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https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
95573eb9dd
* feat(Data): add option to predict 3 classes * feat(Evaluation): added ability to evaluate 3 class predictions * chore(Config): set sensible config for regression models * feat(Data): added option to use balanced or imbalanced three-class data * feat(Evaluate): correctly track "no_of_samples" now that we have three classes * chore(Sweep): remove probably not useful scaler values from sweep
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', 'none']
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distribution: categorical
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include_original_data_in_ensemble:
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values: False
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method:
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value: 'classification'
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no_of_classes:
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values: ['two', 'three-balanced', 'three-imbalanced']
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distribution: categorical
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forecasting_horizon:
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values: 1
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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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value: True
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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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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: [] |