lale.lib.rasl.task_graphs module¶
- class lale.lib.rasl.task_graphs.Prio[source]¶
Bases:
ABC
Abstract base class for scheduling priority in task graphs.
- class lale.lib.rasl.task_graphs.PrioBatch[source]¶
Bases:
Prio
Execute tasks from earlier batches first.
- class lale.lib.rasl.task_graphs.PrioResourceAware[source]¶
Bases:
Prio
Execute tasks with less non-resident data first.
- class lale.lib.rasl.task_graphs.PrioStep[source]¶
Bases:
Prio
Execute tasks from earlier steps first, like nested-loop algorithm.
- lale.lib.rasl.task_graphs.cross_val_score(pipeline: TrainablePipeline[TrainableIndividualOp], batches: Iterable[Tuple[Any, Any]], scoring: MetricMonoidFactory, cv, unique_class_labels: List[Union[str, int, float]], max_resident: Optional[int], prio: Prio, same_fold: bool, verbose: int) List[float] [source]¶
Replacement for sklearn’s cross_val_score function (early interface, subject to change).
- lale.lib.rasl.task_graphs.cross_validate(pipeline: TrainablePipeline[TrainableIndividualOp], batches: Iterable[Tuple[Any, Any]], scoring: MetricMonoidFactory, cv, unique_class_labels: List[Union[str, int, float]], max_resident: Optional[int], prio: Prio, same_fold: bool, return_estimator: bool, verbose: int) Dict[str, Union[List[float], List[TrainedPipeline]]] [source]¶
Replacement for sklearn’s cross_validate function (early interface, subject to change).
- lale.lib.rasl.task_graphs.fit_with_batches(pipeline: TrainablePipeline[TrainableIndividualOp], batches_train: Iterable[Tuple[Any, Any]], batches_valid: Optional[List[Tuple[Any, Any]]], scoring: Optional[MetricMonoidFactory], unique_class_labels: List[Union[str, int, float]], max_resident: Optional[int], prio: Prio, partial_transform: Union[bool, str], verbose: int, progress_callback: Optional[Callable[[float, float, int, bool], None]]) TrainedPipeline[TrainedIndividualOp] [source]¶
Replacement for the fit method on a pipeline (early interface, subject to change).
- lale.lib.rasl.task_graphs.is_associative(op: TrainableIndividualOp) bool [source]¶
Is the operator pre-trained or does it implement MonoidFactory?
- lale.lib.rasl.task_graphs.is_incremental(op: TrainableIndividualOp) bool [source]¶
Does the operator have a partial_fit method or is it pre-trained?
- lale.lib.rasl.task_graphs.is_pretrained(op: TrainableIndividualOp) bool [source]¶
Is the operator frozen-trained or does it lack a fit method?