#Three lines to make our compiler able to draw:

import sys

import matplotlib

matplotlib.use('Agg')

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import numpy as np

import matplotlib.pyplot as plt

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

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n = 10000

ratio = .95

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

n_1 = int(ratio * n)

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

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# below are the probabilities obtained from a hypothetical model that always predicts the majority class

# probability of predicting class 1 is going to be 100%

y_proba = np.array([1]*n)

y_pred = y_proba > .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')

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plot_roc_curve(y, y_proba)

print(f'model 1 AUC score: {roc_auc_score(y, y_proba)}')

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#Two  lines to make our compiler able to draw:

plt.savefig(sys.stdout.buffer)

sys.stdout.flush()

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