def discover_petri_net_inductive(log: Union[EventLog, pd.DataFrame], noise_threshold: float = 0.0) -> Tuple[
PetriNet, Marking, Marking]:
"""
Discovers a Petri net using the IMDFc algorithm
Parameters
--------------
log
Event log
noise_threshold
Noise threshold (default: 0.0)
Returns
--------------
petri_net
Petri net
initial_marking
Initial marking
final_marking
Final marking
"""
if type(log) not in [pd.DataFrame, EventLog, EventStream]: raise Exception("the method can be applied only to a traditional event log!")
pt = discover_process_tree_inductive(log, noise_threshold)
from pm4py.convert import convert_to_petri_net
return convert_to_petri_net(pt)
def discover_petri_net_inductive(
log: Union[EventLog, pd.DataFrame],
noise_threshold: float = 0.0) -> Tuple[PetriNet, Marking, Marking]:
"""
Discovers a Petri net using the IMDFc algorithm
Parameters
--------------
log
Event log
noise_threshold
Noise threshold (default: 0.0)
Returns
--------------
petri_net
Petri net
initial_marking
Initial marking
final_marking
Final marking
"""
pt = discover_process_tree_inductive(log, noise_threshold)
from pm4py.convert import convert_to_petri_net
return convert_to_petri_net(pt)
def check_is_fitting(*args, activity_key=xes_constants.DEFAULT_NAME_KEY):
"""
Checks if a trace object is fit against a process model
Parameters
-----------------
trace
Trace object (trace / variant)
model
Model (process tree, Petri net, BPMN, ...)
activity_key
Activity key (optional)
Returns
-----------------
is_fit
Boolean value (True if the trace fits; False if the trace does not)
"""
from pm4py.util import variants_util
from pm4py.convert import convert_to_process_tree, convert_to_petri_net
trace = args[0]
model = args[1:]
try:
model = convert_to_process_tree(*model)
except:
# the model cannot be expressed as a process tree, let's say if at least can be expressed as a Petri net
model = convert_to_petri_net(*model)
if not isinstance(trace, Trace):
activities = variants_util.get_activities_from_variant(trace)
trace = Trace()
for act in activities:
trace.append(Event({activity_key: act}))
if isinstance(model, ProcessTree):
return __check_is_fit_process_tree(trace, model, activity_key=activity_key)
elif isinstance(model, tuple) and isinstance(model[0], PetriNet):
return __check_is_fit_petri_net(trace, model[0], model[1], model[2], activity_key=activity_key)
def __check_is_fit_process_tree(trace, tree, activity_key=xes_constants.DEFAULT_NAME_KEY):
"""
Check if a trace object is fit against a process tree model
Parameters
-----------------
trace
Trace
tree
Process tree
activity_key
Activity key (optional)
Returns
-----------------
is_fit
Boolean value (True if the trace fits; False if the trace does not)
"""
from pm4py.discovery import discover_footprints
log = EventLog()
log.append(trace)
fp_tree = discover_footprints(tree)
fp_log = discover_footprints(log)
fp_conf_res = conformance_diagnostics_footprints(fp_log, fp_tree)[0]
# CHECK 1) if footprints already say is not fit, then return False
# (if they say True, it might be a false positive)
if not fp_conf_res["is_footprints_fit"]:
return False
else:
from pm4py.convert import convert_to_petri_net
net, im, fm = convert_to_petri_net(tree)
tbr_conf_res = conformance_diagnostics_token_based_replay(log, net, im, fm)[0]
# CHECK 2) if TBR says that is fit, then return True
# (if they say False, it might be a false negative)
if tbr_conf_res["trace_is_fit"]:
return True
else:
# CHECK 3) alignments definitely say if the trace is fit or not if the previous methods fail
align_conf_res = conformance_diagnostics_alignments(log, tree)[0]
return align_conf_res["fitness"] == 1.0