Removes unused run options

main.py

@@ -22,37 +22,26 @@ def run(kwargs:dict, model_paramas_list: list, filename_list:list):
     @return:
     """
     params = kwargs[PARAMS]
-    run_option = 'baseline'
     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')
+    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'])
 
 
 if __name__=='__main__':