multi_1.py

import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt 

#generating synthetic data
number_of_classes = 5

samples_per_class= 70

labels = np.concatenate([[i]*samples_per_class for i in range(number_of_classes)])

print(labels)

predictions = np.stack([np.random.uniform(0,1,samples_per_class*number_of_classes) for _ in range(number_of_classes)]).T