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| import unittest | |
| import pm4py | |
| from pm4py.algo.conformance.tokenreplay import algorithm as token_based_replay | |
| from pm4py.algo.conformance.alignments.petri_net import algorithm as alignments | |
| from pm4py.algo.discovery.log_skeleton import algorithm as log_skeleton_discovery | |
| from pm4py.algo.conformance.log_skeleton import algorithm as log_skeleton_conformance | |
| from pm4py.algo.discovery.footprints import algorithm as footprints_discovery | |
| from pm4py.algo.conformance.footprints.variants import log_model, trace_extensive | |
| from pm4py.objects.log.importer.xes import importer as xes_importer | |
| class DiagnDfConfChecking(unittest.TestCase): | |
| def test_tbr_normal(self): | |
| log = xes_importer.apply("input_data/running-example.xes") | |
| net, im, fm = pm4py.discover_petri_net_inductive(log, noise_threshold=0.2) | |
| replayed_traces = token_based_replay.apply(log, net, im, fm) | |
| diagn_df = token_based_replay.get_diagnostics_dataframe(log, replayed_traces) | |
| def test_tbr_backwards(self): | |
| log = xes_importer.apply("input_data/running-example.xes") | |
| net, im, fm = pm4py.discover_petri_net_inductive(log, noise_threshold=0.2) | |
| replayed_traces = token_based_replay.apply(log, net, im, fm, variant=token_based_replay.Variants.BACKWARDS) | |
| diagn_df = token_based_replay.get_diagnostics_dataframe(log, replayed_traces, variant=token_based_replay.Variants.BACKWARDS) | |
| def test_align(self): | |
| log = xes_importer.apply("input_data/running-example.xes") | |
| net, im, fm = pm4py.discover_petri_net_inductive(log, noise_threshold=0.2) | |
| aligned_traces = alignments.apply(log, net, im, fm) | |
| diagn_df = alignments.get_diagnostics_dataframe(log, aligned_traces) | |
| def test_log_skeleton(self): | |
| log = xes_importer.apply("input_data/running-example.xes") | |
| log_skeleton = log_skeleton_discovery.apply(log, parameters={log_skeleton_discovery.Variants.CLASSIC.value.Parameters.NOISE_THRESHOLD: 0.05}) | |
| conf_result = log_skeleton_conformance.apply(log, log_skeleton) | |
| diagn_df = log_skeleton_conformance.get_diagnostics_dataframe(log, conf_result) | |
| def test_footprints_classic(self): | |
| log = xes_importer.apply("input_data/running-example.xes") | |
| fp_log = footprints_discovery.apply(log, variant=footprints_discovery.Variants.TRACE_BY_TRACE) | |
| tree = pm4py.discover_process_tree_inductive(log, noise_threshold=0.2) | |
| fp_model = footprints_discovery.apply(tree) | |
| conf_result = log_model.apply(fp_log, fp_model) | |
| diagn_df = log_model.get_diagnostics_dataframe(log, conf_result) | |
| def test_footprints_extensive(self): | |
| log = xes_importer.apply("input_data/running-example.xes") | |
| fp_log = footprints_discovery.apply(log, variant=footprints_discovery.Variants.TRACE_BY_TRACE) | |
| tree = pm4py.discover_process_tree_inductive(log, noise_threshold=0.2) | |
| fp_model = footprints_discovery.apply(tree) | |
| conf_result = trace_extensive.apply(fp_log, fp_model) | |
| diagn_df = trace_extensive.get_diagnostics_dataframe(log, conf_result) | |
| if __name__ == "__main__": | |
| unittest.main() | |