A tf-idf word-frequency array

In this exercise, you'll create a tf-idf word frequency array for a toy collection of documents. For this, use the TfidfVectorizer from sklearn. It transforms a list of documents into a word frequency array, which it outputs as a csr_matrix. It has fit() and transform() methods like other sklearn objects.

You are given a list documents of toy documents about pets. Its contents have been printed in the IPython Shell.

Instructions

Import TfidfVectorizer from sklearn.feature_extraction.text.
Create a TfidfVectorizer instance called tfidf.
Apply .fit_transform() method of tfidf to documents and assign the result to csr_mat. This is a word-frequency array in csr_matrix format.
Inspect csr_mat by calling its .toarray() method and printing the result. This has been done for you.
The columns of the array correspond to words. Get the list of words by calling the .get_feature_names() method of tfidf, and assign the result to words.

Take Hint (-30xp)