mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-07 16:17:49 +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>
58 lines
1.1 KiB
YAML
58 lines
1.1 KiB
YAML
program: run_pipeline.py
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method: grid
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project: price-forecasting
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name: Finding best hyperparameters for price prediction
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# early_terminate:
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# type: hyperband
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# min_iter: 2000
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# metric:
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# goal: maximize
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# name: sharpe
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parameters:
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path :
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value: 'data/'
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sliding_window_size:
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values: [50, 90, 130, 160, 180, 280, 380, 500]
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distribution: categorical
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retrain_every:
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values: [7, 14, 30, 60, 100]
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distribution: categorical
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scaler:
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values: ['minmax', 'normalize', 'minmax', 'standardize', 'none']
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distribution: categorical
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include_original_data_in_ensemble:
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value: True
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method:
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value: 'classification'
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forecasting_horizon:
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values: [1,2,3,4,5,6,7,8,9,10]
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distribution: categorical
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load_other_assets:
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value: False
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log_returns:
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value: True
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own_features:
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value: []
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other_features:
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value: []
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index_column:
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value: 'int'
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level_1_models:
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value: ["LR", "LDA", "KNN", "CART", "NB", "AB", "RF"]
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distribution: constant
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level_2_models:
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value: ['Ensemble_CART']
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distribution: constant
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