Spaces:
Sleeping
Sleeping
File size: 10,724 Bytes
8097001 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 |
import os
import unittest
from pm4py.algo.conformance.alignments.petri_net import algorithm as align_alg
from pm4py.algo.conformance.tokenreplay import algorithm as tr_alg
from pm4py.algo.discovery.alpha import algorithm as alpha_miner
from pm4py.algo.discovery.dfg import algorithm as dfg_mining
from pm4py.algo.discovery.heuristics import algorithm as heuristics_miner
from pm4py.algo.discovery.inductive import algorithm as inductive_miner
from pm4py.algo.discovery.transition_system import algorithm as ts_disc
from pm4py.algo.evaluation import algorithm as eval_alg
from pm4py.algo.evaluation.generalization import algorithm as generalization
from pm4py.algo.evaluation.precision import algorithm as precision_evaluator
from pm4py.algo.evaluation.replay_fitness import algorithm as rp_fit
from pm4py.algo.evaluation.simplicity import algorithm as simplicity
from pm4py.objects.conversion.log import converter as log_conversion
from pm4py.objects.log.exporter.xes import exporter as xes_exporter
from pm4py.objects.log.importer.xes import importer as xes_importer
from pm4py.objects.log.util import dataframe_utils
from pm4py.util import constants, pandas_utils
from pm4py.objects.conversion.process_tree import converter as process_tree_converter
class MainFactoriesTest(unittest.TestCase):
def test_nonstandard_exporter(self):
log = xes_importer.apply(os.path.join("input_data", "running-example.xes"))
xes_exporter.apply(log, os.path.join("test_output_data", "running-example.xes"),
variant=xes_exporter.Variants.LINE_BY_LINE)
os.remove(os.path.join("test_output_data", "running-example.xes"))
def test_alphaminer_log(self):
log = xes_importer.apply(os.path.join("input_data", "running-example.xes"))
net, im, fm = alpha_miner.apply(log)
aligned_traces_tr = tr_alg.apply(log, net, im, fm)
aligned_traces_alignments = align_alg.apply(log, net, im, fm)
evaluation = eval_alg.apply(log, net, im, fm)
fitness = rp_fit.apply(log, net, im, fm)
precision = precision_evaluator.apply(log, net, im, fm)
gen = generalization.apply(log, net, im, fm)
sim = simplicity.apply(net)
def test_memory_efficient_iterparse(self):
log = xes_importer.apply(os.path.join("input_data", "running-example.xes"),
variant=xes_importer.Variants.ITERPARSE_MEM_COMPRESSED)
def test_alphaminer_stream(self):
df = pandas_utils.read_csv(os.path.join("input_data", "running-example.csv"))
df = dataframe_utils.convert_timestamp_columns_in_df(df, timest_format=constants.DEFAULT_TIMESTAMP_PARSE_FORMAT)
stream = log_conversion.apply(df, variant=log_conversion.TO_EVENT_STREAM)
net, im, fm = alpha_miner.apply(stream)
aligned_traces_tr = tr_alg.apply(stream, net, im, fm)
aligned_traces_alignments = align_alg.apply(stream, net, im, fm)
evaluation = eval_alg.apply(stream, net, im, fm)
fitness = rp_fit.apply(stream, net, im, fm)
precision = precision_evaluator.apply(stream, net, im, fm)
gen = generalization.apply(stream, net, im, fm)
sim = simplicity.apply(net)
def test_alphaminer_df(self):
log = pandas_utils.read_csv(os.path.join("input_data", "running-example.csv"))
log = dataframe_utils.convert_timestamp_columns_in_df(log, timest_format=constants.DEFAULT_TIMESTAMP_PARSE_FORMAT)
net, im, fm = alpha_miner.apply(log)
aligned_traces_tr = tr_alg.apply(log, net, im, fm)
aligned_traces_alignments = align_alg.apply(log, net, im, fm)
evaluation = eval_alg.apply(log, net, im, fm)
fitness = rp_fit.apply(log, net, im, fm)
precision = precision_evaluator.apply(log, net, im, fm)
gen = generalization.apply(log, net, im, fm)
sim = simplicity.apply(net)
def test_inductiveminer_log(self):
log = xes_importer.apply(os.path.join("input_data", "running-example.xes"))
process_tree = inductive_miner.apply(log)
net, im, fm = process_tree_converter.apply(process_tree)
aligned_traces_tr = tr_alg.apply(log, net, im, fm)
aligned_traces_alignments = align_alg.apply(log, net, im, fm)
evaluation = eval_alg.apply(log, net, im, fm)
fitness = rp_fit.apply(log, net, im, fm)
precision = precision_evaluator.apply(log, net, im, fm)
gen = generalization.apply(log, net, im, fm)
sim = simplicity.apply(net)
def test_inductiveminer_df(self):
log = pandas_utils.read_csv(os.path.join("input_data", "running-example.csv"))
log = dataframe_utils.convert_timestamp_columns_in_df(log, timest_format=constants.DEFAULT_TIMESTAMP_PARSE_FORMAT)
process_tree = inductive_miner.apply(log)
net, im, fm = process_tree_converter.apply(process_tree)
aligned_traces_tr = tr_alg.apply(log, net, im, fm)
aligned_traces_alignments = align_alg.apply(log, net, im, fm)
evaluation = eval_alg.apply(log, net, im, fm)
fitness = rp_fit.apply(log, net, im, fm)
precision = precision_evaluator.apply(log, net, im, fm)
gen = generalization.apply(log, net, im, fm)
sim = simplicity.apply(net)
def test_heu_log(self):
log = xes_importer.apply(os.path.join("input_data", "running-example.xes"))
net, im, fm = heuristics_miner.apply(log)
aligned_traces_tr = tr_alg.apply(log, net, im, fm)
aligned_traces_alignments = align_alg.apply(log, net, im, fm)
evaluation = eval_alg.apply(log, net, im, fm)
fitness = rp_fit.apply(log, net, im, fm)
precision = precision_evaluator.apply(log, net, im, fm)
gen = generalization.apply(log, net, im, fm)
sim = simplicity.apply(net)
def test_heu_stream(self):
df = pandas_utils.read_csv(os.path.join("input_data", "running-example.csv"))
df = dataframe_utils.convert_timestamp_columns_in_df(df, timest_format=constants.DEFAULT_TIMESTAMP_PARSE_FORMAT)
stream = log_conversion.apply(df, variant=log_conversion.TO_EVENT_STREAM)
net, im, fm = heuristics_miner.apply(stream)
aligned_traces_tr = tr_alg.apply(stream, net, im, fm)
aligned_traces_alignments = align_alg.apply(stream, net, im, fm)
evaluation = eval_alg.apply(stream, net, im, fm)
fitness = rp_fit.apply(stream, net, im, fm)
precision = precision_evaluator.apply(stream, net, im, fm)
gen = generalization.apply(stream, net, im, fm)
sim = simplicity.apply(net)
def test_heu_df(self):
log = pandas_utils.read_csv(os.path.join("input_data", "running-example.csv"))
log = dataframe_utils.convert_timestamp_columns_in_df(log, timest_format=constants.DEFAULT_TIMESTAMP_PARSE_FORMAT)
net, im, fm = heuristics_miner.apply(log)
aligned_traces_tr = tr_alg.apply(log, net, im, fm)
aligned_traces_alignments = align_alg.apply(log, net, im, fm)
evaluation = eval_alg.apply(log, net, im, fm)
fitness = rp_fit.apply(log, net, im, fm)
precision = precision_evaluator.apply(log, net, im, fm)
gen = generalization.apply(log, net, im, fm)
sim = simplicity.apply(net)
def test_dfg_log(self):
log = xes_importer.apply(os.path.join("input_data", "running-example.xes"))
dfg = dfg_mining.apply(log)
def test_dfg_stream(self):
df = pandas_utils.read_csv(os.path.join("input_data", "running-example.csv"))
df = dataframe_utils.convert_timestamp_columns_in_df(df, timest_format=constants.DEFAULT_TIMESTAMP_PARSE_FORMAT)
stream = log_conversion.apply(df, variant=log_conversion.TO_EVENT_STREAM)
dfg = dfg_mining.apply(stream)
def test_dfg_df(self):
df = pandas_utils.read_csv(os.path.join("input_data", "running-example.csv"))
df = dataframe_utils.convert_timestamp_columns_in_df(df, timest_format=constants.DEFAULT_TIMESTAMP_PARSE_FORMAT)
dfg = dfg_mining.apply(df)
def test_ts_log(self):
log = xes_importer.apply(os.path.join("input_data", "running-example.xes"))
ts = ts_disc.apply(log)
def test_ts_stream(self):
df = pandas_utils.read_csv(os.path.join("input_data", "running-example.csv"))
df = dataframe_utils.convert_timestamp_columns_in_df(df, timest_format=constants.DEFAULT_TIMESTAMP_PARSE_FORMAT)
stream = log_conversion.apply(df, variant=log_conversion.TO_EVENT_STREAM)
ts = ts_disc.apply(stream)
def test_ts_df(self):
df = pandas_utils.read_csv(os.path.join("input_data", "running-example.csv"))
df = dataframe_utils.convert_timestamp_columns_in_df(df, timest_format=constants.DEFAULT_TIMESTAMP_PARSE_FORMAT)
ts = ts_disc.apply(df)
def test_csvimp_xesexp(self):
df = pandas_utils.read_csv(os.path.join("input_data", "running-example.csv"))
df = dataframe_utils.convert_timestamp_columns_in_df(df, timest_format=constants.DEFAULT_TIMESTAMP_PARSE_FORMAT)
log0 = log_conversion.apply(df, variant=log_conversion.TO_EVENT_STREAM)
log = log_conversion.apply(log0, variant=log_conversion.TO_EVENT_LOG)
stream = log_conversion.apply(log0, variant=log_conversion.TO_EVENT_STREAM)
df = log_conversion.apply(log0, variant=log_conversion.TO_DATA_FRAME)
xes_exporter.apply(log, "ru.xes")
xes_exporter.apply(stream, "ru.xes")
xes_exporter.apply(df, "ru.xes")
os.remove('ru.xes')
def test_xesimp_xesexp(self):
log0 = xes_importer.apply(os.path.join("input_data", "running-example.xes"))
log = log_conversion.apply(log0, variant=log_conversion.TO_EVENT_LOG)
stream = log_conversion.apply(log0, variant=log_conversion.TO_EVENT_STREAM)
df = log_conversion.apply(log0, variant=log_conversion.TO_DATA_FRAME)
xes_exporter.apply(log, "ru.xes")
xes_exporter.apply(stream, "ru.xes")
xes_exporter.apply(df, "ru.xes")
os.remove('ru.xes')
def test_pdimp_xesexp(self):
log0 = pandas_utils.read_csv(os.path.join("input_data", "running-example.csv"))
log0 = dataframe_utils.convert_timestamp_columns_in_df(log0, timest_format=constants.DEFAULT_TIMESTAMP_PARSE_FORMAT)
log = log_conversion.apply(log0, variant=log_conversion.TO_EVENT_LOG)
stream = log_conversion.apply(log0, variant=log_conversion.TO_EVENT_STREAM)
df = log_conversion.apply(log0, variant=log_conversion.TO_DATA_FRAME)
xes_exporter.apply(log, "ru.xes")
xes_exporter.apply(stream, "ru.xes")
xes_exporter.apply(df, "ru.xes")
os.remove('ru.xes')
if __name__ == "__main__":
unittest.main()
|