def _extract_parameters(self, settings, structure, parameters,
**kwargs) -> None:
bpmn, process_graph = structure
p_extractor = spm.StructureParametersMiner(self.log_train, bpmn,
process_graph, settings)
num_inst = len(self.log_valdn.caseid.unique())
# Get minimum date
start_time = (self.log_valdn.start_timestamp.min().strftime(
"%Y-%m-%dT%H:%M:%S.%f+00:00"))
p_extractor.extract_parameters(num_inst, start_time,
parameters['resource_pool'])
if p_extractor.is_safe:
parameters = {**parameters, **p_extractor.parameters}
# print parameters in xml bimp format
xml.print_parameters(
os.path.join(settings['output'],
settings['file'].split('.')[0] + '.bpmn'),
os.path.join(settings['output'],
settings['file'].split('.')[0] + '.bpmn'),
parameters)
self.log_valdn.rename(columns={'user': '******'}, inplace=True)
self.log_valdn['source'] = 'log'
self.log_valdn['run_num'] = 0
self.log_valdn['role'] = 'SYSTEM'
self.log_valdn = self.log_valdn[~self.log_valdn.task.
isin(['Start', 'End'])]
else:
raise RuntimeError('Parameters extraction error')
def extract_parameters(self) -> None:
print("-- Mining Simulation Parameters --")
p_extractor = par.ParameterMiner(self.log, self.bpmn,
self.process_graph, self.settings)
p_extractor.extract_parameters()
self.process_stats = p_extractor.process_stats
# print parameters in xml bimp format
xml.print_parameters(
os.path.join(self.settings['output'],
self.settings['file'].split('.')[0] + '.bpmn'),
os.path.join(self.settings['output'],
self.settings['file'].split('.')[0] + '.bpmn'),
p_extractor.parameters)
self.process_stats = pd.DataFrame.from_records(self.process_stats)
def extract_parameters(self, **kwargs) -> None:
print("-- Mining Simulation Parameters --")
p_extractor = par.ParameterMiner(self.log_train, self.bpmn,
self.process_graph, self.settings)
num_inst = len(pd.DataFrame(self.log_test).caseid.unique())
p_extractor.extract_parameters(num_inst)
self.process_stats = self.process_stats.merge(
p_extractor.resource_table[['resource', 'role']],
on='resource',
how='left')
# print parameters in xml bimp format
xml.print_parameters(
os.path.join(self.settings['output'],
self.settings['file'].split('.')[0] + '.bpmn'),
os.path.join(self.settings['output'],
self.settings['file'].split('.')[0] + '.bpmn'),
p_extractor.parameters)
def single_exec(settings):
"""Main aplication method"""
# Read settings from config file
settings = read_settings(settings)
# Output folder creation
if not os.path.exists(settings['output']):
os.makedirs(settings['output'])
os.makedirs(os.path.join(settings['output'], 'sim_data'))
# Copy event-log to output folder
copyfile(os.path.join(settings['input'], settings['file']),
os.path.join(settings['output'], settings['file']))
# Event log reading
log = lr.LogReader(os.path.join(settings['output'], settings['file']),
settings['timeformat'])
# Execution steps
mining_structure(settings, settings['epsilon'], settings['eta'])
bpmn = br.BpmnReader(os.path.join(settings['output'],
settings['file'].split('.')[0]+'.bpmn'))
process_graph = gph.create_process_structure(bpmn)
# Evaluate alignment
chk.evaluate_alignment(process_graph, log, settings)
print("-- Mining Simulation Parameters --")
parameters, process_stats = par.extract_parameters(log, bpmn, process_graph)
xml.print_parameters(os.path.join(settings['output'],
settings['file'].split('.')[0]+'.bpmn'),
os.path.join(settings['output'],
settings['file'].split('.')[0]+'.bpmn'),
parameters)
response = list()
status = 'ok'
sim_values = list()
if settings['simulation']:
# if settings['analysis']:
process_stats = pd.DataFrame.from_records(process_stats)
for rep in range(settings['repetitions']):
print("Experiment #" + str(rep + 1))
try:
simulate(settings, rep)
process_stats = process_stats.append(measure_stats(settings,
bpmn, rep),
ignore_index=True,
sort=False)
sim_values.append(gen.mesurement(process_stats, settings, rep))
except:
status = 'fail'
break
data = {'alg_manag': settings['alg_manag'],
'epsilon': settings['epsilon'],
'eta': settings['eta'],
'output': settings['output']
}
if status == 'ok':
loss = (1 - np.mean([x['act_norm'] for x in sim_values]))
if loss < 0:
response.append({**{'loss': loss, 'status': 'fail'}, **data})
else:
response.append({**{'loss': loss, 'status': status}, **data})
else:
response.append({**{'loss': 1, 'status': status}, **data})
return response
# -*- coding: utf-8 -*-
from extraction import parameter_extraction as par
from support_modules.readers import readers as rd
from support_modules.writers import xml_writer as xml_bimp
from sys import argv
log_file_name = argv[1]
bpmn_file_name = argv[2]
output_file_bimp = argv[3]
print("-- Mining Simulation Parameters --")
timeformat = "%Y-%m-%dT%H:%M:%S"
log_columns_numbers = "[0]"
log, bpmn = rd.read_inputs(timeformat, log_columns_numbers, log_file_name, bpmn_file_name, False)
print("-- Successfully read log and model --")
parameters,_ = par.extract_parameters(log, bpmn)
xml_bimp.print_parameters(bpmn_file_name, output_file_bimp, parameters)