def main():
# BASIC SETUP
logger = set_logger()
args = NET_parse_args(
main_description=
"Training launcher for Neural net classifier on HIGGS benchmark")
logger.info(args)
flush(logger)
# INFO
model = build_model(args, -1)
os.makedirs(model.results_directory, exist_ok=True)
config = Config()
config_table = evaluate_config(config)
config_table.to_csv(
os.path.join(model.results_directory, 'config_table.csv'))
# RUN
if not args.conditional_only:
eval_table = get_eval_table(args, model.results_directory)
if not args.estimate_only:
eval_conditional = get_eval_conditional(args, model.results_directory)
if not args.estimate_only and not args.conditional_only:
eval_table = pd.concat([eval_table, eval_conditional], axis=1)
# EVALUATION
print_line()
print_line()
print(eval_table)
print_line()
print_line()
eval_table.to_csv(
os.path.join(model.results_directory, 'evaluation.csv'))
gather_images(model.results_directory)
def main():
# BASIC SETUP
logger = set_logger()
args = REG_parse_args(
main_description=
"Training launcher for Marginal Regressor on HIGGS benchmark")
logger.info(args)
flush(logger)
# INFO
model = build_model(args, -1)
os.makedirs(model.results_directory, exist_ok=True)
config = Config()
config_table = evaluate_config(config)
config_table.to_csv(
os.path.join(model.results_directory, 'config_table.csv'))
# RUN
eval_table = get_eval_table(args, model.results_directory)
# EVALUATION
print_line()
print_line()
print(eval_table)
print_line()
print_line()
eval_table.to_csv(os.path.join(model.results_directory, 'evaluation.csv'))
gather_images(model.results_directory)
def main():
# BASIC SETUP
logger = set_logger()
args = REG_parse_args(
main_description=
"Training launcher for Gradient boosting on S3D2 benchmark")
logger.info(args)
flush(logger)
# INFO
model = build_model(args, -1)
os.makedirs(model.results_directory, exist_ok=True)
config = Config()
config_table = evaluate_config(config)
config_table.to_csv(
os.path.join(model.results_directory, 'config_table.csv'))
# RUN
results = [run(args, i_cv) for i_cv in range(N_ITER)]
results = pd.concat(results, ignore_index=True)
results.to_csv(os.path.join(model.results_directory, 'estimations.csv'))
# EVALUATION
eval_table = evaluate_estimator(config.INTEREST_PARAM_NAME, results)
print_line()
print_line()
print(eval_table)
print_line()
print_line()
eval_table.to_csv(os.path.join(model.results_directory, 'evaluation.csv'))
gather_images(model.results_directory)
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():
# BASIC SETUP
logger = set_logger()
args = INFERNO_parse_args(
main_description=
"Training launcher for Gradient boosting on S3D2 benchmark")
logger.info(args)
flush(logger)
# INFO
model = build_model(args, -1)
os.makedirs(model.results_directory, exist_ok=True)
config = Config()
config_table = evaluate_config(config)
config_table.to_csv(
os.path.join(model.results_directory, 'config_table.csv'))
# RUN
if args.load_run:
logger.info(f'Loading previous runs [{args.start_cv},{args.end_cv}[')
directory = model.results_directory
estimations = load_estimations(directory,
start_cv=args.start_cv,
end_cv=args.end_cv)
conditional_estimations = load_conditional_estimations(
directory, start_cv=args.start_cv, end_cv=args.end_cv)
else:
logger.info(f'Running runs [{args.start_cv},{args.end_cv}[')
results = [
run(args, i_cv) for i_cv in range(args.start_cv, args.end_cv)
]
estimations = [e0 for e0, e1 in results]
estimations = pd.concat(estimations, ignore_index=True)
conditional_estimations = [e1 for e0, e1 in results]
conditional_estimations = pd.concat(conditional_estimations)
estimations.to_csv(os.path.join(model.results_directory,
'estimations.csv'))
conditional_estimations.to_csv(
os.path.join(model.results_directory, 'conditional_estimations.csv'))
# EVALUATION
eval_table = evaluate_estimator(config.INTEREST_PARAM_NAME, estimations)
eval_conditional = evaluate_conditional_estimation(
conditional_estimations,
interest_param_name=config.INTEREST_PARAM_NAME)
eval_table = pd.concat([eval_table, eval_conditional], axis=1)
print_line()
print_line()
print(eval_table)
print_line()
print_line()
eval_table.to_csv(os.path.join(model.results_directory, 'evaluation.csv'))
gather_images(model.results_directory)
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)
def main():
logger = set_logger()
logger.info("Hello world !")
os.makedirs(DIRECTORY, exist_ok=True)
set_plot_config()
args = None
config = GGConfig()
config_table = evaluate_config(config)
config_table.to_csv(os.path.join(DIRECTORY, 'config_table.csv'))
results = [run(args, i_cv) for i_cv in range(N_ITER)]
results = pd.concat(results, ignore_index=True)
results.to_csv(os.path.join(DIRECTORY, 'results.csv'))
# EVALUATION
eval_table = evaluate_estimator(config.TRUE.interest_parameters_names,
results)
print_line()
print_line()
print(eval_table)
print_line()
print_line()
eval_table.to_csv(os.path.join(DIRECTORY, 'evaluation.csv'))
gather_images(DIRECTORY)
