#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

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()

)

​

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')

​

fpr, tpr, thresholds = roc_curve(y, y_prob_2)

plt.plot(fpr, tpr)

​

#Two  lines to make our compiler able to draw:

plt.savefig(sys.stdout.buffer)

sys.stdout.flush()

​