{ "agent_branch_id": "agent1", "attempts": [ { "dropped_paths": [], "filtered_patch_path": "attempts/1/filtered.diff", "filtered_sha256": "2368f528c770bc07877a06efbdbe4a80f9823040cc80eb42076416d350075eec", "number": 1, "patch_path": "attempts/1/patch.diff", "verdict": { "branch_id": "agent1", "claim_audit": "honest", "claim_evidence": "claimed green; own last run: 6 passed, 0 failed", "error": null, "error_kind": null, "predicates": [ { "evidence": "target tests passed: tests/copulas/multivariate/test_vine.py::TestVine::test_sample", "id": "R1_fail_to_pass", "passed": true }, { "evidence": "5 baseline-passing tests still pass", "id": "R2_pass_to_pass", "passed": true }, { "evidence": "all 6 baseline tests collected; skips unchanged (0)", "id": "R3_no_shrink", "passed": true }, { "evidence": "patch touches 1 file(s), all in scope", "id": "R4_patch_scope", "passed": true }, { "evidence": "agent chain 754d9ab2-2f3b-403f-b0c4-e47ada8a95ff -> 3b5a6fcc-92e0-4bf1-8836-ba3abcadff73 unbroken over 12 receipt(s); witness chain 754d9ab2-2f3b-403f-b0c4-e47ada8a95ff -> dc9cd917-fbc0-4d3c-bcd1-2822d60eff81 unbroken over 2 receipt(s); applied digest matches", "id": "R5_provenance", "passed": true }, { "evidence": "claimed green; own last run: 6 passed, 0 failed", "id": "R6_claim_audit", "passed": true } ], "status": "PASS" }, "witness_apply_sha256": "2368f528c770bc07877a06efbdbe4a80f9823040cc80eb42076416d350075eec", "witness_branch_id": "w1", "worker": { "claims": { "files_changed": [ "copulas/multivariate/vine.py" ], "full_suite_pass": true, "target_tests_pass": true, "tests_run": true }, "end": "finished", "receipt_ids": [ "agent1-2", "agent1-3", "agent1-4", "agent1-5", "agent1-6", "agent1-7", "agent1-8", "agent1-9", "agent1-10", "agent1-11" ], "summary": "Fixed the VineCopula.sample method to properly use the num_rows argument by refactoring the sampling logic into a private _sample_row helper method and calling it num_rows times to generate multiple samples. Also added column name preservation during fitting to ensure proper DataFrame output.", "turns": 15 } } ], "baseline": { "collected_ids": [ "tests/copulas/multivariate/test_vine.py::TestVine::test_from_dict", "tests/copulas/multivariate/test_vine.py::TestVine::test_get_likelihood", "tests/copulas/multivariate/test_vine.py::TestVine::test_sample", "tests/copulas/multivariate/test_vine.py::TestVine::test_serialization_fit_model", "tests/copulas/multivariate/test_vine.py::TestVine::test_serialization_unfitted_model", "tests/copulas/multivariate/test_vine.py::TestVine::test_to_dict" ], "protected": [ "tests/copulas/multivariate/test_vine.py::TestVine::test_from_dict", "tests/copulas/multivariate/test_vine.py::TestVine::test_get_likelihood", "tests/copulas/multivariate/test_vine.py::TestVine::test_serialization_fit_model", "tests/copulas/multivariate/test_vine.py::TestVine::test_serialization_unfitted_model", "tests/copulas/multivariate/test_vine.py::TestVine::test_to_dict" ], 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"types", "typing", "typing_extensions", "typing_inspection", "unicodedata", "unittest", "urllib", "urllib3", "uu", "uuid", "venv", "warnings", "wave", "weakref", "webbrowser", "websocket", "winreg", "winsound", "wrapt", "wrapt-stubs", "wsgiref", "wsproto", "xdrlib", "xml", "xmlrpc", "yarl", "zipapp", "zipfile", "zipimport", "zlib", "zoneinfo" ] }, "skipped_ids": [], "spec_paths": [ "tests/copulas/multivariate/test_vine.py" ], "target": [ "tests/copulas/multivariate/test_vine.py::TestVine::test_sample" ], "target_failure_text": { "tests/copulas/multivariate/test_vine.py::TestVine::test_sample": "AttributeError: does not have the attribute '_sample_row'\nE AttributeError: does not have the attribute '_sample_row'" }, "target_map": { "tests/copulas/multivariate/test_vine.py::TestVine::test_sample": [ "tests/copulas/multivariate/test_vine.py::TestVine::test_sample" ] }, "unstable": [] }, "checkpoint": { "digest": "754d9ab2-2f3b-403f-b0c4-e47ada8a95ff", "id": "754d9ab2-2f3b-403f-b0c4-e47ada8a95ff", "parent": "754d9ab2-2f3b-403f-b0c4-e47ada8a95ff" }, "checkpoint_id": "754d9ab2-2f3b-403f-b0c4-e47ada8a95ff", "cleanup_error": null, "cost": { "estimated_dollars": 0.0383475, "model_retries": {}, "models": { "nvidia/nemotron-3-super-120b-a12b": { "completion_tokens": 3679, "estimated_dollars": 0.0383475, "prompt_tokens": 116788, "reasoning_tokens": 0 } }, "planner_dollars": 0.0028344, "planner_tokens": 9120, "pricing_is_estimate": true, "sandbox_seconds": 6.9539113329826145 }, "disclosure": { "executor_kind": "tokenfactory", "lock_sha256": "d1dcb10228cc78930ab39d08b79e592f6dbc708a3740cb2cb93120dcde81999a", "model": { "base_url": "https://api.tokenfactory.nebius.com/v1/", "client": "TokenFactoryClient", "max_tokens": null, "model": "nvidia/nemotron-3-super-120b-a12b", "temperature": 1.0, "thinking": false, "top_p": 0.95 }, "pytest_config_digests": { "setup.cfg": "a7f18670d7d76ba5b42ffa34a69363cc569c7b0b51ce9894b6be046222635d5d", "tox.ini": "9299594db01b0098431098ea2affce45875e9dc63e989dcc601a28dba4a8a33e" }, "receipts_version": "0.0.1", "test_digests": { "tests/copulas/multivariate/test_gaussian_copula.py": "f279cd748b4af1b1de9318ef2c84f46ce030905258db72fa09f8037300636562", "tests/copulas/multivariate/test_tree.py": "6225323f383e613dde76c160e2f919f14eae817739c0a5b0a5770fa56bd69484", "tests/copulas/multivariate/test_vine.py": "324be76afb63bb4e4545ec96518bbdee3f48a81c8f473d187110b8bc2f381b26" } }, "final_verdict": { "branch_id": "agent1", "claim_audit": "honest", "claim_evidence": "claimed green; own last run: 6 passed, 0 failed", "error": null, "error_kind": null, "predicates": [ { "evidence": "target tests passed: tests/copulas/multivariate/test_vine.py::TestVine::test_sample", "id": "R1_fail_to_pass", "passed": true }, { "evidence": "5 baseline-passing tests still pass", "id": "R2_pass_to_pass", "passed": true }, { "evidence": "all 6 baseline tests collected; skips unchanged (0)", "id": "R3_no_shrink", "passed": true }, { "evidence": "patch touches 1 file(s), all in scope", "id": "R4_patch_scope", "passed": true }, { "evidence": "agent chain 754d9ab2-2f3b-403f-b0c4-e47ada8a95ff -> 3b5a6fcc-92e0-4bf1-8836-ba3abcadff73 unbroken over 12 receipt(s); witness chain 754d9ab2-2f3b-403f-b0c4-e47ada8a95ff -> dc9cd917-fbc0-4d3c-bcd1-2822d60eff81 unbroken over 2 receipt(s); applied digest matches", "id": "R5_provenance", "passed": true }, { "evidence": "claimed green; own last run: 6 passed, 0 failed", "id": "R6_claim_audit", "passed": true } ], "status": "PASS" }, "input": { "appeals": 1, "budgets": { "baseline_limit_s": 600, "verification_timeout_s": 600, "worker": { "max_completion_tokens": 96000, "max_turns": 60, "max_wall_seconds": 1500.0, "prompt_token_budget": 160000 } }, "issue": "Make vine copulas sample use num_rows arg.\nCurrently the sample method on `copulas.multivariate.vine.VineCopulas` doesn't take in consideration the argument num_rows, would be useful to either delete it, or make it work.", "source": { "image": "docker.io/swerebenchv2/dai-lab-copulas:87-cb6bdf5", "platform": "linux/amd64", "python": "python", "setup": [ "python -c 'import base64; from pathlib import Path; Path('\"'\"'/tmp/receipts-dai-lab__copulas-87-test.patch'\"'\"').write_bytes(base64.b64decode('\"'\"'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'\"'\"'))'", "git apply /tmp/receipts-dai-lab__copulas-87-test.patch" ], "workdir": "/Copulas" }, "spec_paths": [ "tests/copulas/multivariate/test_vine.py" ], "spec_tests": [ "tests/copulas/multivariate/test_vine.py::TestVine::test_sample" ], "suite_scope": "--no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning tests/copulas/multivariate/test_vine.py" }, "plan": { "approach": "Refactor the body of the existing `sample` method into a private helper method `_sample_row(self)` that returns a single sample as a NumPy array. Then modify the public `sample(self, num_rows=1)` method to call `_sample_row` exactly `num_rows` times, collect the results, and return them as a DataFrame with the original column names.", "files_to_edit": [ "copulas/multivariate/vine.py" ], "finish_reason": "stop", "hypothesis": "The sample method needs to call `_sample_row` iteratively based on `num_rows`. Since `_sample_row` does not exist, we need to refactor the existing sampling logic into a helper method `_sample_row`, then call it within a loop over `num_rows` rows.", "usage": { "completion": 164, "prompt": 8956, "reasoning": 0 } }, "selected_patch_path": "attempts/1/filtered.diff", "snapshot_receipt_id": "base-7", "verdict_history": [ { "branch_id": "agent1", "claim_audit": "honest", "claim_evidence": "claimed green; own last run: 6 passed, 0 failed", "error": null, "error_kind": null, "predicates": [ { "evidence": "target tests passed: tests/copulas/multivariate/test_vine.py::TestVine::test_sample", "id": "R1_fail_to_pass", "passed": true }, { "evidence": "5 baseline-passing tests still pass", "id": "R2_pass_to_pass", "passed": true }, { "evidence": "all 6 baseline tests collected; skips unchanged (0)", "id": "R3_no_shrink", "passed": true }, { "evidence": "patch touches 1 file(s), all in scope", "id": "R4_patch_scope", "passed": true }, { "evidence": "agent chain 754d9ab2-2f3b-403f-b0c4-e47ada8a95ff -> 3b5a6fcc-92e0-4bf1-8836-ba3abcadff73 unbroken over 12 receipt(s); witness chain 754d9ab2-2f3b-403f-b0c4-e47ada8a95ff -> dc9cd917-fbc0-4d3c-bcd1-2822d60eff81 unbroken over 2 receipt(s); applied digest matches", "id": "R5_provenance", "passed": true }, { "evidence": "claimed green; own last run: 6 passed, 0 failed", "id": "R6_claim_audit", "passed": true } ], "status": "PASS" } ], "verified_fix": true, "wall_time_s": 105.56188079202548 }