def test_AeventuallyBeventuallyC_neg(self):
df = pd.read_csv(os.path.join("input_data", "running-example.csv"))
df = dataframe_utils.convert_timestamp_columns_in_df(df)
filt_A_ev_B_ev_C_neg = ltl_checker.eventually_follows(
df, ["check ticket", "decide", "pay compensation"],
parameters={ltl_checker.Parameters.POSITIVE: False})
def filter_eventually_follows_relation(log: Union[EventLog, pd.DataFrame], relations: List[str], retain: bool = True) -> \
Union[EventLog, pd.DataFrame]:
"""
Retain traces that contain any of the specified 'eventually follows' relations.
For example, if relations == [('a','b'),('a','c')] and log [<a,b,c>,<a,c,b>,<a,d,b>]
the resulting log will contain traces describing [<a,b,c>,<a,c,b>,<a,d,b>].
Parameters
---------------
log
Log object
relations
List of activity name pairs, which are allowed/forbidden paths
retain
Parameter that says whether the paths
should be kept/removed
Returns
----------------
filtered_log
Filtered log object
"""
if type(log) not in [pd.DataFrame, EventLog, EventStream]:
raise Exception(
"the method can be applied only to a traditional event log!")
parameters = get_properties(log)
if check_is_pandas_dataframe(log):
from pm4py.algo.filtering.pandas.ltl import ltl_checker
parameters[ltl_checker.Parameters.POSITIVE] = retain
if retain:
cases = set()
else:
cases = set(log[constants.CASE_CONCEPT_NAME])
for path in relations:
filt_log = ltl_checker.eventually_follows(log,
path,
parameters=parameters)
this_traces = set(filt_log[constants.CASE_CONCEPT_NAME])
if retain:
cases = cases.union(this_traces)
else:
cases = cases.intersection(this_traces)
return log[log[constants.CASE_CONCEPT_NAME].isin(cases)]
else:
from pm4py.algo.filtering.log.ltl import ltl_checker
parameters[ltl_checker.Parameters.POSITIVE] = retain
if retain:
cases = set()
else:
cases = set(id(trace) for trace in log)
for path in relations:
filt_log = ltl_checker.eventually_follows(log,
path,
parameters=parameters)
this_traces = set(id(trace) for trace in filt_log)
if retain:
cases = cases.union(this_traces)
else:
cases = cases.intersection(this_traces)
filtered_log = EventLog(attributes=log.attributes,
extensions=log.extensions,
omni_present=log.omni_present,
classifiers=log.classifiers,
properties=log.properties)
for trace in log:
if id(trace) in cases:
filtered_log.append(trace)
return filtered_log