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drift/sweep.yaml
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2021-12-23 14:13:52 +01:00

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YAML

program: run_sweep.py
method: bayes
project: price-forecasting
name: Finding best hyperparameters for price prediction
# early_terminate:
# type: hyperband
# min_iter: 2000
metric:
goal: maximize
name: sharpe
parameters:
path :
value: 'data/'
expanding_window:
values: [True, False]
distribution: categorical
sliding_window_size:
values: [180, 280, 380]
distribution: categorical
retrain_every:
values: [10, 20, 30]
distribution: categorical
scaler:
values: ['minmax', 'none']
distribution: categorical
include_original_data_in_ensemble:
value: False
method:
value: 'classification'
no_of_classes:
values: ['two', 'three-balanced', 'three-imbalanced']
distribution: categorical
forecasting_horizon:
value: 1
load_other_assets:
values: [True, False]
distribution: categorical
log_returns:
value: True
index_column:
value: 'int'
level_1_models:
values: [["LR"], ["LDA"], ["KNN"], ["CART"], ["NB"], ["AB"], ["RF"]]
distribution: categorical
level_2_models:
value: []
own_features:
values: [['only_mom', 'date_days'], [], ['level_1', 'date_days'], ['level_1', 'date_days', 'level_2']]
distribution: categorical
other_features:
value: []