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saving the DFA version of model when the postprocessing is requested #2

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saving the DFA version of model when the postprocessing is requested #2

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319e6cb
Add PDF saving for DFA version
baharhadadi Dec 12, 2022
43c158b
Add PDF saving for DFA version
baharhadadi Dec 12, 2022
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3 changes: 2 additions & 1 deletion PRINS/run_PRINS.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,8 +58,9 @@
m_sys, _, _, _ = instance.run(mint_timeout=args.timeout,
ignore_values=args.ignore_values,
save_pdf=args.save_pdf,
det = args.det != 'none',
num_workers=args.num_workers,
use_pickle=args.use_pickle)
if args.det != 'none':
PRINS.postprocess(m_sys, determinize_technique=args.det)
PRINS.postprocess(m_sys, determinize_technique=args.det, save_pdf=args.save_pdf, output_dir = os.path.join(args.output_dir, args.system))
logger.info('run_PRINS ends without error(s).')
15 changes: 11 additions & 4 deletions PRINS/src/main/PRINS.py
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Expand Up @@ -70,7 +70,7 @@ def __init__(self, system: str, logs, output_dir: str):
self.l_vectors = convert_df_into_l_vectors(self.logs_df, include_component=True)
self.components = natsorted(self.logs_df.component.unique())

def run(self, mint_timeout=3600, mint_param=2, ignore_values=False, save_pdf=True, num_workers=4, use_pickle=False):
def run(self, mint_timeout=3600, mint_param=2, ignore_values=False, save_pdf=True, det = False, num_workers=4, use_pickle=False):
"""Run PRINS (main algorithm).

:param mint_timeout: mint timeout (sec) (default=3600)
Expand Down Expand Up @@ -169,8 +169,8 @@ def run(self, mint_timeout=3600, mint_param=2, ignore_values=False, save_pdf=Tru
lc_div_score = len(set(all_components)) / len(all_components)
print(f'lcDivScore: {lc_div_score:.3f}')

# (optional) Saving the Results as a pdf file
if save_pdf:
# (optional) Saving the Results as a pdf file if deterministic technique is not asked
if save_pdf and not det:
if len(m_sys.states) < 1000:
m_sys.save_pdf(output_dir=self.output_dir)
else:
Expand Down Expand Up @@ -253,7 +253,7 @@ def partition_log_by_component(l_vector: list) -> list:
return partitioned_log

@staticmethod
def postprocess(m_sys: 'NFA', determinize_technique: str = 'hybrid-1'):
def postprocess(m_sys: 'NFA', determinize_technique: str = 'hybrid-1', save_pdf=True, output_dir = "/."):
"""
Postprocessing function.
Currently, we only convert NFA to DFA.
Expand All @@ -279,4 +279,11 @@ def postprocess(m_sys: 'NFA', determinize_technique: str = 'hybrid-1'):
print(f'Post-processed model (m_sys): states={len(dfa_sys.states)}, transitions={len(dfa_sys.transitions)}')
logger.info(f'Post-processed model (m_sys): states={len(dfa_sys.states)}, transitions={len(dfa_sys.transitions)}')

# (optional) Saving the Results as a pdf file
if save_pdf:
if len(dfa_sys.states) < 1000:
dfa_sys.save_pdf(output_dir)
else:
print(f'Do not save model.pdf because of too many states: {len(m_sys.states)}')

return dfa_sys, time.time() - start_time