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| import os | |
| import unittest | |
| from pm4py.algo.conformance.alignments.petri_net import algorithm as align_alg | |
| from pm4py.algo.discovery.alpha import algorithm as alpha_alg | |
| from pm4py.algo.discovery.inductive import algorithm as inductive_miner | |
| from pm4py.objects import petri_net | |
| from pm4py.objects.log.importer.xes import importer as xes_importer | |
| from tests.constants import INPUT_DATA_DIR | |
| from pm4py.objects.conversion.process_tree import converter as process_tree_converter | |
| class AlignmentTest(unittest.TestCase): | |
| def test_alignment_alpha(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 = xes_importer.apply(os.path.join(INPUT_DATA_DIR, "running-example.xes")) | |
| net, marking, fmarking = alpha_alg.apply(log) | |
| final_marking = petri_net.obj.Marking() | |
| for p in net.places: | |
| if not p.out_arcs: | |
| final_marking[p] = 1 | |
| for trace in log: | |
| cf_result = \ | |
| align_alg.apply(trace, net, marking, final_marking, variant=align_alg.VERSION_DIJKSTRA_NO_HEURISTICS)[ | |
| 'alignment'] | |
| is_fit = True | |
| for couple in cf_result: | |
| if not (couple[0] == couple[1] or couple[0] == ">>" and couple[1] is None): | |
| is_fit = False | |
| if not is_fit: | |
| raise Exception("should be fit") | |
| def test_alignment_pnml(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 = xes_importer.apply(os.path.join(INPUT_DATA_DIR, "running-example.xes")) | |
| process_tree = inductive_miner.apply(log) | |
| net, marking, final_marking = process_tree_converter.apply(process_tree) | |
| for trace in log: | |
| cf_result = \ | |
| align_alg.apply(trace, net, marking, final_marking, variant=align_alg.VERSION_DIJKSTRA_NO_HEURISTICS)[ | |
| 'alignment'] | |
| is_fit = True | |
| for couple in cf_result: | |
| if not (couple[0] == couple[1] or couple[0] == ">>" and couple[1] is None): | |
| is_fit = False | |
| if not is_fit: | |
| raise Exception("should be fit") | |
| def test_tree_align_receipt(self): | |
| import pm4py | |
| log = pm4py.read_xes("input_data/receipt.xes") | |
| tree = pm4py.discover_process_tree_inductive(log, noise_threshold=0.2) | |
| al = pm4py.conformance_diagnostics_alignments(log, tree, return_diagnostics_dataframe=False) | |
| def test_tree_align_reviewing(self): | |
| import pm4py | |
| log = pm4py.read_xes("compressed_input_data/04_reviewing.xes.gz") | |
| tree = pm4py.discover_process_tree_inductive(log, noise_threshold=0.2) | |
| al = pm4py.conformance_diagnostics_alignments(log, tree, return_diagnostics_dataframe=False) | |
| def test_tree_align_reviewing_classifier(self): | |
| import pm4py | |
| log = xes_importer.apply("compressed_input_data/04_reviewing.xes.gz") | |
| for trace in log: | |
| for event in trace: | |
| event["concept:name"] = event["concept:name"] + "+" + event["lifecycle:transition"] | |
| tree = pm4py.discover_process_tree_inductive(log, noise_threshold=0.2) | |
| al = pm4py.conformance_diagnostics_alignments(log, tree, return_diagnostics_dataframe=False) | |
| def test_tree_align_reviewing_classifier_different_key(self): | |
| import pm4py | |
| log = xes_importer.apply("compressed_input_data/04_reviewing.xes.gz") | |
| for trace in log: | |
| for event in trace: | |
| event["@@classifier"] = event["concept:name"] + "+" + event["lifecycle:transition"] | |
| from pm4py.algo.discovery.inductive import algorithm as inductive_miner | |
| tree = inductive_miner.apply(log, parameters={inductive_miner.Parameters.ACTIVITY_KEY: "@@classifier"}) | |
| from pm4py.algo.conformance.alignments.process_tree.variants import search_graph_pt | |
| al = search_graph_pt.apply(log, tree, parameters={search_graph_pt.Parameters.ACTIVITY_KEY: "@@classifier"}) | |
| def test_variant_state_eq_a_star(self): | |
| import pm4py | |
| log = pm4py.read_xes("input_data/running-example.xes") | |
| net, im, fm = pm4py.discover_petri_net_inductive(log) | |
| align_alg.apply(log, net, im, fm, variant=align_alg.Variants.VERSION_STATE_EQUATION_A_STAR) | |
| def test_variant_dijkstra_less_memory(self): | |
| import pm4py | |
| log = pm4py.read_xes("input_data/running-example.xes") | |
| net, im, fm = pm4py.discover_petri_net_inductive(log) | |
| align_alg.apply(log, net, im, fm, variant=align_alg.Variants.VERSION_DIJKSTRA_LESS_MEMORY) | |
| def test_variant_tweaked_state_eq_a_star(self): | |
| import pm4py | |
| log = pm4py.read_xes("input_data/running-example.xes") | |
| net, im, fm = pm4py.discover_petri_net_inductive(log) | |
| align_alg.apply(log, net, im, fm, variant=align_alg.Variants.VERSION_TWEAKED_STATE_EQUATION_A_STAR) | |
| if __name__ == "__main__": | |
| unittest.main() | |