`limen.sfd`

Package ready-to-run Single-File Decoders that plug directly into the Universal Experiment Loop.

Canonical docs

What this package owns

Owns packaged SFD modules and the reference architectures they rely on. Does not own experiment execution, data retrieval, or the lower-level indicator, feature, scaler, and transform libraries it composes.

Key entry points

Entry point	Use it when	Notes
`logreg_binary`	You want the standard manifest-driven binary-classification SFD	Exported at the package root
`random_binary`	You want a baseline classifier for comparison	Exported at the package root
`xgboost_regressor`	You want a regression-style SFD	Exported at the package root
`foundational_sfd`	You want the full catalog of packaged SFDs	Subpackage with production-oriented SFD modules
`reference_architecture`	You want model-function templates without the packaged data pipeline	Starting point for custom SFD work

Adjacent modules

limen.experiment runs SFDs and consumes params() plus either manifest() or custom prep/model functions.
limen.data, limen.indicators, limen.features, limen.transforms, and limen.scalers provide the building blocks that manifest-driven SFDs wire together.
limen.metrics is commonly used inside the reference architecture model functions.

Quick orientation

sfd/
├── foundational_sfd/          # Packaged SFDs with params + manifest
│   ├── logreg_binary.py
│   ├── random_binary.py
│   ├── xgboost_regressor.py
│   └── tabpfn_binary.py       # Optional
└── reference_architecture/    # Model-function implementations and templates
    ├── logreg_binary.py
    ├── random_binary.py
    ├── xgboost_regressor.py
    └── tabpfn_binary.py

Things to know

A foundational SFD typically owns the experiment shape, while the matching reference architecture owns the model training logic.
tabpfn_binary is optional and remains unavailable when tabpfn is not installed.
The simplest path to a new SFD is to copy an existing foundational SFD, adjust params(), and modify the manifest chain.
SFD modules are expected to stay cheap and stateless at import and manifest-construction time.

Canonical docs​

What this package owns​

Key entry points​

Adjacent modules​

Quick orientation​

Things to know​

Read next​