{"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":"5f3b3c04-6822-597d-b7db-591049e8bbbb","task_key":"default--test--931","task_revision_id":"1","upstream_id":"931","short_description":"I am using python and scikit-learn to find cosine similarity between item…","config":"default","split":"test","body":"{\"library\":\"Sklearn\",\"prompt\":\"Problem:\\n\\nI am using python and scikit-learn to find cosine similarity between item descriptions.\\n\\nA have a df, for example:\\n\\nitems    description\\n\\n1fgg     abcd ty\\n2hhj     abc r\\n3jkl     r df\\nI did following procedures:\\n\\n1) tokenizing each description\\n\\n2) transform the corpus into vector space using tf-idf\\n\\n3) calculated cosine distance between each description text as a measure of similarity. distance = 1 - cosinesimilarity(tfidf_matrix)\\n\\nMy goal is to have a similarity matrix of items like this and answer the question like: \\\"What is the similarity between the items 1ffg and 2hhj :\\n\\n        1fgg    2hhj    3jkl\\n1ffg    1.0     0.8     0.1\\n2hhj    0.8     1.0     0.0\\n3jkl    0.1     0.0     1.0\\nHow to get this result? Thank you for your time.\\n\\nA:\\n\\n<code>\\nimport numpy as np\\nimport pandas as pd\\nimport sklearn\\nfrom sklearn.feature_extraction.text import TfidfVectorizer\\ndf = load_data()\\ntfidf = TfidfVectorizer()\\n</code>\\ncosine_similarity_matrix = ... # 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":[]}