train.optimizer.lr:
  type: float
  lower: 0.01
  upper: 1.0
  log: True
model.num_of_trees:
  type: int
  lower: 3
  upper: 5
vertical.algo:
  type: cate
  choices: ['lr', 'xgb']
feat_engr.type:
  type: cate
  choices: ['', 'min_max_norm', 'instance_norm', 'standardization', 'log_transform', 'uniform_binning', 'quantile_binning', 'correlation_filter', 'variance_filter', 'iv_filter']
condition1:
  type: equal
  child: model.num_of_trees
  parent: vertical.algo
  value: 'xgb'
condition2:
  type: equal
  child: train.optimizer.lr
  parent: vertical.algo
  value: 'lr'