下面是我在python中创建tfidf矩阵的函数
def tf_idf(self,job_id,method='local'):
jobtext = self.get_job_text ( job_id , method=method )
tfidf_vectorizer = TfidfVectorizer( max_df=0.8 , max_features=200000 ,
min_df=0.2 , stop_words='english' ,
use_idf=True , tokenizer=self.tokenize_and_stem(jobtext), ngram_range=(1, 3) )
#tfidf_vectorizer.fit(jobtext)
tfidf_matrix = tfidf_vectorizer.fit_transform(jobtext) #fit the vectorizer to synopses
print(tfidf_matrix.shape)
我收到了以下错误：
追踪（最近一次呼叫最后一次）：
File ".../employment_skills_extraction-master/api/process_request.py", line 206, in <module> main() File ".../employment_skills_extraction-master/api/process_request.py", line 202, in main print pr.process(json.dumps(test)) File ".../employment_skills_extraction-master/api/process_request.py", line 188, in process termVector=self.tf_idf(job_id) File ".../employment_skills_extraction-master/api/process_request.py", line 174, in tf_idf tfidf_matrix = tfidf_vectorizer.fit_transform(jobtext) #fit the vectorizer to synopses File "/usr/local/lib/python2.7/dist-packages/sklearn/feature_extraction/text.py", line 1285, in fit_transform X = super(TfidfVectorizer, self).fit_transform(raw_documents) File "/usr/local/lib/python2.7/dist-packages/sklearn/feature_extraction/text.py", line 804, in fit_transform self.fixed_vocabulary_) File "/usr/local/lib/python2.7/dist-packages/sklearn/feature_extraction/text.py", line 739, in _count_vocab for feature in analyze(doc): File "/usr/local/lib/python2.7/dist-packages/sklearn/feature_extraction/text.py", line 236, in <lambda> tokenize(preprocess(self.decode(doc))), stop_words) TypeError: 'list' object is not callable
请帮助我收到此错误的原因？
答案 0 :(得分：0)
TypeError: 'list' object is not callable看起来像错误的相关部分，它涉及您的变量job_id，这可能不是您认为的那样。无论它应该是什么，它可能是一个包含你想要的东西的列表（我不知道多长时间）。
如果在函数的第二行插入一行，并更改变量名称，使其保持优雅，如下所示：
job_id_element = job_id[0]
jobtext = self.get_job_text ( job_id_element , method=method )
它可能会奏效。
只需检查变量job_id的内容，并考虑是否需要它的第一个元素 - 我写的0 - 或者len(job_id)是你需要的最后一个而不是0，或者可能是另一个。