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AdaBoost_Project2/AdaBoost.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu Jul 26 20:14:03 2018
+
+@author: wzy
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+
+"""
+函数说明：加载文件
+
+Parameters:
+    fileName - 文件名
+    
+Returns:
+    dataMat - 数据矩阵
+    labelMat - 数据标签
+
+Modify:
+    2018-07-26
+"""
+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
+
+
+"""
+函数说明：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
+
+Modify:
+    2018-07-26
+"""
+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
+
+
+"""
+函数说明：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
+
+Modify:
+    2018-07-26
+"""
+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
+
+
+"""
+函数说明：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
+
+Modify:
+    2018-07-26
+"""
+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
+
+
+"""
+函数说明：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
+
+Modify:
+    2018-07-26
+"""
+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))
+