如何在熊猫数据帧上使用sklearn TFIdfVectorizer

Question

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我的目标是产生一个看起来像这样的数据框：

classification  ID  word1   word2   word3   word4
foo foo foo foo foo foo

TSV的长文本字段中的ech单词作为特征（列）出现，并且其值是单词TFIDF。

我可以尝试手动进行此操作，但是我希望使用sklearn's TFIDFVECTORIZER来产生此结果。但是，我需要对字段中的文本进行预处理，以遵循某些准则。

到目前为止，我可以读取sklearn's TFIDFVECTORIZER文件，创建数据框，并对文本进行预处理。我遇到的麻烦是将我的文本格式设置功能组合在一起，然后将其传递给.tsv

下面是我所拥有的：

TFIDFVECTORIZER

哪个生产：

import nltk, string, csv, operator, re, collections, sys, struct, zlib, ast, io, math, time
from nltk.tokenize import word_tokenize, RegexpTokenizer
from nltk.corpus import stopwords
from collections import defaultdict, Counter
from bs4 import BeautifulSoup as soup
from math import sqrt
from itertools import islice
import pandas as pd

# This function removes numbers from an array
def remove_nums(arr): 
    # Declare a regular expression
    pattern = '[0-9]'  
    # Remove the pattern, which is a number
    arr = [re.sub(pattern, '', i) for i in arr]    
    # Return the array with numbers removed
    return arr

# This function cleans the passed in paragraph and parses it
def get_words(para):   
    # Create a set of stop words
    stop_words = set(stopwords.words('english'))
    # Split it into lower case    
    lower = para.lower().split()
    # Remove punctuation
    no_punctuation = (nopunc.translate(str.maketrans('', '', string.punctuation)) for nopunc in lower)
    # Remove integers
    no_integers = remove_nums(no_punctuation)
    # Remove stop words
    dirty_tokens = (data for data in no_integers if data not in stop_words)
    # Ensure it is not empty
    tokens = [data for data in dirty_tokens if data.strip()]
    # Ensure there is more than 1 character to make up the word
    tokens = [data for data in tokens if len(data) > 1]

    # Return the tokens
    return tokens 

def main():

    tsv_file = "filepath"
    print(tsv_file)
    csv_table=pd.read_csv(tsv_file, sep='\t')
    csv_table.columns = ['rating', 'ID', 'text']

    s = pd.Series(csv_table['text'])
    new = s.str.cat(sep=' ')
    vocab = get_words(new)
    print(vocab)

main()

但是，我不确定这是否是允许['decent', 'terribly', 'inconsistent', 'food', 'ive', 'great', 'dishes', 'terrible', 'ones', 'love', 'chaat', 'times', 'great', 'fried', 'greasy', 'mess', 'bad', 'way', 'good', 'way', 'usually', 'matar', 'paneer', 'great', 'oversalted', 'peas', 'plain', 'bad', 'dont', 'know', 'coinflip', 'good', 'food', 'oversalted', 'overcooked', 'bowl', 'either', 'way', 'portions', 'generous', 'looks', 'arent', 'everything', 'little', 'divito', 'looks', 'little', 'scary', 'looking', 'like', 'ive', 'said', 'cant', 'judge', 'book', 'cover', 'necessarily', 'kind', 'place', 'take', 'date', 'unless', 'shes', 'blind', 'hungry', 'man', 'oh', 'man', 'food', 'ever', 'good', 'ordered', 'breakfast', 'lunch', 'dinner', 'fantastico', 'make', 'homemade', 'corn', 'tortillas', 'several', 'salsas', 'breakfast', 'burritos', 'world', 'cost', 'mcdonalds', 'meal', 'family', 'eats', 'frequently', 'frankly', 'tired', 正常工作的正确格式。当我尝试使用它时，我使用了正确运行的以下代码：

TFIDFVECTORIZER

但是只是给了我类似的结果：

from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer()
feature_matrix = tfidf.fit_transform(csv_table['text'])
df = pd.DataFrame(data=feature_matrix.todense(), columns=tfidf.get_feature_names())
print(df)

而且我不知道我在看什么。如何使用TFIDFVECTORIZER来实现我的目标，即使用TFIDF值创建每个单词的特征矩阵（在应用了清洗逻辑之后）？

Answer 1