【python代码实现】决策树分类算法、朴素贝叶斯分类算法以及人工神经网络分类算法的代码及数据

# 决策树分类算法 # 创建数据集 def createdataset(): dataSet = [[0, 2, 0, 0, 'N'], [0, 2, 0, 1, 'N'], [1, 2, 0, 0, 'Y'], [2, 1, 0, 0, 'Y'], [2, 0, 1, 0, 'Y'], [2, 0, 1, 1, 'N'], [1, 0, 1, 1, 'Y'], [0, 1, 0, 0, 'N'], [0, 0, 1, 0, 'Y'], [2, 1, 1, 0, 'Y'], [0, 1, 1, 1, 'Y'], [1, 1, 0, 1, 'Y'], [1, 2, 1, 0, 'Y'], [2, 1, 0, 1, 'N'], ] labels = ['age', 'income', 'job', 'credit'] return dataSet, labels ds1, lab = createdataset() print(ds1) print(lab) # 计算给定数据集的香农熵 from math import log def calcShannonEnt(dataSet): numEntries = len(dataSet) labelCounts = {} for featVec in dataSet: currentLabel = featVec[-1] if currentLabel not in labelCounts.keys(): labelCounts[currentLabel] = 0 labelCounts[currentLabel] += 1 shannonEnt = 0.0 for key in labelCounts: prob = float(labelCounts[key]) / numEntries shannonEnt -= prob * log(prob, 2) return shannonEnt shan = calcShannonEnt(ds1) print(shan) # 按照给定特征划分数据集 # dataSet：待划分的数据集 # axis：划分数据集的特征--数据的第几列 # value：需要返回的特征值 def splitDataSet(dataSet, axis, value): retDataSet = [] for featVec in dataSet: if featVec[axis] == value: reducedFeatVec = featVec[:axis] # 获取从第0列到特征列的数据 reducedFeatVec.extend(featVec[axis + 1:]) # 获取从特征列之后的数据 retDataSet.append(reducedFeatVec) return retDataSet def chooseBestFeatureToSplit(dataSet): numFeatures = len(dataSet[0]) - 1 baseEntropy = calcShannonEnt(dataSet) bestInfoGain = 0.0; bestFeature = -1 for i in range(numFeatures): featList = [example[i] for example in dataSet] uniqueVals = set(featList) newEntroy = 0.0 for value in uniqueVals: subDataSet = splitDataSet(dataSet, i, value) prop = len(subDataSet) / float(len(dataSet)) newEntroy += prop * calcShannonEnt(subDataSet) infoGain = baseEntropy - newEntroy if (infoGain > bestInfoGain): bestInfoGain = infoGain bestFeature = i return bestFeature col = chooseBestFeatureToSplit(ds1) print(col) # 辅助函数，将相同路径的样本归为出现最多次的分类结果 def majorityCnt(classList): classCount = {} for vote in classList: if vote not in classCount.keys(): classCount[vote] = 0 classCount[vote] += 1 sortedClassCount = sorted(classList.iteritems(), key=operator.itemgetter(1), reverse=True) # 利用operator操作键值排序字典 return sortedClassCount[0][0] # 创建树的函数 def createTree(dataSet, labels): classList = [example[-1] for example in dataSet] if classList.count(classList[0]) == len(classList): return classList[0] if len(dataSet[0]) == 1: return majorityCnt(classList) bestFeat = chooseBestFeatureToSplit(dataSet) bestFeatLabel = labels[bestFeat] myTree = {bestFeatLabel: {}} del (labels[bestFeat]) featValues = [example[bestFeat] for example in dataSet] uniqueVals = set(featValues) for value in uniqueVals: subLabels = labels[:] myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value), subLabels) return myTree # 根据样本得出决策树 Tree = createTree(ds1, lab) print("决策树：") print(Tree) # 决策树绘制 import matplotlib.pyplot as plt decisionNode = dict(boxstyle="sawtooth", fc="0.8") leafNode = dict(boxstyle="round4", fc="0.8") arrow_args = dict(arrowstyle="<-") # 获取叶节点的数目 def getNumLeafs(myTree): numLeafs = 0 firstStr = list(myTree.keys())[0] secondDict = myTree[firstStr] for key in secondDict.keys(): if type(secondDict[key]).__name__ == 'dict': # 测试节点的数据是否为字典，以此判断是否为叶节点 numLeafs += getNumLeafs(secondDict[key]) else: numLeafs += 1 return numLeafs # 获取树的层数 def getTreeDepth(myTree): maxDepth = 0 firstStr = list(myTree.keys())[0] secondDict = myTree[firstStr] for key in secondDict.keys(): if type(secondDict[key]).__name__ == 'dict': # 测试节点的数据是否为字典，以此判断是否为叶节点 thisDepth = 1 + getTreeDepth(secondDict[key]) else: thisDepth = 1 if thisDepth > maxDepth: maxDepth = thisDepth return maxDepth # 绘制节点 def plotNode(nodeTxt, centerPt, parentPt, nodeType): createPlot.ax1.annotate(nodeTxt, xy=parentPt, xycoords='axes fraction', xytext=centerPt, textcoords='axes fraction', va="center", ha="center", bbox=nodeType, arrowprops=arrow_args) # 绘制连接线 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, va="center", ha="center", rotation=30) # 绘制树结构 def plotTree(myTree, parentPt, nodeTxt): # if the first key tells you what feat was split on numLeafs = getNumLeafs(myTree) # this determines the x width of this tree depth = getTreeDepth(myTree) firstStr = list(myTree.keys())[0] # the text label for this node should be this 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 type(secondDict[ key]).__name__ == 'dict': # test to see if the nodes are dictonaires, if not they are leaf nodes plotTree(secondDict[key], cntrPt, str(key)) # recursion else: # it's a leaf node print the leaf node 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 # 创建决策树图形 def createPlot(inTree): fig = plt.figure(1, facecolor='white') fig.clf() axprops = dict(xticks=[], yticks=[]) createPlot.ax1 = plt.subplot(111, frameon=False, **axprops) # no ticks # createPlot.ax1 = plt.subplot(111, frameon=False) #ticks for demo puropses 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.savefig('决策时.png', dpi=300, bbox_inches='tight') plt.show() createPlot(Tree) # 使用生成的决策树模型，对测试样本进行分类 def classify(inputtree, featlabels, testvec): firststr = list(inputtree.keys())[0] seconddict = inputtree[firststr] featindex = featlabels.index(firststr) for key in seconddict.keys(): if testvec[featindex] == key: if type(seconddict[key]).__name__ == 'dict': classlabel = classify(seconddict[key], featlabels, testvec) else: classlabel = seconddict[key] return classlabel labels = ['age', 'income', 'job', 'credit'] tsvec = [0, 0, 1, 1] print('result:', classify(Tree, labels, tsvec)) tsvec1 = [0, 2, 0, 1] print('result