def execute_script():
# imports a XES event log
log = pm4py.read_xes(os.path.join("..", "tests", "input_data", "receipt.xes"))
# converts the log into a list of events (not anymore grouped in cases)
event_stream = pm4py.convert_to_event_stream(log)
# calculates a process tree using the IMf algorithm (50% noise)
tree = pm4py.discover_process_tree_inductive(log, noise_threshold=0.5)
# discovers the footprint matrix from the process tree
footprints = fp_discovery.apply(tree)
# creates a live event stream (an object that distributes the messages to the algorithm)
live_stream = LiveEventStream()
# creates the TBR streaming conformance checking object
conf_obj = streaming_fp_conf.apply(footprints)
# register the conformance checking object to the live event stream
live_stream.register(conf_obj)
# start the recording of events from the live event stream
live_stream.start()
# append each event of the original log to the live event stream
# (so it is sent to the conformance checking algorithm)
for index, event in enumerate(event_stream):
live_stream.append(event)
# stops the live event stream
live_stream.stop()
# sends a termination signal to the conformance checking algorithm;
# the conditions on the closure of all the cases are checked
# (for each case, it is checked whether the end activity of the case
# is possible according to the footprints)
diagn_df = conf_obj.get()
conf_obj.terminate_all()
print(diagn_df)
print(diagn_df[diagn_df["is_fit"] == False])
def execute_script():
log = pm4py.read_xes("../tests/input_data/running-example.xes")
dfg, sa, ea = pm4py.discover_dfg(log)
tree = pm4py.discover_process_tree_inductive(log)
heu_net = pm4py.discover_heuristics_net(log)
net, im, fm = pm4py.discover_petri_net_alpha(log)
bpmn = pm4py.convert_to_bpmn(tree)
ts = ts_discovery.apply(log)
x_cases, y_cases = case_statistics.get_kde_caseduration(log)
gviz1 = dfg_visualizer.apply(dfg)
gviz2 = tree_visualizer.apply(tree)
gviz3 = hn_visualizer.apply(heu_net)
gviz4 = pn_visualizer.apply(net, im, fm)
gviz5 = bpmn_visualizer.apply(bpmn)
gviz6 = ts_visualizer.apply(ts)
gviz7 = graphs_visualizer.apply(x_cases, y_cases, variant=graphs_visualizer.Variants.CASES,
parameters={graphs_visualizer.Variants.CASES.value.Parameters.FORMAT: "svg"})
print("1", len(dfg_visualizer.serialize_dot(gviz1)))
print("1", len(dfg_visualizer.serialize(gviz1)))
print("2", len(tree_visualizer.serialize_dot(gviz2)))
print("2", len(tree_visualizer.serialize(gviz2)))
print("3", len(hn_visualizer.serialize(gviz3)))
print("4", len(pn_visualizer.serialize_dot(gviz4)))
print("4", len(pn_visualizer.serialize(gviz4)))
print("5", len(bpmn_visualizer.serialize_dot(gviz5)))
print("5", len(bpmn_visualizer.serialize(gviz5)))
print("6", len(ts_visualizer.serialize_dot(gviz6)))
print("6", len(ts_visualizer.serialize(gviz6)))
print("7", len(graphs_visualizer.serialize(gviz7)))
def execute_script():
log = pm4py.read_xes(
os.path.join("..", "tests", "input_data", "running-example.xes"))
alpha_petri_net, alpha_im, alpha_fm = pm4py.discover_petri_net_alpha(log)
heuristics_petri_net, heuristics_im, heuristics_fm = pm4py.discover_petri_net_heuristics(
log)
tree = pm4py.discover_process_tree_inductive(log)
print("tree discovered by inductive miner=")
print(tree)
inductive_petri_net, inductive_im, inductive_fm = pt_converter.apply(tree)
print("is_wf_net alpha", is_wf_net.apply(alpha_petri_net))
print("is_wf_net heuristics", is_wf_net.apply(heuristics_petri_net))
print("is_wf_net inductive", is_wf_net.apply(inductive_petri_net))
print(
"woflan alpha",
woflan.apply(alpha_petri_net,
alpha_im,
alpha_fm,
parameters={
woflan.Parameters.RETURN_ASAP_WHEN_NOT_SOUND: True,
woflan.Parameters.PRINT_DIAGNOSTICS: False
}))
print(
"woflan heuristics",
woflan.apply(heuristics_petri_net,
heuristics_im,
heuristics_fm,
parameters={
woflan.Parameters.RETURN_ASAP_WHEN_NOT_SOUND: True,
woflan.Parameters.PRINT_DIAGNOSTICS: False
}))
print(
"woflan inductive",
woflan.apply(inductive_petri_net,
inductive_im,
inductive_fm,
parameters={
woflan.Parameters.RETURN_ASAP_WHEN_NOT_SOUND: True,
woflan.Parameters.PRINT_DIAGNOSTICS: False
}))
try:
tree_alpha = wf_net_converter.apply(alpha_petri_net, alpha_im,
alpha_fm)
print(tree_alpha)
except:
traceback.print_exc()
try:
tree_heuristics = wf_net_converter.apply(heuristics_petri_net,
heuristics_im, heuristics_fm)
print(tree_heuristics)
except:
traceback.print_exc()
try:
tree_inductive = wf_net_converter.apply(inductive_petri_net,
inductive_im, inductive_fm)
print(tree_inductive)
pm4py.view_process_tree(tree_inductive, format="svg")
except:
traceback.print_exc()
def test_footprints_extensive(self):
log = pm4py.read_xes("input_data/running-example.xes")
fp_log = footprints_discovery.apply(
log, variant=footprints_discovery.Variants.TRACE_BY_TRACE)
tree = pm4py.discover_process_tree_inductive(log, noise_threshold=0.2)
fp_model = footprints_discovery.apply(tree)
conf_result = trace_extensive.apply(fp_log, fp_model)
diagn_df = trace_extensive.get_diagnostics_dataframe(log, conf_result)
def test_tree_align_reviewing_classifier(self):
import pm4py
log = pm4py.read_xes("compressed_input_data/04_reviewing.xes.gz")
for trace in log:
for event in trace:
event["concept:name"] = event["concept:name"] + "+" + event[
"lifecycle:transition"]
tree = pm4py.discover_process_tree_inductive(log, noise_threshold=0.2)
al = pm4py.conformance_diagnostics_alignments(log, tree)
def execute_script():
log = pm4py.read_xes(
os.path.join("..", "tests", "input_data", "receipt.xes"))
tree = pm4py.discover_process_tree_inductive(log)
aligned_traces = pm4py.conformance_diagnostics_alignments(log, tree)
tree = search_graph_pt_frequency_annotation.apply(tree, aligned_traces)
gviz = pt_visualizer.apply(
tree,
parameters={"format": "svg"},
variant=pt_visualizer.Variants.FREQUENCY_ANNOTATION)
pt_visualizer.view(gviz)
def execute_script():
log = pm4py.read_xes(
os.path.join("..", "tests", "input_data", "receipt.xes"))
# the tree discovered by inductive miner is huge and can replay the behavior of the log
tree = pm4py.discover_process_tree_inductive(log)
pm4py.view_process_tree(tree, "svg")
# to make a more effective replay, remove the elements that are not being used during the replay of the trace
# (that are the skippable ones, with empty intersection with the trace)
tree_first_trace = reducer.apply(tree,
log[0],
variant=reducer.Variants.TREE_TR_BASED)
pm4py.view_process_tree(tree_first_trace, "svg")
def execute_script():
log = pm4py.read_xes("../tests/compressed_input_data/02_teleclaims.xes.gz")
tree = pm4py.discover_process_tree_inductive(log, noise_threshold=0.3)
net, im, fm = pm4py.convert_to_petri_net(tree)
# reduce the log to one trace per variant
log = filtering_utils.keep_one_trace_per_variant(log)
for index, trace in enumerate(log):
print(index)
aa = time.time()
check_tree = pm4py.check_is_fitting(trace, tree)
bb = time.time()
check_petri = pm4py.check_is_fitting(trace, net, im, fm)
cc = time.time()
print("check on tree: ", check_tree, "time", bb - aa)
print("check on Petri net: ", check_petri, "time", cc - bb)
print()
def execute_script():
# read an event log
log = pm4py.read_xes("../tests/compressed_input_data/02_teleclaims.xes.gz")
# log = pm4py.read_xes("../tests/input_data/receipt.xes")
print("number of variants of the original log ->",
len(pm4py.get_variants_as_tuples(log)))
# discover a process model
tree = pm4py.discover_process_tree_inductive(log)
# simulate a log out of the model (to have another log that is similar to the original)
aa = time.time()
min_trace_length = bottomup_discovery.get_min_trace_length(tree)
simulated_log = tree_playout.apply(
tree,
variant=tree_playout.Variants.EXTENSIVE,
parameters={"max_trace_length": min_trace_length + 2})
print("number of variants of the simulated log -> ", len(simulated_log))
# apply the alignments between this log and the model
bb = time.time()
aligned_traces = logs_alignment.apply(log, simulated_log)
cc = time.time()
print(aligned_traces[0])
print("playout time", bb - aa)
print("alignments time", cc - bb)
print("TOTAL", cc - aa)
print(alignment_based.evaluate(aligned_traces))
# apply the anti alignments between this log and the model
dd = time.time()
anti_aligned_traces = logs_alignment.apply(
log,
simulated_log,
parameters={
logs_alignment.Variants.EDIT_DISTANCE.value.Parameters.PERFORM_ANTI_ALIGNMENT:
True
})
ee = time.time()
print(anti_aligned_traces[0])
print("anti alignments time", ee - dd)
print(alignment_based.evaluate(anti_aligned_traces))
def test_inductive_miner_tree(self):
log = pm4py.read_xes("input_data/running-example.xes")
tree = pm4py.discover_process_tree_inductive(log)
tree = pm4py.discover_process_tree_inductive(log, noise_threshold=0.2)
import tempfile
LOGS_FOLDER = "../compressed_input_data"
for log_name in os.listdir(LOGS_FOLDER):
if "xes" in log_name:
bpmn_output_path = tempfile.NamedTemporaryFile(suffix=".bpmn")
bpmn_output_path.close()
bpmn_output_path = bpmn_output_path.name
log_path = os.path.join(LOGS_FOLDER, log_name)
print("")
print(log_path)
log = pm4py.read_xes(log_path)
fp_log = pm4py.algo.discovery.footprints.log.variants.entire_event_log.apply(
log)
tree = pm4py.discover_process_tree_inductive(log)
generic.tree_sort(tree)
fp_tree = pm4py.algo.discovery.footprints.tree.variants.bottomup.apply(
tree)
fp_conf = pm4py.algo.conformance.footprints.variants.log_extensive.apply(
fp_log, fp_tree)
fitness0 = pm4py.algo.conformance.footprints.util.evaluation.fp_fitness(
fp_log, fp_tree, fp_conf)
precision0 = pm4py.algo.conformance.footprints.util.evaluation.fp_precision(
fp_log, fp_tree)
print("fitness 0 = ", fitness0)
print("precision 0 = ", precision0)
net, im, fm = pm4py.objects.conversion.process_tree.variants.to_petri_net.apply(
tree)
bpmn_graph = pm4py.objects.conversion.wf_net.variants.to_bpmn.apply(
net, im, fm)
def test_tree_align_reviewing(self):
import pm4py
log = pm4py.read_xes("compressed_input_data/04_reviewing.xes.gz")
tree = pm4py.discover_process_tree_inductive(log, noise_threshold=0.2)
al = pm4py.conformance_diagnostics_alignments(log, tree)
def test_tree_align_receipt(self):
import pm4py
log = pm4py.read_xes("input_data/receipt.xes")
tree = pm4py.discover_process_tree_inductive(log, noise_threshold=0.2)
al = pm4py.conformance_diagnostics_alignments(log, tree)
def test_bpmn_layouting(self):
log = pm4py.read_xes(os.path.join("input_data", "running-example.xes"))
tree = pm4py.discover_process_tree_inductive(log)
bpmn_graph = tree_converter.apply(
tree, variant=tree_converter.Variants.TO_BPMN)
bpmn_graph = bpmn_layouter.apply(bpmn_graph)
def execute_script():
ENABLE_VISUALIZATION = True
# reads a XES into an event log
log1 = pm4py.read_xes("../tests/input_data/running-example.xes")
# reads a CSV into a dataframe
df = pd.read_csv("../tests/input_data/running-example.csv")
# formats the dataframe with the mandatory columns for process mining purposes
df = pm4py.format_dataframe(df, case_id="case:concept:name", activity_key="concept:name",
timestamp_key="time:timestamp")
# converts the dataframe to an event log
log2 = pm4py.convert_to_event_log(df)
# converts the log read from XES into a stream and dataframe respectively
stream1 = pm4py.convert_to_event_stream(log1)
df2 = pm4py.convert_to_dataframe(log1)
# writes the log1 to a XES file
pm4py.write_xes(log1, "ru1.xes")
dfg, dfg_sa, dfg_ea = pm4py.discover_dfg(log1)
petri_alpha, im_alpha, fm_alpha = pm4py.discover_petri_net_alpha(log1)
petri_inductive, im_inductive, fm_inductive = pm4py.discover_petri_net_inductive(log1)
petri_heuristics, im_heuristics, fm_heuristics = pm4py.discover_petri_net_heuristics(log1)
tree_inductive = pm4py.discover_process_tree_inductive(log1)
heu_net = pm4py.discover_heuristics_net(log1)
pm4py.write_dfg(dfg, dfg_sa, dfg_ea, "ru_dfg.dfg")
pm4py.write_pnml(petri_alpha, im_alpha, fm_alpha, "ru_alpha.pnml")
pm4py.write_pnml(petri_inductive, im_inductive, fm_inductive, "ru_inductive.pnml")
pm4py.write_pnml(petri_heuristics, im_heuristics, fm_heuristics, "ru_heuristics.pnml")
pm4py.write_ptml(tree_inductive, "ru_inductive.ptml")
dfg, dfg_sa, dfg_ea = pm4py.read_dfg("ru_dfg.dfg")
petri_alpha, im_alpha, fm_alpha = pm4py.read_pnml("ru_alpha.pnml")
petri_inductive, im_inductive, fm_inductive = pm4py.read_pnml("ru_inductive.pnml")
petri_heuristics, im_heuristics, fm_heuristics = pm4py.read_pnml("ru_heuristics.pnml")
tree_inductive = pm4py.read_ptml("ru_inductive.ptml")
pm4py.save_vis_petri_net(petri_alpha, im_alpha, fm_alpha, "ru_alpha.png")
pm4py.save_vis_petri_net(petri_inductive, im_inductive, fm_inductive, "ru_inductive.png")
pm4py.save_vis_petri_net(petri_heuristics, im_heuristics, fm_heuristics, "ru_heuristics.png")
pm4py.save_vis_process_tree(tree_inductive, "ru_inductive_tree.png")
pm4py.save_vis_heuristics_net(heu_net, "ru_heunet.png")
pm4py.save_vis_dfg(dfg, dfg_sa, dfg_ea, "ru_dfg.png")
pm4py.save_vis_events_per_time_graph(log1, "ev_time.png")
pm4py.save_vis_case_duration_graph(log1, "cd.png")
pm4py.save_vis_dotted_chart(log1, "dotted_chart.png")
pm4py.save_vis_performance_spectrum(log1, ["register request", "decide"], "ps.png")
if ENABLE_VISUALIZATION:
pm4py.view_petri_net(petri_alpha, im_alpha, fm_alpha, format="svg")
pm4py.view_petri_net(petri_inductive, im_inductive, fm_inductive, format="svg")
pm4py.view_petri_net(petri_heuristics, im_heuristics, fm_heuristics, format="svg")
pm4py.view_process_tree(tree_inductive, format="svg")
pm4py.view_heuristics_net(heu_net, format="svg")
pm4py.view_dfg(dfg, dfg_sa, dfg_ea, format="svg")
aligned_traces = pm4py.conformance_diagnostics_alignments(log1, petri_inductive, im_inductive, fm_inductive)
replayed_traces = pm4py.conformance_diagnostics_token_based_replay(log1, petri_inductive, im_inductive, fm_inductive)
fitness_tbr = pm4py.fitness_token_based_replay(log1, petri_inductive, im_inductive, fm_inductive)
print("fitness_tbr", fitness_tbr)
fitness_align = pm4py.fitness_alignments(log1, petri_inductive, im_inductive, fm_inductive)
print("fitness_align", fitness_align)
precision_tbr = pm4py.precision_token_based_replay(log1, petri_inductive, im_inductive, fm_inductive)
print("precision_tbr", precision_tbr)
precision_align = pm4py.precision_alignments(log1, petri_inductive, im_inductive, fm_inductive)
print("precision_align", precision_align)
print("log start activities = ", pm4py.get_start_activities(log2))
print("df start activities = ", pm4py.get_start_activities(df2))
print("log end activities = ", pm4py.get_end_activities(log2))
print("df end activities = ", pm4py.get_end_activities(df2))
print("log attributes = ", pm4py.get_event_attributes(log2))
print("df attributes = ", pm4py.get_event_attributes(df2))
print("log org:resource values = ", pm4py.get_event_attribute_values(log2, "org:resource"))
print("df org:resource values = ", pm4py.get_event_attribute_values(df2, "org:resource"))
print("start_activities len(filt_log) = ", len(pm4py.filter_start_activities(log2, ["register request"])))
print("start_activities len(filt_df) = ", len(pm4py.filter_start_activities(df2, ["register request"])))
print("end_activities len(filt_log) = ", len(pm4py.filter_end_activities(log2, ["pay compensation"])))
print("end_activities len(filt_df) = ", len(pm4py.filter_end_activities(df2, ["pay compensation"])))
print("attributes org:resource len(filt_log) (cases) cases = ",
len(pm4py.filter_event_attribute_values(log2, "org:resource", ["Ellen"], level="case")))
print("attributes org:resource len(filt_log) (cases) events = ",
len(pm4py.filter_event_attribute_values(log2, "org:resource", ["Ellen"], level="event")))
print("attributes org:resource len(filt_df) (events) cases = ",
len(pm4py.filter_event_attribute_values(df2, "org:resource", ["Ellen"], level="case")))
print("attributes org:resource len(filt_df) (events) events = ",
len(pm4py.filter_event_attribute_values(df2, "org:resource", ["Ellen"], level="event")))
print("attributes org:resource len(filt_df) (events) events notpositive = ",
len(pm4py.filter_event_attribute_values(df2, "org:resource", ["Ellen"], level="event", retain=False)))
print("rework df = ", pm4py.get_rework_cases_per_activity(df2))
print("rework log = ", pm4py.get_rework_cases_per_activity(log2))
print("cases overlap df = ", pm4py.get_case_overlap(df2))
print("cases overlap log = ", pm4py.get_case_overlap(log2))
print("cycle time df = ", pm4py.get_cycle_time(df2))
print("cycle time log = ", pm4py.get_cycle_time(log2))
pm4py.view_events_distribution_graph(df2, format="svg")
pm4py.view_events_distribution_graph(log2, format="svg")
print("variants log = ", pm4py.get_variants_as_tuples(log2))
print("variants df = ", pm4py.get_variants_as_tuples(df2))
print("variants filter log = ",
len(pm4py.filter_variants(log2, [
("register request", "examine thoroughly", "check ticket", "decide", "reject request")])))
print("variants filter df = ",
len(pm4py.filter_variants(df2, [
("register request", "examine thoroughly", "check ticket", "decide", "reject request")])))
print("paths filter log len = ",
len(pm4py.filter_directly_follows_relation(log2, [("register request", "examine casually")])))
print("paths filter dataframe len = ",
len(pm4py.filter_directly_follows_relation(df2, [("register request", "examine casually")])))
print("timeframe filter log events len = ",
len(pm4py.filter_time_range(log2, "2011-01-01 00:00:00", "2011-02-01 00:00:00", mode="events")))
print("timeframe filter log traces_contained len = ",
len(pm4py.filter_time_range(log2, "2011-01-01 00:00:00", "2011-02-01 00:00:00", mode="traces_contained")))
print("timeframe filter log traces_intersecting len = ",
len(pm4py.filter_time_range(log2, "2011-01-01 00:00:00", "2011-02-01 00:00:00", mode="traces_intersecting")))
print("timeframe filter df events len = ",
len(pm4py.filter_time_range(df2, "2011-01-01 00:00:00", "2011-02-01 00:00:00", mode="events")))
print("timeframe filter df traces_contained len = ",
len(pm4py.filter_time_range(df2, "2011-01-01 00:00:00", "2011-02-01 00:00:00", mode="traces_contained")))
print("timeframe filter df traces_intersecting len = ",
len(pm4py.filter_time_range(df2, "2011-01-01 00:00:00", "2011-02-01 00:00:00", mode="traces_intersecting")))
# remove the temporary files
os.remove("ru1.xes")
os.remove("ru_dfg.dfg")
os.remove("ru_alpha.pnml")
os.remove("ru_inductive.pnml")
os.remove("ru_heuristics.pnml")
os.remove("ru_inductive.ptml")
os.remove("ru_alpha.png")
os.remove("ru_inductive.png")
os.remove("ru_heuristics.png")
os.remove("ru_inductive_tree.png")
os.remove("ru_heunet.png")
os.remove("ru_dfg.png")
os.remove("ev_time.png")
os.remove("cd.png")
os.remove("dotted_chart.png")
os.remove("ps.png")
wt_log = pm4py.discover_working_together_network(log2)
wt_df = pm4py.discover_working_together_network(df2)
print("log working together", wt_log)
print("df working together", wt_df)
print("log subcontracting", pm4py.discover_subcontracting_network(log2))
print("df subcontracting", pm4py.discover_subcontracting_network(df2))
print("log working together", pm4py.discover_working_together_network(log2))
print("df working together", pm4py.discover_working_together_network(df2))
print("log similar activities", pm4py.discover_activity_based_resource_similarity(log2))
print("df similar activities", pm4py.discover_activity_based_resource_similarity(df2))
print("log org roles", pm4py.discover_organizational_roles(log2))
print("df org roles", pm4py.discover_organizational_roles(df2))
pm4py.view_sna(wt_log)
pm4py.save_vis_sna(wt_df, "ru_wt_df.png")
os.remove("ru_wt_df.png")
