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import osimport numpy as npimport pandas as pdfrom sklearn import datasetsfrom sklearn import preprocessingfrom sklearn import neighborsfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysisfrom sklearn import svmfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.model_selection import train_test_splitfrom sklearn.model_selection import StratifiedKFoldfrom sklearn.linear_model import LogisticRegressionfrom sklearn.model_selection import GridSearchCVfrom time import timefrom sklearn.naive_bayes import MultinomialNBfrom sklearn import treefrom sklearn.ensemble import GradientBoostingClassifier#读取sklearn自带的数据集（鸢尾花）def getData_1():    iris = datasets.load_iris()    X = iris.data   #样本特征矩阵，150*4矩阵，每行一个样本，每个样本维度是4    y = iris.target #样本类别矩阵，150维行向量，每个元素代表一个样本的类别#读取本地excel表格内的数据集（抽取每类60%样本组成训练集，剩余样本组成测试集）#返回一个元祖，其内有4个元素（类型均为numpy.ndarray）：#（1）归一化后的训练集矩阵，每行为一个训练样本，矩阵行数=训练样本总数，矩阵列数=每个训练样本的特征数#（2）每个训练样本的类标#（3）归一化后的测试集矩阵，每行为一个测试样本，矩阵行数=测试样本总数，矩阵列数=每个测试样本的特征数#（4）每个测试样本的类标#【注】归一化采用“最大最小值”方法。def getData_2():    fPath = 'D:\分类算法\binary_classify_data.txt'    if os.path.exists(fPath):        data = pd.read_csv(fPath,header=None,skiprows=1,names=['class0','pixel0','pixel1','pixel2','pixel3'])        X_train1, X_test1, y_train1, y_test1 = train_test_split(data, data['class0'], test_size = 0.4, random_state = 0)        min_max_scaler = preprocessing.MinMaxScaler()   #归一化        X_train_minmax = min_max_scaler.fit_transform(np.array(X_train1))        X_test_minmax = min_max_scaler.fit_transform(np.array(X_test1))        return (X_train_minmax, np.array(y_train1), X_test_minmax, np.array(y_test1))    else:        print ('No such file or directory!')#读取本地excel表格内的数据集（每类随机生成K个训练集和测试集的组合）#【K的含义】假设一共有1000个样本，K取10，那么就将这1000个样本切分10份（一份100个），那么就产生了10个测试集#对于每一份的测试集，剩余900个样本即作为训练集#结果返回一个字典：键为集合编号（1train, 1trainclass, 1test, 1testclass, 2train, 2trainclass, 2test, 2testclass...），值为数据#其中1train和1test为随机生成的第一组训练集和测试集（1trainclass和1testclass为训练样本类别和测试样本类别），其他以此类推def getData_3():    fPath = 'D:\\分类算法\\binary_classify_data.txt'    if os.path.exists(fPath):        #读取csv文件内的数据，        dataMatrix = np.array(pd.read_csv(fPath,header=None,skiprows=1,names=['class0','pixel0','pixel1','pixel2','pixel3']))        #获取每个样本的特征以及类标        rowNum, colNum = dataMatrix.shape[0], dataMatrix.shape[1]        sampleData = []        sampleClass = []        for i in range(0, rowNum):            tempList = list(dataMatrix[i,:])            sampleClass.append(tempList[0])            sampleData.append(tempList[1:])        sampleM = np.array(sampleData)  #二维矩阵，一行是一个样本，行数=样本总数，列数=样本特征数        classM = np.array(sampleClass)  #一维列向量，每个元素对应每个样本所属类别        #调用StratifiedKFold方法生成训练集和测试集        skf = StratifiedKFold(n_splits = 10)        setDict = {}    #创建字典，用于存储生成的训练集和测试集        count = 1        for trainI, testI in skf.split(sampleM, classM):            trainSTemp = [] #用于存储当前循环抽取出的训练样本数据            trainCTemp = [] #用于存储当前循环抽取出的训练样本类标            testSTemp = []  #用于存储当前循环抽取出的测试样本数据            testCTemp = []  #用于存储当前循环抽取出的测试样本类标            #生成训练集            trainIndex = list(trainI)            for t1 in range(0, len(trainIndex)):                trainNum = trainIndex[t1]                trainSTemp.append(list(sampleM[trainNum, :]))                trainCTemp.append(list(classM)[trainNum])            setDict[str(count) + 'train'] = np.array(trainSTemp)            setDict[str(count) + 'trainclass'] = np.array(trainCTemp)            #生成测试集            testIndex = list(testI)            for t2 in range(0, len(testIndex)):                testNum = testIndex[t2]                testSTemp.append(list(sampleM[testNum, :]))                testCTemp.append(list(classM)[testNum])            setDict[str(count) + 'test'] = np.array(testSTemp)            setDict[str(count) + 'testclass'] = np.array(testCTemp)            count += 1        return setDict    else:        print ('No such file or directory!')#K近邻（K Nearest Neighbor）def KNN():    clf = neighbors.KNeighborsClassifier()    return clf#线性鉴别分析（Linear Discriminant Analysis）def LDA():    clf = LinearDiscriminantAnalysis()    return clf#支持向量机（Support Vector Machine）def SVM():    clf = svm.SVC()    return clf#逻辑回归（Logistic Regression）def LR():    clf = LogisticRegression()    return clf#随机森林决策树（Random Forest）def RF():    clf = RandomForestClassifier()    return clf#多项式朴素贝叶斯分类器def native_bayes_classifier():    clf = MultinomialNB(alpha = 0.01)    return clf#决策树def decision_tree_classifier():    clf = tree.DecisionTreeClassifier()    return clf#GBDTdef gradient_boosting_classifier():    clf = GradientBoostingClassifier(n_estimators = 200)    return clf#计算识别率def getRecognitionRate(testPre, testClass):    testNum = len(testPre)    rightNum = 0    for i in range(0, testNum):        if testClass[i] == testPre[i]:            rightNum += 1    return float(rightNum) / float(testNum)#report函数，将调参的详细结果存储到本地F盘（路径可自行修改，其中n_top是指定输出前多少个最优参数组合以及该组合的模型得分）def report(results, n_top=5488):    f = open('F:/grid_search_rf.txt', 'w')    for i in range(1, n_top + 1):        candidates = np.flatnonzero(results['rank_test_score'] == i)        for candidate in candidates:            f.write("Model with rank: {0}".format(i) + '\n')            f.write("Mean validation score: {0:.3f} (std: {1:.3f})".format(                  results['mean_test_score'][candidate],                  results['std_test_score'][candidate]) + '\n')            f.write("Parameters: {0}".format(results['params'][candidate]) + '\n')            f.write("\n")    f.close()#自动调参（以随机森林为例）def selectRFParam():    clf_RF = RF()    param_grid = {"max_depth": [3,15],                  "min_samples_split": [3, 5, 10],                  "min_samples_leaf": [3, 5, 10],                  "bootstrap": [True, False],                  "criterion": ["gini", "entropy"],                  "n_estimators": range(10,50,10)}                  # "class_weight": [{0:1,1:13.24503311,2:1.315789474,3:12.42236025,4:8.163265306,5:31.25,6:4.77326969,7:19.41747573}],                  # "max_features": range(3,10),                  # "warm_start": [True, False],                  # "oob_score": [True, False],                  # "verbose": [True, False]}    grid_search = GridSearchCV(clf_RF, param_grid=param_grid, n_jobs=4)    start = time()    T = getData_2()    #获取数据集    grid_search.fit(T[0], T[1]) #传入训练集矩阵和训练样本类标    print("GridSearchCV took %.2f seconds for %d candidate parameter settings."          % (time() - start, len(grid_search.cv_results_['params'])))    report(grid_search.cv_results_)#“主”函数1（KFold方法生成K个训练集和测试集，即数据集采用getData_3()函数获取，计算这K个组合的平均识别率）def totalAlgorithm_1():    #获取各个分类器    clf_KNN = KNN()    clf_LDA = LDA()    clf_SVM = SVM()    clf_LR = LR()    clf_RF = RF()    clf_NBC = native_bayes_classifier()    clf_DTC = decision_tree_classifier()    clf_GBDT = gradient_boosting_classifier()    #获取训练集和测试集    setDict = getData_3()    setNums = len(setDict.keys()) / 4  #一共生成了setNums个训练集和setNums个测试集，它们之间是一一对应关系    #定义变量，用于将每个分类器的所有识别率累加    KNN_rate = 0.0    LDA_rate = 0.0    SVM_rate = 0.0    LR_rate = 0.0    RF_rate = 0.0    NBC_rate = 0.0    DTC_rate = 0.0    GBDT_rate = 0.0    for i in range(1, int(setNums + 1)):        trainMatrix = setDict[str(i) + 'train']        trainClass = setDict[str(i) + 'trainclass']        testMatrix = setDict[str(i) + 'test']        testClass = setDict[str(i) + 'testclass']        #输入训练样本        clf_KNN.fit(trainMatrix, trainClass)        clf_LDA.fit(trainMatrix, trainClass)        clf_SVM.fit(trainMatrix, trainClass)        clf_LR.fit(trainMatrix, trainClass)        clf_RF.fit(trainMatrix, trainClass)        clf_NBC.fit(trainMatrix, trainClass)        clf_DTC.fit(trainMatrix, trainClass)        clf_GBDT.fit(trainMatrix, trainClass)        #计算识别率        KNN_rate += getRecognitionRate(clf_KNN.predict(testMatrix), testClass)        LDA_rate += getRecognitionRate(clf_LDA.predict(testMatrix), testClass)        SVM_rate += getRecognitionRate(clf_SVM.predict(testMatrix), testClass)        LR_rate += getRecognitionRate(clf_LR.predict(testMatrix), testClass)        RF_rate += getRecognitionRate(clf_RF.predict(testMatrix), testClass)        NBC_rate += getRecognitionRate(clf_NBC.predict(testMatrix), testClass)        DTC_rate += getRecognitionRate(clf_DTC.predict(testMatrix), testClass)        GBDT_rate += getRecognitionRate(clf_GBDT.predict(testMatrix), testClass)    #输出各个分类器的平均识别率（K个训练集测试集，计算平均）    print    print    print    print('K Nearest Neighbor mean recognition rate: ', KNN_rate / float(setNums))    print('Linear Discriminant Analysis mean recognition rate: ', LDA_rate / float(setNums))    print('Support Vector Machine mean recognition rate: ', SVM_rate / float(setNums))    print('Logistic Regression mean recognition rate: ', LR_rate / float(setNums))    print('Random Forest mean recognition rate: ', RF_rate / float(setNums))    print('Native Bayes Classifier mean recognition rate: ', NBC_rate / float(setNums))    print('Decision Tree Classifier mean recognition rate: ', DTC_rate / float(setNums))    print('Gradient Boosting Decision Tree mean recognition rate: ', GBDT_rate / float(setNums))#“主”函数2（每类前x%作为训练集，剩余作为测试集，即数据集用getData_2()方法获取，计算识别率）def totalAlgorithm_2():    #获取各个分类器    clf_KNN = KNN()    clf_LDA = LDA()    clf_SVM = SVM()    clf_LR = LR()    clf_RF = RF()    clf_NBC = native_bayes_classifier()    clf_DTC = decision_tree_classifier()    clf_GBDT = gradient_boosting_classifier()    #获取训练集和测试集    T = getData_2()    trainMatrix, trainClass, testMatrix, testClass = T[0], T[1], T[2], T[3]    #输入训练样本    clf_KNN.fit(trainMatrix, trainClass)    clf_LDA.fit(trainMatrix, trainClass)    clf_SVM.fit(trainMatrix, trainClass)    clf_LR.fit(trainMatrix, trainClass)    clf_RF.fit(trainMatrix, trainClass)    clf_NBC.fit(trainMatrix, trainClass)    clf_DTC.fit(trainMatrix, trainClass)    clf_GBDT.fit(trainMatrix, trainClass)    #输出各个分类器的识别率    print('K Nearest Neighbor recognition rate: ', getRecognitionRate(clf_KNN.predict(testMatrix), testClass))    print('Linear Discriminant Analysis recognition rate: ', getRecognitionRate(clf_LDA.predict(testMatrix), testClass))    print('Support Vector Machine recognition rate: ', getRecognitionRate(clf_SVM.predict(testMatrix), testClass))    print('Logistic Regression recognition rate: ', getRecognitionRate(clf_LR.predict(testMatrix), testClass))    print('Random Forest recognition rate: ', getRecognitionRate(clf_RF.predict(testMatrix), testClass))    print('Native Bayes Classifier recognition rate: ', getRecognitionRate(clf_NBC.predict(testMatrix), testClass))    print('Decision Tree Classifier recognition rate: ', getRecognitionRate(clf_DTC.predict(testMatrix), testClass))    print('Gradient Boosting Decision Tree recognition rate: ', getRecognitionRate(clf_GBDT.predict(testMatrix), testClass))if __name__ == '__main__':    print('K个训练集和测试集的平均识别率')    totalAlgorithm_1()    print('每类前x%训练，剩余测试，各个模型的识别率')    totalAlgorithm_2()    selectRFParam()    print('随机森林参数调优完成！')

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