# Dataset and Splits
data_root: DATA/missing_data/
dataset_class: dataset.TUDatasetInterfaceMissingData
dataset_name: benzene
data_splits_file: DATA_SPLITS/CHEMICAL/benzene/benzene_outer1_inner1.splits


# Hardware
device:  cuda
max_cpus:  32
max_gpus: 3
gpus_per_task:  0.5


# 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_gaussian
experiment: pydgn.experiment.supervised_task.SupervisedTask
higher_results_are_better: False
evaluate_every: 1
final_training_runs: 3

grid:
  supervised_config:
    model: model.GSPN
    checkpoint: True
    shuffle: True
    batch_size: 32
    epochs: 20


    # Model specific arguments #

    embeddings_folder: 'UNSUP_GSPN_EMBEDDINGS_BASIC/'

    convolution_class: model.GSPNBaseConv
    emission_class: model.GSPNGaussianEmission

    num_layers:
      - 1
    avg_parameters_across_layers:
      - False
    num_mixtures:
      - 40
      - 5
      - 15
      - 20

    init_max_variance: 30

    num_hidden_neurons: 0

    init_kmeans: True
    add_self_loops: False

    # ------------------------ #

    # Optimizer
    optimizer:
      - class_name: pydgn.training.callback.optimizer.Optimizer
        args:
          optimizer_class_name: torch.optim.Adam
          lr:
            - 0.1
            - 0.001
          accumulate_gradients: False  # do/do not accumulate gradient across mini-batches

    # Scheduler
    scheduler: null

    # Loss metric
    loss: metric.GSPNNodeLogLikelihood

    # Score metric
    scorer:
      - class_name: pydgn.training.callback.metric.MultiScore
        args:
          main_scorer: metric.GSPNNodeLogLikelihood

    # 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: 10
          monitor: validation_main_score
          mode: min
          checkpoint: True

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