removed rescaling of noise_threshold

pyinsights/log_skeleton/log_skeleton.py

@@ -428,31 +428,23 @@ def get_non_conforming_cases(self, noise_threshold=0, cases_to_compare=None):
  Checks for each trace in the log, whether it is fitting or not.
  :return: dataframe with ids of non-conforming cases
  """
- # get the number of activities in the log
- query = PQL()
- query.add(
- PQLColumn(name="num", query=f""" COUNT(DISTINCT "{activity_table}"."{act_col}") """))
- num_activities_df = datamodel.get_data_frame(query)
- num_activities = num_activities_df["num"][0]
-
- # rescale noise threshold because old one is not practically usable
- # values based on empirical oberservations
- noise_threshold_scaled = 0.35 + noise_threshold*0.7
- lsk = self.get_log_skeleton(noise_threshold_scaled)
-
+ # get lsk
+ lsk = self.get_log_skeleton(noise_threshold)
+ # get lsk per trace
  lsk_compare_traces = self.get_log_skeleton_per_case(
  case_id=cases_to_compare)
 
  # check for each case if relation is subset of lsk
 
  non_conforming = {case for relation in lsk_compare_traces.keys(
- ) for case in lsk_compare_traces[relation].keys() if not self._conforms(lsk_compare_traces, relation, case, lsk, noise_threshold_scaled, num_activities)}
+ ) for case in lsk_compare_traces[relation].keys() if not self._conforms(lsk_compare_traces, relation, case, lsk)}
 
+ # return non-conforming cases as df
  df = pd.DataFrame(columns=[case_col], data=non_conforming)
 
  return df
 
- def _conforms(self, lsk_traces, relation, case, lsk, noise_threshold, num_activities):
+ def _conforms(self, lsk_traces, relation, case, lsk):
  """checks if relation of trace conforms to lsk
  Args:
  lsk_traces (_type_): _description_