#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 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
def plot_roc_curve(true_y, y_prob):
"""
plots the roc curve based of the probabilities
"""
