SYMBOL INDEX (2302 symbols across 310 files)

FILE: demo/analysis.py
  function run_analysis (line 9) | def run_analysis(Exper_folder:Path, tasks, methods, seeds, args):

FILE: demo/causal_analysis.py
  function run_analysis (line 9) | def run_analysis(Exper_folder:Path, tasks, methods, seeds, args):

FILE: demo/comparison/analysis_hypervolume.py
  function load_and_prepare_data (line 28) | def load_and_prepare_data(file_path, objectives):
  function load_data (line 55) | def load_data(workload, algorithm, seed):
  function collect_all_data (line 63) | def collect_all_data(workload):
  function calculate_mean_hypervolume (line 75) | def calculate_mean_hypervolume(
  function calculate_hypervolumes (line 111) | def calculate_hypervolumes(
  function analyze_and_compare_algorithms (line 139) | def analyze_and_compare_algorithms(workload_results):
  function matrix_to_latex (line 182) | def matrix_to_latex(analysis_results, caption):
  function load_workloads (line 277) | def load_workloads():

FILE: demo/comparison/analysis_plot.py
  function load_and_prepare_data (line 37) | def load_and_prepare_data(file_path):
  function load_data (line 65) | def load_data(workload, algorithm, seed):
  function collect_all_data (line 73) | def collect_all_data(workload):
  function dynamic_plot (line 85) | def dynamic_plot(workload, algorithm, seed):
  function save_individual_frames (line 233) | def save_individual_frames(workload, algorithm, seed):
  function load_workloads (line 264) | def load_workloads():
  function plot_pareto_front (line 311) | def plot_pareto_front(workload):
  function plot_all (line 345) | def plot_all(workload, algorithm=""):
  function plot_all_2d (line 377) | def plot_all_2d(workload, algorithm=""):

FILE: demo/comparison/experiment_gcc.py
  function execute_tasks (line 25) | def execute_tasks(tasks, args):
  function split_into_segments (line 33) | def split_into_segments(lst, n):
  function get_workloads (line 39) | def get_workloads(workloads, split_index, total_splits=10):
  function load_features (line 47) | def load_features():
  function configure_experiment (line 53) | def configure_experiment(workload, features, seed, optimizer_name, exp_p...
  function main (line 77) | def main(optimizers = [], repeat=5, budget=500, init_number=21):
  function main_debug (line 122) | def main_debug(repeat=1, budget=20, init_number=10):

FILE: demo/comparison/experiment_llvm.py
  function execute_tasks (line 25) | def execute_tasks(tasks, args):
  function split_into_segments (line 33) | def split_into_segments(lst, n):
  function get_workloads (line 39) | def get_workloads(workloads, split_index, total_splits=10):
  function load_features (line 47) | def load_features(file_path):
  function configure_experiment (line 52) | def configure_experiment(workload, features, seed, optimizer_name, exp_p...
  function main (line 75) | def main(optimizers = [], repeat=5, budget=500, init_number=21):
  function main_debug (line 103) | def main_debug(repeat=1, budget=20, init_number=10):

FILE: demo/comparison/plot.py
  function create_plots (line 17) | def create_plots(data, file_name, format="pdf"):
  function load_data (line 59) | def load_data(workload, algorithm, seed):
  function load_and_prepare_data (line 67) | def load_and_prepare_data(file_path):
  function get_data_ranges (line 91) | def get_data_ranges(data):
  function rescale_data (line 97) | def rescale_data(data, original_range, target_range):
  function map_data_to_mysql_ranges (line 104) | def map_data_to_mysql_ranges(data, gcc_llvm_range, mysql_range):
  function invert_mapping (line 113) | def invert_mapping(value, min_val, max_val):

FILE: demo/comparison/plot_samples_dbms.py
  function load_and_prepare_data (line 27) | def load_and_prepare_data(file_path):
  function load_data (line 58) | def load_data(workload):
  function plot_pareto_front (line 64) | def plot_pareto_front(workload):
  function plot_all (line 84) | def plot_all(workload):

FILE: demo/comparison/start_server.py
  function generate_index_html (line 14) | def generate_index_html():
  function start_http_server (line 31) | def start_http_server():

FILE: demo/correlation_analysis.py
  function run_analysis (line 9) | def run_analysis(Exper_folder:Path, tasks, methods, seeds, args):

FILE: demo/experiment_lsh_validity.py
  function generate_random_string (line 50) | def generate_random_string(length):
  function generate_dataset_config (line 55) | def generate_dataset_config():
  function create_experiment_datasets (line 98) | def create_experiment_datasets(dm, num_datasets):
  function get_shingles (line 104) | def get_shingles(text, ngram=5):
  function cal_jacard_similarity (line 108) | def cal_jacard_similarity(cfg1, cfg2):
  function validity_experiment (line 118) | def validity_experiment(n_tables, num_replicates=3, jacard_lower_bound =...

FILE: demo/experiments.py
  function run_experiments (line 21) | def run_experiments(tasks, args):

FILE: demo/importances/cal_relationship.py
  function load_and_prepare_data (line 31) | def load_and_prepare_data(file_path, objectives):
  function cal_dcor (line 56) | def cal_dcor(df, objectives):
  function cal_spearman_corr (line 69) | def cal_spearman_corr(df, objectives):
  function cal_pearson_corr (line 86) | def cal_pearson_corr(df, objectives):
  function generate_grid_plot (line 103) | def generate_grid_plot(dcor_values_dict):

FILE: demo/importances/draw_obj_heatmap.py
  function generate_grid_plot_combine (line 22) | def generate_grid_plot_combine(dcor_values_dicts):
  function generate_grid_plot (line 49) | def generate_grid_plot(dcor_values_dict, file_name):

FILE: demo/importances/get_feature_importances.py
  function load_and_prepare_data (line 29) | def load_and_prepare_data(file_path, objectives):
  function calculate_feature_importances (line 54) | def calculate_feature_importances(df, objective):
  function aggregate_importances (line 71) | def aggregate_importances(importances_list):
  function combine_and_rank_features (line 80) | def combine_and_rank_features(importances_list):
  function get_top_combined_features (line 94) | def get_top_combined_features(common_features, combined_ranked, total_fe...
  function find_common_features (line 113) | def find_common_features(importances_list):
  function train_and_evaluate_model (line 139) | def train_and_evaluate_model(
  function get_workloads_improved (line 171) | def get_workloads_improved():
  function get_features_for_exp (line 250) | def get_features_for_exp(workloads, repetitions=5):

FILE: demo/random_sample_compiler.py
  function run_experiments (line 24) | def run_experiments(tasks, args):
  function split_into_segments (line 32) | def split_into_segments(lst, n):

FILE: demo/random_sample_dbms.py
  function run_experiments (line 23) | def run_experiments(tasks, args):
  function split_into_segments (line 31) | def split_into_segments(lst, n):

FILE: demo/sampling/random_sample_compiler.py
  function run_experiments (line 25) | def run_experiments(tasks, args):
  function split_into_segments (line 33) | def split_into_segments(lst, n):

FILE: demo/sampling/random_sample_dbms.py
  function run_experiments (line 27) | def run_experiments(tasks, args):
  function split_into_segments (line 35) | def split_into_segments(lst, n):

FILE: setup.py
  function get_extra_requirements (line 6) | def get_extra_requirements(folder='./extra_requirements'):
  function build_docker_image (line 26) | def build_docker_image(image_name, docker_dir):
  function init_absolut_docker (line 37) | def init_absolut_docker():

FILE: tests/EXP_NSGA2.py
  class HPOProblem (line 7) | class HPOProblem(Problem):
    method __init__ (line 8) | def __init__(self, task_name, budget_type, budget, seed, workload):
    method _evaluate (line 17) | def _evaluate(self, X, out, *args, **kwargs):

FILE: tests/EXP_NSGA2_restart.py
  class HPOProblem (line 14) | class HPOProblem(Problem):
    method __init__ (line 15) | def __init__(self, task_name, budget_type, budget, seed, workload, dat...
    method _evaluate (line 58) | def _evaluate(self, X, out, *args, **kwargs):

FILE: tests/EXP_bohb.py
  function objective (line 10) | def objective(config, budget):
  function get_configspace (line 15) | def get_configspace():

FILE: tests/EXP_grid.py
  function sobol_search (line 5) | def sobol_search(n_samples, task_name, budget_type, budget, seed, worklo...

FILE: tests/EXP_hebo.py
  function objective (line 10) | def objective(config):
  function get_design_space (line 15) | def get_design_space():

FILE: tests/EXP_hyperopt.py
  function objective (line 9) | def objective(params):
  function get_hyperopt_space (line 16) | def get_hyperopt_space():

FILE: tests/EXP_random.py
  function random_search (line 5) | def random_search(n_trials, task_name, budget_type, budget, seed, worklo...

FILE: tests/EXP_smac.py
  function objective (line 12) | def objective(configuration, seed: int = 0):
  function get_configspace (line 19) | def get_configspace():

FILE: tests/EXP_tpe.py
  class formal_obj (line 14) | class formal_obj(ObjectiveFunc):
    method __init__ (line 15) | def __init__(self, f):
    method __call__ (line 18) | def __call__(self, eval_config: Dict[str, Any]) -> Tuple[Dict[str, flo...
  function get_configspace (line 26) | def get_configspace():

FILE: tests/data_analysis.py
  function load_data (line 12) | def load_data(data_folder):
  function get_non_dominated_solutions (line 36) | def get_non_dominated_solutions(data):
  function plot_non_dominated_solutions (line 43) | def plot_non_dominated_solutions(ax, solutions, label, color):
  function compare_nsga2_results_all (line 49) | def compare_nsga2_results_all(res):
  function compare_nsga2_results (line 86) | def compare_nsga2_results(res):
  function calculate_variable_importance (line 116) | def calculate_variable_importance(res):
  function plot_variable_importance (line 150) | def plot_variable_importance(importance):
  function visualize_data_with_metrics (line 172) | def visualize_data_with_metrics(data, metric_name, output_file):

FILE: transopt/ResultAnalysis/AnalysisBase.py
  class Result (line 18) | class Result():
    method __init__ (line 22) | def __init__(self):
  class AnalysisBase (line 29) | class AnalysisBase(abc.ABC, metaclass=abc.ABCMeta):
    method __init__ (line 30) | def __init__(self, exper_folder, methods, seeds, tasks, start = 0, end...
    method read_data_from_kb (line 41) | def read_data_from_kb(self):
    method save_results_to_json (line 74) | def save_results_to_json(self, file_path):
    method load_results_from_json (line 78) | def load_results_from_json(self, file_path):
    method get_results_by_order (line 87) | def get_results_by_order(self, order=None):
    method assign_colors_to_methods (line 133) | def assign_colors_to_methods(self):
    method get_color_for_method (line 172) | def get_color_for_method(self, method:Union[List,str]):
    method get_methods (line 198) | def get_methods(self):
    method get_task_names (line 207) | def get_task_names(self):
    method get_seeds (line 216) | def get_seeds(self):

FILE: transopt/ResultAnalysis/AnalysisPipeline.py
  function analysis_pipeline (line 11) | def analysis_pipeline(Exper_folder, tasks, methods, seeds, args):

FILE: transopt/ResultAnalysis/AnalysisReport.py
  function pdf_to_png (line 6) | def pdf_to_png(pictures_path):
  function create_details_report (line 20) | def create_details_report(details_folders, save_path):
  function create_table_report (line 116) | def create_table_report(save_path):
  function create_report (line 239) | def create_report(save_path):

FILE: transopt/ResultAnalysis/CasualAnalysis.py
  function casual_analysis (line 8) | def casual_analysis(Exper_folder, tasks, methods, seeds, args):

FILE: transopt/ResultAnalysis/CompileTex.py
  function compile_tex (line 6) | def compile_tex(tex_path, output_folder):

FILE: transopt/ResultAnalysis/CorrelationAnalysis.py
  function correlation_analysis (line 9) | def correlation_analysis(Exper_folder, tasks, methods, seeds, args):
  function MutualInformation (line 19) | def MutualInformation(ab:AnalysisBase, dataset_name, method, seed):

FILE: transopt/ResultAnalysis/MakeGif.py
  function make_gif (line 4) | def make_gif(folder_path):

FILE: transopt/ResultAnalysis/PFAnalysis.py
  function parego_analysis (line 8) | def parego_analysis(Exper_folder, tasks, methods, seeds, args):

FILE: transopt/ResultAnalysis/PlotAnalysis.py
  function plot_register (line 20) | def plot_register(name):
  function plot_sk (line 31) | def plot_sk(ab:AnalysisBase, save_path:Path):
  function convergence_rate (line 106) | def convergence_rate(ab:AnalysisBase, save_path:Path, **kwargs):
  function save_traj_data (line 203) | def save_traj_data(ab, save_path):
  function traj2latex (line 259) | def traj2latex(ab: AnalysisBase, save_path: Path):
  function plot_violin (line 361) | def plot_violin(ab:AnalysisBase, save_path, **kwargs):
  function plot_box (line 435) | def plot_box(ab:AnalysisBase, save_path, **kwargs):
  function dbscan_analysis (line 516) | def dbscan_analysis(ab: AnalysisBase, save_path, **kwargs):
  function plot_heatmap (line 640) | def plot_heatmap(ab:AnalysisBase, save_path, **kwargs):

FILE: transopt/ResultAnalysis/TableAnalysis.py
  function Tabel_register (line 13) | def Tabel_register(name):
  function record_mean_std (line 22) | def record_mean_std(ab:AnalysisBase, save_path, **kwargs):
  function record_convergence_rate (line 75) | def record_convergence_rate(ab:AnalysisBase, save_path, **kwargs):

FILE: transopt/ResultAnalysis/TableToLatex.py
  function matrix_to_latex (line 5) | def matrix_to_latex(Data: Dict, col_names, row_names, caption, oder="min"):

FILE: transopt/ResultAnalysis/TrackOptimization.py
  function track_register (line 9) | def track_register(name):

FILE: transopt/agent/app.py
  function create_app (line 13) | def create_app():
  function main (line 276) | def main():

FILE: transopt/agent/chat/openai_chat.py
  function dict_to_string (line 19) | def dict_to_string(dictionary):
  class Message (line 23) | class Message(BaseModel):
    method get_content_string (line 33) | def get_content_string(self) -> str:
    method to_dict (line 41) | def to_dict(self) -> Dict[str, Any]:
    method log (line 48) | def log(self, level: Optional[str] = None):
  class OpenAIChat (line 61) | class OpenAIChat:
    method __init__ (line 64) | def __init__(
    method _get_prompt (line 85) | def _get_prompt(self):
    method client (line 93) | def client(self):
    method invoke_model (line 101) | def invoke_model(self, messages: List[Dict]) -> ChatCompletion:
    method get_response (line 335) | def get_response(self, user_input) -> str:
    method call_manager_function (line 355) | def call_manager_function(self, function_name, **kwargs):
    method _initialize_modules (line 375) | def _initialize_modules(self):
    method get_all_problems (line 384) | def get_all_problems(self):
    method get_optimization_techniques (line 419) | def get_optimization_techniques(self):
    method set_optimization_problem (line 469) | def set_optimization_problem(self, problem_name, workload, budget):
    method set_space_refiner (line 482) | def set_space_refiner(self, refiner):
    method set_sampler (line 486) | def set_sampler(self, Sampler):
    method set_pretrain (line 491) | def set_pretrain(self, Pretrain):
    method set_model (line 495) | def set_model(self, Model):
    method set_normalizer (line 499) | def set_normalizer(self, Normalizer):
    method set_metadata (line 503) | def set_metadata(self, module_name, dataset_name):
    method run_optimization (line 507) | def run_optimization(self):
    method show_configuration (line 552) | def show_configuration(self):
    method install_package (line 556) | def install_package(self, package_name: str) -> str:

FILE: transopt/agent/chat/yaml_generator.py
  function get_prompt (line 9) | def get_prompt(file_name: str) -> str:
  function parse_response (line 19) | def parse_response(response: str) -> Dict[str, Any]:
  function main (line 29) | def main():

FILE: transopt/agent/config.py
  class Config (line 2) | class Config:
  class RunningConfig (line 10) | class RunningConfig:
    method __new__ (line 14) | def __new__(cls, *args, **kwargs):
    method __init__ (line 21) | def __init__(self):
    method set_tasks (line 27) | def set_tasks(self, tasks):
    method set_optimizer (line 30) | def set_optimizer(self, optimizer):
    method set_metadata (line 36) | def set_metadata(self, metadata):

FILE: transopt/agent/registry.py
  class Registry (line 1) | class Registry:
    method __init__ (line 2) | def __init__(self):
    method register (line 5) | def register(self, name=None, cls=None, **kwargs):
    method get (line 20) | def get(self, name):
    method list_names (line 23) | def list_names(self):
    method __getitem__ (line 26) | def __getitem__(self, item):
    method __contains__ (line 29) | def __contains__(self, item):

FILE: transopt/agent/run_cli.py
  function set_task (line 11) | def set_task(services, args):
  function set_optimizer (line 24) | def set_optimizer(services, args):

FILE: transopt/agent/services.py
  class Services (line 20) | class Services:
    method __init__ (line 21) | def __init__(self, task_queue, result_queue, lock):
    method chat (line 40) | def chat(self, user_input):
    method _initialize_modules (line 44) | def _initialize_modules(self):
    method get_modules (line 60) | def get_modules(self):
    method get_comparision_modules (line 143) | def get_comparision_modules(self):
    method search_dataset (line 184) | def search_dataset(self, search_method, dataset_name, dataset_info):
    method convert_metadata (line 203) | def convert_metadata(self, conditions):
    method comparision_search (line 232) | def comparision_search(self, conditions):
    method set_metadata (line 256) | def set_metadata(self, dataset_names):
    method receive_tasks (line 260) | def receive_tasks(self, tasks_info):
    method receive_optimizer (line 280) | def receive_optimizer(self, optimizer_info):
    method receive_metadata (line 285) | def receive_metadata(self, metadata_info):
    method get_all_datasets (line 291) | def get_all_datasets(self):
    method get_experiment_datasets (line 295) | def get_experiment_datasets(self):
    method construct_dataset_info (line 299) | def construct_dataset_info(self, task_set, running_config, seed):
    method get_metadata (line 345) | def get_metadata(self, module_name):
    method save_data (line 356) | def save_data(self, dataset_name, parameters, observations, iteration):
    method remove_dataset (line 363) | def remove_dataset(self, dataset_name):
    method run_optimize (line 372) | def run_optimize(self, seeds):
    method _run_optimize_process (line 383) | def _run_optimize_process(self, seed):
    method terminate_task (line 455) | def terminate_task(self, pid):
    method update_process_info (line 474) | def update_process_info(self, pid, updates):
    method get_all_process_info (line 480) | def get_all_process_info(self):
    method get_box_plot_data (line 483) | def get_box_plot_data(self, task_names):
    method get_report_charts (line 502) | def get_report_charts(self, task_name):
    method get_report_traj (line 523) | def get_report_traj(self, task_name):
    method construct_footprint_data (line 538) | def construct_footprint_data(self, name, var_data, ranges, initial_num...
    method construct_statistic_trajectory_data (line 547) | def construct_statistic_trajectory_data(self, task_names):
    method construct_trajectory_data (line 577) | def construct_trajectory_data(self, name, obj_data, obj_type="minimize"):
    method construct_importance_data (line 611) | def construct_importance_data(self, name, var_data, obj_data, variables):
    method get_configuration (line 615) | def get_configuration(self):

FILE: transopt/agent/testood.py
  function plot_acc_scatter (line 33) | def plot_acc_scatter(train_acc, test_acc):
  function setUp (line 59) | def setUp():
  function list_pth_files (line 63) | def list_pth_files(directory):

FILE: transopt/analysis/compile_tex.py
  function compile_tex (line 6) | def compile_tex(tex_path, output_folder):

FILE: transopt/analysis/mds.py
  class FootPrint (line 7) | class FootPrint:
    method __init__ (line 8) | def __init__(self, X, range):
    method calculate_distances (line 19) | def calculate_distances(self):
    method init_distances (line 37) | def init_distances(self, config_ids, exclude_configs=False):
    method update_distances (line 54) | def update_distances(self, X, distances, config, rejection_threshold=0...
    method get_random_boundary_points (line 88) | def get_random_boundary_points(self, num_samples):
    method get_mds (line 99) | def get_mds(self):
    method plot_embedding (line 107) | def plot_embedding(self):

FILE: transopt/analysis/parameter_network.py
  function calculate_importances (line 11) | def calculate_importances(X, y):
  function calculate_interaction (line 23) | def calculate_interaction(X, y):
  function plot_network (line 57) | def plot_network(X, y, nodes):

FILE: transopt/analysis/table.py
  class Result (line 12) | class Result():
    method __init__ (line 13) | def __init__(self):
  function get_results (line 20) | def get_results(task_names):
  function record_mean_std (line 62) | def record_mean_std(task_names, save_path, **kwargs):

FILE: transopt/analysis/table_to_latex.py
  function matrix_to_latex (line 5) | def matrix_to_latex(Data: Dict, col_names, row_names, caption, oder="min"):

FILE: transopt/benchmark/CSSTuning/Compiler.py
  class GCCTuning (line 12) | class GCCTuning(NonTabularProblem):
    method __init__ (line 19) | def __init__(self, task_name, budget_type, budget, seed, workload, kno...
    method get_configuration_space (line 36) | def get_configuration_space(self):
    method get_fidelity_space (line 49) | def get_fidelity_space(self):
    method get_objectives (line 52) | def get_objectives(self) -> dict:
    method get_problem_type (line 66) | def get_problem_type(self):
    method objective_function (line 69) | def objective_function(self, configuration: dict, fidelity = None, see...
  class LLVMTuning (line 79) | class LLVMTuning(NonTabularProblem):
    method __init__ (line 86) | def __init__(self, task_name, budget_type, budget, seed, workload, kno...
    method get_configuration_space (line 103) | def get_configuration_space(self):
    method get_fidelity_space (line 116) | def get_fidelity_space(self):
    method get_objectives (line 119) | def get_objectives(self) -> dict:
    method get_problem_type (line 126) | def get_problem_type(self):
    method objective_function (line 129) | def objective_function(self, configuration: dict, fidelity = None, see...

FILE: transopt/benchmark/CSSTuning/DBMS.py
  class MySQLTuning (line 12) | class MySQLTuning(NonTabularProblem):
    method __init__ (line 19) | def __init__(self, task_name, budget_type, budget, seed, workload, kno...
    method get_configuration_space (line 30) | def get_configuration_space(self):
    method get_fidelity_space (line 47) | def get_fidelity_space(self):
    method get_objectives (line 50) | def get_objectives(self) -> dict:
    method get_problem_type (line 56) | def get_problem_type(self):
    method objective_function (line 59) | def objective_function(self, configuration: dict, fidelity = None, see...

FILE: transopt/benchmark/HBOROB/algorithms.py
  function get_algorithm_class (line 23) | def get_algorithm_class(algorithm_name):
  class Algorithm (line 29) | class Algorithm(torch.nn.Module):
    method __init__ (line 36) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method update (line 40) | def update(self, minibatches, unlabeled=None):
    method predict (line 50) | def predict(self, x):
  class MLP (line 54) | class MLP(nn.Module):
    method __init__ (line 56) | def __init__(self, n_inputs, n_outputs, hparams):
    method forward (line 66) | def forward(self, x):
  class ResNet (line 78) | class ResNet(torch.nn.Module):
    method __init__ (line 80) | def __init__(self, input_shape, hparams):
    method forward (line 111) | def forward(self, x):
    method train (line 115) | def train(self, mode=True):
    method freeze_bn (line 122) | def freeze_bn(self):
  class ERM (line 129) | class ERM(Algorithm):
    method __init__ (line 134) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method update (line 150) | def update(self, minibatches, unlabeled=None):
    method predict (line 161) | def predict(self, x):

FILE: transopt/benchmark/HPO/HPO.py
  class HPO_base (line 28) | class HPO_base(NonTabularProblem):
    method __init__ (line 49) | def __init__(
    method create_train_loaders (line 141) | def create_train_loaders(self, batch_size):
    method create_test_loaders (line 158) | def create_test_loaders(self, batch_size):
    method save_checkpoint (line 179) | def save_checkpoint(self, filename):
    method get_configuration_space (line 188) | def get_configuration_space(
    method get_fidelity_space (line 207) | def get_fidelity_space(
    method train (line 215) | def train(self, configuration: dict):
    method save_epoch_results (line 278) | def save_epoch_results(self, results):
    method evaluate_loader (line 283) | def evaluate_loader(self, loader):
    method get_score (line 295) | def get_score(self, configuration: dict):
    method objective_function (line 327) | def objective_function(
    method get_objectives (line 361) | def get_objectives(self) -> Dict:
    method get_problem_type (line 364) | def get_problem_type(self):
  class HPO_ERM (line 369) | class HPO_ERM(HPO_base):
    method __init__ (line 370) | def __init__(
  function test_all_combinations (line 393) | def test_all_combinations():

FILE: transopt/benchmark/HPO/HPOAdaBoost.py
  class XGBoostBenchmark (line 28) | class XGBoostBenchmark(NonTabularProblem):
    method __init__ (line 35) | def __init__(
    method get_data (line 97) | def get_data(self) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.nda...
    method shuffle_data (line 108) | def shuffle_data(self, seed=None):
    method objective_function (line 115) | def objective_function(
    method objective_function_test (line 177) | def objective_function_test(self, configuration: Union[Dict],
    method get_configuration_space (line 235) | def get_configuration_space(self, seed: Union[int, None] = None):
    method get_fidelity_space (line 264) | def get_fidelity_space(self, seed: Union[int, None] = None):
    method get_meta_information (line 291) | def get_meta_information(self) -> Dict:
    method _get_pipeline (line 312) | def _get_pipeline(self, max_depth: int, eta: float, min_child_weight: ...
    method get_objectives (line 380) | def get_objectives(self) -> Dict:
    method get_problem_type (line 383) | def get_problem_type(self):

FILE: transopt/benchmark/HPO/HPOSVM.py
  class SupportVectorMachine (line 24) | class SupportVectorMachine(NonTabularProblem):
    method __init__ (line 41) | def __init__(
    method get_data (line 91) | def get_data(self) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.nda...
    method shuffle_data (line 102) | def shuffle_data(self, seed=None):
    method objective_function (line 109) | def objective_function(
    method objective_function_test (line 178) | def objective_function_test(self, configuration: Union[Dict],
    method get_pipeline (line 253) | def get_pipeline(self, C: float, gamma: float) -> pipeline.Pipeline:
    method get_configuration_space (line 270) | def get_configuration_space(self, seed: Union[int, None] = None):
    method get_fidelity_space (line 293) | def get_fidelity_space(self, seed: Union[int, None] = None):
    method get_meta_information (line 323) | def get_meta_information(self):
    method get_objectives (line 349) | def get_objectives(self) -> Dict:
    method get_problem_type (line 352) | def get_problem_type(self):

FILE: transopt/benchmark/HPO/HPOXGBoost.py
  class XGBoostBenchmark (line 28) | class XGBoostBenchmark(NonTabularProblem):
    method __init__ (line 35) | def __init__(
    method get_data (line 97) | def get_data(self) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.nda...
    method shuffle_data (line 108) | def shuffle_data(self, seed=None):
    method objective_function (line 115) | def objective_function(
    method objective_function_test (line 177) | def objective_function_test(self, configuration: Union[Dict],
    method get_configuration_space (line 235) | def get_configuration_space(self, seed: Union[int, None] = None):
    method get_fidelity_space (line 264) | def get_fidelity_space(self, seed: Union[int, None] = None):
    method get_meta_information (line 291) | def get_meta_information(self) -> Dict:
    method _get_pipeline (line 312) | def _get_pipeline(self, max_depth: int, eta: float, min_child_weight: ...
    method get_objectives (line 380) | def get_objectives(self) -> Dict:
    method get_problem_type (line 383) | def get_problem_type(self):

FILE: transopt/benchmark/HPO/algorithms.py
  function get_algorithm_class (line 28) | def get_algorithm_class(algorithm_name):
  class Algorithm (line 34) | class Algorithm(torch.nn.Module):
    method __init__ (line 41) | def __init__(self, input_shape, num_classes, architecture, model_size,...
    method update (line 51) | def update(self, minibatches, unlabeled=None):
    method predict (line 61) | def predict(self, x):
  class ERM (line 64) | class ERM(Algorithm):
    method __init__ (line 69) | def __init__(self, input_shape, num_classes, architecture, model_size,...
    method update (line 87) | def update(self, minibatches, unlabeled=None):
    method predict (line 114) | def predict(self, x):
  class GLMNet (line 117) | class GLMNet(Algorithm):
    method __init__ (line 122) | def __init__(self, input_shape, num_classes, architecture, model_size,...
    method update (line 146) | def update(self, minibatches, unlabeled=None):
    method predict (line 170) | def predict(self, x):
  class BayesianNN (line 174) | class BayesianNN(Algorithm):
    method __init__ (line 179) | def __init__(self, input_shape, num_classes, hparams):
    method model (line 204) | def model(self, x, y=None):
    method guide (line 217) | def guide(self, x, y=None):
    method update (line 224) | def update(self, minibatches, unlabeled=None):
    method predict (line 235) | def predict(self, x):

FILE: transopt/benchmark/HPO/augmentation.py
  function mixup_data (line 7) | def mixup_data(x, y, alpha=0.3, device='cpu'):
  function mixup_criterion (line 23) | def mixup_criterion(criterion, pred, y_a, y_b, lam):
  class Cutout (line 31) | class Cutout(object):
    method __init__ (line 38) | def __init__(self, n_holes = None, length = None):
    method __call__ (line 48) | def __call__(self, img):
  class ImageNetPolicy (line 80) | class ImageNetPolicy(object):
    method __init__ (line 93) | def __init__(self, fillcolor=(128, 128, 128)):
    method __call__ (line 126) | def __call__(self, img):
    method __repr__ (line 130) | def __repr__(self):
  class CIFAR10Policy (line 134) | class CIFAR10Policy(object):
    method __init__ (line 147) | def __init__(self, fillcolor=(128, 128, 128)):
    method __call__ (line 180) | def __call__(self, img):
    method __repr__ (line 184) | def __repr__(self):
  class CIFAR10PolicyPhotometric (line 188) | class CIFAR10PolicyPhotometric(object):
    method __init__ (line 201) | def __init__(self, fillcolor=(128, 128, 128)):
    method __call__ (line 228) | def __call__(self, img):
    method __repr__ (line 232) | def __repr__(self):
  class CIFAR10PolicyGeometric (line 236) | class CIFAR10PolicyGeometric(object):
    method __init__ (line 249) | def __init__(self, fillcolor=(128, 128, 128)):
    method __call__ (line 258) | def __call__(self, img):
    method __repr__ (line 262) | def __repr__(self):
  class SVHNPolicy (line 266) | class SVHNPolicy(object):
    method __init__ (line 279) | def __init__(self, fillcolor=(128, 128, 128)):
    method __call__ (line 312) | def __call__(self, img):
    method __repr__ (line 316) | def __repr__(self):
  class SubPolicy (line 320) | class SubPolicy(object):
    method __init__ (line 321) | def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, mag...
    method __call__ (line 363) | def __call__(self, img):

FILE: transopt/benchmark/HPO/datasets.py
  function data_transform (line 26) | def data_transform(dataset_name, augmentation_name=None):
  function get_dataset_class (line 69) | def get_dataset_class(dataset_name):
  function num_environments (line 75) | def num_environments(dataset_name):
  class Dataset (line 78) | class Dataset:
    method __getitem__ (line 85) | def __getitem__(self, index):
    method __len__ (line 88) | def __len__(self):
  class RobCifar10 (line 91) | class RobCifar10(Dataset):
    method __init__ (line 92) | def __init__(self, root=None, augment=False):
    method get_available_test_set_names (line 166) | def get_available_test_set_names(self):
    method get_test_set (line 173) | def get_test_set(self, name):
    method get_all_test_sets (line 180) | def get_all_test_sets(self):
  class RobCifar100 (line 186) | class RobCifar100(Dataset):
    method __init__ (line 187) | def __init__(self, root, augment=False):
    method get_transform (line 226) | def get_transform(self, augment):
    method get_test_set (line 242) | def get_test_set(self, name):
    method get_all_test_sets (line 249) | def get_all_test_sets(self):
  class RobImageNet (line 256) | class RobImageNet(Dataset):
    method __init__ (line 257) | def __init__(self, root, augment=False):
    method get_transform (line 281) | def get_transform(self, augment):
    method get_test_set (line 301) | def get_test_set(self, name):
    method get_all_test_sets (line 308) | def get_all_test_sets(self):
  function test_dataset (line 314) | def test_dataset(dataset_name='cifar10', num_samples=5):
  function visualize_dataset_tsne (line 364) | def visualize_dataset_tsne(dataset_name='cifar10', n_samples=1000, perpl...

FILE: transopt/benchmark/HPO/fast_data_loader.py
  class _InfiniteSampler (line 5) | class _InfiniteSampler(torch.utils.data.Sampler):
    method __init__ (line 7) | def __init__(self, sampler):
    method __iter__ (line 10) | def __iter__(self):
  class InfiniteDataLoader (line 15) | class InfiniteDataLoader:
    method __init__ (line 16) | def __init__(self, dataset, batch_size, num_workers):
    method __iter__ (line 34) | def __iter__(self):
    method __len__ (line 38) | def __len__(self):
  class FastDataLoader (line 41) | class FastDataLoader:
    method __init__ (line 44) | def __init__(self, dataset, batch_size, num_workers):
    method __iter__ (line 61) | def __iter__(self):
    method __len__ (line 65) | def __len__(self):

FILE: transopt/benchmark/HPO/hparams_registry.py
  function get_hparams (line 5) | def get_hparams(algorithm, dataset, random_seed, model_size=None, archit...
  function default_hparams (line 34) | def default_hparams(algorithm, dataset, model_size='small', architecture...
  function random_hparams (line 37) | def random_hparams(algorithm, dataset, seed, model_size='small', archite...
  function get_hparam_space (line 40) | def get_hparam_space(algorithm, model_size=None, architecture='resnet'):
  function test_hparam_registry (line 77) | def test_hparam_registry():

FILE: transopt/benchmark/HPO/image_options.py
  class ShearX (line 5) | class ShearX(object):
    method __init__ (line 6) | def __init__(self, fillcolor=(128, 128, 128)):
    method __call__ (line 9) | def __call__(self, x, magnitude):
  class ShearY (line 15) | class ShearY(object):
    method __init__ (line 16) | def __init__(self, fillcolor=(128, 128, 128)):
    method __call__ (line 19) | def __call__(self, x, magnitude):
  class TranslateX (line 25) | class TranslateX(object):
    method __init__ (line 26) | def __init__(self, fillcolor=(128, 128, 128)):
    method __call__ (line 29) | def __call__(self, x, magnitude):
  class TranslateY (line 35) | class TranslateY(object):
    method __init__ (line 36) | def __init__(self, fillcolor=(128, 128, 128)):
    method __call__ (line 39) | def __call__(self, x, magnitude):
  class Rotate (line 45) | class Rotate(object):
    method __call__ (line 48) | def __call__(self, x, magnitude):
  class Color (line 53) | class Color(object):
    method __call__ (line 54) | def __call__(self, x, magnitude):
  class Posterize (line 58) | class Posterize(object):
    method __call__ (line 59) | def __call__(self, x, magnitude):
  class Solarize (line 63) | class Solarize(object):
    method __call__ (line 64) | def __call__(self, x, magnitude):
  class Contrast (line 68) | class Contrast(object):
    method __call__ (line 69) | def __call__(self, x, magnitude):
  class Sharpness (line 73) | class Sharpness(object):
    method __call__ (line 74) | def __call__(self, x, magnitude):
  class Brightness (line 78) | class Brightness(object):
    method __call__ (line 79) | def __call__(self, x, magnitude):
  class AutoContrast (line 83) | class AutoContrast(object):
    method __call__ (line 84) | def __call__(self, x, magnitude):
  class Equalize (line 88) | class Equalize(object):
    method __call__ (line 89) | def __call__(self, x, magnitude):
  class Invert (line 93) | class Invert(object):
    method __call__ (line 94) | def __call__(self, x, magnitude):

FILE: transopt/benchmark/HPO/misc.py
  class _SplitDataset (line 17) | class _SplitDataset(torch.utils.data.Dataset):
    method __init__ (line 19) | def __init__(self, underlying_dataset, keys):
    method __getitem__ (line 23) | def __getitem__(self, key):
    method __len__ (line 25) | def __len__(self):
  function split_dataset (line 28) | def split_dataset(dataset, n, seed=0):
  function accuracy (line 42) | def accuracy(network, loader, device):
  function print_row (line 64) | def print_row(row, colwidth=10, latex=False):
  class LossPlotter (line 80) | class LossPlotter:
    method __init__ (line 81) | def __init__(self):
    method update (line 91) | def update(self, classification_loss, reconstruction_loss):
    method show (line 119) | def show(self):

FILE: transopt/benchmark/HPO/networks.py
  function Featurizer (line 21) | def Featurizer(input_shape, architecture, model_size, hparams):
  class Identity (line 37) | class Identity(nn.Module):
    method __init__ (line 39) | def __init__(self):
    method forward (line 42) | def forward(self, x):
  class MLP (line 46) | class MLP(nn.Module):
    method __init__ (line 48) | def __init__(self, n_inputs, n_outputs, hparams):
    method forward (line 58) | def forward(self, x):
  class ResNet (line 69) | class ResNet(torch.nn.Module):
    method __init__ (line 71) | def __init__(self, input_shape, model_size, hparams):
    method forward (line 108) | def forward(self, x):
    method train (line 112) | def train(self, mode=True):
    method freeze_bn (line 119) | def freeze_bn(self):
  function conv3x3 (line 125) | def conv3x3(in_planes, out_planes, stride=1):
  function conv_init (line 135) | def conv_init(m):
  class wide_basic (line 145) | class wide_basic(nn.Module):
    method __init__ (line 146) | def __init__(self, in_planes, planes, dropout_rate, stride=1):
    method forward (line 161) | def forward(self, x):
  class WideResNet (line 169) | class WideResNet(nn.Module):
    method __init__ (line 171) | def __init__(self, input_shape, model_size, hparams):
    method _wide_layer (line 201) | def _wide_layer(self, block, planes, num_blocks, dropout, stride):
    method forward (line 211) | def forward(self, x):
  class DenseNet (line 222) | class DenseNet(nn.Module):
    method __init__ (line 224) | def __init__(self, input_shape, model_size, hparams):
    method forward (line 249) | def forward(self, x):
  class ht_CNN (line 256) | class ht_CNN(nn.Module):
    method __init__ (line 265) | def __init__(self, input_shape):
    method forward (line 279) | def forward(self, x):
  class CNN (line 300) | class CNN(nn.Module):
    method __init__ (line 304) | def __init__(self, input_shape, hparams):
    method forward (line 316) | def forward(self, x):
  class ContextNet (line 330) | class ContextNet(nn.Module):
    method __init__ (line 331) | def __init__(self, input_shape):
    method forward (line 346) | def forward(self, x):
  class AlexNet (line 349) | class AlexNet(nn.Module):
    method __init__ (line 351) | def __init__(self, input_shape, hparams):
    method forward (line 380) | def forward(self, x):
  function Classifier (line 388) | def Classifier(in_features, out_features, dropout=0.5, is_nonlinear=False):

FILE: transopt/benchmark/HPO/visualization.py
  function get_cifar10_data (line 8) | def get_cifar10_data(transform):

FILE: transopt/benchmark/HPO/wide_resnet.py
  function conv3x3 (line 17) | def conv3x3(in_planes, out_planes, stride=1):
  function conv_init (line 27) | def conv_init(m):
  class wide_basic (line 37) | class wide_basic(nn.Module):
    method __init__ (line 38) | def __init__(self, in_planes, planes, dropout_rate, stride=1):
    method forward (line 55) | def forward(self, x):
  class Wide_ResNet (line 63) | class Wide_ResNet(nn.Module):
    method __init__ (line 65) | def __init__(self, input_shape, depth, widen_factor, dropout_rate):
    method _wide_layer (line 87) | def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride):
    method forward (line 97) | def forward(self, x):

FILE: transopt/benchmark/HPOB/HpobBench.py
  class HPOb (line 12) | class HPOb():
    method __init__ (line 13) | def __init__(self, search_space_id, data_set_id, xdim, path='./Benchma...
    method transfer (line 39) | def transfer(self, X):
    method normalize (line 42) | def normalize(self, X):
    method data_num (line 45) | def data_num(self):
    method get_var (line 48) | def get_var(self, indexs):
    method get_idx (line 52) | def get_idx(self, vars):
    method get_all_unobserved_var (line 62) | def get_all_unobserved_var(self):
    method get_all_unobserved_idxs (line 65) | def get_all_unobserved_idxs(self):
    method f (line 68) | def f(self,X, indexs):
  function calculate_correlation (line 98) | def calculate_correlation(x1, y1, X2, Y2):

FILE: transopt/benchmark/HPOOOD/algorithms.py
  function get_algorithm_class (line 56) | def get_algorithm_class(algorithm_name):
  class Algorithm (line 62) | class Algorithm(torch.nn.Module):
    method __init__ (line 69) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method update (line 73) | def update(self, minibatches, unlabeled=None):
    method predict (line 83) | def predict(self, x):
  class ERM (line 86) | class ERM(Algorithm):
    method __init__ (line 91) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method update (line 107) | def update(self, minibatches, unlabeled=None):
    method predict (line 118) | def predict(self, x):
  class Fish (line 122) | class Fish(Algorithm):
    method __init__ (line 128) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method create_clone (line 142) | def create_clone(self, device):
    method fish (line 153) | def fish(self, meta_weights, inner_weights, lr_meta):
    method update (line 159) | def update(self, minibatches, unlabeled=None):
    method predict (line 178) | def predict(self, x):
  class ARM (line 182) | class ARM(ERM):
    method __init__ (line 184) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method predict (line 192) | def predict(self, x):
  class AbstractDANN (line 207) | class AbstractDANN(Algorithm):
    method __init__ (line 210) | def __init__(self, input_shape, num_classes, num_domains,
    method update (line 246) | def update(self, minibatches, unlabeled=None):
    method predict (line 294) | def predict(self, x):
  class DANN (line 297) | class DANN(AbstractDANN):
    method __init__ (line 299) | def __init__(self, input_shape, num_classes, num_domains, hparams):
  class CDANN (line 304) | class CDANN(AbstractDANN):
    method __init__ (line 306) | def __init__(self, input_shape, num_classes, num_domains, hparams):
  class IRM (line 311) | class IRM(ERM):
    method __init__ (line 314) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method _irm_penalty (line 320) | def _irm_penalty(logits, y):
    method update (line 330) | def update(self, minibatches, unlabeled=None):
  class RDM (line 366) | class RDM(ERM):
    method __init__ (line 368) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method my_cdist (line 372) | def my_cdist(self, x1, x2):
    method gaussian_kernel (line 381) | def gaussian_kernel(self, x, y, gamma=[0.0001, 0.001, 0.01, 0.1, 1, 10...
    method mmd (line 391) | def mmd(self, x, y):
    method _moment_penalty (line 398) | def _moment_penalty(p_mean, q_mean, p_var, q_var):
    method _kl_penalty (line 402) | def _kl_penalty(p_mean, q_mean, p_var, q_var):
    method _js_penalty (line 405) | def _js_penalty(self, p_mean, q_mean, p_var, q_var):
    method update (line 411) | def update(self, minibatches, unlabeled=None, held_out_minibatches=None):
  class VREx (line 475) | class VREx(ERM):
    method __init__ (line 477) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method update (line 482) | def update(self, minibatches, unlabeled=None):
  class Mixup (line 521) | class Mixup(ERM):
    method __init__ (line 527) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method update (line 531) | def update(self, minibatches, unlabeled=None):
  class GroupDRO (line 553) | class GroupDRO(ERM):
    method __init__ (line 558) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method update (line 563) | def update(self, minibatches, unlabeled=None):
  class MLDG (line 587) | class MLDG(ERM):
    method __init__ (line 594) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method update (line 599) | def update(self, minibatches, unlabeled=None):
  class AbstractMMD (line 703) | class AbstractMMD(ERM):
    method __init__ (line 708) | def __init__(self, input_shape, num_classes, num_domains, hparams, gau...
    method my_cdist (line 716) | def my_cdist(self, x1, x2):
    method gaussian_kernel (line 724) | def gaussian_kernel(self, x, y, gamma=[0.001, 0.01, 0.1, 1, 10, 100,
    method mmd (line 734) | def mmd(self, x, y):
    method update (line 753) | def update(self, minibatches, unlabeled=None):
  class MMD (line 781) | class MMD(AbstractMMD):
    method __init__ (line 786) | def __init__(self, input_shape, num_classes, num_domains, hparams):
  class CORAL (line 791) | class CORAL(AbstractMMD):
    method __init__ (line 796) | def __init__(self, input_shape, num_classes, num_domains, hparams):
  class MTL (line 801) | class MTL(Algorithm):
    method __init__ (line 808) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method update (line 829) | def update(self, minibatches, unlabeled=None):
    method update_embeddings_ (line 840) | def update_embeddings_(self, features, env=None):
    method predict (line 851) | def predict(self, x, env=None):
  class SagNet (line 856) | class SagNet(Algorithm):
    method __init__ (line 862) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method forward_c (line 914) | def forward_c(self, x):
    method forward_s (line 918) | def forward_s(self, x):
    method randomize (line 922) | def randomize(self, x, what="style", eps=1e-5):
    method update (line 946) | def update(self, minibatches, unlabeled=None):
    method predict (line 974) | def predict(self, x):
  class RSC (line 978) | class RSC(ERM):
    method __init__ (line 979) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method update (line 986) | def update(self, minibatches, unlabeled=None):
  class SD (line 1035) | class SD(ERM):
    method __init__ (line 1040) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method update (line 1045) | def update(self, minibatches, unlabeled=None):
  class ANDMask (line 1060) | class ANDMask(ERM):
    method __init__ (line 1066) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method update (line 1071) | def update(self, minibatches, unlabeled=None):
    method mask_grads (line 1090) | def mask_grads(self, tau, gradients, params):
  class IGA (line 1105) | class IGA(ERM):
    method __init__ (line 1111) | def __init__(self, in_features, num_classes, num_domains, hparams):
    method update (line 1114) | def update(self, minibatches, unlabeled=None):
  class SelfReg (line 1147) | class SelfReg(ERM):
    method __init__ (line 1148) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method update (line 1167) | def update(self, minibatches, unlabeled=None):
  class SANDMask (line 1236) | class SANDMask(ERM):
    method __init__ (line 1242) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method update (line 1257) | def update(self, minibatches, unlabeled=None):
    method mask_grads (line 1278) | def mask_grads(self, gradients, params):
  class Fishr (line 1301) | class Fishr(Algorithm):
    method __init__ (line 1304) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method _init_optimizer (line 1327) | def _init_optimizer(self):
    method update (line 1334) | def update(self, minibatches, unlabeled=None):
    method compute_fishr_penalty (line 1362) | def compute_fishr_penalty(self, all_logits, all_y, len_minibatches):
    method _get_grads (line 1367) | def _get_grads(self, logits, y):
    method _get_grads_var_per_domain (line 1384) | def _get_grads_var_per_domain(self, dict_grads, len_minibatches):
    method _compute_distance_grads_var (line 1404) | def _compute_distance_grads_var(self, grads_var_per_domain):
    method predict (line 1428) | def predict(self, x):
  class TRM (line 1431) | class TRM(Algorithm):
    method __init__ (line 1437) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method neum (line 1463) | def neum(v, model, batch):
    method update (line 1498) | def update(self, minibatches, unlabeled=None):
    method predict (line 1590) | def predict(self, x):
    method train (line 1593) | def train(self):
    method eval (line 1596) | def eval(self):
  class IB_ERM (line 1599) | class IB_ERM(ERM):
    method __init__ (line 1602) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method update (line 1612) | def update(self, minibatches, unlabeled=None):
  class IB_IRM (line 1656) | class IB_IRM(ERM):
    method __init__ (line 1659) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method _irm_penalty (line 1670) | def _irm_penalty(logits, y):
    method update (line 1680) | def update(self, minibatches, unlabeled=None):
  class AbstractCAD (line 1733) | class AbstractCAD(Algorithm):
    method __init__ (line 1738) | def __init__(self, input_shape, num_classes, num_domains,
    method bn_loss (line 1775) | def bn_loss(self, z, y, dom_labels):
    method update (line 1856) | def update(self, minibatches, unlabeled=None):
    method predict (line 1877) | def predict(self, x):
  class CAD (line 1881) | class CAD(AbstractCAD):
    method __init__ (line 1889) | def __init__(self, input_shape, num_classes, num_domains, hparams):
  class CondCAD (line 1893) | class CondCAD(AbstractCAD):
    method __init__ (line 1900) | def __init__(self, input_shape, num_classes, num_domains, hparams):
  class Transfer (line 1904) | class Transfer(Algorithm):
    method __init__ (line 1907) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method loss_gap (line 1935) | def loss_gap(self, minibatches, device):
    method update (line 1947) | def update(self, minibatches, unlabeled=None):
    method update_second (line 1971) | def update_second(self, minibatches, unlabeled=None):
    method predict (line 1998) | def predict(self, x):
  class AbstractCausIRL (line 2002) | class AbstractCausIRL(ERM):
    method __init__ (line 2004) | def __init__(self, input_shape, num_classes, num_domains, hparams, gau...
    method my_cdist (line 2012) | def my_cdist(self, x1, x2):
    method gaussian_kernel (line 2020) | def gaussian_kernel(self, x, y, gamma=[0.001, 0.01, 0.1, 1, 10, 100,
    method mmd (line 2030) | def mmd(self, x, y):
    method update (line 2049) | def update(self, minibatches, unlabeled=None):
  class CausIRL_MMD (line 2086) | class CausIRL_MMD(AbstractCausIRL):
    method __init__ (line 2088) | def __init__(self, input_shape, num_classes, num_domains, hparams):
  class CausIRL_CORAL (line 2093) | class CausIRL_CORAL(AbstractCausIRL):
    method __init__ (line 2095) | def __init__(self, input_shape, num_classes, num_domains, hparams):
  class EQRM (line 2100) | class EQRM(ERM):
    method __init__ (line 2105) | def __init__(self, input_shape, num_classes, num_domains, hparams, dis...
    method risk (line 2114) | def risk(self, x, y):
    method update (line 2117) | def update(self, minibatches, unlabeled=None):
  class ADRMX (line 2145) | class ADRMX(Algorithm):
    method __init__ (line 2147) | def __init__(self, input_shape, num_classes, num_domains, hparams):
    method update (line 2197) | def update(self, minibatches, unlabeled=None):
    method predict (line 2282) | def predict(self, x):

FILE: transopt/benchmark/HPOOOD/collect_results.py
  function find_jsonl_files (line 16) | def find_jsonl_files(directory):
  function find_dirs (line 29) | def find_dirs(directory):
  function remove_empty_directories (line 37) | def remove_empty_directories(directory):
  function plot_bins (line 51) | def plot_bins(test_data, val_data, save_file_name):
  function plot_traj (line 63) | def plot_traj(test_data, val_data, save_file_name):
  function plot_scatter (line 75) | def plot_scatter(x, y, values, save_file_name):
  function print_table (line 88) | def print_table(table, header_text, row_labels, col_labels, colwidth=10,

FILE: transopt/benchmark/HPOOOD/download.py
  function stage_path (line 22) | def stage_path(data_dir, name):
  function download_and_extract (line 31) | def download_and_extract(url, dst, remove=True):
  function download_vlcs (line 99) | def download_vlcs(data_dir):
  function download_mnist (line 109) | def download_mnist(data_dir):
  function download_pacs (line 117) | def download_pacs(data_dir):
  function download_office_home (line 130) | def download_office_home(data_dir):
  function download_domain_net (line 143) | def download_domain_net(data_dir):
  function download_terra_incognita (line 169) | def download_terra_incognita(data_dir):
  function download_sviro (line 268) | def download_sviro(data_dir):
  function download_spawrious (line 281) | def download_spawrious(data_dir, remove=True):

FILE: transopt/benchmark/HPOOOD/fast_data_loader.py
  class _InfiniteSampler (line 5) | class _InfiniteSampler(torch.utils.data.Sampler):
    method __init__ (line 7) | def __init__(self, sampler):
    method __iter__ (line 10) | def __iter__(self):
  class InfiniteDataLoader (line 15) | class InfiniteDataLoader:
    method __init__ (line 16) | def __init__(self, dataset, weights, batch_size, num_workers):
    method __iter__ (line 41) | def __iter__(self):
    method __len__ (line 45) | def __len__(self):
  class FastDataLoader (line 48) | class FastDataLoader:
    method __init__ (line 51) | def __init__(self, dataset, batch_size, num_workers):
    method __iter__ (line 68) | def __iter__(self):
    method __len__ (line 72) | def __len__(self):

FILE: transopt/benchmark/HPOOOD/hparams_registry.py
  function _define_hparam (line 5) | def _define_hparam(hparams, hparam_name, default_val, random_val_fn):
  function _hparams (line 9) | def _hparams(algorithm, dataset, random_seed):
  function default_hparams (line 224) | def default_hparams(algorithm, dataset):
  function random_hparams (line 228) | def random_hparams(algorithm, dataset, seed):
  function get_hparams (line 231) | def get_hparams(algorithm, dataset):

FILE: transopt/benchmark/HPOOOD/hpoood.py
  function make_record (line 38) | def make_record(step, hparams_seed, envs):
  class HPOOOD_base (line 53) | class HPOOOD_base(NonTabularProblem):
    method __init__ (line 118) | def __init__(
    method save_checkpoint (line 259) | def save_checkpoint(self, filename):
    method get_configuration_space (line 271) | def get_configuration_space(
    method get_fidelity_space (line 293) | def get_fidelity_space(
    method train (line 312) | def train(self, configuration: dict):
    method get_score (line 383) | def get_score(self, configuration: dict):
    method objective_function (line 406) | def objective_function(
    method get_objectives (line 435) | def get_objectives(self) -> Dict:
    method get_problem_type (line 438) | def get_problem_type(self):
  class ERMOOD (line 444) | class ERMOOD(HPOOOD_base):
    method __init__ (line 445) | def __init__(
  class IRMOOD (line 451) | class IRMOOD(HPOOOD_base):
    method __init__ (line 452) | def __init__(
  class ARMOOD (line 458) | class ARMOOD(HPOOOD_base):
    method __init__ (line 459) | def __init__(
  class MixupOOD (line 465) | class MixupOOD(HPOOOD_base):
    method __init__ (line 466) | def __init__(
  class DANNOOD (line 472) | class DANNOOD(HPOOOD_base):
    method __init__ (line 473) | def __init__(

FILE: transopt/benchmark/HPOOOD/misc.py
  function distance (line 20) | def distance(h1, h2):
  function proj (line 28) | def proj(delta, adv_h, h):
  function l2_between_dicts (line 41) | def l2_between_dicts(dict_1, dict_2):
  class MovingAverage (line 50) | class MovingAverage:
    method __init__ (line 52) | def __init__(self, ema, oneminusema_correction=True):
    method update (line 58) | def update(self, dict_data):
  function make_weights_for_balanced_classes (line 81) | def make_weights_for_balanced_classes(dataset):
  function pdb (line 101) | def pdb():
  function seed_hash (line 107) | def seed_hash(*args):
  function print_separator (line 114) | def print_separator():
  function print_row (line 117) | def print_row(row, colwidth=10, latex=False):
  class _SplitDataset (line 131) | class _SplitDataset(torch.utils.data.Dataset):
    method __init__ (line 133) | def __init__(self, underlying_dataset, keys):
    method __getitem__ (line 137) | def __getitem__(self, key):
    method __len__ (line 139) | def __len__(self):
  function split_dataset (line 142) | def split_dataset(dataset, n, seed=0):
  function random_pairs_of_minibatches (line 156) | def random_pairs_of_minibatches(minibatches):
  function split_meta_train_test (line 172) | def split_meta_train_test(minibatches, num_meta_test=1):
  function accuracy (line 188) | def accuracy(network, loader, weights, device):
  class Tee (line 214) | class Tee:
    method __init__ (line 215) | def __init__(self, fname, mode="a"):
    method write (line 219) | def write(self, message):
    method flush (line 224) | def flush(self):
  class ParamDict (line 228) | class ParamDict(OrderedDict):
    method __init__ (line 233) | def __init__(self, *args, **kwargs):
    method _prototype (line 236) | def _prototype(self, other, op):
    method __add__ (line 244) | def __add__(self, other):
    method __rmul__ (line 247) | def __rmul__(self, other):
    method __neg__ (line 252) | def __neg__(self):
    method __rsub__ (line 255) | def __rsub__(self, other):
    method __truediv__ (line 261) | def __truediv__(self, other):
  class Kernel (line 270) | class Kernel(torch.nn.Module):
    method __init__ (line 273) | def __init__(self, bw=None):
    method _diffs (line 277) | def _diffs(self, test_Xs, train_Xs):
    method forward (line 283) | def forward(self, test_Xs, train_Xs):
    method sample (line 286) | def sample(self, train_Xs):
  class GaussianKernel (line 290) | class GaussianKernel(Kernel):
    method forward (line 293) | def forward(self, test_Xs, train_Xs):
    method sample (line 307) | def sample(self, train_Xs):
    method cdf (line 312) | def cdf(self, test_Xs, train_Xs):
  function estimate_bandwidth (line 319) | def estimate_bandwidth(x, method="silverman"):
  class KernelDensityEstimator (line 338) | class KernelDensityEstimator(torch.nn.Module):
    method __init__ (line 341) | def __init__(self, train_Xs, kernel='gaussian', bw_select='Gauss-optim...
    method device (line 362) | def device(self):
    method forward (line 366) | def forward(self, x):
    method sample (line 369) | def sample(self, n_samples):
    method cdf (line 373) | def cdf(self, x):
  class Distribution1D (line 384) | class Distribution1D:
    method __init__ (line 385) | def __init__(self, dist_function=None):
    method parameters (line 394) | def parameters(self):
    method create_dist (line 397) | def create_dist(self):
    method estimate_parameters (line 403) | def estimate_parameters(self, x):
    method log_prob (line 406) | def log_prob(self, x):
    method cdf (line 409) | def cdf(self, x):
    method icdf (line 412) | def icdf(self, q):
    method sample (line 415) | def sample(self, n=1):
    method sample_n (line 421) | def sample_n(self, n=10):
  function continuous_bisect_fun_left (line 425) | def continuous_bisect_fun_left(f, v, lo, hi, n_steps=32):
  class Normal (line 437) | class Normal(Distribution1D):
    method __init__ (line 438) | def __init__(self, location=0, scale=1):
    method parameters (line 444) | def parameters(self):
    method estimate_parameters (line 447) | def estimate_parameters(self, x):
    method icdf (line 453) | def icdf(self, q):
  class Nonparametric (line 466) | class Nonparametric(Distribution1D):
    method __init__ (line 467) | def __init__(self, use_kde=True, bw_select='Gauss-optimal'):
    method parameters (line 474) | def parameters(self):
    method estimate_parameters (line 477) | def estimate_parameters(self, x):
    method icdf (line 484) | def icdf(self, q):
  class SupConLossLambda (line 509) | class SupConLossLambda(torch.nn.Module):
    method __init__ (line 510) | def __init__(self, lamda: float=0.5, temperature: float=0.07):
    method forward (line 515) | def forward(self, features: torch.Tensor, labels: torch.Tensor, domain...

FILE: transopt/benchmark/HPOOOD/networks.py
  function remove_batch_norm_from_resnet (line 12) | def remove_batch_norm_from_resnet(model):
  class Identity (line 36) | class Identity(nn.Module):
    method __init__ (line 38) | def __init__(self):
    method forward (line 41) | def forward(self, x):
  class MLP (line 45) | class MLP(nn.Module):
    method __init__ (line 47) | def __init__(self, n_inputs, n_outputs, hparams):
    method forward (line 57) | def forward(self, x):
  class ResNet (line 69) | class ResNet(torch.nn.Module):
    method __init__ (line 71) | def __init__(self, input_shape, hparams):
    method forward (line 102) | def forward(self, x):
    method train (line 106) | def train(self, mode=True):
    method freeze_bn (line 113) | def freeze_bn(self):
  class MNIST_CNN (line 119) | class MNIST_CNN(nn.Module):
    method __init__ (line 128) | def __init__(self, input_shape):
    method forward (line 142) | def forward(self, x):
  class ContextNet (line 164) | class ContextNet(nn.Module):
    method __init__ (line 165) | def __init__(self, input_shape):
    method forward (line 180) | def forward(self, x):
  function Featurizer (line 184) | def Featurizer(input_shape, hparams):
  function Classifier (line 198) | def Classifier(in_features, out_features, is_nonlinear=False):
  class WholeFish (line 210) | class WholeFish(nn.Module):
    method __init__ (line 211) | def __init__(self, input_shape, num_classes, hparams, weights=None):
    method reset_weights (line 224) | def reset_weights(self, weights):
    method forward (line 227) | def forward(self, x):

FILE: transopt/benchmark/HPOOOD/ooddatasets.py
  function get_dataset_class (line 19) | def get_dataset_class(dataset_name):
  function num_environments (line 26) | def num_environments(dataset_name):
  class MultipleDomainDataset (line 30) | class MultipleDomainDataset:
    method __getitem__ (line 37) | def __getitem__(self, index):
    method __len__ (line 40) | def __len__(self):
  class Debug (line 44) | class Debug(MultipleDomainDataset):
    method __init__ (line 45) | def __init__(self, root, test_envs, hparams):
  class Debug28 (line 58) | class Debug28(Debug):
  class Debug224 (line 62) | class Debug224(Debug):
  class MultipleEnvironmentMNIST (line 67) | class MultipleEnvironmentMNIST(MultipleDomainDataset):
    method __init__ (line 68) | def __init__(self, root, environments, dataset_transform, input_shape,
  class ColoredMNIST (line 99) | class ColoredMNIST(MultipleEnvironmentMNIST):
    method __init__ (line 102) | def __init__(self, root, test_envs, hparams):
    method color_dataset (line 109) | def color_dataset(self, images, labels, environment):
    method torch_bernoulli_ (line 132) | def torch_bernoulli_(self, p, size):
    method torch_xor_ (line 135) | def torch_xor_(self, a, b):
  class RotatedMNIST (line 139) | class RotatedMNIST(MultipleEnvironmentMNIST):
    method __init__ (line 142) | def __init__(self, root, test_envs, hparams):
    method rotate_dataset (line 146) | def rotate_dataset(self, images, labels, angle):
  class MultipleEnvironmentImageFolder (line 162) | class MultipleEnvironmentImageFolder(MultipleDomainDataset):
    method __init__ (line 163) | def __init__(self, root, test_envs, augment, hparams):
  class VLCS (line 203) | class VLCS(MultipleEnvironmentImageFolder):
    method __init__ (line 206) | def __init__(self, root, test_envs, hparams):
  class PACS (line 210) | class PACS(MultipleEnvironmentImageFolder):
    method __init__ (line 213) | def __init__(self, root, test_envs, hparams):
  class DomainNet (line 217) | class DomainNet(MultipleEnvironmentImageFolder):
    method __init__ (line 220) | def __init__(self, root, test_envs, hparams):
  class OfficeHome (line 224) | class OfficeHome(MultipleEnvironmentImageFolder):
    method __init__ (line 227) | def __init__(self, root, test_envs, hparams):
  class TerraIncognita (line 231) | class TerraIncognita(MultipleEnvironmentImageFolder):
    method __init__ (line 234) | def __init__(self, root, test_envs, hparams):
  class SVIRO (line 238) | class SVIRO(MultipleEnvironmentImageFolder):
    method __init__ (line 241) | def __init__(self, root, test_envs, hparams):
  class WILDSEnvironment (line 246) | class WILDSEnvironment:
    method __init__ (line 247) | def __init__(
    method __getitem__ (line 264) | def __getitem__(self, i):
    method __len__ (line 274) | def __len__(self):
  class WILDSDataset (line 278) | class WILDSDataset(MultipleDomainDataset):
    method __init__ (line 280) | def __init__(self, dataset, metadata_name, test_envs, augment, hparams):
    method metadata_values (line 318) | def metadata_values(self, wilds_dataset, metadata_name):
  class WILDSCamelyon (line 324) | class WILDSCamelyon(WILDSDataset):
    method __init__ (line 327) | def __init__(self, root, test_envs, hparams):
  class WILDSFMoW (line 333) | class WILDSFMoW(WILDSDataset):
    method __init__ (line 336) | def __init__(self, root, test_envs, hparams):
  class CustomImageFolder (line 343) | class CustomImageFolder(Dataset):
    method __init__ (line 347) | def __init__(self, folder_path, class_index, limit=None, transform=None):
    method __len__ (line 355) | def __len__(self):
    method __getitem__ (line 358) | def __getitem__(self, index):
  class SpawriousBenchmark (line 368) | class SpawriousBenchmark(MultipleDomainDataset):
    method __init__ (line 374) | def __init__(self, train_combinations, test_combinations, root_dir, au...
    method _prepare_data_lists (line 380) | def _prepare_data_lists(self, train_combinations, test_combinations, r...
    method _create_data_list (line 405) | def _create_data_list(self, combinations, root_dir, transforms):
    method build_type1_combination (line 444) | def build_type1_combination(self,group,test,filler):
    method build_type2_combination (line 467) | def build_type2_combination(self,group,test):
  class SpawriousO2O_easy (line 487) | class SpawriousO2O_easy(SpawriousBenchmark):
    method __init__ (line 488) | def __init__(self, root_dir, test_envs, hparams):
  class SpawriousO2O_medium (line 495) | class SpawriousO2O_medium(SpawriousBenchmark):
    method __init__ (line 496) | def __init__(self, root_dir, test_envs, hparams):
  class SpawriousO2O_hard (line 503) | class SpawriousO2O_hard(SpawriousBenchmark):
    method __init__ (line 504) | def __init__(self, root_dir, test_envs, hparams):
  class SpawriousM2M_easy (line 511) | class SpawriousM2M_easy(SpawriousBenchmark):
    method __init__ (line 512) | def __init__(self, root_dir, test_envs, hparams):
  class SpawriousM2M_medium (line 518) | class SpawriousM2M_medium(SpawriousBenchmark):
    method __init__ (line 519) | def __init__(self, root_dir, test_envs, hparams):
  class SpawriousM2M_hard (line 525) | class SpawriousM2M_hard(SpawriousBenchmark):
    method __init__ (line 527) | def __init__(self, root_dir, test_envs, hparams):

FILE: transopt/benchmark/HPOOOD/wide_resnet.py
  function conv3x3 (line 17) | def conv3x3(in_planes, out_planes, stride=1):
  function conv_init (line 27) | def conv_init(m):
  class wide_basic (line 37) | class wide_basic(nn.Module):
    method __init__ (line 38) | def __init__(self, in_planes, planes, dropout_rate, stride=1):
    method forward (line 55) | def forward(self, x):
  class Wide_ResNet (line 63) | class Wide_ResNet(nn.Module):
    method __init__ (line 65) | def __init__(self, input_shape, depth, widen_factor, dropout_rate):
    method _wide_layer (line 87) | def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride):
    method forward (line 97) | def forward(self, x):

FILE: transopt/benchmark/RL/LunarlanderBenchmark.py
  function lunar_lander_simulation (line 20) | def lunar_lander_simulation(w, print_reward=False, seed=1, dimension=12):
  function heuristic_controller (line 47) | def heuristic_controller(s, w, is_continuous=True):
  function heuristic_controller5d (line 74) | def heuristic_controller5d(s, w, is_continuous=True):
  function heuristic_controller10d (line 166) | def heuristic_controller10d(s, w, is_continuous=True):
  function vanilla_heuristic (line 193) | def vanilla_heuristic(s, is_continuous=False):
  class LunarlanderBenchmark (line 227) | class LunarlanderBenchmark(NonTabularProblem):
    method __init__ (line 236) | def __init__(self, task_name, task_id, budget, seed, task_type="non-ta...
    method objective_function (line 242) | def objective_function(
    method get_configuration_space (line 254) | def get_configuration_space(
    method get_fidelity_space (line 281) | def get_fidelity_space(
    method get_meta_information (line 304) | def get_meta_information(self) -> Dict:

FILE: transopt/benchmark/instantiate_problems.py
  function InstantiateProblems (line 6) | def InstantiateProblems(

FILE: transopt/benchmark/problem_base/base.py
  class ProblemBase (line 16) | class ProblemBase(abc.ABC):
    method __init__ (line 17) | def __init__(self, seed: Union[int, np.random.RandomState, None] = Non...
    method f (line 45) | def f(self, configuration, fidelity=None, seed=None, **kwargs) -> Dict:
    method objective_function (line 53) | def objective_function(
    method get_configuration_space (line 66) | def get_configuration_space(self) -> SearchSpace:
    method check_validity (line 80) | def check_validity(self, configuration, fidelity):
    method __call__ (line 111) | def __call__(self, configuration: Dict, **kwargs) -> float:
    method get_fidelity_space (line 120) | def get_fidelity_space(self) -> FidelitySpace:
    method get_objectives (line 134) | def get_objectives(self) -> dict:
    method problem_type (line 150) | def problem_type(self):
    method num_objectives (line 154) | def num_objectives(self):
    method num_variables (line 158) | def num_variables(self):

FILE: transopt/benchmark/problem_base/non_tab_problem.py
  class NonTabularProblem (line 18) | class NonTabularProblem(ProblemBase):
    method __init__ (line 19) | def __init__(
    method get_budget_type (line 36) | def get_budget_type(self) -> str:
    method get_budget (line 47) | def get_budget(self) -> int:
    method get_name (line 58) | def get_name(self) -> str:
    method get_type (line 69) | def get_type(self) -> str:
    method get_input_dim (line 80) | def get_input_dim(self) -> int:
    method get_objective_num (line 91) | def get_objective_num(self) -> int:
    method lock (line 94) | def lock(self):
    method unlock (line 97) | def unlock(self):
    method get_lock_state (line 100) | def get_lock_state(self) -> bool:
    method workloads (line 105) | def workloads(self):
    method fidelity (line 110) | def fidelity(self):

FILE: transopt/benchmark/problem_base/tab_problem.py
  class TabularProblem (line 18) | class TabularProblem(ProblemBase):
    method __init__ (line 19) | def __init__(
    method f (line 135) | def f(
    method objective_function (line 148) | def objective_function(
    method sample_dataframe (line 177) | def sample_dataframe(key, df, p_remove=0.):
    method get_configuration_type (line 199) | def get_configuration_type(self):
    method get_configuration_space (line 205) | def get_configuration_space(
    method get_fidelity_space (line 240) | def get_fidelity_space(
    method get_meta_information (line 261) | def get_meta_information(self) -> Dict:
    method get_budget (line 264) | def get_budget(self) -> int:
    method get_name (line 275) | def get_name(self) -> str:
    method get_type (line 286) | def get_type(self) -> str:
    method get_input_dim (line 297) | def get_input_dim(self) -> int:
    method get_objective_num (line 308) | def get_objective_num(self) -> int:
    method lock (line 311) | def lock(self):
    method unlock (line 314) | def unlock(self):
    method get_lock_state (line 317) | def get_lock_state(self) -> bool:
    method get_dataset_size (line 321) | def get_dataset_size(self):
    method get_var_by_idx (line 324) | def get_var_by_idx(self, idx):
    method get_idx_by_var (line 327) | def get_idx_by_var(self, vectors):
    method get_unobserved_vars (line 330) | def get_unobserved_vars(self):
    method get_unobserved_idxs (line 333) | def get_unobserved_idxs(self):

FILE: transopt/benchmark/problem_base/transfer_problem.py
  class TransferProblem (line 13) | class TransferProblem:
    method __init__ (line 14) | def __init__(self, seed: Union[int, np.random.RandomState, None] = Non...
    method add_task_to_id (line 21) | def add_task_to_id(
    method add_task (line 35) | def add_task(
    method del_task_by_id (line 46) | def del_task_by_id(self, del_id, name):
    method get_cur_id (line 49) | def get_cur_id(self):
    method get_tasks_num (line 52) | def get_tasks_num(self):
    method get_unsolved_num (line 55) | def get_unsolved_num(self):
    method get_rest_budget (line 58) | def get_rest_budget(self):
    method get_query_num (line 61) | def get_query_num(self):
    method get_cur_budgettype (line 64) | def get_cur_budgettype(self):
    method get_cur_budget (line 67) | def get_cur_budget(self):
    method get_curname (line 70) | def get_curname(self):
    method get_curdim (line 73) | def get_curdim(self):
    method get_curobj_info (line 76) | def get_curobj_info(self):
    method get_cur_fidelity_info (line 79) | def get_cur_fidelity_info(self) -> Dict:
    method get_cur_searchspace_info (line 82) | def get_cur_searchspace_info(self) -> Dict:
    method get_cur_searchspace (line 86) | def get_cur_searchspace(self) -> SearchSpace:
    method get_curtask (line 90) | def get_curtask(self):
    method get_cur_seed (line 94) | def get_cur_seed(self):
    method get_cur_task_id (line 97) | def get_cur_task_id(self):
    method get_cur_workload (line 100) | def get_cur_workload(self):
    method sync_query_num (line 104) | def sync_query_num(self, query_num: int):
    method roll (line 107) | def roll(self):
    method lock (line 110) | def lock(self):
    method unlock (line 113) | def unlock(self):
    method get_lockstate (line 116) | def get_lockstate(self):
    method get_task_type (line 119) | def get_task_type(self):
    method get_dataset_size (line 130) | def get_dataset_size(self):
    method get_var_by_idx (line 134) | def get_var_by_idx(self, idx):
    method get_idx_by_var (line 138) | def get_idx_by_var(self, vectors):
    method get_unobserved_vars (line 142) | def get_unobserved_vars(self):
    method get_unobserved_idxs (line 146) | def get_unobserved_idxs(self):
    method add_query_num (line 150) | def add_query_num(self):
    method f (line 154) | def f(
  class RemoteTransferOptBenchmark (line 211) | class RemoteTransferOptBenchmark(TransferProblem):
    method __init__ (line 212) | def __init__(
    method add_task_to_id (line 219) | def add_task_to_id(
    method f (line 231) | def f(
    method _package_data (line 261) | def _package_data(
    method _execute_experiment (line 278) | def _execute_experiment(self, data):

FILE: transopt/benchmark/synthetic/MovingPeakBenchmark.py
  class MovingPeakGenerator (line 15) | class MovingPeakGenerator:
    method __init__ (line 16) | def __init__(
    method get_MPB (line 99) | def get_MPB(self):
    method cal_width_shift (line 102) | def cal_width_shift(self):
    method cal_height_shift (line 106) | def cal_height_shift(self):
    method cal_peak_shift (line 110) | def cal_peak_shift(self, previous_shift):
    method change (line 116) | def change(self):
    method current_optimal (line 120) | def current_optimal(self, peak_shape=None):
    method transfer (line 127) | def transfer(self, X):
    method normalize (line 132) | def normalize(self, X):
    method optimizers (line 141) | def optimizers(self):
    method _fix_bound (line 149) | def _fix_bound(data, bound):
  class MovingPeakBenchmark (line 160) | class MovingPeakBenchmark(NonTabularProblem):
    method __init__ (line 161) | def __init__(
    method peak_function_cone (line 181) | def peak_function_cone(self, x):
    method peak_function_sharp (line 185) | def peak_function_sharp(self, x):
    method peak_function_hilly (line 189) | def peak_function_hilly(self, x):
    method objective_function (line 197) | def objective_function(
    method get_configuration_space (line 223) | def get_configuration_space(
    method get_fidelity_space (line 250) | def get_fidelity_space(
    method get_meta_information (line 271) | def get_meta_information(self) -> Dict:

FILE: transopt/benchmark/synthetic/MultiObjBenchmark.py
  class AckleySphereOptBenchmark (line 16) | class AckleySphereOptBenchmark(NonTabularProblem):
    method __init__ (line 17) | def __init__(
    method objective_function (line 51) | def objective_function(
    method get_configuration_space (line 78) | def get_configuration_space(
    method get_fidelity_space (line 105) | def get_fidelity_space(
    method get_meta_information (line 126) | def get_meta_information(self) -> Dict:

FILE: transopt/benchmark/synthetic/synthetic_problems.py
  class SyntheticProblemBase (line 19) | class SyntheticProblemBase(NonTabularProblem):
    method __init__ (line 25) | def __init__(
    method get_fidelity_space (line 36) | def get_fidelity_space(self) -> FidelitySpace:
    method get_objectives (line 40) | def get_objectives(self) -> Dict:
    method get_problem_type (line 43) | def get_problem_type(self):
  class SphereOptBenchmark (line 49) | class SphereOptBenchmark(SyntheticProblemBase):
    method __init__ (line 50) | def __init__(
    method objective_function (line 79) | def objective_function(
    method get_configuration_space (line 101) | def get_configuration_space(self) -> SearchSpace:
  class RastriginOptBenchmark (line 108) | class RastriginOptBenchmark(SyntheticProblemBase):
    method __init__ (line 109) | def __init__(
    method objective_function (line 138) | def objective_function(
    method get_configuration_space (line 161) | def get_configuration_space(self) -> SearchSpace:
  class SchwefelOptBenchmark (line 168) | class SchwefelOptBenchmark(SyntheticProblemBase):
    method __init__ (line 169) | def __init__(
    method objective_function (line 198) | def objective_function(
    method get_configuration_space (line 222) | def get_configuration_space(self) -> SearchSpace:
  class LevyROptBenchmark (line 230) | class LevyROptBenchmark(SyntheticProblemBase):
    method __init__ (line 231) | def __init__(
    method objective_function (line 260) | def objective_function(
    method get_configuration_space (line 291) | def get_configuration_space(self) -> SearchSpace:
  class GriewankOptBenchmark (line 298) | class GriewankOptBenchmark(SyntheticProblemBase):
    method __init__ (line 299) | def __init__(
    method objective_function (line 328) | def objective_function(
    method get_configuration_space (line 353) | def get_configuration_space(self) -> SearchSpace:
  class RosenbrockOptBenchmark (line 360) | class RosenbrockOptBenchmark(SyntheticProblemBase):
    method __init__ (line 361) | def __init__(
    method objective_function (line 390) | def objective_function(
    method get_configuration_space (line 415) | def get_configuration_space(self) -> SearchSpace:
  class DropwaveROptBenchmark (line 422) | class DropwaveROptBenchmark(SyntheticProblemBase):
    method __init__ (line 423) | def __init__(
    method objective_function (line 456) | def objective_function(
    method get_configuration_space (line 479) | def get_configuration_space(self) -> SearchSpace:
  class LangermannOptBenchmark (line 486) | class LangermannOptBenchmark(SyntheticProblemBase):
    method __init__ (line 487) | def __init__(
    method objective_function (line 520) | def objective_function(
    method get_configuration_space (line 546) | def get_configuration_space(self) -> SearchSpace:
  class RotatedHyperEllipsoidOptBenchmark (line 553) | class RotatedHyperEllipsoidOptBenchmark(SyntheticProblemBase):
    method __init__ (line 554) | def __init__(
    method objective_function (line 583) | def objective_function(
    method get_configuration_space (line 606) | def get_configuration_space(self) -> SearchSpace:
  class SumOfDifferentPowersOptBenchmark (line 613) | class SumOfDifferentPowersOptBenchmark(SyntheticProblemBase):
    method __init__ (line 614) | def __init__(
    method objective_function (line 643) | def objective_function(
    method get_configuration_space (line 667) | def get_configuration_space(self) -> SearchSpace:
  class StyblinskiTangOptBenchmark (line 674) | class StyblinskiTangOptBenchmark(SyntheticProblemBase):
    method __init__ (line 675) | def __init__(
    method objective_function (line 704) | def objective_function(
    method get_configuration_space (line 726) | def get_configuration_space(self) -> SearchSpace:
  class PowellOptBenchmark (line 733) | class PowellOptBenchmark(SyntheticProblemBase):
    method __init__ (line 734) | def __init__(
    method objective_function (line 763) | def objective_function(
    method get_configuration_space (line 792) | def get_configuration_space(self) -> SearchSpace:
  class DixonPriceOptBenchmark (line 799) | class DixonPriceOptBenchmark(SyntheticProblemBase):
    method __init__ (line 800) | def __init__(
    method objective_function (line 834) | def objective_function(
    method get_configuration_space (line 860) | def get_configuration_space(self) -> SearchSpace:
  class cpOptBenchmark (line 867) | class cpOptBenchmark(SyntheticProblemBase):
    method __init__ (line 868) | def __init__(
    method objective_function (line 902) | def objective_function(
    method get_configuration_space (line 928) | def get_configuration_space(self) -> SearchSpace:
  class mpbOptBenchmark (line 935) | class mpbOptBenchmark(SyntheticProblemBase):
    method __init__ (line 936) | def __init__(
    method objective_function (line 970) | def objective_function(
  function get_configuration_space (line 995) | def get_configuration_space(self) -> SearchSpace:
  class Ackley (line 1005) | class Ackley(SyntheticProblemBase):
    method __init__ (line 1006) | def __init__(
    method objective_function (line 1039) | def objective_function(
    method get_configuration_space (line 1063) | def get_configuration_space(self) -> SearchSpace:
  class EllipsoidOptBenchmark (line 1071) | class EllipsoidOptBenchmark(SyntheticProblemBase):
    method __init__ (line 1072) | def __init__(
    method objective_function (line 1103) | def objective_function(
    method get_configuration_space (line 1129) | def get_configuration_space(self) -> SearchSpace:
  class DiscusOptBenchmark (line 1136) | class DiscusOptBenchmark(SyntheticProblemBase):
    method __init__ (line 1137) | def __init__(
    method objective_function (line 1168) | def objective_function(
    method get_configuration_space (line 1194) | def get_configuration_space(self) -> SearchSpace:
  class BentCigarOptBenchmark (line 1201) | class BentCigarOptBenchmark(SyntheticProblemBase):
    method __init__ (line 1202) | def __init__(
    method objective_function (line 1233) | def objective_function(
    method get_configuration_space (line 1259) | def get_configuration_space(self) -> SearchSpace:
  class SharpRidgeOptBenchmark (line 1266) | class SharpRidgeOptBenchmark(SyntheticProblemBase):
    method __init__ (line 1267) | def __init__(
    method objective_function (line 1298) | def objective_function(
    method get_configuration_space (line 1329) | def get_configuration_space(self) -> SearchSpace:
  class GriewankRosenbrockOptBenchmark (line 1336) | class GriewankRosenbrockOptBenchmark(SyntheticProblemBase):
    method __init__ (line 1337) | def __init__(
    method objective_function (line 1366) | def objective_function(
    method get_configuration_space (line 1396) | def get_configuration_space(self) -> SearchSpace:
  class KatsuuraOptBenchmark (line 1403) | class KatsuuraOptBenchmark(SyntheticProblemBase):
    method __init__ (line 1404) | def __init__(
    method objective_function (line 1433) | def objective_function(
    method get_configuration_space (line 1465) | def get_configuration_space(self) -> SearchSpace:
  function visualize_function (line 1471) | def visualize_function(func_name, n_points=100):

FILE: transopt/datamanager/database.py
  class DatabaseDaemon (line 46) | class DatabaseDaemon:
    method __init__ (line 47) | def __init__(self, data_path, task_queue, result_queue, stop_event):
    method run (line 53) | def run(self):
  class Database (line 74) | class Database:
    method __init__ (line 75) | def __init__(self, db_file_name="database.db"):
    method close (line 99) | def close(self):
    method _execute (line 104) | def _execute(self, task, args=(), timeout=None, commit=True):
    method query_exec (line 116) | def query_exec(cursor, query, params, fetchone, fetchall, many):
    method execute (line 127) | def execute(
    method executemany (line 144) | def executemany(
    method start_transaction (line 161) | def start_transaction(self):
    method commit_transaction (line 164) | def commit_transaction(self):
    method rollback_transaction (line 167) | def rollback_transaction(self):
    method get_experiment_datasets (line 174) | def get_experiment_datasets(self):
    method get_all_datasets (line 185) | def get_all_datasets(self):
    method get_table_list (line 194) | def get_table_list(self):
    method check_table_exist (line 203) | def check_table_exist(self, name):
    method create_table (line 212) | def create_table(self, name, dataset_cfg, overwrite=False, is_experime...
    method remove_table (line 297) | def remove_table(self, name):
    method create_or_update_config (line 315) | def create_or_update_config(self, name, dataset_cfg, is_experiment=Tru...
    method query_config (line 338) | def query_config(self, name):
    method query_dataset_info (line 348) | def query_dataset_info(self, name):
    method create_or_update_metadata (line 372) | def create_or_update_metadata(self, table_name, metadata, commit=True):
    method get_all_metadata (line 413) | def get_all_metadata(self):
    method search_tables_by_metadata (line 420) | def search_tables_by_metadata(self, search_params):
    method insert_data (line 449) | def insert_data(
    method _get_conditions (line 513) | def _get_conditions(self, rowid=None, conditions=None):
    method update_data (line 557) | def update_data(self, table, data, rowid=None, conditions=None):
    method delete_data (line 598) | def delete_data(self, table, rowid=None, conditions=None):
    method select_data (line 617) | def select_data(
    method get_num_row (line 669) | def get_num_row(self, table):
    method get_column_names (line 673) | def get_column_names(self, table):

FILE: transopt/datamanager/lsh.py
  class LSHCache (line 6) | class LSHCache:
    method __init__ (line 7) | def __init__(self, hasher, num_bands=10):
    method add (line 31) | def add(self, key, vector):
    method query (line 67) | def query(self, vector):

FILE: transopt/datamanager/manager.py
  class DataManager (line 11) | class DataManager:
    method __new__ (line 15) | def __new__(cls, *args, **kwargs):
    method __init__ (line 21) | def __init__(
    method _initialize_lsh_cache (line 33) | def _initialize_lsh_cache(self, num_hashes, char_ngram, num_bands, ran...
    method _add_lsh_vector (line 45) | def _add_lsh_vector(self, dataset_name, dataset_info):
    method _construct_vector (line 49) | def _construct_vector(self, dataset_info):
    method search_similar_datasets (line 68) | def search_similar_datasets(self, problem_config):
    method search_datasets_by_name (line 73) | def search_datasets_by_name(self, dataset_name):
    method get_dataset_info (line 80) | def get_dataset_info(self, dataset_name):
    method get_experiment_datasets (line 83) | def get_experiment_datasets(self):
    method get_all_datasets (line 86) | def get_all_datasets(self):
    method create_dataset (line 89) | def create_dataset(self, dataset_name, dataset_info, overwrite=True):
    method insert_data (line 95) | def insert_data(self, dataset_name, data):
    method remove_dataset (line 98) | def remove_dataset(self, dataset_name):
    method teardown (line 101) | def teardown(self):
  function main (line 107) | def main():

FILE: transopt/datamanager/minhash.py
  class MinHasher (line 7) | class MinHasher:
    method __init__ (line 8) | def __init__(self, num_hashes, char_ngram, random_state=None):
    method num_seeds (line 30) | def num_seeds(self):
    method get_shingles (line 33) | def get_shingles(self, text):
    method fingerprint (line 40) | def fingerprint(self, text):
    method estimate_similarity (line 53) | def estimate_similarity(self, fp1, fp2):
  function jaccard_similarity (line 57) | def jaccard_similarity(set1, set2):

FILE: transopt/optimizer/MultiObjOptimizer/CauMOpt.py
  function calculate_gini_index (line 14) | def calculate_gini_index(labels):
  function features_by_gini (line 21) | def features_by_gini(data, labels):
  class CauMO (line 47) | class CauMO(BOBase):
    method __init__ (line 48) | def __init__(self, config: Dict, rate_oversampling = 4, seed = 0, **kw...
    method initial_sample (line 66) | def initial_sample(self):
    method random_sample (line 69) | def random_sample(self, num_samples: int) -> List[Dict]:
    method update_model (line 95) | def update_model(self, Data):
    method create_model (line 121) | def create_model(self, X, Y):
    method set_data (line 185) | def set_data(self, X, Y):
    method fit_data (line 203) | def fit_data(self, X, Y):
    method suggest (line 221) | def suggest(self, n_suggestions: Union[None, int] = None) -> List[Dict]:
    method observe (line 244) | def observe(self, input_vectors: Union[List[Dict], Dict], output_value...
    method predict (line 252) | def predict(self, X, full_cov=False):
    method raw_predict (line 287) | def raw_predict(self, X, model):
    method raw_predict_var (line 294) | def raw_predict_var(self, X, trees,  predictions, min_variance=0.1):
    method model_reset (line 313) | def model_reset(self):
    method get_fmin (line 317) | def get_fmin(self):
    method get_fmin_by_id (line 323) | def get_fmin_by_id(self, idx):

FILE: transopt/optimizer/MultiObjOptimizer/MoeadEGO.py
  class MoeadEGO (line 17) | class MoeadEGO(BOBase):
    method __init__ (line 18) | def __init__(self, config: Dict, **kwargs):
    method initial_sample (line 35) | def initial_sample(self):
    method random_sample (line 38) | def random_sample(self, num_samples: int) -> List[Dict]:
    method update_model (line 65) | def update_model(self, Data):
    method create_model (line 93) | def create_model(self, X, Y):
    method suggest (line 111) | def suggest(self, n_suggestions: Union[None, int] = None) -> List[Dict]:
    method predict (line 134) | def predict(self, X, full_cov=False):
    method predict_by_id (line 150) | def predict_by_id(self, X, idx, full_cov=False):
    method model_reset (line 164) | def model_reset(self):
    method get_fmin (line 168) | def get_fmin(self):
    method get_fmin_by_id (line 174) | def get_fmin_by_id(self, idx):

FILE: transopt/optimizer/MultiObjOptimizer/ParEGO.py
  class ParEGO (line 12) | class ParEGO(BOBase):
    method __init__ (line 13) | def __init__(self, config: Dict, **kwargs):
    method scalarization (line 31) | def scalarization(self, Y: np.ndarray, rho):
    method initial_sample (line 45) | def initial_sample(self):
    method suggest (line 48) | def suggest(self, n_suggestions: Union[None, int] = None) -> List[Dict]:
    method update_model (line 73) | def update_model(self, Data):
    method create_model (line 99) | def create_model(self, X, Y):
    method predict (line 113) | def predict(self, X, full_cov=False):
    method random_sample (line 129) | def random_sample(self, num_samples: int) -> List[Dict]:
    method model_reset (line 155) | def model_reset(self):
    method get_fmin (line 159) | def get_fmin(self):

FILE: transopt/optimizer/MultiObjOptimizer/SMSEGO.py
  class SMSEGO (line 15) | class SMSEGO(BOBase):
    method __init__ (line 16) | def __init__(self, config:Dict, **kwargs):
    method initial_sample (line 33) | def initial_sample(self):
    method suggest (line 36) | def suggest(self, n_suggestions:Union[None, int] = None) ->List[Dict]:
    method update_model (line 56) | def update_model(self, Data):
    method create_model (line 81) | def create_model(self, X, Y):
    method predict (line 97) | def predict(self, X, full_cov=False):
    method random_sample (line 116) | def random_sample(self, num_samples: int) -> List[Dict]:
    method model_reset (line 140) | def model_reset(self):
    method get_fmin (line 144) | def get_fmin(self):

FILE: transopt/optimizer/SingleObjOptimizer/KrigingOptimizer.py
  class KrigingEA (line 18) | class KrigingEA(BOBase):
    method __init__ (line 19) | def __init__(self, config: Dict, **kwargs):
    method initial_sample (line 57) | def initial_sample(self):
    method suggest (line 60) | def suggest(self, n_suggestions: Union[None, int] = None) -> List[Dict]:
    method observe (line 82) | def observe(self, input_vectors: Union[List[Dict], Dict], output_value...
    method update_model (line 107) | def update_model(self, Data):
    method create_model (line 129) | def create_model(self, X, Y):
    method create_ea (line 134) | def create_ea(self):
    method predict (line 145) | def predict(self, X):
    method sample (line 152) | def sample(self, num_samples: int) -> List[Dict]:
    method model_reset (line 178) | def model_reset(self):
    method get_fmin (line 181) | def get_fmin(self):
    method reset (line 185) | def reset(self, task_name:str, design_space:Dict, search_sapce:Union[N...
    method model_manage_strategy (line 193) | def model_manage_strategy(self):
  class EAProblem (line 225) | class EAProblem(Problem):
    method __init__ (line 226) | def __init__(self, space, predict):
    method _evaluate (line 239) | def _evaluate(self, x, out, *args, **kwargs):

FILE: transopt/optimizer/SingleObjOptimizer/LFL.py
  class LFLOptimizer (line 23) | class LFLOptimizer(BOBase):
    method __init__ (line 24) | def __init__(self, config:Dict, **kwargs):
    method reset (line 51) | def reset(self, design_space:Dict, search_sapce:Union[None, Dict] = No...
    method initial_sample (line 60) | def initial_sample(self):
    method random_sample (line 71) | def random_sample(self, num_samples: int) -> List[Dict]:
    method combine_data (line 97) | def combine_data(self):
    method suggest (line 103) | def suggest(self, n_suggestions:Union[None, int] = None)->List[Dict]:
    method create_model (line 127) | def create_model(self, X_list, Y_list, mf=None, prior:list=[]):
    method update_model (line 166) | def update_model(self, Data):
    method predict (line 218) | def predict(self, X):
    method var_predict (line 243) | def var_predict(self, X):
    method obj_posterior_samples (line 261) | def obj_posterior_samples(self, X, sample_size):
    method get_fmin (line 283) | def get_fmin(self):
    method set_XY (line 289) | def set_XY(self, X=None, Y=None):
    method samples (line 307) | def samples(self, gp):
    method posterior_samples_f (line 320) | def posterior_samples_f(self,X, model_id, size=10):
    method posterior_samples (line 339) | def posterior_samples(self, X, model_id, size=10):
    method get_model_para (line 361) | def get_model_para(self):
    method update_prior (line 372) | def update_prior(self, parameters):

FILE: transopt/optimizer/SingleObjOptimizer/MetaLearningOptimizer.py
  function get_model (line 28) | def get_model(
  class MetaBOOptimizer (line 42) | class MetaBOOptimizer(OptimizerBase):
    method __init__ (line 45) | def __init__(self, Xdim, bounds, kernel='RBF', likelihood=None, model_...
    method create_model (line 71) | def create_model(self, model_name, Meta_data, Target_data):
    method updateModel (line 112) | def updateModel(self, Target_data):
    method resetModel (line 131) | def resetModel(self, Source_data, Target_data):
    method predict (line 137) | def predict(self, X):
    method get_fmin (line 183) | def get_fmin(self):
    method set_XY (line 196) | def set_XY(self, X=None, Y=None):
    method samples (line 214) | def samples(self, gp):
    method posterior_samples_f (line 227) | def posterior_samples_f(self,X, model_id, size=10):
    method posterior_samples (line 246) | def posterior_samples(self, X, model_id, size=10):

FILE: transopt/optimizer/SingleObjOptimizer/MultitaskOptimizer.py
  class MultitaskBO (line 17) | class MultitaskBO(BOBase):
    method __init__ (line 18) | def __init__(self, config:Dict, **kwargs):
    method initial_sample (line 39) | def initial_sample(self):
    method random_sample (line 42) | def random_sample(self, num_samples: int) -> List[Dict]:
    method suggest (line 66) | def suggest(self, n_suggestions:Union[None, int] = None) ->List[Dict]:
    method update_model (line 90) | def update_model(self, Data):
    method create_model (line 125) | def create_model(self, X_list, Y_list, mf=None, prior:list=[]):
    method set_XY (line 151) | def set_XY(self, X=None, Y=None):
    method model_reset (line 169) | def model_reset(self):
    method predict (line 172) | def predict(self, X):
    method get_fmin (line 196) | def get_fmin(self):

FILE: transopt/optimizer/SingleObjOptimizer/PROptimizer.py
  class PREA (line 19) | class PREA(BayesianOptimizerBase):
    method __init__ (line 20) | def __init__(self, config: Dict, **kwargs):
    method initial_sample (line 63) | def initial_sample(self):
    method suggest (line 66) | def suggest(self, n_suggestions: Union[None, int] = None) -> List[Dict]:
    method observe (line 88) | def observe(self, input_vectors: Union[List[Dict], Dict], output_value...
    method update_model (line 115) | def update_model(self, Data):
    method create_model (line 132) | def create_model(self, X, Y):
    method create_ea (line 138) | def create_ea(self):
    method predict (line 149) | def predict(self, X):
    method sample (line 157) | def sample(self, num_samples: int) -> List[Dict]:
    method model_reset (line 183) | def model_reset(self):
    method get_fmin (line 186) | def get_fmin(self):
    method reset (line 190) | def reset(self, task_name: str, design_space: Dict, search_sapce: Unio...
    method model_manage_strategy (line 198) | def model_manage_strategy(self):
  class EAProblem (line 230) | class EAProblem(Problem):
    method __init__ (line 231) | def __init__(self, space, predict):
    method _evaluate (line 244) | def _evaluate(self, x, out, *args, **kwargs):

FILE: transopt/optimizer/SingleObjOptimizer/RBFNOptimizer.py
  class RbfnEA (line 19) | class RbfnEA(BayesianOptimizerBase):
    method __init__ (line 20) | def __init__(self, config: Dict, **kwargs):
    method initial_sample (line 78) | def initial_sample(self):
    method suggest (line 81) | def suggest(self, n_suggestions: Union[None, int] = None) -> List[Dict]:
    method observe (line 103) | def observe(self, input_vectors: Union[List[Dict], Dict], output_value...
    method update_model (line 127) | def update_model(self, Data):
    method create_model (line 150) | def create_model(self, X, Y):
    method create_ea (line 158) | def create_ea(self):
    method predict (line 169) | def predict(self, X):
    method sample (line 176) | def sample(self, num_samples: int) -> List[Dict]:
    method model_reset (line 202) | def model_reset(self):
    method get_fmin (line 205) | def get_fmin(self):
    method reset (line 210) | def reset(self, task_name: str, design_space: Dict, search_sapce: Unio...
    method model_manage_strategy (line 218) | def model_manage_strategy(self):
  class EAProblem (line 249) | class EAProblem(Problem):
    method __init__ (line 250) | def __init__(self, space, predict):
    method _evaluate (line 263) | def _evaluate(self, x, out, *args, **kwargs):

FILE: transopt/optimizer/SingleObjOptimizer/RGPEOptimizer.py
  class RGPEOptimizer (line 12) | class RGPEOptimizer(BOBase):
    method __init__ (line 13) | def __init__(self, config: Dict, **kwargs):
    method initial_sample (line 33) | def initial_sample(self):
    method random_sample (line 36) | def random_sample(self, num_samples: int) -> List[Dict]:
    method model_reset (line 60) | def model_reset(self):
    method meta_update (line 69) | def meta_update(self):
    method suggest (line 71) | def suggest(self, n_suggestions:Union[None, int] = None) ->List[Dict]:
    method create_model (line 91) | def create_model(self):
    method create_model (line 94) | def create_model(self, model_name, Source_data, Target_data):
    method updateModel (line 133) | def updateModel(self, Target_data):
    method reset_target (line 152) | def reset_target(self):
    method meta_add (line 155) | def meta_add(self, meta_data):
    method resetModel (line 158) | def resetModel(self, Source_data, Target_data):
    method get_train_time (line 162) | def get_train_time(self):
    method get_fit_time (line 165) | def get_fit_time(self):
    method predict (line 169) | def predict(
    method obj_posterior_samples (line 179) | def obj_posterior_samples(self, X, sample_size):
    method update_model (line 193) | def update_model(self, Data: Dict):
    method get_fmin (line 205) | def get_fmin(self):
    method set_XY (line 211) | def set_XY(self, X=None, Y=None):
    method samples (line 229) | def samples(self, gp):
    method posterior_samples_f (line 242) | def posterior_samples_f(self,X, model_id, size=10):
    method posterior_samples (line 261) | def posterior_samples(self, X, model_id, size=10):

FILE: transopt/optimizer/SingleObjOptimizer/TPEOptimizer.py
  class TPEOptimizer (line 11) | class TPEOptimizer(BOBase):
    method __init__ (line 12) | def __init__(self, config:Dict, **kwargs):
    method initial_sample (line 37) | def initial_sample(self):
    method suggest (line 40) | def suggest(self, n_suggestions:Union[None, int] = None) ->List[Dict]:
    method update_model (line 60) | def update_model(self, Data):
    method create_model (line 80) | def create_model(self, X, Y):
    method predict (line 85) | def predict(self, X):
    method random_sample (line 102) | def random_sample(self, num_samples: int) -> List[Dict]:
    method model_reset (line 126) | def model_reset(self):
    method get_fmin (line 129) | def get_fmin(self):
    method posterior_samples (line 135) | def posterior_samples(self, X, model_id, size=10):

FILE: transopt/optimizer/SingleObjOptimizer/VizerOptimizer.py
  class Vizer (line 12) | class Vizer(BOBase):
    method __init__ (line 14) | def __init__(self, config: Dict, **kwargs):
    method model_reset (line 34) | def model_reset(self):
    method initial_sample (line 43) | def initial_sample(self):
    method random_sample (line 46) | def random_sample(self, num_samples: int) -> List[Dict]:
    method suggest (line 70) | def suggest(self, n_suggestions:Union[None, int] = None) ->List[Dict]:
    method meta_update (line 90) | def meta_update(self):
    method meta_add (line 93) | def meta_add(self, Data:List[Dict]):
    method create_model (line 96) | def create_model(self):
    method update_model (line 100) | def update_model(self, Data):
    method MetaFitModel (line 113) | def MetaFitModel(self, metadata):
    method get_train_time (line 125) | def get_train_time(self):
    method get_fit_time (line 128) | def get_fit_time(self):
    method predict (line 132) | def predict(
    method obj_posterior_samples (line 142) | def obj_posterior_samples(self, X, sample_size):
    method get_fmin (line 164) | def get_fmin(self):
    method samples (line 170) | def samples(self, gp):
    method posterior_samples_f (line 184) | def posterior_samples_f(self,X, model_id, size=10):
    method posterior_samples (line 203) | def posterior_samples(self, X, model_id, size=10):

FILE: transopt/optimizer/acquisition_function/ConformalLCB.py
  class ConformalLCB (line 8) | class ConformalLCB(AcquisitionBase):
    method __init__ (line 28) | def __init__(self, model, space, optimizer, config):
    method _compute_acq (line 36) | def _compute_acq(self, x):
    method _compute_acq_withGradients (line 48) | def _compute_acq_withGradients(self, x):

FILE: transopt/optimizer/acquisition_function/acf_base.py
  class AcquisitionBase (line 11) | class AcquisitionBase(object):
    method __init__ (line 23) | def __init__(self, cost_withGradients=None, **kwargs):
    method fromDict (line 37) | def fromDict(model, space, optimizer, cost_withGradients, config):
    method link (line 40) | def link(self, model, space):
    method link_model (line 44) | def link_model(self, model):
    method link_space (line 47) | def link_space(self, space):
    method acquisition_function (line 64) | def acquisition_function(self,x):
    method acquisition_function_withGradients (line 74) | def acquisition_function_withGradients(self, x):
    method optimize (line 85) | def optimize(self, duplicate_manager=None):
    method _compute_acq (line 95) | def _compute_acq(self,x):
    method _compute_acq_withGradients (line 99) | def _compute_acq_withGradients(self, x):

FILE: transopt/optimizer/acquisition_function/ei.py
  class AcquisitionEI (line 11) | class AcquisitionEI(AcquisitionBase):
    method __init__ (line 24) | def __init__(self, config):
    method _compute_acq (line 38) | def _compute_acq(self, x):
    method _compute_acq_withGradients (line 47) | def _compute_acq_withGradients(self, x):

FILE: transopt/optimizer/acquisition_function/get_acf.py
  function get_acf (line 4) | def get_acf(acf_name, **kwargs):

FILE: transopt/optimizer/acquisition_function/lcb.py
  class AcquisitionLCB (line 10) | class AcquisitionLCB(AcquisitionBase):
    method __init__ (line 30) | def __init__(self, config):
    method _compute_acq (line 37) | def _compute_acq(self, x):
    method _compute_acq_withGradients (line 45) | def _compute_acq_withGradients(self, x):

FILE: transopt/optimizer/acquisition_function/model_manage/CMAESBest.py
  class CMAESBest (line 13) | class CMAESBest(AcquisitionBase):
    method __init__ (line 16) | def __init__(self, config):
    method link_space (line 39) | def link_space(self, space):
    method optimize (line 61) | def optimize(self, duplicate_manager=None):
    method _compute_acq (line 71) | def _compute_acq(self, x):
    method _compute_acq_withGradients (line 74) | def _compute_acq_withGradients(self, x):
  class EAProblem (line 78) | class EAProblem(Problem):
    method __init__ (line 79) | def __init__(self, space, predict):
    method _evaluate (line 92) | def _evaluate(self, x, out, *args, **kwargs):

FILE: transopt/optimizer/acquisition_function/model_manage/CMAESGeneration.py
  class CMAESGeneration (line 11) | class CMAESGeneration(AcquisitionBase):
    method __init__ (line 14) | def __init__(self, config):
    method link_space (line 37) | def link_space(self, space):
    method optimize (line 59) | def optimize(self, duplicate_manager=None):
    method _compute_acq (line 70) | def _compute_acq(self, x):
    method _compute_acq_withGradients (line 73) | def _compute_acq_withGradients(self, x):
  class EAProblem (line 77) | class EAProblem(Problem):
    method __init__ (line 78) | def __init__(self, space, predict):
    method _evaluate (line 91) | def _evaluate(self, x, out, *args, **kwargs):

FILE: transopt/optimizer/acquisition_function/model_manage/CMAESPreSelect.py
  class CMAESPreSelect (line 11) | class CMAESPreSelect(AcquisitionBase):
    method __init__ (line 14) | def __init__(self, config):
    method link_space (line 37) | def link_space(self, space):
    method optimize (line 59) | def optimize(self, duplicate_manager=None):
    method _compute_acq (line 78) | def _compute_acq(self, x):
    method _compute_acq_withGradients (line 81) | def _compute_acq_withGradients(self, x):
  class EAProblem (line 85) | class EAProblem(Problem):
    method __init__ (line 86) | def __init__(self, space, predict):
    method _evaluate (line 99) | def _evaluate(self, x, out, *args, **kwargs):

FILE: transopt/optimizer/acquisition_function/model_manage/DEBest.py
  class DEBest (line 11) | class DEBest(AcquisitionBase):
    method __init__ (line 14) | def __init__(self, config):
    method link_space (line 37) | def link_space(self, space):
    method optimize (line 59) | def optimize(self, duplicate_manager=None):
    method _compute_acq (line 69) | def _compute_acq(self, x):
    method _compute_acq_withGradients (line 72) | def _compute_acq_withGradients(self, x):
  class EAProblem (line 76) | class EAProblem(Problem):
    method __init__ (line 77) | def __init__(self, space, predict):
    method _evaluate (line 90) | def _evaluate(self, x, out, *args, **kwargs):

FILE: transopt/optimizer/acquisition_function/model_manage/DEGeneration.py
  class DEGeneration (line 11) | class DEGeneration(AcquisitionBase):
    method __init__ (line 14) | def __init__(self, config):
    method link_space (line 37) | def link_space(self, space):
    method optimize (line 59) | def optimize(self, duplicate_manager=None):
    method _compute_acq (line 70) | def _compute_acq(self, x):
    method _compute_acq_withGradients (line 73) | def _compute_acq_withGradients(self, x):
  class EAProblem (line 77) | class EAProblem(Problem):
    method __init__ (line 78) | def __init__(self, space, predict):
    method _evaluate (line 91) | def _evaluate(self, x, out, *args, **kwargs):

FILE: transopt/optimizer/acquisition_function/model_manage/DEPreSelect.py
  class DEPreSelect (line 11) | class DEPreSelect(AcquisitionBase):
    method __init__ (line 14) | def __init__(self, config):
    method link_space (line 37) | def link_space(self, space):
    method optimize (line 59) | def optimize(self, duplicate_manager=None):
    method _compute_acq (line 78) | def _compute_acq(self, x):
    method _compute_acq_withGradients (line 81) | def _compute_acq_withGradients(self, x):
  class EAProblem (line 85) | class EAProblem(Problem):
    method __init__ (line 86) | def __init__(self, space, predict):
    method _evaluate (line 99) | def _evaluate(self, x, out, *args, **kwargs):

FILE: transopt/optimizer/acquisition_function/model_manage/GABest.py
  class GABest (line 11) | class GABest(AcquisitionBase):
    method __init__ (line 14) | def __init__(self, config):
    method link_space (line 37) | def link_space(self, space):
    method optimize (line 59) | def optimize(self, duplicate_manager=None):
    method _compute_acq (line 69) | def _compute_acq(self, x):
    method _compute_acq_withGradients (line 72) | def _compute_acq_withGradients(self, x):
  class EAProblem (line 76) | class EAProblem(Problem):
    method __init__ (line 77) | def __init__(self, space, predict):
    method _evaluate (line 90) | def _evaluate(self, x, out, *args, **kwargs):

FILE: transopt/optimizer/acquisition_function/model_manage/GAGeneration.py
  class GAGeneration (line 11) | class GAGeneration(AcquisitionBase):
    method __init__ (line 14) | def __init__(self, config):
    method link_space (line 37) | def link_space(self, space):
    method optimize (line 59) | def optimize(self, duplicate_manager=None):
    method _compute_acq (line 70) | def _compute_acq(self, x):
    method _compute_acq_withGradients (line 73) | def _compute_acq_withGradients(self, x):
  class EAProblem (line 77) | class EAProblem(Problem):
    method __init__ (line 78) | def __init__(self, space, predict):
    method _evaluate (line 91) | def _evaluate(self, x, out, *args, **kwargs):

FILE: transopt/optimizer/acquisition_function/model_manage/GAPreSelect.py
  class GAPreSelect (line 11) | class GAPreSelect(AcquisitionBase):
    method __init__ (line 14) | def __init__(self, config):
    method link_space (line 37) | def link_space(self, space):
    method optimize (line 59) | def optimize(self, duplicate_manager=None):
    method _compute_acq (line 78) | def _compute_acq(self, x):
    method _compute_acq_withGradients (line 81) | def _compute_acq_withGradients(self, x):
  class EAProblem (line 85) | class EAProblem(Problem):
    method __init__ (line 86) | def __init__(self, space, predict):
    method _evaluate (line 99) | def _evaluate(self, x, out, *args, **kwargs):

FILE: transopt/optimizer/acquisition_function/model_manage/PSOBest.py
  class PSOBest (line 11) | class PSOBest(AcquisitionBase):
    method __init__ (line 14) | def __init__(self, config):
    method link_space (line 37) | def link_space(self, space):
    method optimize (line 59) | def optimize(self, duplicate_manager=None):
    method _compute_acq (line 69) | def _compute_acq(self, x):
    method _compute_acq_withGradients (line 72) | def _compute_acq_withGradients(self, x):
  class EAProblem (line 76) | class EAProblem(Problem):
    method __init__ (line 77) | def __init__(self, space, predict):
    method _evaluate (line 90) | def _evaluate(self, x, out, *args, **kwargs):

FILE: transopt/optimizer/acquisition_function/model_manage/PSOGeneration.py
  class PSOGeneration (line 11) | class PSOGeneration(AcquisitionBase):
    method __init__ (line 14) | def __init__(self, config):
    method link_space (line 37) | def link_space(self, space):
    method optimize (line 59) | def optimize(self, duplicate_manager=None):
    method _compute_acq (line 70) | def _compute_acq(self, x):
    method _compute_acq_withGradients (line 73) | def _compute_acq_withGradients(self, x):
  class EAProblem (line 77) | class EAProblem(Problem):
    method __init__ (line 78) | def __init__(self, space, predict):
    method _evaluate (line 91) | def _evaluate(self, x, out, *args, **kwargs):

FILE: transopt/optimizer/acquisition_function/model_manage/PSOPreSelect.py
  class PSOPreSelect (line 11) | class PSOPreSelect(AcquisitionBase):
    method __init__ (line 14) | def __init__(self, config):
    method link_space (line 37) | def link_space(self, space):
    method optimize (line 59) | def optimize(self, duplicate_manager=None):
    method _compute_acq (line 78) | def _compute_acq(self, x):
    method _compute_acq_withGradients (line 81) | def _compute_acq_withGradients(self, x):
  class EAProblem (line 85) | class EAProblem(Problem):
    method __init__ (line 86) | def __init__(self, space, predict):
    method _evaluate (line 99) | def _evaluate(self, x, out, *args, **kwargs):

FILE: transopt/optimizer/acquisition_function/moeadego.py
  class MOEADEGO (line 14) | class MOEADEGO:
    method __init__ (line 15) | def __init__(self, model, space, optimizer, config):
    method _compute_acq (line 28) | def _compute_acq(self, x):
    method set_model_id (line 36) | def set_model_id(self, idx):
    method optimize (line 38) | def optimize(self, duplicate_manager=None):

FILE: transopt/optimizer/acquisition_function/pi.py
  class AcquisitionPI (line 10) | class AcquisitionPI(AcquisitionBase):
    method __init__ (line 23) | def __init__(self, config):
    method _compute_acq (line 36) | def _compute_acq(self, x):
    method _compute_acq_withGradients (line 45) | def _compute_acq_withGradients(self, x):

FILE: transopt/optimizer/acquisition_function/piei.py
  class AcquisitionpiEI (line 10) | class AcquisitionpiEI(AcquisitionBase):
    method __init__ (line 23) | def __init__(self, Model, space, optimizer, cost_withGradients=None, j...
    method _compute_acq (line 35) | def _compute_acq(self, x):
    method _compute_prior (line 45) | def _compute_prior(self, x):
    method _compute_acq_withGradients (line 48) | def _compute_acq_withGradients(self, x):

FILE: transopt/optimizer/acquisition_function/sequential.py
  class Sequential (line 4) | class Sequential(EvaluatorBase):
    method __init__ (line 12) | def __init__(self, acquisition, batch_size=1):
    method compute_batch (line 15) | def compute_batch(self, duplicate_manager=None,context_manager=None):

FILE: transopt/optimizer/acquisition_function/smsego.py
  class SMSEGO (line 13) | class SMSEGO:
    method __init__ (line 14) | def __init__(self, model, space, optimizer, config):
    method _compute_acq (line 21) | def _compute_acq(self, x):
    method optimize (line 39) | def optimize(self, duplicate_manager=None):

FILE: transopt/optimizer/acquisition_function/taf.py
  class AcquisitionTAF (line 12) | class AcquisitionTAF(AcquisitionBase):
    method __init__ (line 25) | def __init__(self, config):
    method _compute_acq (line 39) | def _compute_acq(self, x):
    method _compute_acq_withGradients (line 62) | def _compute_acq_withGradients(self, x):

FILE: transopt/optimizer/construct_optimizer.py
  function ConstructOptimizer (line 9) | def ConstructOptimizer(optimizer_config: dict = None, seed: int = 0) -> BO:
  function ConstructSelector (line 59) | def ConstructSelector(optimizer_config, dict = None, seed: int = 0):

FILE: transopt/optimizer/model/HyperBO.py
  class hyperbo (line 31) | class hyperbo():
    method __init__ (line 32) | def __init__(self, seed = 0):
    method pretrain (line 56) | def pretrain(self, Meta_data, Target_data):
    method retrain (line 86) | def retrain(self, Target_data):
    method predict (line 107) | def predict(self, X, subset_data_id:Union[int, str] = 0):

FILE: transopt/optimizer/model/bohb.py
  class KDEMultivariate (line 9) | class KDEMultivariate(sm.nonparametric.KDEMultivariate):
    method __init__ (line 10) | def __init__(self, configurations):
  class Log (line 19) | class Log():
    method __init__ (line 20) | def __init__(self, size):
    method __getitem__ (line 25) | def __getitem__(self, index):
    method __setitem__ (line 28) | def __setitem__(self, index, value):
    method __repr__ (line 31) | def __repr__(self):
  class BOHB (line 47) | class BOHB:
    method __init__ (line 48) | def __init__(self, configspace, evaluate, max_budget, min_budget,
    method optimize (line 71) | def optimize(self):
    method get_sample (line 123) | def get_sample(self):

FILE: transopt/optimizer/model/deepkernel.py
  class Metric (line 30) | class Metric(object):
    method __init__ (line 31) | def __init__(self,prefix='train: '):
    method update (line 35) | def update(self,loss,noise,mse):
    method reset (line 40) | def reset(self,):
    method report (line 45) | def report(self):
    method get (line 50) | def get(self):
  function totorch (line 56) | def totorch(x,device):
  class MLP (line 60) | class MLP(nn.Module):
    method __init__ (line 61) | def __init__(self, input_size, hidden_size=[32,32,32,32], dropout=0.0):
    method forward (line 70) | def forward(self,x):
  class ExactGPLayer (line 80) | class ExactGPLayer(gpytorch.models.ExactGP):
    method __init__ (line 81) | def __init__(self, train_x, train_y, likelihood,config,dims ):
    method forward (line 92) | def forward(self, x):
  class DeepKernelGP (line 99) | class DeepKernelGP(nn.Module):
    method __init__ (line 100) | def __init__(self, config = {}):
    method get_model_likelihood_mll (line 126) | def get_model_likelihood_mll(self, train_size):
    method fit (line 139) | def fit(self,
    method load_checkpoint (line 192) | def load_checkpoint(self, checkpoint):
    method predict (line 199) | def predict(self, X_pen):
    method continuous_maximization (line 219) | def continuous_maximization( self, dim, bounds, acqf):
    method get_fmin (line 225) | def get_fmin(self):

FILE: transopt/optimizer/model/dyhpo.py
  class FeatureExtractor (line 15) | class FeatureExtractor(nn.Module):
    method __init__ (line 19) | def __init__(self, configuration):
    method forward (line 58) | def forward(self, x, budgets, learning_curves):
  class GPRegressionModel (line 91) | class GPRegressionModel(gpytorch.models.ExactGP):
    method __init__ (line 95) | def __init__(
    method forward (line 114) | def forward(self, x):
  class DyHPO (line 122) | class DyHPO:
    method __init__ (line 126) | def __init__(
    method restart_optimization (line 195) | def restart_optimization(self):
    method get_model_likelihood_mll (line 210) | def get_model_likelihood_mll(
    method train_pipeline (line 233) | def train_pipeline(self, data: Dict[str, torch.Tensor], load_checkpoin...
    method predict_pipeline (line 334) | def predict_pipeline(
    method load_checkpoint (line 374) | def load_checkpoint(self):
    method save_checkpoint (line 383) | def save_checkpoint(self, state: Dict =None):
    method get_state (line 404) | def get_state(self) -> Dict[str, Dict]:

FILE: transopt/optimizer/model/get_model.py
  function get_model (line 5) | def get_model(model_name, **kwargs):

FILE: transopt/optimizer/model/gp.py
  class GP (line 15) | class GP(Model):
    method __init__ (line 17) | def __init__(
    method kernel (line 44) | def kernel(self):
    method noise_variance (line 49) | def noise_variance(self):
    method kernel (line 54) | def kernel(self, kernel: Kern):
    method meta_fit (line 73) | def meta_fit(
    method fit (line 81) | def fit(
    method predict (line 126) | def predict(
    method _raw_predict (line 136) | def _raw_predict(
    method predict_posterior_mean (line 164) | def predict_posterior_mean(self, X) -> np.ndarray:
    method predict_posterior_covariance (line 188) | def predict_posterior_covariance(self, x1, x2) -> np.ndarray:
    method compute_kernel (line 215) | def compute_kernel(self, x1, x2) -> np.ndarray:
    method compute_kernel_diagonal (line 232) | def compute_kernel_diagonal(self, X) -> np.ndarray:
    method sample (line 249) | def sample(
    method get_fmin (line 271) | def get_fmin(self):

FILE: transopt/optimizer/model/hebo.py
  class HEBO (line 41) | class HEBO(AbstractOptimizer):
    method __init__ (line 45) | def __init__(self, space, model_name = 'gpy', rand_sample = None, acq_...
    method quasi_sample (line 64) | def quasi_sample(self, n, fix_input = None):
    method model_config (line 79) | def model_config(self):
    method get_best_id (line 112) | def get_best_id(self, fix_input : dict = None) -> int:
    method suggest (line 128) | def suggest(self, n_suggestions=1, fix_input = None):
    method check_unique (line 199) | def check_unique(self, rec : pd.DataFrame) -> [bool]:
    method observe (line 202) | def observe(self, X, y):
    method best_x (line 221) | def best_x(self)->pd.DataFrame:
    method best_y (line 228) | def best_y(self)->float:

FILE: transopt/optimizer/model/mhgp.py
  class MHGP (line 27) | class MHGP(Model):
    method __init__ (line 38) | def __init__(self,
    method _compute_residuals (line 62) | def _compute_residuals(self, X: np.ndarray, Y: np.ndarray) -> np.ndarray:
    method _update_meta_data (line 89) | def _update_meta_data(self, *gps: GP):
    method _meta_fit_single_gp (line 94) | def _meta_fit_single_gp(
    method meta_fit (line 124) | def meta_fit(
    method fit (line 156) | def fit(
    method predict (line 184) | def predict(
    method predict_posterior_mean (line 202) | def predict_posterior_mean(self, X: np.ndarray, idx: int = None) -> np...
    method predict_posterior_covariance (line 232) | def predict_posterior_covariance(self, x1: np.ndarray, x2: np.ndarray)...
    method get_fmin (line 244) | def get_fmin(self):

FILE: transopt/optimizer/model/mlp.py
  function compute_irm_penalty (line 21) | def compute_irm_penalty(losses, dummy):
  class Net (line 26) | class Net(nn.Module):
    method __init__ (line 27) | def __init__(self, input_dim, dropout_rate=0.3):
    method forward (line 35) | def forward(self, x):
  class MLP (line 47) | class MLP(Model):
    method __init__ (line 48) | def __init__(self, config):
    method meta_fit (line 58) | def meta_fit(
    method fit (line 66) | def fit(
    method predict (line 150) | def predict(
    method get_fmin (line 163) | def get_fmin(self):
    method save_plots (line 168) | def save_plots(self, train_losses, val_losses, X_val, y_val, output_di...

FILE: transopt/optimizer/model/model_base.py
  class Model (line 6) | class Model(ABC):
    method __init__ (line 9) | def __init__(self):
    method X (line 15) | def X(self) -> np.ndarray:
    method y (line 20) | def y(self) -> np.ndarray:
    method meta_fit (line 25) | def meta_fit(self, metadata, **kwargs):
    method fit (line 37) | def fit(self, X, Y, **kwargs):
    method predict (line 48) | def predict(self, X) -> (np.ndarray, np.ndarray):

FILE: transopt/optimizer/model/moeadego.py
  class MoeadEGO (line 12) | class MoeadEGO(Model):
    method __init__ (line 13) | def __init__(
    method fit (line 35) | def fit(self, X, Y):
    method predict (line 43) | def predict(self, X, full_cov=False):
    method _create_model (line 46) | def _create_model(self, X, Y):
    method _update_model (line 64) | def _update_model(self, X, Y):
    method _make_prediction (line 81) | def _make_prediction(self, X, full_cov=False):
    method _make_prediction_by_id (line 98) | def _make_prediction_by_id(self, X, idx, full_cov=False):

FILE: transopt/optimizer/model/mtgp.py
  class MTGP (line 29) | class MTGP(GP):
    method __init__ (line 58) | def __init__(
    method meta_fit (line 79) | def meta_fit(
    method fit (line 95) | def fit(
    method _raw_predict (line 163) | def _raw_predict(

FILE: transopt/optimizer/model/neuralprocess.py
  class NeuralProcess (line 9) | class NeuralProcess(Model):
    method __init__ (line 10) | def __init__(self):

FILE: transopt/optimizer/model/parego.py
  class ParEGO (line 10) | class ParEGO(Model):
    method __init__ (line 11) | def __init__(self, seed=0, normalize=True, **options):
    method fit (line 21) | def fit(self, X, Y):
    method predict (line 29) | def predict(self, X, full_cov=False):
    method _scalarization (line 32) | def _scalarization(self, Y: np.ndarray, rho):
    method _create_model (line 43) | def _create_model(self, X, Y):
    method _update_model (line 50) | def _update_model(self, X, Y):
    method _make_prediction (line 63) | def _make_prediction(self, X, full_cov=False):

FILE: transopt/optimizer/model/pr.py
  class PR (line 12) | class PR(Model):
    method __init__ (line 13) | def __init__(
    method meta_fit (line 29) | def meta_fit(
    method fit (line 37) | def fit(
    method predict (line 63) | def predict(

FILE: transopt/optimizer/model/rbfn.py
  class RegressionDataset (line 16) | class RegressionDataset(Dataset):
    method __init__ (line 18) | def __init__(self, inputs, targets):
    method __len__ (line 22) | def __len__(self):
    method __getitem__ (line 25) | def __getitem__(self, index):
  class RbfNet (line 31) | class RbfNet(nn.Module):
    method __init__ (line 32) | def __init__(self, centers, beta):
    method kernel_fun (line 40) | def kernel_fun(self, batches):
    method forward (line 47) | def forward(self, x):
  class rbfn (line 53) | class rbfn(object):
    method __init__ (line 54) | def __init__(self, dataset, max_epoch=30, batch_size=5, lr=0.01, num_c...
    method train (line 78) | def train(self):
    method predict (line 98) | def predict(self, x):
    method cluster (line 105) | def cluster(self):
    method calculate_beta (line 111) | def calculate_beta(self):
    method update_dataset (line 120) | def update_dataset(self, dataset):
  class RBFN (line 130) | class RBFN(Model):
    method __init__ (line 131) | def __init__(
    method meta_fit (line 155) | def meta_fit(
    method fit (line 163) | def fit(
    method predict (line 199) | def predict(

FILE: transopt/optimizer/model/rf.py
  class RF (line 27) | class RF(Model):
    method __init__ (line 28) | def __init__(
    method meta_fit (line 55) | def meta_fit(
    method fit (line 63) | def fit(
    method predict (line 82) | def predict(
    method _raw_predict (line 89) | def _raw_predict(
    method _raw_predic_var (line 104) | def _raw_predic_var(self, X, trees, predictions, min_variance=0.0):
    method sample (line 121) | def sample(
    method get_fmin (line 143) | def get_fmin(self):

FILE: transopt/optimizer/model/rgpe.py
  function roll_col (line 14) | def roll_col(X: np.ndarray, shift: int) -> np.ndarray:
  function compute_ranking_loss (line 21) | def compute_ranking_loss(
  class RGPE (line 48) | class RGPE(Model):
    method __init__ (line 49) | def __init__(
    method _meta_fit_single_gp (line 75) | def _meta_fit_single_gp(
    method meta_fit (line 103) | def meta_fit(self,
    method fit (line 132) | def fit(self,
    method predict (line 157) | def predict(
    method _calculate_weights (line 184) | def _calculate_weights(self, alpha: float = 0.0):
    method _calculate_weights_with_no_observations (line 324) | def _calculate_weights_with_no_observations(self):
    method _calculate_weights_with_one_observation (line 346) | def _calculate_weights_with_one_observation(self):
    method _update_meta_data (line 398) | def _update_meta_data(self, *gps: GPy.models.GPRegression):
    method meta_update (line 403) | def meta_update(self):
    method set_XY (line 406) | def set_XY(self, Data:Dict):
    method print_Weights (line 410) | def print_Weights(self):
    method get_Weights (line 414) | def get_Weights(self):
    method loss (line 420) | def loss(self, task_uid: int) -> np.ndarray:
    method posterior_samples_f (line 429) | def posterior_samples_f(self,X, size=10, **predict_kwargs):
    method posterior_samples (line 451) | def posterior_samples(self, X, size=10, Y_metadata=None, likelihood=No...
    method get_fmin (line 476) | def get_fmin(self):

FILE: transopt/optimizer/model/sgpt.py
  function roll_col (line 13) | def roll_col(X: np.ndarray, shift: int) -> np.ndarray:
  class SGPT (line 20) | class SGPT(Model):
    method __init__ (line 21) | def __init__(
    method _meta_fit_single_gp (line 49) | def _meta_fit_single_gp(
    method meta_fit (line 77) | def meta_fit(self,
    method fit (line 106) | def fit(self,
    method predict (line 132) | def predict(self, X, return_full: bool = False, with_noise: bool = Fal...
    method Epanechnikov_kernel (line 155) | def Epanechnikov_kernel(self, X1, X2):
    method _calculate_weights (line 164) | def _calculate_weights(self, alpha: float = 0.0):
    method posterior_samples_f (line 215) | def posterior_samples_f(self,X, size=10, **predict_kwargs):
    method posterior_samples (line 237) | def posterior_samples(self, X, size=10, Y_metadata=None, likelihood=No...
    method get_fmin (line 262) | def get_fmin(self):

FILE: transopt/optimizer/model/smsego.py
  class SMSEGO (line 10) | class SMSEGO(Model):
    method __init__ (line 11) | def __init__(self, seed=0, normalize=True, **options):
    method fit (line 21) | def fit(self, X, Y):
    method predict (line 29) | def predict(self, X, full_cov=False):
    method _create_model (line 32) | def _create_model(self, X, Y):
    method _update_model (line 40) | def _update_model(self, X, Y):
    method _make_prediction (line 53) | def _make_prediction(self, X, full_cov=False):

FILE: transopt/optimizer/model/utils.py
  function is_pd (line 10) | def is_pd(a: np.ndarray) -> bool:
  function nearest_pd (line 27) | def nearest_pd(a: np.ndarray) -> np.ndarray:
  function compute_cholesky (line 51) | def compute_cholesky(matrix: np.ndarray) -> np.ndarray:
  class FixedKernel (line 83) | class FixedKernel(Fixed):
    method __init__ (line 89) | def __init__(
    method to_dict (line 113) | def to_dict(self) -> dict:
  function compute_alpha (line 123) | def compute_alpha(model: "GP", x) -> np.ndarray:
  class CrossTaskKernel (line 149) | class CrossTaskKernel(BasisFuncKernel):
    method __init__ (line 152) | def __init__(
    method _phi (line 169) | def _phi(self, X: np.ndarray) -> np.ndarray:

FILE: transopt/optimizer/normalizer/normalizer_base.py
  class NormalizerBase (line 5) | class NormalizerBase(ABC):
    method __init__ (line 6) | def __init__(self, config):
    method fit (line 9) | def fit(self, X, Y):
    method transform (line 12) | def transform(self, X = None, Y = None):
    method inverse_transform (line 15) | def inverse_transform(self, X = None, Y = None):

FILE: transopt/optimizer/normalizer/standerd.py
  class Standard_normalizer (line 29) | class Standard_normalizer(NormalizerBase):
    method __init__ (line 30) | def __init__(self, config, metadata =  None, metadata_info = None):
    method fit (line 35) | def fit(self, X, Y):
    method transform (line 38) | def transform(self, X = None, Y = None):
    method inverse_transform (line 45) | def inverse_transform(self, X = None, Y = None):

FILE: transopt/optimizer/optimizer_base/EvoOptimizerBase.py
  class EVOBase (line 13) | class EVOBase(OptimizerBase):
    method __init__ (line 17) | def __init__(self, config):

FILE: transopt/optimizer/optimizer_base/base.py
  class OptimizerBase (line 4) | class OptimizerBase(abc.ABC, metaclass=abc.ABCMeta):
    method __init__ (line 11) | def __init__(self, config, **kwargs):
    method suggest (line 27) | def suggest(self, n_suggestions:Union[None, int] = None)->List[Dict]:
    method observe (line 45) | def observe(self, input_vectors: Union[List[Dict], Dict], output_value...

FILE: transopt/optimizer/optimizer_base/bo.py
  class BO (line 17) | class BO(OptimizerBase):
    method __init__ (line 22) | def __init__(self, Refiner, Sampler, ACF, Pretrain, Model, Normalizer,...
    method link_task (line 42) | def link_task(self, task_name:str, search_space: SearchSpace):
    method search_space_refine (line 51) | def search_space_refine(self, metadata = None, metadata_info = None):
    method sample_initial_set (line 57) | def sample_initial_set(self, metadata = None, metadata_info = None):
    method pretrain (line 60) | def pretrain(self, metadata = None, metadata_info = None):
    method meta_fit (line 66) | def meta_fit(self, metadata = None, metadata_info = None):
    method fit (line 77) | def fit(self):
    method suggest (line 85) | def suggest(self):
    method observe (line 95) | def observe(self, X: np.ndarray, Y: List[Dict]) -> None:

FILE: transopt/optimizer/pretrain/deepkernelpretrain.py
  class Metric (line 20) | class Metric(object):
    method __init__ (line 21) | def __init__(self,prefix='train: '):
    method update (line 25) | def update(self,loss,noise,mse):
    method reset (line 30) | def reset(self,):
    method report (line 35) | def report(self):
    method get (line 40) | def get(self):
  function totorch (line 46) | def totorch(x,device):
  class MLP (line 50) | class MLP(nn.Module):
    method __init__ (line 51) | def __init__(self, input_size, hidden_size=[32,32,32,32], dropout=0.0):
    method forward (line 60) | def forward(self,x):
  class ExactGPLayer (line 70) | class ExactGPLayer(gpytorch.models.ExactGP):
    method __init__ (line 71) | def __init__(self, train_x, train_y, likelihood,config,dims ):
    method forward (line 82) | def forward(self, x):
  class DeepKernelPretrain (line 90) | class DeepKernelPretrain(nn.Module):
    method __init__ (line 91) | def __init__(self, config = {}):
    method set_data (line 118) | def set_data(self, metadata, metadata_info= None):
    method get_tasks (line 134) | def get_tasks(self,):
    method get_model_likelihood_mll (line 138) | def get_model_likelihood_mll(self, train_size):
    method epoch_end (line 150) | def epoch_end(self):
    method meta_train (line 154) | def meta_train(self, epochs = 50000, lr = 0.0001):
    method train_loop (line 164) | def train_loop(self, epoch, optimizer, scheduler=None):
    method test_loop (line 197) | def test_loop(self, task, train):
    method get_batch (line 215) | def get_batch(self,task):
    method get_support_and_queries (line 229) | def get_support_and_queries(self,task, train=False):
    method save_checkpoint (line 247) | def save_checkpoint(self, checkpoint):
    method load_checkpoint (line 254) | def load_checkpoint(self, checkpoint):

FILE: transopt/optimizer/pretrain/get_pretrain.py
  function get_pretrain (line 5) | def get_pretrain(pretrain_name, **kwargs):

FILE: transopt/optimizer/pretrain/hyper_bo.py
  class HyperBOPretrain (line 7) | class HyperBOPretrain(PretrainBase):
    method __init__ (line 8) | def __init__(self, config) -> None:

FILE: transopt/optimizer/pretrain/pretrain_base.py
  class PretrainBase (line 3) | class PretrainBase:
    method __init__ (line 4) | def __init__(self) -> None:

FILE: transopt/optimizer/refiner/box.py
  class BoxRefiner (line 5) | class BoxRefiner(RefinerBase):
    method __init__ (line 6) | def __init__(self, config) -> None:

FILE: transopt/optimizer/refiner/ellipse.py
  class EllipseRefiner (line 5) | class EllipseRefiner(RefinerBase):
    method __init__ (line 6) | def __init__(self, config) -> None:

FILE: transopt/optimizer/refiner/get_refiner.py
  function get_refiner (line 5) | def get_refiner(refiner_name, **kwargs):

FILE: transopt/optimizer/refiner/prune.py
  class Prune (line 6) | class Prune(RefinerBase):
    method __init__ (line 7) | def __init__(self, config) -> None:
    method refine (line 10) | def refine(self, search_space, metadata=None):
    method check_metadata_avaliable (line 14) | def check_metadata_avaliable(self, metadata):

FILE: transopt/optimizer/refiner/refiner_base.py
  class RefinerBase (line 4) | class RefinerBase:
    method __init__ (line 5) | def __init__(self, config) -> None:
    method refine (line 8) | def refine(self, search_space, metadata=None):
    method check_metadata_avaliable (line 12) | def check_metadata_avaliable(self, metadata):

FILE: transopt/optimizer/sampler/get_sampler.py
  function get_sampler (line 5) | def get_sampler(sampler_name, **kwargs):

FILE: transopt/optimizer/sampler/grid.py
  class GridSampler (line 6) | class GridSampler(Sampler):
    method generate_grid_for_variable (line 7) | def generate_grid_for_variable(self, var_range, is_discrete, steps):
    method sample (line 18) | def sample(self, search_space, steps=5, metadata=None):

FILE: transopt/optimizer/sampler/lhs.py
  class LatinHypercubeSampler (line 10) | class LatinHypercubeSampler(Sampler):
    method sample (line 11) | def sample(self, search_space:SearchSpace, metadata = None):

FILE: transopt/optimizer/sampler/lhs_BAK.py
  function lhs (line 11) | def lhs(d, samples=None, criterion=None, iterations=5, correlation_matri...
  function _lhsclassic (line 63) | def _lhsclassic(d, samples):
  function _lhscentered (line 69) | def _lhscentered(d, samples):
  function _lhsmaximin (line 76) | def _lhsmaximin(d, samples, iterations, lhstype):
  function _lhscorrelate (line 96) | def _lhscorrelate(d, samples, iterations):
  function _lhsmu (line 114) | def _lhsmu(d, samples=None, corr=None, M=5):

FILE: transopt/optimizer/sampler/random.py
  class RandomSampler (line 7) | class RandomSampler(Sampler):
    method sample (line 8) | def sample(self, search_space, metadata = None):

FILE: transopt/optimizer/sampler/sampler_base.py
  class Sampler (line 2) | class Sampler:
    method __init__ (line 3) | def __init__(self, n_samples, config) -> None:
    method sample (line 7) | def sample(self, search_space, metadata=None):
    method change_n_samples (line 10) | def change_n_samples(self, n_samples):
    method check_metadata_avaliable (line 13) | def check_metadata_avaliable(self, metadata):

FILE: transopt/optimizer/sampler/sobel.py
  class SobolSampler (line 8) | class SobolSampler(Sampler):
    method sample (line 9) | def sample(self, search_space, metadata = None):

FILE: transopt/optimizer/selector/fuzzy_selector.py
  class FuzzySelector (line 6) | class FuzzySelector(SelectorBase):
    method __init__ (line 7) | def __init__(self, config):
    method fetch_data (line 10) | def fetch_data(self, tasks_info):

FILE: transopt/optimizer/selector/lsh_selector.py
  class LSHSelector (line 5) | class LSHSelector(SelectorBase):
    method __init__ (line 6) | def __init__(self, config):
    method fetch_data (line 10) | def fetch_data(self, tasks_info):

FILE: transopt/optimizer/selector/selector_base.py
  class SelectorBase (line 6) | class SelectorBase:
    method __init__ (line 7) | def __init__(self, config):
    method fetch_data (line 13) | def fetch_data(self, tasks_info):

FILE: transopt/remote/experiment_client.py
  class ExperimentClient (line 5) | class ExperimentClient:
    method __init__ (line 6) | def __init__(self, server_url, timeout=10):
    method _handle_response (line 10) | def _handle_response(self, response):
    method start_experiment (line 17) | def start_experiment(self, params):
    method get_experiment_result (line 27) | def get_experiment_result(self, task_id):
    method wait_for_result (line 39) | def wait_for_result(self, task_id, poll_interval=2):

FILE: transopt/remote/experiment_server.py
  class ExperimentServer (line 5) | class ExperimentServer:
    method __init__ (line 6) | def __init__(self, task_handler):
    method _validate_params (line 11) | def _validate_params(self, params):
    method _setup_routes (line 15) | def _setup_routes(self):
    method run (line 54) | def run(self, host="0.0.0.0", port=5001):

FILE: transopt/remote/experiment_tasks.py
  class DebugTask (line 11) | class DebugTask(Task):
    method on_failure (line 12) | def on_failure(self, exc, task_id, args, kwargs, einfo):
    method on_success (line 15) | def on_success(self, retval, task_id, args, kwargs):
    method after_return (line 18) | def after_return(self, status, retval, task_id, args, kwargs, einfo):
  class ExperimentTaskHandler (line 22) | class ExperimentTaskHandler:
    method __init__ (line 23) | def __init__(self):
    method run_experiment (line 27) | def run_experiment(self, params):
    method start_experiment (line 57) | def start_experiment(self, params):

FILE: transopt/space/fidelity_space.py
  class FidelitySpace (line 7) | class FidelitySpace:
    method __init__ (line 8) | def __init__(self, fidelity_variables):
    method fidelity_names (line 12) | def fidelity_names(self):
    method get_fidelity_range (line 16) | def get_fidelity_range(self):

FILE: transopt/space/search_space.py
  class SearchSpace (line 7) | class SearchSpace:
    method __init__ (line 8) | def __init__(self, variables):
    method __getitem__ (line 23) | def __getitem__(self, item):
    method __contains__ (line 27) | def __contains__(self, item):
    method get_design_variables (line 30) | def get_design_variables(self):
    method get_design_variable (line 33) | def get_design_variable(self, name):
    method get_hyperparameter_names (line 36) | def get_hyperparameter_names(self):
    method get_hyperparameter_types (line 39) | def get_hyperparameter_types(self):
    method map_to_design_space (line 43) | def map_to_design_space(self, values: np.ndarray) -> dict:
    method map_from_design_space (line 64) | def map_from_design_space(self, values_dict: dict) -> np.ndarray:
    method update_range (line 81) | def update_range(self, name, new_range: tuple):

FILE: transopt/space/variable.py
  class Variable (line 4) | class Variable:
    method __init__ (line 5) | def __init__(self, name, type_):
    method search_space_range (line 10) | def search_space_range(self):
    method map2design (line 13) | def map2design(self, value):
    method map2search (line 17) | def map2search(self, value):
  class Continuous (line 22) | class Continuous(Variable):
    method __init__ (line 23) | def __init__(self, name, range_):
    method search_space_range (line 30) | def search_space_range(self):
    method map2design (line 33) | def map2design(self, value):
    method map2search (line 36) | def map2search(self, value):
  class Categorical (line 40) | class Categorical(Variable):
    method __init__ (line 41) | def __init__(self, name, categories):
    method search_space_range (line 49) | def search_space_range(self):
    method map2design (line 52) | def map2design(self, value):
    method map2search (line 55) | def map2search(self, value):
  class Integer (line 60) | class Integer(Variable):
    method __init__ (line 61) | def __init__(self, name, range_):
    method search_space_range (line 68) | def search_space_range(self):
    method map2design (line 71) | def map2design(self, value):
    method map2search (line 75) | def map2search(self, value):
  class LargeInteger (line 78) | class LargeInteger(Variable):
    method __init__ (line 79) | def __init__(self, name, range_):
    method search_space_range (line 85) | def search_space_range(self):
    method map2design (line 91) | def map2design(self, value):
    method map2search (line 95) | def map2search(self, value):
  class ExponentialInteger (line 99) | class ExponentialInteger(Variable):
    method __init__ (line 100) | def __init__(self, name, range_):
    method search_space_range (line 109) | def search_space_range(self):
    method map2design (line 114) | def map2design(self, value):
    method map2search (line 117) | def map2search(self, value):
  class LogContinuous (line 121) | class LogContinuous(Variable):
    method __init__ (line 122) | def __init__(self, name, range_):
    method search_space_range (line 129) | def search_space_range(self):
    method map2design (line 132) | def map2design(self, value):
    method map2search (line 135) | def map2search(self, value):

FILE: transopt/utils/Initialization.py
  function InitData (line 9) | def InitData(Init_method, KB, Init, Xdim, Dty, **kwargs):

FILE: transopt/utils/Kernel.py
  function construct_multi_objective_kernel (line 10) | def construct_multi_objective_kernel(input_dim, output_dim, base_kernel=...

FILE: transopt/utils/Normalization.py
  function get_normalizer (line 6) | def get_normalizer(name):
  function normalize_with_power_transform (line 20) | def normalize_with_power_transform(data: Union[np.ndarray, list], mean=N...
  function rank_normalize_with_power_transform (line 64) | def rank_normalize_with_power_transform(data: Union[np.ndarray, list]):
  function normalize (line 119) | def normalize(data:Union[List, Dict, np.ndarray], mean=None, std=None):

FILE: transopt/utils/Prior.py
  class Prior (line 13) | class Prior(object):
    method __new__ (line 16) | def __new__(cls, *args, **kwargs):
    method pdf (line 25) | def pdf(self, x):
    method plot (line 28) | def plot(self):
    method __repr__ (line 36) | def __repr__(self, *args, **kwargs):
  class Gaussian (line 40) | class Gaussian(Prior):
    method __new__ (line 53) | def __new__(cls, mu=0, sigma=1):  # Singleton:
    method __init__ (line 67) | def __init__(self, mu, sigma):
    method __str__ (line 73) | def __str__(self):
    method lnpdf (line 76) | def lnpdf(self, x):
    method lnpdf_grad (line 79) | def lnpdf_grad(self, x):
    method rvs (line 82) | def rvs(self, n):
    method getstate (line 85) | def getstate(self):
    method setstate (line 88) | def setstate(self, state):
  class Uniform (line 94) | class Uniform(Prior):
    method __new__ (line 97) | def __new__(cls, lower=0, upper=1):  # Singleton:
    method __init__ (line 111) | def __init__(self, lower, upper):
    method __str__ (line 122) | def __str__(self):
    method lnpdf (line 125) | def lnpdf(self, x):
    method lnpdf_grad (line 129) | def lnpdf_grad(self, x):
    method rvs (line 132) | def rvs(self, n):
  class LogGaussian (line 142) | class LogGaussian(Gaussian):
    method __new__ (line 155) | def __new__(cls, mu=0, sigma=1, name=''):  # Singleton:
    method __init__ (line 169) | def __init__(self, mu, sigma, name):
    method __str__ (line 176) | def __str__(self):
    method lnpdf (line 179) | def lnpdf(self, x):
    method lnpdf_grad (line 182) | def lnpdf_grad(self, x):
    method rvs (line 185) | def rvs(self, n):
    method getstate (line 188) | def getstate(self):
    method setstate (line 191) | def setstate(self, state):
  class MultivariateGaussian (line 197) | class MultivariateGaussian(Prior):
    method __new__ (line 210) | def __new__(cls, mu=0, var=1):  # Singleton:
    method __init__ (line 226) | def __init__(self, mu, var):
    method __str__ (line 237) | def __str__(self):
    method summary (line 240) | def summary(self):
    method pdf (line 243) | def pdf(self, x):
    method lnpdf (line 247) | def lnpdf(self, x):
    method lnpdf_grad (line 252) | def lnpdf_grad(self, x):
    method rvs (line 257) | def rvs(self, n):
    method plot (line 260) | def plot(self):
    method __getstate__ (line 268) | def __getstate__(self):
    method __setstate__ (line 271) | def __setstate__(self, state):
  function gamma_from_EV (line 282) | def gamma_from_EV(E, V):
  class Gamma (line 287) | class Gamma(Prior):
    method __new__ (line 300) | def __new__(cls, a=1, b=.5, name = ''):  # Singleton:
    method a (line 315) | def a(self):
    method b (line 319) | def b(self):
    method __init__ (line 322) | def __init__(self, a, b, name=''):
    method __str__ (line 328) | def __str__(self):
    method summary (line 331) | def summary(self):
    method lnpdf (line 342) | def lnpdf(self, x):
    method lnpdf_grad (line 345) | def lnpdf_grad(self, x):
    method rvs (line 348) | def rvs(self, n):
    method getstate (line 352) | def getstate(self):
    method update (line 355) | def update(self, value):
    method from_EV (line 360) | def from_EV(E, V):
    method __getstate__ (line 372) | def __getstate__(self):
    method __setstate__ (line 375) | def __setstate__(self, state):
  class InverseGamma (line 380) | class InverseGamma(Gamma):
    method __str__ (line 393) | def __str__(self):
    method summary (line 396) | def summary(self):
    method from_EV (line 400) | def from_EV(E, V):
    method lnpdf (line 403) | def lnpdf(self, x):
    method lnpdf_grad (line 406) | def lnpdf_grad(self, x):
    method rvs (line 409) | def rvs(self, n):
  class DGPLVM_KFDA (line 412) | class DGPLVM_KFDA(Prior):
    method __init__ (line 436) | def __init__(self, lambdaa, sigma2, lbl, kern, x_shape):
    method get_class_label (line 449) | def get_class_label(self, y):
    method compute_cls (line 457) | def compute_cls(self, x):
    method x_reduced (line 470) | def x_reduced(self, cls):
    method compute_lst_ni (line 476) | def compute_lst_ni(self):
    method compute_a (line 490) | def compute_a(self, lst_ni):
    method compute_A (line 503) | def compute_A(self, lst_ni):
    method lnpdf (line 513) | def lnpdf(self, x):
    method lnpdf_grad (line 524) | def lnpdf_grad(self, x):
    method rvs (line 536) | def rvs(self, n):
    method __str__ (line 539) | def __str__(self):
    method __getstate___ (line 542) | def __getstate___(self):
    method __setstate__ (line 545) | def __setstate__(self, state):
  class DGPLVM (line 559) | class DGPLVM(Prior):
    method __new__ (line 570) | def __new__(cls, sigma2, lbl, x_shape):
    method __init__ (line 573) | def __init__(self, sigma2, lbl, x_shape):
    method get_class_label (line 582) | def get_class_label(self, y):
    method compute_cls (line 590) | def compute_cls(self, x):
    method compute_Mi (line 601) | def compute_Mi(self, cls):
    method compute_indices (line 610) | def compute_indices(self, x):
    method compute_listIndices (line 621) | def compute_listIndices(self, data_idx):
    method compute_Sb (line 637) | def compute_Sb(self, cls, M_i, M_0):
    method compute_Sw (line 646) | def compute_Sw(self, cls, M_i):
    method compute_sig_beta_Bi (line 658) | def compute_sig_beta_Bi(self, data_idx, M_i, M_0, lst_idx_all):
    method compute_wj (line 681) | def compute_wj(self, data_idx, M_i):
    method compute_sig_alpha_W (line 692) | def compute_sig_alpha_W(self, data_idx, lst_idx_all, W_i):
    method lnpdf (line 709) | def lnpdf(self, x):
    method lnpdf_grad (line 723) | def lnpdf_grad(self, x):
    method rvs (line 761) | def rvs(self, n):
    method __str__ (line 764) | def __str__(self):
  class DGPLVM_Lamda (line 773) | class DGPLVM_Lamda(Prior, Parameterized):
    method __init__ (line 794) | def __init__(self, sigma2, lbl, x_shape, lamda, name='DP_prior'):
    method get_class_label (line 807) | def get_class_label(self, y):
    method compute_cls (line 815) | def compute_cls(self, x):
    method compute_Mi (line 826) | def compute_Mi(self, cls):
    method compute_indices (line 835) | def compute_indices(self, x):
    method compute_listIndices (line 846) | def compute_listIndices(self, data_idx):
    method compute_Sb (line 862) | def compute_Sb(self, cls, M_i, M_0):
    method compute_Sw (line 871) | def compute_Sw(self, cls, M_i):
    method compute_sig_beta_Bi (line 883) | def compute_sig_beta_Bi(self, data_idx, M_i, M_0, lst_idx_all):
    method compute_wj (line 906) | def compute_wj(self, data_idx, M_i):
    method compute_sig_alpha_W (line 917) | def compute_sig_alpha_W(self, data_idx, lst_idx_all, W_i):
    method lnpdf (line 934) | def lnpdf(self, x):
    method lnpdf_grad (line 955) | def lnpdf_grad(self, x):
    method rvs (line 1010) | def rvs(self, n):
    method __str__ (line 1013) | def __str__(self):
  class DGPLVM_T (line 1018) | class DGPLVM_T(Prior):
    method __init__ (line 1039) | def __init__(self, sigma2, lbl, x_shape, vec):
    method get_class_label (line 1050) | def get_class_label(self, y):
    method compute_cls (line 1058) | def compute_cls(self, x):
    method compute_Mi (line 1069) | def compute_Mi(self, cls):
    method compute_indices (line 1079) | def compute_indices(self, x):
    method compute_listIndices (line 1090) | def compute_listIndices(self, data_idx):
    method compute_Sb (line 1106) | def compute_Sb(self, cls, M_i, M_0):
    method compute_Sw (line 1115) | def compute_Sw(self, cls, M_i):
    method compute_sig_beta_Bi (line 1127) | def compute_sig_beta_Bi(self, data_idx, M_i, M_0, lst_idx_all):
    method compute_wj (line 1150) | def compute_wj(self, data_idx, M_i):
    method compute_sig_alpha_W (line 1161) | def compute_sig_alpha_W(self, data_idx, lst_idx_all, W_i):
    method lnpdf (line 1178) | def lnpdf(self, x):
    method lnpdf_grad (line 1196) | def lnpdf_grad(self, x):
    method rvs (line 1238) | def rvs(self, n):
    method __str__ (line 1241) | def __str__(self):
  class HalfT (line 1247) | class HalfT(Prior):
    method __new__ (line 1258) | def __new__(cls, A, nu):  # Singleton:
    method __init__ (line 1268) | def __init__(self, A, nu):
    method __str__ (line 1273) | def __str__(self):
    method lnpdf (line 1276) | def lnpdf(self, theta):
    method lnpdf_grad (line 1293) | def lnpdf_grad(self, theta):
    method rvs (line 1302) | def rvs(self, n):
  class Exponential (line 1311) | class Exponential(Prior):
    method __new__ (line 1322) | def __new__(cls, l):  # Singleton:
    method __init__ (line 1332) | def __init__(self, l):
    method __str__ (line 1335) | def __str__(self):
    method summary (line 1338) | def summary(self):
    method lnpdf (line 1346) | def lnpdf(self, x):
    method lnpdf_grad (line 1349) | def lnpdf_grad(self, x):
    method rvs (line 1352) | def rvs(self, n):
  class StudentT (line 1355) | class StudentT(Prior):
    method __new__ (line 1369) | def __new__(cls, mu=0, sigma=1, nu=4):  # Singleton:
    method __init__ (line 1383) | def __init__(self, mu, sigma, nu):
    method __str__ (line 1389) | def __str__(self):
    method lnpdf (line 1392) | def lnpdf(self, x):
    method lnpdf_grad (line 1396) | def lnpdf_grad(self, x):
    method rvs (line 1399) | def rvs(self, n):

FILE: transopt/utils/Read.py
  function read_file (line 6) | def read_file(file_path)->pd.DataFrame:
  function read_url (line 47) | def read_url(url):

FILE: transopt/utils/Visualization.py
  function visual_contour (line 15) | def visual_contour(
  function visual_oned (line 162) | def visual_oned(
  function visual_pf (line 307) | def visual_pf(

FILE: transopt/utils/check.py
  function check_dir (line 8) | def  check_dir(self):
  function check_url (line 14) | def check_url(url):
  function check_ip_address (line 22) | def check_ip_address(ip_address):

FILE: transopt/utils/encoding.py
  function target_encoding (line 3) | def target_encoding(df:pds.DataFrame, column_name, target_name):
  function multitarget_encoding (line 29) | def multitarget_encoding(df:pds.DataFrame, column_name, target_names):

FILE: transopt/utils/hypervolume.py
  function find_pareto (line 5) | def find_pareto(X, y):
  function find_pareto_only_y (line 41) | def find_pareto_only_y(y):
  function create_cells (line 71) | def create_cells(pf, ref, ref_inv=None):
  function find_pareto_from_posterior (line 142) | def find_pareto_from_posterior(X, mean, y):
  function calc_hypervolume (line 183) | def calc_hypervolume(y, w_ref):

FILE: transopt/utils/log.py
  function get_logger (line 10) | def get_logger(logger_name: str) -> logging.Logger:

FILE: transopt/utils/openml_data_manager.py
  function _check_dir (line 36) | def _check_dir(path: Path):
  function get_openml100_taskids (line 48) | def get_openml100_taskids():
  function get_openmlcc18_taskids (line 69) | def get_openmlcc18_taskids():
  function _load_data (line 86) | def _load_data(task_id: int):
  class DataManager (line 123) | class DataManager(abc.ABC, metaclass=abc.ABCMeta):
    method __init__ (line 132) | def __init__(self):
    method load (line 136) | def load(self):
    method create_save_directory (line 142) | def create_save_directory(self, save_dir: Path):
    method _download_file_with_progressbar (line 156) | def _download_file_with_progressbar(self, data_url: str, data_file: Pa...
    method _untar_data (line 179) | def _untar_data(self, compressed_file: Path, save_dir: Union[Path, Non...
    method _unzip_data (line 189) | def _unzip_data(self, compressed_file: Path, save_dir: Union[Path, Non...
  class HoldoutDataManager (line 197) | class HoldoutDataManager(DataManager):
    method __init__ (line 210) | def __init__(self):
  class CrossvalidationDataManager (line 221) | class CrossvalidationDataManager(DataManager):
    method __init__ (line 233) | def __init__(self):
  class OpenMLHoldoutDataManager (line 242) | class OpenMLHoldoutDataManager(HoldoutDataManager):
    method __init__ (line 262) | def __init__(self, openml_task_id: int, rng: Union[int, np.random.Rand...
    method load (line 276) | def load(self) -> Tuple[np.ndarray, np.ndarray, np.ndarray,
    method replace_nans_in_cat_columns (line 303) | def replace_nans_in_cat_columns(X_train: np.ndarray, X_valid: np.ndarr...
  class OpenMLCrossvalidationDataManager (line 329) | class OpenMLCrossvalidationDataManager(CrossvalidationDataManager):
    method __init__ (line 349) | def __init__(self, openml_task_id: int, rng: Union[int, np.random.Rand...
    method load (line 363) | def load(self):

FILE: transopt/utils/pareto.py
  function convert_minimization (line 10) | def convert_minimization(Y, obj_type=None):
  function find_pareto_front (line 32) | def find_pareto_front(Y, return_index=False, obj_type=None, eps=1e-8):
  function check_pareto (line 54) | def check_pareto(Y, obj_type=None):
  function calc_hypervolume (line 74) | def calc_hypervolume(Y, ref_point, obj_type=None):
  function calc_pred_error (line 88) | def calc_pred_error(Y, Y_pred_mean, average=False):

FILE: transopt/utils/path.py
  function get_library_path (line 5) | def get_library_path():
  function get_absolut_path (line 15) | def get_absolut_path():
  function get_log_file_path (line 26) | def get_log_file_path():

FILE: transopt/utils/plot.py
  function plot2D (line 5) | def plot2D(X, Y, c='black', ls='', marker='o', fillstyle=None, label=Non...
  function plot3D (line 36) | def plot3D(X, Y, Z, c='black', ls='', marker='o', fillstyle=None, label=...
  function surface3D (line 65) | def surface3D(X_grid, Y_grid, cmap=cm.Blues, ax=None, file=None, show=Fa...

FILE: transopt/utils/profile.py
  function profile_function (line 4) | def profile_function(filename=None):

FILE: transopt/utils/rng_helper.py
  function get_rng (line 14) | def get_rng(rng: Union[int, np.random.RandomState, None] = None,
  function _cast_int_to_random_state (line 42) | def _cast_int_to_random_state(rng: Union[int, np.random.RandomState]) ->...
  function serialize_random_state (line 62) | def serialize_random_state(random_state: np.random.RandomState) -> Tuple...
  function deserialize_random_state (line 68) | def deserialize_random_state(random_state: Tuple[int, List, int, int, in...

FILE: transopt/utils/serialization.py
  class InputData (line 8) | class InputData:
  class TaskData (line 13) | class TaskData:
  function output_to_ndarray (line 39) | def output_to_ndarray(Y: List[Dict]) -> np.ndarray:
  function multioutput_to_ndarray (line 49) | def multioutput_to_ndarray(output_value: List[Dict], num_output:int) -> ...
  function convert_np_to_bulidin (line 62) | def convert_np_to_bulidin(obj):

FILE: transopt/utils/sk.py
  function skDemo (line 27) | def skDemo(n=5) :
  class o (line 53) | class o:
    method __init__ (line 54) | def __init__(i,**d) : i.__dict__.update(**d)
  class THE (line 56) | class THE:
  function cliffsDeltaSlow (line 73) | def cliffsDeltaSlow(lst1,lst2, dull = THE.cliffs.dull):
  function cliffsDelta (line 84) | def cliffsDelta(lst1, lst2,  dull=THE.cliffs.dull):
  function bootstrap (line 106) | def bootstrap(y0,z0,conf=THE.bs.conf,b=THE.bs.b):
  function same (line 147) | def same(x): return x
  class Mine (line 149) | class Mine:
    method identify (line 152) | def identify(i):
    method __repr__ (line 156) | def __repr__(i):
  class Rx (line 167) | class Rx(Mine):
    method __init__ (line 169) | def __init__(i, rx="",vals=[], key=same):
    method tiles (line 176) | def tiles(i,lo=0,hi=1): return  xtile(i.vals,lo,hi)
    method __lt__ (line 177) | def __lt__(i,j):        return i.med < j.med
    method __eq__ (line 178) | def __eq__(i,j):
    method __repr__ (line 181) | def __repr__(i):
    method xpect (line 183) | def xpect(i,j,b4):
    method data (line 191) | def data(**d):
    method fileIn (line 196) | def fileIn(f):
    method sum (line 211) | def sum(rxs):
    method show (line 220) | def show(rxs):
    method write (line 228) | def write(rxs):
    method sk (line 238) | def sk(rxs):
  function pairs (line 263) | def pairs(lst):
  function words (line 270) | def words(f):
  function xtile (line 276) | def xtile(lst,lo,hi,
  function thing (line 309) | def thing(x):
  function _cliffsDelta (line 318) | def _cliffsDelta():
  function bsTest (line 327) | def bsTest(n=1000,mu1=10,sigma1=1,mu2=10.2,sigma2=1):

FILE: transopt/utils/weights.py
  function _set_weight (line 5) | def _set_weight(w, c, v, unit, s, n_obj, dim):
  function _no_weight (line 20) | def _no_weight(unit, s, dim):
  function init_weight (line 30) | def init_weight(n_obj, n_sample):
  function tchebycheff (line 48) | def tchebycheff(X, W, ideal=None, normalize=False):

FILE: webui/src/App.js
  function App (line 24) | function App() {

FILE: webui/src/components/CalendarView/index.js
  constant THEME_BG (line 7) | const THEME_BG = CALENDAR_EVENT_STYLE
  function CalendarView (line 9) | function CalendarView({calendarEvents, addNewEvent, openDayDetail}){

FILE: webui/src/components/Cards/TitleCard.js
  function TitleCard (line 4) | function TitleCard({title, children, topMargin, TopSideButtons}){

FILE: webui/src/components/Input/InputText.js
  function InputText (line 4) | function InputText({labelTitle, labelStyle, type, containerStyle, defaul...

FILE: webui/src/components/Input/SearchBar.js
  function SearchBar (line 5) | function SearchBar({searchText, styleClass, placeholderText, setSearchTe...

FILE: webui/src/components/Input/SelectBox.js
  function SelectBox (line 8) | function SelectBox(props){

FILE: webui/src/components/Input/TextAreaInput.js
  function TextAreaInput (line 4) | function TextAreaInput({labelTitle, labelStyle, type, containerStyle, de...

FILE: webui/src/components/Input/ToogleInput.js
  function ToogleInput (line 4) | function ToogleInput({labelTitle, labelStyle, type, containerStyle, defa...

FILE: webui/src/components/Typography/ErrorText.js
  function ErrorText (line 1) | function ErrorText({styleClass, children}){

FILE: webui/src/components/Typography/HelperText.js
  function HelperText (line 1) | function HelperText({className, children}){

FILE: webui/src/components/Typography/Subtitle.js
  function Subtitle (line 1) | function Subtitle({styleClass, children}){

FILE: webui/src/components/Typography/Title.js
  function Title (line 1) | function Title({className, children}){

FILE: webui/src/containers/Header.js
  function Header (line 14) | function Header(){

FILE: webui/src/containers/Layout.js
  function Layout (line 11) | function Layout(){

FILE: webui/src/containers/LeftSidebar.js
  function LeftSidebar (line 7) | function LeftSidebar(){

FILE: webui/src/containers/ModalLayout.js
  function ModalLayout (line 9) | function ModalLayout(){

FILE: webui/src/containers/PageContent.js
  function PageContent (line 12) | function PageContent(){

FILE: webui/src/containers/RightSidebar.js
  function RightSidebar (line 9) | function RightSidebar(){

FILE: webui/src/containers/SidebarSubmenu.js
  function SidebarSubmenu (line 6) | function SidebarSubmenu({submenu, name, icon}){

FILE: webui/src/containers/SuspenseContent.js
  function SuspenseContent (line 1) | function SuspenseContent(){

FILE: webui/src/features/algorithm/components/OptTable.js
  function OptTable (line 47) | function OptTable({ optimizer }) {

FILE: webui/src/features/algorithm/components/SelectPlugin.js
  function SelectAlgorithm (line 12) | function SelectAlgorithm({SpaceRefiner, Sampler, Pretrain, Model, ACF, D...

FILE: webui/src/features/algorithm/index.js
  class Algorithm (line 9) | class Algorithm extends React.Component {
    method constructor (line 10) | constructor(props) {
    method render (line 29) | render() {

FILE: webui/src/features/analytics/charts/Box.js
  function Box (line 9) | function Box({ BoxData }) {

FILE: webui/src/features/analytics/charts/Trajectory.js
  class Trajectory (line 44) | class Trajectory extends Component {
    method constructor (line 45) | constructor(props) {
    method render (line 49) | render() {

FILE: webui/src/features/analytics/components/SelectTask.js
  function ASearch (line 14) | function ASearch({key, name, restField, remove, selections}) {
  function SelectTask (line 125) | function SelectTask({selections, handleClick}) {

FILE: webui/src/features/analytics/index.js
  class Analytics (line 17) | class Analytics extends React.Component {
    method constructor (line 18) | constructor(props) {
    method render (line 66) | render() {

FILE: webui/src/features/calendar/CalendarEventsBodyRightDrawer.js
  constant THEME_BG (line 3) | const THEME_BG = CALENDAR_EVENT_STYLE
  function CalendarEventsBodyRightDrawer (line 5) | function CalendarEventsBodyRightDrawer({filteredEvents}){

FILE: webui/src/features/calendar/index.js
  constant INITIAL_EVENTS (line 12) | const INITIAL_EVENTS = CALENDAR_INITIAL_EVENTS
  function Calendar (line 14) | function Calendar(){

FILE: webui/src/features/charts/components/BarChart.js
  function BarChart (line 15) | function BarChart(){

FILE: webui/src/features/charts/components/DoughnutChart.js
  function DoughnutChart (line 18) | function DoughnutChart(){

FILE: webui/src/features/charts/components/LineChart.js
  function LineChart (line 26) | function LineChart(){

FILE: webui/src/features/charts/components/PieChart.js
  function PieChart (line 18) | function PieChart(){

FILE: webui/src/features/charts/components/ScatterChart.js
  function ScatterChart (line 16) | function ScatterChart(){

FILE: webui/src/features/charts/components/StackBarChart.js
  function StackBarChart (line 15) | function StackBarChart(){

FILE: webui/src/features/charts/index.js
  function Charts (line 13) | function Charts(){

FILE: webui/src/features/chatbot/ChatBot.js
  class ChatBot (line 7) | class ChatBot extends React.Component {
    method render (line 9) | render() {

FILE: webui/src/features/chatbot/components/ChatUI.js
  function ChatUI (line 6) | function ChatUI() {

FILE: webui/src/features/common/components/ConfirmationModalBody.js
  function ConfirmationModalBody (line 7) | function ConfirmationModalBody({ extraObject, closeModal}){

FILE: webui/src/features/common/components/NotificationBodyRightDrawer.js
  function NotificationBodyRightDrawer (line 1) | function NotificationBodyRightDrawer(){

FILE: webui/src/features/dashboard/components/AmountStats.js
  function AmountStats (line 3) | function AmountStats({}){

FILE: webui/src/features/dashboard/components/BarChart.js
  function BarChart (line 15) | function BarChart({ ImportanceData }){

FILE: webui/src/features/dashboard/components/DashboardStats.js
  function DashboardStats (line 1) | function DashboardStats({title, icon, value, description, colorIndex}){

FILE: webui/src/features/dashboard/components/DashboardTopBar.js
  function DashboardTopBar (line 21) | function DashboardTopBar({updateDashboardPeriod}){

FILE: webui/src/features/dashboard/components/DoughnutChart.js
  function DoughnutChart (line 18) | function DoughnutChart(){

FILE: webui/src/features/dashboard/components/Footprint.js
  function Footprint (line 16) | function Footprint({ ScatterData = {} }) { // 提供默认值为空对象

FILE: webui/src/features/dashboard/components/Importance.js
  function Importance (line 3) | function Importance() {

FILE: webui/src/features/dashboard/components/PageStats.js
  function PageStats (line 5) | function PageStats({}){

FILE: webui/src/features/dashboard/components/ScatterChart.js
  function Footprint (line 38) | function Footprint({ ScatterData = {} }) {

FILE: webui/src/features/dashboard/components/UserChannels.js
  function UserChannels (line 11) | function UserChannels(){

FILE: webui/src/features/dashboard/index.js
  class Dashboard (line 15) | class Dashboard extends React.Component {
    method constructor (line 16) | constructor(props) {
    method componentDidMount (line 109) | componentDidMount() {
    method componentWillUnmount (line 114) | componentWillUnmount() {
    method render (line 149) | render() {

FILE: webui/src/features/documentation/DocComponents.js
  function DocComponents (line 13) | function DocComponents(){

FILE: webui/src/features/documentation/DocFeatures.js
  function Features (line 13) | function Features(){

FILE: webui/src/features/documentation/DocGettingStarted.js
  function GettingStarted (line 11) | function GettingStarted(){

FILE: webui/src/features/documentation/components/DocComponentsContent.js
  function DocComponentsContent (line 12) | function DocComponentsContent(){

FILE: webui/src/features/documentation/components/DocComponentsNav.js
  function DocComponentsNav (line 3) | function DocComponentsNav({activeIndex}){

FILE: webui/src/features/documentation/components/FeaturesContent.js
  function FeaturesContent (line 6) | function FeaturesContent(){

FILE: webui/src/features/documentation/components/FeaturesNav.js
  function FeaturesNav (line 3) | function FeaturesNav({activeIndex}){

FILE: webui/src/features/documentation/components/GettingStartedContent.js
  function GettingStartedContent (line 6) | function GettingStartedContent(){

FILE: webui/src/features/documentation/components/GettingStartedNav.js
  function GettingStartedNav (line 3) | function GettingStartedNav({activeIndex}){

FILE: webui/src/features/experiment/components/DashboardStats.js
  function DashboardStats (line 1) | function DashboardStats({title, icon, value, description, colorIndex}){

FILE: webui/src/features/experiment/components/SearchData.js
  function SearchData (line 18) | function SearchData({set_dataset}) {

FILE: webui/src/features/experiment/components/SelectAlgorithm.js
  function SelectAlgorithm (line 13) | function SelectAlgorithm({ SpaceRefiner, Sampler, Pretrain, Model, ACF, ...

FILE: webui/src/features/experiment/components/SelectData.js
  function SelectData (line 14) | function SelectData({DatasetData, updateTable, DatasetSelector}) {
  function Info (line 179) | function Info({isExact, data}) {

FILE: webui/src/features/experiment/components/SelectTask.js
  function TaskTable (line 8) | function TaskTable({ tasks }) {
  function SelectTask (line 32) | function SelectTask({ data, updateTable }) {

FILE: webui/src/features/experiment/index.js
  class Experiment (line 11) | class Experiment extends React.Component {
    method constructor (line 12) | constructor(props) {
    method render (line 76) | render() {

FILE: webui/src/features/integration/index.js
  constant INITIAL_INTEGRATION_LIST (line 7) | const INITIAL_INTEGRATION_LIST = [
  function Integration (line 17) | function Integration(){

FILE: webui/src/features/leads/components/AddLeadModalBody.js
  constant INITIAL_LEAD_OBJ (line 8) | const INITIAL_LEAD_OBJ = {
  function AddLeadModalBody (line 14) | function AddLeadModalBody({closeModal}){

FILE: webui/src/features/leads/index.js
  function Leads (line 26) | function Leads(){

FILE: webui/src/features/run/components/DataTable.js
  function DataTable (line 6) | function DataTable({datasets, optimizer}) {

FILE: webui/src/features/run/components/OptTable.js
  function OptTable (line 6) | function OptTable({optimizer}) {

FILE: webui/src/features/run/components/Run.js
  function Run (line 13) | function Run() {

FILE: webui/src/features/run/components/RunProgress.js
  class RunProgress (line 9) | class RunProgress extends React.Component {
    method constructor (line 10) | constructor(props) {
    method componentDidMount (line 21) | componentDidMount() {
    method componentWillUnmount (line 26) | componentWillUnmount() {
    method render (line 83) | render() {

FILE: webui/src/features/run/components/TaskTable.js
  function TaskTable (line 6) | function TaskTable({tasks}) {

FILE: webui/src/features/run/index.js
  class RunPage (line 14) | class RunPage extends React.Component {
    method constructor (line 15) | constructor(props) {
    method render (line 25) | render() {

FILE: webui/src/features/seldata/components/DataTable.js
  function DataTable (line 6) | function DataTable({ SpaceRefiner, SpaceRefinerDataSelector, SpaceRefine...

FILE: webui/src/features/seldata/components/SearchData.js
  function SearchData (line 18) | function SearchData({set_dataset}) {

FILE: webui/src/features/seldata/components/SelectData.js
  function SelectData (line 14) | function SelectData({DatasetData, updateTable, DatasetSelector}) {
  function Info (line 179) | function Info({isExact, data}) {

FILE: webui/src/features/seldata/index.js
  class Dataselector (line 14) | class Dataselector extends React.Component {
    method constructor (line 15) | constructor(props) {
    method render (line 65) | render() {

FILE: webui/src/features/settings/billing/index.js
  constant BILLS (line 9) | const BILLS = [
  function Billing (line 29) | function Billing(){

FILE: webui/src/features/settings/profilesettings/index.js
  function ProfileSettings (line 10) | function ProfileSettings(){

FILE: webui/src/features/settings/team/index.js
  constant TEAM_MEMBERS (line 23) | const TEAM_MEMBERS = [
  function Team (line 33) | function Team(){

FILE: webui/src/features/transactions/index.js
  function Transactions (line 57) | function Transactions(){

FILE: webui/src/features/user/ForgotPassword.js
  function ForgotPassword (line 8) | function ForgotPassword(){

FILE: webui/src/features/user/LandingIntro.js
  function LandingIntro (line 5) | function LandingIntro(){

FILE: webui/src/features/user/Login.js
  function Login (line 7) | function Login(){

FILE: webui/src/features/user/Register.js
  function Register (line 7) | function Register(){

FILE: webui/src/features/user/components/TemplatePointers.js
  function TemplatePointers (line 1) | function TemplatePointers(){

FILE: webui/src/pages/GettingStarted.js
  function ExternalPage (line 5) | function ExternalPage(){

FILE: webui/src/pages/protected/404.js
  function InternalPage (line 6) | function InternalPage(){

FILE: webui/src/pages/protected/Algorithm.js
  function InternalPage (line 6) | function InternalPage(){

FILE: webui/src/pages/protected/Analytics.js
  function InternalPage (line 6) | function InternalPage(){

FILE: webui/src/pages/protected/Bills.js
  function InternalPage (line 6) | function InternalPage(){

FILE: webui/src/pages/protected/Blank.js
  function InternalPage (line 7) | function InternalPage(){

FILE: webui/src/pages/protected/Calendar.js
  function InternalPage (line 6) | function InternalPage(){

FILE: webui/src/pages/protected/Charts.js
  function InternalPage (line 6) | function InternalPage(){

FILE: webui/src/pages/protected/ChatOpt.js
  function InternalPage (line 6) | function InternalPage(){

FILE: webui/src/pages/protected/Dashboard.js
  function InternalPage (line 6) | function InternalPage(){

FILE: webui/src/pages/protected/Experiment.js
  function InternalPage (line 6) | function InternalPage(){

FILE: webui/src/pages/protected/Integration.js
  function InternalPage (line 6) | function InternalPage(){

FILE: webui/src/pages/protected/Leads.js
  function InternalPage (line 6) | function InternalPage(){

FILE: webui/src/pages/protected/ProfileSettings.js
  function InternalPage (line 6) | function InternalPage(){

FILE: webui/src/pages/protected/Run.js
  function InternalPage (line 6) | function InternalPage(){

FILE: webui/src/pages/protected/Seldata.js
  function InternalPage (line 6) | function InternalPage(){

FILE: webui/src/pages/protected/Team.js
  function InternalPage (line 6) | function InternalPage(){

FILE: webui/src/pages/protected/Transactions.js
  function InternalPage (line 6) | function InternalPage(){

FILE: webui/src/pages/protected/Welcome.js
  function InternalPage (line 7) | function InternalPage(){