fixed problem with lsk

pyinsights/log_skeleton/log_skeleton.py

@@ -44,7 +44,6 @@ def get_log_skeleton(self, noise_threshold=0):
  """
  log_skeleton = None
  # Get the extended log
-
  equivalence, always_after, always_before, never_together, directly_follows \
  = self._get_relations(noise_threshold)
 
@@ -179,13 +178,16 @@ def _get_equivalence(self, noise_threshold, case_id=None):
  occ_max = groups_expanded[max]
  occ_min = groups_expanded[min]
 
+ occurrences_max = len(groups_expanded[max])
+
  # get difference betwwen two profiles
+
  differences = occ_max.merge(
  occ_min, indicator=True, how='left').loc[lambda x: x['_merge'] != 'both']
 
- # if the two profiles deviate no more than noise * num_cases
+ # if the two profiles deviate no more than noise * size of larger profile
  # they are equivalent
- if len(differences) <= num_cases*noise_threshold:
+ if len(differences) <= occurrences_max*noise_threshold:
  equivalence.add(pair)
 
  return equivalence
@@ -244,16 +246,18 @@ def _get_always_after(self, noise_threshold, case_id=None):
  # fill nas so they don't screw up max
  pos_per_case.fillna(0, inplace=True)
 
+ # length of act1's profile
+ num = groups_expanded[pair[0]][case_col].nunique()
+
  # handling if both activities are the same
  if pair[0] == pair[1]:
  # if they actually occur more than 1 time in every case --> they are in the relation
- if np.sum(merged[case_col].value_counts() > 1) >= num_cases*(1-noise_threshold):
+ if np.sum(merged[case_col].value_counts() > 1) >= num*(1-noise_threshold):
  always_after.add(pair)
  # else test if the last occurrence of act2 is after the last occurrence of act1
- # in at least (1-noise) percent of cases
- elif np.sum(pos_per_case["order_x"] <= pos_per_case["order_y"]) >= num_cases*(1-noise_threshold):
+ # in at least (1-noise) percent of times act1 occurs
+ elif np.sum(pos_per_case["order_x"] <= pos_per_case["order_y"]) >= num*(1-noise_threshold):
  always_after.add(pair)
-
  return always_after
 
  def _get_always_before(self, noise_threshold, case_id=None):
@@ -306,15 +310,17 @@ def _get_always_before(self, noise_threshold, case_id=None):
  test = grouped.agg({'order_x': 'min',
  'order_y': 'min'})
 
+ # length of act1's profile
+ num = groups_expanded[pair[0]][case_col].nunique()
  # handling if both activities are the same
  if pair[0] == pair[1]:
  # if they actually occur more than 1 time in every case --> they are in the relation
- if np.sum(merged[case_col].value_counts() > 1) >= num_cases*(1-noise_threshold):
+ if np.sum(merged[case_col].value_counts() > 1) >= num*(1-noise_threshold):
  always_before.add(pair)
 
  # else test if the first occurrence of act1 is after the first occurrence of act2
  # in more than (1-noise) times act1 occurs
- elif np.sum(test["order_x"] >= test["order_y"]) >= num_cases*(1-noise_threshold):
+ elif np.sum(test["order_x"] >= test["order_y"]) >= num*(1-noise_threshold):
  always_before.add(pair)
 
  return always_before
@@ -372,7 +378,7 @@ def _get_never_together(self, noise_threshold, case_id=None):
  num = len(occ_max)
  # check if smaller profile and larger profile do not occur together
  # more than num_cases * noise
- if np.sum(occ_max.isin(occ_min)) <= num_cases*(noise_threshold):
+ if np.sum(occ_max.isin(occ_min)) <= num*(noise_threshold):
  never_together.add(pair)
  return never_together
 
@@ -417,11 +423,8 @@ def _get_directly_follows(self, noise_threshold, case_id=None):
  i = 1
  bar = tqdm(total=iterations)
  bar.set_description("Calculating directly-follows")
- for _, row in edge_table.iterrows():
- directly_follows.add((row["SOURCE"], row["TARGET"]))
-
- bar.update(1)
- i += 1
+ directly_follows = set(
+ edge_table[["SOURCE", "TARGET"]].itertuples(name=None, index=False))
 
  return directly_follows
 
@@ -430,6 +433,8 @@ 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
  """
+ events = self.connector.events()
+ num_activities = events[act_col].nunique()
  # get lsk
  lsk = self.get_log_skeleton(noise_threshold)
  # get lsk per trace
@@ -439,30 +444,39 @@ def get_non_conforming_cases(self, noise_threshold=0, cases_to_compare=None):
  # 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)}
+ ) for case in lsk_compare_traces[relation].keys() if not self._conforms(lsk_compare_traces, relation, case, lsk, noise_threshold, num_activities)}
 
  # 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):
+ def _conforms(self, lsk_traces, relation, case, lsk, noise_threshold, num_activities):
  """checks if relation of trace conforms to lsk
+ relaxed problem: the difference between the relations can have up to noise-threshold * number of pairs elements, and still conform
  Args:
  lsk_traces (_type_): _description_
  relation (_type_): _description_
  case (_type_): _description_
  lsk (_type_): _description_
-
  Returns:
  bool: conformity
  """
  if relation == "activ_freq":
  for act in lsk[relation].keys():
  if not lsk_traces[relation][case][act].issubset(lsk[relation][act]):
  return False
+
+ elif relation not in ['equivalence', 'never_together']:
+ num_pairs = num_activities**2
+ difference = lsk_traces[relation][case].difference(lsk[relation])
+ if len(difference) > num_pairs*noise_threshold:
+ return False
  else:
- if not lsk_traces[relation][case].issubset(lsk[relation]):
+ num_pairs = math.factorial(
+ num_activities) / math.factorial(num_activities-2)
+ difference = lsk_traces[relation][case].difference(lsk[relation])
+ if len(difference) > num_pairs*noise_threshold:
  return False
  return True
 
@@ -883,9 +897,7 @@ def _get_directly_follows_per_case(self, case_id=None):
 
  bar = tqdm(total=iterations)
  bar.set_description("Calculating directly- for cases")
- for _, row in edge_table.iterrows():
- df_all_cases[row[case_col]].add((row["SOURCE"], row["TARGET"]))
-
- bar.update(1)
+ df_all_cases = {case_col: set((source, target)) for (
+ case_col, source, target) in tqdm(edge_table[[case_col, "SOURCE", "TARGET"]].itertuples(name=None, index=False))}
 
  return df_all_cases