def test_alignment(self):
log = xes_importer.apply(
os.path.join("input_data", "running-example.xes"))
from pm4py.algo.discovery.alpha import algorithm as alpha_miner
net, im, fm = alpha_miner.apply(log)
from pm4py.algo.conformance.alignments import algorithm as alignments
aligned_traces = alignments.apply(
log,
net,
im,
fm,
variant=alignments.Variants.VERSION_STATE_EQUATION_A_STAR)
aligned_traces = alignments.apply(
log,
net,
im,
fm,
variant=alignments.Variants.VERSION_DIJKSTRA_NO_HEURISTICS)
from pm4py.evaluation.replay_fitness import evaluator as rp_fitness_evaluator
fitness = rp_fitness_evaluator.apply(
log,
net,
im,
fm,
variant=rp_fitness_evaluator.Variants.ALIGNMENT_BASED)
evaluation = rp_fitness_evaluator.evaluate(
aligned_traces,
variant=rp_fitness_evaluator.Variants.ALIGNMENT_BASED)
from pm4py.evaluation.precision import evaluator as precision_evaluator
precision = precision_evaluator.apply(
log,
net,
im,
fm,
variant=rp_fitness_evaluator.Variants.ALIGNMENT_BASED)
def test_alignment(self):
log = xes_importer.apply(os.path.join("input_data", "running-example.xes"))
from pm4py.algo.discovery.alpha import algorithm as alpha_miner
net, im, fm = alpha_miner.apply(log)
from pm4py.algo.conformance.alignments import algorithm as alignments
aligned_traces = alignments.apply(log, net, im, fm, variant=alignments.Variants.VERSION_STATE_EQUATION_A_STAR)
aligned_traces = alignments.apply(log, net, im, fm, variant=alignments.Variants.VERSION_DIJKSTRA_NO_HEURISTICS)
def test_importingPetriLogAlignment(self):
# to avoid static method warnings in tests,
# that by construction of the unittest package have to be expressed in such way
self.dummy_variable = "dummy_value"
imported_petri1, marking1, fmarking1 = petri_importer.apply(
os.path.join(INPUT_DATA_DIR, "running-example.pnml"))
log = xes_importer.apply(
os.path.join(INPUT_DATA_DIR, "running-example.xes"))
final_marking = petri.petrinet.Marking()
for p in imported_petri1.places:
if not p.out_arcs:
final_marking[p] = 1
for trace in log:
cf_result = align_alg.apply(
trace,
imported_petri1,
marking1,
final_marking,
variant=align_alg.VERSION_DIJKSTRA_NO_HEURISTICS)['alignment']
is_fit = True
for couple in cf_result:
if not (couple[0] == couple[1]
or couple[0] == ">>" and couple[1] is None):
is_fit = False
if not is_fit:
raise Exception("should be fit")
def execute_script():
log = importer.apply(os.path.join("..", "tests", "input_data", "running-example.xes"))
net, im, fm = inductive_miner.apply(log)
aligned_traces = alignments.apply(log, net, im, fm)
gviz = visualizer.apply(log, aligned_traces,
parameters={visualizer.Variants.CLASSIC.value.Parameters.FORMAT: "svg"})
visualizer.view(gviz)
def conformance_diagnostics_alignments(
log: EventLog, petri_net: PetriNet, initial_marking: Marking,
final_marking: Marking) -> List[Dict[str, Any]]:
"""
Apply the alignments algorithm between a log and a Petri net
The methods return the full alignment diagnostics.
Parameters
-------------
log
Event log
petri_net
Petri net
initial_marking
Initial marking
final_marking
Final marking
Returns
-------------
aligned_traces
A list of alignments for each trace of the log (in the same order as the traces in the event log)
"""
from pm4py.algo.conformance.alignments import algorithm as alignments
return alignments.apply(log, petri_net, initial_marking, final_marking)
def apply(log, petri_net, initial_marking, final_marking, align_variant=alignments.DEFAULT_VARIANT, parameters=None):
"""
Evaluate fitness based on alignments
Parameters
----------------
log
Event log
petri_net
Petri net
initial_marking
Initial marking
final_marking
Final marking
align_variant
Variants of the alignments to apply
parameters
Parameters of the algorithm
Returns
---------------
dictionary
Containing two keys (percFitTraces and averageFitness)
"""
if align_variant == decomp_alignments.Variants.RECOMPOS_MAXIMAL.value:
alignment_result = decomp_alignments.apply(log, petri_net, initial_marking, final_marking,
variant=align_variant, parameters=parameters)
else:
alignment_result = alignments.apply(log, petri_net, initial_marking, final_marking, variant=align_variant,
parameters=parameters)
return evaluate(alignment_result)
def getFitness(net,m0,mf,log,size_of_run):
'''
Prefix-aware fitness
:param net:
:param m0:
:param mf:
:param log:
:param size_of_run:
:return:
'''
sum=0
for l in log._list:
model_cost_function = dict()
sync_cost_function = dict()
for t in net.transitions:
if t.label is not None:
model_cost_function[t]=2**(len(l)+size_of_run-getIndexOfT(t,m0))
sync_cost_function[t]=0
trace_cost_function = dict()
for a in range (0,len(l)):
trace_cost_function[a]=2**(len(l)+size_of_run-a)
parameters = {}
parameters[alignments.Variants.VERSION_STATE_EQUATION_A_STAR.value.Parameters.PARAM_MODEL_COST_FUNCTION] = model_cost_function
parameters[alignments.Variants.VERSION_STATE_EQUATION_A_STAR.value.Parameters.PARAM_SYNC_COST_FUNCTION] = sync_cost_function
parameters[alignments.Variants.VERSION_STATE_EQUATION_A_STAR.value.Parameters.PARAM_TRACE_COST_FUNCTION] = trace_cost_function
ali = alignments.apply(l, net, m0, mf, parameters=parameters, variant=versions.dijkstra_no_heuristics)
if ali == None:
vizu.apply(net, m0, mf).view()
sum+=ali['cost']
return sum
def get_alignments_decoration(net, im, fm, log=None, aligned_traces=None, parameters=None):
"""
Get a decoration for the Petri net based on alignments
Parameters
-------------
net
Petri net
im
Initial marking
fm
Final marking
log
Event log
aligned_traces
Aligned traces
parameters
Parameters of the algorithm
Returns
-------------
decorations
Decorations to use
"""
if parameters is None:
parameters = {}
if aligned_traces is None and log is not None:
from pm4py.algo.conformance.alignments import algorithm as alignments
aligned_traces = alignments.apply(log, net, im, fm, parameters={"ret_tuple_as_trans_desc": True})
decorations = {}
net_transitions = {}
for trans in net.transitions:
net_transitions[trans.name] = trans
for align_trace0 in aligned_traces:
align_trace = align_trace0["alignment"]
for move in align_trace:
move_trans_name = move[0][1]
activity_trace_name = move[0][0]
if move_trans_name in net_transitions:
trans = net_transitions[move_trans_name]
if trans not in decorations:
decorations[trans] = {"count_fit": 0, "count_move_on_model": 0}
if activity_trace_name == ">>":
decorations[trans]["count_move_on_model"] = decorations[trans]["count_move_on_model"] + 1
else:
decorations[trans]["count_fit"] = decorations[trans]["count_fit"] + 1
for trans in decorations:
if trans.label is not None:
decorations[trans]["label"] = trans.label + " (" + str(
decorations[trans]["count_move_on_model"]) + "," + str(decorations[trans]["count_fit"]) + ")"
decorations[trans]["color"] = get_transitions_color(decorations[trans]["count_move_on_model"],
decorations[trans]["count_fit"])
return decorations
def g():
aa = time.time()
aligned_traces = alignments.apply(
Shared.log,
Shared.net,
Shared.im,
Shared.fm,
variant=alignments.Variants.VERSION_DIJKSTRA_NO_HEURISTICS)
bb = time.time()
print(bb - aa)
def f():
aa = time.time()
aligned_traces = alignments.apply(
Shared.log,
Shared.net,
Shared.im,
Shared.fm,
variant=alignments.Variants.VERSION_DIJKSTRA_LESS_MEMORY)
bb = time.time()
print(bb - aa)
def test_inductiveminer_log(self):
log = xes_importer.apply(
os.path.join("input_data", "running-example.xes"))
net, im, fm = inductive_miner.apply(log)
aligned_traces_tr = tr_alg.apply(log, net, im, fm)
aligned_traces_alignments = align_alg.apply(log, net, im, fm)
evaluation = eval_alg.apply(log, net, im, fm)
fitness = rp_fit.apply(log, net, im, fm)
precision = precision_evaluator.apply(log, net, im, fm)
gen = generalization.apply(log, net, im, fm)
sim = simplicity.apply(net)
def test_alphaminer_df(self):
log = pd.read_csv(os.path.join("input_data", "running-example.csv"))
log = dataframe_utils.convert_timestamp_columns_in_df(log)
net, im, fm = alpha_miner.apply(log)
aligned_traces_tr = tr_alg.apply(log, net, im, fm)
aligned_traces_alignments = align_alg.apply(log, net, im, fm)
evaluation = eval_alg.apply(log, net, im, fm)
fitness = rp_fit.apply(log, net, im, fm)
precision = precision_evaluator.apply(log, net, im, fm)
gen = generalization.apply(log, net, im, fm)
sim = simplicity.apply(net)
def test_inductiveminer_stream(self):
df = pd.read_csv(os.path.join("input_data", "running-example.csv"))
df = dataframe_utils.convert_timestamp_columns_in_df(df)
stream = log_conversion.apply(df,
variant=log_conversion.TO_EVENT_STREAM)
net, im, fm = inductive_miner.apply(stream)
aligned_traces_tr = tr_alg.apply(stream, net, im, fm)
aligned_traces_alignments = align_alg.apply(stream, net, im, fm)
evaluation = eval_alg.apply(stream, net, im, fm)
fitness = rp_fit.apply(stream, net, im, fm)
precision = precision_evaluator.apply(stream, net, im, fm)
gen = generalization.apply(stream, net, im, fm)
sim = simplicity.apply(net)
def conformance_diagnostics_alignments(log: EventLog, *args) -> List[Dict[str, Any]]:
"""
Apply the alignments algorithm between a log and a process model.
The methods return the full alignment diagnostics.
Parameters
-------------
log
Event log
args
Specification of the process model
Returns
-------------
aligned_traces
A list of alignments for each trace of the log (in the same order as the traces in the event log)
"""
if len(args) == 3:
if type(args[0]) is PetriNet:
# Petri net alignments
from pm4py.algo.conformance.alignments import algorithm as alignments
return alignments.apply(log, args[0], args[1], args[2])
elif type(args[0]) is dict or type(args[0]) is Counter:
# DFG alignments
from pm4py.objects.dfg.utils import dfg_alignment
return dfg_alignment.apply(log, args[0], args[1], args[2])
elif len(args) == 1:
if type(args[0]) is ProcessTree:
# process tree alignments
from pm4py.algo.conformance.tree_alignments import algorithm as tree_alignments
return tree_alignments.apply(log, args[0])
# try to convert to Petri net
import pm4py
from pm4py.algo.conformance.alignments import algorithm as alignments
net, im, fm = pm4py.convert_to_petri_net(*args)
return alignments.apply(log, net, im, fm)
def test_alignment_pnml(self):
# to avoid static method warnings in tests,
# that by construction of the unittest package have to be expressed in such way
self.dummy_variable = "dummy_value"
log = xes_importer.apply(
os.path.join(INPUT_DATA_DIR, "running-example.xes"))
net, marking, final_marking = inductive_miner.apply(log)
for trace in log:
cf_result = \
align_alg.apply(trace, net, marking, final_marking, variant=align_alg.VERSION_DIJKSTRA_NO_HEURISTICS)[
'alignment']
is_fit = True
for couple in cf_result:
if not (couple[0] == couple[1]
or couple[0] == ">>" and couple[1] is None):
is_fit = False
if not is_fit:
raise Exception("should be fit")
def execute_script():
log = pm4py.read_xes(
os.path.join("..", "tests", "input_data", "receipt.xes"))
print("number of cases", len(log))
print("number of events", sum(len(x) for x in log))
print("number of variants", len(pm4py.get_variants(log)))
ac = get.get_attribute_values(log, "concept:name")
dfg, sa, ea = pm4py.discover_dfg(log)
perc = 0.5
dfg, sa, ea, ac = dfg_filtering.filter_dfg_on_activities_percentage(
dfg, sa, ea, ac, perc)
dfg, sa, ea, ac = dfg_filtering.filter_dfg_on_paths_percentage(
dfg, sa, ea, ac, perc)
aa = time.time()
aligned_traces = dfg_alignment.apply(log, dfg, sa, ea)
bb = time.time()
net, im, fm = pm4py.convert_to_petri_net(dfg, sa, ea)
for trace in aligned_traces:
if trace["cost"] != trace["internal_cost"]:
print(trace)
pass
print(bb - aa)
print(sum(x["visited_states"] for x in aligned_traces))
print(sum(x["cost"] // 10000 for x in aligned_traces))
gviz = visualizer.apply(dfg,
activities_count=ac,
parameters={
"start_activities": sa,
"end_activities": ea,
"format": "svg"
})
visualizer.view(gviz)
cc = time.time()
aligned_traces2 = petri_alignments.apply(
log,
net,
im,
fm,
variant=petri_alignments.Variants.VERSION_DIJKSTRA_LESS_MEMORY)
dd = time.time()
print(dd - cc)
print(sum(x["visited_states"] for x in aligned_traces2))
print(sum(x["cost"] // 10000 for x in aligned_traces2))
def conformance_alignments(log, petri_net, initial_marking, final_marking):
"""
Apply the alignments algorithm between a log and a Petri net
Parameters
-------------
log
Event log
petri_net
Petri net
initial_marking
Initial marking
final_marking
Final marking
Returns
-------------
aligned_traces
A list of alignments for each trace of the log
"""
from pm4py.algo.conformance.alignments import algorithm as alignments
return alignments.apply(log, petri_net, initial_marking, final_marking)
t0 = time.time()
token_replay.apply(a32f0n00_log, a32f0n00_net, a32f0n00_im,
a32f0n00_fm)
t1 = time.time()
T3[0] = (t1 - t0)
T3[2] = math.ceil(T3[1] / (T3[0] + 0.00000001) * 1000.0)
print(
"TEST 3 - Applying token-based replay between A32F0N00 log and model - %.5f s (test score: %d)"
% (T3[0], T3[2]))
if ENABLE_TESTS:
# TEST 4: perform alignments between A32F0N00 log and model
t0 = time.time()
alignments.apply(
a32f0n00_log,
a32f0n00_net,
a32f0n00_im,
a32f0n00_fm,
variant=alignments.Variants.VERSION_DIJKSTRA_NO_HEURISTICS)
t1 = time.time()
T4[0] = (t1 - t0)
T4[2] = math.ceil(T4[1] / (T4[0] + 0.00000001) * 1000.0)
print(
"TEST 4 - Applying alignments between A32F0N00 log and model - %.5f s (test score: %d)"
% (T4[0], T4[2]))
if ENABLE_TESTS:
# TEST 5: perform playout of the a32f0n00 Petri net
t0 = time.time()
playout.apply(a32f0n00_net, a32f0n00_im, a32f0n00_fm)
t1 = time.time()
T5[0] = (t1 - t0)
createAlignments.py : compute alignment costs with pm4py
'''
# read parameter : python3 createAlignments.py output_file xes_file pnml_file
argv=sys.argv
filename=argv[1]
net, m0, mf = importer.importer.apply(argv[3])
log = xes_importer.apply(argv[2])
# compute alignment for variants only
variants = get_variants.get_variants(log)
start=time.time()
for l in variants:
ali=alignments.apply(variants[l][0],net, m0,mf,variant=versions.dijkstra_no_heuristics)
# export in a CSV file with : trace, trace with moves, run, run with moves, cost, frequency
if ali:
writer=open(filename,"a")
alignment=ali["alignment"]
cost=ali["cost"]
phrase1=""
phrase1None=""
phrase2=""
phrase2None=""
for (a,b) in alignment:
if a!=">>":
# activities are separated with :::
phrase1+=a+":::"
phrase1None+=a+":::"
if b!=">>" and b!=None:
def test_align(self):
log = pm4py.read_xes("input_data/running-example.xes")
net, im, fm = pm4py.discover_petri_net_inductive(log,
noise_threshold=0.2)
aligned_traces = alignments.apply(log, net, im, fm)
diagn_df = alignments.get_diagnostics_dataframe(log, aligned_traces)
def get_attributes(log,
decision_points,
attributes,
use_trace_attributes,
trace_attributes,
k,
net,
initial_marking,
final_marking,
decision_points_names,
parameters=None):
"""
This method aims to construct for each decision place a table where for each decision place a list if given with the
label of the later decision and as value the given attributes
:param log: Log on which the method is applied
:param alignments: Computed alignments for a log and a model
:param decision_points: Places that have multiple outgoing arcs
:param attributes: Attributes that are considered
:param use_trace_attributes: If trace attributes have to be considered or not
:param trace_attributes: List of trace attributes that are considered
:param k: Taking k last activities into account
:return: Dictionary that has as keys the decision places. The value for this key is a list.
The content of these lists are tuples. The first element of these tuples is information regrading the attributes,
the second element of these tuples is the transition which chosen in a decision.
"""
if parameters is None:
parameters = {}
I = {}
for key in decision_points:
I[key] = []
A = {}
for attri in attributes:
A[attri] = None
i = 0
# first, take a look at the variants
variants_idxs = variants_module.get_variants_from_log_trace_idx(
log, parameters=parameters)
one_variant = []
for variant in variants_idxs:
one_variant.append(variant)
# TODO: Token based replay code mit paramter für nur varianten einbeziehen ausstatten
replay_result = token_replay.apply(log,
net,
initial_marking,
final_marking,
parameters=parameters)
replay_result = simplify_token_replay(replay_result)
count = 0
for variant in replay_result:
if variant['trace_fitness'] == 1.0:
for trace_index in variants_idxs[one_variant[count]]:
last_k_list = [None] * k
trace = log[trace_index]
if use_trace_attributes:
for attribute in trace_attributes:
# can be done here since trace attributes does not change for whole trace
A[attribute] = trace.attributes[attribute]
j = 0
# j is a pointer which points to the current event inside a trace
for transition in variant['activated_transitions']:
for key, value in decision_points_names.items():
if transition.label in value:
for element in last_k_list:
if element != None:
if transition.label != None:
I[key].append(
(element.copy(), transition.label))
else:
I[key].append(
(element.copy(), transition.name))
for attri in attributes:
# print(variant, transition.label, j)
if attri in trace[j]:
# only add the attribute information if it is present in the event
A[attri] = trace[j][attri]
# add A to last_k_list. Using modulo to access correct entry
last_k_list[j % k] = A.copy()
if transition.label != None:
if not j + 1 >= len(trace):
# Problem otherwise: If there are tau-transition after the last event related transition,
# the pointer j which points to the current event in a trace, gets out of range
j += 1
else:
example_trace = log[variants_idxs[one_variant[count]][0]]
align_parameters = copy(parameters)
align_parameters[star.Parameters.
PARAM_ALIGNMENT_RESULT_IS_SYNC_PROD_AWARE] = True
alignment = ali.apply(example_trace,
net,
initial_marking,
final_marking,
parameters=align_parameters)['alignment']
for trace_index in variants_idxs[one_variant[count]]:
last_k_list = [None] * k
trace = log[trace_index]
if use_trace_attributes:
for attribute in trace_attributes:
# can be done here since trace attributes does not change for whole trace
A[attribute] = trace.attributes[attribute]
j = 0
for el in alignment:
if el[1][1] != '>>':
# If move in model
for key, value in decision_points.items():
if el[0][1] in value:
for element in last_k_list:
if element != None:
# only add those entries where information is provided
if el[1][1] == None:
# for some dt algorithms, the entry None might be a problem, since it is left out later
I[key].append(
(element.copy(), el[0][1]))
else:
I[key].append(
(element.copy(), el[1][1]))
if el[1][0] != '>>' and el[1][1] != '>>':
# If there is a move in log and model
for attri in attributes:
if attri in trace[j]:
# only add the attribute information if it is present in the event
A[attri] = trace[j][attri]
# add A to last_k_list. Using modulo to access correct entry
last_k_list[j % k] = A.copy()
if el[1][0] != '>>':
# only go to next event in trace if the current event has been aligned
# TODO: Discuss if this is correct or can lead to problems
j += 1
count += 1
return I