program: metalearn/train_caml.py
method: random
metric:
  goal: maximize
  name: "best_Mean Val rate @ 0.995_value"

parameters:
  num_covariates_layer:
    values: [2,4,6]

  num_treatment_layer:
    values: [2,4,6]

  dropout:
    values: [0, 0.1]

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

  dim_hidden_treatment:
    values: [128, 256]

  dim_hidden_covariates:
    values: [128, 256]

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

  sin_weight_decay:
    values: [0.005]

  caml_k:
    values: [1,10,50]

  independence_regularisation_coeff:
    values: [0.0,0.01,0.1,1.0]

  batch_norm:
    values: ["false","true"]

  deterministic_sample:
    values: ["false"]

  all_tasks_baseline:
    values: ["true"]

  task_sample_weighting:
    values: ["enable"]

  all_tasks_baseline_eval_prob:
    max: 0.5
    min: 0.05

  graphite_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]

  meta_batch_size:
    values: [1, 4]

  batch_size:
    values: [512]

