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| from pm4py.objects.log.importer.xes import importer as xes_importer | |
| from pm4py.algo.discovery.inductive import algorithm as inductive_miner | |
| from pm4py.algo.discovery.footprints import algorithm as fp_discovery | |
| from pm4py.algo.conformance.footprints import algorithm as fp_conformance | |
| from pm4py.statistics.traces.generic.log import case_statistics | |
| from pm4py.algo.discovery.dfg import algorithm as dfg_discovery | |
| from pm4py.algo.filtering.log.paths import paths_filter | |
| from pm4py.util.vis_utils import human_readable_stat | |
| from pm4py.algo.filtering.log.variants import variants_filter | |
| from pm4py.statistics.variants.log import get as variants_get | |
| from examples import examples_conf | |
| import os | |
| import importlib.util | |
| def execute_script(): | |
| log = xes_importer.apply(os.path.join("..", "tests", "input_data", "receipt.xes")) | |
| throughput_time = case_statistics.get_median_case_duration(log) | |
| variants, variants_times = variants_get.get_variants_along_with_case_durations(log) | |
| dfg = dfg_discovery.apply(log) | |
| filtered_log = variants_filter.filter_log_variants_percentage(log, 0.2) | |
| # filtered_log = log | |
| tree = inductive_miner.apply(filtered_log) | |
| fp_log = fp_discovery.apply(log, variant=fp_discovery.Variants.ENTIRE_EVENT_LOG) | |
| fp_model = fp_discovery.apply(tree) | |
| conf = fp_conformance.apply(fp_log, fp_model) | |
| conf_occ = sorted([(x, dfg[x]) for x in conf], key=lambda y: (y[1], y[0][0], y[0][1]), reverse=True) | |
| print( | |
| "source activity\t\ttarget activity\t\toccurrences\t\tthroughput time log\t\tthroughput time traces with path") | |
| for i in range(min(10, len(conf_occ))): | |
| path = conf_occ[i][0] | |
| occ = conf_occ[i][1] | |
| red_log = paths_filter.apply(log, [path]) | |
| red_throughput_time = case_statistics.get_median_case_duration(red_log) | |
| print("%s\t\t%s\t\t%d\t\t%s\t\t%s" % ( | |
| path[0], path[1], occ, human_readable_stat(throughput_time), human_readable_stat(red_throughput_time))) | |
| variants_length = sorted([(x, len(variants[x])) for x in variants.keys()], key=lambda y: (y[1], y[0]), reverse=True) | |
| print("\nvariant\t\toccurrences\t\tthroughput time log\t\tthroughput time traces with path") | |
| for i in range(min(10, len(variants_length))): | |
| var = variants_length[i][0] | |
| vark = str(var) | |
| if len(vark) > 10: | |
| vark = vark[:10] | |
| occ = variants_length[i][1] | |
| fp_log_var = fp_discovery.apply(variants[var], variant=fp_discovery.Variants.ENTIRE_EVENT_LOG) | |
| conf_var = fp_conformance.apply(fp_log_var, fp_model) | |
| is_fit = str(len(conf_var) == 0) | |
| var_throughput = case_statistics.get_median_case_duration(variants[var]) | |
| print("%s\t\t%d\t\t%s\t\t%s\t\t%s" % (vark, occ, is_fit, throughput_time, human_readable_stat(var_throughput))) | |
| if importlib.util.find_spec("graphviz"): | |
| from pm4py.algo.conformance.footprints.util import tree_visualization | |
| from pm4py.visualization.process_tree import visualizer as pt_visualizer | |
| # print(conf_occ) | |
| conf_colors = tree_visualization.apply(tree, conf) | |
| if True: | |
| gviz = pt_visualizer.apply(tree, parameters={"format": examples_conf.TARGET_IMG_FORMAT, | |
| pt_visualizer.Variants.WO_DECORATION.value.Parameters.COLOR_MAP: conf_colors, | |
| pt_visualizer.Variants.WO_DECORATION.value.Parameters.ENABLE_DEEPCOPY: False}) | |
| pt_visualizer.view(gviz) | |
| if __name__ == "__main__": | |
| execute_script() | |