Lightweight workbench flows

Small synchronous helpers in alphaswarm.ml.flows that let users iterate on a dataset without spinning up a full Experiment. Surfaced at POST /ml/flows/{flow}/preview, POST /ml/flows/{flow}/preview-task (Celery), and GET /ml/flows (catalog).

Catalog

Flow	Purpose	Backend
linear	Ridge / Lasso / ElasticNet / BayesianRidge with IC + RMSE / MAE	sklearn
decomposition	STL trend / seasonal / residual	statsmodels
forecast	Prophet / sktime-naive / ARIMA / ETS / Theta / AutoARIMA	mixed
regression_diagnostics	OLS coef table, R^2, F-stat, Durbin-Watson	statsmodels
unit_root	ADF / KPSS unit-root tests	statsmodels
acf_pacf	Auto- and partial-autocorrelation series	statsmodels
granger_causality	Granger causality between two columns	statsmodels
cointegration	Engle-Granger pair cointegration	statsmodels
garch	GARCH(p, q) volatility model + horizon	arch
change_point	PELT / RBF kernel change points	ruptures
clustering	KMeans / DBSCAN / HDBSCAN on the feature matrix	sklearn / hdbscan
pca_summary	PCA variance + factor loadings	sklearn

REST surface

# List every flow + its parameter schema
curl http://localhost:8000/ml/flows | jq

# Sync run a flow
curl -XPOST http://localhost:8000/ml/flows/linear/preview \
  -d '{"dataset_cfg": {...}, "estimator": "ridge", "alpha": 1.0}' \
  -H 'content-type: application/json'

# Background run via Celery (returns TaskAccepted)
curl -XPOST http://localhost:8000/ml/flows/garch/preview-task \
  -d '{"dataset_cfg": {...}, "column": "close", "p": 1, "q": 1, "horizon": 10}' \
  -H 'content-type: application/json'

Webui workbench drawer

The ML Experiment Builder (/ml/builder) ships an "Interactive Workbench" drawer on its toolbar. Pick a flow, fill in the per-flow form (auto-generated from GET /ml/flows), and submit — the result table renders inline so you never leave the canvas.

Tutorials

• 01_quick_ridge_workbench.yaml
• 02_stl_decompose_workbench.yaml
• 03_arima_garch_diagnostics.yaml

Adding a new flow

1. Implement run_<flow>_flow(...) in alphaswarm/ml/flows.py returning a FlowResult.
2. Add a dispatch branch in run_flow(flow, payload).
3. Add an entry in list_flows() so the webui form reflects the new parameters automatically.
4. (Optional) Wrap as a notebook helper in alphaswarm/ml/adhoc/.