mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-02 13:47:47 +00:00
Mark Aron Szulyovszky
86 lines
2.7 KiB
Python
86 lines
2.7 KiB
Python
from pydantic import BaseModel, validator
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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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from sklearn.base import BaseEstimator
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from dataclasses import dataclass
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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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directional_models_meta: 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: bool
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sliding_window_size_base: int
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sliding_window_size_meta: 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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forecasting_horizon: int
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directional_models: list[str]
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meta_models: list[str]
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@dataclass
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class Config:
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directional_models_meta: 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: bool
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sliding_window_size_base: int
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sliding_window_size_meta: 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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forecasting_horizon: int
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no_of_classes: Literal["two", "three-balanced", "three-imbalanced"]
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mode: Literal["training", "inference"]
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directional_model: Model
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meta_model: Model
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@validator("directional_model", "meta_model")
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def check_model(cls, v):
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assert isinstance(v, BaseEstimator)
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return v
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@validator("event_filter")
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def check_event_filter(cls, v):
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assert isinstance(v, EventFilter)
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return v
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@validator("labeling")
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def check_labeling(cls, v):
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assert isinstance(v, EventLabeller)
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return v
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# class Config:
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# arbitrary_types_allowed = True
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