小编thi*_*tbl的帖子

给出以下示例:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import NMF
from sklearn.pipeline import Pipeline
import pandas as pd

pipe = Pipeline([
    ("tf_idf", TfidfVectorizer()),
    ("nmf", NMF())
])

data = pd.DataFrame([["Salut comment tu vas", "Hey how are you today", "I am okay and you ?"]]).T
data.columns = ["test"]

pipe.fit_transform(data.test)

我想在scikit学习管道中获得与tf_idf输出相对应的中间数据状态(在tf_idf上的fit_transform但不是NMF之后)或NMF输入.或者用另一种方式说出来,这与申请相同

TfidfVectorizer().fit_transform(data.test)

我知道pipe.named_steps ["tf_idf"] ti获得中间变换器,但我无法获取数据,只能使用此方法获取变换器的参数.