ROC.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul  4 09:56:52 2023

@author: paweljakuszyk
"""

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
import matplotlib.pyplot as plt
import os

# Set float_format to display up to 3 decimal places
pd.options.display.float_format = '{:.3f}'.format

path_to_write='/Volumes/ms/seropositive_project/figures'

# Create the folder if it doesn't exist
if not os.path.exists(path_to_write):
    os.makedirs(path_to_write)

#read in the data
df = pd.read_csv("/Volumes/ms/PJ_gRatio/Stata/data_ready.csv", sep=',')

#define the predictor variables and the response variable
X = data[['student', 'balance', 'income']]
y = data['default']

#split the dataset into training (70%) and testing (30%) sets
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=0) 

#instantiate the model
log_regression = LogisticRegression()

#fit the model using the training data
log_regression.fit(X_train,y_train)

#define metrics
y_pred_proba = log_regression.predict_proba(X_test)[::,1]
fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)

#create ROC curve
plt.plot(fpr,tpr)
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()

#define metrics
y_pred_proba = log_regression.predict_proba(X_test)[::,1]
fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)
auc = metrics.roc_auc_score(y_test, y_pred_proba)

#create ROC curve
plt.plot(fpr,tpr,label="AUC="+str(auc))
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.legend(loc=4)
plt.show()