diff --git a/TweetSentQuant/experiments_NPP.py b/TweetSentQuant/experiments_NPP.py
index 37d93f9..bd9b808 100644
--- a/TweetSentQuant/experiments_NPP.py
+++ b/TweetSentQuant/experiments_NPP.py
@@ -58,6 +58,7 @@ def quantification_ensembles():
     param_mod_sel = {
         'sample_size': settings.SAMPLE_SIZE,
         'n_repetitions': 1000,
+        'protocol': 'npp',
         'verbose': False
     }
     common = {
@@ -72,13 +73,13 @@ def quantification_ensembles():
     # hyperparameters will be evaluated within each quantifier of the ensemble, and so the typical model selection
     # will be skipped (by setting hyperparameters to None)
     hyper_none = None
-    #yield 'epaccmaeptr', EPACC(newLR(), optim='mae', policy='ptr', **common), hyper_none
+    yield 'epaccmaeptr', EPACC(newLR(), optim='mae', policy='ptr', **common), hyper_none
     yield 'epaccmaemae1k', EPACC(newLR(), optim='mae', policy='mae', **common), hyper_none
     # yield 'esldmaeptr', EEMQ(newLR(), optim='mae', policy='ptr', **common), hyper_none
     # yield 'esldmaemae', EEMQ(newLR(), optim='mae', policy='mae', **common), hyper_none
 
-    #yield 'epaccmraeptr', EPACC(newLR(), optim='mrae', policy='ptr', **common), hyper_none
-    #yield 'epaccmraemrae', EPACC(newLR(), optim='mrae', policy='mrae', **common), hyper_none
+    yield 'epaccmraeptr', EPACC(newLR(), optim='mrae', policy='ptr', **common), hyper_none
+    yield 'epaccmraemrae', EPACC(newLR(), optim='mrae', policy='mrae', **common), hyper_none
     #yield 'esldmraeptr', EEMQ(newLR(), optim='mrae', policy='ptr', **common), hyper_none
     #yield 'esldmraemrae', EEMQ(newLR(), optim='mrae', policy='mrae', **common), hyper_none