sample, var, n)
else:
logger.info(
" No datasets available for evaluation of model trained with datasets: %s , generator variation: %s with %s events.",
sample, var, n)
logger.info("ABORTING")
sys.exit()
loading = Loader()
carl = RatioEstimator()
carl.load('models/' + sample + '/' + var + '_carl_' + str(n))
evaluate = ['train', 'val']
for i in evaluate:
r_hat, _ = carl.evaluate(x='data/' + sample + '/' + var + '/X0_' + i +
'_' + str(n) + '.npy')
w = 1. / r_hat
loading.load_result(
x0='data/' + sample + '/' + var + '/X0_' + i + '_' + str(n) + '.npy',
x1='data/' + sample + '/' + var + '/X1_' + i + '_' + str(n) + '.npy',
weights=w,
label=i,
do=sample,
var=var,
plot=True,
n=n,
path=p,
)
carl.evaluate_performance(
x='data/' + sample + '/' + var + '/X_val_' + str(n) + '.npy',
y='data/' + sample + '/' + var + '/y_val_' + str(n) + '.npy')
r_hat, s_hat = carl.evaluate(x='data/' + global_name + '/X0_' + i + '_' +
str(n) + '.npy')
print("s_hat = {}".format(s_hat))
print("r_hat = {}".format(r_hat))
w = 1. / r_hat # I thought r_hat = p_{1}(x) / p_{0}(x) ???
print("w = {}".format(w))
print("<evaluate.py::__init__>:: Loading Result for {}".format(i))
loading.load_result(
x0='data/' + global_name + '/X0_' + i + '_' + str(n) + '.npy',
x1='data/' + global_name + '/X1_' + i + '_' + str(n) + '.npy',
w0='data/' + global_name + '/w0_' + i + '_' + str(n) + '.npy',
w1='data/' + global_name + '/w1_' + i + '_' + str(n) + '.npy',
metaData='data/' + global_name + '/metaData_' + str(n) + '.pkl',
weights=w,
features=features,
#weightFeature=weightFeature,
label=i,
plot=True,
nentries=n,
#TreeName=treename,
#pathA=p+nominal+".root",
#pathB=p+variation+".root",
global_name=global_name,
plot_ROC=opts.plot_ROC,
plot_obs_ROC=opts.plot_obs_ROC,
)
# Evaluate performance
carl.evaluate_performance(
x='data/' + global_name + '/X_val_' + str(n) + '.npy',
y='data/' + global_name + '/y_val_' + str(n) + '.npy')