def test_filter_paths_percentage(self):
from pm4py.algo.filtering.dfg import dfg_filtering
log = pm4py.read_xes("input_data/running-example.xes")
dfg, sa, ea = pm4py.discover_dfg(log)
act_count = pm4py.get_attribute_values(log, "concept:name")
dfg_filtering.filter_dfg_on_paths_percentage(dfg, sa, ea, act_count,
0.3)
def execute_script():
log = pm4py.read_xes(
os.path.join("..", "tests", "input_data", "receipt.xes"))
activities = pm4py.get_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(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 test_dfg_align(self):
import pm4py
from pm4py.algo.filtering.dfg import dfg_filtering
from pm4py.algo.conformance.alignments.dfg import algorithm as dfg_alignment
log = pm4py.read_xes(os.path.join("input_data", "running-example.xes"))
dfg, sa, ea = pm4py.discover_dfg(log)
act_count = pm4py.get_attribute_values(log, "concept:name")
dfg, sa, ea, act_count = dfg_filtering.filter_dfg_on_activities_percentage(
dfg, sa, ea, act_count, 0.5)
dfg, sa, ea, act_count = dfg_filtering.filter_dfg_on_paths_percentage(
dfg, sa, ea, act_count, 0.5)
aligned_traces = dfg_alignment.apply(log, dfg, sa, ea)
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"] // 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():
log = pm4py.read_xes("../tests/input_data/receipt.xes")
dfg, sa, ea = pm4py.discover_dfg(log)
act_count = pm4py.get_event_attribute_values(log, "concept:name")
# keep the specified amount of activities
dfg, sa, ea, act_count = dfg_filtering.filter_dfg_on_activities_percentage(
dfg, sa, ea, act_count, 0.3)
# keep the specified amount of paths
dfg, sa, ea, act_count = dfg_filtering.filter_dfg_on_paths_percentage(
dfg, sa, ea, act_count, 0.3)
# view the DFG
gviz = dfg_visualizer.apply(
dfg,
activities_count=act_count,
parameters={
dfg_visualizer.Variants.FREQUENCY.value.Parameters.START_ACTIVITIES:
sa,
dfg_visualizer.Variants.FREQUENCY.value.Parameters.END_ACTIVITIES:
ea,
dfg_visualizer.Variants.FREQUENCY.value.Parameters.FORMAT: "svg"
})
dfg_visualizer.view(gviz)
