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| import logging | |
| import os, sys | |
| import unittest | |
| from pm4py.objects.conversion.log import converter as log_conversion | |
| from pm4py.algo.conformance.tokenreplay import algorithm as token_replay | |
| from pm4py.algo.conformance.tokenreplay.variants.token_replay import NoConceptNameException | |
| from pm4py.algo.discovery.inductive import algorithm as inductive_miner | |
| from pm4py.objects import petri_net | |
| from pm4py.objects.log.util import dataframe_utils | |
| from pm4py.util import constants, pandas_utils | |
| from pm4py.objects.log.importer.xes import importer as xes_importer | |
| from pm4py.objects.log.util import sampling, sorting, index_attribute | |
| from pm4py.objects.petri_net.exporter import exporter as petri_exporter | |
| from pm4py.visualization.petri_net.common import visualize as pn_viz | |
| from pm4py.objects.conversion.process_tree import converter as process_tree_converter | |
| # from tests.constants import INPUT_DATA_DIR, OUTPUT_DATA_DIR, PROBLEMATIC_XES_DIR | |
| INPUT_DATA_DIR = "input_data" | |
| OUTPUT_DATA_DIR = "test_output_data" | |
| PROBLEMATIC_XES_DIR = "xes_importer_tests" | |
| COMPRESSED_INPUT_DATA = "compressed_input_data" | |
| class InductiveMinerTest(unittest.TestCase): | |
| def obtain_petri_net_through_im(self, log_name, variant=inductive_miner.Variants.IM): | |
| # to avoid static method warnings in tests, | |
| # that by construction of the unittest package have to be expressed in such way | |
| self.dummy_variable = "dummy_value" | |
| if ".xes" in log_name: | |
| log = xes_importer.apply(log_name) | |
| else: | |
| df = pandas_utils.read_csv(log_name) | |
| df = dataframe_utils.convert_timestamp_columns_in_df(df, timest_format=constants.DEFAULT_TIMESTAMP_PARSE_FORMAT) | |
| log = log_conversion.apply(df, variant=log_conversion.Variants.TO_EVENT_LOG) | |
| process_tree = inductive_miner.apply(log) | |
| net, marking, final_marking = process_tree_converter.apply(process_tree) | |
| return log, net, marking, final_marking | |
| def test_applyImdfToXES(self): | |
| # to avoid static method warnings in tests, | |
| # that by construction of the unittest package have to be expressed in such way | |
| self.dummy_variable = "dummy_value" | |
| # calculate and compare Petri nets obtained on the same log to verify that instances | |
| # are working correctly | |
| log1, net1, marking1, fmarking1 = self.obtain_petri_net_through_im( | |
| os.path.join(INPUT_DATA_DIR, "running-example.xes")) | |
| log2, net2, marking2, fmarking2 = self.obtain_petri_net_through_im( | |
| os.path.join(INPUT_DATA_DIR, "running-example.xes")) | |
| log1 = sorting.sort_timestamp(log1) | |
| log1 = sampling.sample(log1) | |
| log1 = index_attribute.insert_trace_index_as_event_attribute(log1) | |
| log2 = sorting.sort_timestamp(log2) | |
| log2 = sampling.sample(log2) | |
| log2 = index_attribute.insert_trace_index_as_event_attribute(log2) | |
| petri_exporter.apply(net1, marking1, os.path.join(OUTPUT_DATA_DIR, "running-example.pnml")) | |
| os.remove(os.path.join(OUTPUT_DATA_DIR, "running-example.pnml")) | |
| self.assertEqual(len(net1.places), len(net2.places)) | |
| final_marking = petri_net.obj.Marking() | |
| for p in net1.places: | |
| if not p.out_arcs: | |
| final_marking[p] = 1 | |
| aligned_traces = token_replay.apply(log1, net1, marking1, final_marking) | |
| del aligned_traces | |
| def test_applyImdfToCSV(self): | |
| # to avoid static method warnings in tests, | |
| # that by construction of the unittest package have to be expressed in such way | |
| self.dummy_variable = "dummy_value" | |
| # calculate and compare Petri nets obtained on the same log to verify that instances | |
| # are working correctly | |
| log1, net1, marking1, fmarking1 = self.obtain_petri_net_through_im( | |
| os.path.join(INPUT_DATA_DIR, "running-example.csv")) | |
| log2, net2, marking2, fmarking2 = self.obtain_petri_net_through_im( | |
| os.path.join(INPUT_DATA_DIR, "running-example.csv")) | |
| log1 = sorting.sort_timestamp(log1) | |
| log1 = sampling.sample(log1) | |
| log1 = index_attribute.insert_trace_index_as_event_attribute(log1) | |
| log2 = sorting.sort_timestamp(log2) | |
| log2 = sampling.sample(log2) | |
| log2 = index_attribute.insert_trace_index_as_event_attribute(log2) | |
| petri_exporter.apply(net1, marking1, os.path.join(OUTPUT_DATA_DIR, "running-example.pnml")) | |
| os.remove(os.path.join(OUTPUT_DATA_DIR, "running-example.pnml")) | |
| self.assertEqual(len(net1.places), len(net2.places)) | |
| final_marking = petri_net.obj.Marking() | |
| for p in net1.places: | |
| if not p.out_arcs: | |
| final_marking[p] = 1 | |
| aligned_traces = token_replay.apply(log1, net1, marking1, final_marking) | |
| del aligned_traces | |
| def test_imdfVisualizationFromXES(self): | |
| # to avoid static method warnings in tests, | |
| # that by construction of the unittest package have to be expressed in such way | |
| self.dummy_variable = "dummy_value" | |
| log, net, marking, fmarking = self.obtain_petri_net_through_im( | |
| os.path.join(INPUT_DATA_DIR, "running-example.xes")) | |
| log = sorting.sort_timestamp(log) | |
| log = sampling.sample(log) | |
| log = index_attribute.insert_trace_index_as_event_attribute(log) | |
| petri_exporter.apply(net, marking, os.path.join(OUTPUT_DATA_DIR, "running-example.pnml")) | |
| os.remove(os.path.join(OUTPUT_DATA_DIR, "running-example.pnml")) | |
| gviz = pn_viz.graphviz_visualization(net) | |
| final_marking = petri_net.obj.Marking() | |
| for p in net.places: | |
| if not p.out_arcs: | |
| final_marking[p] = 1 | |
| aligned_traces = token_replay.apply(log, net, marking, final_marking) | |
| del gviz | |
| del aligned_traces | |
| def test_inductive_miner_new_log(self): | |
| import pm4py | |
| log = pm4py.read_xes("input_data/running-example.xes", return_legacy_log_object=True) | |
| tree = pm4py.discover_process_tree_inductive(log, noise_threshold=0.2) | |
| def test_inductive_miner_new_df(self): | |
| import pm4py | |
| log = pm4py.read_xes("input_data/running-example.xes") | |
| tree = pm4py.discover_process_tree_inductive(log, noise_threshold=0.2) | |
| def test_inductive_miner_new_log_dfg(self): | |
| import pm4py | |
| from pm4py.objects.dfg.obj import DFG | |
| log = pm4py.read_xes("input_data/running-example.xes", return_legacy_log_object=True) | |
| dfg, sa, ea = pm4py.discover_dfg(log) | |
| typed_dfg = DFG(dfg, sa, ea) | |
| tree = pm4py.discover_process_tree_inductive(typed_dfg, noise_threshold=0.2) | |
| def test_inductive_miner_new_df_dfg(self): | |
| import pm4py | |
| log = pm4py.read_xes("input_data/running-example.xes", return_legacy_log_object=False) | |
| typed_dfg = pm4py.discover_dfg_typed(log) | |
| tree = pm4py.discover_process_tree_inductive(typed_dfg, noise_threshold=0.2) | |
| def test_inductive_miner_new_log_variants(self): | |
| import pm4py | |
| from pm4py.util.compression.dtypes import UVCL | |
| from pm4py.algo.discovery.inductive.variants.imf import IMFUVCL | |
| from pm4py.algo.discovery.inductive.dtypes.im_ds import IMDataStructureUVCL | |
| log = pm4py.read_xes("input_data/running-example.xes", return_legacy_log_object=True) | |
| variants = pm4py.get_variants(log) | |
| uvcl = UVCL() | |
| for var, occ in variants.items(): | |
| uvcl[var] = len(occ) | |
| parameters = {"noise_threshold": 0.2} | |
| imfuvcl = IMFUVCL(parameters) | |
| tree = imfuvcl.apply(IMDataStructureUVCL(uvcl), parameters=parameters) | |
| def test_inductive_miner_new_df_variants(self): | |
| import pm4py | |
| from pm4py.util.compression.dtypes import UVCL | |
| from pm4py.algo.discovery.inductive.variants.imf import IMFUVCL | |
| from pm4py.algo.discovery.inductive.dtypes.im_ds import IMDataStructureUVCL | |
| log = pm4py.read_xes("input_data/running-example.xes", return_legacy_log_object=True) | |
| variants = pm4py.get_variants(log) | |
| uvcl = UVCL() | |
| for var, occ in variants.items(): | |
| uvcl[var] = len(occ) | |
| parameters = {"noise_threshold": 0.2} | |
| imfuvcl = IMFUVCL(parameters) | |
| tree = imfuvcl.apply(IMDataStructureUVCL(uvcl), parameters=parameters) | |
| if __name__ == "__main__": | |
| unittest.main() | |