数据挖掘实验2

实验二：决策树要求：实现决策树分类算法,在两种不同的数据集上(iris.txt 和wine.txt)比较算法的性能。

有趣的故事介绍一下决策树。

[白话决策树模型](/shujuwajue/2441.html)首先第一个数据集iris.txt。

iris数据集记录的是鸢尾植物。

Scikit-learn自带了iris数据集。

其中iris.data记录的就是它的四个属性：萼片/花瓣的长和宽。

一个150*4的矩阵。

Iris.target就是每一行对应的鸢尾植物的种类，一共有三种。

测试结果：可以看到，本算法的性能大约是，准确率为0.673333333333。

附录-Python代码：import sysfrom math import logimport operatorfrom numpy import meandef get_labels(train_file):'''返回所有数据集labels(列表)'''labels = []for index,line in enumerate(open(train_file,'rU').readlines()):label = line.strip().split(',')[-1]labels.append(label)return labelsdef format_data(dataset_file):'''返回dataset(列表集合)和features(列表)'''dataset = []for index,line in enumerate(open(dataset_file,'rU').readlines()):line = line.strip()fea_and_label = line.split(',')dataset.append([float(fea_and_label[i]) for i in range(len(fea_and_label)-1)]+[fea_and_label[len(fea_and_label)-1]])#features = [dataset[0][i] for i in range(len(dataset[0])-1)]#sepal length（花萼长度）、sepal width（花萼宽度）、petal length（花瓣长度）、petal width（花瓣宽度）features = ['sepal_length','sepal_width','petal_length','petal_width']return dataset,featuresdef split_dataset(dataset,feature_index,labels):'''按指定feature划分数据集，返回四个列表:@dataset_less:指定特征项的属性值＜=该特征项平均值的子数据集@dataset_greater:指定特征项的属性值＞该特征项平均值的子数据集@label_less:按指定特征项的属性值＜=该特征项平均值切割后子标签集@label_greater:按指定特征项的属性值＞该特征项平均值切割后子标签集'''dataset_less = []dataset_greater = []label_less = []label_greater = []datasets = []for data in dataset:datasets.append(data[0:4])mean_value = mean(datasets,axis = 0)[feature_index] #数据集在该特征项的所有取值的平均值for data in dataset:if data[feature_index] > mean_value:dataset_greater.append(data)label_greater.append(data[-1])else:dataset_less.append(data)label_less.append(data[-1])return dataset_less,dataset_greater,label_less,label_greaterdef cal_entropy(dataset):'''计算数据集的熵大小'''n = len(dataset)label_count = {}for data in dataset:label = data[-1]if label_count.has_key(label):label_count[label] += 1else:label_count[label] = 1entropy = 0for label in label_count:prob = float(label_count[label])/nentropy -= prob*log(prob,2)#print 'entropy:',entropyreturn entropydef cal_info_gain(dataset,feature_index,base_entropy):'''计算指定特征对数据集的信息增益值g(D,F) = H(D)-H(D/F) = entropy(dataset) -sum{1,k}(len(sub_dataset)/len(dataset))*entropy(sub_dataset)@base_entropy = H(D)'''datasets = []for data in dataset:datasets.append(data[0:4])#print datasetsmean_value = mean(datasets,axis = 0)[feature_index] #计算指定特征的所有数据集值的平均值#print mean_valuedataset_less = []dataset_greater = []for data in dataset:if data[feature_index] > mean_value:dataset_greater.append(data)else:dataset_less.append(data)#条件熵 H(D/F)condition_entropy = float(len(dataset_less))/len(dataset)*cal_entropy(dataset_less) + float(len(dataset_greater))/len(dataset)*cal_entropy(dataset_greater)#print 'info_gain:',base_entropy - condition_entropyreturn base_entropy - condition_entropydef cal_info_gain_ratio(dataset,feature_index):'''计算信息增益比 gr(D,F) = g(D,F)/H(D)'''base_entropy = cal_entropy(dataset)'''if base_entropy == 0:return 1'''info_gain = cal_info_gain(dataset,feature_index,base_entropy)info_gain_ratio = info_gain/base_entropyreturn info_gain_ratiodef choose_best_fea_to_split(dataset,features):'''根据每个特征的信息增益比大小，返回最佳划分数据集的特征索引'''#base_entropy = cal_entropy(dataset)split_fea_index = -1max_info_gain_ratio = 0.0for i in range(len(features)):#info_gain = cal_info_gain(dataset,i,base_entropy)#info_gain_ratio = info_gain/base_entropyinfo_gain_ratio = cal_info_gain_ratio(dataset,i)if info_gain_ratio > max_info_gain_ratio:max_info_gain_ratio = info_gain_ratiosplit_fea_index = ireturn split_fea_indexdef most_occur_label(labels):'''返回数据集中出现次数最多的label'''label_count = {}for label in labels:if label not in label_count.keys():label_count[label] = 1else:label_count[label] += 1sorted_label_count = sorted(label_count.iteritems(),key = operator.itemgetter(1),reverse = True)return sorted_label_count[0][0]def build_tree(dataset,labels,features):'''创建决策树@dataset:训练数据集@labels:数据集中包含的所有label(可重复)@features:可进行划分的特征集'''#若数据集为空,返回NULLif len(labels) == 0:return 'NULL'#若数据集中只有一种label,返回该labelif len(labels) == len(labels[0]):return labels[0]#若没有可划分的特征集,则返回数据集中出现次数最多的labelif len(features) == 0:return most_occur_label(labels)#若数据集趋于稳定，则返回数据集中出现次数最多的labelif cal_entropy(dataset) == 0:return most_occur_label(labels)split_feature_index = choose_best_fea_to_split(dataset,features)split_feature = features[split_feature_index]decesion_tree = {split_feature:{}}#若划分特征的信息增益比小于阈值,则返回数据集中出现次数最多的labelif cal_info_gain_ratio(dataset,split_feature_index) < 0.3:return most_occur_label(labels)del(features[split_feature_index])dataset_less,dataset_greater,labels_less,labels_greater =split_dataset(dataset,split_feature_index,labels)decesion_tree[split_feature]['<='] = build_tree(dataset_less,labels_less,features)decesion_tree[split_feature]['>'] =build_tree(dataset_greater,labels_greater,features)return decesion_treedef store_tree(decesion_tree,filename):'''把决策树以二进制格式写入文件'''import picklewriter = open(filename,'w')pickle.dump(decesion_tree,writer)writer.close()def read_tree(filename):'''从文件中读取决策树，返回决策树'''import picklereader = open(filename,'rU')return pickle.load(reader)def classify(decesion_tree,features,test_data,mean_values):'''对测试数据进行分类, decesion_tree : {'petal_length': {'<=': {'petal_width': {'<=':'Iris-setosa', '>': {'sepal_width': {'<=': 'Iris-versicolor', '>': {'sepal_length': {'<=': 'Iris-setosa', '>': 'Iris-versicolor'}}}}}}, '>': 'Iris-virginica'}}'''first_fea = decesion_tree.keys()[0]fea_index = features.index(first_fea)if test_data[fea_index] <= mean_values[fea_index]:sub_tree = decesion_tree[first_fea]['<=']if type(sub_tree) == dict:return classify(sub_tree,features,test_data,mean_values)else:return sub_treeelse:sub_tree = decesion_tree[first_fea]['>']if type(sub_tree) == dict:return classify(sub_tree,features,test_data,mean_values)else:return sub_treedef get_means(train_dataset):'''获取训练数据集各个属性的数据平均值'''dataset = []for data in train_dataset:dataset.append(data[0:4])mean_values = mean(dataset,axis = 0) #数据集在该特征项的所有取值的平均值return mean_valuesdef run(train_file,test_file):'''主函数'''labels = get_labels(train_file)train_dataset,train_features = format_data(train_file)decesion_tree = build_tree(train_dataset,labels,train_features)print 'decesion_tree :',decesion_treestore_tree(decesion_tree,'decesion_tree')mean_values = get_means(train_dataset)test_dataset,test_features = format_data(test_file)n = len(test_dataset)correct = 0for test_data in test_dataset:label = classify(decesion_tree,test_features,test_data,mean_values)#print 'classify_label correct_label:',label,test_data[-1]if label == test_data[-1]:correct += 1print "准确率: ",correct/float(n)#############################################################if __name__ == '__main__':if len(sys.argv) != 3:print "please use: python decision.py train_file test_file"sys.exit()train_file = sys.argv[1]test_file = sys.argv[2]run(train_file,test_file)。