"""Contains the implementations for active learning methods
Implements the supported active learning methods.
"""
from collections.abc import Callable
from pydantic import Field
from ..base import CalibrationMethodBase, CalibrationWorkflowBase
from ..data_model import CalibrationModel
from .skactiveml_wrapper import SkActiveMLActiveLearning
TASK = "active_learning"
IMPLEMENTATIONS: dict[str, type[CalibrationWorkflowBase]] = dict(
skactiveml=SkActiveMLActiveLearning
)
[docs]
def get_implementations() -> dict[str, type[CalibrationWorkflowBase]]:
"""Get the calibration implementations for active learning.
Returns:
Dict[str, type[CalibrationWorkflowBase]]: The dictionary
of calibration implementations for active learning.
"""
return IMPLEMENTATIONS
[docs]
class ActiveLearningMethodModel(CalibrationModel):
"""The active learning method data model.
Args:
BaseModel (CalibrationModel): The calibration base model class.
"""
query_strategy: str = Field(
description="The active learning query strategy",
default="greedy_sampling_target",
)
[docs]
class ActiveLearningMethod(CalibrationMethodBase):
"""The active learning method class."""
def __init__(
self,
calibration_func: Callable,
specification: ActiveLearningMethodModel,
engine: str = "skactiveml",
implementation: CalibrationWorkflowBase | None = None,
) -> None:
"""ActiveLearningMethod constructor.
Args:
calibration_func (Callable): The calibration function.
For example, a simulation function or objective function.
specification (ActiveLearningMethodModel): The calibration
specification.
engine (str, optional): The active learning backend.
Defaults to "emukit".
implementation (CalibrationWorkflowBase | None): The
calibration workflow implementation.
"""
super().__init__(
calibration_func,
specification,
TASK,
engine,
IMPLEMENTATIONS,
implementation,
)