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"""
@author: zj
@file: multi-roc-nn.py
@time: 2020-01-11
"""
"""
@author: zj
@file: 2-roc.py
@time: 2020-01-10
"""
from mnist_reader import load_mnist
from nn_classifier import NN
import numpy as np
import matplotlib.pyplot as plt
from scipy import interp
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.metrics import auc
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
def load_data():
# Import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target
# shuffle and split training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5)
return X_train, X_test, y_train, y_test
if __name__ == '__main__':
X_train, X_test, y_train, y_test = load_data()
n_classes = 3
# 数据标准化
x_train = X_train.astype(np.float64)
x_test = X_test.astype(np.float64)
mu = np.mean(x_train, axis=0)
var = np.var(x_train, axis=0)
eps = 1e-8
x_train = (x_train - mu) / np.sqrt(np.maximum(var, eps))
x_test = (x_test - mu) / np.sqrt(np.maximum(var, eps))
# 定义分类器，训练和预测
classifier = NN(None, input_dim=4, num_classes=3)
classifier.train(x_train, y_train, num_iters=100, batch_size=8, verbose=True)
res_labels, y_score = classifier.predict(x_test)
# print(y_score)
# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
# Binarize the output 将类别标签二值化
y_test = label_binarize(y_test, classes=[0, 1, 2])
# one vs rest方式计算每个类别的TPR/FPR以及AUC
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])
# Compute micro-average ROC curve and ROC area
# 微平均方式计算TPR/FPR，最后得到AUC
fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
# 直接调用函数计算
micro_auc = roc_auc_score(y_test, y_score, average='micro')
lw = 2
# plt.figure()
# plt.plot(fpr[2], tpr[2], color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[2])
# 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()
# 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
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])
# 直接调用函数计算
macro_auc = roc_auc_score(y_test, y_score, average='macro')
print(roc_auc)
print('micro auc:', micro_auc)
print('macro auc:', macro_auc)
# Plot all ROC curves
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 = ['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('Some extension of Receiver operating characteristic to multi-class')
plt.legend(loc="lower right")
plt.show()
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