def run(args, i_cv):
logger = logging.getLogger()
print_line()
logger.info('Running iter n°{}'.format(i_cv))
print_line()
# LOAD/GENERATE DATA
logger.info('Set up data generator')
config = Config()
seed = SEED + i_cv * 5
train_generator, valid_generator, test_generator = get_generators_torch(
seed, cuda=args.cuda)
train_generator = GeneratorCPU(train_generator)
valid_generator = GeneratorCPU(valid_generator)
test_generator = GeneratorCPU(test_generator)
# SET MODEL
logger.info('Set up classifier')
model = build_model(args, i_cv)
os.makedirs(model.results_path, exist_ok=True)
flush(logger)
# TRAINING / LOADING
train_or_load_classifier(model,
train_generator,
config.CALIBRATED,
config.N_TRAINING_SAMPLES,
retrain=args.retrain)
# MEASUREMENT
results = measurement(model, i_cv, config, valid_generator, test_generator)
print(results)
return results
def main():
# BASIC SETUP
logger = set_logger()
args = REG_parse_args(
main_description="Training launcher for Regressor on S3D2 benchmark")
logger.info(args)
flush(logger)
# Setup model
logger.info("Setup model")
model = build_model(args, 0)
os.makedirs(model.results_directory, exist_ok=True)
# Setup data
logger.info("Setup data")
config = Config()
config_table = evaluate_config(config)
config_table.to_csv(
os.path.join(model.results_directory, 'config_table.csv'))
seed = SEED + 99999
train_generator, valid_generator, test_generator = get_generators_torch(
seed, cuda=args.cuda, GeneratorClass=GeneratorClass)
train_generator = GeneratorCPU(train_generator)
train_generator = TrainGenerator(param_generator, train_generator)
valid_generator = GeneratorCPU(valid_generator)
test_generator = GeneratorCPU(test_generator)
i_cv = 0
result_row = {'i_cv': i_cv}
# TRAINING / LOADING
train_or_load_neural_net(model, train_generator, retrain=args.retrain)
# CHECK TRAINING
result_row.update(evaluate_neural_net(model, prefix='valid'))
evaluate_regressor(model, prefix='valid')
print_line()
result_table = [
run_iter(model, result_row, i, test_config, valid_generator,
test_generator)
for i, test_config in enumerate(config.iter_test_config())
]
result_table = pd.DataFrame(result_table)
result_table.to_csv(os.path.join(model.results_directory, 'results.csv'))
logger.info('Plot params')
param_names = [CALIB_PARAM_NAME]
for name in param_names:
plot_params(name,
result_table,
title=model.full_name,
directory=model.results_directory)
logger.info('DONE')
def main():
logger = set_logger()
directory = os.path.join(DIRECTORY, "nll_contour")
os.makedirs(directory, exist_ok=True)
args = parse_args()
i_cv = 0
seed = SEED + i_cv * 5
train_generator, valid_generator, test_generator = get_generators_torch(seed, cuda=args.cuda)
train_generator = GeneratorCPU(train_generator)
valid_generator = GeneratorCPU(valid_generator)
test_generator = GeneratorCPU(test_generator)
model = load_some_NN()
config = Config()
config_table = evaluate_config(config)
os.makedirs(os.path.join(directory, model.name), exist_ok=True)
config_table.to_csv(os.path.join(directory, model.name, 'config_table.csv'))
for i_iter, test_config in enumerate(config.iter_test_config()):
do_iter(test_config, model, i_iter, valid_generator, test_generator, directory)
