metrics.py

from keras.models import *
import tensorflow as tf
from sklearn import metrics
import numpy as np


def acc(y_true, y_pred):
    return K.mean(K.equal(K.round(y_true), K.round(y_pred)), axis=-1)

def prec(y_true, y_pred):
    y_true = K.round(y_true)
    y_pred = K.round(y_pred)
    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 rec(y_true, y_pred):
    y_true = K.round(y_true)
    y_pred = K.round(y_pred)
    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 f_one(y_true, y_pred, beta=1):
    y_true = K.round(y_true)
    y_pred = K.round(y_pred)
    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 = prec(y_true, y_pred)
    r = rec(y_true, y_pred)
    bb = beta ** 2
    fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon())
    return fbeta_score