import os import numpy as np import sys from datetime import datetime import gc path = 'H:\大三上大作业\python大作业\date' import jieba with open(r'H:\大三上大作业\python大作业\stopsCN.txt', encoding='utf-8') as f: stopwords = f.read().split('\n') #print(stopwords.shape)#查看停用的字符数量 # for w in stopwords:#查看stopwords文件数据 # print(w) #文本预处理 def processing(tokens): tokens = "".join([char for char in tokens if char.isalpha()])# 去掉非字母汉字的字符 tokens = [token for token in jieba.cut(tokens, cut_all=True) if len(token) >= 2]#分词 tokens = " ".join([token for token in tokens if token not in stopwords])# 去掉停用词 return tokens tokenList = [] targetList = [] for root, dirs, files in os.walk(path): # print(root)#地址 # print(dirs)#子目录 # print(files)#详细文件名 for f in files: filePath = os.path.join(root, f)#地址拼接 with open(filePath, encoding='utf-8') as f: content = f.read() target = filePath.split('\\')[-2] targetList.append(target) tokenList.append(processing(content)) #建模 from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import GaussianNB, MultinomialNB from sklearn.model_selection import cross_val_score from sklearn.metrics import classification_report x_train, x_test, y_train, y_test = train_test_split(tokenList, targetList, test_size=0.3, stratify=targetList) vectorizer = TfidfVectorizer() X_train = vectorizer.fit_transform(x_train) X_test = vectorizer.transform(x_test) from sklearn.naive_bayes import MultinomialNB mnb = MultinomialNB() module = mnb.fit(X_train, y_train) y_predict = module.predict(X_test) scores = cross_val_score(mnb, X_test, y_test, cv=5) print("验证结果:%.3f" % scores.mean()) print("分类结果:\n", classification_report(y_predict, y_test)) import collections # 测试集和预测集的各类新闻数量 testCount = collections.Counter(y_test) predCount = collections.Counter(y_predict) print('实际:', testCount, '\n', '预测', predCount) # 建立标签列表,实际结果与预测结果 nameList = list(testCount.keys()) testList = list(testCount.values()) predictList = list(predCount.values()) x = list(range(len(nameList))) print("类别:", nameList, '\n', "实际:", testList, '\n', "预测:", predictList) # 画图 import matplotlib.pyplot as plt from pylab import mpl mpl.rcParams['font.sans-serif'] = ['FangSong'] # 指定字体 plt.figure(figsize=(7,5)) total_width, n = 0.6, 2 width = total_width / n plt.bar(x, testList, width=width,label='实际',fc = 'black') for i in range(len(x)): x[i] = x[i] + width plt.bar(x, predictList,width=width,label='预测',tick_label = nameList,fc='r') plt.grid() plt.title('实际和预测对比图',fontsize=17) plt.xlabel('新闻类别',fontsize=17) plt.ylabel('频数',fontsize=17) plt.legend(fontsize =17) plt.tick_params(labelsize=15) plt.show()
大作业之中文文本分类(终稿)
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