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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()
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