if (X0_eventnum is not None):
if (X0_eventnum.shape[0] != X0.shape[0]):
print(
'problem when loading, #eventnumbers does not match #events, exit')
exit(1)
else:
# some samples won't have an eventnumber,
# but ATLAS ones should, since we use it to propagate the weight to reco-level events
if (DEBUG):
print("No eventnumber found in dataset.")
# load model and evaluate weights:
carl = RatioEstimator()
if (DEBUG):
print('Loading model from:', model_out_path)
carl.load(model_out_path + '/carl/')
r_hat, s_hat = carl.evaluate(X0)
# prevent -ve weights (should be rounding only):
r_hat = ensure_positive_weight(r_hat)
# prevent 0-division
r_hat = force_nonzero(r_hat, zero_w_bound)
weights = 1. / r_hat
# ensure <weights>=1 after cropping
weights = weights * len(weights) / weights.sum()
maxweight = -1
if (crop_weight_perc > 0):
weights, _maxweight = crop_weight_nperc(weights, crop_weight_perc)
maxweight = max(maxweight, _maxweight)
logger = logging.getLogger(__name__)
if os.path.exists('data/' + global_name + '/X_train_' + str(n) +
'.npy') and os.path.exists('data/' + global_name +
'/metaData_' + str(n) + '.pkl'):
logger.info(
" Doing calibration of model trained with datasets: [{},{}], with {} events.",
nominal, variation, n)
else:
logger.info(
" No datasets available for evaluation of model trained with datasets: [{},{}] with {} events."
.format(nominal, variation, n))
logger.info("ABORTING")
sys.exit()
carl = RatioEstimator()
carl.load('models/' + global_name + '_carl_' + str(n))
#load
evaluate = ['train']
X = 'data/' + global_name + '/X_train_' + str(n) + '.npy'
y = 'data/' + global_name + '/y_train_' + str(n) + '.npy'
w = 'data/' + global_name + '/w_train_' + str(n) + '.npy'
r_hat, s_hat = carl.evaluate(X)
calib = CalibratedClassifier(carl, global_name=global_name)
calib.fit(X=X, y=y, w=w)
p0, p1, r_cal = calib.predict(X=X)
w_cal = 1 / r_cal
loading.load_calibration(
y_true=y,
p1_raw=s_hat,
p1_cal=p1,
label='calibrated',
logger = logging.getLogger(__name__)
if os.path.exists('data/' + sample + '/' + var + '/X_train_' + str(n) +
'.npy'):
logger.info(
" Doing evaluation of model trained with datasets: %s , generator variation: %s with %s events.",
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-torch inference###
#get the weight from carl-torch (weightCT) evaluated on the same model used for carlAthena and the root file from carlAthena
eventVarsCT = ['Njets', 'MET']
eventVarsCA = ['Njets', 'MET', 'weight']
jetVars = ['Jet_Pt', 'Jet_Mass']
lepVars = ['Lepton_Pt']
xCT, _ = load(f=p + '/test.root',
events=eventVarsCT,
jets=jetVars,
leps=lepVars,
n=int(n),
t='Tree',
do=sample)
xCT = xCT[sorted(xCT.columns)]
carl = RatioEstimator()
carl.load('models/' + sample + '/' + var + '_carl_2000001')
r_hat, s_hat = carl.evaluate(x=xCT.to_numpy())
weightCT = 1. / r_hat
###carlAthena inference###
#load sample with weight infered from carlAthena
xCA, _ = load(f=p + '/test.root',
events=eventVarsCA,
jets=jetVars,
leps=lepVars,
n=int(n),
t='Tree')
weightCA = xCA.weight
###compare weights###
# draw histograms comparing weight from carl-torch (weightCT) from weight infered through carlAthena (ca.weight)