python - 在Python代码中实现多类文本分类的n-gram

Question

我是Python新手，正在研究建筑行业契约(Contract)文件的多类文本分类。我在代码中实现 n 元语法时遇到问题，这些代码是我通过从不同的在线资源获得帮助而生成的。我想在我的代码中实现一元语法、二元语法和三元语法。在这方面的任何帮助都将受到高度赞赏。

我在代码的 Tfidf 部分尝试了二元组和三元组，但它有效。

    df = pd.read_csv('projectdataayes.csv')
    df = df[pd.notnull(df['types'])]
    my_types = ['Requirement','Non-Requirement']

    #converting to lower case
    df['description'] = df.description.map(lambda x: x.lower()) 

    #Removing the punctuation
    df['description'] = df.description.str.replace('[^\w\s]', '')  

    #splitting the word into tokens
    df['description'] = df['description'].apply(tokenize.word_tokenize) 

    #stemming
    stemmer = PorterStemmer()
    df['description'] = df['description'].apply(lambda x: [stemmer.stem(y) for y in x]) 

    print(df[:10])

    ## This converts the list of words into space-separated strings
    df['description'] = df['description'].apply(lambda x: ' '.join(x))
    count_vect = CountVectorizer()  
    counts = count_vect.fit_transform(df['description']) 


    X_train, X_test, y_train, y_test = train_test_split(counts, df['types'], test_size=0.3, random_state=39) 

    tfidf_vect_ngram = TfidfVectorizer(analyzer='word', 
    token_pattern=r'\w{1,}', ngram_range=(2,3), max_features=5000)
    tfidf_vect_ngram.fit(df['description'])
    X_train_Tfidf =  tfidf_vect_ngram.transform(X_train)
    X_test_Tfidf =  tfidf_vect_ngram.transform(X_test)

    model = MultinomialNB().fit(X_train, y_train)

文件“C:\Users\fhassan\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py”，第 328 行，位于 tokenize(预处理(self.decode(doc))), stop_words)

文件“C:\Users\fhassan\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py”，第 256 行，位于 返回 lambda x: strip_accents(x.lower())

文件“C:\Users\fhassan\anaconda3\lib\site-packages\scipy\sparse\base.py”，第 686 行，getattr 引发 AttributeError(attr + "未找到")

属性错误:未找到下层

Answer 1

首先，您在文本上安装矢量化器:

tfidf_vect_ngram.fit(df['description']) 

然后尝试将其应用于计数:

counts = count_vect.fit_transform(df['description'])
X_train, X_test, y_train, y_test = train_test_split(counts, df['types'], test_size=0.3, random_state=39) 
tfidf_vect_ngram.transform(X_train)

您需要将矢量化器应用于文本，而不是计数:

X_train, X_test, y_train, y_test = train_test_split(df['description'], df['types'], test_size=0.3, random_state=39)
tfidf_vect_ngram.transform(X_train)