Kunthawat Greethong d87a1ada39 [verified] Cross-theme surprise normalization + historical factor store (P4 enabler) ...

A. Cross-theme comparability:
- compute_theme_surprises now weight-normalizes by total |weight| (weighted
  average), so every theme surprise on same [-1,1] scale regardless of factor
  count/weight (retail 0.189->0.145; auto_credit 1.0->0.64).

B. Historical factor store (enables learning macro/demographic factors):
- New factor_history.py: append-only per-factor JSONL, dedupes unchanged
  values, rejects non-finite, records every FACTORS value each scheduler run.
- scheduler.py: jobs carry fetch_module; refresh_all records factor history
  (non-fatal); added bank_npl job.
- GET /api/v1/learning/factors?min_points= reports n_points/learnable per
  factor so users see when P4 learning unlocks (validated query parsing).
- weight_learning: generic learn_factor_series() aggregator (momentum reuses).

Independent review deleg_5dd358e3 passed=true (empty security/logic arrays);
its two robustness suggestions applied (finite guard in record(), clean 400 on
bad min_points). 234 tests pass; Vite build passes.

2026-08-27 07:32:16 +07:00

5.1 KiB

Raw Blame History

View Raw