def load_dataframe():
df = pm4py.read_csv("event_log.csv", sep=";")
df = pm4py.objects.log.util.dataframe_utils.convert_timestamp_columns_in_df(
df)
df = df.sort_values(['start time', 'end time'])
df["event id"] = df.index.astype(str)
df = df.reset_index()
return df
def test_csv(self):
df = pm4py.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")
log2 = pm4py.convert_to_event_log(df)
stream1 = pm4py.convert_to_event_stream(log2)
df2 = pm4py.convert_to_dataframe(log2)
pm4py.write_xes(log2, "test_output_data/log.xes")
pm4py.write_csv(df, "test_output_data/log.csv")
os.remove("test_output_data/log.xes")
os.remove("test_output_data/log.csv")
def execute_script():
log_path = os.path.join("..", "tests", "input_data",
"interval_event_log.csv")
dataframe = pm4py.read_csv(log_path)
log_path = os.path.join("..", "tests", "input_data", "reviewing.xes")
log = pm4py.read_xes(log_path)
dataframe = pm4py.convert_to_dataframe(log)
parameters = {}
#parameters[constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY] = "start_timestamp"
parameters[constants.PARAMETER_CONSTANT_TIMESTAMP_KEY] = "time:timestamp"
parameters[constants.PARAMETER_CONSTANT_ACTIVITY_KEY] = "concept:name"
parameters[constants.PARAMETER_CONSTANT_CASEID_KEY] = "case:concept:name"
parameters["strict"] = True
parameters["format"] = "svg"
start_activities = sa_get.get_start_activities(dataframe,
parameters=parameters)
end_activities = ea_get.get_end_activities(dataframe,
parameters=parameters)
att_count = att_get.get_attribute_values(dataframe,
"concept:name",
parameters=parameters)
parameters["start_activities"] = start_activities
parameters["end_activities"] = end_activities
soj_time = soj_time_get.apply(dataframe, parameters=parameters)
print("soj_time")
print(soj_time)
conc_act = conc_act_get.apply(dataframe, parameters=parameters)
print("conc_act")
print(conc_act)
efg = efg_get.apply(dataframe, parameters=parameters)
print("efg")
print(efg)
dfg_freq, dfg_perf = df_statistics.get_dfg_graph(
dataframe, measure="both", start_timestamp_key="start_timestamp")
dfg_gv_freq = dfg_vis_fact.apply(dfg_freq,
activities_count=att_count,
variant=dfg_vis_fact.Variants.FREQUENCY,
soj_time=soj_time,
parameters=parameters)
dfg_vis_fact.view(dfg_gv_freq)
dfg_gv_perf = dfg_vis_fact.apply(dfg_perf,
activities_count=att_count,
variant=dfg_vis_fact.Variants.PERFORMANCE,
soj_time=soj_time,
parameters=parameters)
dfg_vis_fact.view(dfg_gv_perf)
net, im, fm = dfg_conv.apply(dfg_freq)
gviz = pn_vis.apply(net, im, fm, parameters=parameters)
pn_vis.view(gviz)
def execute_script():
df = pm4py.read_csv("../tests/input_data/interval_event_log.csv")
log = pm4py.read_xes("../tests/input_data/interval_event_log.xes")
heu_net = plusplus.apply_heu(log, parameters={"heu_net_decoration": "performance"})
heu_net_2 = plusplus.apply_heu_pandas(df, parameters={"heu_net_decoration": "performance"})
gviz = visualizer.apply(heu_net, parameters={"format": "svg"})
visualizer.view(gviz)
gviz2 = visualizer.apply(heu_net_2, parameters={"format": "svg"})
visualizer.view(gviz2)
net1, im1, fm1 = plusplus.apply(log)
net2, im2, fm2 = plusplus.apply(log)
gviz3 = pn_visualizer.apply(net1, im1, fm1, parameters={"format": "svg"})
pn_visualizer.view(gviz3)
gviz4 = pn_visualizer.apply(net2, im2, fm2, parameters={"format": "svg"})
pn_visualizer.view(gviz4)
def execute_script():
ENABLE_VISUALIZATION = False
# reads a XES into an event log
log1 = pm4py.read_xes("../tests/input_data/running-example.xes")
# reads a CSV into a dataframe
df = pm4py.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")
# writes the df to a CSV file
pm4py.write_csv(df, "ru1.csv")
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, 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"],
how="cases")))
print(
"attributes org:resource len(filt_log) (cases) events = ",
len(
pm4py.filter_attribute_values(log2,
"org:resource", ["Ellen"],
how="events")))
print(
"attributes org:resource len(filt_df) (events) cases = ",
len(
pm4py.filter_attribute_values(df2,
"org:resource", ["Ellen"],
how="cases")))
print(
"attributes org:resource len(filt_df) (events) events = ",
len(
pm4py.filter_attribute_values(df2,
"org:resource", ["Ellen"],
how="events")))
print(
"attributes org:resource len(filt_df) (events) events notpositive = ",
len(
pm4py.filter_attribute_values(df2,
"org:resource", ["Ellen"],
how="events",
positive=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, percentage=0.8)))
print(
"paths filter log len = ",
len(
pm4py.filter_paths(log2,
[("register request", "examine casually")])))
print(
"paths filter dataframe len = ",
len(pm4py.filter_paths(df2,
[("register request", "examine casually")])))
print(
"timeframe filter log events len = ",
len(
pm4py.filter_timestamp(log2,
"2011-01-01 00:00:00",
"2011-02-01 00:00:00",
how="events")))
print(
"timeframe filter log traces_contained len = ",
len(
pm4py.filter_timestamp(log2,
"2011-01-01 00:00:00",
"2011-02-01 00:00:00",
how="traces_contained")))
print(
"timeframe filter log traces_intersecting len = ",
len(
pm4py.filter_timestamp(log2,
"2011-01-01 00:00:00",
"2011-02-01 00:00:00",
how="traces_intersecting")))
print(
"timeframe filter df events len = ",
len(
pm4py.filter_timestamp(df2,
"2011-01-01 00:00:00",
"2011-02-01 00:00:00",
how="events")))
print(
"timeframe filter df traces_contained len = ",
len(
pm4py.filter_timestamp(df2,
"2011-01-01 00:00:00",
"2011-02-01 00:00:00",
how="traces_contained")))
print(
"timeframe filter df traces_intersecting len = ",
len(
pm4py.filter_timestamp(df2,
"2011-01-01 00:00:00",
"2011-02-01 00:00:00",
how="traces_intersecting")))
# remove the temporary files
os.remove("ru1.xes")
os.remove("ru1.csv")
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")