mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-07 08:07:49 +00:00
Mark Aron Szulyovszky
6ae8acf70e
* feat(Models): added `debug_future_lookahead`, sped up LogisticRegression & DecisionTreeClassifier * feat(Training): added ability to train on expanding_window * feat(Models): tuned some hyperparameters, added expanding_window to sweep config, fixed tests * feat(Models): tune parameters of ensemble models * fix(Config): use window size that works with ensembling
53 lines
1.3 KiB
YAML
53 lines
1.3 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: [90, 130, 160, 180, 280, 380]
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distribution: categorical
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retrain_every:
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values: [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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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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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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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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