Source code for lightwood.ensemble.base

from typing import List, Optional

import pandas as pd

from lightwood.mixer.base import BaseMixer
from import EncodedDs
from lightwood.api.types import PredictionArguments, SubmodelData

[docs]class BaseEnsemble: """ Base class for all ensembles. Ensembles wrap sets of Lightwood mixers, with the objective of generating better predictions based on the output of each mixer. There are two important methods for any ensemble to work: 1. `__init__()` should prepare all mixers and internal ensemble logic. 2. `__call__()` applies any aggregation rules to generate final predictions based on the output of each mixer. Class Attributes: - mixers: List of mixers the ensemble will use. - supports_proba: For classification tasks, whether the ensemble supports yielding per-class scores rather than only returning the predicted label. NOTE: this ensemble is not functional. Do not use it when generating custom JsonAI objects, as the learning process will fail. """ # noqa data: EncodedDs mixers: List[BaseMixer] best_index: int # @TODO: maybe only applicable to BestOf supports_proba: bool prepared: bool submodel_data: List[SubmodelData] def __init__(self, target, mixers: List[BaseMixer], data: EncodedDs, fit: Optional[bool] = True) -> None: = data self.mixers = mixers self.best_index = 0 self.supports_proba = False self.submodel_data = [] def __call__(self, ds: EncodedDs, args: PredictionArguments) -> pd.DataFrame: raise NotImplementedError()