tfidfvectorizer

I need to get matrix of TF-IDF features from the text stored in columns of a huge dataframe , loaded from a CSV file (which cannot fit in memory). I am trying to iterate over dataframe using chunks but it is returning generator objects which is not an expected variable type for the method TfidfVectorizer . I guess I am doing something wrong while writing a generator method ChunkIterator shown below. import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer #Will work only for small Dataset csvfilename = 'data_elements.csv' df = pd.read_csv(csvfilename) vectorizer =

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

Why is the value of the vectorized corpus different from the value obtained through the idf_ attribute? Should not the idf_ attribute just return the inverse document frequency (IDF) in the same way it appears in the corpus vectorized? from sklearn.feature_extraction.text import TfidfVectorizer corpus = ["This is very strange", "This is very nice"] vectorizer = TfidfVectorizer() corpus = vectorizer.fit_transform(corpus) print(corpus) Corpus vectorized: (0, 2) 0.6300993445179441 (0, 4) 0.44832087319911734 (0, 0) 0.44832087319911734 (0, 3) 0.44832087319911734 (1, 1) 0.6300993445179441 (1, 4) 0