mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
3eb3ea94e3
* refactor(Training): added InferenceResult & TrainedModel types * refactor(Pipeline): introduced TrainingOutcome, BetSizingWithMetaOutcome, etc. * fix(Pipeline): getting it to compile * refactor(WalkForward): separate preprocessing step * feat(Pipeline): separate out transformations processing step * refactor(Pipeline): use the Directional model terminology, put bet_sizing into pipeline instead of hiding it in a step * refactor(WalkForward): moved functions to separate folder * fix(WalkForward): use sparse array to store models, process transformations in parallel (lot faster) * fix(Tests): and evaluation * fix(Tests): for realz * fix(Inference): preloading everything now, renamed primary models to directional models * fix(BetSizing): was running transformations on the wrong data, oops * fix(BetSizing): concatenated on the wrong axis accidentally * fix(Reporting): able to use the new Stats type * fix(BetSizing): renamed int column names * fix(Portfolio): name the column properly * fix(Reporting): rename the correct Series, lol * fix(Inference): walk_forwad_inference() can deal with models not being aligned with the starting index * fix(WalkForward): accidentally using the wrong index * fix(WalkForward): use the correct indicies to fetch last model/transformations * fix(CI): changed the name of the results
67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
from pydantic import BaseModel
|
|
from typing import Literal, Optional
|
|
from labeling.types import EventFilter
|
|
from models.base import Model
|
|
from data_loader.types import DataCollection, DataSource
|
|
from feature_extractors.types import FeatureExtractor, ScalerTypes
|
|
from labeling.types import EventFilter, EventLabeller
|
|
|
|
|
|
# RawConfig is needed to ensure we can declare config presets here with static typing, we then convert it to Config
|
|
class RawConfig(BaseModel):
|
|
directional_models_meta: bool
|
|
dimensionality_reduction: bool
|
|
n_features_to_select: int
|
|
expanding_window_base: bool
|
|
expanding_window_meta: bool
|
|
sliding_window_size_base: int
|
|
sliding_window_size_meta: int
|
|
retrain_every: int
|
|
scaler: Literal['normalize', 'minmax', 'standardize']
|
|
|
|
assets: list[str]
|
|
target_asset: str
|
|
other_assets: list[str]
|
|
exogenous_data: list[str]
|
|
load_non_target_asset: bool
|
|
own_features: list[str]
|
|
other_features: list[str]
|
|
exogenous_features: list[str]
|
|
event_filter: Literal['none', 'cusum_vol', 'cusum_fixed']
|
|
labeling: Literal['two_class', 'three_class_balanced', 'three_class_imbalanced']
|
|
|
|
directional_models: list[str]
|
|
meta_models: list[str]
|
|
|
|
|
|
class Config(BaseModel):
|
|
directional_models_meta: bool
|
|
dimensionality_reduction: bool
|
|
n_features_to_select: int
|
|
expanding_window_base: bool
|
|
expanding_window_meta: bool
|
|
sliding_window_size_base: int
|
|
sliding_window_size_meta: int
|
|
retrain_every: int
|
|
scaler: Literal['normalize', 'minmax', 'standardize']
|
|
|
|
assets: DataCollection
|
|
target_asset: DataSource
|
|
other_assets: DataCollection
|
|
exogenous_data: DataCollection
|
|
load_non_target_asset: bool
|
|
own_features: list[tuple[str, FeatureExtractor, list[int]]]
|
|
other_features: list[tuple[str, FeatureExtractor, list[int]]]
|
|
exogenous_features: list[tuple[str, FeatureExtractor, list[int]]]
|
|
event_filter: EventFilter
|
|
labeling: EventLabeller
|
|
no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced']
|
|
|
|
directional_models: list[Model]
|
|
meta_models: list[Model]
|
|
|
|
class Config:
|
|
arbitrary_types_allowed = True
|
|
|
|
|