python实现二分类和多分类的ROC曲线教程

脚本专栏 2024/11/2 佚名

3 2 1

圆月山庄资源网 Design By www.vgjia.com

基本概念

precision：预测为对的当中，原本为对的比例（越大越好，1为理想状态）

recall：原本为对的当中，预测为对的比例（越大越好，1为理想状态）

F-measure：F度量是对准确率和召回率做一个权衡（越大越好，1为理想状态，此时precision为1，recall为1）

accuracy：预测对的（包括原本是对预测为对，原本是错的预测为错两种情形）占整个的比例（越大越好，1为理想状态）

fp rate：原本是错的预测为对的比例（越小越好，0为理想状态）

tp rate：原本是对的预测为对的比例（越大越好，1为理想状态）

ROC曲线通常在Y轴上具有真阳性率，在X轴上具有假阳性率。这意味着图的左上角是“理想”点 - 误报率为零，真正的正率为1。这不太现实，但它确实意味着曲线下面积（AUC）通常更好。

二分类问题：ROC曲线

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
start_time = time.time()
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve
from sklearn.metrics import auc
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import recall_score,accuracy_score
from sklearn.metrics import precision_score,f1_score
from keras.optimizers import Adam,SGD,sgd
from keras.models import load_model

print('读取数据')
X_train = np.load('x_train-rotate_2.npy')
Y_train = np.load('y_train-rotate_2.npy')
print(X_train.shape)
print(Y_train.shape)

print('获取测试数据和验证数据')
X_train, X_valid, Y_train, Y_valid = train_test_split(X_train, Y_train, test_size=0.1, random_state=666)

Y_train = np.asarray(Y_train,np.uint8)
Y_valid = np.asarray(Y_valid,np.uint8)
X_valid = np.array(X_valid, np.float32) / 255.

print('获取模型')
model = load_model('./model/InceptionV3_model.h5')
opt = Adam(lr=1e-4)
model.compile(optimizer=opt, loss='binary_crossentropy')

print("Predicting")
Y_pred = model.predict(X_valid)
Y_pred = [np.argmax(y) for y in Y_pred] # 取出y中元素最大值所对应的索引
Y_valid = [np.argmax(y) for y in Y_valid]

# micro：多分类　　
# weighted：不均衡数量的类来说，计算二分类metrics的平均
# macro：计算二分类metrics的均值，为每个类给出相同权重的分值。
precision = precision_score(Y_valid, Y_pred, average='weighted')
recall = recall_score(Y_valid, Y_pred, average='weighted')
f1_score = f1_score(Y_valid, Y_pred, average='weighted')
accuracy_score = accuracy_score(Y_valid, Y_pred)
print("Precision_score:",precision)
print("Recall_score:",recall)
print("F1_score:",f1_score)
print("Accuracy_score:",accuracy_score)

# 二分类　ＲＯＣ曲线
# roc_curve:真正率（True Positive Rate , TPR）或灵敏度（sensitivity）
# 横坐标：假正率（False Positive Rate , FPR）
fpr, tpr, thresholds_keras = roc_curve(Y_valid, Y_pred)
auc = auc(fpr, tpr)
print("AUC : ", auc)
plt.figure()
plt.plot([0, 1], [0, 1], 'k--')
plt.plot(fpr, tpr, label='Keras (area = {:.3f})'.format(auc))
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC curve')
plt.legend(loc='best')
plt.savefig("../images/ROC/ROC_2分类.png")
plt.show()

print("--- %s seconds ---" % (time.time() - start_time))

ROC图如下所示：

多分类问题：ROC曲线

ROC曲线通常用于二分类以研究分类器的输出。为了将ROC曲线和ROC区域扩展到多类或多标签分类，有必要对输出进行二值化。⑴可以每个标签绘制一条ROC曲线。⑵也可以通过将标签指示符矩阵的每个元素视为二元预测（微平均）来绘制ROC曲线。⑶另一种用于多类别分类的评估方法是宏观平均，它对每个标签的分类给予相同的权重。

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
start_time = time.time()
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve
from sklearn.metrics import auc
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import recall_score,accuracy_score
from sklearn.metrics import precision_score,f1_score
from keras.optimizers import Adam,SGD,sgd
from keras.models import load_model
from itertools import cycle
from scipy import interp
from sklearn.preprocessing import label_binarize

nb_classes = 5
print('读取数据')
X_train = np.load('x_train-resized_5.npy')
Y_train = np.load('y_train-resized_5.npy')
print(X_train.shape)
print(Y_train.shape)

print('获取测试数据和验证数据')
X_train, X_valid, Y_train, Y_valid = train_test_split(X_train, Y_train, test_size=0.1, random_state=666)

Y_train = np.asarray(Y_train,np.uint8)
Y_valid = np.asarray(Y_valid,np.uint8)
X_valid = np.asarray(X_valid, np.float32) / 255.

print('获取模型')
model = load_model('./model/SE-InceptionV3_model.h5')
opt = Adam(lr=1e-4)
model.compile(optimizer=opt, loss='categorical_crossentropy')

print("Predicting")
Y_pred = model.predict(X_valid)
Y_pred = [np.argmax(y) for y in Y_pred] # 取出y中元素最大值所对应的索引
Y_valid = [np.argmax(y) for y in Y_valid]

# Binarize the output
Y_valid = label_binarize(Y_valid, classes=[i for i in range(nb_classes)])
Y_pred = label_binarize(Y_pred, classes=[i for i in range(nb_classes)])

# micro：多分类　　
# weighted：不均衡数量的类来说，计算二分类metrics的平均
# macro：计算二分类metrics的均值，为每个类给出相同权重的分值。
precision = precision_score(Y_valid, Y_pred, average='micro')
recall = recall_score(Y_valid, Y_pred, average='micro')
f1_score = f1_score(Y_valid, Y_pred, average='micro')
accuracy_score = accuracy_score(Y_valid, Y_pred)
print("Precision_score:",precision)
print("Recall_score:",recall)
print("F1_score:",f1_score)
print("Accuracy_score:",accuracy_score)

# roc_curve:真正率（True Positive Rate , TPR）或灵敏度（sensitivity）
# 横坐标：假正率（False Positive Rate , FPR）

# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(nb_classes):
 fpr[i], tpr[i], _ = roc_curve(Y_valid[:, i], Y_pred[:, i])
 roc_auc[i] = auc(fpr[i], tpr[i])

# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve(Y_valid.ravel(), Y_pred.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(nb_classes)]))

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

# Finally average it and compute AUC
mean_tpr /= nb_classes

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

# Plot all ROC curves
lw = 2
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(nb_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.savefig("../images/ROC/ROC_5分类.png")
plt.show()

print("--- %s seconds ---" % (time.time() - start_time))

ROC图如下所示：

以上这篇python实现二分类和多分类的ROC曲线教程就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。

python,二分类,多分类,ROC曲线

标签：

python,二分类,多分类,ROC曲线

圆月山庄资源网 Design By www.vgjia.com

广告合作：本站广告合作请联系QQ：858582 申请时备注：广告合作（否则不回）
免责声明：本站文章均来自网站采集或用户投稿，网站不提供任何软件下载或自行开发的软件！如有用户或公司发现本站内容信息存在侵权行为，请邮件告知！ 858582#qq.com

圆月山庄资源网 Design By www.vgjia.com

评论“python实现二分类和多分类的ROC曲线教程”

暂无评论...

www.vgjia.com 圆月山庄资源网

139,976互联网资源

144,792高清电影

21,817无损音乐

631,128技术资源

python实现二分类和多分类的ROC曲线教程

python,二分类,多分类,ROC曲线

Python如何在windows环境安装pip及rarfile

keras训练曲线,混淆矩阵,CNN层输出可视化实例

评论“python实现二分类和多分类的ROC曲线教程”

RTX 5090要首发性能要翻倍！三星展示GDDR7显存

更新日志

友情链接

python实现二分类和多分类的ROC曲线教程

python,二分类,多分类,ROC曲线

Python如何在windows环境安装pip及rarfile

keras训练曲线,混淆矩阵,CNN层输出可视化实例

评论“python实现二分类和多分类的ROC曲线教程”

RTX 5090要首发 性能要翻倍！三星展示GDDR7显存

更新日志

友情链接

RTX 5090要首发性能要翻倍！三星展示GDDR7显存