# Telegram Bot
# telegram_config_file: telegram_config.yml

# Dataset and Splits
data_root: DATA
dataset_class: dataset.OGBGDatasetInterface
dataset_name:  ogbg-molpcba
data_splits_file:  DATA_SPLITS/CHEMICAL/ogbg_molpcba/ogbg_molpcba_outer1_inner1.splits


# Hardware
device:  cuda
max_cpus:  64
max_gpus: 3
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: unsupervised_dgi_embedding_generation_categorical
experiment: unsupervised_embedding_generation.EmbeddingTask
higher_results_are_better: False  # not used here - only embedding generation
evaluate_every: 1
final_training_runs: 1

grid:
  unsupervised_config:
    model: baselines.DGI
    checkpoint: True
    shuffle: True
    batch_size: 512
    epochs: 100


    # Model specific arguments #

    embeddings_folder: 'UNSUP_DGI_EMBEDDINGS_BASIC/'

    num_layers:
      - 2
      - 3
      - 5

    dim_embedding:
      - 32
      - 128
      - 256

    num_hidden_neurons: 0  # not used

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

    # Optimizer
    optimizer:
      - class_name: pydgn.training.callback.optimizer.Optimizer
        args:
          optimizer_class_name: torch.optim.Adam
          lr:
            - 0.1
            - 0.01
          accumulate_gradients: False  # do/do not accumulate gradient across mini-batches

    # Scheduler
    scheduler: null

    # Loss metric
    loss: metric.DGILoss

    # Score metric
    scorer: metric.DGILoss

    # 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: 50
          monitor: validation_main_score
          mode: min
          checkpoint: True

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