def apply(log, parameters=None):
"""
Discovers a Petri net using Heuristics Miner
Parameters
------------
log
Event log
parameters
Possible parameters of the algorithm,
including: activity_key, case_id_glue, timestamp_key,
dependency_thresh, and_measure_thresh, min_act_count, min_dfg_occurrences, dfg_pre_cleaning_noise_thresh
Returns
------------
net
Petri net
im
Initial marking
fm
Final marking
"""
if parameters is None:
parameters = {}
heu_net = apply_heu(log, parameters=parameters)
net, im, fm = hn_conv_factory.apply(heu_net, parameters=parameters)
return net, im, fm
def apply_dfg(dfg,
activities=None,
activities_occurrences=None,
start_activities=None,
end_activities=None,
parameters=None):
"""
Discovers a Petri net using Heuristics Miner
Parameters
------------
dfg
Directly-Follows Graph
activities
(If provided) list of activities of the log
activities_occurrences
(If provided) dictionary of activities occurrences
start_activities
(If provided) dictionary of start activities occurrences
end_activities
(If provided) dictionary of end activities occurrences
parameters
Possible parameters of the algorithm,
including: activity_key, case_id_glue, timestamp_key,
dependency_thresh, and_measure_thresh, min_act_count, min_dfg_occurrences, dfg_pre_cleaning_noise_thresh,
loops_length_two_thresh
Returns
------------
net
Petri net
im
Initial marking
fm
Final marking
"""
if parameters is None:
parameters = {}
heu_net = apply_heu_dfg(dfg,
activities=activities,
activities_occurrences=activities_occurrences,
start_activities=start_activities,
end_activities=end_activities,
parameters=parameters)
net, im, fm = hn_conv_factory.apply(heu_net, parameters=parameters)
return net, im, fm
def apply_pandas(df, parameters=None):
"""
Discovers a Petri net using Heuristics Miner
Parameters
------------
df
Pandas dataframe
parameters
Possible parameters of the algorithm,
including: activity_key, case_id_glue, timestamp_key,
dependency_thresh, and_measure_thresh, min_act_count, min_dfg_occurrences, dfg_pre_cleaning_noise_thresh,
loops_length_two_thresh
Returns
------------
net
Petri net
im
Initial marking
fm
Final marking
"""
if parameters is None:
parameters = {}
activity_key = parameters[
constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] if constants.PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY
case_id_glue = parameters[
constants.
PARAMETER_CONSTANT_CASEID_KEY] if constants.PARAMETER_CONSTANT_CASEID_KEY in parameters else CASE_CONCEPT_NAME
timestamp_key = parameters[
constants.
PARAMETER_CONSTANT_TIMESTAMP_KEY] if constants.PARAMETER_CONSTANT_TIMESTAMP_KEY in parameters else xes.DEFAULT_TIMESTAMP_KEY
start_activities = pd_sa_filter.get_start_activities(df,
parameters=parameters)
end_activities = pd_ea_filter.get_end_activities(df, parameters=parameters)
activities_occurrences = pd_attributes.get_attribute_values(
df, activity_key, parameters=parameters)
activities = list(activities_occurrences.keys())
if timestamp_key in df:
dfg = df_statistics.get_dfg_graph(df,
case_id_glue=case_id_glue,
activity_key=activity_key,
timestamp_key=timestamp_key)
dfg_window_2 = df_statistics.get_dfg_graph(df,
case_id_glue=case_id_glue,
activity_key=activity_key,
timestamp_key=timestamp_key,
window=2)
frequency_triples = get_freq_triples.get_freq_triples(
df,
case_id_glue=case_id_glue,
activity_key=activity_key,
timestamp_key=timestamp_key)
else:
dfg = df_statistics.get_dfg_graph(df,
case_id_glue=case_id_glue,
activity_key=activity_key,
sort_timestamp_along_case_id=False)
dfg_window_2 = df_statistics.get_dfg_graph(
df,
case_id_glue=case_id_glue,
activity_key=activity_key,
sort_timestamp_along_case_id=False,
window=2)
frequency_triples = get_freq_triples.get_freq_triples(
df,
case_id_glue=case_id_glue,
activity_key=activity_key,
timestamp_key=timestamp_key,
sort_timestamp_along_case_id=False)
heu_net = apply_heu_dfg(dfg,
activities=activities,
activities_occurrences=activities_occurrences,
start_activities=start_activities,
end_activities=end_activities,
dfg_window_2=dfg_window_2,
freq_triples=frequency_triples,
parameters=parameters)
net, im, fm = hn_conv_factory.apply(heu_net, parameters=parameters)
return net, im, fm
