如何在 pandas dataframe 上使用 sklearn TFIdfVectorizer

Question

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我的目標是產生一個看起來像這樣的 dataframe：

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

其中 TSV 的長文本字段中的 ech 單詞作為特征（列）出現，其值是單詞 TFIDF。

我可以手動嘗試 go ，但我希望使用sklearn's TFIDFVECTORIZER來產生這個。 但是，我需要對字段中的文本進行預處理，以遵循某些准則。

到目前為止，我可以讀取.tsv文件，創建 dataframe，並對文本進行預處理。 我遇到的麻煩是將我的文本格式化功能組合起來，然后將其傳遞給TFIDFVECTORIZER

以下是我所擁有的：

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()

產生：

['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', 

但是，我不確定這是否是允許TFIDFVECTORIZER正常工作的正確格式。 當我嘗試使用它時，我使用了以下運行正常的代碼：

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)

但只是給了我這樣的結果：

  (0, 4147)     0.09801030349526582
  (0, 4482)     0.11236176486916101
  (0, 6304)     0.13511683683910816
  :     :
  (1998, 11298) 0.08469000607646575
  (1998, 500)   0.10185473904595721
  (1998, 3196)  0.07801251063240894

而且我不知道我在看什么。 如何使用 TFIDFVECTORIZER 來實現我的目標，即使用 TFIDF 值創建每個單詞的特征矩陣（在應用我的清理邏輯之后）？

Answer 1

fit_transform 的 output 是一個稀疏矩陣，因此您需要將其轉換為密集形式，並包含您可以嘗試的清理步驟：

s = pd.Series(csv_table['text'])
corpus = s.apply(lambda s: ' '.join(get_words(s)))

vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)

df = pd.DataFrame(data=X.todense(), columns=vectorizer.get_feature_names())
print(df)

基本上，您需要做的是在將其傳遞給fit_transform之前，對csv_table['text'] （ s中的元素）中的每個文檔應用您的清理程序（ get_words ）。