mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-09 17:11:05 +00:00
Daniel Szemerey
55f083638f
* feat: Basic scaffolding up for inference process after training. * feat: Saving and loading models works. Inference works nearly. * feat: Added inference pipeline. * feat: Saving model now accoring to date and time; loading models now selects from latest file. Fixed the creation of dictionary of models. * feat: Added lightweight asset config, but full pipeline. * feat: Added new naming for dictionary. * fix: Fixed dictionary naming convention. * fix: Fixed naming again, now the model structure is good * fix: Changed the output path and the return values from run_pipeline. * feat: Added function to make sure folder exists for output models. Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com>
134 lines
4.3 KiB
Python
134 lines
4.3 KiB
Python
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def get_dev_config() -> tuple[dict, dict, dict]:
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training_config = dict(
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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_meta_labeling = False,
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sliding_window_size_primary = 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' 'none'
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)
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data_config = dict(
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assets = ['daily_only_btc'],
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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 = ['z_score'],
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index_column= 'int',
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method= 'classification',
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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 = ["LR_two_class"]
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model_config = dict(
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primary_models = regression_models if data_config['method'] == 'regression' else 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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training_config = dict(
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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_meta_labeling = True,
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sliding_window_size_primary = 380,
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sliding_window_size_meta_labeling = 240,
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retrain_every = 20,
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scaler = 'minmax', # 'normalize' 'minmax' 'standardize' 'none'
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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 = ['z_score'],
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index_column= 'int',
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method= 'classification',
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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", "RF"]
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classification_models = ["LR_two_class", "LDA", "NB", "RF", "XGB_two_class", "LGBM", "StaticMom"]
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meta_labeling_models = ['LR_two_class', 'LGBM']
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ensemble_model = 'Average'
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model_config = dict(
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primary_models = regression_models if data_config['method'] == 'regression' else 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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training_config = dict(
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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_meta_labeling = True,
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sliding_window_size_primary = 380,
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sliding_window_size_meta_labeling = 240,
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retrain_every = 20,
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scaler = 'minmax', # 'normalize' 'minmax' 'standardize' 'none'
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)
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data_config = dict(
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assets = ['daily_crypto_lightweight'],
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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' ],
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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= '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 = ['LR_two_class', 'SVC']
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meta_labeling_models = ['LR_two_class', 'LGBM']
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ensemble_model = 'Average'
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model_config = dict(
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primary_models = regression_models if data_config['method'] == 'regression' else 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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