# Dataset and Splits
data_root: DATA/missing_data/
dataset_class: dataset.TUDatasetInterfaceMissingData
dataset_name: toluene
data_splits_file: DATA_SPLITS/CHEMICAL/toluene/toluene_outer1_inner1.splits

# Hardware
device:  cuda
max_cpus:  8
max_gpus: 1
gpus_per_task:  1


# Data Loading
dataset_getter: pydgn.data.provider.DataProvider
data_loader:
  class_name: torch_geometric.loader.DataLoader
  args:
    num_workers : 1
    pin_memory: True


# Reproducibility
seed: 42


# Experiment
result_folder: GSPN_RESULTS/UNSUPERVISED
exp_name: missing_data_baseline
experiment: pydgn.experiment.supervised_task.SupervisedTask
higher_results_are_better: False
evaluate_every: 1
final_training_runs: 1

grid:
  supervised_config:
    model: baseline_mask.GaussianImputation
    checkpoint: True
    shuffle: True
    batch_size: 200000
    epochs: 1


    # Optimizer
    optimizer:
      - class_name: pydgn.training.callback.optimizer.Optimizer
        args:
          optimizer_class_name: torch.optim.Adam
          lr:
            - 0.1
          accumulate_gradients: False  # do/do not accumulate gradient across mini-batches

    # Scheduler
    scheduler: null

    # Loss metric
    loss: metric.FakeLoss

    # Score metric
    scorer:
      - class_name: pydgn.training.callback.metric.MultiScore
        args:
          main_scorer: metric.GSPNNodeLogLikelihood
          mse: metric.MissingFeaturesMSE
          mae: metric.MissingFeaturesMAE

    # Readout (optional)
    readout: null

    # Training engine
    engine: pydgn.training.engine.TrainingEngine

    # Gradient clipper (optional)
    gradient_clipper: null

    # Early stopper
    early_stopper:
      - class_name: pydgn.training.callback.early_stopping.PatienceEarlyStopper
        args:
          patience: 1
          monitor: validation_main_score
          mode: min
          checkpoint: True

    # Plotter of metrics
    plotter: pydgn.training.callback.plotter.Plotter
