基于贝叶斯的文本分类

import pandas as pd
import jieba
import numpy
df_news = pd.read_table('val.txt',names=['category','theme','URL','content'],encoding='utf-8')
df_news = df_news.dropna()
print(df_news.head())

df_news.shape
#分词：使用结吧分词器,此处加不加tolist()结果是一样的
content = df_news.content.values.tolist()#从数据中拿到content列数据并且将其转化为list格式
print (content[1000])

content_S = []
#将content的里面的值遍历后进行分词
for line in content:
    current_segment = jieba.lcut(line)
    if len(current_segment) > 1 and current_segment != '\r\n': #换行符
        content_S.append(current_segment)
print(content_S[1000])

df_content=pd.DataFrame({'content_S':content_S})
print(df_content.head())
stopwords=pd.read_csv("stopwords.txt",index_col=False,sep="\t",quoting=3,names=['stopword'], encoding='utf-8')
stopwords.head(20)

#第二步:去掉停用词
def drop_stopwords(contents,stopwords):
    contents_clean = []
    all_words = []
    for line in contents:
        line_clean = []
        for word in line:
            if word in stopwords:
                continue
            line_clean.append(word)
            all_words.append(str(word))
        contents_clean.append(line_clean)
    return contents_clean,all_words
contents = df_content.content_S.values.tolist()
stopwords = stopwords.stopword.values.tolist()
contents_clean,all_words = drop_stopwords(contents,stopwords)
df_content=pd.DataFrame({'contents_clean':contents_clean})
print(df_content.head())
df_all_words=pd.DataFrame({'all_words':all_words})
print(df_all_words.head())
#groupby当前这个词出现的次数有多大
words_count=df_all_words.groupby(by=['all_words'])['all_words'].agg({"count":numpy.size})
words_count=words_count.reset_index().sort_values(by=["count"],ascending=False)
words_count.head()

from wordcloud import WordCloud
import matplotlib.pyplot as plt

import matplotlib
matplotlib.rcParams['figure.figsize'] = (10.0, 5.0)

wordcloud=WordCloud(font_path="./data/simhei.ttf",background_color="white",max_font_size=80)
word_frequence = {x[0]:x[1] for x in words_count.head(100).values}
wordcloud=wordcloud.fit_words(word_frequence)
plt.imshow(wordcloud)
plt.show()
#TF-IDF
import jieba.analyse
index = 2400
print (df_news['content'][index])
content_S_str = "".join(content_S[index])#“”的意思是起到填加空格的作用
print ("  ".join(jieba.analyse.extract_tags(content_S_str, topK=5, withWeight=False)))
# topK:返回几个 TF/IDF 权重最大的关键词，默认值为20。
# withWeight:是否一并返回关键词权重值，默认值为False。

#LDA ：主题模型
#用gensim进行建模的流程
from gensim import corpora, models, similarities
import gensim

#做映射，相当于词袋
dictionary = corpora.Dictionary(contents_clean)#映射字典
corpus = [dictionary.doc2bow(sentence) for sentence in contents_clean]#语料

df_train=pd.DataFrame({'contents_clean':contents_clean,'label':df_news['category']})
df_train.tail()
df_train.label.unique()#指的是当前这一列中有多少个不重复的值
#用数字来替换相应的文本
label_mapping = {"汽车": 1, "财经": 2, "科技": 3, "健康": 4, "体育":5, "教育": 6,"文化": 7,"军事": 8,"娱乐": 9,"时尚": 0}
df_train['label'] = df_train['label'].map(label_mapping)
print(df_train.head())

from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(df_train['contents_clean'].values, df_train['label'].values, random_state=1)
x_train[0][1]
words = []
for line_index in range(len(x_train)):
    try:
        #x_train[line_index][word_index] = str(x_train[line_index][word_index])
        words.append(' '.join(x_train[line_index]))#列表转字符串的方法join
        #将列表转换成字符串，用空格进行分开
    except:
        print (line_index,word_index)
words[0]

from sklearn.feature_extraction.text import CountVectorizer#CountVectorizer指的是把一个词或者一句话转化成向量
texts=["dog cat fish","dog cat cat","fish bird", 'bird']
cv = CountVectorizer()
cv_fit=cv.fit_transform(texts)
print(cv.get_feature_names())
print(cv_fit.toarray())
print(cv_fit.toarray().sum(axis=0))


from sklearn.feature_extraction.text import CountVectorizer
texts=["dog cat fish","dog cat cat","fish bird", 'bird']
cv = CountVectorizer(ngram_range=(1,4))
cv_fit=cv.fit_transform(texts)

print(cv.get_feature_names())
print(cv_fit.toarray())


print(cv_fit.toarray().sum(axis=0))

from sklearn.feature_extraction.text import CountVectorizer

vec = CountVectorizer(analyzer='word', max_features=4000,  lowercase = False)
vec.fit(words)

from sklearn.naive_bayes import MultinomialNB
classifier = MultinomialNB()
classifier.fit(vec.transform(words), y_train)

test_words = []
for line_index in range(len(x_test)):
    try:
        #x_train[line_index][word_index] = str(x_train[line_index][word_index])
        test_words.append(' '.join(x_test[line_index]))
    except:
         print (line_index,word_index)
test_words[0]
classifier.score(vec.transform(test_words), y_test)
from sklearn.feature_extraction.text import TfidfVectorizer

vectorizer = TfidfVectorizer(analyzer='word', max_features=4000,  lowercase = False)
vectorizer.fit(words)
from sklearn.naive_bayes import MultinomialNB
classifier = MultinomialNB()
classifier.fit(vectorizer.transform(words), y_train)
classifier.score(vectorizer.transform(test_words), y_test)