import keras.backend as K
from keras.activations import elu


def get_custom_objects():
    return dict([
        ("precision", precision),
        ("recall", recall),
        ("f1_score", f1_score),
        ("selu", selu)
    ])


def selu(x):
    """Scaled Exponential Linear Unit. (Klambauer et al., 2017)
    # Arguments
        x: A tensor or variable to compute the activation function for.
    # References
        - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
    # copied from keras.io
    """
    alpha = 1.6732632423543772848170429916717
    scale = 1.0507009873554804934193349852946
    return scale * elu(x, alpha)


def get_metric_functions():
    return [precision, recall, f1_score]


def precision(y_true, y_pred):
    # Count positive samples.
    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)))
    return true_positives / (predicted_positives + K.epsilon())


def recall(y_true, y_pred):
    # Count positive samples.
    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)))
    return true_positives / (possible_positives + K.epsilon())


def f1_score(y_true, y_pred):
    return f_score(1)(y_true, y_pred)


def f05_score(y_true, y_pred):
    return f_score(0.5)(y_true, y_pred)


def f_score(beta):
    def _f(y_true, y_pred):
        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
    
    return _f