{"abstract":"The fitted feature projection changes the dimensionality of transformed numeric rows.","category":"Feature preprocessing fit scope","checks":7,"contract":"Train and rows are nonempty rectangular numeric matrices with equal width; threshold is nonnegative. Fit population variance per training column and retain columns with variance strictly greater than threshold, preserving original order.","contract_signature":"train, rows, threshold","evaluation_group":"w3-feature-preprocessing-fit-scope-variance-feature-selection","failed_approach":"Reversing the selected feature indices preserves the width but changes column ownership.","family":"w3-feature-preprocessing-fit-scope-variance-feature-selection-threshold-boundary","id":"FA-100386","implementations":{"attempt":{"sha256":"4cf9c12e2d6732c5df24e294a3e83759997b02bff41aa06b25829e39fc5117ba","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(train, rows, threshold):\n    cols=[j for j in range(len(train[0])) if sum((r[j]-sum(x[j] for x in train)/len(train))**2 for r in train)/len(train)>threshold]; cols=list(reversed(cols)); return [[r[j] 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('strict cutoff excludes equality',solve([[0,0],[0,2]],[[1,2]],1),[[]])\ncheck('two active columns ordered',solve([[0,0],[2,4]],[[1,3]],0.5),[[1,3]])\ncheck('constant feature removed',solve([[N,0],[N,2]],[[N+2,1]],0.5),[[1]])\ncheck('no active features',solve([[1],[1]],[[8]],0),[[]])\ncheck('empty transform',solve([[0],[2]],[],0.5),[])\ncheck('single feature above cutoff',solve([[0],[4]],[[2]],1),[[2]])\ncheck('empty selection keeps row',solve([[3],[3]],[[N]],0),[[]])\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":"fe3ae3179398b93cd43622a81049cc6846e73caf712b2f62f5943a367b875476","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(train, rows, threshold):\n    cols=[j for j in range(len(train[0])) if sum((r[j]-sum(x[j] for x in train)/len(train))**2 for r in train)/len(train)>=threshold]; return [[r[j] 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('strict cutoff excludes equality',solve([[0,0],[0,2]],[[1,2]],1),[[]])\ncheck('two active columns ordered',solve([[0,0],[2,4]],[[1,3]],0.5),[[1,3]])\ncheck('constant feature removed',solve([[N,0],[N,2]],[[N+2,1]],0.5),[[1]])\ncheck('no active features',solve([[1],[1]],[[8]],0),[[]])\ncheck('empty transform',solve([[0],[2]],[],0.5),[])\ncheck('single feature above cutoff',solve([[0],[4]],[[2]],1),[[2]])\ncheck('empty selection keeps row',solve([[3],[3]],[[N]],0),[[]])\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":"This toy selector uses population variance for numeric columns and does not claim compatibility with a production estimator. 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-variance-feature-selection-threshold-boundary","generated_at":"2026-09-30T16:48:34.921270+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"A fitted column projection demonstrates how training-only statistics control later feature transforms.","root_cause":"The cutoff comparison includes features whose variance equals the requested threshold.","sha256":"f00821f5367984d29c5607b40395686a4f5cf6867f67eb40b048519e04e319de","title":"Variance selector includes a feature at the strict cutoff · case 01","variant":1,"variant_policy":"The fixture code references N, supplied as 1 through 5. Fixed boundary checks may repeat. These records share one mechanism and must remain together in evaluation splits.","verified":true,"visibility":"public","verification":{"attempt":{"elapsed_ms":27.118,"exit_code":1,"observations":[{"actual":[[]],"check":"strict cutoff excludes equality","expected":[[]],"passed":true},{"actual":[[3,1]],"check":"two active columns ordered","expected":[[1,3]],"passed":false},{"actual":[[1]],"check":"constant feature removed","expected":[[1]],"passed":true},{"actual":[[]],"check":"no active features","expected":[[]],"passed":true},{"actual":[],"check":"empty transform","expected":[],"passed":true},{"actual":[[2]],"check":"single feature above cutoff","expected":[[2]],"passed":true},{"actual":[[]],"check":"empty selection keeps row","expected":[[]],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"strict cutoff excludes equality\", \"actual\": [[]], \"expected\": [[]], \"passed\": true}, {\"check\": \"two active columns ordered\", \"actual\": [[3, 1]], \"expected\": [[1, 3]], \"passed\": false}, {\"check\": \"constant feature removed\", \"actual\": [[1]], \"expected\": [[1]], \"passed\": true}, {\"check\": \"no active features\", \"actual\": [[]], \"expected\": [[]], \"passed\": true}, {\"check\": \"empty transform\", \"actual\": [], \"expected\": [], \"passed\": true}, {\"check\": \"single feature above cutoff\", \"actual\": [[2]], \"expected\": [[2]], \"passed\": true}, {\"check\": \"empty selection keeps row\", \"actual\": [[]], \"expected\": [[]], \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":27.405,"exit_code":1,"observations":[{"actual":[[2]],"check":"strict cutoff excludes equality","expected":[[]],"passed":false},{"actual":[[1,3]],"check":"two active columns ordered","expected":[[1,3]],"passed":true},{"actual":[[1]],"check":"constant feature removed","expected":[[1]],"passed":true},{"actual":[[8]],"check":"no active features","expected":[[]],"passed":false},{"actual":[],"check":"empty transform","expected":[],"passed":true},{"actual":[[2]],"check":"single feature above cutoff","expected":[[2]],"passed":true},{"actual":[[1]],"check":"empty selection keeps row","expected":[[]],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"strict cutoff excludes equality\", \"actual\": [[2]], \"expected\": [[]], \"passed\": false}, {\"check\": \"two active columns ordered\", \"actual\": [[1, 3]], \"expected\": [[1, 3]], \"passed\": true}, {\"check\": \"constant feature removed\", \"actual\": [[1]], \"expected\": [[1]], \"passed\": true}, {\"check\": \"no active features\", \"actual\": [[8]], \"expected\": [[]], \"passed\": false}, {\"check\": \"empty transform\", \"actual\": [], \"expected\": [], \"passed\": true}, {\"check\": \"single feature above cutoff\", \"actual\": [[2]], \"expected\": [[2]], \"passed\": true}, {\"check\": \"empty selection keeps row\", \"actual\": [[1]], \"expected\": [[]], \"passed\": false}], \"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."}}