Machine Learning in action --CART--regTrees.py
最近在自学机器学习，应导师要求，先把《Machine Learning with R》动手刷了一遍，感觉R真不能算是一门计算机语言，感觉也就是一个功能复杂的计算器。所以这次就决定使用经典教材《Machine Learning in action》。因为开学得换work station ，怕到时候代码又丢了，所以就索性开个博客，把代码上传上来。
因为书上的原代码有很多错误，并且网上的许多博客的代码也是没有改正的，这次我把修正过的代码po上来
version：python3.5
talk is cheap show me the code
函数定义代码
#coding=utf-8
'''
Created on Aug 19, 2017
Tree-Based Regression Methods
@author: Edgis
'''
from numpy import *
from tkinter import *
import matplotlib.pyplot as plt
import matplotlib
def loadDataSet(fileName):
dataMat = []
fr = open(fileName)
lines = fr.readlines()
for line in lines:
curLine = line.strip().split('\t')
#将 每一行映射成浮点数
fltLine = list(map(float, curLine))
dataMat.append(fltLine)
return dataMat
def binSplitDataSet(dataSet, feature, value):
mat0 = dataSet[nonzero(dataSet[:,feature] > value)[0], :]
mat1 = dataSet[nonzero(dataSet[:,feature] <= value)[0], :]
return mat0, mat1
#求总方差
def regErr(dataSet):
return var(dataSet[:,-1]) * shape(dataSet)[0]
def regLeaf(dataSet):
return mean(dataSet[:,-1]) #返回叶节点,回归树中的目标变量的均值
def createTree(dataSet, leafType = regLeaf, errType = regErr, ops=(1,4)):
feat, val = chooseBestSplit(dataSet, leafType, errType, ops) #将数据集进行切分
if feat == None:
return val
else:
retTree = {}
retTree["spIndex"] = feat
retTree["spVal"] = val
lSet, rSet = binSplitDataSet(dataSet, feat, val)
retTree["left"] = createTree(lSet, leafType, errType, ops) #递归切分
retTree["right"] = createTree(rSet, leafType, errType, ops)
return retTree
def chooseBestSplit(dataSet, leafType=regLeaf, errType=regErr, ops=(1,4)):
tolS = ops[0] ; tolN = ops[1]
# 如果剩余特征值的数目为1，那么就不再切分而返回
if len(set(dataSet[:,-1].T.tolist()[0])) == 1:
return None, leafType(dataSet)
else:
m , n= shape(dataSet)
S = errType(dataSet)
bestS = inf
bestIndex = 0
bestValue = 0
for featIndex in range(n-1): #对特征进行遍历
for splitVal in set((dataSet[:,featIndex].T.A.tolist())[0]): #对特征值进行遍历 set()-->convert list to dict
mat0 , mat1 = binSplitDataSet(dataSet, featIndex, splitVal)
if (shape(mat0)[0] <tolN ) or (shape(mat1)[0] < tolN):
continue
newS = errType(mat0) + errType(mat1)
if newS < bestS :
bestIndex = featIndex
bestValue = splitVal
bestS = newS
if (S - bestS) < tolS: #假如误差不大，则退出
return None, leafType(dataSet)
mat0, mat1 = binSplitDataSet(dataSet, bestIndex, bestValue)
if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN):
return None, leafType(dataSet)
return bestIndex, bestValue
def isTree(obj):
return (type(obj).__name__ == 'dict')
def getMean(tree):
if isTree(tree['right']):
tree['right'] = getMean(tree['right'])
if isTree(tree['left']):
tree['left'] = getMean(tree['left'])
return (tree['left'] + tree['right'])/ 2.0
def prune(tree, testData):
if shape(testData)[0] == 0:
return getMean(tree)
if (isTree(tree['right']) or isTree(tree['left'])):# left or right 是树结构
lSet, rSet = binSplitDataSet(testData, tree['spIndex'], tree['spVal'])
if isTree(tree['left']):
tree['left'] = prune(tree['left'], lSet)
if isTree(tree['right']):
tree['right'] = prune(tree['right'], rSet)
if not isTree(tree['left']) and not isTree(tree['right']):
lSet, rSet = binSplitDataSet(testData, tree['spIndex'], tree['spVal'])
errorNoMerge = sum(power(lSet[:,-1] - tree['left'], 2)) +\
sum(power(rSet[:,-1] - tree['right'], 2))
treeMean = (tree['left'] + tree['right'])/2.0
errorMerge = sum(power(testData[:, -1] - treeMean , 2))
if errorMerge < errorNoMerge :
print("merging")
return treeMean
else:
return tree
else:
return tree
def linearSolve(dataSet):
m, n = shape(dataSet)
# X, Y 格式化
X = mat(ones((m, n))) ; Y = mat(ones((m, 1)))
X[:, 1:n] = dataSet[:, 0:n-1]
Y = dataSet[:, -1]
xTx = X.T * X
if linalg.det(xTx) == 0:
raise NameError('This matrix is singular, cannot do inverse, ','\n'
,'try increasing the second value of ops')
ws = xTx.I * (X.T * Y)
return ws, X, Y
def modelLeaf(dataSet):
ws, X, Y = linearSolve(dataSet)
return ws
def modelErr(dataSet):
ws ,X, Y = linearSolve(dataSet)
yHat = X * ws
return sum(power(Y - yHat , 2))
def regTreeEval(model, inDat):
return float(model)
def modelTreeEval(model, inDat):
n = shape(inDat)[1]
X = mat(ones((1, n+1)))
X[:, 1:n+1] = inDat
return float(X * model)
def treeForeCast(tree, inDat, modelEval = regTreeEval):
if not isTree(tree):
return modelEval(tree, inDat)
if inDat[tree['spIndex']] > tree['spVal']:
if isTree(tree['left']):
return treeForeCast(tree['left'], inDat, modelEval)
else:
return modelEval(tree['left'], inDat)
else:
if isTree(tree['right']):
return treeForeCast(tree['right'], inDat, modelEval)
else:
return modelEval(tree['right'], inDat)
def createForeCast(tree, testData, modelEval = regTreeEval):
m = len(testData)
yHat = mat(zeros((m, 1)))
for i in range(m):
yHat[i,0] = treeForeCast(tree, mat(testData[i]), modelEval)
print("yHat : ",'\n',yHat)
return yHat
上面代码块只是定义了主要的函数，离运行还差一点。由于书原文中，采用了使用 iPython 命令行的运行方式，但是博主比较懒，所以干脆舍弃掉原来的方式。
废话不多少，直接上代码
实验1
if __name__=="__main__":
testMat = mat(eye(4))
print("testMat:",'\n',testMat)
mat0, mat1 = binSplitDataSet(testMat, 1, 0.5)
print("mat0",'\n',mat0)
print("mat1",'\n',mat1)实验2 ：
if __name__ == "__main__":
myDat = loadDataSet('ex0.txt')
myDat = mat(myDat)
print("myDat ",'\n',myDat)
myMat = mat(myDat)
print("myMat ",'\n',myMat)
createTree(myMat)
print("createTree(myMat) ",'\n',createTree(myMat))
实验3 ：
if __name__ == "__main__":
myDat = loadDataSet('ex2.txt')
myDat = mat(myDat)
print("myDat ", '\n', myDat)
myMat = mat(myDat)
print("myMat ", '\n', myMat)
createTree(myMat, ops=(10000,4))
print("createTree(myMat) ", '\n', createTree(myMat, ops=(10,4)))
实验4 ：
if __name__ == "__main__":
myDat2 = loadDataSet('ex2.txt')
myDat2 = mat(myDat2)
myTree = createTree(myDat2, ops=(0,1))
print("myTree " ,myTree)
myDatTest = loadDataSet('ex2test.txt')
myMat2Test = mat(myDatTest)
print("pruned Tree", prune(myTree, myMat2Test))实验5 ：
if __name__ == "__main__":
myDat2 = loadDataSet('exp2.txt')
myMat2 = mat(myDat2)
xcord1 = myMat2[:, 0]
ycord1 = myMat2[:, -1]
print(myMat2)
myTree = createTree(myMat2, modelLeaf, modelErr, (1, 10))
实验6 ：
if __name__ == "__main__":
#回归树
trainMat = mat(loadDataSet('bikeSpeedVsIq_train.txt'))
testMat = mat(loadDataSet('bikeSpeedVsIq_test.txt'))
myTree = createTree(trainMat, ops = (1, 20))
yHat = createForeCast(myTree, testMat[:,0])
print('回归树： ',corrcoef(yHat, testMat[:,1], rowvar=0)[0,1])
#模型树
myTree1 = createTree(trainMat, modelLeaf, modelErr, (1,20))
yHat = createForeCast(myTree1, testMat[:,0], modelTreeEval)
print('模型树： ',corrcoef(yHat, testMat[:,1], rowvar=0)[0,1])
#标准线性回归
ws, X, Y = linearSolve(trainMat)
for i in range(shape(testMat)[0]):
yHat[i] = testMat[i,0] * ws[1,0] +ws[0,0]
print('标准线性回归树： ',corrcoef(yHat, testMat[:,1], rowvar=0)[0,1])
更多请戳github
https://github.com/Edgis/Machine-learning-in-action/blob/master/regTrees.py
