def get_eval_table(args, results_directory):
logger = logging.getLogger()
if args.load_run:
logger.info(f'Loading previous runs [{args.start_cv},{args.end_cv}[')
estimations = load_estimations(results_directory,
start_cv=args.start_cv,
end_cv=args.end_cv)
else:
logger.info(f'Running runs [{args.start_cv},{args.end_cv}[')
estimations = [
run_estimation(args, i_cv)
for i_cv in range(args.start_cv, args.end_cv)
]
estimations = pd.concat(estimations, ignore_index=True)
estimations.to_csv(os.path.join(results_directory, 'estimations.csv'))
# EVALUATION
eval_table = evaluate_estimator(Config.INTEREST_PARAM_NAME, estimations)
print_line()
print_line()
print(eval_table)
print_line()
print_line()
eval_table.to_csv(
os.path.join(results_directory, 'estimation_evaluation.csv'))
return eval_table
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 load_cheat_evaluation_config(loader, benchmark_name):
estimations = loader.load_estimations()
estimations["mu"] = estimations["cheat_mu"]
estimations["mu_error"] = estimations["cheat_sigma_mu"]
evaluation = evaluate_estimator(Config.INTEREST_PARAM_NAME, estimations)
config_table = loader.load_config_table()
evaluation = evaluation.join(config_table, rsuffix='_')
evaluation['model_full_name'] = loader.model_full_name
evaluation['benchmark_name'] = benchmark_name
evaluation['base_name'] = loader.base_name
return evaluation
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()
logger.info("Hello world !")
os.makedirs(DIRECTORY, exist_ok=True)
set_plot_config()
args = None
config = Config()
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)
def main():
# BASIC SETUP
logger = set_logger()
args = INFERNO_parse_args(
main_description="Training launcher for Regressor on S3D2 benchmark")
logger.info(args)
flush(logger)
# INFO
model = build_model(args, -1)
pb_config = Config()
# 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.directory, 'results.csv'))
# EVALUATION
eval_table = evaluate_estimator(pb_config.INTEREST_PARAM_NAME, results)
print_line()
print_line()
print(eval_table)
print_line()
print_line()
eval_table.to_csv(os.path.join(model.directory, 'evaluation.csv'))
gather_images(model.directory)
