import numpy as np
from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score, roc_curve
n = 10000
y = np.array([0] * n + [1] * n)
#
y_prob_1 = np.array(
np.random.uniform(.25, .5, n//2).tolist() +
np.random.uniform(.3, .7, n).tolist() +
np.random.uniform(.5, .75, n//2).tolist()
)
y_prob_2 = np.array(
np.random.uniform(0, .4, n//2).tolist() +
np.random.uniform(.3, .7, n).tolist() +
np.random.uniform(.6, 1, n//2).tolist()
)
print(f'model 1 accuracy score: {accuracy_score(y, y_prob_1>.5)}')
print(f'model 2 accuracy score: {accuracy_score(y, y_prob_2>.5)}')
print(f'model 1 AUC score: {roc_auc_score(y, y_prob_1)}')
print(f'model 2 AUC score: {roc_auc_score(y, y_prob_2)}')
