This simulation is to parctice the procedure for building a package on PyPi.

Please read the following paper for more information: M. Heydarian, T. Doyle, and R. Samavi, MLCM: Multi-Label Confusion Matrix, IEEE Access, 2022

import numpy as np
import sklearn.metrics as skm
from mlcm import mlcm

number_of_samples = 1000
number_of_classes = 5
label_true = np.random.randint(2, size=(number_of_samples, number_of_classes))
label_pred = np.random.randint(2, size=(number_of_samples, number_of_classes))

conf_mat,normal_conf_mat = mlcm.cm(label_true,label_pred)
print('\nRaw confusion Matrix:')
print(conf_mat)
print('\nNormalized confusion Matrix (%):')
print(normal_conf_mat)

one_vs_rest = mlcm.stats(conf_mat)