实现本文的文本数据可以在THUCTC下载也可以自己手动爬虫生成，
本文主要参考：/hao5335156/article/details/82716923
nb表示朴素贝叶斯
rf表示随机森林
lg表示逻辑回归
初学者（我）通过本程序的学习可以巩固python基础，学会python文本的处理，和分类器的调用。方便接下来的机器学习的学习。
各个参数直观的含义：
# -*- coding: utf-8 -*-"""Created on Thu Nov 29 13:00:46 @author: caoqu"""import matplotlib.pyplot as pltimport randomimport osimport jiebafrom sklearn.naive_bayes import MultinomialNB as NB from sklearn.linear_model.logistic import LogisticRegression as LR from sklearn.ensemble import RandomForestClassifier as RF # 文本处理 --> 生成训练集 测试集 词频集 def text_processor(text_path, test_size=0.2):folder_list = os.listdir(text_path)data_list=[] # 每个元素均为一篇文章class_list=[] # 对应于每篇文章的类别# 一个循环读取一个类别的文件夹for folder in folder_list:new_folder_path = os.path.join(text_path, folder)# 类别列表# 由于THUCTC文本巨多，所以我从每个类别的文本列表中随机抽取200个文本用于训练和测试，可以自行修改files = random.sample(os.listdir(new_folder_path), 200) # 一个循环读取一篇文章for file in files:with open(os.path.join(new_folder_path, file), 'r', encoding='UTF-8') as fp:raw = fp.read()word_cut = jieba.cut(raw, cut_all=False) #精确模式切分文章word_list = list(word_cut)# 一篇文章一个 word_listdata_list.append(word_list)class_list.append(folder.encode('utf-8')) # 划分训练集和测试集# data_class_list[[word_list_one[], 体育], [word_list_two[], 财经], ..., [...]]data_class_list = list(zip(data_list, class_list)) random.shuffle(data_class_list)# 打乱顺序index = int(len(data_class_list) * test_size) + 1 # 训测比为 8:2train_list = data_class_list[index:] test_list = data_class_list[:index]train_data_list, train_class_list = zip(*train_list) # (word_list_one[],...), (体育,...)test_data_list, test_class_list = zip(*test_list)# 统计词频 all_words_dict{"key_word_one":100, "key_word_two":200, ...}all_words_dict = {}for word_list in train_data_list:for word in word_list:if all_words_dict.get(word) != None:all_words_dict[word] += 1else:all_words_dict[word] = 1all_words_tuple_list = sorted(all_words_dict.items(), key=lambda f: f[1], reverse=True)# 按值降序排序 all_words_list = list(list(zip(*all_words_tuple_list))[0])# all_words_list[word_one, word_two, ...] return all_words_list, train_data_list, test_data_list, train_class_list, test_class_list# 选取特征词def words_dict(all_words_list, deleteN, stopwords_set=set()):feature_words = []n = 1for t in range(deleteN, len(all_words_list), 1):if n > 1000: # 维度最大1000break# 非数字 非停用词 长度 1-4 之间if not all_words_list[t].isdigit() and all_words_list[t] not in stopwords_set and 1 < len(all_words_list[t]) < 5:feature_words.append(all_words_list[t])n += 1return feature_words# 文本特征def text_features(train_data_list, test_data_list, feature_words):def text_feature_(text, feature_words):text_words = set(text)features = [1 if word in text_words else 0 for word in feature_words]return featurestrain_feature_list = [text_feature_(text, feature_words) for text in train_data_list]test_feature_list = [text_feature_(text, feature_words) for text in test_data_list]return train_feature_list, test_feature_list# 对停用词去重def make_word_set(words_file):words_set = set()with open(words_file, 'r', encoding='UTF-8') as fp:for line in fp.readlines():word = line.strip()if len(word)>0 and word not in words_set:words_set.add(word)return words_set# 列表求均值def average(accuracy_list):sum = 0for i in accuracy_list:sum += ireturn round(sum/len(accuracy_list),3)# 分类 同时输出准确率等def text_classifier(train_feature_list, test_feature_list, train_class_list, test_class_list, flag):if flag == 'nb':# 朴素贝叶斯分类器 拟合 默认拉普拉斯平滑 不指定先验概率先验概率classifier = NB().fit(train_feature_list, train_class_list)if flag == 'lg':# 逻辑回归分类器 指定liblinear为求解最优化问题的算法 最大迭代数 多分类问题策略classifier = LR(solver='liblinear',max_iter=5000, multi_class='auto').fit(train_feature_list, train_class_list)if flag == 'rf':# 随机森林分类器classifier = RF(n_estimators=200).fit(train_feature_list, train_class_list)test_accuracy = classifier.score(test_feature_list, test_class_list) # 测试准确率return test_accuracydef start(flag):folder_path = 'D:/WorkSpace/THUCTC/THUCNews/'# 请修改成自己的路径all_words_list, train_data_list, test_data_list, train_class_list, test_class_list = text_processor(folder_path, test_size=0.2)stopwords_set = make_word_set('D:/WorkSpace/tmp/py/stop_words_cn.txt')# 文本特征的提取和分类deleteNs = range(0,1000,20)test_accuracy_list = []# 每循环一次，去除前 20 个最高词频，直到去除 980 个最高词频为止for deleteN in deleteNs:feature_words = words_dict(all_words_list, deleteN, stopwords_set)train_feature_list, test_feature_list = text_features(train_data_list, test_data_list, feature_words)if flag == 'nb':test_accuracy = text_classifier(train_feature_list, test_feature_list, train_class_list, test_class_list, flag='nb')if flag == 'lg':test_accuracy = text_classifier(train_feature_list, test_feature_list, train_class_list, test_class_list, flag='lg')if flag == 'rf':test_accuracy = text_classifier(train_feature_list, test_feature_list, train_class_list, test_class_list, flag='rf')test_accuracy_list.append(test_accuracy)print(flag + '平均准确度：', average(test_accuracy_list))print(flag + '最大准确度：', round(max(test_accuracy_list), 3))return deleteNs, test_accuracy_listif __name__ == "__main__":plt.figure(figsize=(13, 11))for i in range(5):# 1 flag = 'nb'nb_deleteNs, nb_accuracy_list = start(flag)flag = 'lg'lg_deleteNs, lg_accuracy_list = start(flag)flag = 'rf'rf_deleteNs, rf_accuracy_list = start(flag)# 绘图plt.title('Relationship of deleteNs and test_accuracy')plt.xlabel('deleteNs')plt.ylabel('test_accuracy')plt.grid()plt.plot(nb_deleteNs, nb_accuracy_list, 'b', label='nb')plt.plot(lg_deleteNs, lg_accuracy_list, 'k', label='lg')plt.plot(rf_deleteNs, rf_accuracy_list, 'r', label='rf')plt.annotate('大', xy=((nb_accuracy_list.index(max(nb_accuracy_list))-1)*20, max(nb_accuracy_list)))plt.annotate('大', xy=((lg_accuracy_list.index(max(lg_accuracy_list))-1)*20, max(lg_accuracy_list)))plt.annotate('大', xy=((rf_accuracy_list.index(max(rf_accuracy_list))-1)*20, max(rf_accuracy_list)))plt.legend() plt.show()
运行结果：
其他参数请自行修改
