def run(args, i_cv):
logger = logging.getLogger()
print_line()
logger.info('Running iter n°{}'.format(i_cv))
print_line()
directory = os.path.join(DIRECTORY, f'cv_{i_cv}')
os.makedirs(directory, exist_ok=True)
config = GGConfig()
seed = SEED + i_cv * 5
test_seed = seed + 2
result_table = [
run_iter(i_cv, i, test_config, test_seed, directory)
for i, test_config in enumerate(config.iter_test_config())
]
result_table = pd.DataFrame(result_table)
result_table.to_csv(os.path.join(directory, 'results.csv'))
logger.info('Plot params')
param_names = config.PARAM_NAMES
for name in param_names:
plot_params(name,
result_table,
title='Likelihood fit',
directory=directory)
return result_table
def run(args, i_cv):
logger = logging.getLogger()
print_line()
logger.info('Running iter n°{}'.format(i_cv))
print_line()
directory = os.path.join(DIRECTORY, f'cv_{i_cv}')
os.makedirs(directory, exist_ok=True)
config = Config()
seed = SEED + i_cv * 5
train_generator = Generator(seed)
valid_generator = Generator(seed + 1)
test_generator = Generator(seed + 2)
N_BINS = 10
X_train, y_train, w_train = train_generator.generate(
*config.CALIBRATED, n_samples=config.N_TRAINING_SAMPLES)
compute_summaries = HistogramSummaryComputer(n_bins=N_BINS).fit(X_train)
result_table = [
run_iter(compute_summaries, i_cv, i, test_config, valid_generator,
test_generator, directory)
for i, test_config in enumerate(config.iter_test_config())
]
result_table = pd.DataFrame(result_table)
result_table.to_csv(os.path.join(directory, 'results.csv'))
logger.info('Plot params')
param_names = config.PARAM_NAMES
for name in param_names:
plot_params(name,
result_table,
title='Likelihood fit',
directory=directory)
return result_table
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 = GeneratorTorch(seed, cuda=args.cuda)
valid_generator = Generator(seed+1)
test_generator = Generator(seed+2)
# 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_neural_net(model, train_generator, retrain=args.retrain)
# MEASUREMENT
result_row = {'i_cv': i_cv}
results = []
for test_config in config.iter_test_config():
logger.info(f"Running test set : {test_config.TRUE}, {test_config.N_TESTING_SAMPLES} samples")
for threshold in np.linspace(0, 1, 500):
result_row = {'i_cv': i_cv}
result_row['threshold'] = threshold
result_row.update(test_config.TRUE.to_dict(prefix='true_'))
result_row['n_test_samples'] = test_config.N_TESTING_SAMPLES
X, y, w = valid_generator.generate(*config.TRUE, n_samples=config.N_VALIDATION_SAMPLES)
proba = model.predict_proba(X)
decision = proba[:, 1]
selected = decision > threshold
beta = np.sum(y[selected] == 0)
gamma = np.sum(y[selected] == 1)
result_row['beta'] = beta
result_row['gamma'] = gamma
X, y, w = test_generator.generate(*config.TRUE, n_samples=config.N_VALIDATION_SAMPLES)
proba = model.predict_proba(X)
decision = proba[:, 1]
selected = decision > threshold
n_selected = np.sum(selected)
n_selected_bkg = np.sum(y[selected] == 0)
n_selected_sig = np.sum(y[selected] == 1)
result_row['n'] = n_selected
result_row['b'] = n_selected_bkg
result_row['s'] = n_selected_sig
result_row['s_sqrt_n'] = n_selected_sig / np.sqrt(n_selected)
result_row['s_sqrt_b'] = n_selected_sig / np.sqrt(n_selected)
results.append(result_row.copy())
results = pd.DataFrame(results)
print(results)
return results
def run(args, i_cv):
logger = logging.getLogger()
print_line()
logger.info('Running iter n°{}'.format(i_cv))
print_line()
result_row = {'i_cv': i_cv}
# LOAD/GENERATE DATA
logger.info('Set up data generator')
config = Config()
seed = SEED + i_cv * 5
# train_generator = Generator(seed)
# valid_generator = Generator(seed+1)
test_generator = Generator(seed + 2)
# SET MODEL
# logger.info('Set up classifier')
model = build_model(args, i_cv)
# flush(logger)
# TRAINING / LOADING
# train_or_load_classifier(model, train_generator, config.CALIBRATED, config.N_TRAINING_SAMPLES, retrain=args.retrain)
# CHECK TRAINING
logger.info('Generate validation data')
# X_valid, y_valid, w_valid = valid_generator.generate(*config.CALIBRATED, n_samples=config.N_VALIDATION_SAMPLES)
# result_row.update(evaluate_classifier(model, X_valid, y_valid, w_valid, prefix='valid'))
# MEASUREMENT
N_BINS = 10
# evaluate_summary_computer(model, X_valid, y_valid, w_valid, n_bins=N_BINS, prefix='valid_', suffix='')
result_table = [
run_iter(model,
result_row,
i,
i_cv,
args,
test_config,
test_generator,
n_bins=N_BINS)
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_path, 'results.csv'))
logger.info('Plot params')
param_names = config.PARAM_NAMES
for name in param_names:
plot_params(name,
result_table,
title=model.full_name,
directory=model.path)
logger.info('DONE')
return result_table
def run_estimation(args, i_cv):
logger = logging.getLogger()
print_line()
logger.info('Running iter n°{}'.format(i_cv))
print_line()
result_row = {'i_cv': i_cv}
# LOAD/GENERATE DATA
logger.info('Set up data generator')
config = Config()
seed = SEED + i_cv * 5
train_generator = Generator(seed)
valid_generator = Generator(seed + 1)
test_generator = Generator(seed + 2)
train_generator = TrainGenerator(param_generator, train_generator)
# SET MODEL
logger.info('Set up regressor')
model = build_model(args, i_cv)
os.makedirs(model.results_path, exist_ok=True)
flush(logger)
# TRAINING / LOADING
train_or_load_neural_net(model, train_generator, retrain=args.retrain)
# CHECK TRAINING
logger.info('Generate validation data')
X_valid, y_valid, w_valid = valid_generator.generate(
*config.CALIBRATED, n_samples=config.N_VALIDATION_SAMPLES)
result_row.update(evaluate_neural_net(model, prefix='valid'))
evaluate_regressor(model, prefix='valid')
# MEASUREMENT
calib_rescale = load_calib_rescale(DATA_NAME, BENCHMARK_NAME)
result_row['nfcn'] = NCALL
iter_results = [
run_estimation_iter(model, result_row, i, test_config, valid_generator,
test_generator, calib_rescale)
for i, test_config in enumerate(config.iter_test_config())
]
result_table = pd.DataFrame(iter_results)
result_table.to_csv(os.path.join(model.results_path, 'estimations.csv'))
logger.info('Plot params')
param_names = config.PARAM_NAMES
for name in param_names:
plot_params(name,
result_table,
title=model.full_name,
directory=model.results_path)
logger.info('DONE')
return result_table
def run(args, i_cv):
logger = logging.getLogger()
print_line()
logger.info('Running iter n°{}'.format(i_cv))
print_line()
result_row = {'i_cv': i_cv}
# LOAD/GENERATE DATA
logger.info('Set up data generator')
config = Config()
seed = SEED + i_cv * 5
train_generator = Generator(seed)
train_generator = TrainGenerator(param_generator, train_generator)
valid_generator = Generator(seed+1)
test_generator = Generator(seed+2)
# 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_pivot(model, train_generator, config.N_TRAINING_SAMPLES*N_AUGMENT, retrain=args.retrain)
# CHECK TRAINING
logger.info('Generate validation data')
X_valid, y_valid, w_valid = valid_generator.generate(*config.CALIBRATED, n_samples=config.N_VALIDATION_SAMPLES)
result_row.update(evaluate_neural_net(model, prefix='valid'))
result_row.update(evaluate_classifier(model, X_valid, y_valid, w_valid, prefix='valid'))
# MEASUREMENT
N_BINS = 10
evaluate_summary_computer(model, X_valid, y_valid, w_valid, n_bins=N_BINS, prefix='valid_', suffix='')
iter_results = [run_iter(model, result_row, i, test_config, valid_generator, test_generator, n_bins=N_BINS)
for i, test_config in enumerate(config.iter_test_config())]
result_table = [e0 for e0, e1 in iter_results]
result_table = pd.DataFrame(result_table)
result_table.to_csv(os.path.join(model.results_path, 'estimations.csv'))
logger.info('Plot params')
param_names = config.PARAM_NAMES
for name in param_names:
plot_params(name, result_table, title=model.full_name, directory=model.results_path)
conditional_estimate = pd.concat([e1 for e0, e1 in iter_results])
conditional_estimate['i_cv'] = i_cv
fname = os.path.join(model.results_path, "conditional_estimations.csv")
conditional_estimate.to_csv(fname)
logger.info('DONE')
return result_table, conditional_estimate
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 = TrainGenerator(param_generator, Generator(seed))
valid_generator = Generator(seed + 1)
test_generator = Generator(seed + 2)
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 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 = Generator(seed)
train_generator = TrainGenerator(param_generator, train_generator)
valid_generator = Generator(seed+1)
test_generator = Generator(seed+2)
# 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_pivot(model, train_generator, config.N_TRAINING_SAMPLES*N_AUGMENT, retrain=args.retrain)
some_fisher = compute_fisher(*compute_bins(model, valid_generator, config, n_bins=3), config.TRUE.mu)
some_fisher_bis = compute_fisher(*compute_bins(model, valid_generator, config, n_bins=3), config.TRUE.mu)
assert some_fisher == some_fisher_bis, f"Fisher info should be deterministic but found : {some_fisher} =/= {some_fisher_bis}"
# MEASUREMENT
result_row = {'i_cv': i_cv}
results = []
for test_config in config.iter_test_config():
logger.info(f"Running test set : {test_config.TRUE}, {test_config.N_TESTING_SAMPLES} samples")
for n_bins in range(1, 30):
result_row = {'i_cv': i_cv}
gamma_array, beta_array = compute_bins(model, valid_generator, test_config, n_bins=n_bins)
fisher = compute_fisher(gamma_array, beta_array, test_config.TRUE.mu)
result_row.update({f'gamma_{i}' : gamma for i, gamma in enumerate(gamma_array, 1)})
result_row.update({f'beta_{i}' : beta for i, beta in enumerate(beta_array, 1)})
result_row.update(test_config.TRUE.to_dict(prefix='true_'))
result_row['n_test_samples'] = test_config.N_TESTING_SAMPLES
result_row['fisher'] = fisher
result_row['n_bins'] = n_bins
results.append(result_row.copy())
results = pd.DataFrame(results)
print(results)
return results
def run_conditional_estimation(args, i_cv):
logger = logging.getLogger()
print_line()
logger.info('Running iter n°{}'.format(i_cv))
print_line()
result_row = {'i_cv': i_cv}
# LOAD/GENERATE DATA
logger.info('Set up data generator')
config = Config()
seed = SEED + i_cv * 5
train_generator = Generator(seed)
valid_generator = Generator(seed + 1)
test_generator = Generator(seed + 2)
train_generator = TrainGenerator(param_generator, train_generator)
# SET MODEL
logger.info('Set up regressor')
model = build_model(args, i_cv)
os.makedirs(model.results_path, exist_ok=True)
flush(logger)
# TRAINING / LOADING
train_or_load_neural_net(model, train_generator, retrain=args.retrain)
# CHECK TRAINING
logger.info('Generate validation data')
X_valid, y_valid, w_valid = valid_generator.generate(
*config.CALIBRATED, n_samples=config.N_VALIDATION_SAMPLES)
result_row.update(evaluate_neural_net(model, prefix='valid'))
evaluate_regressor(model, prefix='valid')
# MEASUREMENT
result_row['nfcn'] = NCALL
iter_results = [
run_conditional_estimation_iter(model, result_row, i, test_config,
valid_generator, test_generator)
for i, test_config in enumerate(config.iter_test_config())
]
conditional_estimate = pd.concat(iter_results)
conditional_estimate['i_cv'] = i_cv
fname = os.path.join(model.results_path, "conditional_estimations.csv")
conditional_estimate.to_csv(fname)
logger.info('DONE')
return result_table
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)
def main():
# BASIC SETUP
logger = set_logger()
args = GB_parse_args(
main_description=
"Training launcher for Gradient boosting on S3D2 benchmark")
logger.info(args)
flush(logger)
# Config
config = Config()
config.TRUE = Parameter(rescale=0.9, mu=0.1)
train_generator = Generator(SEED)
valid_generator = Generator(SEED + 1)
test_generator = Generator(SEED + 2)
X_test, y_test, w_test = test_generator.generate(
*config.TRUE, n_samples=config.N_TESTING_SAMPLES)
# for nuisance in p(nuisance | data)
nuisance_param_sample = [
param_generator().nuisance_parameters for _ in range(25)
]
average_list = []
variance_list = []
result_table = []
for nuisance_params in nuisance_param_sample:
logger.info(f"nuisance_params = {nuisance_params}")
estimator_values = []
for i_cv in range(N_ITER):
clf = build_model(args, i_cv)
parameters = Parameter(*nuisance_params,
config.CALIBRATED.interest_parameters)
print(parameters)
n_samples = config.N_TRAINING_SAMPLES
X_train, y_train, w_train = train_generator.generate(
*parameters, n_samples=n_samples)
logger.info(f"Training {clf.full_name}")
clf.fit(X_train, y_train, w_train)
compute_summaries = ClassifierSummaryComputer(clf, n_bins=10)
nll_computer = NLLComputer(compute_summaries,
valid_generator,
X_test,
w_test,
config=config)
compute_nll = lambda mu: nll_computer(*nuisance_params, mu)
minimizer = get_minimizer(compute_nll)
results = evaluate_minuit(minimizer,
[config.TRUE.interest_parameters])
estimator_values.append(results['mu'])
results['i_cv'] = i_cv
results.update(params_to_dict(parameters, suffix='true'))
result_table.append(results.copy())
average_list.append(np.mean(estimator_values))
variance_list.append(np.var(estimator_values))
model = build_model(args, 0)
model.set_info(DATA_NAME, BENCHMARK_NAME, 0)
save_directory = model.results_path
os.makedirs(save_directory, exist_ok=True)
result_table = pd.DataFrame(result_table)
result_table.to_csv(os.path.join(save_directory, 'results.csv'))
logger.info(f"average_list {average_list}")
logger.info(f"variance_list {variance_list}")
v_stat = np.mean(variance_list)
v_syst = np.var(average_list)
v_total = v_stat + v_syst
logger.info(f"V_stat = {v_stat}")
logger.info(f"V_syst = {v_syst}")
logger.info(f"V_total = {v_total}")
eval_dict = {"V_stat": v_stat, "V_syst": v_syst, "V_total": v_total}
eval_path = os.path.join(save_directory, 'info.json')
with open(eval_path, 'w') as f:
json.dump(eval_dict, f)
