Source code for tests.alignment_test

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


[docs]class AlignmentTest(unittest.TestCase):
[docs] 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")
[docs] 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")) net, marking, final_marking = inductive_miner.apply(log) 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")
[docs] 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)
[docs] 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)
[docs] def test_tree_align_reviewing_classifier(self): import pm4py log = pm4py.read_xes("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)
[docs] def test_tree_align_reviewing_classifier_different_key(self): import pm4py log = pm4py.read_xes("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.variants.im_clean import algorithm as im_clean tree = im_clean.apply_tree(log, parameters={im_clean.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"})
[docs] 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)
[docs] 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)
[docs] 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()