model

superduper.ext.sklearn.model

Source code

Estimator

Estimator(self,
     identifier: str,
     db: dataclasses.InitVar[typing.Optional[ForwardRef('Datalayer')]] = None,
     uuid: str = None,
     *,
     trainer: Optional[superduper.ext.sklearn.model.SklearnTrainer] = None,
     artifacts: 'dc.InitVar[t.Optional[t.Dict]]' = None,
     signature: Literal['*args',
     '**kwargs',
     '*args,
    **kwargs',
     'singleton'] = 'singleton',
     datatype: 'EncoderArg' = None,
     output_schema: 't.Optional[Schema]' = None,
     flatten: 'bool' = False,
     model_update_kwargs: 't.Dict' = None,
     predict_kwargs: 't.Dict' = None,
     compute_kwargs: 't.Dict' = None,
     validation: 't.Optional[Validation]' = None,
     metric_values: 't.Dict' = None,
     object: sklearn.base.BaseEstimator,
     preprocess: Optional[Callable] = None,
     postprocess: Optional[Callable] = None) -> None

Parameter	Description
identifier	Identifier of the leaf.
db	Datalayer instance.
uuid	UUID of the leaf.
artifacts	A dictionary of artifacts paths and DataType objects
signature	Model signature.
datatype	DataType instance.
output_schema	Output schema (mapping of encoders).
flatten	Flatten the model outputs.
model_update_kwargs	The kwargs to use for model update.
predict_kwargs	Additional arguments to use at prediction time.
compute_kwargs	Kwargs used for compute backend job submit. Example (Ray backend): compute_kwargs = dict(resources=...).
validation	The validation Dataset instances to use.
metric_values	The metrics to evaluate on.
object	The estimator object from sklearn.
trainer	The trainer to use.
preprocess	The preprocessing function to use.
postprocess	The postprocessing function to use.

Estimator model.

This is a model that can be trained and used for prediction.

SklearnTrainer

SklearnTrainer(self,
     identifier: str,
     db: dataclasses.InitVar[typing.Optional[ForwardRef('Datalayer')]] = None,
     uuid: str = None,
     *,
     artifacts: 'dc.InitVar[t.Optional[t.Dict]]' = None,
     key: 'ModelInputType',
     select: 'Query',
     transform: 't.Optional[t.Callable]' = None,
     metric_values: 't.Dict' = None,
     signature: 'Signature' = '*args',
     data_prefetch: 'bool' = False,
     prefetch_size: 'int' = 1000,
     prefetch_factor: 'int' = 100,
     in_memory: 'bool' = True,
     compute_kwargs: 't.Dict' = None,
     fit_params: Dict = None,
     predict_params: Dict = None,
     y_preprocess: Optional[Callable] = None) -> None

Parameter	Description
identifier	Identifier of the leaf.
db	Datalayer instance.
uuid	UUID of the leaf.
artifacts	A dictionary of artifacts paths and DataType objects
key	Model input type key.
select	Model select query for training.
transform	(optional) transform callable.
metric_values	Dictionary for metric defaults.
signature	Model signature.
data_prefetch	Boolean for prefetching data before forward pass.
prefetch_size	Prefetch batch size.
prefetch_factor	Prefetch factor for data prefetching.
in_memory	If training in memory.
compute_kwargs	Kwargs for compute backend.
fit_params	The parameters to pass to fit.
predict_params	The parameters to pass to `predict
y_preprocess	The preprocessing function to use for the target.

A trainer for sklearn models.