mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-05 15:17:46 +00:00
Mark Aron Szulyovszky
1cd0119589
* fix(FeatureExtractor): apply log to transform some series to normality * feat(DataLoader): add ability of not returning returns when they're not needed (exogenous data), applied log to certain features * feat(FeatureExtractors): added standard scaling for exogenous data * feat(FeatureSelection): scale data with the passed in scaler before doing feature-selection * fix(Config): sweep config * feat(Models): output probability, store it * feat(Core): added caching to select_features() and load_data() * fix(Dependencies): added diskcache * fix(Training): error when creating results DF * feat(Models): added xgboost, fixed tests * refactor(Cache): moved hashing to a separate function, created wrapper functions to separate business logic and caching * fix(Tests): new syntax * fix(Model): XGboost can't handle -1 class, so we'll use the deprecated label_encoder fornow * fix(Model): XGBoost config * feat(Cache): add run_clear_cache script * fix(Pipeline) accidentally re-instatiating all_predictions for each asset
132 lines
4.4 KiB
Python
132 lines
4.4 KiB
Python
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def get_default_level_1_daily_config() -> tuple[dict, dict, dict]:
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training_config = dict(
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dimensionality_reduction = True,
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feature_selection = True,
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n_features_to_select = 30,
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expanding_window_level1 = False,
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expanding_window_level2 = False,
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sliding_window_size_level1 = 380,
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sliding_window_size_level2 = 1,
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retrain_every = 20,
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scaler = 'minmax', # 'normalize' 'minmax' 'standardize' 'none'
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include_original_data_in_ensemble = False,
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)
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data_config = dict(
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assets = ['daily_crypto'],
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other_assets = [],
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exogenous_data = [],
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load_non_target_asset= True,
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log_returns= True,
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forecasting_horizon = 1,
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own_features = ['level_2', 'date_days'],
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other_features = ['single_mom'],
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exogenous_features = ['standard_scaling'],
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index_column= 'int',
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method= 'classification',
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no_of_classes= 'three-balanced',
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narrow_format = False,
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)
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regression_models = ["Lasso"]
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classification_models = ["KNN"]
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model_config = dict(
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level_1_models = regression_models if data_config['method'] == 'regression' else classification_models,
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level_2_model = None
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)
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return model_config, training_config, data_config
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def get_default_level_2_hourly_config() -> tuple[dict, dict, dict]:
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training_config = dict(
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dimensionality_reduction = True,
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feature_selection = True,
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n_features_to_select = 30,
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expanding_window_level1 = True,
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expanding_window_level2 = False,
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sliding_window_size_level1 = 2480,
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sliding_window_size_level2 = 1,
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retrain_every = 100,
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scaler = 'minmax', # 'normalize' 'minmax' 'standardize' 'none'
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include_original_data_in_ensemble = False,
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)
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data_config = dict(
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assets = ['hourly_crypto'],
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other_assets = [],
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exogenous_data = [],
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load_non_target_asset= True,
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log_returns= True,
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forecasting_horizon = 1,
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own_features = ['level_2', 'date_days', 'lags_up_to_5'],
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other_features = ['level_2'],
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exogenous_features = ['standard_scaling'],
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index_column= 'int',
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method= 'classification',
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no_of_classes= 'three-balanced',
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narrow_format = False,
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)
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regression_models = ["Lasso", "KNN", "RF"]
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regression_ensemble_model = 'KNN'
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classification_models = ["LDA", "KNN", "CART", "RF", "StaticMom"]
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classification_ensemble_model = 'Ensemble_Average'
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model_config = dict(
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level_1_models = regression_models if data_config['method'] == 'regression' else classification_models,
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level_2_model = regression_ensemble_model if data_config['method'] == 'regression' else classification_ensemble_model
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)
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return model_config, training_config, data_config
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def get_default_level_2_daily_config() -> tuple[dict, dict, dict]:
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training_config = dict(
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dimensionality_reduction = True,
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feature_selection = True,
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n_features_to_select = 30,
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expanding_window_level1 = True,
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expanding_window_level2 = False,
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sliding_window_size_level1 = 380,
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sliding_window_size_level2 = 1,
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retrain_every = 20,
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scaler = 'minmax', # 'normalize' 'minmax' 'standardize' 'none'
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include_original_data_in_ensemble = False,
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)
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data_config = dict(
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assets = ['daily_crypto'],
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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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log_returns= True,
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forecasting_horizon = 1,
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own_features = ['level_2', 'date_days', 'lags_up_to_5'],
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other_features = ['level_2', 'lags_up_to_5'],
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exogenous_features = ['standard_scaling'],
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index_column= 'int',
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method= 'classification',
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no_of_classes= 'three-balanced',
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narrow_format = False,
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)
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regression_models = ["Lasso", "KNN", "RF"]
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regression_ensemble_model = 'KNN'
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classification_models = ['LR', 'LDA', 'KNN', 'CART', 'NB', 'AB', 'RF', 'XGB', 'StaticMom']
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classification_ensemble_model = 'Ensemble_Average'
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model_config = dict(
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level_1_models = regression_models if data_config['method'] == 'regression' else classification_models,
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level_2_model = regression_ensemble_model if data_config['method'] == 'regression' else classification_ensemble_model
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)
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return model_config, training_config, data_config
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