feat(Events): added EventFilter, EventLabeller (#186)

config/types.py

@@ -0,0 +1,68 @@
+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):
+    primary_models_meta_labeling: bool
+    dimensionality_reduction: bool
+    n_features_to_select: int
+    expanding_window_base: bool
+    expanding_window_meta_labeling: bool
+    sliding_window_size_base: int
+    sliding_window_size_meta_labeling: 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']
+
+    primary_models: list[str]
+    meta_labeling_models: list[str]
+    ensemble_model: Optional[str]
+
+
+class Config(BaseModel):
+    primary_models_meta_labeling: bool
+    dimensionality_reduction: bool
+    n_features_to_select: int
+    expanding_window_base: bool
+    expanding_window_meta_labeling: bool
+    sliding_window_size_base: int
+    sliding_window_size_meta_labeling: 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']
+
+    primary_models: list[tuple[str, Model]]
+    meta_labeling_models: list[tuple[str, Model]]
+    ensemble_model: Optional[tuple[str, Model]]
+
+    class Config:
+        arbitrary_types_allowed = True
+
+