TabPFNRel

Looking for usage documentation? Check out Relational predictions.

`TabPFNRel`

Fit a relational context with local or hosted TabPFN inference. Select the inference backend.

TabPFNRel(
    *,
    model: Literal["client", "local", "local-2026-08-15", "client-2026-08-15", "client-2026-09-18", "client-2026-09-28", "local-2026-09-28"],
)

Parameters

Parameter	Type	Default	Description
`model`	`Literal[“client”, “local”, “local-2026-08-15”, “client-2026-08-15”, “client-2026-09-18”, “client-2026-09-28”, “local-2026-09-28”]`	Required	`"client"` (hosted API) or `"local"` for the latest recipe; a dated selector such as `"client-2026-09-28"` pins one.

`TabPFNRel.trials`

View source

Return tuning results, or None when no tuning has completed. Returns

Type	Description
`list[TrialResult] \| None`	—

`TabPFNRel.config`

View source

Return the fitted configuration, or None before a successful fit. Returns

Type	Description
`dict[str, Any] \| None`	—

`TabPFNRel.fit`

View source

Fit a model on the context and return self.

TabPFNRel.fit(
    context: PredictiveContext,
    *,
    n_trials: int = 0,
    seed: int = 0,
    cache_dir: str | Path | None = None,
) -> TabPFNRel

Parameters

Parameter	Type	Default	Description
`context`	`PredictiveContext`	Required	The database and training task.
`n_trials`	`int`	`0`	Tuning trials; `0` fits the default configuration. Hosted trials use API quota.
`seed`	`int`	`0`	Random seed for tuning and fitting.
`cache_dir`	`str \| Path \| None`	`None`	Directory for cached DFS features, reused by `predict`.

Returns

Type	Description
`TabPFNRel`	—

`TabPFNRel.predict`

View source

Predict the query’s entities and timestamp using the fitted context.

TabPFNRel.predict(
    query: PredictiveQuery,
    *,
    cache_dir: str | Path | None = None,
) -> pd.DataFrame

Parameters

Parameter	Type	Default	Description
`query`	`PredictiveQuery`	Required	The entities and anchor timestamp to predict.
`cache_dir`	`str \| Path \| None`	`None`	Feature cache; `None` reuses the one passed to `fit`.

Returns

Type	Description
`pd.DataFrame`	One row per entity: the time and entity columns and `<target_col>_pred`.

Reference

tabpfn

tabpfn-client

tabpfn-extensions

tabpfn-rel

REST API

`TabPFNRel`

`TabPFNRel.trials`

`TabPFNRel.config`

`TabPFNRel.fit`

`TabPFNRel.predict`

​TabPFNRel

​TabPFNRel.trials

​TabPFNRel.config

​TabPFNRel.fit

​TabPFNRel.predict

`TabPFNRel`

`TabPFNRel.trials`

`TabPFNRel.config`

`TabPFNRel.fit`

`TabPFNRel.predict`