mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-03 06:07:50 +00:00
Mark Aron Szulyovszky
69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
from pydantic import BaseModel
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from typing import Literal, Optional
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from labeling.types import EventFilter
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from models.base import Model
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from data_loader.types import DataCollection, DataSource
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from feature_extractors.types import FeatureExtractor, ScalerTypes
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from labeling.types import EventFilter, EventLabeller
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# RawConfig is needed to ensure we can declare config presets here with static typing, we then convert it to Config
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class RawConfig(BaseModel):
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primary_models_meta_labeling: bool
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dimensionality_reduction: bool
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n_features_to_select: int
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expanding_window_base: bool
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expanding_window_meta_labeling: bool
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sliding_window_size_base: int
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sliding_window_size_meta_labeling: int
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retrain_every: int
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scaler: Literal['normalize', 'minmax', 'standardize']
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assets: list[str]
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target_asset: str
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other_assets: list[str]
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exogenous_data: list[str]
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load_non_target_asset: bool
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own_features: list[str]
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other_features: list[str]
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exogenous_features: list[str]
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event_filter: Literal['none', 'cusum_vol', 'cusum_fixed']
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labeling: Literal['two_class', 'three_class_balanced', 'three_class_imbalanced']
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primary_models: list[str]
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meta_labeling_models: list[str]
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ensemble_model: Optional[str]
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class Config(BaseModel):
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primary_models_meta_labeling: bool
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dimensionality_reduction: bool
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n_features_to_select: int
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expanding_window_base: bool
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expanding_window_meta_labeling: bool
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sliding_window_size_base: int
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sliding_window_size_meta_labeling: int
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retrain_every: int
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scaler: Literal['normalize', 'minmax', 'standardize']
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assets: DataCollection
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target_asset: DataSource
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other_assets: DataCollection
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exogenous_data: DataCollection
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load_non_target_asset: bool
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own_features: list[tuple[str, FeatureExtractor, list[int]]]
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other_features: list[tuple[str, FeatureExtractor, list[int]]]
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exogenous_features: list[tuple[str, FeatureExtractor, list[int]]]
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event_filter: EventFilter
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labeling: EventLabeller
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no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced']
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primary_models: list[tuple[str, Model]]
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meta_labeling_models: list[tuple[str, Model]]
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ensemble_model: Optional[tuple[str, Model]]
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class Config:
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arbitrary_types_allowed = True
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