from CalcshannonEnt import *
import operator
import matplotlib.pyplot as plt


def majorityCnt(classList):
    classCount = {}
    for vote in classList:
        if vote not in classCount.keys():
            classCount[vote] = 0
        classCount[vote] += 1
    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
    return sortedClassCount[0][0]

def createTree(dataSet,labels,featLabels):
    classList= [example[-1] for example in dataSet] #取分类标签(是否放贷:yes or no)
    if classList.count(classList[0])==len(classList): #如果类别完全相同则停止继续划分
        return classList[0]
    if len(dataSet[0])==1 or len(labels)==0:
        return majorityCnt(classList)
    bestFeat=chooseBestFeatureToSplit(dataSet) #选择最优特征
    bestFeatLabel=labels[bestFeat]
    featLabels.append(bestFeatLabel)
    myTree={bestFeatLabel:{}}  #根据最优特征的标签生成树
    del(labels[bestFeat]) #删除已经使用特征标签
    featValues=[example[bestFeat] for example in dataSet]#得到训练集中所有最优特征的属性值
    uniqueVals=set(featValues) #去掉重复的属性值
    for value in uniqueVals:
        myTree[bestFeatLabel][value]=createTree(splitDataSet(dataSet,bestFeat,value),labels,featLabels)
    return myTree


def getNumLeafs(myTree):
    numLeafs = 0  # 初始化叶子
    firstStr = next(iter(
        myTree))  # python3中myTree.keys()返回的是dict_keys,不在是list,所以不能使用myTree.keys()[0]的方法获取结点属性，可以使用list(myTree.keys())[0]
    secondDict = myTree[firstStr]  # 获取下一组字典
    for key in secondDict.keys():
        if type(secondDict[key]).__name__ == 'dict':  # 测试该结点是否为字典，如果不是字典，代表此结点为叶子结点
            numLeafs += getNumLeafs(secondDict[key])
        else:
            numLeafs += 1
    return numLeafs


"""
函数说明:获取决策树的层数

Parameters:
    myTree - 决策树
Returns:
    maxDepth - 决策树的层数
"""


def getTreeDepth(myTree):
    maxDepth = 0  # 初始化决策树深度
    firstStr = next(iter(
        myTree))  # python3中myTree.keys()返回的是dict_keys,不在是list,所以不能使用myTree.keys()[0]的方法获取结点属性，可以使用list(myTree.keys())[0]
    secondDict = myTree[firstStr]  # 获取下一个字典
    for key in secondDict.keys():
        if type(secondDict[key]).__name__ == 'dict':  # 测试该结点是否为字典，如果不是字典，代表此结点为叶子结点
            thisDepth = 1 + getTreeDepth(secondDict[key])
        else:
            thisDepth = 1
        if thisDepth > maxDepth: maxDepth = thisDepth  # 更新层数
    return maxDepth


"""
函数说明:绘制结点

Parameters:
    nodeTxt - 结点名
    centerPt - 文本位置
    parentPt - 标注的箭头位置
    nodeType - 结点格式
Returns:
    无
"""


def plotNode(nodeTxt, centerPt, parentPt, nodeType):
    arrow_args = dict(arrowstyle="<-")  # 定义箭头格式
    #font = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=14)  # 设置中文字体
    createPlot.ax1.annotate(nodeTxt, xy=parentPt, xycoords='axes fraction',  # 绘制结点
                            xytext=centerPt, textcoords='axes fraction',
                            va="center", ha="center", bbox=nodeType, arrowprops=arrow_args)


"""
函数说明:标注有向边属性值

Parameters:
    cntrPt、parentPt - 用于计算标注位置
    txtString - 标注的内容
Returns:
    无
"""


def plotMidText(cntrPt, parentPt, txtString):
    xMid = (parentPt[0] - cntrPt[0]) / 2.0 + cntrPt[0]  # 计算标注位置
    yMid = (parentPt[1] - cntrPt[1]) / 2.0 + cntrPt[1]
    createPlot.ax1.text(xMid, yMid, txtString, va="center", ha="center", rotation=30)


"""
函数说明:绘制决策树

Parameters:
    myTree - 决策树(字典)
    parentPt - 标注的内容
    nodeTxt - 结点名
Returns:
    无
"""


def plotTree(myTree, parentPt, nodeTxt):
    decisionNode = dict(boxstyle="sawtooth", fc="0.8")  # 设置结点格式
    leafNode = dict(boxstyle="round4", fc="0.8")  # 设置叶结点格式
    numLeafs = getNumLeafs(myTree)  # 获取决策树叶结点数目，决定了树的宽度
    depth = getTreeDepth(myTree)  # 获取决策树层数
    firstStr = next(iter(myTree))  # 下个字典
    cntrPt = (plotTree.xOff + (1.0 + float(numLeafs)) / 2.0 / plotTree.totalW, plotTree.yOff)  # 中心位置
    plotMidText(cntrPt, parentPt, nodeTxt)  # 标注有向边属性值
    plotNode(firstStr, cntrPt, parentPt, decisionNode)  # 绘制结点
    secondDict = myTree[firstStr]  # 下一个字典，也就是继续绘制子结点
    plotTree.yOff = plotTree.yOff - 1.0 / plotTree.totalD  # y偏移
    for key in secondDict.keys():
        if type(secondDict[key]).__name__ == 'dict':  # 测试该结点是否为字典，如果不是字典，代表此结点为叶子结点
            plotTree(secondDict[key], cntrPt, str(key))  # 不是叶结点，递归调用继续绘制
        else:  # 如果是叶结点，绘制叶结点，并标注有向边属性值
            plotTree.xOff = plotTree.xOff + 1.0 / plotTree.totalW
            plotNode(secondDict[key], (plotTree.xOff, plotTree.yOff), cntrPt, leafNode)
            plotMidText((plotTree.xOff, plotTree.yOff), cntrPt, str(key))
    plotTree.yOff = plotTree.yOff + 1.0 / plotTree.totalD


"""
函数说明:创建绘制面板

Parameters:
    inTree - 决策树(字典)
Returns:
    无
"""


def createPlot(inTree):
    fig = plt.figure(1, facecolor='white')  # 创建fig
    fig.clf()  # 清空fig
    axprops = dict(xticks=[], yticks=[])
    createPlot.ax1 = plt.subplot(111, frameon=False, **axprops)  # 去掉x、y轴
    plotTree.totalW = float(getNumLeafs(inTree))  # 获取决策树叶结点数目
    plotTree.totalD = float(getTreeDepth(inTree))  # 获取决策树层数
    plotTree.xOff = -0.5 / plotTree.totalW;
    plotTree.yOff = 1.0;  # x偏移
    plotTree(inTree, (0.5, 1.0), '')  # 绘制决策树
    plt.show()  # 显示绘制结果


if __name__=="__main__":
    dataSet,labels=createDataSet()
    featlabels=[]
    myTree=createTree(dataSet,labels,featlabels)
    print(myTree)
    createPlot(myTree)