diff --git a/TweetSentQuant/experiments.py b/TweetSentQuant/experiments.py
index 5867255..e31d15e 100644
--- a/TweetSentQuant/experiments.py
+++ b/TweetSentQuant/experiments.py
@@ -16,9 +16,7 @@ import argparse
 import torch
 import shutil
 
-
-DEBUG = False
-
+qp.environ['SAMPLE_SIZE'] = settings.SAMPLE_SIZE
 
 def newLR():
     return LogisticRegression(max_iter=1000, solver='lbfgs', n_jobs=-1)
@@ -118,8 +116,6 @@ def save_results(dataset_name, model_name, optim_loss, *results):
 
 def run(experiment):
 
-    qp.environ['SAMPLE_SIZE'] = settings.SAMPLE_SIZE
-
     optim_loss, dataset_name, (model_name, model, hyperparams) = experiment
 
     if is_already_computed(dataset_name, model_name, optim_loss=optim_loss):
diff --git a/TweetSentQuant/gen_tables.py b/TweetSentQuant/gen_tables.py
index 16dadee..b68b733 100644
--- a/TweetSentQuant/gen_tables.py
+++ b/TweetSentQuant/gen_tables.py
@@ -33,6 +33,7 @@ nice = {
     'hdy': 'HDy',
     'dys': 'DyS',
     'epaccmaeptr': 'E(PACC)$_\mathrm{Ptr}$',
+    'epaccmaemae': 'E(PACC)$_\mathrm{AE}$',
     'svmperf':'',
     'sanders': 'Sanders',
     'semeval13': 'SemEval13',
@@ -117,7 +118,7 @@ if __name__ == '__main__':
     datasets = qp.datasets.TWITTER_SENTIMENT_DATASETS_TEST
     evaluation_measures = [qp.error.ae, qp.error.rae]
     gao_seb_methods = ['cc', 'acc', 'pcc', 'pacc', 'sld', 'svmq', 'svmkld', 'svmnkld']
-    new_methods = ['hdy', 'quanet', 'epaccptr']
+    new_methods = ['hdy', 'quanet', 'epaccmaeptr', 'epaccmaemae']
 
     gao_seb_ranks, gao_seb_results = get_ranks_from_Gao_Sebastiani()
 
diff --git a/quapy/method/aggregative.py b/quapy/method/aggregative.py
index 0053767..850a141 100644
--- a/quapy/method/aggregative.py
+++ b/quapy/method/aggregative.py
@@ -367,7 +367,8 @@ class EMQ(AggregativeProbabilisticQuantifier):
             s += 1
 
         if not converged:
-            raise UserWarning('the method has reached the maximum number of iterations; it might have not converged')
+            #raise UserWarning('the method has reached the maximum number of iterations; it might have not converged')
+            print('[warning] the method has reached the maximum number of iterations; it might have not converged')
 
         return qs, ps