python单分类预测模版，输出支持度，多种分类器，str的csv转float

预测结果为1到11中的1个

首先加载数据，训练数据，训练标签，预测数据，预测标签：

if __name__=="__main__":  
    
    importTrainContentdata()
    importTestContentdata()
    importTrainlabeldata()
    importTestlabeldata()

traindata = []
testdata = []
trainlabel = []
testlabel = []

def importTrainContentdata():
    file = 'F:/goverment/myfinalcode/train_big.csv'
    fo=open(file) 
    ls=[]
    for line in fo:
        line=line.replace("\t",",")
        line=line.replace("\n",",")
        line=line.replace("\"",",")
        ls.append(line.split(","))
    for i in ls:
        li=[]
        for j in i:
            if j == '':
                continue
            li.append(float(j))
        traindata.append(li)

def importTestContentdata():
    file = 'F:/goverment/myfinalcode/test_big.csv'
    fo=open(file) 
    ls=[]
    for line in fo:
        line=line.replace("\t",",")
        line=line.replace("\n",",")
        line=line.replace("\"",",")
        ls.append(line.split(","))
    for i in ls:
        li=[]
        for j in i:
            if j == '':
                continue
            li.append(float(j))
        testdata.append(li)   

#导入类别的训练和测试数据
def importTrainlabeldata():
    file = 'F:/goverment/myfinalcode/train_big_label.xls'
    wb = xlrd.open_workbook(file)
    ws = wb.sheet_by_name("Sheet1")
    for r in range(ws.nrows):
        col = []
        for c in range(1):
            col.append(ws.cell(r, c).value)
        trainlabel.append(col)
        
def importTestlabeldata():
    file = 'F:/goverment/myfinalcode/test_big_label.xls'
    wb = xlrd.open_workbook(file)
    ws = wb.sheet_by_name("Sheet1")
    for r in range(ws.nrows):
        col = []
        for c in range(1):
            col.append(ws.cell(r, c).value)
        testlabel.append(col)

其中训练数据，预测数据是csv文件格式，而且是str，要转为float并一排排放入lis，然后将所有lis放入traindata或testdata中，但csv中是以","隔开的，所以要将"\t"等都转为","，需要利用

ls.append(line.split(","))放入ls中，但仍然是str型的，我又另外转化成了float，后来发
现不转化也是可以的，可能它后来会在即转化吧。

之后运用多种分类器，调参数参考
http://scikit-learn.org/stable/supervised_learning.html#supervised-learning
 然后选出尽量好的分类器，提高准确率

 '''
    #19%
    from sklearn import neighbors
    knn=neighbors.KNeighborsClassifier(n_neighbors=75, leaf_size=51, weights='distance',p=2)
    knn.fit(traindata, trainlabel)
    predict=knn.predict(testdata)
    '''

    '''
    #这个不行
    from sklearn.neural_network import MLPClassifier
    import numpy as np
    traindata = np.array(traindata)#TypeError: cannot perform reduce with flexible type
    traindata = traindata.astype(float)
    
    trainlabel = np.array(trainlabel)
    trainlabel = trainlabel.astype(float)
    
    testdata=np.array(testdata)
    testdata = testdata.astype(float)
    model=MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto',
       beta_1=0.9, beta_2=0.999, early_stopping=False,
       epsilon=1e-08, hidden_layer_sizes=(5, 2), learning_rate='constant',
       learning_rate_init=0.001, max_iter=200, momentum=0.9,
       nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,
       solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,
       warm_start=False)
    model.fit(traindata, trainlabel)
    predict = model.predict(testdata)
    '''
  
    
    '''
    #19%
    from sklearn.tree import DecisionTreeClassifier
    model=DecisionTreeClassifier(class_weight='balanced',max_features=68,splitter='best',random_state=5)
    model.fit(traindata, trainlabel)
    predict = model.predict(testdata)

    
    这个不行
    from sklearn.naive_bayes import MultinomialNB
    clf = MultinomialNB(alpha=0.052).fit(traindata, trainlabel)  
    #clf.fit(traindata, trainlabel)  
    predict=clf.predict(testdata)
    '''
    
    '''17%
    from sklearn.svm import SVC
    clf = SVC(C=150,kernel='rbf', degree=51, gamma='auto',coef0=0.0,shrinking=False,probability=False,tol=0.001,cache_size=300, class_weight=None,verbose=False,max_iter=-1,decision_function_shape=None,random_state=None)
    clf.fit(traindata, trainlabel)
    predict=clf.predict(testdata)
    '''
    
    '''0.5%
    from sklearn.naive_bayes import GaussianNB
    import numpy as np
    gnb = GaussianNB()
    traindata = np.array(traindata)#TypeError: cannot perform reduce with flexible type
    traindata = traindata.astype(float)   
    trainlabel = np.array(trainlabel)
    trainlabel = trainlabel.astype(float)
    testdata=np.array(testdata)
    testdata = testdata.astype(float)
    predict = gnb.fit(traindata, trainlabel).predict(testdata)
    '''
    
    '''16%
    from sklearn.naive_bayes import BernoulliNB
    import numpy as np
    gnb = BernoulliNB()
    traindata = np.array(traindata)#TypeError: cannot perform reduce with flexible type
    traindata = traindata.astype(float)
    
    trainlabel = np.array(trainlabel)
    trainlabel = trainlabel.astype(float)
    
    testdata=np.array(testdata)
    testdata = testdata.astype(float)
    predict = gnb.fit(traindata, trainlabel).predict(testdata)
    '''
    
    from sklearn.ensemble import RandomForestClassifier
    forest = RandomForestClassifier(n_estimators=500,random_state=5, warm_start=False, min_impurity_decrease=0.0,min_samples_split=15)  # 生成随机森林多分类器
    
    predict = forest.fit(traindata, trainlabel).predict(testdata)

输出准确率，我还把预测结果输出到txt中，方便分析。

s=len(predict)
    
    f=open('F:/goverment/myfinalcode/predict.txt', 'w')
    for i in range(s):
       f.write(str(predict[i]))
       f.write('\n')
    f.write("写好了")
    f.close()
    
    
    
    k=0
    print(s)
    
    for i in range(s):
        if testlabel[i] == predict[i]:
            k=k+1
    print("精度为:",k*1.0/s)

接下来是输出所有标签的支持度

    print('我要开始输出支持度啦')
    attribute_proba=forest.predict_proba(testdata)
    #print(forest.predict_proba(testdata))#输出各个标签的概率
    print(type(attribute_proba))
    
    
    import xlwt
    myexcel = xlwt.Workbook()
    sheet = myexcel.add_sheet('sheet')
    si=-1
    sj=-1
    for i in attribute_proba:
        si=si+1
        for j in i:
            sj=sj+1
            sheet.write(si,sj,str(j))
        sj=-1
    myexcel.save("attribute_proba_small.xls") 

运行结果如下：

但是这样还不够，我还要输出前3个的预测结果的编号和支持度。
我开了个类attri，key用来放编号，weight则放支持度。
之后对每一条记录的所有的预测概率（支持度）遍历3次。每次找出概率最大的一个，挑出后把编号和
概率存好，并把这个值变为0，再寻找挑出最大的一个，循环3次。存好后输出到excel

    '''接下来输出每组概率最大的四个的编号'''
    class attri:
        def __init__(self):
            self.key=0
            self.weight=0.0
    label=[]
    for i in attribute_proba:
        lis=[]
        k=0
        while k<3:
            k=k+1
            p=1
            mm=0
            sj=-1
            for j in i:
                sj=sj+1
                if j>mm:
                    mm=j
                    p=sj
            i[p]=0#难道是从1开始？我一开始写了i【p-1】但debug时发现不对
            a=attri()
            a.key=p
            a.weight=mm
            lis.append(a)
        label.append(lis)
    print('挑几个输出') 
    import xlwt
    myexcel = xlwt.Workbook()
    sheet = myexcel.add_sheet('sheet')
    si=-2
    sj=-1
    for i in label:
        si=si+2
        for j in i:
            sj=sj+1
            sheet.write(si,sj,str(j.key))
            sheet.write(si+1,sj,str(j.weight))
        sj=-1
    myexcel.save("proba_big.xls")

运行结果如下：

自学得真辛苦啊，这些都是我的学习成果，准确还是可以在提高的，对你有帮助的话，点个赞吧，嘿嘿。