{"task": {"agent_timeout": 1800, "task": "888", "verifier_timeout": 1800, "instruction": "# 888: DS-1000 Task\n\n## Prompt\nProblem:\n\nIs there any package in Python that does data transformation like Box-Cox transformation to eliminate skewness of data? In R this could be done using caret package:\n\nset.seed(1)\npredictors = data.frame(x1 = rnorm(1000,\n                                   mean = 5,\n                                   sd = 2),\n                        x2 = rexp(1000,\n                                  rate=10))\n\nrequire(caret)\n\ntrans = preProcess(predictors,\n                   c(\"BoxCox\", \"center\", \"scale\"))\npredictorsTrans = data.frame(\n      trans = predict(trans, predictors))\nI know about sklearn, but I was unable to find functions to do Box-Cox transformation.\nHow can I use sklearn to solve this?\n\nA:\n\n<code>\nimport numpy as np\nimport pandas as pd\nimport sklearn\ndata = load_data()\nassert type(data) == np.ndarray\n</code>\nbox_cox_data = ... # 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": []}