classes) == intent)[0]] = 1
        else:
            curr[np.where(np.array(classes) == sample)[0]] = 1
        y.append(curr)
    y = np.asarray(y)
    return y
Convert labels to one-hot vectors for multi-class multi-label classification

    Args:
        labels: list of samples where each sample is a class or a list of classes which sample belongs with
        classes: array of classes' names

    Returns:
        2d array with one-hot representation of given samples