{"abstract":"Balanced example weights divide by total examples instead of class frequency.","category":"Training label alignment","checks":7,"contract":"For labels, return M/(K*count(label)) per row, where M is row count and K the number of observed classes; empty input returns empty.","contract_signature":"labels","evaluation_group":"training-label-alignment-inverse-frequency-example-weight","failed_approach":"Using only inverse class frequency gives a different total scale and fails the declared normalization.","family":"w3-training-label-alignment-class-balanced-sample-weight","id":"FA-98036","implementations":{"attempt":{"sha256":"96c9e86894413036ac038a2de5db1b357ee53c1a8823c024fe0f1b2bef935879","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(labels):\n    from collections import Counter\n    c=Counter(labels)\n    return [1/c[y] for y in labels]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('imbalanced classes',solve(['a','a','b']),[0.75,0.75,1.5])\ncheck('balanced classes',solve(['a','b']),[1.0,1.0])\ncheck('empty',solve([]),[])\ncheck('single class',solve(['x']*N),[1.0]*N)\ncheck('three classes',solve(['c','a','b']),[1.0,1.0,1.0])\ncheck('one rare class',solve(['a','a','a','b']),[2/3,2/3,2/3,2.0])\ncheck('order preserved',solve(['b','a','b']),[0.75,1.5,0.75])\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":"32c6ad43554adb38124edbc933515fba2ec6dc88dc6ca9427e65a667249d6f15","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(labels):\n    return [1/len(labels) for _ in labels] if labels else []\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\ncheck('imbalanced classes',solve(['a','a','b']),[0.75,0.75,1.5])\ncheck('balanced classes',solve(['a','b']),[1.0,1.0])\ncheck('empty',solve([]),[])\ncheck('single class',solve(['x']*N),[1.0]*N)\ncheck('three classes',solve(['c','a','b']),[1.0,1.0,1.0])\ncheck('one rare class',solve(['a','a','a','b']),[2/3,2/3,2/3,2.0])\ncheck('order preserved',solve(['b','a','b']),[0.75,1.5,0.75])\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":"Class labels are hashable; floating outputs are exact simple ratios in these fixtures. 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-training-label-alignment-class-balanced-sample-weight","generated_at":"2026-09-30T16:48:08.407499+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"A small deterministic training-data operation makes label ownership and ordering explicit.","root_cause":"Every row receives the same inverse total weight regardless of its class count.","sha256":"440ee96410cb7cdf8d93d272f53dcb15b0f2f86a82fc43e790d58fb7e53d9147","title":"Balanced example weights divide by total examples instead of class frequency · 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":27.111,"exit_code":1,"observations":[{"actual":[0.5,0.5,1.0],"check":"imbalanced classes","expected":[0.75,0.75,1.5],"passed":false},{"actual":[1.0,1.0],"check":"balanced classes","expected":[1.0,1.0],"passed":true},{"actual":[],"check":"empty","expected":[],"passed":true},{"actual":[1.0],"check":"single class","expected":[1.0],"passed":true},{"actual":[1.0,1.0,1.0],"check":"three classes","expected":[1.0,1.0,1.0],"passed":true},{"actual":[0.3333333333333333,0.3333333333333333,0.3333333333333333,1.0],"check":"one rare class","expected":[0.6666666666666666,0.6666666666666666,0.6666666666666666,2.0],"passed":false},{"actual":[0.5,1.0,0.5],"check":"order preserved","expected":[0.75,1.5,0.75],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"imbalanced classes\", \"actual\": [0.5, 0.5, 1.0], \"expected\": [0.75, 0.75, 1.5], \"passed\": false}, {\"check\": \"balanced classes\", \"actual\": [1.0, 1.0], \"expected\": [1.0, 1.0], \"passed\": true}, {\"check\": \"empty\", \"actual\": [], \"expected\": [], \"passed\": true}, {\"check\": \"single class\", \"actual\": [1.0], \"expected\": [1.0], \"passed\": true}, {\"check\": \"three classes\", \"actual\": [1.0, 1.0, 1.0], \"expected\": [1.0, 1.0, 1.0], \"passed\": true}, {\"check\": \"one rare class\", \"actual\": [0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 1.0], \"expected\": [0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 2.0], \"passed\": false}, {\"check\": \"order preserved\", \"actual\": [0.5, 1.0, 0.5], \"expected\": [0.75, 1.5, 0.75], \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":24.883,"exit_code":1,"observations":[{"actual":[0.3333333333333333,0.3333333333333333,0.3333333333333333],"check":"imbalanced classes","expected":[0.75,0.75,1.5],"passed":false},{"actual":[0.5,0.5],"check":"balanced classes","expected":[1.0,1.0],"passed":false},{"actual":[],"check":"empty","expected":[],"passed":true},{"actual":[1.0],"check":"single class","expected":[1.0],"passed":true},{"actual":[0.3333333333333333,0.3333333333333333,0.3333333333333333],"check":"three classes","expected":[1.0,1.0,1.0],"passed":false},{"actual":[0.25,0.25,0.25,0.25],"check":"one rare class","expected":[0.6666666666666666,0.6666666666666666,0.6666666666666666,2.0],"passed":false},{"actual":[0.3333333333333333,0.3333333333333333,0.3333333333333333],"check":"order preserved","expected":[0.75,1.5,0.75],"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"imbalanced classes\", \"actual\": [0.3333333333333333, 0.3333333333333333, 0.3333333333333333], \"expected\": [0.75, 0.75, 1.5], \"passed\": false}, {\"check\": \"balanced classes\", \"actual\": [0.5, 0.5], \"expected\": [1.0, 1.0], \"passed\": false}, {\"check\": \"empty\", \"actual\": [], \"expected\": [], \"passed\": true}, {\"check\": \"single class\", \"actual\": [1.0], \"expected\": [1.0], \"passed\": true}, {\"check\": \"three classes\", \"actual\": [0.3333333333333333, 0.3333333333333333, 0.3333333333333333], \"expected\": [1.0, 1.0, 1.0], \"passed\": false}, {\"check\": \"one rare class\", \"actual\": [0.25, 0.25, 0.25, 0.25], \"expected\": [0.6666666666666666, 0.6666666666666666, 0.6666666666666666, 2.0], \"passed\": false}, {\"check\": \"order preserved\", \"actual\": [0.3333333333333333, 0.3333333333333333, 0.3333333333333333], \"expected\": [0.75, 1.5, 0.75], \"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."}}