def handle(self, *args, **options):
#args = parse_args(args)
setup_logging(options['loglevel'])
script_config = LSTMHyperoptTrainingScriptConfig(args.config_file)
_logger.debug("Starting training an LSTM with {} configuration".format(args.config_file))
since_date = args.since_date.split('-')
_logger.debug("since_date parameter is {} ".format(since_date))
which_minute = int(args.which_minute)
_logger.debug("which_minute parameter is {} ".format(which_minute))
if which_minute <= 0 :
which_minutes = range(60)
else:
which_minutes = [which_minute]
if not os.path.exists(script_config.output_models_path):
os.makedirs(script_config.output_models_path)
if not os.path.exists(script_config.output_results_path):
os.makedirs(script_config.output_results_path)
tic = time.time()
train_lstm(script_config,since_date,which_minutes)
toc = time.time()
_logger.debug("Training has ended, took {} seconds overall".format(toc-tic))
def handle(self, *args, **options):
# reading parameters
self.config_file = os.path.join(settings.CONFIG_DIR,
options['config_file'])
self.log_success('Config file read from parameters {}'.format(
self.config_file))
script_config = LSTMHyperoptTrainingScriptConfig(self.config_file)
self.log_success('Script-config: {}'.format(script_config))
try:
model_package_path = options['model_package_path']
if model_package_path == '':
model_package_path = None
except Exception as e:
self.log_success(
'Error trying to read model_package_path parameter : {}'.
format(e))
self.log_success(
'Model package file to call predict_lstm is {}'.format(
model_package_path))
try:
future_steps = int(options['future_steps'])
except Exception as e:
self.log_success(
'Error trying to read future_steps parameter : {}'.format(e))
self.log_success(
'Future steps to call predict_lstm is {}'.format(future_steps))
try:
step_mins = int(options['step_mins'])
except Exception as e:
self.log_success(
'Error trying to read step_mins parameter : {}'.format(e))
self.log_success('Each step is {} minutes'.format(step_mins))
# starting the predicction process
self.predict_lstm(script_config, model_package_path, future_steps,
step_mins)
def handle(self, *args, **options):
self.config_file = os.path.join(settings.CONFIG_DIR,
options['config_file'])
self.log_success('Config file read from parameters {}'.format(
self.config_file))
since_date = options['since_date'].split('-')
self.log_success('Since date argument {}'.format(since_date))
which_minute = int(options['which_minute'])
self.log_success('which_minute argument {}'.format(which_minute))
if which_minute <= 0:
which_minutes = range(60)
else:
which_minutes = [which_minute]
script_config = LSTMHyperoptTrainingScriptConfig(self.config_file)
self.log_success('Script-config: {}'.format(script_config))
self.train_lstm(script_config, since_date, which_minutes)
def handle(self, *args, **options):
# parameter parsing
self.config_file = os.path.join(settings.CONFIG_DIR,
options['config_file'])
self.log_success('Config file read from parameters {}'.format(
self.config_file))
since_date = options['since_date'].split('-')
self.log_success('Since date argument {}'.format(since_date))
which_minute = int(options['which_minute'])
self.log_success('which_minute argument {}'.format(which_minute))
if which_minute <= 0:
# if we pass negative values, we choose to use the whole hour
which_minutes = range(60)
else:
which_minutes = [which_minute]
# instantiate the configuration
script_config = LSTMHyperoptTrainingScriptConfig(self.config_file)
self.log_success('Script-config: {}'.format(script_config))
# call the training process
self.train_lstm(script_config, since_date, which_minutes)
def main(args):
"""Main entry point allowing external calls
Args:
args ([str]): command line parameter list
"""
args = parse_args(args)
setup_logging(args.loglevel)
script_config = LSTMHyperoptTrainingScriptConfig(args.config_file)
_logger.debug("Starting training an LSTM with {} configuration".format(args.config_file))
if not os.path.exists(script_config.output_models_path):
os.makedirs(script_config.output_models_path)
if not os.path.exists(script_config.output_results_path):
os.makedirs(script_config.output_results_path)
tic = time.time()
train_lstm(script_config)
toc = time.time()
_logger.debug("Training has ended, took {} seconds overall".format(toc-tic))