veas_pilot_forecast_cv_xgboost.yaml

# @package _global_

# example hyperparameter optimization of some experiment with Optuna:
# python train.py -m hparams_search=mnist_optuna experiment=example

defaults:
  - veas_pilot_cv_base
  - override /experiment: veas_pilot_forecast
  - override /model: veas_pilot_xgboost_forecast

model:
  lags_future_covariates:
    _target_: builtins.tuple
    _args_:
      - - ${model_lags}
        - 0
  early_stopping_rounds: 10

# here we define Optuna hyperparameter search
# it optimizes for value returned from function with @hydra.main decorator
# docs: https://hydra.cc/docs/next/plugins/optuna_sweeper
hydra:
  sweeper:
    study_name: veas_pilot-forecast-cv-xgboost
    params:
      model_lags: range(1, ${max_lags})
      ++model.output_chunk_length: choice(1, 2, 3, ${eval.kwargs.forecast_horizon})
      ++model.max_depth: range(3, 10)
      ++model.learning_rate: interval(1e-3,1e-1)
      ++model.reg_lambda: range(0.0, 4.5, 0.5)
      ++model.reg_alpha: range(0.0, 4.5, 0.5)
      ++model.min_child_weight: range(1,6,2)
      ++model.subsample: range(0.4, 1.0, 0.1)
      ++model.colsample_bytree: range(0.4, 1.0, 0.1)
      use_inputs.oxygen: choice(True, False)
      use_inputs.ammonium: choice(True, False)
      use_inputs.filterpressure_1: choice(True, False)
      use_inputs.filterpressure_8: choice(True, False)
      use_inputs.turb: choice(True, False)
      use_inputs.temp: choice(True, False)
      use_inputs.methanol: choice(True, False)
      use_inputs.orto-p: choice(True, False)
      use_inputs.tunnelwater: choice(True, False)
      # TODO: add gamma?