Add Scripts 39b/c/d/e + 40b + 43: anchor-based FAR diagnostics

Spike checkpoint in response to codex rounds 28-30 review:

- 39b/c: signature-level dip test on Big-4 and non-Big-4 marginals
- 39d: dHash discrete-value robustness (raw vs jittered + histogram
  valleys + firm residualization); confirms within-firm dHash dip
  rejection is integer-mass-point artefact
- 39e: dHash firm-residualized + jittered 2x2 factorial decomposition;
  confirms Big-4 pooled dh "multimodality" is composition + integer
  artefact (centered + jittered p=0.35, 0/5 seeds reject)
- 40b: inter-CPA per-pair FAR sweep (cos + dh marginal + joint +
  conditional); replicates v3 cos>0.95 FAR=0.0006 and provides
  v4-new dh FAR curve
- 43: pool-normalized per-signature FAR (codex round-30 fix for
  per-pair vs per-signature conflation); per-sig FAR for deployed
  any-pair rule = 11.02%, per-firm structure shows Firm A 20% vs
  B/C/D <1%

These scripts replace the distributional path (K=3 mixture / dip /
antimode) with anchor-based threshold derivation. Companion
artefacts in reports/v4_big4/{signature_level_diptest,
midsmall_signature_diptest, dhash_discrete_robustness,
inter_cpa_far_sweep, pool_normalized_far}/.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
2026-05-13 14:08:49 +08:00
parent 6db5d635f5
commit d4f370bd5e
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#!/usr/bin/env python3
"""
Script 39b: Signature-Level Dip Test (multimodality at the signature cloud)
============================================================================
Phase 5 pre-emptive evidence. Script 34 / 36 already report Hartigan
dip tests on the 437 accountant-level (cos_mean, dh_mean) means and
both marginals reject unimodality at p < 5e-4. Reviewers may ask
whether the same multimodality is detectable at the signature level
itself (n = 150,442 Big-4 signatures) and whether the multimodality
is a within-firm or only a between-firm phenomenon.
This script supplies the missing dip evidence on the raw signature
cloud. It is a *diagnostic* in the same role as Scripts 34/36 dip
tests: it does not derive an operational threshold; it characterises
the marginal distributions of (cos, dh_indep) at the signature level.
Outputs:
reports/v4_big4/signature_level_diptest/
sig_diptest_results.json
sig_diptest_report.md
Tests performed:
A. Pooled Big-4 marginals (cos, dh_indep), n = 150,442
B. Per-firm marginals (Firm A / B / C / D separately)
"""
import json
import sqlite3
import numpy as np
import diptest
from pathlib import Path
from datetime import datetime
from scipy import stats
from scipy.signal import find_peaks
DB = '/Volumes/NV2/PDF-Processing/signature-analysis/signature_analysis.db'
OUT = Path('/Volumes/NV2/PDF-Processing/signature-analysis/reports/'
'v4_big4/signature_level_diptest')
OUT.mkdir(parents=True, exist_ok=True)
BIG4 = ('勤業眾信聯合', '安侯建業聯合', '資誠聯合', '安永聯合')
ALIAS = {'勤業眾信聯合': 'Firm A',
'安侯建業聯合': 'Firm B',
'資誠聯合': 'Firm C',
'安永聯合': 'Firm D'}
N_BOOT = 2000
def load_big4_signatures():
conn = sqlite3.connect(DB)
cur = conn.cursor()
cur.execute('''
SELECT s.assigned_accountant, a.firm,
s.max_similarity_to_same_accountant,
CAST(s.min_dhash_independent AS REAL)
FROM signatures s
JOIN accountants a ON s.assigned_accountant = a.name
WHERE s.assigned_accountant IS NOT NULL
AND s.max_similarity_to_same_accountant IS NOT NULL
AND s.min_dhash_independent IS NOT NULL
AND a.firm IN (?, ?, ?, ?)
''', BIG4)
rows = cur.fetchall()
conn.close()
return rows
def kde_dip(values, n_boot=N_BOOT):
arr = np.asarray(values, dtype=float)
arr = arr[np.isfinite(arr)]
dip, pval = diptest.diptest(arr, boot_pval=True, n_boot=n_boot)
kde = stats.gaussian_kde(arr, bw_method='silverman')
xs = np.linspace(arr.min(), arr.max(), 2000)
density = kde(xs)
peaks, _ = find_peaks(density, prominence=density.max() * 0.02)
antimodes = []
for i in range(len(peaks) - 1):
seg = density[peaks[i]:peaks[i + 1]]
if not len(seg):
continue
local = peaks[i] + int(np.argmin(seg))
antimodes.append(float(xs[local]))
return {
'n': int(len(arr)),
'dip': float(dip),
'dip_pvalue': float(pval),
'unimodal_alpha05': bool(pval > 0.05),
'n_modes': int(len(peaks)),
'mode_locations': [float(xs[p]) for p in peaks],
'antimodes': antimodes,
'n_boot': int(n_boot),
}
def _fmt_p(p):
if p == 0.0:
return '< 5e-4 (no bootstrap replicate exceeded observed dip)'
return f'{p:.4g}'
def main():
print('=' * 72)
print('Script 39b: Signature-Level Dip Test')
print('=' * 72)
rows = load_big4_signatures()
cos_all = np.array([r[2] for r in rows], dtype=float)
dh_all = np.array([r[3] for r in rows], dtype=float)
firms = np.array([ALIAS[r[1]] for r in rows])
print(f'\nLoaded {len(rows):,} Big-4 signatures')
for f in sorted(set(firms)):
print(f' {f}: {(firms == f).sum():,}')
results = {
'meta': {
'script': '39b',
'timestamp': datetime.now().isoformat(timespec='seconds'),
'n_total': int(len(rows)),
'n_boot': N_BOOT,
'note': ('Signature-level Hartigan dip test on Big-4 '
'(cos, dh_indep) marginals; pooled and per-firm.'),
},
'pooled': {},
'per_firm': {},
}
# A. Pooled
print('\n[A] Pooled Big-4')
for desc, arr in [('cos', cos_all), ('dh_indep', dh_all)]:
r = kde_dip(arr)
results['pooled'][desc] = r
print(f' {desc}: n={r["n"]:,}, dip={r["dip"]:.5f}, '
f'p={_fmt_p(r["dip_pvalue"])}, n_modes={r["n_modes"]}')
# B. Per-firm
print('\n[B] Per-firm')
for f in sorted(set(firms)):
mask = firms == f
results['per_firm'][f] = {}
for desc, arr in [('cos', cos_all[mask]), ('dh_indep', dh_all[mask])]:
r = kde_dip(arr)
results['per_firm'][f][desc] = r
print(f' {f} {desc}: n={r["n"]:,}, dip={r["dip"]:.5f}, '
f'p={_fmt_p(r["dip_pvalue"])}, n_modes={r["n_modes"]}')
json_path = OUT / 'sig_diptest_results.json'
json_path.write_text(json.dumps(results, indent=2, ensure_ascii=False),
encoding='utf-8')
print(f'\n[json] {json_path}')
md = ['# Signature-Level Dip Test (Script 39b)',
'',
f'Generated: {results["meta"]["timestamp"]}',
f'Bootstrap replicates: {N_BOOT}',
'',
'## A. Pooled Big-4 signature cloud',
'',
f'n = {results["meta"]["n_total"]:,} signatures',
'',
'| Marginal | dip | p (boot) | n_modes | unimodal @0.05 |',
'|---|---|---|---|---|']
for desc in ['cos', 'dh_indep']:
r = results['pooled'][desc]
md.append(f'| {desc} | {r["dip"]:.5f} | {_fmt_p(r["dip_pvalue"])} | '
f'{r["n_modes"]} | {r["unimodal_alpha05"]} |')
md += ['', '## B. Per-firm signature-level dip tests', '',
'| Firm | Marginal | n | dip | p (boot) | n_modes | unimodal @0.05 |',
'|---|---|---|---|---|---|---|']
for f in sorted(results['per_firm']):
for desc in ['cos', 'dh_indep']:
r = results['per_firm'][f][desc]
md.append(f'| {f} | {desc} | {r["n"]:,} | {r["dip"]:.5f} | '
f'{_fmt_p(r["dip_pvalue"])} | {r["n_modes"]} | '
f'{r["unimodal_alpha05"]} |')
md += ['',
'## Reading guide',
'',
('A unimodality rejection at the signature level confirms '
'multimodal structure independent of accountant-level '
'aggregation. A within-firm rejection further indicates the '
'multimodality is not solely a between-firm artefact. A '
'within-firm non-rejection (e.g., Firm A) is consistent with '
'that firm being concentrated in a single mechanism corner.'),
'',
('All thresholds and operational classifiers remain those of '
'v3.x §III-K and v4.0 §III-J; this script supplies diagnostic '
'evidence only.'),
'']
md_path = OUT / 'sig_diptest_report.md'
md_path.write_text('\n'.join(md), encoding='utf-8')
print(f'[md ] {md_path}')
if __name__ == '__main__':
main()