Spaces:
Sleeping
Sleeping
| import os | |
| import traceback | |
| from pm4py.util import constants, pandas_utils | |
| import time | |
| import pm4py | |
| from pm4py.algo.discovery.inductive import algorithm as im_clean | |
| from pm4py.statistics.variants.log import get as variants_get | |
| LOGS_FOLDER = "../compressed_input_data" | |
| CLASSIFIER = "@@classifier" | |
| ENABLE_ALIGNMENTS = True | |
| NOISE_THRESHOLD = 0.2 | |
| VARIANT = im_clean.Variants.IM | |
| ENABLE_MULTIPROCESSING = False | |
| if __name__ == "__main__": | |
| for log_name in os.listdir(LOGS_FOLDER): | |
| if "xes" in log_name or "parquet" in log_name: | |
| try: | |
| log_path = os.path.join(LOGS_FOLDER, log_name) | |
| print("") | |
| print(log_path) | |
| if "xes" in log_name: | |
| from pm4py.statistics.attributes.log import get as attributes_get_log | |
| log = pm4py.read_xes(log_path, return_legacy_log_object=True) | |
| for trace in log: | |
| for event in trace: | |
| if True and "lifecycle:transition" in event: | |
| event["@@classifier"] = event["concept:name"] + "+" + event["lifecycle:transition"] | |
| # event["concept:name"] = event["concept:name"] + "+" + event["lifecycle:transition"] | |
| else: | |
| event["@@classifier"] = event["concept:name"] | |
| activities = set(attributes_get_log.get_attribute_values(log, CLASSIFIER).keys()) | |
| variants = variants_get.get_variants(log, parameters={"pm4py:param:activity_key": CLASSIFIER}) | |
| fp_log = pm4py.algo.discovery.footprints.log.variants.entire_event_log.apply(log, parameters={ | |
| "pm4py:param:activity_key": CLASSIFIER}) | |
| elif "parquet" in log_name: | |
| from pm4py.statistics.attributes.pandas import get as attributes_get_pandas | |
| dataframe = pandas_utils.DATAFRAME.read_parquet(log_path) | |
| activities = set(attributes_get_pandas.get_attribute_values(dataframe, CLASSIFIER).keys()) | |
| variants = pm4py.get_variants_as_tuples(dataframe) | |
| variants = {",".join(x): y for x, y in variants.items()} | |
| fp_log = pm4py.algo.discovery.footprints.log.variants.entire_dataframe.apply(dataframe) | |
| print("start tree_im_clean") | |
| tree_im_clean = im_clean.apply(log, variant=VARIANT, parameters={"pm4py:param:activity_key": CLASSIFIER, | |
| "noise_threshold": NOISE_THRESHOLD, "multiprocessing": ENABLE_MULTIPROCESSING}) | |
| print(tree_im_clean) | |
| print("end tree_im_clean") | |
| fp_tree_clean = pm4py.algo.discovery.footprints.tree.variants.bottomup.apply(tree_im_clean) | |
| if not activities.issubset(fp_tree_clean["activities"]): | |
| print("ALERT! activities of the tree are less than the ones in the log!") | |
| print(activities.difference(fp_tree_clean["activities"])) | |
| time.sleep(5) | |
| fp_conf_im_clean = pm4py.algo.conformance.footprints.variants.log_extensive.apply(fp_log, fp_tree_clean) | |
| fitness_im_clean = pm4py.algo.conformance.footprints.util.evaluation.fp_fitness(fp_log, fp_tree_clean, | |
| fp_conf_im_clean) | |
| if ENABLE_ALIGNMENTS: | |
| from pm4py.algo.conformance.alignments.process_tree.variants import search_graph_pt | |
| alignments_clean = search_graph_pt.apply(log, tree_im_clean, parameters={ | |
| search_graph_pt.Parameters.ACTIVITY_KEY: CLASSIFIER}) | |
| from pm4py.algo.evaluation.replay_fitness.variants import alignment_based | |
| fitness_al_clean = alignment_based.evaluate(alignments_clean)["average_trace_fitness"] | |
| if fitness_al_clean < fitness_im_clean: | |
| print("ALERT", fitness_al_clean, fitness_im_clean) | |
| time.sleep(5) | |
| #input() | |
| else: | |
| print("OK ALIGNMENTS", fitness_al_clean) | |
| precision_im_clean = pm4py.algo.conformance.footprints.util.evaluation.fp_precision(fp_log, fp_tree_clean) | |
| print("IMCLEAN fp-fitness=%.3f fp-precision=%.3f" % (fitness_im_clean, precision_im_clean)) | |
| except: | |
| traceback.print_exc() | |
| input() | |