6a68ecb4d4b6faafee46a64366ceffbaacda2d02
2
Commits
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ad8566f1ae |
feat(settings): per-company data-analysis opt-in gating the calibration corpus (#1346) (#2007)
* feat(settings): per-company opt-in for data analysis of bookkeeping outcomes (#1346) Adds company_settings.data_analysis_opt_in (default false, no grandfathering) and gates every path that reads bookkeeping outcomes across companies on it: POST /api/agent/categorize/outcome stops writing calibration samples for companies that have not opted in, and the backtest / calibration-fit scripts filter to opted-in company ids. One helper (lib/company/data-analysis.ts) is the single gate for future analysis paths. A toggle on Inställningar > Företag states plainly what is analysed (proposed vs booked account, amount, confidence; no free text, no personal data) in sv and en. The flag is UI-only by design: consent is a human action, so it is absent from the v1 REST / MCP settings pick lists. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_015nAd8XJ2RPCmG2eKoLBdna * fix(settings): make data-analysis consent copy true for the backtest path (#1346) Addresses adversarial review findings on PR #2007: - Findings 1-3 (consent narrower than the gated processing): the flag also gates scripts/backtest-categorize.ts, which re-runs transaction descriptions, merchant names and matched underlag through the model. The sv/en toggle help and disclosure now state that explicitly as "evaluation runs" and no longer claim that free text or underlag are excluded. The migration header and COMMENT, the lib/company/data-analysis.ts docstring, the backtest script header and the DECISIONS line say the same. Kept the gate (un-gating would put the script back to reading every company with no consent at all). A test pins that both locales name those inputs and contain no "no free text / no underlag" denial. - Finding 4 (member sees an active switch that RLS rejects): the toggle is now enabled only for owner/admin, matching the company_settings update policy; the disclosure says only administrators can change the choice. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_015nAd8XJ2RPCmG2eKoLBdna * fix(scripts): address round-2 review findings (#1346) 1. [minor] Opted-in company filter was an unbounded PostgREST `in` list in the URL (scripts/fit-categorize-calibration.ts, scripts/backtest-categorize.ts). Both scripts now read the opted-in ids through a shared, paginated helper (listDataAnalysisOptedInCompanyIds, fetchAllRows so the pre-fetch no longer caps at 1000) and query per chunk of 100 ids (chunkCompanyIds). The fit script pages each chunk on the id PK; the backtest merges per-chunk results and re-cuts to the N most recent overall. Early exit on zero opt-ins is kept. Pinned with tests in lib/company/__tests__/data-analysis.test.ts. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_015nAd8XJ2RPCmG2eKoLBdna * fix(scripts): coerce a null transaction description in the backtest (#1346) The typed row from the chunked consent query made description nullable, which TransactionForSelect does not accept; fall back to the original description or an empty string, as the untyped row did implicitly before. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_015nAd8XJ2RPCmG2eKoLBdna --------- Co-authored-by: Jakob Wennberg <311770904+jakobwennberg-oss@users.noreply.github.com> Co-authored-by: Claude Fable 5 <noreply@anthropic.com> |
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704bf93e08 |
feat(categorize): confidence calibration engine + measurement loop (cascade step 4) (#1784)
Turns the selector's raw confidence into a score that means what it says. - lib/agent/categorize/calibration.ts: the engine. Isotonic regression (pool-adjacent-violators, distribution-free + monotonic) over (confidence, was_correct) samples → a calibrator; plus reliabilityByBucket, ECE, and bandFor(). bandFor NEVER returns 'auto' without a fitted calibrator (no silent booking on an unproven score) and never auto-books above an amount cap. 12 engine tests (overconfidence pulled down, underconfidence lifted, monotonicity, ECE, band gating). - Measurement loop: migration categorize_calibration_samples (append-only, company-scoped RLS, confidence CHECK [0,1]) + POST /api/agent/categorize/ outcome logging one sample (proposed vs actually booked) fire-and-forget from QuickReviewDialog on a successful book (sandbox skipped). AiCategorizeProposal surfaces the proposal metadata via onProposal. - scripts/fit-categorize-calibration.ts (read-only): prints the reliability diagram + ECE + fitted calibrator once data has accumulated. Fitting needs a few hundred real outcomes, so nothing calibrates today — the loop starts collecting, and "säker" stays uncalibrated (no auto-book) until the data proves it. 131 unit tests green; RLS covered by a pg-real test. Co-authored-by: Jakob Wennberg <311770904+jakobwennberg-oss@users.noreply.github.com> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> |