File size: 9,730 Bytes
bdf9096
 
 
 
 
 
 
8c2c8d0
222513d
8c2c8d0
bdf9096
8c2c8d0
 
bdf9096
 
99bcc04
f137caa
 
bdf9096
 
 
 
 
 
 
 
 
ddfaf7c
bdf9096
 
 
 
 
8c2c8d0
bdf9096
 
 
 
 
 
b49ce56
bdf9096
 
 
 
b49ce56
bdf9096
 
222513d
bdf9096
 
 
 
 
 
 
 
8c2c8d0
bdf9096
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8c2c8d0
bdf9096
 
 
 
8c2c8d0
bdf9096
 
 
 
 
 
 
 
 
8036cbd
b49ce56
 
8036cbd
b49ce56
8036cbd
8c2c8d0
bdf9096
 
 
8c2c8d0
bdf9096
 
8c2c8d0
 
 
 
 
bdf9096
b49ce56
 
bdf9096
8c2c8d0
bdf9096
 
 
99bcc04
bdf9096
 
 
 
 
 
99bcc04
bdf9096
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c55374f
bdf9096
 
 
 
 
 
c55374f
bdf9096
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8c2c8d0
 
 
 
bdf9096
 
 
 
 
 
 
 
 
 
 
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
199
200
201
202
203
import json
import multiprocessing
import os
import pandas as pd
import subprocess

from datetime import datetime as dt
from functools import partialmethod
from itertools import repeat
from pm4py import convert_to_bpmn, read_bpmn, convert_to_petri_net, check_soundness
from pm4py import discover_petri_net_inductive, discover_petri_net_ilp, discover_petri_net_heuristics
from pm4py import fitness_alignments
from pm4py import precision_alignments
from pm4py.objects.bpmn.obj import BPMN
from pm4py.objects.log.importer.xes import importer as xes_importer
from gedi.utils.io_helpers import dump_features_json
from gedi.utils.param_keys import INPUT_PATH, OUTPUT_PATH
from gedi.utils.param_keys.benchmark import MINERS
from tqdm import tqdm

class BenchmarkTest:
    def __init__(self, params=None, event_logs=None):
        start = dt.now()
        print("=========================== BenchmarkTest =============================")

        print(f"INFO: Running with {params}")

        if event_logs is None or len(event_logs) == 0:
            log_path = params[INPUT_PATH]
            if log_path.endswith(".xes"):
                event_logs = [""]
            else:
                try:
                    event_logs =sorted([filename for filename in os.listdir(log_path) if filename.endswith(".xes")])
                except FileNotFoundError:
                    print(f"        FAILED: Cannot find {params[INPUT_PATH]}" )
                    return
        if params != None:
            self.params = params

        log_counter = [*range(0,len(event_logs))]

        if True:
             num_cores = multiprocessing.cpu_count() if len(
                        event_logs) >= multiprocessing.cpu_count() else len(event_logs)
             #self.benchmark_wrapper((event_logs[0],0), miners=self.params[MINERS])# TESTING
             with multiprocessing.Pool(num_cores) as p:
                 print(f"INFO: Benchmark starting at {start.strftime('%H:%M:%S')} using {num_cores} cores for {len(event_logs)} files...")
                 p.starmap(self.benchmark_wrapper, zip(event_logs, log_counter, repeat(self.params[MINERS])))

             # Aggregates metafeatures in saved Jsons into dataframe
             self.root_path = self.params[INPUT_PATH]
             path_to_json = f"output/benchmark/{str(self.root_path).split('/',1)[1]}"
             if path_to_json.endswith(".xes"):
                path_to_json = path_to_json.rsplit("/",1)[0]
             df = pd.DataFrame()
             # Iterate over the files in the directory
             for filename in sorted(os.listdir(path_to_json)):
                 if filename.endswith('.json'):
                     i_path = os.path.join(path_to_json, filename)
                     with open(i_path) as f:
                         data = json.load(f)
                         temp_df = pd.DataFrame([data])
                         df = pd.concat([df, temp_df], ignore_index = True)
             benchmark_results = df
             #print(benchmark_results)

        self.filename = os.path.split(self.root_path)[-1].replace(".xes","") + '_benchmark.csv'
        self.filepath = os.path.join("output", "benchmark", self.filename)
        os.makedirs(os.path.split(self.filepath)[0], exist_ok=True)
        benchmark_results.to_csv(self.filepath, index=False)

        self.results = benchmark_results
        print(benchmark_results)
        print(f"SUCCESS: BenchmarkTest took {dt.now()-start} sec for {len(params[MINERS])} miners"+\
              f" and {len(benchmark_results)} event-logs. Saved benchmark to {self.filepath}.")
        print("========================= ~ BenchmarkTest =============================")

    def benchmark_wrapper(self, event_log, log_counter=0, miners=['ind']):
        dump_path = os.path.join(self.params[OUTPUT_PATH],
                                 os.path.split(self.params[INPUT_PATH])[-1])
        dump_path= os.path.join(self.params[OUTPUT_PATH],
                                os.path.join(*os.path.normpath(self.params[INPUT_PATH]).split(os.path.sep)[1:]))
        if dump_path.endswith(".xes"):
            event_log = os.path.split(dump_path)[-1]
            dump_path = os.path.split(dump_path)[0]

        benchmark_results = pd.DataFrame()
        if isinstance(event_log, str):
            log_name = event_log.replace(".xes", "")
            results = {'log': log_name}
        else:
            log_name = "gen_el_"+str(log_counter)
            results = {"log": event_log}

        for miner in miners:
            miner_cols = [f"fitness_{miner}", f"precision_{miner}", f"fscore_{miner}", f"size_{miner}", f"cfc_{miner}", f"pnsize_{miner}"]# f"generalization_{miner}",f"simplicity_{miner}"]
            start_miner = dt.now()
            benchmark_results =  [round(x, 4) for x in self.benchmark_discovery(results['log'],  miner, self.params)]
            results[f"fitness_{miner}"] = benchmark_results[0]
            results[f"precision_{miner}"] = benchmark_results[1]
            results[f"fscore_{miner}"] = round(2*(benchmark_results[0]*benchmark_results[1]/
                                                  (benchmark_results[0]+ benchmark_results[1])), 4)
            results[f"size_{miner}"]= benchmark_results[2]
            results[f"pnsize_{miner}"]= benchmark_results[4]
            results[f"cfc_{miner}"]= benchmark_results[3]

        results['log'] = log_name

        print(f"    SUCCESS: {miner} miner for {results} took {dt.now()-start_miner} sec.")
        dump_features_json(results, os.path.join(dump_path, log_name), content_type="benchmark")
        return

    def split_miner_wrapper(self, log_path="data/real_event_logs/BPI_Challenges/BPI_Challenge_2012.xes"):
        jar_path = os.path.join("gedi","libs","split-miner-1.7.1-all.jar")
        filename = os.path.split(log_path)[-1].rsplit(".",1)[0]
        bpmn_path = os.path.join("output", "bpmns_split", filename)
        os.makedirs(os.path.split(bpmn_path)[0], exist_ok=True)
        command = [
                "java",
                "-cp",
                f"{os.getcwd()}/gedi/libs/sm2.jar:{os.getcwd()}/tag/libs/lib/*",
                "au.edu.unimelb.services.ServiceProvider",
                "SM2",
                f"{os.getcwd()}/{log_path}",
                f"{os.getcwd()}/{bpmn_path}",
                "0.05"
                ]
        print("COMMAND", " ".join(command))
        output = subprocess.run(
            command,
            capture_output=True,
            text=True,
        )
        try:
            if "\nERROR:" in output.stdout:
                print("FAILED: SplitMiner could not create BPMN for", log_path)
                print("     SplitMiner:", output.stderr)
                return None
            return read_bpmn(bpmn_path+'.bpmn')
        except ValueError:
            print(output.stdout)

    def benchmark_discovery(self, log, miner, params=None):
        """
        Runs discovery algorithms on a specific log and returns their performance.

        :param str/EventLog log: log from pipeline step before or string to .xes file.
        :param str miner: Specifies process discovery miner to be run on log.
        :param Dict params: Params from config file

        """
        #print("Running benchmark_discovery with", self, log, miner, params)
        NOISE_THRESHOLD = 0.2
        miner_params=''
        tqdm.__init__ = partialmethod(tqdm.__init__, disable=True)
        start_bench = dt.now()

        if type(log) is str:
            if params[INPUT_PATH].endswith('.xes'):
                log_path = params[INPUT_PATH]
            else:
                log_path = os.path.join(params[INPUT_PATH], log+".xes")
            success_msg = f"        SUCCESS: Benchmarking event-log {log} with {miner} took "# {dt.now()-start_bench} sec."
            try:
                log = xes_importer.apply(f"{log_path}", parameters={"show_progress_bar": False})
            except FileNotFoundError:
                print(f"        FAILED: Cannot find {log_path}" )
        else:
            log=log
            success_msg = f"        SUCCESS: Benchmarking one event-log with {miner} took "# {dt.now()-start_bench} sec."
        if miner == 'sm':
            bpmn_graph = self.split_miner_wrapper(log_path)
            if bpmn_graph is None:
                return None
            '''TESTING
            from pm4py.visualization.bpmn.visualizer import apply as get_bpmn_fig
            from pm4py.visualization.bpmn.visualizer import matplotlib_view as view_bpmn_fig
            bpmn_fig = get_bpmn_fig(bpmn_graph)
            view_bpmn_fig(bpmn_fig)
            '''
            net, im, fm = convert_to_petri_net(bpmn_graph)
            is_sound = check_soundness(net, im, fm)
        else:
            if miner == 'imf':
                miner = 'inductive'
                miner_params = f', noise_threshold={NOISE_THRESHOLD}'
            elif miner == 'ind':
                miner = 'inductive'
            elif miner == 'heu':
                miner = 'heuristics'
            net, im, fm = eval(f"discover_petri_net_{miner}(log {miner_params})")
            bpmn_graph = convert_to_bpmn(net, im, fm)
        fitness = fitness_alignments(log, net, im, fm)['log_fitness']
        precision = precision_alignments(log, net, im, fm)
        pn_size = len(net._PetriNet__places)
        size = len(bpmn_graph._BPMN__nodes)
        cfc = sum([isinstance(node, BPMN.ExclusiveGateway) for node in bpmn_graph._BPMN__nodes])
        #generalization = generalization_evaluator.apply(log, net, im, fm)
        #simplicity = simplicity_evaluator.apply(net)
        print(success_msg + f"{dt.now()-start_bench} sec.")
        return fitness, precision, size, cfc, pn_size#, generalization, simplicity