47 lines
1.6 KiB
Python
47 lines
1.6 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Mon Nov 20 19:06:57 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 import neighbors
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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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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=3)
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#模型
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clf=neighbors.KNeighborsClassifier(algorithm='ball_tree')#创建分类器对象，
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clf.fit(train_data,train_target)#训练模型
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joblib.dump(clf, './file/knn.model')
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print("forestrandom.model has been saved to 'file/knn.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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