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_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 = GeneratorTorch(seed, cuda=args.cuda)
train_generator = TrainGenerator(train_generator, 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)
# 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
evaluate_summary_computer(model,
X_valid,
y_valid,
w_valid,
n_bins=N_BINS,
prefix='valid_',
suffix='')
iter_results = [
run_conditional_estimation_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())
]
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 conditional_estimate
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)
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
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_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.results_path)
logger.info('DONE')
return result_table
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(r=0.1, lam=2.7, 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 = []
all_results = []
for nuisance_params in nuisance_param_sample:
logger.info(f"nuisance_params = {nuisance_params}")
estimator_values = []
results = {name : value for name, value in zip(config.TRUE.nuisance_parameters_names, nuisance_params)}
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}")
# TODO : is it OK to provide w_train to the classifier or useless ?
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.update(evaluate_minuit(minimizer, [config.TRUE.interest_parameters]))
all_results.append(results.copy())
# TODO : Add results to some csv
estimator_values.append(results['mu'])
average_list.append(np.mean(estimator_values))
variance_list.append(np.var(estimator_values))
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}")
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_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)
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
calib_r = load_calib_r(DATA_NAME, BENCHMARK_NAME)
calib_lam = load_calib_lam(DATA_NAME, BENCHMARK_NAME)
evaluate_summary_computer(model, X_valid, y_valid, w_valid, n_bins=N_BINS, prefix='valid_', suffix='')
iter_results = [run_estimation_iter(model, result_row, i, test_config, valid_generator, test_generator, calib_r, calib_lam, n_bins=N_BINS)
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_iter(i_cv, i_iter, config, seed, directory):
logger = logging.getLogger()
logger.info('-' * 45)
logger.info(f'iter : {i_iter}')
result_row = dict(i_cv=i_cv, i=i_iter)
iter_directory = os.path.join(directory, f'iter_{i_iter}')
os.makedirs(iter_directory, exist_ok=True)
logger.info(f"True Parameters = {config.TRUE}")
suffix = f'-mu={config.TRUE.mu:1.2f}_r={config.TRUE.r}_lambda={config.TRUE.lam}'
generator = Generator(seed) # test_generator
data, label = generator.sample_event(*config.TRUE,
size=config.N_TESTING_SAMPLES)
result_row['n_test_samples'] = config.N_TESTING_SAMPLES
debug_label(label)
compute_nll = lambda r, lam, mu: generator.nll(data, r, lam, mu)
plot_nll_around_min(compute_nll, config.TRUE, iter_directory, suffix)
logger.info('Prepare minuit minimizer')
minimizer = get_minimizer(compute_nll, config.CALIBRATED,
config.CALIBRATED_ERROR)
minimizer.precision = None
result_row.update(
evaluate_minuit(minimizer, config.TRUE, iter_directory, suffix=suffix))
return result_row
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 explore_links():
generator = Generator(SEED)
config = Config()
N_SAMPLES = 30_000
feature_names = list(generator.feature_names) + ['Label', 'classifier', 'bin', 'log_p']
mu_range = np.linspace(min(config.RANGE.mu), max(config.RANGE.mu), num=18)
all_params = {"min": config.MIN, "true":config.TRUE, "max":config.MAX}
# all_params = {"true":config.TRUE}
clf = load_some_clf()
all_average_df = {}
for params_name, orig_params in all_params.items():
print(f"computing link between X and mu using {params_name}...")
average_list = []
target_list = []
for mu in mu_range:
params = Parameter(*orig_params.nuisance_parameters, mu)
data, label, weight = generator.generate(*params, n_samples=N_SAMPLES)
sum_weight = np.sum(weight)
average_array = np.sum(data*weight.reshape(-1, 1), axis=0) / sum_weight
average_label = np.sum(label*weight, axis=0) / sum_weight
proba = clf.predict_proba(data)
decision = proba[:, 1]
log_p = np.log(decision / (1 - decision))
average_log_p = np.sum(log_p*weight, axis=0) / sum_weight
average_clf = np.sum(decision*weight, axis=0) / sum_weight
average_bin = np.sum((decision > 0.9)*weight, axis=0) / sum_weight
average_array = np.hstack([average_array, average_label, average_clf, average_bin, average_log_p])
average_list.append(average_array)
target_list.append(mu)
average_df = pd.DataFrame(np.array(average_list), columns=feature_names)
all_average_df[params_name] = average_df
for name in feature_names:
for params_name, average_df in all_average_df.items():
plt.scatter(average_df[name], target_list, label=params_name)
plt.title(f'Link between weighted mean({name}) and mu')
plt.ylabel('mu')
plt.xlabel(f'weighted mean({name})')
plt.legend()
plt.savefig(os.path.join(DIRECTORY, f'link_{name}.png'))
plt.clf()
def run_iter(i_cv, i_iter, config, seed, directory):
# Init
logger = logging.getLogger()
print_line()
logger.info('running iter n°{}'.format(i_iter))
directory = os.path.join(directory, f'iter_{i_iter}')
os.makedirs(directory, exist_ok=True)
results = dict(i_cv=i_cv, i=i_iter)
# Config
# DATA_N_SAMPLES = config.N_TESTING_SAMPLES
DATA_N_SAMPLES = 9000
R_MIN = config.TRUE.r - 0.3
R_MAX = config.TRUE.r + 0.3
LAM_MIN = config.TRUE.lam - 1
LAM_MAX = config.TRUE.lam + 1
MU_MIN = max(0, config.TRUE.mu - 0.1)
MU_MAX = min(1.0, config.TRUE.mu + 0.1)
R_N_SAMPLES = 21
LAM_N_SAMPLES = 22
MU_N_SAMPLES = 23
# Prior
prior_r = stats.uniform(loc=R_MIN, scale=R_MAX - R_MIN)
prior_lam = stats.uniform(loc=LAM_MIN, scale=LAM_MAX - LAM_MIN)
prior_mu = stats.uniform(loc=MU_MIN, scale=MU_MAX - MU_MIN)
# Param grid
r_grid = np.linspace(R_MIN, R_MAX, R_N_SAMPLES)
lam_grid = np.linspace(LAM_MIN, LAM_MAX, LAM_N_SAMPLES)
mu_grid = np.linspace(MU_MIN, MU_MAX, MU_N_SAMPLES)
# Data Generator
generator = Generator(seed)
data, label = generator.sample_event(*config.TRUE, size=DATA_N_SAMPLES)
debug_label(label)
# Compute likelihood
shape = (R_N_SAMPLES, LAM_N_SAMPLES, MU_N_SAMPLES)
n_elements = np.prod(shape)
logger.info(f"3D grid has {n_elements} elements")
log_likelihood = np.zeros(shape)
log_prior_proba = np.zeros(shape)
for i, j, k in get_iter_prod(R_N_SAMPLES,
LAM_N_SAMPLES,
MU_N_SAMPLES,
progress_bar=True):
log_likelihood[i, j, k] = generator.log_proba_density(
data, r_grid[i], lam_grid[j], mu_grid[k]).sum()
log_prior_proba[i, j, k] = prior_r.logpdf(r_grid[i]) \
+ prior_lam.logpdf(lam_grid[j]) \
+ prior_mu.logpdf(mu_grid[k])
debug_log_proba(log_likelihood, log_prior_proba)
# Normalization
posterior_r_lam_mu = softmax(log_likelihood + log_prior_proba)
debug_posterior(posterior_r_lam_mu)
# Marginal posterior param proba
marginal_r = posterior_r_lam_mu.sum(axis=2).sum(axis=1)
marginal_lam = posterior_r_lam_mu.sum(axis=2).sum(axis=0)
marginal_mu = posterior_r_lam_mu.sum(axis=1).sum(axis=0)
marginal_r_lam = posterior_r_lam_mu.sum(axis=2)
assert marginal_r.shape == r_grid.shape, "sum along the wrong axis for marginal r"
assert marginal_lam.shape == lam_grid.shape, "sum along the wrong axis for marginal lam"
assert marginal_mu.shape == mu_grid.shape, "sum along the wrong axis for marginal mu"
assert marginal_r_lam.shape == (
R_N_SAMPLES,
LAM_N_SAMPLES), "sum along the wrong axis for marginal (r, lam)"
debug_marginal(marginal_r, "r")
debug_marginal(marginal_lam, "lam")
debug_marginal(marginal_mu, "mu")
debug_marginal(marginal_r_lam, "r_lam")
# Conditional posterior
posterior_mu = np.divide(posterior_r_lam_mu,
marginal_r_lam.reshape(R_N_SAMPLES, LAM_N_SAMPLES,
1),
out=np.zeros_like(posterior_r_lam_mu),
where=(posterior_r_lam_mu != 0))
# Minor check
logger.debug("probability densities should sum to one")
debug_proba_sum_one(posterior_mu *
marginal_r_lam.reshape(R_N_SAMPLES, LAM_N_SAMPLES, 1))
debug_proba_sum_one(posterior_r_lam_mu)
debug_proba_sum_one(marginal_r)
debug_proba_sum_one(marginal_mu)
# Compute estimator values
sig_ratio = np.sum(label == 1) / DATA_N_SAMPLES
expect_mu = expectancy(mu_grid, marginal_mu)
var_mu = variance(mu_grid, marginal_mu)
std_mu = np.sqrt(var_mu)
expect_r = expectancy(r_grid, marginal_r)
var_r = variance(r_grid, marginal_r)
std_r = np.sqrt(var_r)
expect_lam = expectancy(lam_grid, marginal_lam)
var_lam = variance(lam_grid, marginal_lam)
std_lam = np.sqrt(var_lam)
stat_err = stat_uncertainty(mu_grid,
posterior_mu,
marginal_r_lam,
reshape=(1, 1, -1))
syst_err = syst_uncertainty(mu_grid,
posterior_mu,
marginal_r_lam,
reshape=(1, 1, -1))
i_max, j_max, k_max = np.unravel_index(np.argmax(log_likelihood),
log_likelihood.shape)
assert np.max(log_likelihood) == log_likelihood[
i_max, j_max, k_max], "max and argmax should point to the same value"
# Save estimator values
results['mu'] = expect_mu
results['mu' + _TRUTH] = config.TRUE.mu
results['mu_std'] = std_mu
results['mu' + _ERROR] = var_mu
results['mu_stat'] = stat_err
results['mu_syst'] = syst_err
results['r'] = expect_r
results['r' + _TRUTH] = config.TRUE.r
results['r_std'] = std_r
results['r' + _ERROR] = var_r
results['lam'] = expect_lam
results['lam' + _TRUTH] = config.TRUE.lam
results['lam_std'] = std_lam
results['lam' + _ERROR] = var_lam
# Log estimator values
logger.info(f"True mu value = {config.TRUE.mu}")
logger.info(f"Sig ratio = {sig_ratio}")
logger.info(f"E[mu|x] = {expect_mu}")
logger.info(f"Var[mu|x] = {var_mu}")
logger.info(f"sqrt(Var[mu|x]) = {std_mu}")
logger.info(f"stat_uncertainty = {stat_err}")
logger.info(f"syst_uncertainty = {syst_err}")
logger.info(f"Var - stat = {var_mu - stat_err}")
logger.info(f"argmax_mu p(mu|x) = {mu_grid[np.argmax(marginal_mu)]}")
logger.info(
f"argmax_r_mu logp(x|r, mu) = {r_grid[i_max]} {mu_grid[j_max]}")
# Minor checks
debug_min_max(marginal_mu, 'p(mu | x)')
debug_min_max(marginal_lam, 'p(lam | x)')
debug_min_max(marginal_r, 'p(r | x)')
debug_min_max(posterior_mu, 'p(mu | x, r)')
debug_min_max(posterior_r_lam_mu, 'p(mu, r | x)')
# Plots
plot_infer(mu_grid,
marginal_mu,
expected_value=expect_mu,
true_value=config.TRUE.mu,
std=std_mu,
name='mu',
directory=directory,
fname='marginal_mu.png')
plot_infer(r_grid,
marginal_r,
expected_value=expect_r,
true_value=config.TRUE.r,
std=std_r,
name='r',
directory=directory,
fname='marginal_r.png')
plot_infer(lam_grid,
marginal_lam,
expected_value=expect_lam,
true_value=config.TRUE.lam,
std=std_lam,
name='lam',
directory=directory,
fname='marginal_lam.png')
# plot_distrib(data, generator, config.TRUE, expect_r, expect_mu,
# title="data distribution", directory=directory, fname='data_distrib.png')
return results
def features():
config = Config()
N_SAMPLES = 10_000
R_MIN = -0.3
R_MAX = 0.3
LAM_MIN = 2
LAM_MAX = 4
MU_MIN = 0.0
MU_MAX = 1.0
generator = Generator(SEED)
X, label = generator.sample_event(config.TRUE.r, config.TRUE.lam, config.TRUE.mu, size=N_SAMPLES)
n_sig = np.sum(label==1)
n_bkg = np.sum(label==0)
print(f"nb of signal = {n_sig}")
print(f"nb of backgrounds = {n_bkg}")
df = pd.DataFrame(X, columns=["x1","x2","x3"])
df['label'] = label
g = sns.PairGrid(df, vars=["x1","x2","x3"], hue='label')
g = g.map_upper(sns.scatterplot)
g = g.map_diag(sns.kdeplot)
g = g.map_lower(sns.kdeplot, n_levels=6)
g = g.add_legend()
# g = g.map_offdiag(sns.kdeplot, n_levels=6)
g.savefig(os.path.join(DIRECTORY, 'pairgrid.png'))
plt.clf()
nll = generator.nll(X, config.TRUE.r, config.TRUE.lam, config.TRUE.mu)
print(f"NLL = {nll}")
R_RANGE = np.linspace(R_MIN, R_MAX, 100)
nll = [generator.nll(X, r, config.TRUE.lam, config.TRUE.mu) for r in R_RANGE]
min_nll = R_RANGE[np.argmin(nll)]
plt.plot(R_RANGE, nll, label="nll(r)")
plt.axvline(config.TRUE.r, c="orange", label="true r")
plt.axvline(min_nll, c="red", label="min nll")
plt.xlabel("r")
plt.ylabel("NLL")
plt.title("NLL according to r param")
plt.legend()
plt.tight_layout()
plt.savefig(os.path.join(DIRECTORY, 'NLL_r.png'))
plt.clf()
LAM_RANGE = np.linspace(LAM_MIN, LAM_MAX, 100)
nll = [generator.nll(X, config.TRUE.r, lam, config.TRUE.mu) for lam in LAM_RANGE]
min_nll = LAM_RANGE[np.argmin(nll)]
plt.plot(LAM_RANGE, nll, label="nll(lam)")
plt.axvline(config.TRUE.lam, c="orange", label="true lam")
plt.axvline(min_nll, c="red", label="min nll")
plt.xlabel("$\lambda$")
plt.ylabel("NLL")
plt.title("NLL according to $\lambda$ param")
plt.legend()
plt.tight_layout()
plt.savefig(os.path.join(DIRECTORY, 'NLL_lambda.png'))
plt.clf()
MU_RANGE = np.linspace(MU_MIN, MU_MAX, 100)
nll = [generator.nll(X, config.TRUE.r, config.TRUE.lam, mu) for mu in MU_RANGE]
min_nll = MU_RANGE[np.argmin(nll)]
plt.plot(MU_RANGE, nll, label="nll(mu)")
plt.axvline(config.TRUE.mu, c="orange", label="true mu")
plt.axvline(min_nll, c="red", label="min nll")
plt.xlabel("$\mu$")
plt.ylabel("NLL")
plt.title("NLL according to $\mu$ param")
plt.legend()
plt.tight_layout()
plt.savefig(os.path.join(DIRECTORY, 'NLL_mu.png'))
plt.clf()