program: metalearn/train_caml.py
method: random
metric:
  goal: maximize
  name: "best_Mean Val rate @ 0.998_value"

parameters:
  meta_batch_size:
    values: [1]

  num_como_layers:
    values: [2,4,6]

  num_covariates_layer:
    values: [2,4,6]

  num_propensity_layers:
    values: [2,4,6]

  num_treatment_layer:
    values: [2,4,6]

  pro_dropout:
    values: [0, 0.1]

  l1_reg:
    values: [0, 1e-7, 5e-7]

  dim_output:
    values: [128, 256]

  dim_hidden_treatment:
    values: [128, 256]

  dim_hidden_covariates:
    values: [128, 256]

  dim_hidden_como:
    values: [128, 256]

  dim_hidden_propensity:
    values: [16, 32, 64, 128]

  learning_rate:
    values: [0.003, 0.001, 0.0003, 0.0001]

  lr:
    values: [0.003, 0.001, 0.0003, 0.0001]

  pro_lr:
    values: [0.003, 0.001, 0.0003, 0.0001]

  sin_weight_decay:
    values: [0.0, 0.005]

  pro_weight_decay:
    values: [0.0, 0.005]

  gnn_weight_decay:
    values: [0.0, 0.005]

  caml_k:
    values: [1,10,50]

  deterministic_sample:
    values: ["false"]

  all_tasks_baseline:
    values: ["false"]

  task_sample_weighting:
    values: ["enable"]

  all_tasks_baseline_eval_prob:
    max: 0.5
    min: 0.05

  sin_learner:
    values: ["true"]

  task_embedding_config:
    values: ["early_concat"]

  use_task_embeddings:
    values: ["true"]

  step:
    values: ["y0"]

  n_iterations:
    values: [10000]

  val_interval:
    values: [50]

  sin_learner_stage1_loc:
    values: ['<local/path/removed/for/anonymity>/model_500.pt']

