59 lines
2.3 KiB
Python
59 lines
2.3 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Mon Oct 30 20:00:50 2017
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@author: wf
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"""
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from sklearn import metrics
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from sklearn.svm import SVC
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from sklearn.model_selection import train_test_split
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from featurepossess import generate
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from sklearn.externals import joblib
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sql_matrix=generate("./data/sqlnew.csv","./data/sql_matrix.csv",1)
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nor_matrix=generate("./data/normal_less.csv","./data/nor_matrix.csv",0)
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df = pd.read_csv(sql_matrix)
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df.to_csv("./data/all_matrix.csv",encoding="utf_8_sig",index=False)
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df = pd.read_csv( nor_matrix)
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df.to_csv("./data/all_matrix.csv",encoding="utf_8_sig",index=False, header=False, mode='a+')
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# with open('sql_matrix', 'ab') as f:
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# f.write(open('nor_matrix', 'rb').read())
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feature_max = pd.read_csv('./data/all_matrix.csv')
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arr=feature_max.values
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data = np.delete(arr, -1, axis=1) #删除最后一列
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#print(arr)
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target=arr[:,7]
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#随机划分训练集和测试集
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train_data,test_data,train_target,test_target = train_test_split(data,target,test_size=0.3,random_state=8)
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clf = SVC(kernel='rbf')#创建分类器对象，采用概率估计，默认为False
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clf.fit(train_data, train_target)#用训练数据拟合分类器模型
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joblib.dump(clf, './file/svm.model')
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print("svm.model has been saved to 'file/svm.model'")
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#clf = joblib.load('svm.model')
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y_pred=clf.predict(test_data)#预测
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print("y_pred:%s"%y_pred)
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print("test_target:%s"%test_target)
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#Verify
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print('Precision:%.3f' %metrics.precision_score(y_true=test_target,y_pred=y_pred))#查全率
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print('Recall:%.3f' %metrics.recall_score(y_true=test_target,y_pred=y_pred))#查准率
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print(metrics.confusion_matrix(y_true=test_target,y_pred=y_pred))#混淆矩阵
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#print('F1:%.3f' %metrics.f1_score(y_true=test_target,y_pred=y_pred))#F1度量
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#fpr,tpr,thresholds=metrics.roc_curve(y_true=test_target,y_score=y_pred)
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#print(fpr,tpr,thresholds)
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#print('auc:%.3f' %metrics.auc(fpr,tpr))
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#print('auc:%.3f' %metrics.roc_auc_score(y_true=test_target,y_score=y_pred))
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#plt.figure(1)
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#plt.axis([0,1,0,1])#设置横轴纵轴最大坐标
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#plt.plot([0,1],[0,1],'k--')#绘制对角线曲线
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#plt.plot(fpr,tpr,label='ROCcurve')#有问题，只有3个点
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#plt.xlabel('False positive rate')#x轴标签
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#plt.ylabel('True positive rate')#y轴标签
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#plt.title('ROC curve')
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#plt.legend(loc='best')#生成图例
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#plt.show()#显示图形
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