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lib/item-classes.js

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  1. // Category-conditional basket-quantity plausibility (deterministic, zero deps).
  2. // ---------------------------------------------------------------------------
  3. // Shared by the engine's ITEM_QTY_ANOMALY check (lib/engine.js) and the
  4. // behavior scorer's qty_over_class_cap feature (lib/behavior-model.js). The
  5. // Python trainer mirrors these tables and the qtyCap rule in
  6. // merchant-trust-data/models/behavior/train_behavior.py — keep the three in
  7. // lockstep.
  8. //
  9. // Classes:
  10. // bulk consumables sold in bulk — huge line quantities are plausible
  11. // (a box of 500 disposable gloves is a normal household order)
  12. // gift resellable/recurring value (gift cards, subscriptions) — a line
  13. // quantity above the base cap is a classic cash-out pattern
  14. // finite durable/personal goods — 500 pairs of shoes is not a basket
  15. // service per-occasion services — quantity barely composes; low cap
  16. //
  17. // Unknown categories fall back to `finite` (conservative: flag and ask
  18. // rather than silently allow an unclassifiable bulk order).
  20. import { statThreshold } from './evstats.js';
  22. export const CATEGORY_CLASS = {
  23. bulk: ['groceries', 'household', 'home_improvement'],
  24. gift: ['gift_card', 'subscriptions', 'membership'],
  25. finite: ['clothing', 'electronics', 'sporting_goods', 'cosmetics', 'books'],
  26. service: ['dining', 'food_delivery', 'fuel', 'hotel', 'transport'],
  27. };
  29. export const BASE_CAPS = { bulk: 500, gift: 2, finite: 12, service: 20 };
  31. const CLASS_OF = new Map(
  32. Object.entries(CATEGORY_CLASS).flatMap(([cls, cats]) => cats.map(c => [c, cls]))
  33. );
  35. export function categoryClass(category) {
  36. return CLASS_OF.get(String(category ?? '').trim().toLowerCase()) ?? 'finite';
  37. }
  39. export function baseCap(category) {
  40. return BASE_CAPS[categoryClass(category)];
  41. }
  43. /** Adaptive cap for one line item: never below the class base, raised to
  44. * 3× the customer's own observed maximum in that category when the trained
  45. * profile carries qty_max_by_category (self-adjusts to what the user
  46. * usually orders). Returns { cap, adaptive }. */
  47. export function qtyCap(category, qtyMaxByCategory) {
  48. const base = baseCap(category);
  49. const observed = Math.max(0, Number(qtyMaxByCategory?.[category]) || 0);
  50. return observed * 3 > base
  51. ? { cap: observed * 3, adaptive: true }
  52. : { cap: base, adaptive: false };
  53. }
  55. /** Statistically-informed cap for one line item: fits the customer's own
  56. * per-category quantity sample (GEV / GPD-POT / robust MAD — lib/evstats.js)
  57. * and clamps the result between the deterministic floor and a hard ceiling.
  58. * Returns null when the sample is too small for any method (caller falls
  59. * back to `qtyCap`). Engine-side enforcement only — the trained feature 14
  60. * and the Python trainer stay on the stable `qtyCap` heuristic above. */
  61. export function statCap(category, samples, qtyMaxByCategory) {
  62. const base = baseCap(category);
  63. const observed = Math.max(0, Number(qtyMaxByCategory?.[category]) || 0);
  64. const incumbent = qtyCap(category, qtyMaxByCategory).cap;
  65. const st = statThreshold(samples, { floor: base, observedMax: observed, heuristicFloor: incumbent });
  66. if (!st || !st.used.length) return null; // no fit spoke → caller keeps qtyCap
  67. return { cap: st.value, adaptive: true, method: st.used, n: st.n, detail: st.detail };
  68. }