keras_metrics.py

def mae(y_true, y_pred):
    from keras import backend as K
    return K.mean(K.abs(y_pred - y_true), axis=-1)


def mse(y_true, y_pred):
    from keras import backend as K
    return K.mean(K.square(y_pred - y_true), axis=-1)


def rmae(y_true, y_pred):
    from keras import backend as K
    return K.sqrt(K.mean(K.abs(y_pred - y_true), axis=-1))


def rmse(y_true, y_pred):
    from keras import backend as K
    return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))


def mape(y_true, y_pred):
    from keras import backend as K
    diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true),
                                            K.epsilon(),
                                            None))
    return 100. * K.mean(diff, axis=-1)


def msle(y_true, y_pred):
    from keras import backend as K
    first_log = K.log(K.clip(y_pred, K.epsilon(), None) + 1.)
    second_log = K.log(K.clip(y_true, K.epsilon(), None) + 1.)
    return K.mean(K.square(first_log - second_log), axis=-1)


def rmsle(y_true, y_pred):
    from keras import backend as K
    first_log = K.log(K.clip(y_pred, K.epsilon(), None) + 1.)
    second_log = K.log(K.clip(y_true, K.epsilon(), None) + 1.)
    return K.sqrt(K.mean(K.square(first_log - second_log), axis=-1))


def matthews(y_true, y_pred):

    from keras import backend as K
    y_pred_pos = K.round(K.clip(y_pred, 0, 1))
    y_pred_neg = 1 - y_pred_pos

    y_pos = K.round(K.clip(y_true, 0, 1))
    y_neg = 1 - y_pos

    tp = K.sum(y_pos * y_pred_pos)
    tn = K.sum(y_neg * y_pred_neg)

    fp = K.sum(y_neg * y_pred_pos)
    fn = K.sum(y_pos * y_pred_neg)

    numerator = (tp * tn - fp * fn)
    denominator = K.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))

    return numerator / (denominator + K.epsilon())


def precision(y_true, y_pred):

    from keras import backend as K
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
    precision = true_positives / (predicted_positives + K.epsilon())
    return precision


def recall(y_true, y_pred):

    from keras import backend as K
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
    recall = true_positives / (possible_positives + K.epsilon())
    return recall


def fbeta(y_true, y_pred, beta=1):

    from keras import backend as K
    if beta < 0:
        raise ValueError('The lowest choosable beta is zero (only precision).')

    # If there are no true positives, fix the F score at 0 like sklearn.
    if K.sum(K.round(K.clip(y_true, 0, 1))) == 0:
        return 0

    p = precision(y_true, y_pred)
    r = recall(y_true, y_pred)
    bb = beta ** 2
    fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon())
    return fbeta_score


def f1score(y_true, y_pred):

    return fbeta(y_true, y_pred, beta=1)