ROC.py

from numpy import loadtxt
from keras.models import load_model
from sklearn.model_selection import train_test_split
import matplotlib
import h5py
from numpy import expand_dims
matplotlib.use('Agg')
import sys, os, numpy
import tensorflow

#Establish the input and output data for each dataset, expanding dimensions for compatability with Conv1D layer input

#First Dataset
with h5py.File('/data/t3home000/aidandc/testingDataver3HH.h5', 'r') as hf:
    dataset = hf["Testing Data"][:]

A = dataset[:,0:len(dataset[0])-1]
b = dataset[:,len(dataset[0])-1]
A = expand_dims(A,axis=3)

#Second Dataset
with h5py.File('/data/t3home000/aidandc/testingDataHHThreeV.h5', 'r') as hf:
    dataset1 = hf["Testing Data"][:]

A1 = dataset1[:,0:len(dataset1[0])-1]
b1 = dataset1[:,len(dataset1[0])-1]
A1 = expand_dims(A1,axis=3)

#Third Dataset
with h5py.File('/data/t3home000/aidandc/testingDataHHFiveV.h5', 'r') as hf:
    dataset2 = hf["Testing Data"][:]

A2 = dataset2[:,0:len(dataset2[0])-1]
b2 = dataset2[:,len(dataset2[0])-1]
A2 = expand_dims(A2,axis=3)

from sklearn.metrics import roc_curve
from sklearn.metrics import auc

import matplotlib.pyplot as plt

#Create plot for ROC
plt.figure(1)
plt.plot([0,1],[0,1],'k--')

#Load in respective model for the datasets
model1 = load_model('modelOne.h5')
model2 = load_model('modelTwo.h5')
model3 = load_model('modelThree.h5')

#Creating ROC curves based on model predictions for each dataset
Ab_pred_keras = model1.predict(A).ravel()
fpr_Ab, tpr_Ab, thresholds_Ab = roc_curve(b,Ab_pred_keras)
auc_Ab = auc(fpr_Ab,tpr_Ab)
plt.plot(fpr_Ab,tpr_Ab,label='dZ+dXY 1 Vertex (area={:.3f})'.format(auc_Ab))

Bc_pred_keras = model2.predict(A1).ravel()
fpr_Bc, tpr_Bc, thresholds_Bc = roc_curve(b1,Bc_pred_keras)
auc_Bc = auc(fpr_Bc, tpr_Bc)
plt.plot(fpr_Bc,tpr_Bc,label='dZ+dXY 3 Vertex (area={:.3f})'.format(auc_Bc))

Cd_pred_keras = model3.predict(A2).ravel()
fpr_Cd, tpr_Cd, thresholds_Cd = roc_curve(b2,Cd_pred_keras)
auc_Cd = auc(fpr_Cd, tpr_Cd)
plt.plot(fpr_Cd, tpr_Cd, label='dZ+dXY 5 Vertex (area={:.3f})'.format(auc_Cd))

#Establish labels and save image
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('L1BTag Model ROC Curves')
plt.legend(loc='best')
plt.savefig('ROCCurves.png')