{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"ds-1000","formal_name":"DS-1000","introduction":"DS-1000 builds 1,000 data-science problems from real StackOverflow questions across seven libraries including NumPy, Pandas and Matplotlib. The problems are perturbed so that recalling the original answer does not solve them.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://ds1000-code-gen.github.io/","indexing_mode":"index","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[],"history":"","construction":"","limitations":"","saturation":"","scores":[]}},"task_id":"08d44688-fb42-555a-9e21-c9ddd1400001","task_key":"default--test--873","task_revision_id":"1","upstream_id":"873","short_description":"My goal is to input some queries and find out which query is most similar to a…","config":"default","split":"test","body":"{\"library\":\"Sklearn\",\"prompt\":\"Problem:\\n\\nMy goal is to input some queries and find out which query is most similar to a set of documents.\\n\\nSo far I have calculated the tf-idf of the documents doing the following:\\n\\nfrom sklearn.feature_extraction.text import TfidfVectorizer\\n\\ndef get_term_frequency_inverse_data_frequency(documents):\\n    vectorizer = TfidfVectorizer()\\n    matrix = vectorizer.fit_transform(documents)\\n    return matrix\\n\\ndef get_tf_idf_query_similarity(documents, query):\\n    tfidf = get_term_frequency_inverse_data_frequency(documents)\\nThe problem I am having is now that I have tf-idf of the documents what operations do I perform on the query so I can find the cosine similarity to the documents? The answer should be like a 3*5 matrix of the similarities.\\n\\nA:\\n\\n<code>\\nimport numpy as np\\nimport pandas as pd\\nfrom sklearn.feature_extraction.text import TfidfVectorizer\\nqueries, documents = load_data()\\nassert type(queries) == list\\nassert type(documents) == list\\ntfidf = TfidfVectorizer()\\ntfidf.fit_transform(documents)\\n</code>\\ncosine_similarities_of_queries = ... # put solution in this variable\\nBEGIN SOLUTION\\n<code>\\n\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://ds1000-code-gen.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}