Implementation of Log Loss from scratch and comparism to Scikit-Learn Log Loss Metric

log_loss.py

import numpy as np
from sklearn.metrics import log_loss
np.random.seed(0)#Setting our seed to make our results reproducable

#Creating a sample target and a sample predictions probabilites
targets = np.random.randint(low=0,high=2,size=100)
probs = np.random.rand(100)

#Using Scikit-Learn Log Loss Metric
sklearn_loss = log_loss(
    y_true=targets,
    y_pred=probs
)
print("Scikit Learn Loss = ",sklearn_loss)

#Implementation from Sratch
#Let's Create our custom log_loss metrics

def customLogLoss(label,probs):
    if len(label) != len(probs):
        print("Length of Target and Length of Preds are not same")
    else:
        losses = []
        for yt,yp in zip(label,probs):
            first_part = yt * np.log(yp)
            second_part = (1-yt) * np.log(1-yp)
            merge = -1 * (first_part + second_part)
            losses.append(merge)
        return np.mean(losses)#Taking the mean or average of the losses

custom_loss = customLogLoss(label=targets,probs=probs)
print("Custom Func Loss = ",custom_loss)