TASK $2$

Below, you will need to implement Jaccard ranking function using the following mathematical expression:

score$(q,d)=\frac{|qd|}{|qd|}$

In $[6]$: N def vsm$\_$jaccard$($indexer$,$ query$\_$tokens, docs$)$:

Calculate the Jaccard similarity coefficients for a given set of documents using the Vector Space Model $($VSM$).$

Args:

indexer $($Indexer$)$: An instance of the Indexer class containing the inverted index.

query$\_$tokens $($dict$)$: A dictionary representing the query with terms as keys and their frequencies as values.

docs $($list$)$: A list of document IDs for which the scores need to be calculated.

Returns:

dict: A dictionary containing document IDs as keys and their corresponding scores as values.

results $=\{\}$

for doc$\_$id in docs:

common$\_$terms $=$ set$($query$\_$tokens.keys$())$ & set$($indexer$.$document$\_$term$\_$vector$[$doc$\_$id$].$keys$())$

intersection$\_$size $,$ indexer.document$\_$term$\_$vector$[$doc$\_$id$][$term$])$ for term in common$\_$terms$)$

score $=$ intersection$\_$size $/$ union$\_$size if union$\_$size $0$ else $0$

results$[$doc$\_$id$]=$ score

return results

Let's now test the Jaccard Ranking function by running the following cell:

In $[7]$:

AssertionError:

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