6072d8795a
train_baseline now returns mae + rmse alongside mape/smape/r2. MAE is the
user-facing metric ("predicted SAP within N points"); RMSE the quadratic
counterpart. Both come straight from sklearn.
New sample_weight_fn parameter: callable(y_train) -> per-row weights.
Threads into LGBMRegressor.fit's sample_weight argument. Default None
preserves existing behaviour.
Default tail strategy exposed as low_sap_tail_weight(y, threshold=58,
weight=3): 3x weight where SAP < 58. Threshold picked from slice 16h's
per-decile residuals — decile 0 (SAP 1-58) carries 17% MAPE vs <5% body.
Three TDD tracers, all AAA.
|
||
|---|---|---|
| .. | ||
| ara | ||
| ml_training_data | ||
| README.md | ||
Services
Each subdirectory is a deployable unit — typically a Lambda image. Own pyproject.toml, own Dockerfile, own deps. Lambda bundle contains only that service's deps + its workspace deps.
| Service | Purpose |
|---|---|
ara/ |
The Domna retrofit modelling backend — ingestion + modelling pipelines, all 9 services in PRD §9.2. |
Other Domna services (address2uprn, hubspot, pashub, ecmk, magicplan) live in the legacy backend/ and etl/ trees for now; they are slated to migrate here as their owners pick them up — see PRD §11. When that work starts, scaffold the service under services/<name>/ and add it to the workspace members in the root pyproject.toml.
Service boundary
A service can import domain.*, import repos.*, import fetchers.*, import utils.* (workspace deps). It cannot import another service's modules — they are separate distributions with no cross-import path. This is the structural enforcement of the modelling/ingestion separation (ADR-0003).