mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-01 21:27:46 +00:00
Daniel Szemerey
516c8bcc87
* fix, feat: Fixed inference processing data. Add transformation attribute. * feat: Added transformations step, refractored the loop to make more sense (divided the train and inference loop). * feat: Truncated models over time and transformations over time. Fixed some typing aswell. * fix: Fixed a number of out of array problems. * feat: Inference now works! * fix(Steps): runtime error not checking for None * fix(Steps): preloaded transformers are not optional anymore, sped up training by temporary increasing the retrain_every * fix(CI): disable ray memory monitoring * refactor(Inference): removed truncate_models and replaced it with filling X with NaN until inference should start * feat(Inference): added index_from parameter * fix(Tests): walk_forward test * refactor(Pipeline): only predict one asset * refactor(Inference): removed select_models step, inference code moved to run_inference.py so it matches convention (similar to run_pipeline.py) * fix(Evaluation): adjust transaction costs * fix(Config): adjusted retrain_every Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com> Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
137 lines
4.4 KiB
Python
137 lines
4.4 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'
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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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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 = ["LogisticRegression_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 = 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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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", "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 = 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 = 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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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 = ['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"]
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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 = 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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