### 关于 `plotDecisionTree` 模块 #### 使用说明 为了可视化决策树，通常会使用特定函数或库来绘制图形表示。虽然 Python 中没有名为 `plotDecisionTree` 的标准模块，但可以通过自定义函数或其他第三方库实现相同功能。一种常见的方式是利用 matplotlib 和一些辅助函数完成此操作。 对于具体绘图过程中的节点绘制命令如下所示： ```python def plotNode(value, centerPt, parentPt, nodeType): createPlot.ax1.annotate( value, xy=parentPt, xycoords='axes fraction', xytext=centerPt, textcoords='axes fraction', va="center", ha="center", bbox=nodeType, arrowprops=dict(arrowstyle="<-")) ``` 上述代码片段展示了如何创建一个用于标注并连接两个点之间关系的方法[^1]。 #### 示例代码 下面给出一段完整的示例代码，该例子不仅包含了前面提到过的 `plotNode()` 函数，还实现了整个决策树的构建与显示逻辑： ```python import matplotlib.pyplot as plt # 定义文本框和箭头格式 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="<-") def getNumLeafs(myTree): numLeafs = 0 firstStr = list(myTree.keys())[0] secondDict = myTree[firstStr] for key in secondDict.keys(): if type(secondDict[key]).__name__=='dict': numLeafs += getNumLeafs(secondDict[key]) else: numLeafs +=1 return numLeafs def getTreeDepth(myTree): 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): 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): xMid = (parentPt[0]-cntrPt[0])/2.0 + cntrPt[0] yMid = (parentPt[1]-cntrPt[1])/2.0 + cntrPt[1] createPlot.ax1.text(xMid, yMid, txtString) def plotTree(myTree, parentPt, nodeTxt): 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 = 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 = 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 def createPlot(inTree): 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() myTree=retrieveTree(0) createPlot(myTree) ``` 这段程序首先导入必要的包，并设置了一些全局变量以及辅助性的函数，最后通过调用 `createPlot()` 来实际渲染一棵简单的二叉决策树。