PM4py 二开记录(6) ：get_events_distribution log中挖掘足迹模型

最新推荐文章于 2026-01-06 22:02:48 发布

原创最新推荐文章于 2026-01-06 22:02:48 发布 · 360 阅读

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#数据库 #前端

本文介绍了如何使用PM4Py库对事件日志中的活动进行关系分析，包括直接跟随关系、并行行为的发现，以及通过诱导式挖掘发现过程模型。之后，文章展示了足迹发现和一致性检查的方法，以评估日志与模型的符合度。

推断日志/模型的任何两个活动之间的关系。

直接跟随关系(>)：活动B紧随A。A在前，B在后 (dfg)
直接被跟随关系(<)：活动A紧随B。B在前，A在后 (sequence)
并行行为(||)：可能是A后面跟着B，B后面跟着A (parallel)

import os
from pm4py.objects.log.importer.xes import importer
from pm4py.algo.filtering.log.variants import variants_filter
from pm4py.algo.discovery.inductive import algorithm as inductive_miner
from pm4py.objects.conversion.process_tree import converter
from pm4py.algo.discovery.footprints import algorithm as footprints_discovery
from pm4py.algo.conformance.footprints import algorithm as footprints_conformance
from pm4py.visualization.footprints import visualizer as fp_visualizer
from examples import examples_conf

if __name__ == "__main__":
    # import a log
    log = importer.apply(os.path.join("..", "tests", "input_data", "receipt.xes"))
    # found a filtered version of the log that is used to discover a process model
    filtered_log = variants_filter.filter_log_variants_percentage(log, 0.2)
    # discover a process tree using inductive miner
    tree = inductive_miner.apply(filtered_log)
    print(tree)
    # apply the conversion of a process tree into a Petri net
    net, im, fm = converter.apply(tree)
    # Footprints discovery: discover a list of footprints
    # for all the cases of the log
    fp_log = footprints_discovery.apply(log)

    # discover the footpritns from the Petri net
    fp_net = footprints_discovery.apply(net, im)

    # finds the footprints for the entire log (not case-by-case, but taking
    # the relations that appear inside the entire log)
    fp_log_entire = footprints_discovery.apply(log, variant=footprints_discovery.Variants.ENTIRE_EVENT_LOG)
    # visualize the footprint table
    gviz = fp_visualizer.apply(fp_log_entire, fp_net, parameters={"format": examples_conf.TARGET_IMG_FORMAT})
    fp_visualizer.view(gviz)


    # discover the footpritns from the process tree
    fp_tree = footprints_discovery.apply(tree)
    print(len(fp_tree["sequence"]), len(fp_tree["parallel"]), len(fp_net["sequence"]), len(fp_net["parallel"]))
    print(fp_tree["sequence"] == fp_net["sequence"] and fp_tree["parallel"] == fp_net["parallel"])
    # apply the footprints conformance checking
    conf = footprints_conformance.apply(fp_log, fp_net)
    i = 0
    for trace_an in conf:
        if trace_an:
            # print the first anomalous trace (containing deviations
            # that are contained in the trace but not allowed by the model
            print(log[i].attributes['concept:name'])
            print(trace_an) 
            i = i + 1