机器学习实战—— Chap07.AdaBoost

爱被打了一巴掌 2023-06-18 06:51 38阅读 0赞

import numpy as np
    import matplotlib.pyplot as plt
    
    """
    Desc:
        创建单层决策树的数据集
    
    Parameters:
        None
        
    Returns:
        dataMat - 数据矩阵
        classLabels - 数据标签
    """
    def loadsimpData():
        datMat = np.matrix([[1. , 2.1],
                            [1.5, 1.6],
                            [1.3, 1. ],
                            [1. , 1. ],
                            [2. , 1. ]])
        classLabels = [1.0, 1.0, -1.0, -1.0, 1.0]
        return datMat, classLabels
    
    
    """
    Desc:
        单层决策树分类函数
    
    Parameters:
        dataMatrix - 数据矩阵
        dimen - 第dimen列，也就是第几个特征
        threshVal - 阈值
        threshIneq - 标志
        
    Returns:
        retArray - 分类结果
    """
    def stumpClassify(dataMatrix, dimen, threshVal, threshIneq):
        # 初始化retArray为全1列向量
        retArray = np.ones((np.shape(dataMatrix)[0], 1))
        if threshIneq == 'lt':
            # 如果小于阈值则赋值为-1
            retArray[dataMatrix[:, dimen] <= threshVal] = -1.0
        else:
            # 如果大于阈值则赋值为-1
            retArray[dataMatrix[:, dimen] > threshVal] = -1.0
        return retArray
    
    
    """
    Desc:
        找到数据集上最佳的单层决策树
    
    Parameters:
        dataArr - 数据矩阵
        classLabels - 数据标签
        D - 样本权重,每个样本权重相等 1/n
        
    Returns:
        bestStump - 最佳单层决策树信息
        minError - 最小误差
    """
    def buildStump(dataArr, classLabels, D):
        # 输入数据转为矩阵(5, 2)
        dataMatrix = np.mat(dataArr)
        # 将标签矩阵进行转置(5, 1)
        labelMat = np.mat(classLabels).T
        # m=5, n=2
        m, n = np.shape(dataMatrix)
        numSteps = 10.0
        bestStump = {
        }
        # (5, 1)全零列矩阵
        bestClasEst = np.mat(np.zeros((m, 1)))
        # 最小误差初始化为正无穷大inf
        minError = float('inf')
        # 遍历所有特征
        for i in range(n):
            # 找到(每列)特征中的最小值和最大值
            rangeMin = dataMatrix[:, i].min()
            rangeMax = dataMatrix[:, i].max()
            # 计算步长
            stepSize = (rangeMax - rangeMin) / numSteps
            for j in range(-1, int(numSteps) + 1):
                # 大于和小于的情况均遍历，lt:Less than  gt:greater than
                for inequal in ['lt', 'gt']:
                    # 计算阈值
                    threshVal = (rangeMin + float(j) * stepSize)
                    # 计算分类结果
                    predictedVals = stumpClassify(dataMatrix, i, threshVal, inequal)
                    # 初始化误差矩阵
                    errArr = np.mat(np.ones((m, 1)))
                    # 分类正确的，赋值为0
                    errArr[predictedVals == labelMat] = 0
                    # 计算误差
                    weightedError = D.T * errArr
                    print("split: dim %d, thresh %.2f, thresh ineqal: %s, the weighted error is %.3f" % (i, threshVal, inequal, weightedError))
                    # 找到误差最小的分类方式
                    if weightedError < minError:
                        minError = weightedError
                        bestClasEst = predictedVals.copy()
                        bestStump['dim'] = i
                        bestStump['thresh'] = threshVal
                        bestStump['ineq'] = inequal
        return bestStump, minError, bestClasEst
    
    
    """
    Desc:
        使用AdaBoost进行优化
    
    Parameters:
        dataArr - 数据矩阵
        classLabels - 数据标签
        numIt - 最大迭代次数
        
    Returns:
        weakClassArr - 存储单层决策树的list
        aggClassEsc - 训练的label
    """
    def adaBoostTrainDS(dataArr, classLabels, numIt=40):
        weakClassArr = []
        # 获取数据集的行数
        m = np.shape(dataArr)[0]
        # 样本权重，每个样本权重相等，即1/n
        D = np.mat(np.ones((m, 1)) / m)
        # 初始化为全零列
        aggClassEst = np.mat(np.zeros((m, 1)))
        # 迭代
        for i in range(numIt):
            # 构建单层决策树
            bestStump, error, classEst = buildStump(dataArr, classLabels, D)
            # print("D:", D.T)
            # 计算弱学习算法权重alpha，使error不等于0，因为分母不能为0
            alpha = float(0.5 * np.log((1.0 - error) / max(error, 1e-16)))
            # 存储弱学习算法权重
            bestStump['alpha'] = alpha
            # 存储单层决策树
            weakClassArr.append(bestStump)
            # 打印最佳分类结果
            # print("classEst: ", classEst.T)
            # 计算e的指数项
            expon = np.multiply(-1 * alpha * np.mat(classLabels).T, classEst)
            # 计算递推公式的分子
            D = np.multiply(D, np.exp(expon))
            # 根据样本权重公式，更新样本权重
            D = D / D.sum()
            # 计算AdaBoost误差，当误差为0的时候，退出循环
            # 以下为错误率累计计算
            aggClassEst += alpha * classEst
            # print("aggClassEst: ", aggClassEst.T)
            # 计算误差
            aggErrors = np.multiply(np.sign(aggClassEst) != np.mat(classLabels).T, np.ones((m, 1)))
            errorRate = aggErrors.sum() / m
            # print("total error:", errorRate)
            if errorRate == 0.0:
                # 误差为0退出循环
                break
        return weakClassArr, aggClassEst
    
    
    """
    Desc:
        AdaBoost分类函数
    
    Parameters:
        datToClass - 待分类样例
        classifierArr - 训练好的分类器
        
    Returns:
        分类结果
    """
    def adaClassify(datToClass, classifierArr):
        dataMatrix = np.mat(datToClass)
        m = np.shape(dataMatrix)[0]
        aggClassEst = np.mat(np.zeros((m, 1)))
        for i in range(len(classifierArr)):
            # 遍历所有分类器进行分类
            classEst = stumpClassify(dataMatrix, classifierArr[i]['dim'], classifierArr[i]['thresh'], classifierArr[i]['ineq'])
            aggClassEst += classifierArr[i]['alpha'] * classEst
            print(aggClassEst)
        return np.sign(aggClassEst)
    
    
    """
    Desc:
        数据可视化
    
    Parameters:
        dataMat - 数据矩阵
        classLabels - 数据标签
        
    Returns:
        None
    """
    def showDataSet(dataMat, labelMat):
        # 正样本
        data_plus = []
        # 负样本
        data_minus = []
        for i in range(len(dataMat)):
            if labelMat[i] > 0:
                data_plus.append(dataMat[i])
            else:
                data_minus.append(dataMat[i])
        # 转换为numpy矩阵
        data_plus_np = np.array(data_plus)
        # 转换为numpy矩阵
        data_minus_np = np.array(data_minus)
        # 正样本散点图（scatter）
        # transpose转置
        plt.scatter(np.transpose(data_plus_np)[0], np.transpose(data_plus_np)[1])
        # 负样本散点图（scatter）
        plt.scatter(np.transpose(data_minus_np)[0], np.transpose(data_minus_np)[1])
        # 显示
        plt.show()

if __name__ == '__main__':
        dataArr, classLabels = loadsimpData()
        weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, classLabels)
        print(adaClassify([[0, 0], [5, 5]], weakClassArr))
        # print('weakClassArr:\n', weakClassArr)
        # print('aggClassEst:\n', aggClassEst)
        # showDataSet(dataArr, classLabels)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MTcyODQzMQ_size_16_color_FFFFFF_t_70]

import numpy as np
    import matplotlib.pyplot as plt
    
    """
    Desc:
        加载文件
    
    Parameters:
        fileName - 文件名
        
    Returns:
        dataMat - 数据矩阵
        labelMat - 数据标签
    """
    def loadDataSet(fileName):
        # 特征个数
        numFeat = len((open(fileName).readline().split('\t')))
        dataMat = []
        labelMat = []
        fr = open(fileName)
        for line in fr.readlines():
            lineArr = []
            curLine = line.strip().split('\t')
            for i in range(numFeat - 1):
                lineArr.append(float(curLine[i]))
            dataMat.append(lineArr)
            labelMat.append(float(curLine[-1]))
        return dataMat, labelMat
    
    
    """
    Desc:
        单层决策树分类函数
    
    Parameters:
        dataMatrix - 数据矩阵
        dimen - 第dimen列，也就是第几个特征
        threshVal - 阈值
        threshIneq - 标志
        
    Returns:
        retArray - 分类结果
    """
    def stumpClassify(dataMatrix, dimen, threshVal, threshIneq):
        # 初始化retArray为全1列向量
        retArray = np.ones((np.shape(dataMatrix)[0], 1))
        if threshIneq == 'lt':
            # 如果小于阈值则赋值为-1
            retArray[dataMatrix[:, dimen] <= threshVal] = -1.0
        else:
            # 如果大于阈值则赋值为-1
            retArray[dataMatrix[:, dimen] > threshVal] = -1.0
        return retArray
    
    
    """
    Desc:
        找到数据集上最佳的单层决策树
    
    Parameters:
        dataArr - 数据矩阵
        classLabels - 数据标签
        D - 样本权重,每个样本权重相等 1/n
        
    Returns:
        bestStump - 最佳单层决策树信息
        minError - 最小误差
        bestClassEst - 最佳的分类结果
    """
    def buildStump(dataArr, classLabels, D):
        # 输入数据转为矩阵(5, 2)
        dataMatrix = np.mat(dataArr)
        # 将标签矩阵进行转置(5, 1)
        labelMat = np.mat(classLabels).T
        # m=5, n=2
        m, n = np.shape(dataMatrix)
        numSteps = 10.0
        bestStump = {
        }
        # (5, 1)全零列矩阵
        bestClasEst = np.mat(np.zeros((m, 1)))
        # 最小误差初始化为正无穷大inf
        minError = float('inf')
        # 遍历所有特征
        for i in range(n):
            # 找到(每列)特征中的最小值和最大值
            rangeMin = dataMatrix[:, i].min()
            rangeMax = dataMatrix[:, i].max()
            # 计算步长
            stepSize = (rangeMax - rangeMin) / numSteps
            for j in range(-1, int(numSteps) + 1):
                # 大于和小于的情况均遍历，lt:Less than  gt:greater than
                for inequal in ['lt', 'gt']:
                    # 计算阈值
                    threshVal = (rangeMin + float(j) * stepSize)
                    # 计算分类结果
                    predictedVals = stumpClassify(dataMatrix, i, threshVal, inequal)
                    # 初始化误差矩阵
                    errArr = np.mat(np.ones((m, 1)))
                    # 分类正确的，赋值为0
                    errArr[predictedVals == labelMat] = 0
                    # 计算误差
                    weightedError = D.T * errArr
                    print("split: dim %d, thresh %.2f, thresh ineqal: %s, the weighted error is %.3f" % (i, threshVal, inequal, weightedError))
                    # 找到误差最小的分类方式
                    if weightedError < minError:
                        minError = weightedError
                        bestClasEst = predictedVals.copy()
                        bestStump['dim'] = i
                        bestStump['thresh'] = threshVal
                        bestStump['ineq'] = inequal
        return bestStump, minError, bestClasEst
    
    
    """
    Desc:
        使用AdaBoost进行优化
    
    Parameters:
        dataArr - 数据矩阵
        classLabels - 数据标签
        numIt - 最大迭代次数
        
    Returns:
        weakClassArr - 存储单层决策树的list
        aggClassEsc - 训练的label
    """
    def adaBoostTrainDS(dataArr, classLabels, numIt=60):
        weakClassArr = []
        # 获取数据集的行数
        m = np.shape(dataArr)[0]
        # 样本权重，每个样本权重相等，即1/n
        D = np.mat(np.ones((m, 1)) / m)
        # 初始化为全零列
        aggClassEst = np.mat(np.zeros((m, 1)))
        # 迭代
        for i in range(numIt):
            # 构建单层决策树
            bestStump, error, classEst = buildStump(dataArr, classLabels, D)
            # print("D:", D.T)
            # 计算弱学习算法权重alpha，使error不等于0，因为分母不能为0
            alpha = float(0.5 * np.log((1.0 - error) / max(error, 1e-16)))
            # 存储弱学习算法权重
            bestStump['alpha'] = alpha
            # 存储单层决策树
            weakClassArr.append(bestStump)
            # 打印最佳分类结果
            # print("classEst: ", classEst.T)
            # 计算e的指数项
            expon = np.multiply(-1 * alpha * np.mat(classLabels).T, classEst)
            # 计算递推公式的分子
            D = np.multiply(D, np.exp(expon))
            # 根据样本权重公式，更新样本权重
            D = D / D.sum()
            # 计算AdaBoost误差，当误差为0的时候，退出循环
            # 以下为错误率累计计算
            aggClassEst += alpha * classEst
            # print("aggClassEst: ", aggClassEst.T)
            # 计算误差
            aggErrors = np.multiply(np.sign(aggClassEst) != np.mat(classLabels).T, np.ones((m, 1)))
            errorRate = aggErrors.sum() / m
            # print("total error:", errorRate)
            if errorRate == 0.0:
                # 误差为0退出循环
                break
        return weakClassArr, aggClassEst
    
    
    """
    Desc:
        AdaBoost分类函数
    
    Parameters:
        datToClass - 待分类样例
        classifierArr - 训练好的分类器
        
    Returns:
        分类结果
    """
    def adaClassify(datToClass, classifierArr):
        dataMatrix = np.mat(datToClass)
        m = np.shape(dataMatrix)[0]
        aggClassEst = np.mat(np.zeros((m, 1)))
        for i in range(len(classifierArr)):
            # 遍历所有分类器进行分类
            classEst = stumpClassify(dataMatrix, classifierArr[i]['dim'], classifierArr[i]['thresh'], classifierArr[i]['ineq'])
            aggClassEst += classifierArr[i]['alpha'] * classEst
            # print(aggClassEst)
        return np.sign(aggClassEst)

if __name__ == '__main__':
        dataArr, LabelArr = loadDataSet('horseColicTraining2.txt')
        weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, LabelArr)
        testArr, testLabelArr = loadDataSet('horseColicTest2.txt')
        print(weakClassArr)
        predictions = adaClassify(dataArr, weakClassArr)
        errArr = np.mat(np.ones((len(dataArr), 1)))
        print('训练集的错误率:%.3f%%' % float(errArr[predictions != np.mat(LabelArr).T].sum() / len(dataArr) * 100))
        predictions = adaClassify(testArr, weakClassArr)
        errArr = np.mat(np.ones((len(testArr), 1)))
        print('测试集的错误率:%.3f%%' % float(errArr[predictions != np.mat(testLabelArr).T].sum() / len(testArr) * 100))

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MTcyODQzMQ_size_16_color_FFFFFF_t_70 1]

import numpy as np
    from sklearn.ensemble import AdaBoostClassifier
    from sklearn.tree import DecisionTreeClassifier
    
    """
    Desc:
        加载文件
    
    Parameters:
        fileName - 文件名
        
    Returns:
        dataMat - 数据矩阵
        labelMat - 数据标签
    """
    def loadDataSet(fileName):
        # 特征个数
        numFeat = len((open(fileName).readline().split('\t')))
        dataMat = []
        labelMat = []
        fr = open(fileName)
        for line in fr.readlines():
            lineArr = []
            curLine = line.strip().split('\t')
            for i in range(numFeat - 1):
                lineArr.append(float(curLine[i]))
            dataMat.append(lineArr)
            labelMat.append(float(curLine[-1]))
        return dataMat, labelMat

if __name__ == '__main__':
        dataArr, classLabels = loadDataSet('horseColicTraining2.txt')
        testArr, testLabelArr = loadDataSet('horseColicTest2.txt')
        bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2), algorithm="SAMME", n_estimators=10)
        bdt.fit(dataArr, classLabels)
        predictions = bdt.predict(dataArr)
        errArr = np.mat(np.zeros((len(dataArr), 1)))
        for i in range(len(classLabels)):
            if classLabels[i] != predictions[i]:
                errArr[i] = 1
        print('训练集的错误率:%.3f%%' % float(errArr.sum() / len(dataArr) * 100))
        predictions = bdt.predict(testArr)
        errArr = np.mat(np.zeros((len(testArr), 1)))
        for i in range(len(testLabelArr)):
            if testLabelArr[i] != predictions[i]:
                errArr[i] = 1
        print('测试集的错误率:%.3f%%' % float(errArr.sum() / len(testArr) * 100))
        '''
        训练集的错误率:16.054%
    	测试集的错误率:17.910%
        '''

import numpy as np
    import matplotlib.pyplot as plt
    from matplotlib.font_manager import FontProperties
    
    """
    Desc：
        加载文件
    
    Parameters:
        fileName - 文件名
        
    Returns:
        dataMat - 数据矩阵
        labelMat - 数据标签
    """
    def loadDataSet(fileName):
        # 特征个数
        numFeat = len((open(fileName).readline().split('\t')))
        dataMat = []
        labelMat = []
        fr = open(fileName)
        for line in fr.readlines():
            lineArr = []
            curLine = line.strip().split('\t')
            # 将Label列剔除，只存储特征矩阵
            for i in range(numFeat - 1):
                lineArr.append(float(curLine[i]))
            dataMat.append(lineArr)
            labelMat.append(float(curLine[-1]))
        return dataMat, labelMat
    
    
    """
    Desc：
        单层决策树分类函数
    
    Parameters:
        dataMatrix - 数据矩阵
        dimen - 第dimen列，也就是第几个特征
        threshVal - 阈值
        threshIneq - 标志
        
    Returns:
        retArray - 分类结果
    """
    def stumpClassify(dataMatrix, dimen, threshVal, threshIneq):
        # 初始化retArray为全1列向量
        retArray = np.ones((np.shape(dataMatrix)[0], 1))
        # 阈值之外标记为-1，阈值之内标记为1
        # less than
        if threshIneq == 'lt':
            # 如果小于阈值则赋值为-1
            retArray[dataMatrix[:, dimen] <= threshVal] = -1.0
        # greater than
        else:
            # 如果大于阈值则赋值为-1
            retArray[dataMatrix[:, dimen] > threshVal] = -1.0
        return retArray
    
    
    """
    Desc：
        找到数据集上最佳的单层决策树
    
    Parameters:
        dataArr - 数据矩阵
        classLabels - 数据标签
        D - 样本权重,每个样本权重相等 1/n
        
    Returns:
        bestStump - 最佳单层决策树信息
        minError - 最小误差
        bestClassEst - 最佳的分类结果
    """
    def buildStump(dataArr, classLabels, D):
        # 输入数据转为矩阵(5, 2)
        dataMatrix = np.mat(dataArr)
        # 将标签矩阵进行转置(5, 1)
        labelMat = np.mat(classLabels).T
        # m=5, n=2
        m, n = np.shape(dataMatrix)
        numSteps = 10.0
        bestStump = {
        }
        # (5, 1)全零列矩阵
        bestClasEst = np.mat(np.zeros((m, 1)))
        # 最小误差初始化为正无穷大inf
        minError = float('inf')
        # 遍历所有特征
        for i in range(n):
            # 找到(每列)特征中的最小值和最大值
            rangeMin = dataMatrix[:, i].min()
            rangeMax = dataMatrix[:, i].max()
            # 计算步长
            stepSize = (rangeMax - rangeMin) / numSteps
            for j in range(-1, int(numSteps) + 1):
                # 大于和小于的情况均遍历，lt:Less than  gt:greater than
                for inequal in ['lt', 'gt']:
                    # 计算阈值
                    threshVal = (rangeMin + float(j) * stepSize)
                    # 计算分类结果
                    predictedVals = stumpClassify(dataMatrix, i, threshVal, inequal)
                    # 初始化误差矩阵
                    errArr = np.mat(np.ones((m, 1)))
                    # 分类正确的，赋值为0
                    errArr[predictedVals == labelMat] = 0
                    # 计算误差
                    weightedError = D.T * errArr
                    # print("split: dim %d, thresh %.2f, thresh ineqal: %s, the weighted error is %.3f" % (i, threshVal, inequal, weightedError))
                    # 找到误差最小的分类方式
                    if weightedError < minError:
                        minError = weightedError
                        bestClasEst = predictedVals.copy()
                        # 第i个特征
                        bestStump['dim'] = i
                        # 此时的阈值
                        bestStump['thresh'] = threshVal
                        # 分类方式是lt还是gt
                        bestStump['ineq'] = inequal
        return bestStump, minError, bestClasEst
    
    
    """
    Desc：
        使用AdaBoost进行优化
    
    Parameters:
        dataArr - 数据矩阵
        classLabels - 数据标签
        numIt - 最大迭代次数
        
    Returns:
        weakClassArr - 存储单层决策树的list
        aggClassEsc - 训练的label
    """
    def adaBoostTrainDS(dataArr, classLabels, numIt=60):
        weakClassArr = []
        # 获取数据集的行数
        m = np.shape(dataArr)[0]
        # 样本权重，每个样本权重相等，即1/n
        D = np.mat(np.ones((m, 1)) / m)
        # 初始化为全零列
        aggClassEst = np.mat(np.zeros((m, 1)))
        # 迭代
        for i in range(numIt):
            # 构建单层决策树
            bestStump, error, classEst = buildStump(dataArr, classLabels, D)
            # print("D:", D.T)
            # 计算弱学习算法权重alpha，使error不等于0，因为分母不能为0
            alpha = float(0.5 * np.log((1.0 - error) / max(error, 1e-16)))
            # 存储弱学习算法权重
            bestStump['alpha'] = alpha
            # 存储单层决策树
            weakClassArr.append(bestStump)
            # 打印最佳分类结果
            # print("classEst: ", classEst.T)
            # 计算e的指数项
            expon = np.multiply(-1 * alpha * np.mat(classLabels).T, classEst)
            # 计算递推公式的分子
            D = np.multiply(D, np.exp(expon))
            # 根据样本权重公式，更新样本权重
            D = D / D.sum()
            # 计算AdaBoost误差，当误差为0的时候，退出循环
            # 以下为错误率累计计算
            aggClassEst += alpha * classEst
            # print("aggClassEst: ", aggClassEst.T)
            # 计算误差
            aggErrors = np.multiply(np.sign(aggClassEst) != np.mat(classLabels).T, np.ones((m, 1)))
            # 计算误差率
            errorRate = aggErrors.sum() / m
            # print("total error:", errorRate)
            if errorRate == 0.0:
                # 误差为0退出循环
                break
        return weakClassArr, aggClassEst
    
    
    """
    Desc：
        绘制ROC
    
    Parameters:
        predStrengths - 分类器的预测强度
        classLabels - 类别
        
    Returns:
        None
    """
    def plotROC(predStrengths, classLabels):
        # 设置中文字体
        font = FontProperties(fname=r"C:\Windows\Fonts\simsun.ttc", size=14)
        # 检测光标的位置
        cur = (1.0, 1.0)
        # 用于计算AUC
        ySum = 0.0
        # 统计正类的数量
        numPosClas = np.sum(np.array(classLabels) == 1.0)
        # y轴步长
        yStep = 1 / float(numPosClas)
        # x轴步长
        xStep = 1 / float(len(classLabels) - numPosClas)
        # 预测强度顺序  argsort函数返回的是数组值从小到大的索引值
        sortedIndicies = predStrengths.argsort()
        fig = plt.figure()
        fig.clf()
        # 把画布分成1*1的格子。把图形放在第1格
        ax = plt.subplot(111)
        for index in sortedIndicies.tolist()[0]:
            # 多一个TP向y轴移动一步
            if classLabels[index] == 1.0:
                delX = 0
                delY = yStep
            # 多一个FP向x轴移动一步
            else:
                delX = xStep
                delY = 0
                # 高度累加
                ySum += cur[1]
            # 绘制ROC
            ax.plot([cur[0], cur[0] - delX], [cur[1], cur[1] - delY], c='b')
            # 更新绘制光标位置
            cur = (cur[0] - delX, cur[1] - delY)
        ax.plot([0, 1], [0, 1], 'b--')
        plt.title('AdaBoost马疝病检测系统的ROC曲线', FontProperties=font)
        plt.xlabel('假阳率', FontProperties=font)
        plt.ylabel('真阳率', FontProperties=font)
        ax.axis([0, 1, 0, 1])
        # 计算AUC
        print('AUC面积为：', ySum * xStep)
        plt.show()

if __name__ == '__main__':
        dataArr, LabelArr = loadDataSet('horseColicTraining2.txt')
        weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, LabelArr)
        plotROC(aggClassEst.T, LabelArr)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MTcyODQzMQ_size_16_color_FFFFFF_t_70 2]

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