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Running
| import config | |
| import pandas as pd | |
| from datetime import datetime as dt | |
| from tag.generator import GenerateEventLogs | |
| from tag.features import EventLogFeatures | |
| from tag.analyser import FeatureAnalyser | |
| from tag.augmentation import InstanceAugmentator | |
| from tag.benchmark import BenchmarkTest | |
| from tag.plotter import BenchmarkPlotter, FeaturesPlotter, AugmentationPlotter, GenerationPlotter | |
| from utils.default_argparse import ArgParser | |
| from utils.param_keys import * | |
| from utils.param_keys.run_options import * | |
| def run(kwargs:dict, model_paramas_list: list, filename_list:list): | |
| """ | |
| This function chooses the running option for the program. | |
| @param kwargs: dict | |
| contains the running parameters and the event-log file information | |
| @param model_params_list: list | |
| contains a list of model parameters, which are used to analyse this different models. | |
| @param filename_list: list | |
| contains the list of the filenames to load multiple event-logs | |
| @return: | |
| """ | |
| params = kwargs[PARAMS] | |
| run_option = params[RUN_OPTION] | |
| ft = EventLogFeatures(None) | |
| augmented_ft = InstanceAugmentator() | |
| gen = pd.DataFrame(columns=['log']) | |
| if run_option == BASELINE: | |
| for model_params in model_params_list: | |
| if model_params.get(PIPELINE_STEP) == 'instance_augmentation': | |
| augmented_ft = InstanceAugmentator(aug_params=model_params, samples=ft.feat) | |
| AugmentationPlotter(augmented_ft, model_params) | |
| elif model_params.get(PIPELINE_STEP) == 'event_logs_generation': | |
| gen = pd.DataFrame(GenerateEventLogs(model_params).log_config) | |
| #gen = pd.read_csv("output/features/generated/grid_2objectives_enseef_enve/2_enseef_enve_feat.csv") | |
| GenerationPlotter(gen, model_params, output_path="output/plots") | |
| elif model_params.get(PIPELINE_STEP) == 'benchmark_test': | |
| benchmark = BenchmarkTest(model_params, event_logs=gen['log']) | |
| # BenchmarkPlotter(benchmark.features, output_path="output/plots") | |
| elif model_params.get(PIPELINE_STEP) == 'feature_extraction': | |
| ft = EventLogFeatures(**kwargs, logs=gen['log'], ft_params=model_params) | |
| FeaturesPlotter(ft.feat, model_params) | |
| elif model_params.get(PIPELINE_STEP) == "evaluation_plotter": | |
| GenerationPlotter(gen, model_params, output_path=model_params['output_path'], input_path=model_params['input_path']) | |
| elif run_option == COMPARE: | |
| if params[N_COMPONENTS] != 2: | |
| raise ValueError(f'The parameter `{N_COMPONENTS}` has to be 2, but it\'s {params[N_COMPONENTS]}.') | |
| ft = EventLogFeatures(**kwargs) | |
| FeatureAnalyser(ft, params).compare(model_params_list) | |
| else: | |
| raise InvalidRunningOptionError(f'The run_option: `{run_option}` in the (json) configuration ' | |
| f'does not exists or it is not a loading option.\n') | |
| if __name__=='__main__': | |
| start_tag = dt.now() | |
| print(f'INFO: TAG starting {start_tag}') | |
| args = ArgParser().parse('GEDI main') | |
| run_params = config.get_run_params(args.run_params_json) | |
| filename_list, kwargs = config.get_files_and_kwargs(run_params) | |
| if args.result_load_files is None: | |
| model_params_list = config.get_model_params_list(args.alg_params_json) | |
| run(kwargs, model_params_list, filename_list) | |
| else: | |
| load(args.result_load_files, kwargs) | |
| print(f'SUCCESS: TAG took {dt.now()-start_tag} sec.') | |