{"task": {"agent_timeout": 1800, "task": "874", "verifier_timeout": 1800, "instruction": "# 874: DS-1000 Task\n\n## Prompt\nProblem:\n\nMy goal is to input 3 queries and find out which query is most similar to a set of 5 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\ndef solve(queries, documents):\n    tfidf = TfidfVectorizer()\n    tfidf.fit_transform(documents)\n    # return the solution in this function\n    # cosine_similarities_of_queries = solve(queries, documents)\n    ### BEGIN SOLUTION\n\n## What to do\n- Edit `solution/solution.py` so the code passes the DS-1000 tests.\n- Do not access the internet or install new packages; required libraries are preinstalled in the Docker image.\n- Run tests locally via `bash tests/test.sh`.\n\n## Notes\n- Keep the variable names/signatures implied by the prompt/code_context.\n- The evaluator uses the original DS-1000 `code_context` (`test_execution` / `test_string`).\n", "memory": "", "runnable": false, "difficulty": "", "language": "", "cpus": "", "instruction_truncated": false, "category": "", "compose": false, "has_solution": true, "oracle": null, "docker_image": "ds1000:latest", "taskset": "ds1000", "tags": []}, "runs": []}