FA-100436 / Feature preprocessing fit scope / Open access

Learned missingness indicators applies fitted state incorrectly · case 01

Learned missingness indicators applies fitted state incorrectly.

Verified by executionVariant 1 · 7 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The transform applies a plausible but incorrect state convention.

THE FAILURE

The transform applies a plausible but incorrect state convention.

Unsuccessful approach: The partial repair addresses the main convention but mishandles an edge case.

Case 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.

Why this case matters

A small deterministic preprocessing contract makes the fit boundary and transform behavior directly inspectable.

1 / The failure

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(train, rows):
    cols=list(range(len(train[0]))); return [[int(r[j] is None) for j in cols] for r in rows]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('trained missing column',solve([[0,None],[1,2]],[[3,None]]),[[1]])
check('heldout cannot add column',solve([[0,1],[2,3]],[[None,4]]),[[]])
check('heldout cannot remove column',solve([[0,None]],[[4,5]]),[[0]])
check('multiple learned columns',solve([[None,2,None]],[[1,None,3]]),[[0,0]])
check('empty transform',solve([[None]],[]),[])
check('no learned indicators',solve([[1,2]],[[None,3]]),[[]])
check('singleton fit',solve([[None]],[[None]]),[[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
trained missing column	`[[0, 1]]`	`[[1]]`	Failed
heldout cannot add column	`[[1, 0]]`	`[[]]`	Failed
heldout cannot remove column	`[[0, 0]]`	`[[0]]`	Failed
multiple learned columns	`[[0, 1, 0]]`	`[[0, 0]]`	Failed
empty transform	`[]`	`[]`	Passed
no learned indicators	`[[1, 0]]`	`[[]]`	Failed
singleton fit	`[[1]]`	`[[1]]`	Passed

SHA-256 / 8fa1c1fda80ac767100376c996c18d2811920dfdf08586e478b6b2765aa9c3fa

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(train, rows):
    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]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('trained missing column',solve([[0,None],[1,2]],[[3,None]]),[[1]])
check('heldout cannot add column',solve([[0,1],[2,3]],[[None,4]]),[[]])
check('heldout cannot remove column',solve([[0,None]],[[4,5]]),[[0]])
check('multiple learned columns',solve([[None,2,None]],[[1,None,3]]),[[0,0]])
check('empty transform',solve([[None]],[]),[])
check('no learned indicators',solve([[1,2]],[[None,3]]),[[]])
check('singleton fit',solve([[None]],[[None]]),[[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
trained missing column	`[[0]]`	`[[1]]`	Failed
heldout cannot add column	`[[]]`	`[[]]`	Passed
heldout cannot remove column	`[[1]]`	`[[0]]`	Failed
multiple learned columns	`[[1, 1]]`	`[[0, 0]]`	Failed
empty transform	`[]`	`[]`	Passed
no learned indicators	`[[]]`	`[[]]`	Passed
singleton fit	`[[0]]`	`[[1]]`	Failed

SHA-256 / 4c7ecaf30b9bfb3e77d409586c2ca0b346253c8d4b42b3971648db6a81a13df1

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 7 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

Member access is invitation-based. Sign in with your invited account to inspect the repair.

Verification & scope

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.

Observations recorded using Python 3.12.14 at 2026-09-30T16:48:35.363864+00:00.

Case digest / cebc259cf7bceeec0820f3ceb28696c622e42c9046d0bc42baeda22e3c95ee85