Onnowpurbo: Created page with " import pm4py from pm4py.objects.log.importer.xes import factory as xes_importer from pm4py.algo.discovery.inductive import factory as inductive_miner from pm4py.visualiz..."

2024-03-22T04:13:43Z

Created page with " import pm4py from pm4py.objects.log.importer.xes import factory as xes_importer from pm4py.algo.discovery.inductive import factory as inductive_miner from pm4py.visualiz..."

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import pm4py
from pm4py.objects.log.importer.xes import factory as xes_importer
from pm4py.algo.discovery.inductive import factory as inductive_miner
from pm4py.visualization.petrinet import factory as pn_vis_factory

# Load event log
log = xes_importer.import_log("your_event_log.xes")

# Discover process model using Inductive Miner
net, initial_marking, final_marking = inductive_miner.apply(log)

# Visualize Petri net
gviz = pn_vis_factory.apply(net, initial_marking, final_marking)
pn_vis_factory.view(gviz)

# Analyze the model to identify bottlenecks
# One way to detect bottlenecks could be by analyzing the transition
frequencies
transition_freq = {}
for trace in log:
for event in trace:
transition = event["concept:name"]
transition_freq[transition] = transition_freq.get(transition, 0) + 1

# Sort transitions by frequency
sorted_transitions = sorted(transition_freq.items(), key=lambda x: x[1], reverse=True)

# Print the most frequent transitions, which can indicate potential bottlenecks
print("Potential bottlenecks (transitions with highest frequency):")
for transition, freq in sorted_transitions[:5]:
print(transition, "-", freq, "occurrences")

Pm4py: deteksi bottleneck - Revision history

Onnowpurbo: Created page with " import pm4py from pm4py.objects.log.importer.xes import factory as xes_importer from pm4py.algo.discovery.inductive import factory as inductive_miner from pm4py.visualiz..."