Iterative and Adaptive Sampling Methods in Semiconductor Testing

In this repository we provide the code used to evaluate the experiments using the optimization benchmark functions.

Dependencies

• pandas 1.4.3
• scikit-learn 1.1.1
• numpy 1.23.1
• tensorflow 2.9.1
• autotune (learner, benchmark-functions)

Installation

Install dependencies with pip install -r requirements.txt

Usage

Adjust settings in main.py before running tests:

• choose sampler type, has to be in ['random', 'qbc_hetero', 'qbc_homo', 'cross_val', 'lola_voronoi', 'variance']
  sampler = 'random'
• choose benchmark function: ackley, rosenbrock
  benchmark_fct = 'ackley'
• choose dimension of benchmark function
  dim = 8
• choose initial seed
  seed = 12345678
• choose initial dataset size
  iniial_datasize = 50
• choose poolsize of potential new datapoints per iteration
  poolsize = 500
• choose how frequently stopping criteria will be evaluated
  eval_cycle = 1
• choose target mse for stopping criterion
  target_mse = 0.1
• choose tartet r2-score for stopping criterion
  target_r2 = 0.9
• choose maximum datasize
  max_datasize = 150
• choose heterogeneous committee members, have to be in ['knn_sk', 'decision_tree_sk', 'gaussian_process_sk', 'svm_sk', 'mlp_sk']
  committee_hetero = ['knn_sk', 'decision_tree_sk, 'svm_sk']
• choose model type of homogeneous committee members, has to be in ['knn_sk', 'decision_tree_sk', 'gaussian_process_sk', 'svm_sk', 'mlp_sk']
  committee_homo = 'knn_sk'
• choose size of homogeneous committee
  committee_homo_size = 3
• choose error model used by cross-validation based sampler, has to be in ['knn_sk', 'decision_tree_sk', 'gaussian_process_sk', 'svm_sk', 'mlp_sk']
  cross_val_error_model = 'knn_sk'
• choose distance constraints that will be used for qbc and cross-validation, has to be in ['None', 'avg_min_dist', 'max_min_dist']

distance_constraints = {
    'qbc_homo' : None,
    'qbc_hetero' : None,
    'cross_val' : None,
}