{"task": {"agent_timeout": 1800, "task": "928", "verifier_timeout": 1800, "instruction": "# 928: DS-1000 Task\n\n## Prompt\nProblem:\n\nI have set up a GridSearchCV and have a set of parameters, with I will find the best combination of parameters. My GridSearch consists of 12 candidate models total.\n\nHowever, I am also interested in seeing the accuracy score of all of the 12, not just the best score, as I can clearly see by using the .best_score_ method. I am curious about opening up the black box that GridSearch sometimes feels like.\n\nI see a scoring= argument to GridSearch, but I can't see any way to print out scores. Actually, I want the full results of GridSearchCV besides getting the score, in pandas dataframe.\n\nAny advice is appreciated. Thanks in advance.\n\n\nA:\n\n<code>\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import GridSearchCV\nGridSearch_fitted = load_data()\nassert type(GridSearch_fitted) == sklearn.model_selection._search.GridSearchCV\n</code>\nfull_results = ... # 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": []}