CAP_curve_1_variable_1_flag

import numpy as np
import sklearn.datasets
import sklearn.model_selection
import sklearn.linear_model
import numpy
#import compue_auc
import scipy.stats
import random
import pandas as pd
#import numpy as np
from scipy.stats import sem
from sklearn.metrics import roc_auc_score
import matplotlib.pyplot as plt

#https://github.com/APavlides/cap_curve/blob/master/cap_curve.py

data = pd.read_excel(r'ur_path')
"""

if necessary to read specific sheet, use sheet_name = 'ur_sheet'
pd.read_excel(r'ur_path', sheet_name = '')

"""


pred = data['pred']     #change for your column name/names
y_test = data['flag']   #change for your column name
# length of the test data
total = len(y_test)

# Counting '1' labels in test data
one_count = np.sum(y_test)

# counting '0' labels in test data
zero_count = total - one_count

plt.figure(figsize=(10, 6))

# x-axis ranges from 0 to total people contacted
# y-axis ranges from 0 to the total positive outcomes.

plt.plot([0, total], [0, one_count], c='b',
         linestyle='--', label='Random Model')
plt.legend()


lm = [y for _, y in sorted(zip(pred, y_test), reverse = True)]
x = np.arange(0, total + 1)
y = np.append([0], np.cumsum(lm))

plt.plot(x, y, c = 'b', label = 'Random classifier', linewidth = 2)

plt.plot([0, one_count, total], [0, one_count, one_count],
         c = 'grey', linewidth = 2, label = 'Perfect Model')

plt.savefig(f'test.png')