| import importlib |
| import sys |
| import getopt |
| import os |
| import experiment.experiment_script as rbe |
| importlib.reload(rbe) |
|
|
| learning_rate = [0.005, 0.01, 0.05, 0.1, 0.5] |
| patience_round = 50 |
| outlier_index = 1.5 |
|
|
|
|
| def run_batch_experiment(model_learning_rate, |
| augmentation=False, |
| remove_outlier_qtr=False, |
| round_number=1, |
| iteration=200, |
| use_model_weight=True, |
| dataset_name="simple", |
| experiment_version=2, |
| use_early_stopping=False |
| ): |
|
|
| dataset_directory = f'data/{dataset_name}' |
| output_group_directory = f'result/{dataset_name}' |
| os.makedirs(output_group_directory, exist_ok=True) |
| output_directory = f'{output_group_directory}/{dataset_name}_{experiment_version}' |
| output_directory += f"_{model_learning_rate}" |
| if remove_outlier_qtr: |
| output_directory += "_remove_outlier" |
| if augmentation: |
| output_directory += "_augmented" |
| if use_early_stopping: |
| output_directory += "_early_stopping" |
|
|
| try: |
| rbe.run_experiment(data_directory=dataset_directory, epoch=iteration, |
| learning_rate_list=learning_rate, round_number=round_number, |
| folder_name=output_directory, breakpoint=iteration / 2, |
| early_stopping=use_early_stopping, patience_round=patience_round, |
| augmentation=augmentation, |
| outlier_index=outlier_index, |
| remove_outlier_qtr=remove_outlier_qtr, |
| use_model_weight=use_model_weight, |
| model_learning_rate=model_learning_rate) |
| except Exception as e: |
| print("Error in Experiment", e) |
| pass |
|
|
|
|
| def run_in_many_learning_rate(dataset_name="simple", experiment_version=2, round_number=1, iteration=200): |
| model_learning_rate_list = [0.0000001, 0.00000001] |
| early_stopping_list = [False, True] |
| for model_learning_rate in model_learning_rate_list: |
| for early_stopping in early_stopping_list: |
| |
| print( |
| f'LL: {model_learning_rate} / Normal / Eearly Stopping: {early_stopping}') |
| run_batch_experiment(model_learning_rate=model_learning_rate, |
| augmentation=False, |
| remove_outlier_qtr=False, |
| round_number=round_number, |
| iteration=iteration, |
| use_model_weight=True, |
| dataset_name=dataset_name, |
| experiment_version=experiment_version, |
| use_early_stopping=early_stopping |
| ) |
| |
| print( |
| f'LL: {model_learning_rate} / Remove Outlier / Eearly Stopping: {early_stopping}') |
| run_batch_experiment(model_learning_rate=model_learning_rate, |
| augmentation=False, |
| remove_outlier_qtr=True, |
| round_number=round_number, |
| iteration=iteration, |
| use_model_weight=True, |
| dataset_name=dataset_name, |
| experiment_version=experiment_version, |
| use_early_stopping=early_stopping |
| ) |
| |
| print( |
| f'LL: {model_learning_rate} / Augmentation / Eearly Stopping: {early_stopping}') |
| run_batch_experiment(model_learning_rate=model_learning_rate, |
| augmentation=True, |
| remove_outlier_qtr=False, |
| round_number=round_number, |
| iteration=iteration, |
| use_model_weight=True, |
| dataset_name=dataset_name, |
| experiment_version=experiment_version, |
| use_early_stopping=early_stopping |
| ) |
| print( |
| f'LL: {model_learning_rate} / Remove Outlier & Augmentation / Eearly Stopping: {early_stopping}') |
| |
| run_batch_experiment(model_learning_rate=model_learning_rate, |
| augmentation=True, |
| remove_outlier_qtr=True, |
| round_number=round_number, |
| iteration=iteration, |
| use_model_weight=True, |
| dataset_name=dataset_name, |
| experiment_version=experiment_version, |
| use_early_stopping=early_stopping |
| ) |
|
|
|
|
| if __name__ == "__main__": |
| try: |
| opts, args = getopt.getopt(sys.argv[1:], "d:v:r:i:", [ |
| "dataset=", "version=", 'round=', 'iteration=']) |
| except getopt.GetoptError as err: |
| print(err) |
| |
|
|
| for opt, arg in opts: |
| if opt in ("-d", "--dataset"): |
| dataset_name = arg |
| elif opt in ("-v", "--version"): |
| experiment_version = int(arg) |
| elif opt in ("-r", "--round"): |
| round_number = int(arg) |
| elif opt in ("-i", "--iteration"): |
| iteration = int(arg) |
|
|
| print( |
| f"Dataset: {dataset_name} / Version: {experiment_version} / Round: {round_number} / Iteration: {iteration}") |
| run_in_many_learning_rate(dataset_name=dataset_name, |
| experiment_version=experiment_version, |
| round_number=round_number, |
| iteration=iteration) |
|
|
