{"task": {"agent_timeout": 1800, "task": "875", "verifier_timeout": 1800, "instruction": "# 875: DS-1000 Task\n\n## Prompt\nProblem:\n\nGiven a list of variant length features:\n\nfeatures = [\n    ['f1', 'f2', 'f3'],\n    ['f2', 'f4', 'f5', 'f6'],\n    ['f1', 'f2']\n]\nwhere each sample has variant number of features and the feature dtype is str and already one hot.\n\nIn order to use feature selection utilities of sklearn, I have to convert the features to a 2D-array which looks like:\n\n    f1  f2  f3  f4  f5  f6\ns1   1   1   1   0   0   0\ns2   0   1   0   1   1   1\ns3   1   1   0   0   0   0\nHow could I achieve it via sklearn or numpy?\n\nA:\n\n<code>\nimport pandas as pd\nimport numpy as np\nimport sklearn\nfeatures = load_data()\n</code>\nnew_features = ... # 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": []}