[FIXED] Calculate the accuracy of a machine learning model without sklearn

Issue

I'm trying to calculate the accuracy of a model I created using the function below:

def accuracy(y_true, y_pred):
    accuracy = np.mean(y_pred == y_true)
    return accuracy

Sometimes it displays the accuracy correctly and sometimes its incorrect. Can someone explain how can i fix the function to have it display the same accuracy as sklearn accuracy_score. Here's an example of the results I am getting from my method.

y_true
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]

y_pred
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]

KNN classification accuracy:  0.0
KNN classification accuracy sklearn:  0.9428571428571428

Solution

With numpy you can do the following:

import numpy as np
acc = np.sum(np.equal(y_true, y_pred)) / len(y_true)

Answered By - Antoine Dubuis