diff --git a/TweetSentQuant/experiments.py b/TweetSentQuant/experiments.py
index 84269f5..5867255 100644
--- a/TweetSentQuant/experiments.py
+++ b/TweetSentQuant/experiments.py
@@ -63,7 +63,8 @@ def quantification_ensembles():
         'n_jobs': settings.ENSEMBLE_N_JOBS,
         'param_grid': lr_params,
         'param_mod_sel': param_mod_sel,
-        'val_split': 0.4
+        'val_split': 0.4,
+        'min_pos': 10
     }
     
     # hyperparameters will be evaluated within each quantifier of the ensemble, and so the typical model selection
@@ -71,13 +72,13 @@ def quantification_ensembles():
     hyper_none = None
     yield 'epaccmaeptr', EPACC(newLR(), optim='mae', policy='ptr', **common), hyper_none
     yield 'epaccmaemae', 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 '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 'esldmraeptr', EEMQ(newLR(), optim='mrae', policy='ptr', **common), hyper_none
-    yield 'esldmraemrae', EEMQ(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
 
 
 def evaluate_experiment(true_prevalences, estim_prevalences):
@@ -178,8 +179,8 @@ def run(experiment):
                      benchmark_eval.training.prevalence(), test_true_prevalence, test_estim_prevalence,
                      best_params)
 
-    if isinstance(model, QuaNet):
-        model.clean_checkpoint_dir()
+    #if isinstance(model, QuaNet):
+        #model.clean_checkpoint_dir()
 
 
 if __name__ == '__main__':
@@ -195,24 +196,24 @@ if __name__ == '__main__':
     print(f'Result folder: {args.results}')
     np.random.seed(0)
 
-    optim_losses = ['mae'] # ['mae', 'mrae']
+    optim_losses = ['mae', 'mrae']
     datasets = qp.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN
 
-    #models = quantification_models()
-    #Parallel(n_jobs=settings.N_JOBS)(
-    #    delayed(run)(experiment) for experiment in itertools.product(optim_losses, datasets, models)
-    #)
+    models = quantification_models()
+    Parallel(n_jobs=settings.N_JOBS)(
+        delayed(run)(experiment) for experiment in itertools.product(optim_losses, datasets, models)
+    )
 
-    #models = quantification_cuda_models()
-    #Parallel(n_jobs=settings.CUDA_N_JOBS)(
-    #    delayed(run)(experiment) for experiment in itertools.product(optim_losses, datasets, models)
-    #)
+    models = quantification_cuda_models()
+    Parallel(n_jobs=settings.CUDA_N_JOBS)(
+        delayed(run)(experiment) for experiment in itertools.product(optim_losses, datasets, models)
+    )
 
     models = quantification_ensembles()
     Parallel(n_jobs=1)(
         delayed(run)(experiment) for experiment in itertools.product(optim_losses, datasets, models)
     )
 
-    shutil.rmtree(args.checkpointdir, ignore_errors=True)
+    #shutil.rmtree(args.checkpointdir, ignore_errors=True)
 
 
diff --git a/TweetSentQuant/settings.py b/TweetSentQuant/settings.py
index 418385d..8064fa8 100644
--- a/TweetSentQuant/settings.py
+++ b/TweetSentQuant/settings.py
@@ -1,7 +1,7 @@
 import multiprocessing
 
 N_JOBS = -2  #multiprocessing.cpu_count()
-CUDA_N_JOBS = 1
+CUDA_N_JOBS = 2
 ENSEMBLE_N_JOBS = -2
 
 SAMPLE_SIZE = 100
diff --git a/quapy/method/meta.py b/quapy/method/meta.py
index 6251242..94ec99d 100644
--- a/quapy/method/meta.py
+++ b/quapy/method/meta.py
@@ -72,7 +72,8 @@ class Ensemble(BaseQuantifier):
 
         # randomly chooses the prevalences for each member of the ensemble (preventing classes with less than
         # min_pos positive examples)
-        prevs = [_draw_simplex(ndim=data.n_classes, min_val=self.min_pos / len(data)) for _ in range(self.size)]
+        sample_size = len(data) if self.max_sample_size is None else min(self.max_sample_size, len(data))
+        prevs = [_draw_simplex(ndim=data.n_classes, min_val=self.min_pos / sample_size) for _ in range(self.size)]
 
         posteriors = None
         if self.policy == 'ds':
@@ -80,7 +81,7 @@ class Ensemble(BaseQuantifier):
             posteriors, self.post_proba_fn = self.ds_policy_get_posteriors(data)
 
         is_static_policy = (self.policy in qp.error.QUANTIFICATION_ERROR_NAMES)
-        sample_size = len(data) if self.max_sample_size is None else min(self.max_sample_size, len(data))
+
         self.ensemble = Parallel(n_jobs=self.n_jobs)(
             delayed(_delayed_new_instance)(
                 self.base_quantifier, data, val_split, prev, posteriors, keep_samples=is_static_policy,