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utils.py
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#!/usr/bin/env python
# coding=utf8
'''*************************************************************************
> File Name: utils.py
> Author: HUANG Yongxiang
> Mail:
> Created Time: Sat Mar 10 02:11:12 2018
> Usage:
*************************************************************************'''
import os
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc, accuracy_score, confusion_matrix
import numpy as np
import itertools
def plot_confusion_matrix(cm, classes,normalize=False, title='Confusion matrix',cmap=plt.cm.Blues):
if(normalize):
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
plt.figure()
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title, fontsize=16)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
fontsize=16,
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('Ground truth')
plt.xlabel('Prediction')
plt.show()
def plot_auc_curve(fpr, tpr, AUC):
plt.figure()
lw = 1.5
plt.plot(fpr, tpr, color='darkorange',
lw=lw, label='AUC = %0.3f' % AUC)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
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 example')
plt.legend(loc="lower right")
plt.show()
print("confusion_matrix:\r\n",cm2, "\r\n accuaracy of 2-class ",acc2)
print("AUC="+str(AUC))