使用Python的普通方法或者使用pandas库写决策树.zip

# -*- coding: utf-8 -*- """ Created on Fri Jul 19 14:47:14 2019 @author: Caiyunbin """ import pandas as pd import numpy as np from sklearn import tree from sklearn.tree import DecisionTreeClassifier import graphviz #定义一个数据集或者说导入一个数据集 def create_data(): row_data = {'no surfacing':[1,1,1,0,0], 'flippers':[1,1,0,1,1], 'fish':['yes','yes','no','no','no']} dataset = pd.DataFrame(row_data) return dataset #定义香浓熵的算法 def calent(dataset): n = dataset.shape[0] #注意shape的中括号 nclass = dataset.iloc[:,-1].value_counts() p = nclass/n ent = (-p*np.log2(p)).sum() return ent #定义最佳分割的列方法 def best_split(dataset): baseent = calent(dataset) bestgain = 0 axis = -1 for i in range(dataset.shape[1]-1): levels = dataset.iloc[:,i].value_counts().index ents = 0 for j in levels: childset = dataset[dataset.iloc[:,i]==j] ent = calent(childset) ents += (childset.shape[0]/dataset.shape[0])*ent infogain = baseent - ents if (infogain>bestgain): bestgain = infogain axis = i return axis #定义一个划分数据集的方法 def mysplit(dataset,axis,value): col = dataset.columns[axis] redataset = dataset.loc[dataset[col]==value,:].drop(col,axis=1) return redataset #使用递归的方法构建一棵树 def createtree(dataset): colum_all_feature = list(dataset.columns) classlist = dataset.iloc[:,-1].value_counts() #会按照从高到低的顺序进行排列 if classlist[0] == dataset.shape[0] or dataset.shape[1]==1: return classlist.index[0] axis = best_split(dataset) bestfeature = colum_all_feature[axis] mytree = {bestfeature:{}} del colum_all_feature[axis] valuelist = set(dataset.iloc[:,axis]) for value in valuelist: mytree[bestfeature][value] = createtree(mysplit(dataset,axis,value)) return mytree #关于决策树的存储，格式为numpy库npy文件 def save_tree(mytree): np.save('mytree.npy',mytree) #将数在测试集上进行测试 def classy(inputtree,labels,testvec): #判断其中的一条向量 firststr = next(iter(inputtree)) seconddict = inputtree[firststr] featindex = labels.index(firststr) for key in seconddict.keys(): if testvec[featindex]==key: if type(seconddict[key])==dict: classlabel = classy(seconddict[key],labels,testvec) else: classlabel = seconddict[key] return classlabel #查看该模型的性能 def acc_classify(train,test): inputtree = createtree(train) labels = list(train.columns) result = [] for i in range(test.shape[0]): testvec = test.iloc[i,:-1] classlabel = classy(inputtree,labels,testvec) result.append(classlabel) test['predict']=result acc = (test.iloc[:,-1]==test.iloc[:-2]).mean() print('模型的准确率为',acc) #对决策树进行画图 xtrain = dataset.iloc[:,:-1] Ytrain = dataset.iloc[:,-1] labels = Ytrain.unique().tolist Ytrain = Ytrain.apply(lambda x:labels.index(x)) clf = DecisionTreeClassifier() clf = clf.fit(xtrain,Ytrain) tree.export_graphviz(clf) dot_data = tree.export_graphviz(clf,out_file = None, feature_names =['no surfacing','flippers'], class_names=['fish','not fish'], filled = True,rounded = True, special_characters = True, graphviz.source(dot_data)) def main(): dat = create_data() mytree = createtree(dat) print(mytree) if __name__ == '__main__': main() mytree=createtree(dataset) inputtree = mytree