def invoke_model(parameters):
model_params = load_parameters()
model_name = model_params["MODEL_TYPE"]
for parameter in parameters.keys():
model_params[parameter] = parameters[parameter][0]
logger.debug("Assigning to %s the value %s" % (str(parameter), parameters[parameter][0]))
model_name += '_' + str(parameter) + '_' + str(parameters[parameter][0])
model_params["SKIP_VECTORS_HIDDEN_SIZE"] = model_params["TARGET_TEXT_EMBEDDING_SIZE"]
model_params["MODEL_NAME"] = model_name
# models and evaluation results will be stored here
model_params[
"STORE_PATH"] = '/home/lvapeab/smt/software/egocentric-video-description/meta-optimizers/spearmint/trained_models/' + \
model_params["MODEL_NAME"] + '/'
check_params(model_params)
assert model_params['MODE'] == 'training', 'You can only launch Spearmint when training!'
logging.info('Running training.')
train_model(model_params)
results_path = model_params['STORE_PATH'] + '/' + model_params['EVAL_ON_SETS'][0] + '.' + model_params['METRICS'][0]
# Recover the highest metric score
metric_pos_cmd = "head -n 1 " + results_path + \
" |awk -v metric=" + metric_name + \
" 'BEGIN{FS=\",\"}" \
"{for (i=1; i<=NF; i++) if ($i == metric) print i;}'"
metric_pos = \
subprocess.Popen(metric_pos_cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True).communicate()[0][:-1]
cmd = "tail -n +2 " + results_path + \
" |awk -v m_pos=" + str(metric_pos) + \
" 'BEGIN{FS=\",\"}{print $m_pos}'|sort -gr|head -n 1"
ps = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True, env=d)
metric_value = float(ps.communicate()[0])
print "Best %s: %f" % (metric_name, metric_value)
return 1. - metric_value if maximize else metric_value # Spearmint minimizes a function
def invoke_model(parameters):
"""
Loads a model, trains it and evaluates it.
:param parameters: Model parameters
:return: Metric to minimize value.
"""
model_params = load_parameters()
model_name = model_params["MODEL_TYPE"]
for parameter in list(parameters):
model_params[parameter] = parameters[parameter][0]
logger.debug("Assigning to %s the value %s" %
(str(parameter), parameters[parameter][0]))
model_name += '_' + str(parameter) + '_' + str(
parameters[parameter][0])
model_params["MODEL_NAME"] = model_name
# models and evaluation results will be stored here
model_params["STORE_PATH"] = os.path.join('trained_models',
model_params["MODEL_NAME"])
check_params(model_params)
assert model_params[
'MODE'] == 'training', 'You can only launch Spearmint when training!'
logger.info('Running training.')
train_model(model_params)
results_path = os.path.join(
model_params['STORE_PATH'],
model_params['EVAL_ON_SETS'][0] + '.' + model_params['METRICS'][0])
# Recover the highest metric score
metric_pos_cmd = "head -n 1 " + results_path + \
" |awk -v metric=" + metric_name + \
" 'BEGIN{FS=\",\"}" \
"{for (i=1; i<=NF; i++) if ($i == metric) print i;}'"
metric_pos = subprocess.Popen(metric_pos_cmd,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
shell=True).communicate()[0][:-1]
cmd = "tail -n +2 " + results_path + \
" |awk -v m_pos=" + str(metric_pos) + \
" 'BEGIN{FS=\",\"}{print $m_pos}'|sort -gr|head -n 1"
ps = subprocess.Popen(cmd,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
shell=True,
env=d)
metric_value = float(ps.communicate()[0])
print("Best %s: %f" % (metric_name, metric_value))
return 1. - metric_value if maximize else metric_value # Spearmint minimizes a function