问题描述
- python 回归树问题，报错求解决
-
错误提示：
Traceback (most recent call last):File "", line 1, in
runfile('F:/desktop/新建文件夹 (2)/书/machinelearninginaction/Ch09/regTrees.py', wdir='F:/desktop/新建文件夹 (2)/书/machinelearninginaction/Ch09')File "C:UsersshiyingAnaconda2libsite-packagesspyderlibwidgetsexternalshellsitecustomize.py", line 685, in runfile
execfile(filename, namespace)File "C:UsersshiyingAnaconda2libsite-packagesspyderlibwidgetsexternalshellsitecustomize.py", line 71, in execfile
exec(compile(scripttext, filename, 'exec'), glob, loc)File "F:/desktop/新建文件夹 (2)/书/machinelearninginaction/Ch09/regTrees.py", line 140, in
File "F:/desktop/新建文件夹 (2)/书/machinelearninginaction/Ch09/regTrees.py", line 76, in createTree
File "F:/desktop/新建文件夹 (2)/书/machinelearninginaction/Ch09/regTrees.py", line 58, in chooseBestSplit
TypeError: unhashable type: 'matrix'
解决方案
解决方案二：
源码如下：
from numpy import *
def loadDataSet(fileName): #general function to parse tab -delimited floats
dataMat = [] #assume last column is target value
fr = open(fileName)
for line in fr.readlines():
curLine = line.strip().split(' ')
fltLine = map(float,curLine) #map all elements to float()
dataMat.append(fltLine)
return dataMat
def binSplitDataSet(dataSet, feature, value):
mat0 = dataSet[nonzero(dataSet[:,feature] > value)[0],:][0]
mat1 = dataSet[nonzero(dataSet[:,feature] <= value)[0],:][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 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,
try increasing the second value of ops')
ws = xTx.I * (X.T * Y)
return ws,X,Y
def modelLeaf(dataSet):#create linear model and return coeficients
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 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]):
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
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
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('bikeSpeedVsIq_train.txt'))
testMat = mat(loadDataSet('bikeSpeedVsIq_test.txt'))
myregTree=createTree(trainMat, ops=(1,20))
mymodTree=createTree(trainMat, modelLeaf, modelErr, (1,20))
yregHat=createForeCast(myregTree, testMat[:,0])
ymodHat=createForeCast(mymodTree, testMat[:,0], modelTreeEval)
regCo = corrcoef(yregHat, testMat[:,1], rowvar=0)[0,1]
modCo = corrcoef(ymodHat, testMat[:,1], rowvar=0)[0,1]
print "reg", regCo
1. print "model", modCo