{"task": {"agent_timeout": 1800, "task": "880", "verifier_timeout": 1800, "instruction": "# 880: DS-1000 Task\n\n## Prompt\nProblem:\n\nGiven a distance matrix, with similarity between various professors :\n\n              prof1     prof2     prof3\n       prof1     0        0.8     0.9\n       prof2     0.8      0       0.2\n       prof3     0.9      0.2     0\nI need to perform hierarchical clustering on this data, where the above data is in the form of 2-d matrix\n\n       data_matrix=[[0,0.8,0.9],[0.8,0,0.2],[0.9,0.2,0]]\nThe expected number of clusters is 2. I tried checking if I can implement it using sklearn.cluster AgglomerativeClustering but it is considering all the 3 rows as 3 separate vectors and not as a distance matrix. Can it be done using sklearn.cluster AgglomerativeClustering? prefer answer in a list like [label1, label2, ...]\n\nA:\n\n<code>\nimport numpy as np\nimport pandas as pd\nimport sklearn.cluster\ndata_matrix = load_data()\n</code>\ncluster_labels = ... # 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": []}