FA-13791 / Numerical aggregation / Open access
Adjusted partition pair agreement: Both marginal pair counts are taken from joint cells. · case 01
The reduction disagrees with its explicit aggregation oracle.
ROOT CAUSE
Both marginal pair counts are taken from joint cells.
VERIFIED REPAIR
Preserve the adjusted partition pair agreement contract at the identified reduction decision.
Unsuccessful approach: Copying one marginal ignores different cluster-size distributions.
Case contract
Two equal-length integer cluster label arrays describe the same observations. Return exact adjusted Rand index from pair-count contingency reduction. Label names have no numerical meaning. Matching degenerate partitions with zero normalization return "1"; length mismatch returns None.
Why this case matters
Exact bounded examples isolate a reduction defect without floating-point or external-service effects.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
from collections import Counter, defaultdict
import math
import itertools
N = 1
observations = []
def solve(a, b):
if len(a)!=len(b): return None
n=len(a)
if n<2: return "1"
pairs=n*(n-1)//2
joint=Counter(zip(a,b))
ra,rb=Counter(a),Counter(b)
choose=lambda k:k*(k-1)//2
same=sum(choose(v) for v in joint.values())
x=y=same
expected=Fraction(x*y,pairs)
ceiling=Fraction(x+y,2)
return "1" if ceiling==expected else str((same-expected)/(ceiling-expected))
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('regression 1', solve(*([0, 0, 1, 1], [4, 4, 5, 5])), '1')
check('regression 2', solve(*([0, 0, 1, 1], [0, 1, 0, 1])), '-1/2')
check('regression 3', solve(*([], [])), '1')
check('regression 4', solve(*([0], [9])), '1')
check('regression 5', solve(*([0, 0, 0, 1, 2], [1, 1, 2, 2, 2])), '-2/23')
check('regression 6', solve(*([0, 1, 2], [8, 9, 10])), '1')
check('regression 7', solve(*([0, 0], [1])), None)
check('regression 8', solve(*([0, 0, 1, 2, 2, 2], [4, 5, 4, 5, 5, 5])), '34/109')
check("variable label names",solve([N,N,N+1,N+1],[7,7,8,8]),"1")
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression 1 | 1 | 1 | Passed |
| regression 2 | 1 | -1/2 | Failed |
| regression 3 | 1 | 1 | Passed |
| regression 4 | 1 | 1 | Passed |
| regression 5 | 1 | -2/23 | Failed |
| regression 6 | 1 | 1 | Passed |
| regression 7 | None | None | Passed |
| regression 8 | 1 | 34/109 | Failed |
| variable label names | 1 | 1 | Passed |
SHA-256 / 2bc7a03af0550fb20016e42156dff5c15cddd291b6c0c526a22e97d1ccf4b114
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
from collections import Counter, defaultdict
import math
import itertools
N = 1
observations = []
def solve(a, b):
if len(a)!=len(b): return None
n=len(a)
if n<2: return "1"
pairs=n*(n-1)//2
joint=Counter(zip(a,b))
ra,rb=Counter(a),Counter(b)
choose=lambda k:k*(k-1)//2
same=sum(choose(v) for v in joint.values())
x=sum(choose(v) for v in ra.values())
y=x
expected=Fraction(x*y,pairs)
ceiling=Fraction(x+y,2)
return "1" if ceiling==expected else str((same-expected)/(ceiling-expected))
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('regression 1', solve(*([0, 0, 1, 1], [4, 4, 5, 5])), '1')
check('regression 2', solve(*([0, 0, 1, 1], [0, 1, 0, 1])), '-1/2')
check('regression 3', solve(*([], [])), '1')
check('regression 4', solve(*([0], [9])), '1')
check('regression 5', solve(*([0, 0, 0, 1, 2], [1, 1, 2, 2, 2])), '-2/23')
check('regression 6', solve(*([0, 1, 2], [8, 9, 10])), '1')
check('regression 7', solve(*([0, 0], [1])), None)
check('regression 8', solve(*([0, 0, 1, 2, 2, 2], [4, 5, 4, 5, 5, 5])), '34/109')
check("variable label names",solve([N,N,N+1,N+1],[7,7,8,8]),"1")
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression 1 | 1 | 1 | Passed |
| regression 2 | -1/2 | -1/2 | Passed |
| regression 3 | 1 | 1 | Passed |
| regression 4 | 1 | 1 | Passed |
| regression 5 | 1/21 | -2/23 | Failed |
| regression 6 | 1 | 1 | Passed |
| regression 7 | None | None | Passed |
| regression 8 | 29/44 | 34/109 | Failed |
| variable label names | 1 | 1 | Passed |
SHA-256 / f73e1a1e069e85f81b8f27008640c1e53d4c41eab4259462618f8ba532ab0f61
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
from collections import Counter, defaultdict
import math
import itertools
N = 1
observations = []
def solve(a, b):
if len(a)!=len(b): return None
n=len(a)
if n<2: return "1"
pairs=n*(n-1)//2
joint=Counter(zip(a,b))
ra,rb=Counter(a),Counter(b)
choose=lambda k:k*(k-1)//2
same=sum(choose(v) for v in joint.values())
x=sum(choose(v) for v in ra.values())
y=sum(choose(v) for v in rb.values())
expected=Fraction(x*y,pairs)
ceiling=Fraction(x+y,2)
return "1" if ceiling==expected else str((same-expected)/(ceiling-expected))
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('regression 1', solve(*([0, 0, 1, 1], [4, 4, 5, 5])), '1')
check('regression 2', solve(*([0, 0, 1, 1], [0, 1, 0, 1])), '-1/2')
check('regression 3', solve(*([], [])), '1')
check('regression 4', solve(*([0], [9])), '1')
check('regression 5', solve(*([0, 0, 0, 1, 2], [1, 1, 2, 2, 2])), '-2/23')
check('regression 6', solve(*([0, 1, 2], [8, 9, 10])), '1')
check('regression 7', solve(*([0, 0], [1])), None)
check('regression 8', solve(*([0, 0, 1, 2, 2, 2], [4, 5, 4, 5, 5, 5])), '34/109')
check("variable label names",solve([N,N,N+1,N+1],[7,7,8,8]),"1")
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
| Boundary fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression 1 | 1 | 1 | Passed |
| regression 2 | -1/2 | -1/2 | Passed |
| regression 3 | 1 | 1 | Passed |
| regression 4 | 1 | 1 | Passed |
| regression 5 | -2/23 | -2/23 | Passed |
| regression 6 | 1 | 1 | Passed |
| regression 7 | None | None | Passed |
| regression 8 | 34/109 | 34/109 | Passed |
| variable label names | 1 | 1 | Passed |
SHA-256 / f1d717731848ae1508be0aed6706e7ae4b085082a44842998adbcafe1bc5ec7c
Verification & scope
Small offline integer/rational inputs only; no performance, statistical inference, or production-library conformance claim. 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.
Observations recorded using Python 3.12.14 at 2026-09-29T14:39:10.611922+00:00.
Case digest / c34ff3aed774a171162aafc97413e854a923f7841ff6054c1af40432c7f5a3cc