Python 3.x 与'相关的错误；列表'；没有属性的对象'；下'；

我正在做多个集群项目。在我当前的集群项目中，我得到了一个与我的tfidf_矢量器代码相关的错误

以下是我导入的文档：

description_1 = open('description1.txt', 
encoding="utf8").read().lower().split('\n')
description_2 = open('description2.txt', 
encoding="utf8").read().lower().split('\n')
description_3 = open('description3.txt', 
encoding="utf8").read().lower().split('\n')
description_4 = open('description4.txt', 
encoding="utf8").read().lower().split('\n')
description_5 = open('description5.txt', 
encoding="utf8").read().lower().split('\n')
description_6 = open('description6.txt', 
encoding="utf8").read().lower().split('\n')
description_7 = open('description7.txt', 
encoding="utf8").read().lower().split('\n')

descriptions_on = (description_1, description_2, description_3, 
description_4, description_5, description_6, description_7)

descriptions = []

for i in range(len(descriptions_on)):
    item = descriptions_on[i]
    descriptions.append(item)

AttributeError                            Traceback (most recent call 
last)
<timed exec> in <module>

D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
fit_transform(self, raw_documents, y)
   1611         """
   1612         self._check_params()
->     1613         X = super(TfidfVectorizer, 
self).fit_transform(raw_documents)
   1614         self._tfidf.fit(X)
   1615         # X is already a transformed view of raw_documents so

 D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
fit_transform(self, raw_documents, y)
   1029 
   1030         vocabulary, X = self._count_vocab(raw_documents,
 ->1031                                           self.fixed_vocabulary_)
   1032 
   1033         if self.binary:

D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
_count_vocab(self, raw_documents, fixed_vocab)
    941         for doc in raw_documents:
    942             feature_counter = {}
--> 943             for feature in analyze(doc):
    944                 try:
    945                     feature_idx = vocabulary[feature]

D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
<lambda>(doc)
    327                                                tokenize)
    328             return lambda doc: self._word_ngrams(
--> 329                 tokenize(preprocess(self.decode(doc))), 
stop_words)
    330 
    331         else:

 D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
<lambda>(x)
    255 
    256         if self.lowercase:
--> 257             return lambda x: strip_accents(x.lower())
    258         else:
    259             return strip_accents

AttributeError: 'list' object has no attribute 'lower'

print(descriptions)

然后我将这些文档组合起来：

description_1 = open('description1.txt', 
encoding="utf8").read().lower().split('\n')
description_2 = open('description2.txt', 
encoding="utf8").read().lower().split('\n')
description_3 = open('description3.txt', 
encoding="utf8").read().lower().split('\n')
description_4 = open('description4.txt', 
encoding="utf8").read().lower().split('\n')
description_5 = open('description5.txt', 
encoding="utf8").read().lower().split('\n')
description_6 = open('description6.txt', 
encoding="utf8").read().lower().split('\n')
description_7 = open('description7.txt', 
encoding="utf8").read().lower().split('\n')

descriptions_on = (description_1, description_2, description_3, 
description_4, description_5, description_6, description_7)

descriptions = []

for i in range(len(descriptions_on)):
    item = descriptions_on[i]
    descriptions.append(item)

AttributeError                            Traceback (most recent call 
last)
<timed exec> in <module>

D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
fit_transform(self, raw_documents, y)
   1611         """
   1612         self._check_params()
->     1613         X = super(TfidfVectorizer, 
self).fit_transform(raw_documents)
   1614         self._tfidf.fit(X)
   1615         # X is already a transformed view of raw_documents so

 D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
fit_transform(self, raw_documents, y)
   1029 
   1030         vocabulary, X = self._count_vocab(raw_documents,
 ->1031                                           self.fixed_vocabulary_)
   1032 
   1033         if self.binary:

D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
_count_vocab(self, raw_documents, fixed_vocab)
    941         for doc in raw_documents:
    942             feature_counter = {}
--> 943             for feature in analyze(doc):
    944                 try:
    945                     feature_idx = vocabulary[feature]

D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
<lambda>(doc)
    327                                                tokenize)
    328             return lambda doc: self._word_ngrams(
--> 329                 tokenize(preprocess(self.decode(doc))), 
stop_words)
    330 
    331         else:

 D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
<lambda>(x)
    255 
    256         if self.lowercase:
--> 257             return lambda x: strip_accents(x.lower())
    258         else:
    259             return strip_accents

AttributeError: 'list' object has no attribute 'lower'

print(descriptions)

问题出现在这些代码行的某个地方

from sklearn.feature_extraction.text import TfidfVectorizer
from spacy.lang.fr.stop_words import STOP_WORDS as fr_stop
from spacy.lang.en.stop_words import STOP_WORDS as en_stop
#from warnings import filterwarnings
#filterwarnings('ignore')

final_stopwords_list = list(fr_stop) + list(en_stop)

tfidf_vectorizer = TfidfVectorizer(max_df=0.90, max_features=200000,
                             min_df=0.10, stop_words=final_stopwords_list,
                             use_idf=True, tokenizer=tokenize_and_stem, 
ngram_range=(1,3))


%time tfidf_matrix = tfidf_vectorizer.fit_transform(descriptions)

tokenizer=tokenize_and_-stem与一个函数“tokenize_and_-stem”相关，该函数已创建，但未包含在该问题的代码列表中，因为它不恰当

这是我从上面的代码中得到的错误消息：

description_1 = open('description1.txt', 
encoding="utf8").read().lower().split('\n')
description_2 = open('description2.txt', 
encoding="utf8").read().lower().split('\n')
description_3 = open('description3.txt', 
encoding="utf8").read().lower().split('\n')
description_4 = open('description4.txt', 
encoding="utf8").read().lower().split('\n')
description_5 = open('description5.txt', 
encoding="utf8").read().lower().split('\n')
description_6 = open('description6.txt', 
encoding="utf8").read().lower().split('\n')
description_7 = open('description7.txt', 
encoding="utf8").read().lower().split('\n')

descriptions_on = (description_1, description_2, description_3, 
description_4, description_5, description_6, description_7)

descriptions = []

for i in range(len(descriptions_on)):
    item = descriptions_on[i]
    descriptions.append(item)

AttributeError                            Traceback (most recent call 
last)
<timed exec> in <module>

D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
fit_transform(self, raw_documents, y)
   1611         """
   1612         self._check_params()
->     1613         X = super(TfidfVectorizer, 
self).fit_transform(raw_documents)
   1614         self._tfidf.fit(X)
   1615         # X is already a transformed view of raw_documents so

 D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
fit_transform(self, raw_documents, y)
   1029 
   1030         vocabulary, X = self._count_vocab(raw_documents,
 ->1031                                           self.fixed_vocabulary_)
   1032 
   1033         if self.binary:

D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
_count_vocab(self, raw_documents, fixed_vocab)
    941         for doc in raw_documents:
    942             feature_counter = {}
--> 943             for feature in analyze(doc):
    944                 try:
    945                     feature_idx = vocabulary[feature]

D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
<lambda>(doc)
    327                                                tokenize)
    328             return lambda doc: self._word_ngrams(
--> 329                 tokenize(preprocess(self.decode(doc))), 
stop_words)
    330 
    331         else:

 D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
<lambda>(x)
    255 
    256         if self.lowercase:
--> 257             return lambda x: strip_accents(x.lower())
    258         else:
    259             return strip_accents

AttributeError: 'list' object has no attribute 'lower'

print(descriptions)

结果：

description_1 = open('description1.txt', 
encoding="utf8").read().lower().split('\n')
description_2 = open('description2.txt', 
encoding="utf8").read().lower().split('\n')
description_3 = open('description3.txt', 
encoding="utf8").read().lower().split('\n')
description_4 = open('description4.txt', 
encoding="utf8").read().lower().split('\n')
description_5 = open('description5.txt', 
encoding="utf8").read().lower().split('\n')
description_6 = open('description6.txt', 
encoding="utf8").read().lower().split('\n')
description_7 = open('description7.txt', 
encoding="utf8").read().lower().split('\n')

descriptions_on = (description_1, description_2, description_3, 
description_4, description_5, description_6, description_7)

descriptions = []

for i in range(len(descriptions_on)):
    item = descriptions_on[i]
    descriptions.append(item)

AttributeError                            Traceback (most recent call 
last)
<timed exec> in <module>

D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
fit_transform(self, raw_documents, y)
   1611         """
   1612         self._check_params()
->     1613         X = super(TfidfVectorizer, 
self).fit_transform(raw_documents)
   1614         self._tfidf.fit(X)
   1615         # X is already a transformed view of raw_documents so

 D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
fit_transform(self, raw_documents, y)
   1029 
   1030         vocabulary, X = self._count_vocab(raw_documents,
 ->1031                                           self.fixed_vocabulary_)
   1032 
   1033         if self.binary:

D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
_count_vocab(self, raw_documents, fixed_vocab)
    941         for doc in raw_documents:
    942             feature_counter = {}
--> 943             for feature in analyze(doc):
    944                 try:
    945                     feature_idx = vocabulary[feature]

D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
<lambda>(doc)
    327                                                tokenize)
    328             return lambda doc: self._word_ngrams(
--> 329                 tokenize(preprocess(self.decode(doc))), 
stop_words)
    330 
    331         else:

 D:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py in 
<lambda>(x)
    255 
    256         if self.lowercase:
--> 257             return lambda x: strip_accents(x.lower())
    258         else:
    259             return strip_accents

AttributeError: 'list' object has no attribute 'lower'

print(descriptions)

---

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列表，而不是字符串

因此，他们没有标记器的正确格式。
你能试着打印
说明
并给我们看一下样本吗？@UpasanaMittal绝对，我已经编辑了帖子…结果是打印的一部分（说明）。完整输出太长，无法发布。看起来这是列表而不是文本列表，tfidf需要文本列表。我基本上有7个文档，在文章开头发布，每个文档有100行文本。如果您将该文档的行转换为段落，可以吗？