{"abstract":"Learned missingness indicators applies fitted state incorrectly.","category":"Feature preprocessing fit scope","checks":7,"contract":"Train and rows are nonempty rectangular rows. Fit the output feature list as columns containing at least one None in training; emit a 0/1 missing flag for each such column in the same order.","contract_signature":"train, rows","evaluation_group":"w3-feature-preprocessing-fit-scope-learned-missing-indicators","failed_approach":"The partial repair addresses the main convention but mishandles an edge case.","family":"w3-feature-preprocessing-fit-scope-learned-missing-indicators-transform-state","id":"FA-100436","implementations":{"attempt":{"sha256":"4c7ecaf30b9bfb3e77d409586c2ca0b346253c8d4b42b3971648db6a81a13df1","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(train, rows):\n    cols=[j for j in range(len(train[0])) if any(r[j] is None for r in train)]; return [[int(r[j] is not None) for j in cols] for r in rows]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('trained missing column',solve([[0,None],[1,2]],[[3,None]]),[[1]])\ncheck('heldout cannot add column',solve([[0,1],[2,3]],[[None,4]]),[[]])\ncheck('heldout cannot remove column',solve([[0,None]],[[4,5]]),[[0]])\ncheck('multiple learned columns',solve([[None,2,None]],[[1,None,3]]),[[0,0]])\ncheck('empty transform',solve([[None]],[]),[])\ncheck('no learned indicators',solve([[1,2]],[[None,3]]),[[]])\ncheck('singleton fit',solve([[None]],[[None]]),[[1]])\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"8fa1c1fda80ac767100376c996c18d2811920dfdf08586e478b6b2765aa9c3fa","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(train, rows):\n    cols=list(range(len(train[0]))); return [[int(r[j] is None) for j in cols] for r in rows]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('trained missing column',solve([[0,None],[1,2]],[[3,None]]),[[1]])\ncheck('heldout cannot add column',solve([[0,1],[2,3]],[[None,4]]),[[]])\ncheck('heldout cannot remove column',solve([[0,None]],[[4,5]]),[[0]])\ncheck('multiple learned columns',solve([[None,2,None]],[[1,None,3]]),[[0,0]])\ncheck('empty transform',solve([[None]],[]),[])\ncheck('no learned indicators',solve([[1,2]],[[None,3]]),[[]])\ncheck('singleton fit',solve([[None]],[[None]]),[[1]])\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"Toy learned missingness indicators; it models column selection state, not the original feature matrix. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"w3-feature-preprocessing-fit-scope-learned-missing-indicators-transform-state","generated_at":"2026-09-30T16:48:35.363864+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"A small deterministic preprocessing contract makes the fit boundary and transform behavior directly inspectable.","root_cause":"The transform applies a plausible but incorrect state convention.","sha256":"cebc259cf7bceeec0820f3ceb28696c622e42c9046d0bc42baeda22e3c95ee85","title":"Learned missingness indicators applies fitted state incorrectly · case 01","variant":1,"variant_policy":"The same explicitly authored fixtures repeat across all five numbered records; runtime N is unused. These records share one mechanism and must remain together in evaluation splits.","verified":true,"visibility":"public","verification":{"attempt":{"elapsed_ms":26.228,"exit_code":1,"observations":[{"actual":[[0]],"check":"trained missing column","expected":[[1]],"passed":false},{"actual":[[]],"check":"heldout cannot add column","expected":[[]],"passed":true},{"actual":[[1]],"check":"heldout cannot remove column","expected":[[0]],"passed":false},{"actual":[[1,1]],"check":"multiple learned columns","expected":[[0,0]],"passed":false},{"actual":[],"check":"empty transform","expected":[],"passed":true},{"actual":[[]],"check":"no learned indicators","expected":[[]],"passed":true},{"actual":[[0]],"check":"singleton fit","expected":[[1]],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"trained missing column\", \"actual\": [[0]], \"expected\": [[1]], \"passed\": false}, {\"check\": \"heldout cannot add column\", \"actual\": [[]], \"expected\": [[]], \"passed\": true}, {\"check\": \"heldout cannot remove column\", \"actual\": [[1]], \"expected\": [[0]], \"passed\": false}, {\"check\": \"multiple learned columns\", \"actual\": [[1, 1]], \"expected\": [[0, 0]], \"passed\": false}, {\"check\": \"empty transform\", \"actual\": [], \"expected\": [], \"passed\": true}, {\"check\": \"no learned indicators\", \"actual\": [[]], \"expected\": [[]], \"passed\": true}, {\"check\": \"singleton fit\", \"actual\": [[0]], \"expected\": [[1]], \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":27.148,"exit_code":1,"observations":[{"actual":[[0,1]],"check":"trained missing column","expected":[[1]],"passed":false},{"actual":[[1,0]],"check":"heldout cannot add column","expected":[[]],"passed":false},{"actual":[[0,0]],"check":"heldout cannot remove column","expected":[[0]],"passed":false},{"actual":[[0,1,0]],"check":"multiple learned columns","expected":[[0,0]],"passed":false},{"actual":[],"check":"empty transform","expected":[],"passed":true},{"actual":[[1,0]],"check":"no learned indicators","expected":[[]],"passed":false},{"actual":[[1]],"check":"singleton fit","expected":[[1]],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"trained missing column\", \"actual\": [[0, 1]], \"expected\": [[1]], \"passed\": false}, {\"check\": \"heldout cannot add column\", \"actual\": [[1, 0]], \"expected\": [[]], \"passed\": false}, {\"check\": \"heldout cannot remove column\", \"actual\": [[0, 0]], \"expected\": [[0]], \"passed\": false}, {\"check\": \"multiple learned columns\", \"actual\": [[0, 1, 0]], \"expected\": [[0, 0]], \"passed\": false}, {\"check\": \"empty transform\", \"actual\": [], \"expected\": [], \"passed\": true}, {\"check\": \"no learned indicators\", \"actual\": [[1, 0]], \"expected\": [[]], \"passed\": false}, {\"check\": \"singleton fit\", \"actual\": [[1]], \"expected\": [[1]], \"passed\": true}], \"passed\": false}\n"}},"member_only":{"stages":["fixed"],"fields":["implementations.fixed","verification.fixed","harness","repair"],"note":"The verified repair, its recorded checks, the repair description, and the scoring harness are available to members."}}