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https://github.com/webclinic017/drift.git
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refactor(Config): use a Config object instead of dictionary of dictionaries! (#184)
* refactor(Config): use a Config object instead of dictionary of dictionaries! * fix(Config): use default_ensemble_config * fix(Portfolio): fixed portfolio construction
This commit is contained in:
Mark Aron Szulyovszky
+93
-48
@@ -1,19 +1,87 @@
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def get_dev_config() -> tuple[dict, dict, dict]:
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from pydantic import BaseModel
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from typing import Literal, Optional
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from models.base import Model
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from utils.types import DataCollection, DataSource, FeatureExtractor
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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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log_returns: bool
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forecasting_horizon: int
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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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no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced']
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index_column: Literal['date', 'int']
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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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log_returns: bool
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forecasting_horizon: int
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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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no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced']
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index_column: Literal['date', 'int']
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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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def get_dev_config() -> RawConfig:
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training_config = dict(
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regression_models = ["Lasso"]
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classification_models = ["LogisticRegression_two_class"]
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return RawConfig(
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primary_models_meta_labeling = False,
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dimensionality_reduction = False,
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n_features_to_select = 30,
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expanding_window_primary = False,
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expanding_window_base = False,
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expanding_window_meta_labeling = False,
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sliding_window_size_primary = 380,
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sliding_window_size_base = 380,
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sliding_window_size_meta_labeling = 1,
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retrain_every = 20,
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scaler = 'minmax', # 'normalize' 'minmax' 'standardize'
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)
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data_config = dict(
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assets = ['daily_only_btc'],
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target_asset = 'BTC_USD',
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other_assets = [],
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@@ -26,37 +94,31 @@ def get_dev_config() -> tuple[dict, dict, dict]:
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exogenous_features = ['z_score'],
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index_column= 'int',
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no_of_classes= 'two',
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narrow_format = False,
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)
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regression_models = ["Lasso"]
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classification_models = ["LogisticRegression_two_class"]
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model_config = dict(
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primary_models = classification_models,
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meta_labeling_models = [],
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ensemble_model = None
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)
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return model_config, training_config, data_config
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def get_default_ensemble_config() -> tuple[dict, dict, dict]:
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def get_default_ensemble_config() -> RawConfig:
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training_config = dict(
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regression_models = ["Lasso", "KNN", "RFR"]
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classification_models = ["LogisticRegression_two_class", "LDA", "NB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
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meta_labeling_models = ['LogisticRegression_two_class', 'LGBM']
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ensemble_model = 'Average'
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return RawConfig(
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primary_models_meta_labeling = True,
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dimensionality_reduction = False,
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n_features_to_select = 30,
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expanding_window_primary = False,
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expanding_window_base = False,
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expanding_window_meta_labeling = True,
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sliding_window_size_primary = 380,
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sliding_window_size_base = 380,
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sliding_window_size_meta_labeling = 240,
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retrain_every = 10,
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scaler = 'minmax', # 'normalize' 'minmax' 'standardize'
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)
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data_config = dict(
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assets = ['daily_crypto'],
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target_asset = 'BTC_USD',
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other_assets = ['daily_etf'],
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@@ -69,41 +131,34 @@ def get_default_ensemble_config() -> tuple[dict, dict, dict]:
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exogenous_features = ['z_score'],
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index_column= 'int',
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no_of_classes= 'two',
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narrow_format = False,
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)
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regression_models = ["Lasso", "KNN", "RFR"]
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classification_models = ["LogisticRegression_two_class", "LDA", "NB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
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meta_labeling_models = ['LogisticRegression_two_class', 'LGBM']
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ensemble_model = 'Average'
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model_config = dict(
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primary_models = classification_models,
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meta_labeling_models = meta_labeling_models,
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ensemble_model = ensemble_model
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)
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return model_config, training_config, data_config
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def get_lightweight_ensemble_config() -> tuple[dict, dict, dict]:
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def get_lightweight_ensemble_config() -> RawConfig:
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training_config = dict(
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regression_models = ["Lasso", "KNN"]
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classification_models = ['LogisticRegression_two_class', 'SVC']
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meta_labeling_models = ['LogisticRegression_two_class', 'LGBM']
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ensemble_model = 'Average'
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return RawConfig(
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primary_models_meta_labeling = True,
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dimensionality_reduction = True,
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n_features_to_select = 30,
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expanding_window_primary = False,
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expanding_window_base = False,
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expanding_window_meta_labeling = True,
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sliding_window_size_primary = 380,
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sliding_window_size_base = 380,
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sliding_window_size_meta_labeling = 240,
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retrain_every = 40,
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scaler = 'minmax', # 'normalize' 'minmax' 'standardize'
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)
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data_config = dict(
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assets = ['daily_crypto_lightweight'],
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target_asset = 'BTC_USD',
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target_asset = 'BCH_USD',
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other_assets = ['daily_etf'],
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exogenous_data = ['daily_glassnode'],
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load_non_target_asset= True,
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@@ -114,20 +169,10 @@ def get_lightweight_ensemble_config() -> tuple[dict, dict, dict]:
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exogenous_features = ['z_score'],
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index_column= 'int',
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no_of_classes= 'two',
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narrow_format = False,
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)
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regression_models = ["Lasso", "KNN"]
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classification_models = ['LogisticRegression_two_class', 'SVC']
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meta_labeling_models = ['LogisticRegression_two_class', 'LGBM']
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ensemble_model = 'Average'
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model_config = dict(
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primary_models = classification_models,
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meta_labeling_models = meta_labeling_models,
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ensemble_model = ensemble_model
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)
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return model_config, training_config, data_config
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