ML library reference

Per-framework reference for every model wrapper under alphaswarm/ml/models/. Configs live under configs/ml/.

Coverage matrix

Library	Wrapper(s)	Optional extra	Example config
scikit-learn	SklearnRegressorModel, SklearnClassifierModel, SklearnPipelineModel, SklearnStackingModel, SklearnAutoPipelineModel	ml	sklearn_ridge_alpha.yaml, sklearn_stacking_alpha.yaml
LightGBM	LGBModel	ml	alpha158_lgbm.yaml
XGBoost	XGBModel	ml	(in tree zoo)
CatBoost	CatBoostModel	ml	(in tree zoo)
Keras 3	KerasMLPModel, KerasLSTMModel, KerasFunctionalModel, KerasTabTransformerModel	ml-keras	keras_mlp_alpha.yaml, keras_tab_transformer.yaml
TensorFlow native	TFEstimatorModel (linear / DNN / boosted_trees)	ml-tensorflow + ALPHASWARM_TF_NATIVE_ENABLED=true	tf_estimator_dnn.yaml
PyTorch (qlib ports)	LSTMTSModel, TransformerTSModel, TCNTSModel, TabNetModel, HISTModel, GATsModel, TRAModel, …	ml-torch	alpha360_*.yaml
Prophet	ProphetForecastModel	ml-forecast	prophet_forecast_alpha.yaml
sktime	SktimeForecastModel, SktimeReductionForecastModel, AutoETSForecastModel, AutoARIMAForecastModel, ThetaForecastModel, BatsTbatsForecastModel	ml-forecast	sktime_reduction_forecast.yaml, auto_ets_forecast.yaml, auto_arima_forecast.yaml, theta_forecast.yaml
PyOD	PyODAnomalyModel (iforest / knn / ecod / copod / lof / suod / auto_encoder / hbos / mcd / ocsvm / pca)	ml-anomaly	pyod_anomaly_alpha.yaml, pyod_ecod_anomaly.yaml
HuggingFace transformers	HuggingFaceTextSignalModel, HuggingFaceFinBertSentimentModel, HuggingFaceTimeSeriesModel, HuggingFaceGenerativeForecastModel	ml-transformers (+ ALPHASWARM_HF_TIMESERIES_ENABLED=true for time-series)	huggingface_finbert_signal.yaml, hf_finbert_sentiment.yaml, hf_patchtst_forecast.yaml

Adhoc / notebook surface

alphaswarm.ml.adhoc exposes a quick_* namespace for one-off analyses without spelling out a full Experiment config:

from alphaswarm.ml.adhoc import (
    quick_arima,
    quick_ecod,
    quick_finbert_sentiment,
    quick_iforest,
    quick_panel_fixed_effects,
    quick_prophet,
    quick_ridge,
    quick_text_embed,
    quick_theta,
)

# Linear / ridge / elasticnet
ridge = quick_ridge(features_df, target_series, alpha=1.0)
print(ridge.score, ridge.coefficients)

# Anomaly detection
iforest = quick_iforest(features_df, contamination=0.05)
print(iforest.n_anomalies)

# Forecasting
arima = quick_arima(series, horizon=10, order=(1, 1, 1))
prophet = quick_prophet(series, horizon=10)
theta = quick_theta(series, horizon=10)

# Embeddings & sentiment
embeds = quick_text_embed(headlines)
sentiment = quick_finbert_sentiment(headlines)

# Panel diagnostics
fe = quick_panel_fixed_effects(panel, target_col="y", entity_col="vt_symbol")

Where to add a new wrapper

1. Implement the class under alphaswarm/ml/models/<library>.py, subclassing Model.
2. Decorate with @register("Name", kind="model") from alphaswarm.core.registry.
3. Make optional imports lazy (raise RuntimeError mentioning the right extra) so the rest of the registry keeps working.
4. Add a YAML under configs/ml/frameworks/.
5. Add a hermetic test under tests/ml/models/ that monkey-patches the optional dep when needed.
6. Cross-list it here.

See alphaswarm_docs/ml-framework.md for the full registry + Experiment contract.