如何在 pandas 数据帧上使用 sklearn TFIdfVectorizer
How to use sklearn TFIdfVectorizer on pandas dataframe
我正在使用制表符分隔的文件，如下所示：
0 abch7619 Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. 42Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat…..........
1 uewl0928 Duis aute irure d21olor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excep3teur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
0 ahwb3612 Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetur
1 llll2019 adipisci velit, sed quia non numquam eius modi tempora incidunt ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur???? Quis autem vel eum iure reprehenderit qui in ea voluptate velit esse quam nihil molestiae consequatur, vel illum qui dolorem eum fugiat quo voluptas nulla pariatur?
0 jdne2319 At vero eos et accusamus et iusto odio dignissimos ducimus qui blanditiis praesentium voluptatum deleniti atque corrupti quos dolores et quas molestias excepturi sint occaecati cupiditate non provident, similique sunt in culpa qui officia deserunt mollitia animi, id est laborum et dolorum fuga.
1 asbq0918 Et harum quidem rerum facilis est et expedita distinctio................................ Nam libero tempore, cum soluta nobis est eligendi optio cumque nihil impedit quo minus id quod maxime placeat facere possimus, omnis voluptas assumenda est, omnis dolor repellendus. Temporibus autem quibusdam et aut
我的目标是生成如下所示的数据框：
classification ID word1 word2 word3 word4
foo foo foo foo foo foo
TSV 长文本字段中的每个词作为特征（列）出现，其值是词 TFIDF。
我可以尝试手动执行此操作，但我希望使用 sklearn's TFIDFVECTORIZER 来生成此文件。但是，我需要预处理字段中的文本，以遵循某些准则。
到目前为止，我可以读取 .tsv 文件、创建数据框并预处理文本。我遇到的麻烦是组合我的文本格式化函数，然后将其传递给 TFIDFVECTORIZER
以下是我的资料：
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()
产生：
['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，我不确定这是否是允许 TFIDFVECTORIZER 正常工作的正确格式。当我尝试使用它时，我使用了下面的代码 运行 正确：
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)
但只是给了我这样的结果：
(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。我如何使用 TFIDFVECTORIZER 来实现我的目标，即使用 TFIDF 值创建每个单词的特征矩阵（在应用我的清理逻辑之后）？
fit_transform 的输出是一个稀疏矩阵，因此您需要将其转换为密集形式，并包括您可以尝试的清理步骤：
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对 csv_table['text'] 中的每个文档（s 中的元素）应用 清理程序 (get_words) ) 在将其传递给 fit_transform.
之前
我正在使用制表符分隔的文件，如下所示：
0 abch7619 Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. 42Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat…..........
1 uewl0928 Duis aute irure d21olor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excep3teur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
0 ahwb3612 Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetur
1 llll2019 adipisci velit, sed quia non numquam eius modi tempora incidunt ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur???? Quis autem vel eum iure reprehenderit qui in ea voluptate velit esse quam nihil molestiae consequatur, vel illum qui dolorem eum fugiat quo voluptas nulla pariatur?
0 jdne2319 At vero eos et accusamus et iusto odio dignissimos ducimus qui blanditiis praesentium voluptatum deleniti atque corrupti quos dolores et quas molestias excepturi sint occaecati cupiditate non provident, similique sunt in culpa qui officia deserunt mollitia animi, id est laborum et dolorum fuga.
1 asbq0918 Et harum quidem rerum facilis est et expedita distinctio................................ Nam libero tempore, cum soluta nobis est eligendi optio cumque nihil impedit quo minus id quod maxime placeat facere possimus, omnis voluptas assumenda est, omnis dolor repellendus. Temporibus autem quibusdam et aut
我的目标是生成如下所示的数据框：
classification ID word1 word2 word3 word4
foo foo foo foo foo foo
TSV 长文本字段中的每个词作为特征（列）出现，其值是词 TFIDF。
我可以尝试手动执行此操作，但我希望使用 sklearn's TFIDFVECTORIZER 来生成此文件。但是，我需要预处理字段中的文本，以遵循某些准则。
到目前为止，我可以读取 .tsv 文件、创建数据框并预处理文本。我遇到的麻烦是组合我的文本格式化函数，然后将其传递给 TFIDFVECTORIZER
以下是我的资料：
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()
产生：
['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，我不确定这是否是允许 TFIDFVECTORIZER 正常工作的正确格式。当我尝试使用它时，我使用了下面的代码 运行 正确：
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)
但只是给了我这样的结果：
(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。我如何使用 TFIDFVECTORIZER 来实现我的目标，即使用 TFIDF 值创建每个单词的特征矩阵（在应用我的清理逻辑之后）？
fit_transform 的输出是一个稀疏矩阵，因此您需要将其转换为密集形式，并包括您可以尝试的清理步骤：
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对 csv_table['text'] 中的每个文档（s 中的元素）应用 清理程序 (get_words) ) 在将其传递给 fit_transform.