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https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
b6cd6b14fe
* feat(Config): feature extractors are enabled one-by-one with a bool, added previous model to model.fit() * fix(Sweep): removed unused `other_features` parameter that fails sweep * feat(Config): using preset names for defining feature extractors again * fix(Tests): fixed model stub classes
52 lines
1.4 KiB
YAML
52 lines
1.4 KiB
YAML
program: run_sweep.py
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method: bayes
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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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expanding_window:
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values: [True, False]
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distribution: categorical
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sliding_window_size:
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values: [180, 280, 380]
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distribution: categorical
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retrain_every:
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values: [10, 20, 30]
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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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values: [True, False]
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distribution: categorical
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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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values: [True, False]
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distribution: categorical
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log_returns:
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values: [True, False]
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distribution: categorical
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index_column:
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value: 'int'
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level_1_models:
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values: [["LR"], ["LDA"], ["KNN"], ["CART"], ["NB"], ["AB"], ["RF"]]
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distribution: categorical
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level_2_models:
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value: []
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distribution: constant
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own_features:
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values: [['only_mom', 'date_days'], [], ['level_1', 'date_days'], ['level_1', 'date_days', 'level_2']]
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distribution: categorical
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other_features:
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value: [] |