loading ¶

download_all_models ¶

download_all_models(to: Path) -> None

Download all v2 classifier and regressor models into a local directory.

download_model ¶

download_model(
    to: Path,
    *,
    version: Literal["v2"],
    which: Literal["classifier", "regressor"],
    model_name: str | None = None
) -> Literal["ok"] | list[Exception]

Download a TabPFN model, trying all available sources.

Parameters:

Name	Type	Description	Default
`to`	`Path`	The directory to download the model to.	required
`version`	`Literal['v2']`	The version of the model to download.	required
`which`	`Literal['classifier', 'regressor']`	The type of model to download.	required
`model_name`	`str \| None`	Optional specific model name to download.	`None`

Returns:

Type	Description
`Literal['ok'] \| list[Exception]`	"ok" if the model was downloaded successfully, otherwise a list of
`Literal['ok'] \| list[Exception]`	exceptions that occurred that can be handled as desired.

load_model ¶

load_model(*, path: Path, model_seed: int) -> tuple[
    PerFeatureTransformer,
    BCEWithLogitsLoss
    | CrossEntropyLoss
    | FullSupportBarDistribution,
    InferenceConfig,
]

Loads a model from a given path.

Parameters:

Name	Type	Description	Default
`path`	`Path`	Path to the checkpoint	required
`model_seed`	`int`	The seed to use for the model	required

load_model_criterion_config ¶

load_model_criterion_config(
    model_path: None | str | Path,
    *,
    check_bar_distribution_criterion: bool,
    cache_trainset_representation: bool,
    which: Literal["regressor", "classifier"],
    version: Literal["v2"] = "v2",
    download: bool,
    model_seed: int
) -> tuple[
    PerFeatureTransformer,
    BCEWithLogitsLoss
    | CrossEntropyLoss
    | FullSupportBarDistribution,
    InferenceConfig,
]

Load the model, criterion, and config from the given path.

Parameters:

Name	Type	Description	Default
`model_path`	`None \| str \| Path`	The path to the model.	required
`check_bar_distribution_criterion`	`bool`	Whether to check if the criterion is a FullSupportBarDistribution, which is the expected criterion for models trained for regression.	required
`cache_trainset_representation`	`bool`	Whether the model should know to cache the trainset representation.	required
`which`	`Literal['regressor', 'classifier']`	Whether the model is a regressor or classifier.	required
`version`	`Literal['v2']`	The version of the model.	`'v2'`
`download`	`bool`	Whether to download the model if it doesn't exist.	required
`model_seed`	`int`	The seed of the model.	required

Returns:

Type	Description
`tuple[PerFeatureTransformer, BCEWithLogitsLoss \| CrossEntropyLoss \| FullSupportBarDistribution, InferenceConfig]`	The model, criterion, and config.