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drift/sweep.yaml
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program: rnn_sweep.py
method: bayes
project: integer-sequence
name: Finding best hyperparameters for price prediction
early_terminate:
type: hyperband
min_iter: 2000
metric:
goal: maximize
name: accuracy_test
parameters:
path : 'data/'
sliding_window_size:
values:
- 90
- 150
- 365
- 730
distribution: categorical
retrain_every:
values:
- 7
- 14
- 30
- 60
distribution: categorical
scaler: 'minmax' # 'normalize' 'minmax' 'standardize' 'none'
include_original_data_in_ensemble: True
method:
values:
- 'classification'
- 'regression'
distribution: categorical
forecasting_horizon:
value: 1
distribution: constant