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《机器学习实战》CART回归树源码问题：TypeError: list indices must be integers or slices, not tuple

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书中代码1：

def binSplitDataSet(dataSet, feature, value):
    mat0 = dataSet[nonzero(dataSet[:,feature] > value)[0],:][0]
    mat1 = dataSet[nonzero(dataSet[:,feature] <= value)[0],:][0]
    return mat0,mat1

改成：

def binSplitDataSet(dataSet, feature, value):  
    featList = []
    mat0 = []
    mat1 = []
    for featVec in dataSet:
        featList.append(featVec[feature])
    for feat in featList:
        if feat > value:
            mat0.append(dataSet[featList.index(feat)])
        else:
            mat1.append(dataSet[featList.index(feat)])
    return mat0, mat1

书中代码2：

def regLeaf(dataSet):
    return mean(dataSet[:,-1])

改成：

def regLeaf(dataSet):
    valueList = []
    for featVec in dataSet:
        valueList.append(featVec[-1])
    return mean(valueList)

书中代码3：

def regErr(dataSet):
    return var(dataSet[:,-1]) * shape(dataSet)[0]

改成：

def regErr(dataSet):
    valueList = []
    for featVec in dataSet:
        valueList.append(featVec[-1])
    var = 0
    mean = sum(valueList)/len(valueList)
    for value in valueList:
        var += (mean-value)**2
    return var/len(valueList) * shape(dataSet)[0]

书中代码4：

def chooseBestSplit(dataSet, leafType=regLeaf, errType=regErr, ops=(1,4)):
    tolS = ops[0]; tolN = ops[1]
    if len(set(dataSet[:,-1].T.tolist()[0])) == 1:
        return None, leafType(dataSet)
    m,n = shape(dataSet)
    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 (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

改成：

def chooseBestSplit(dataSet, leafType=regLeaf, errType=regErr, ops=(1, 4)):
    tolS = ops[0]  
    tolN = ops[1]  
    valueList = []
    for featVec in dataSet:
        valueList.append(featVec[-1])
    if len(list(set(valueList))) == 1:
        return None, leafType(dataSet)
    m, n = shape(dataSet)
    S = errType(dataSet)  
    bestS = inf  
    bestIndex = 0
    bestValue = 0
    for featIndex in range(n - 1):
        valueList = []
        for featVec in dataSet:
            valueList.append(featVec[featIndex])
        for splitVal in list(set(valueList)):
            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:
        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

运行结果：

 

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