config.yaml

# This is project name
project name: 'd-gnn-auto-pipeline'
wandb_entity: ''

# Input file (This variable contains the actual data entry that will have columns described in the README.md
data_file: 'data/classification_toy.csv'

# Structures location (the directory where all the structures are stored)
structure_location: ''

# Data file

# Problem (Options: regression, classification). classification variable accomodates multi-bin classification as well as binary.
problem_type: classification

#Node features. Options are: kidera or OHE
node_features: 'Kidera'

# Model configuration and Sweeping
parameters:
  # Notes: - All available gpus (torch.cuda.device_count()) will be used.
  Layers:
    distribution: constant 
    value: "H-100-100-100"

  # Format is super important here. The Adjacency type is seperated from the cutoff threshold by '_'. eg..  if you want an alpha shape 5 then use 'DT_5.0'
  Adjacency:
    distribution: categorical
    values:
    - 'DT_5.0'

  lr:
    distribution: constant
    value: 0.0001

  # Seeds shouldn't exceed 5 digits
  seed:
    values: [336,13,42]

  train_batchsize:
    distribution: constant
    value: 64
  
  test_batchsize:
    distribution: constant
    value: 32

  # Early stopping  patience is  10% of the epoch.
  epochs:
    distribution: constant
    value: 500
  
  logger:
    distribution: constant
    value: "wandb"