#Three lines to make our compiler able to draw:

import sys

import matplotlib

matplotlib.use('Agg')

​

import numpy as np

import matplotlib.pyplot as plt

​

from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score, roc_curve

​

n = 10000

ratio = .95

n_0 = int((1-ratio) * n)

n_1 = int(ratio * n)

​

y = np.array([0] * n_0 + [1] * n_1)

​

# below are the probabilities obtained from a hypothetical model that doesn't always predict the mode

y_proba_2 = np.array(

    np.random.uniform(0, .7, n_0).tolist() +

    np.random.uniform(.3, 1, n_1).tolist()

)

y_pred_2 = y_proba_2 > .5

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def plot_roc_curve(true_y, y_prob):

    """

    plots the roc curve based of the probabilities

    """

    fpr, tpr, thresholds = roc_curve(true_y, y_prob)

    plt.plot(fpr, tpr)

    plt.xlabel('False Positive Rate')

    plt.ylabel('True Positive Rate')

​

plot_roc_curve(y, y_proba_2)

print(f'model 2 AUC score: {roc_auc_score(y, y_proba_2)}')

​

#Two  lines to make our compiler able to draw:

plt.savefig(sys.stdout.buffer)

sys.stdout.flush()

​