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_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 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_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():
root = ProcessTree(operator=Operator.SEQUENCE)
choice = ProcessTree(operator=Operator.XOR, parent=root)
parallel = ProcessTree(operator=Operator.PARALLEL, parent=root)
root.children.append(choice)
root.children.append(parallel)
leaf_A = ProcessTree(label="A", parent=choice)
leaf_B = ProcessTree(label="B", parent=choice)
leaf_C = ProcessTree(label="C", parent=choice)
choice.children.append(leaf_A)
choice.children.append(leaf_B)
choice.children.append(leaf_C)
leaf_D = ProcessTree(label="D", parent=parallel)
leaf_E = ProcessTree(label="E", parent=parallel)
leaf_F = ProcessTree(label="F", parent=parallel)
parallel.children.append(leaf_D)
parallel.children.append(leaf_E)
parallel.children.append(leaf_F)
pm4py.view_process_tree(root, format="svg")
# remove leaf_C from choice
choice.children.remove(leaf_C)
# remove the leaf with label "E" from parallel
parallel.children.remove([
parallel.children[i] for i in range(len(parallel.children))
if parallel.children[i].label == "E"
][0])
pm4py.view_process_tree(root, format="svg")
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_tree_inductive(log1)
heu_net = pm4py.discover_heuristics_net(log1)
pm4py.write_dfg(dfg, dfg_sa, dfg_ea, "ru_dfg.dfg")
pm4py.write_petri_net(petri_alpha, im_alpha, fm_alpha, "ru_alpha.pnml")
pm4py.write_petri_net(petri_inductive, im_inductive, fm_inductive,
"ru_inductive.pnml")
pm4py.write_petri_net(petri_heuristics, im_heuristics, fm_heuristics,
"ru_heuristics.pnml")
pm4py.write_process_tree(tree_inductive, "ru_inductive.ptml")
dfg, dfg_sa, dfg_ea = pm4py.read_dfg("ru_dfg.dfg")
petri_alpha, im_alpha, fm_alpha = pm4py.read_petri_net("ru_alpha.pnml")
petri_inductive, im_inductive, fm_inductive = pm4py.read_petri_net(
"ru_inductive.pnml")
petri_heuristics, im_heuristics, fm_heuristics = pm4py.read_petri_net(
"ru_heuristics.pnml")
tree_inductive = pm4py.read_process_tree("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")
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_alignments(log1, petri_inductive,
im_inductive, fm_inductive)
replayed_traces = pm4py.conformance_tbr(log1, petri_inductive,
im_inductive, fm_inductive)
fitness_tbr = pm4py.evaluate_fitness_tbr(log1, petri_inductive,
im_inductive, fm_inductive)
print("fitness_tbr", fitness_tbr)
fitness_align = pm4py.evaluate_fitness_alignments(log1, petri_inductive,
im_inductive,
fm_inductive)
print("fitness_align", fitness_align)
precision_tbr = pm4py.evaluate_precision_tbr(log1, petri_inductive,
im_inductive, fm_inductive)
print("precision_tbr", precision_tbr)
precision_align = pm4py.evaluate_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_attributes(log2))
print("df attributes = ", pm4py.get_attributes(df2))
print("log org:resource values = ",
pm4py.get_attribute_values(log2, "org:resource"))
print("df org:resource values = ",
pm4py.get_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_attribute_values(log2,
"org:resource", ["Ellen"],
level="case")))
print(
"attributes org:resource len(filt_log) (cases) events = ",
len(
pm4py.filter_attribute_values(log2,
"org:resource", ["Ellen"],
level="event")))
print(
"attributes org:resource len(filt_df) (events) cases = ",
len(
pm4py.filter_attribute_values(df2,
"org:resource", ["Ellen"],
level="case")))
print(
"attributes org:resource len(filt_df) (events) events = ",
len(
pm4py.filter_attribute_values(df2,
"org:resource", ["Ellen"],
level="event")))
print(
"attributes org:resource len(filt_df) (events) events notpositive = ",
len(
pm4py.filter_attribute_values(df2,
"org:resource", ["Ellen"],
level="event",
retain=False)))
print("variants log = ", pm4py.get_variants(log2))
print("variants df = ", pm4py.get_variants(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("variants filter percentage = ",
len(pm4py.filter_variants_percentage(log2, threshold=0.8)))
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")