def execute_script():
    log_path = os.path.join("..", "tests", "input_data", "running-example.xes")

    log = xes_importer.apply(log_path)
    tree: ProcessTree = inductive.apply_tree(log)
    gviz = pt_vis.apply(
        tree,
        parameters={
            pt_vis.Variants.WO_DECORATION.value.Parameters.FORMAT: "svg"
        })
    pt_vis.view(gviz)

    print("start calculate approximated alignments")
    approx_alignments = align_approx.apply(log, tree)
    pretty_print_alignments(approx_alignments)
def execute_script():
    log_path = os.path.join("..", "tests", "input_data", "running-example.xes")
    pnml_path = os.path.join("..", "tests", "input_data",
                             "running-example.pnml")

    # log_path = 'C:/Users/bas/Documents/tue/svn/private/logs/a32_logs/a32f0n05.xes'
    # pnml_path = 'C:/Users/bas/Documents/tue/svn/private/logs/a32_logs/a32.pnml'

    log = xes_importer.apply(log_path)
    net, marking, fmarking = petri_importer.apply(pnml_path)

    model_cost_function = dict()
    sync_cost_function = dict()
    for t in net.transitions:
        if t.label is not None:
            model_cost_function[t] = 1000
            sync_cost_function[t] = 0
        else:
            model_cost_function[t] = 1

    alignments = ali.algorithm.apply(log, net, marking, fmarking)
    print(alignments)
    pretty_print_alignments(alignments)
Exemple #3
0
inductive_petri_2, initial_marking, final_marking = inductive_miner.apply(
    log_2)  # Για το precision
"""
ALIGNMENTS
"""
net, initial_marking, final_marking = inductive_miner.apply(log[:1000])
gviz = visualizer.apply(net, initial_marking, final_marking)
alignments = alignment.apply_log(log[:1000], net, initial_marking,
                                 final_marking)

with open(
        'C:/Users/user/Desktop/ΔΙΠΛΩΜΑΤΙΚΗ/ΔΙΠΛΩΜΑΤΙΚΗ_2/pretty_allignment.txt',
        'w') as f:
    with redirect_stdout(f):
        pretty_print_alignments(alignments)
f.close()
"""
SIMPLICITY για Alpha-Miner, Inductive-Miner, Heuristic-Miner και Heuristic-Miner-With-Parameters.

RESULTS:
    simplicity_alpha= 1.0
    simplicity_inductive= 0.6274509803921569
    simplicity_heuristic= 0.5186721991701245
    simplicity_heuristic_with_params= 0.5146443514644352
"""
simplicity_alpha = simplicity_factory.apply(alpha_petri)
simplicity_inductive = simplicity_factory.apply(inductive_petri)
simplicity_heuristic = simplicity_factory.apply(heuristic_petri)
simplicity_heuristic_with_params = simplicity_factory.apply(
    heuristic_with_params_petri)