Source code for tests.algorithm_test

import unittest
from pm4py.objects.log.util import dataframe_utils
from pm4py.objects.log.importer.xes import importer as xes_importer
import os
import pandas as pd


[docs]class AlgorithmTest(unittest.TestCase):
[docs] def test_importing_xes(self): from pm4py.objects.log.importer.xes import importer as xes_importer log = xes_importer.apply(os.path.join("input_data", "running-example.xes"), variant=xes_importer.Variants.ITERPARSE) log = xes_importer.apply(os.path.join("input_data", "running-example.xes"), variant=xes_importer.Variants.LINE_BY_LINE) log = xes_importer.apply(os.path.join("input_data", "running-example.xes"), variant=xes_importer.Variants.ITERPARSE_MEM_COMPRESSED) log = xes_importer.apply(os.path.join("input_data", "running-example.xes"), variant=xes_importer.Variants.CHUNK_REGEX)
[docs] def test_hiearch_clustering(self): from pm4py.algo.clustering.trace_attribute_driven import algorithm as clust_algorithm log = xes_importer.apply(os.path.join("input_data", "receipt.xes"), variant=xes_importer.Variants.LINE_BY_LINE, parameters={xes_importer.Variants.LINE_BY_LINE.value.Parameters.MAX_TRACES: 50}) # raise Exception("%d" % (len(log))) clust_algorithm.apply(log, "responsible", variant=clust_algorithm.Variants.VARIANT_DMM_VEC)
[docs] def test_log_skeleton(self): log = xes_importer.apply(os.path.join("input_data", "running-example.xes")) from pm4py.algo.discovery.log_skeleton import algorithm as lsk_discovery model = lsk_discovery.apply(log) from pm4py.algo.conformance.log_skeleton import algorithm as lsk_conformance conf = lsk_conformance.apply(log, model)
[docs] def test_alignment(self): log = xes_importer.apply(os.path.join("input_data", "running-example.xes")) from pm4py.algo.discovery.alpha import algorithm as alpha_miner net, im, fm = alpha_miner.apply(log) from pm4py.algo.conformance.alignments.petri_net import algorithm as alignments aligned_traces = alignments.apply(log, net, im, fm, variant=alignments.Variants.VERSION_STATE_EQUATION_A_STAR) aligned_traces = alignments.apply(log, net, im, fm, variant=alignments.Variants.VERSION_DIJKSTRA_NO_HEURISTICS) from pm4py.algo.evaluation.replay_fitness import algorithm as rp_fitness_evaluator fitness = rp_fitness_evaluator.apply(log, net, im, fm, variant=rp_fitness_evaluator.Variants.ALIGNMENT_BASED) evaluation = rp_fitness_evaluator.evaluate(aligned_traces, variant=rp_fitness_evaluator.Variants.ALIGNMENT_BASED) from pm4py.algo.evaluation.precision import algorithm as precision_evaluator precision = precision_evaluator.apply(log, net, im, fm, variant=rp_fitness_evaluator.Variants.ALIGNMENT_BASED)
[docs] def test_decomp_alignment(self): log = xes_importer.apply(os.path.join("input_data", "running-example.xes")) from pm4py.algo.discovery.alpha import algorithm as alpha_miner net, im, fm = alpha_miner.apply(log) from pm4py.algo.conformance.alignments.decomposed import algorithm as decomp_align aligned_traces = decomp_align.apply(log, net, im, fm, variant=decomp_align.Variants.RECOMPOS_MAXIMAL)
[docs] def test_tokenreplay(self): log = xes_importer.apply(os.path.join("input_data", "running-example.xes")) from pm4py.algo.discovery.alpha import algorithm as alpha_miner net, im, fm = alpha_miner.apply(log) from pm4py.algo.conformance.tokenreplay import algorithm as token_replay replayed_traces = token_replay.apply(log, net, im, fm, variant=token_replay.Variants.TOKEN_REPLAY) replayed_traces = token_replay.apply(log, net, im, fm, variant=token_replay.Variants.BACKWARDS) from pm4py.algo.evaluation.replay_fitness import algorithm as rp_fitness_evaluator fitness = rp_fitness_evaluator.apply(log, net, im, fm, variant=rp_fitness_evaluator.Variants.TOKEN_BASED) evaluation = rp_fitness_evaluator.evaluate(replayed_traces, variant=rp_fitness_evaluator.Variants.TOKEN_BASED) from pm4py.algo.evaluation.precision import algorithm as precision_evaluator precision = precision_evaluator.apply(log, net, im, fm, variant=precision_evaluator.Variants.ETCONFORMANCE_TOKEN) from pm4py.algo.evaluation.generalization import algorithm as generalization_evaluation generalization = generalization_evaluation.apply(log, net, im, fm, variant=generalization_evaluation.Variants.GENERALIZATION_TOKEN)
[docs] def test_evaluation(self): log = xes_importer.apply(os.path.join("input_data", "running-example.xes")) from pm4py.algo.discovery.alpha import algorithm as alpha_miner net, im, fm = alpha_miner.apply(log) from pm4py.algo.evaluation.simplicity import algorithm as simplicity simp = simplicity.apply(net) from pm4py.algo.evaluation import algorithm as evaluation_method eval = evaluation_method.apply(log, net, im, fm)
[docs] def test_playout(self): log = xes_importer.apply(os.path.join("input_data", "running-example.xes")) from pm4py.algo.discovery.alpha import algorithm as alpha_miner net, im, fm = alpha_miner.apply(log) from pm4py.algo.simulation.playout.petri_net import algorithm log2 = algorithm.apply(net, im, fm)
[docs] def test_tree_generation(self): from pm4py.algo.simulation.tree_generator import algorithm as tree_simulator tree1 = tree_simulator.apply(variant=tree_simulator.Variants.BASIC) tree2 = tree_simulator.apply(variant=tree_simulator.Variants.PTANDLOGGENERATOR)
[docs] def test_alpha_miner_log(self): log = xes_importer.apply(os.path.join("input_data", "running-example.xes")) from pm4py.algo.discovery.alpha import algorithm as alpha_miner net1, im1, fm1 = alpha_miner.apply(log, variant=alpha_miner.Variants.ALPHA_VERSION_CLASSIC) net2, im2, fm2 = alpha_miner.apply(log, variant=alpha_miner.Variants.ALPHA_VERSION_PLUS) from pm4py.algo.discovery.dfg import algorithm as dfg_discovery dfg = dfg_discovery.apply(log) net3, im3, fm3 = alpha_miner.apply_dfg(dfg, variant=alpha_miner.Variants.ALPHA_VERSION_CLASSIC)
[docs] def test_alpha_miner_dataframe(self): df = pd.read_csv(os.path.join("input_data", "running-example.csv")) df = dataframe_utils.convert_timestamp_columns_in_df(df) from pm4py.algo.discovery.alpha import algorithm as alpha_miner net, im, fm = alpha_miner.apply(df, variant=alpha_miner.Variants.ALPHA_VERSION_CLASSIC)
[docs] def test_tsystem(self): log = xes_importer.apply(os.path.join("input_data", "running-example.xes")) from pm4py.algo.discovery.transition_system import algorithm as ts_system tsystem = ts_system.apply(log, variant=ts_system.Variants.VIEW_BASED)
[docs] def test_inductive_miner(self): log = xes_importer.apply(os.path.join("input_data", "running-example.xes")) from pm4py.algo.discovery.inductive import algorithm as inductive_miner net, im, fm = inductive_miner.apply(log, variant=inductive_miner.Variants.IM_CLEAN)
[docs] def test_performance_spectrum(self): log = xes_importer.apply(os.path.join("input_data", "running-example.xes")) from pm4py.algo.discovery.performance_spectrum import algorithm as pspectrum ps = pspectrum.apply(log, ["register request", "decide"]) df = pd.read_csv(os.path.join("input_data", "running-example.csv")) df = dataframe_utils.convert_timestamp_columns_in_df(df) ps = pspectrum.apply(df, ["register request", "decide"])
if __name__ == "__main__": unittest.main()