def run_train():
print "Training machine learning"
try:
MachineLearning.train(MachineLearningBVA)
MachineLearning.train(MachineLearningBOW)
except IOError:
return PERMISSION_DENIED
except MachineLearningTrainError:
return ERROR_INIT_TRAIN
return SUCCESS
def train_machine_learning():
try:
print "Training machine learning"
MachineLearning.train(MachineLearningBVA)
MachineLearning.train(MachineLearningBOW)
os.system("cp {} {}".format(
MachineLearningData.PKGS_CLASSIFICATIONS, LOG_PATH))
except xapian.DatabaseOpeningError:
print "\n\nPlease check if you prepared the AppRecommender data"
print "Try to run the following commands:"
print " $ cd .."
print " $ apprec --init\n"
exit(1)
def run():
load_options = LoadOptions()
load_options.load()
if call_initialize(load_options.options):
print "Initializing AppRecommender"
initialize = Initialize()
initialize.prepare_data()
return SUCCESS
elif call_training(load_options.options):
print "Training machine learning"
MachineLearning.train(MachineLearningBVA)
MachineLearning.train(MachineLearningBOW)
return SUCCESS
else:
return run_apprecommender(load_options.options)
def run():
load_options = LoadOptions()
load_options.load()
if call_initialize(load_options.options):
print "Initializing AppRecommender"
initialize = Initialize()
initialize.prepare_data()
return SUCCESS
elif call_training(load_options.options):
print "Training machine learning"
MachineLearning.train(MachineLearningBVA)
MachineLearning.train(MachineLearningBOW)
return SUCCESS
else:
return run_apprecommender(load_options.options)
def run():
load_options = LoadOptions()
load_options.load()
options = load_options.options
if check_for_flag(options, '-i', '--init'):
print "Initializing AppRecommender"
initialize = Initialize()
initialize.prepare_data()
return SUCCESS
elif check_for_flag(options, '-t', '--train'):
print "Training machine learning"
MachineLearning.train(MachineLearningBVA)
MachineLearning.train(MachineLearningBOW)
return SUCCESS
elif check_for_flag(options, '-c', '--contribute'):
collect_user_data.main()
else:
return run_apprecommender(load_options.options)
def main():
logging.getLogger().disabled = True
# print "Checking dependencies"
# unistalled_dependencies = check_dependencies()
# if len(unistalled_dependencies) > 0:
# print 'These packages need to be installed:', unistalled_dependencies
# return
initial_prints()
if not user_accept_collect_data():
exit(1)
create_log_folder()
MachineLearning.train(MachineLearningBVA)
MachineLearning.train(MachineLearningBOW)
os.system("cp {} {}".format(
MachineLearningData.PKGS_CLASSIFICATIONS, LOG_PATH))
collect_data = Process(target=collect_user_data)
cross_validation_mlbva = Process(
target=ml_cross_validation, args=(LOG_PATH + '/', 'mlbva'))
cross_validation_mlbow = Process(
target=ml_cross_validation, args=(LOG_PATH + '/', 'mlbow'))
collect_data.start()
cross_validation_mlbva.start()
cross_validation_mlbow.start()
os.system('clear')
print "Preparing recommendations..."
collect_user_preferences()
print "\n\nWaiting for data collection complete"
collect_data.join()
cross_validation_mlbva.join()
cross_validation_mlbow.join()
print "\n\nFinished: All files and recommendations were collected"
print "Collect data folder: {0}".format(LOG_PATH)