'Difference between vocabulary and get_features() of TfidfVectorizer?
I have
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
# Train the vectorizer
text="this is a simple example"
singleTFIDF = TfidfVectorizer(ngram_range=(1,2)).fit([text])
singleTFIDF.vocabulary_ # show the word-matrix position pairs
# Analyse the training string - text
single=singleTFIDF.transform([text])
single.toarray()
I would like to associate for each value in single the according feature. What is now the structure of single? How could you map the position of a value in single to the feature?
How can I interpret the indices of vocabulary and get_features()? Are they related? Both have the features with indices according to the documentation. That is confusing?
Sources
This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.
Source: Stack Overflow
| Solution | Source |
|---|