我有文本预处理功能,只需删除停用词:
def text_preprocessing():
df['text'] = df['text'].apply(word_tokenize)
df['text']=df['text'].apply(lambda x: [item for item in x if item not in stopwords])
new_array=[]
for keywords in df['text']: #converts list of words into string
P=" ".join(str(x) for x in keywords)
new_array.append(P)
df['text'] = new_array
return df['text']
我想将text_preprocessing()
传递给另一个函数tf_idf()
,它给出了我基本上做的特征矩阵: -
def tf_idf():
tfidf = TfidfVectorizer()
feature_array = tfidf.fit_transform(text_preprocessing)
keywords_data=pd.DataFrame(feature_array.toarray(), columns=tfidf.get_feature_names())
return keywords_data
我得到了TypeError: 'function' object is not iterable
的错误
您可以简单地将自定义停用词列表传递给TfidfVectorizer,而不是为停用词删除构建其他功能。正如您在下面的示例中所看到的,“test”已成功排除在Tfidf词汇表之外。
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
# Setting up
numbers = np.random.randint(1, 5, 3)
text = ['This is a test.', 'Is this working?', "Let's see."]
df = pd.DataFrame({'text': text, 'numbers': numbers})
# Define custom stop words and instantiate TfidfVectorizer with them
my_stopwords = ['test'] # the list can be longer
tfidf = TfidfVectorizer(stop_words=my_stopwords)
text_tfidf = tfidf.fit_transform(df['text'])
# Optional - concatenating tfidf with df
df_tfidf = pd.DataFrame(text_tfidf.toarray(), columns=tfidf.get_feature_names())
df = pd.concat([df, df_tfidf], axis=1)
# Initial df
df
Out[133]:
numbers text
0 2 This is a test.
1 4 Is this working?
2 3 Let's see.
tfidf.vocabulary_
Out[134]: {'this': 3, 'is': 0, 'working': 4, 'let': 1, 'see': 2}
# Final df
df
Out[136]:
numbers text is let see this working
0 2 This is a test. 0.707107 0.000000 0.000000 0.707107 0.000000
1 4 Is this working? 0.517856 0.000000 0.000000 0.517856 0.680919
2 3 Let's see. 0.000000 0.707107 0.707107 0.000000 0.000000