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| from pm4py.objects.log.importer.xes import importer as xes_importer | |
| from pm4py.algo.discovery.alpha import algorithm as alpha_miner | |
| from pm4py.algo.discovery.inductive import algorithm as inductive_miner | |
| from pm4py.algo.evaluation.earth_mover_distance import algorithm as earth_mover_distance | |
| from pm4py.statistics.variants.log import get as variants_get | |
| from pm4py.algo.simulation.playout.petri_net import algorithm | |
| from pm4py.objects.conversion.process_tree import converter as process_tree_converter | |
| import os | |
| def execute_script(): | |
| M = {("a", "b", "d", "e"): 0.49, ("a", "d", "b", "e"): 0.49, ("a", "c", "d", "e"): 0.01, ("a", "d", "c", "e"): 0.01} | |
| L1 = {("a", "b", "d", "e"): 0.49, ("a", "d", "b", "e"): 0.49, ("a", "c", "d", "e"): 0.01, | |
| ("a", "d", "c", "e"): 0.01} | |
| # the distance between M and L1, that we expect to be zero, is zero according to the EMD | |
| emd = earth_mover_distance.apply(M, L1) | |
| print("M L1 emd distance:", emd) | |
| L2 = {("a", "b", "e"): 0.5, ("a", "b", "d", "e"): 0.245, ("a", "d", "b", "e"): 0.245, ("a", "c", "d", "e"): 0.005, | |
| ("a", "d", "c", "e"): 0.005} | |
| # the distance between M and L2 according to the EMD is 0.1275 (paper value 0.125) | |
| emd = earth_mover_distance.apply(M, L2) | |
| print("M L2 emd distance:", emd) | |
| L3 = {("a", "b", "d", "e"): 0.489, ("a", "d", "b", "e"): 0.489, ("a", "c", "d", "e"): 0.01, | |
| ("a", "d", "c", "e"): 0.01, ("a", "b", "e"): 0.002} | |
| # the distance between M and L3 according to the EMD is 0.0005 (paper value 0.0005), perfect! | |
| emd = earth_mover_distance.apply(M, L3) | |
| print("M L3 emd distance:", emd) | |
| L4 = {("a", "b", "d", "e"): 0.5, ("a", "d", "b", "e"): 0.5} | |
| # the distance between M and L4 according to the EMD is 0.005 (paper value 0.005), perfect! | |
| emd = earth_mover_distance.apply(M, L4) | |
| print("M L4 emd distance:", emd) | |
| L5 = {("a", "c", "d", "e"): 0.5, ("a", "d", "c", "e"): 0.5} | |
| # the distance between M and L5 according to the EMD is 0.245 (paper value 0.245), perfect! | |
| emd = earth_mover_distance.apply(M, L5) | |
| print("M L5 emd distance:", emd) | |
| log = xes_importer.apply(os.path.join("..", "tests", "input_data", "running-example.xes")) | |
| lang_log = variants_get.get_language(log) | |
| net0, im0, fm0 = alpha_miner.apply(log) | |
| process_tree = inductive_miner.apply(log) | |
| net1, im1, fm1 = process_tree_converter.apply(process_tree) | |
| lang_model0 = variants_get.get_language( | |
| algorithm.apply(net0, im0, fm0, variant=algorithm.Variants.STOCHASTIC_PLAYOUT, | |
| parameters={algorithm.Variants.STOCHASTIC_PLAYOUT.value.Parameters.LOG: log})) | |
| lang_model1 = variants_get.get_language( | |
| algorithm.apply(net1, im1, fm1, variant=algorithm.Variants.STOCHASTIC_PLAYOUT, | |
| parameters={algorithm.Variants.STOCHASTIC_PLAYOUT.value.Parameters.LOG: log})) | |
| emd = earth_mover_distance.apply(lang_model0, lang_log) | |
| print("running-example alpha emd distance: ", emd) | |
| emd = earth_mover_distance.apply(lang_model1, lang_log) | |
| print("running-example inductive emd distance: ", emd) | |
| #log = xes_importer.apply(os.path.join("..", "tests", "input_data", "receipt.xes")) | |
| #net, im, fm = inductive_miner.apply(log) | |
| #lang_model = variants_get.get_language(simulator.apply(net, im, fm, variant=simulator.Variants.STOCHASTIC_PLAYOUT, | |
| # parameters={ | |
| # simulator.Variants.STOCHASTIC_PLAYOUT.value.Parameters.LOG: log})) | |
| #emd = earth_mover_distance.apply(lang_model, lang_log) | |
| #print("receipt inductive emd distance: ", emd) | |
| if __name__ == "__main__": | |
| execute_script() | |