# @package _global_

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

defaults:
  - override /datamodule: focus150.yaml
  - override /model: focus150.yaml
  - override /hydra/sweeper: optuna

# choose metric which will be optimized by Optuna
# make sure this is the correct name of some metric logged in lightning module!
optimized_metric: "val/mae_best"

name: "focusMSE_150_hyperparameter_search"

# 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:
    _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper

    # storage URL to persist optimization results
    # for example, you can use SQLite if you set 'sqlite:///example.db'
    storage: null

    # name of the study to persist optimization results
    study_name: focusMAE_150_hyperparameter_search

    # number of parallel workers
    n_jobs: 1

    # 'minimize' or 'maximize' the objective
    direction: minimize

    # total number of runs that will be executed
    n_trials: 20

    # choose Optuna hyperparameter sampler
    # docs: https://optuna.readthedocs.io/en/stable/reference/samplers.html
    sampler:
      _target_: optuna.samplers.TPESampler
      seed: 12345
      n_startup_trials: 10 # number of random sampling runs before optimization starts

    # define range of hyperparameters
    search_space:
      datamodule.batch_size:
        type: categorical
        choices: [64, 128]
      model.lr:
        type: float
        low: 0.0001
        high: 0.2
      model.lin1_size:
        type: categorical
        choices: [64, 128, 256, 512, 1024]
      model.lin2_size:
        type: categorical
        choices: [64, 128, 256, 512, 1024]
      model.lin3_size:
        type: categorical
        choices: [64, 128, 256, 512, 1024]