roc_curve.py

import matplotlib.pyplot as plt
plt.switch_backend('agg')
import numpy as np
import itertools
from sklearn.metrics import roc_curve, auc
from sklearn.preprocessing import label_binarize
from itertools import cycle
from scipy import interp
import sys

'''
To get the ROC curves, please type in the command line: 
        python roc_curve.py file_with_true_labels.txt file_with_predicted_labels.txt
'''
n_classes = 3
lw = 2

# Read saved true and predicted labels.
y_test_t = np.loadtxt(sys.argv[1]) 
y_score_t = np.loadtxt(sys.argv[2])

# Binarize the output
y_test = label_binarize(y_test_t, classes=[0, 1, 2])
y_score = label_binarize(y_score_t, classes=[0, 1, 2])
print(y_test, y_score)

# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])
print(roc_auc)

# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

# Compute macro-average ROC curve and ROC area

# First aggregate all false positive rates
all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))

# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)
for i in range(n_classes):
    mean_tpr += interp(all_fpr, fpr[i], tpr[i])

# Finally average it and compute AUC
mean_tpr /= n_classes

fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
print(fpr, tpr)
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])

# Plot all ROC curves
fig = plt.figure()
plt.plot(fpr["micro"], tpr["micro"],
         label='micro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["micro"]),
         color='deeppink', linestyle=':', linewidth=4)

plt.plot(fpr["macro"], tpr["macro"],
         label='macro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["macro"]),
         color='navy', linestyle=':', linewidth=4)

colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
for i, color in zip(range(n_classes), colors):
    plt.plot(fpr[i], tpr[i], color=color, lw=lw,
             label='ROC curve of class {0} (area = {1:0.2f})'
             ''.format(i, roc_auc[i]))

plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic to multi-class')
plt.legend(loc="lower right")
plt.show()
fig.savefig('ROC_curve.jpeg', dpi=120, bbox_inches='tight')