机器学习实战：第九章 树回归
源代码中有两处错误，在网上查找后解决。
from numpy import * import matplotlib.pyplot as plt def loadDataSet(fileName): dataSet = [] fr=open(fileName) for line in fr.readlines(): curLine = line.strip().split('\t') fltLine=map(float,curLine) dataSet.append(list(fltLine)) return dataSet def binSplitDataSet(dataSet, feature, value): mat0 = dataSet[nonzero(dataSet[:,feature] > value)[0],:] ## 第一处错误 mat1 = dataSet[nonzero(dataSet[:,feature] <= value)[0],:] return mat0,mat1 def regLeaf(dataSet):#returns the value used for each leaf return mean(dataSet[:, -1]) def regErr(dataSet): return var(dataSet[:,-1]) * shape(dataSet)[0] def chooseBestSplit(dataSet, leafType=regLeaf, errType=regErr, ops=(1,4)): tolS = ops[0] tolN = ops[1] #if all the target variables are the same value: quit and return value if len(set(dataSet[:, -1].T.tolist()[0])) == 1: #exit cond 1 return None , leafType(dataSet) m,n = shape(dataSet) #the choice of the best feature is driven by Reduction in RSS error from mean S = errType(dataSet) bestS = inf; bestIndex = 0; bestValue = 0 for featIndex in range(n-1): #for splitVal in set(dataSet[:,featIndex]):
## 第二处错误 for splitVal in set((dataSet[:, featIndex].T.tolist())[0]): 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 the decrease (S-bestS) is less than a threshold don't do the split if (S - bestS) < tolS: return None, leafType(dataSet) #exit cond 2 mat0, mat1 = binSplitDataSet(dataSet, bestIndex, bestValue) if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN): #exit cond 3 return None, leafType(dataSet) return bestIndex,bestValue#returns the best feature to split on #and the value used for that split def createTree(dataSet, leafType=regLeaf, errType=regErr, ops=(1,4)):#assume dataSet is NumPy Mat so we can array filtering feat, val = chooseBestSplit(dataSet, leafType, errType, ops)#choose the best split if feat == None: return val #if the splitting hit a stop condition return val retTree = {} retTree['spInd'] = feat retTree['spVal'] = val lSet, rSet = binSplitDataSet(dataSet, feat, val) retTree['left'] = createTree(lSet, leafType, errType, ops) retTree['right'] = createTree(rSet, leafType, errType, ops) return retTree ########################################### # example construct a simple regression tree myDat1 = loadDataSet(r'train.txt') myMat = mat(myDat1) mytree = createTree(myMat) mytree=createTree(myMat,ops=(1,4)) print(mytree) x=[];y=[] for a in myDat1: x.append(a[:][-2]) y.append(a[:][-1]) plt.scatter(x,y) plt.show() ############################# # cut some branches 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 we have no test data collapse the tree if (isTree(tree['right']) or isTree(tree['left'])):#if the branches are not trees try to prune them lSet, rSet = binSplitDataSet(testData, tree['spInd'], tree['spVal']) if isTree(tree['left']): tree['left'] = prune(tree['left'], lSet) if isTree(tree['right']): tree['right'] = prune(tree['right'], rSet) #if they are now both leafs, see if we can merge them if not isTree(tree['left']) and not isTree(tree['right']): lSet, rSet = binSplitDataSet(testData, tree['spInd'], 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 mydatTest=loadDataSet('test.txt') mymatTest=mat(mydatTest) cut_tree=prune(mytree,mymatTest) print(cut_tree) ################################ #model tree def linearSolve(dataSet): #helper function used in two places m,n = shape(dataSet) X = mat(ones((m,n))); Y = mat(ones((m,1)))#create a copy of data with 1 in 0th postion X[:,1:n] = dataSet[:,0:n-1]; Y = dataSet[:,-1]#and strip out Y xTx = X.T*X if linalg.det(xTx) == 0.0: 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, inData, modelEval=regTreeEval): if not isTree(tree): return modelEval(tree, inData) if inData[tree['spInd']] > tree['spVal']: if isTree(tree['left']): return treeForeCast(tree['left'], inData, modelEval) else: return modelEval(tree['left'], inData) else: if isTree(tree['right']): return treeForeCast(tree['right'], inData, modelEval) else: return modelEval(tree['right'], inData) 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) return yHat trainmat=mat(loadDataSet('train.txt')) testdat=loadDataSet('test.txt') testmat=mat(testdat) print(testmat[:,1]) mytree=createTree(trainmat,ops=(1,4)) yHat=createForeCast(mytree,testmat[:,0]) #print(yHat) co1=corrcoef(yHat,testmat[:,1],rowvar=0)[0,1] print(co1) #mytree=createTree(trainmat,modelLeaf,modelErr,(0,20)) #yHat=createForeCast(mytree,testmat[:,0],modelTreeEval) #co2=corrcoef(yHat,testmat[:,1],rowvar=0)[0,1] #print(co2)
