def test_statistics_log(self):
log = pm4py.read_xes("input_data/running-example.xes")
pm4py.get_start_activities(log)
pm4py.get_end_activities(log)
pm4py.get_event_attributes(log)
pm4py.get_trace_attributes(log)
pm4py.get_event_attribute_values(log, "org:resource")
pm4py.get_variants_as_tuples(log)
def test_statistics_df(self):
df = pd.read_csv("input_data/running-example.csv")
df = pm4py.format_dataframe(df,
case_id="case:concept:name",
activity_key="concept:name",
timestamp_key="time:timestamp")
pm4py.get_start_activities(df)
pm4py.get_end_activities(df)
pm4py.get_event_attributes(df)
pm4py.get_event_attribute_values(df, "org:resource")
pm4py.get_variants_as_tuples(df)
def execute_script():
log = pm4py.read_xes(
os.path.join("..", "tests", "input_data", "receipt.xes"))
activities = pm4py.get_event_attribute_values(log, "concept:name")
dfg, sa, ea = pm4py.discover_dfg(log)
# filters the DFG to make a simpler one
perc = 0.5
dfg, sa, ea, activities = dfg_filtering.filter_dfg_on_activities_percentage(
dfg, sa, ea, activities, perc)
dfg, sa, ea, activities = dfg_filtering.filter_dfg_on_paths_percentage(
dfg, sa, ea, activities, perc)
# creates the simulated log
simulated_log = dfg_playout.apply(dfg, sa, ea)
print(simulated_log)
print(len(simulated_log))
print(sum(x.attributes["probability"] for x in simulated_log))
# shows the two DFGs to show that they are identical
pm4py.view_dfg(dfg, sa, ea, log=log, format="svg")
new_dfg, new_sa, new_ea = pm4py.discover_dfg(simulated_log)
pm4py.view_dfg(new_dfg, new_sa, new_ea, log=simulated_log, format="svg")
for trace in simulated_log:
print(list(x["concept:name"] for x in trace))
print(trace.attributes["probability"],
dfg_playout.get_trace_probability(trace, dfg, sa, ea))
break
dfg, sa, ea = pm4py.discover_dfg(log)
variants = pm4py.get_variants_as_tuples(log)
sum_prob_log_variants = 0.0
for var in variants:
sum_prob_log_variants += dfg_playout.get_trace_probability(
variants[var][0], dfg, sa, ea)
print(
"percentage of behavior allowed from DFG that is in the log (from 0.0 to 1.0): ",
sum_prob_log_variants)
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_as_tuples(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"] // align_utils.STD_MODEL_LOG_MOVE_COST
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"] // align_utils.STD_MODEL_LOG_MOVE_COST
for x in aligned_traces2))
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))
event["@@classifier"] = event["concept:name"]
activities = set(
attributes_get_log.get_attribute_values(log,
CLASSIFIER).keys())
variants = variants_get.get_variants(
log, parameters={"pm4py:param:activity_key": CLASSIFIER})
fp_log = pm4py.algo.discovery.footprints.log.variants.entire_event_log.apply(
log, parameters={"pm4py:param:activity_key": CLASSIFIER})
elif "parquet" in log_name:
from pm4py.statistics.attributes.pandas import get as attributes_get_pandas
dataframe = pd.read_parquet(log_path)
activities = set(
attributes_get_pandas.get_attribute_values(
dataframe, CLASSIFIER).keys())
variants = pm4py.get_variants_as_tuples(dataframe)
variants = {",".join(x): y for x, y in variants.items()}
fp_log = pm4py.algo.discovery.footprints.log.variants.entire_dataframe.apply(
dataframe)
print("start tree_im_clean")
tree_im_clean = im_clean.apply_tree(log,
parameters={
"pm4py:param:activity_key":
CLASSIFIER,
"noise_threshold":
NOISE_THRESHOLD
})
print("end tree_im_clean")
tree_im = inductive_miner.apply_tree_variants(
variants,
variant=inductive_miner.Variants.IM,
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")