{"task": {"agent_timeout": 1800, "task": "931", "verifier_timeout": 1800, "instruction": "# 931: DS-1000 Task\n\n## Prompt\nProblem:\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\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": []}