Restores run file

gedi/__init__.py

@@ -1,8 +1,3 @@
-from .generator import GenerateEventLogs
-from .features import EventLogFeatures
-from .augmentation import InstanceAugmentator
-from .benchmark import BenchmarkTest
-from .plotter import BenchmarkPlotter, FeaturesPlotter, AugmentationPlotter, GenerationPlotter
 from .run import gedi
 
-__all__=[ 'gedi', 'GenerateEventLogs', 'EventLogFeatures', 'FeatureAnalyser', 'InstanceAugmentator', 'BenchmarkTest', 'BenchmarkPlotter', 'FeaturesPlotter', 'AugmentationPlotter', 'GenerationPlotter']
+__all__=[ 'gedi']

gedi/run.py

@@ -0,0 +1,53 @@
+import config
+import pandas as pd
+from datetime import datetime as dt
+from gedi.generator import GenerateEventLogs
+from gedi.features import EventLogFeatures
+from gedi.augmentation import InstanceAugmentator
+from gedi.benchmark import BenchmarkTest
+from gedi.plotter import BenchmarkPlotter, FeaturesPlotter, AugmentationPlotter, GenerationPlotter
+from utils.default_argparse import ArgParser
+from utils.param_keys import *
+
+def run(kwargs:dict, model_params_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]
+    ft = EventLogFeatures(None)
+    augmented_ft = InstanceAugmentator()
+    gen = pd.DataFrame(columns=['log'])
+
+    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'])
+
+def gedi(config_path):
+    """
+    This function runs the GEDI pipeline.
+    @param config_path: str
+        contains the path to the config file
+    @return:
+    """
+    model_params_list = config.get_model_params_list(config_path)
+    run({'params':""}, model_params_list, [])