### Python 实现 ID3 决策树算法的可视化 以下是通过 `matplotlib` 和自定义绘图函数来实现 ID3 决策树可视化的代码示例： ```python import matplotlib.pyplot as plt from matplotlib.patches import Circle, FancyArrowPatch import numpy as np # 定义绘制决策树的参数 decisionNode = dict(boxstyle="sawtooth", fc="0.8") # 决策节点样式 leafNode = dict(boxstyle="round4", fc="0.8") # 叶子节点样式 arrow_args = dict(arrowstyle="<-") # 箭头样式 def plotNode(nodeTxt, centerPt, parentPt, nodeType): """ 绘制单个节点 :param nodeTxt: 节点文字 :param centerPt: 文本位置坐标 :param parentPt: 指向文本的箭头起点坐标 :param nodeType: 节点类型（决策节点或叶子节点） """ createPlot.ax1.annotate( nodeTxt, xy=parentPt, xycoords='axes fraction', xytext=centerPt, textcoords='axes fraction', va="center", ha="center", bbox=nodeType, arrowprops=arrow_args) def getNumLeafs(myTree): """ 获取叶节点的数量 :param myTree: 决策树字典 :return: 叶节点数量 """ numLeafs = 0 firstStr = list(myTree.keys())[0] secondDict = myTree[firstStr] for key in secondDict.keys(): if isinstance(secondDict[key], dict): numLeafs += getNumLeafs(secondDict[key]) else: numLeafs += 1 return numLeafs def getTreeDepth(myTree): """ 获取树的深度 :param myTree: 决策树字典 :return: 树的深度 """ maxDepth = 0 firstStr = list(myTree.keys())[0] secondDict = myTree[firstStr] for key in secondDict.keys(): if isinstance(secondDict[key], dict): thisDepth = 1 + getTreeDepth(secondDict[key]) else: thisDepth = 1 if thisDepth > maxDepth: maxDepth = thisDepth return maxDepth def retrieveTree(i): """ 返回预定义的简单决策树用于测试程序 :param i: 树的选择编号 :return: 决策树字典 """ listOfTrees = [ {'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}}, {'no surfacing': {0: 'no', 1: {'flippers': {0: {'head': {0: 'no', 1: 'yes'}}, 1: 'no'}}}} ] return listOfTrees[i] def plotMidText(cntrPt, parentPt, txtString): """ 在父子节点间填充文本信息 :param cntrPt: 子节点中心坐标 :param parentPt: 父节点中心坐标 :param txtString: 填充的文字 """ xMid = (parentPt[0] - cntrPt[0]) / 2.0 + cntrPt[0] yMid = (parentPt[1] - cntrPt[1]) / 2.0 + cntrPt[1] createPlot.ax1.text(xMid, yMid, txtString, va="center", ha="center", rotation=30) def plotTree(myTree, parentPt, nodeTxt): """ 绘制完整的决策树 :param myTree: 决策树字典 :param parentPt: 父节点坐标 :param nodeTxt: 节点描述文字 """ numLeafs = getNumLeafs(myTree) depth = getTreeDepth(myTree) firstStr = list(myTree.keys())[0] cntrPt = (plotTree.xOff + (1.0 + float(numLeafs)) / 2.0 / plotTree.totalW, plotTree.yOff) plotMidText(cntrPt, parentPt, nodeTxt) plotNode(firstStr, cntrPt, parentPt, decisionNode) secondDict = myTree[firstStr] plotTree.yOff -= 1.0 / plotTree.totalD for key in secondDict.keys(): if isinstance(secondDict[key], dict): plotTree(secondDict[key], cntrPt, str(key)) else: 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 += 1.0 / plotTree.totalD def createPlot(inTree): """ 创建并显示决策树图形 :param inTree: 输入的决策树字典 """ fig = plt.figure(1, facecolor='white') fig.clf() axprops = dict(xticks=[], yticks=[]) createPlot.ax1 = plt.subplot(111, frameon=False, **axprops) plotTree.totalW = float(getNumLeafs(inTree)) plotTree.totalD = float(getTreeDepth(inTree)) plotTree.xOff = -0.5 / plotTree.totalW plotTree.yOff = 1.0 plotTree(inTree, (0.5, 1.0), '') plt.show() # 测试代码 myTree = retrieveTree(1) createPlot(myTree) ``` 此代码实现了基于 `matplotlib` 的决策树可视化功能，能够清晰展示决策树结构及其分支逻辑。 #### 关键说明： - 上述代码中的 `retrieveTree()` 函数提供了简单的预定义决策树供测试使用[^1]。 - `getNumLeafs()` 和 `getTreeDepth()` 方法分别用于获取叶节点数和树的深度，以便合理布局绘图区域[^2]。 - 使用了 `FancyArrowPatch` 来连接父节点与子节点，并支持旋转角度调整以优化视觉效果[^3]。 ---