def __executeSVN(self, command, arg = "", split=False):
command = "svnlook --%s %s %s %s %s" % (self.type, self.txnName, command, self.reposPath, arg)
if command in self.cache:
return self.cache[command]
output = Process.execute(command)
if split:
output = [x.strip() for x in output.split("\n") if x.strip()]
self.cache[command] = output
return self.cache[command]
def run(transaction, config):
check = config.getArray("Checkstyle.CheckFiles", [".*\.java"])
ignore = config.getArray("Checkstyle.IgnoreFiles", [])
files = transaction.getFiles(check, ignore)
java = config.getString("Checkstyle.Java")
classpath = config.getString("Checkstyle.Classpath")
config = config.getString("Checkstyle.ConfigFile")
command = "%s -classpath %s com.puppycrawl.tools.checkstyle.Main -c %s " % (java, classpath, config)
files = [transaction.getFile(oneFile[0]) for oneFile in files.iteritems() if oneFile[1] in ["A", "U", "UU"]]
try:
Process.execute(command + " ".join(files))
except Process.ProcessException, e:
msg = "Coding style errors found:\n\n"
msg += e.output + "\n"
msg += "See Checkstyle documentation for a detailed description: http://checkstyle.sourceforge.net/"
return (msg, 1)
input_sets = SetsGenerator(
input_dir=input_dir,
lang=args.lang,
monolingual_only=args.monolingual,
cognate_decimal=args.cognate_decimal_fraction,
lexicon_csv=args.lexicon,
structures_csv=args.structures,
allow_free_structure_production=args.free_pos)
# I had issues with joblib installation on Ubuntu 16.04.6 LTS
# If prefer="threads", deepcopy input sets. -1 means that all CPUs will be used
# Parallel(n_jobs=-1)(delayed(create_input_for_simulation)(sim) for sim in simulation_range)
parallel_jobs = []
for sim in simulation_range: # first create all input files
parallel_jobs.append(
Process(target=create_input_for_simulation,
args=(results_dir, input_sets, cognate_experiment,
training_num, num_test, l2_decimal,
auxiliary_experiment, sim, args.randomize)))
parallel_jobs[-1].start()
# if number of simulations is larger than number of cores or it is the last simulation, start multiproc.
if len(parallel_jobs
) == available_cpu or sim == simulation_range[-1]:
for p in parallel_jobs:
p.join()
parallel_jobs = []
del input_sets # we no longer need it
if not args.decrease_lrate or args.continue_training: # assumption: when training continues, lrate is NOT reduced
logging.info(
f"Learning rate will NOT be decreased, it is set to {args.final_lrate}"
)