# tft.tfidf

Maps the terms in x to their term frequency * inverse document frequency.

The term frequency of a term in a document is calculated as (count of term in document) / (document size)

The inverse document frequency of a term is, by default, calculated as 1 + log((corpus size + 1) / (count of documents containing term + 1)).

#### Example usage:

``````example strings: [["I", "like", "pie", "pie", "pie"], ["yum", "yum", "pie]]

* <b>`in`</b>: SparseTensor(indices=[[0, 0], [0, 1], [0, 2], [0, 3], [0, 4],
[1, 0], [1, 1], [1, 2]],
values=[1, 2, 0, 0, 0, 3, 3, 0])
* <b>`out`</b>: SparseTensor(indices=[[0, 0], [0, 1], [0, 2], [1, 0], [1, 1]],
values=[1, 2, 0, 3, 0])
SparseTensor(indices=[[0, 0], [0, 1], [0, 2], [1, 0], [1, 1]],
values=[(1/5)*(log(3/2)+1), (1/5)*(log(3/2)+1), (3/5),
(2/3)*(log(3/2)+1), (1/3)]
``````
• `NOTE`: the first doc's duplicate "pie" strings have been combined to one output, as have the second doc's duplicate "yum" strings.

`x` A `SparseTensor` representing int64 values (most likely that are the result of calling `compute_and_apply_vocabulary` on a tokenized string).
`vocab_size` An int - the count of vocab used to turn the string into int64s including any OOV buckets.
`smooth` A bool indicating if the inverse document frequency should be smoothed. If True, which is the default, then the idf is calculated as 1 + log((corpus size + 1) / (document frequency of term + 1)). Otherwise, the idf is 1 +log((corpus size) / (document frequency of term)), which could result in a division by zero error.
`name` (Optional) A name for this operation.

Two `SparseTensor`s with indices [index_in_batch, index_in_bag_of_words]. The first has values vocab_index, which is taken from input `x`. The second has values tfidf_weight.