mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-01 13:17:47 +00:00
Daniel Szemerey
1c1b8b2e54
* feat: Parametricized model selection works now. * feat: Fixed errors. Sweep generates and you can run it, but it gives an error for model.only_columns attribute. * feat: Factored the wandb management, default config managment and the model_dictionary out of the run_pipeline to a seperate file. * fix: Took out prints and fixed the mismatch of ensemble models when classifing. * fix(Models): added StaticMomentum model to the dictionary, hopefully fixed sklearn-ex RandomForestRegressor problem * fix(Dependencies): pin scikit-learn-ex's version, moved map_model_name_to_function to `models` * feat(Sweep): added `run_sweep.py` shortcut * feat(Pipeline): skip training a meta model if array is empty Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
38 lines
1.6 KiB
Python
38 lines
1.6 KiB
Python
from utils.load_data import get_crypto_assets
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import feature_extractors.feature_extractor_presets as feature_extractor_presets
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from models.model_map import model_names_classification, model_names_regression
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def get_default_config() -> tuple[dict, dict, dict]:
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training_config = dict(
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sliding_window_size = 150,
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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 = True,
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)
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data_config = dict(
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path='data/',
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all_assets = get_crypto_assets('data/'),
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load_other_assets= False,
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log_returns= True,
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forecasting_horizon = 1,
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own_features= feature_extractor_presets.date + feature_extractor_presets.level1,
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other_features= [],
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index_column= 'int',
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method= 'classification',
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)
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# regression_models = ["Lasso", "Ridge", "BayesianRidge", "KNN", "AB", "LR", "MLP", "RF", "SVR"]
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regression_models = model_names_regression
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regression_ensemble_models = ['Ensemble_Average']
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# classification_models = ["LR", "LDA", "KNN", "CART", "NB", "AB", "RF", "StaticMom"]
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classification_models = model_names_classification
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classification_ensemble_models = ['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_models = regression_ensemble_models if data_config['method'] == 'regression' else classification_ensemble_models
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)
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return model_config, training_config, data_config |