mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
f85ee6bb9c
* fix(MetaLabeling): previously misinterpreted meta-labeling, now also multiplying base model's prediction with the meta model's prediction * fix(Evaluate): print results * fix(Evaluate): make sure we have numerical stability in returns * fix(Inference): only output and print stats in training mode * fix(Evaluate): don't add miniscule amount to result
69 lines
2.2 KiB
Python
69 lines
2.2 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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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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directional_models: list[str]
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meta_models: list[str]
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class Config(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: 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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mode: Literal['training', 'inference']
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directional_models: list[Model]
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meta_models: list[Model]
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class Config:
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arbitrary_types_allowed = True
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