diff --git a/ClassifierAccuracy/experiments.py b/ClassifierAccuracy/experiments.py
index 6af1f16..3234e99 100644
--- a/ClassifierAccuracy/experiments.py
+++ b/ClassifierAccuracy/experiments.py
@@ -12,7 +12,7 @@ if PROBLEM == 'binary':
     gen_datasets = gen_bin_datasets
 elif PROBLEM == 'multiclass':
     qp.environ['SAMPLE_SIZE'] = 250
-    NUM_TEST = 100
+    NUM_TEST = 1000
     gen_datasets = gen_multi_datasets
 
 
@@ -34,13 +34,14 @@ for (cls_name, h), (dataset_name, (L, V, U)) in itertools.product(gen_classifier
     # instances of ClassifierAccuracyPrediction are bound to the evaluation measure, so they
     # must be nested in the acc-for
     for acc_name, acc_fn in gen_acc_measure():
+        print(f'\tfor measure {acc_name}')
         for (method_name, method) in gen_CAP(h, acc_fn, with_oracle=ORACLE):
             result_path = getpath(basedir, cls_name, acc_name, dataset_name, method_name)
             if os.path.exists(result_path):
-                print(f'\t{method_name}-{acc_name} exists, skipping')
+                print(f'\t\t{method_name}-{acc_name} exists, skipping')
                 continue
 
-            print(f'\t{method_name} computing...')
+            print(f'\t\t{method_name} computing...')
             method, t_train = fit_method(method, V)
             estim_accs, t_test_ave = predictionsCAP(method, test_prot, ORACLE)
             save_json_result(result_path, true_accs[acc_name], estim_accs, t_train, t_test_ave)
@@ -49,10 +50,10 @@ for (cls_name, h), (dataset_name, (L, V, U)) in itertools.product(gen_classifier
     # be nested to the predictions to speed up things
     for (method_name, method) in gen_CAP_cont_table(h):
         if not any_missing(basedir, cls_name, dataset_name, method_name):
-            print(f'\tmethod {method_name} has all results already computed. Skipping.')
+            print(f'\t\tmethod {method_name} has all results already computed. Skipping.')
             continue
 
-        print(f'\tmethod {method_name} computing...')
+        print(f'\t\tmethod {method_name} computing...')
 
         method, t_train = fit_method(method, V)
         estim_accs_dict, t_test_ave = predictionsCAPcont_table(method, test_prot, gen_acc_measure, ORACLE)
@@ -65,7 +66,6 @@ for (cls_name, h), (dataset_name, (L, V, U)) in itertools.product(gen_classifier
 # generate diagonal plots
 print('generating plots')
 for (cls_name, _), (acc_name, _) in itertools.product(gen_classifiers(), gen_acc_measure()):
-    methods = get_method_names()
     plot_diagonal(basedir, cls_name, acc_name)
     for dataset_name, _ in gen_datasets(only_names=True):
         plot_diagonal(basedir, cls_name, acc_name, dataset_name=dataset_name)
diff --git a/ClassifierAccuracy/models_multiclass.py b/ClassifierAccuracy/models_multiclass.py
index 15b80cd..a3bc98d 100644
--- a/ClassifierAccuracy/models_multiclass.py
+++ b/ClassifierAccuracy/models_multiclass.py
@@ -329,7 +329,7 @@ class SebastianiCAP(ClassifierAccuracyPrediction):
 
 class PabloCAP(ClassifierAccuracyPrediction):
 
-    def __init__(self, h, acc_fn, q_class, n_val_samples=50, aggr='mean'):
+    def __init__(self, h, acc_fn, q_class, n_val_samples=100, aggr='mean'):
         self.h = h
         self.acc = acc_fn
         self.q = q_class(h)
@@ -434,7 +434,7 @@ class QuAcc1xN2(CAPContingencyTableQ, QuAcc):
                  add_maxinfsoft=False):
         self.h = h
         self.acc = acc
-        self.q = EmptySaveQuantifier(q_class)
+        self.q = EmptySafeQuantifier(q_class)
         self.add_X = add_X
         self.add_posteriors = add_posteriors
         self.add_maxconf = add_maxconf
@@ -490,7 +490,7 @@ class QuAccNxN(CAPContingencyTableQ, QuAcc):
             y_i = true_labels[pred_labels==class_i]
             data_i = LabelledCollection(X_dot_i, y_i, classes=val.classes_)
 
-            q_i = EmptySaveQuantifier(deepcopy(self.q_class))
+            q_i = EmptySafeQuantifier(deepcopy(self.q_class))
             q_i.fit(data_i)
             self.q.append(q_i)
 
@@ -518,7 +518,7 @@ def safehstack(X, P):
     return XP
 
 
-class EmptySaveQuantifier(BaseQuantifier):
+class EmptySafeQuantifier(BaseQuantifier):
     def __init__(self, surrogate_quantifier: BaseQuantifier):
         self.surrogate = surrogate_quantifier
 
@@ -616,11 +616,12 @@ class ATC(ClassifierAccuracyPrediction):
 
 class DoC(ClassifierAccuracyPrediction):
 
-    def __init__(self, h, acc, sample_size, num_samples=500):
+    def __init__(self, h, acc, sample_size, num_samples=500, clip_vals=(0,1)):
         self.h = h
         self.acc = acc
         self.sample_size = sample_size
         self.num_samples = num_samples
+        self.clip_vals = clip_vals
 
     def _get_post_stats(self, X, y):
         P = get_posteriors_from_h(self.h, X)
@@ -660,6 +661,8 @@ class DoC(ClassifierAccuracyPrediction):
         P = get_posteriors_from_h(self.h, X)
         mc = max_conf(P)
         acc_pred = self.predict_regression(mc)[0]
+        if self.clip_vals is not None:
+            acc_pred = np.clip(acc_pred, *self.clip_vals)
         return acc_pred
 
     """
diff --git a/ClassifierAccuracy/util/commons.py b/ClassifierAccuracy/util/commons.py
index 7d1a55e..2896b61 100644
--- a/ClassifierAccuracy/util/commons.py
+++ b/ClassifierAccuracy/util/commons.py
@@ -6,12 +6,14 @@ from glob import glob
 from pathlib import Path
 from time import time
 import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics import accuracy_score, f1_score
 
-from sklearn.datasets import fetch_rcv1
+from sklearn.datasets import fetch_rcv1, fetch_20newsgroups
 from sklearn.model_selection import GridSearchCV
 
 from ClassifierAccuracy.models_multiclass import *
+from ClassifierAccuracy.util.tabular import Table
 from quapy.method.aggregative import EMQ, ACC, KDEyML
 
 from quapy.data import LabelledCollection
@@ -41,6 +43,16 @@ def gen_multi_datasets(only_names=False)-> [str,[LabelledCollection,LabelledColl
         else:
             dataset = fetch_UCIMulticlassLabelledCollection(dataset_name)
             yield dataset_name, split(dataset)
+    train = fetch_20newsgroups(subset='train', remove=('headers', 'footers', 'quotes'))
+    test  = fetch_20newsgroups(subset='test', remove=('headers', 'footers', 'quotes'))
+    tfidf = TfidfVectorizer(min_df=5, sublinear_tf=True)
+    Xtr = tfidf.fit_transform(train.data)
+    Xte = tfidf.transform((test.data))
+    train = LabelledCollection(instances=Xtr, labels=train.target)
+    U  = LabelledCollection(instances=Xte, labels=test.target)
+    T, V = train.split_stratified(train_prop=0.5, random_state=0)
+    yield "20news", (T, V, U)
+
 
 
 def gen_bin_datasets(only_names=False) -> [str,[LabelledCollection,LabelledCollection,LabelledCollection]]:
@@ -71,7 +83,7 @@ def gen_CAP(h, acc_fn, with_oracle=False)->[str, ClassifierAccuracyPrediction]:
     #yield 'SebCAP-KDE', SebastianiCAP(h, acc_fn, KDEyML)
     #yield 'SebCAPweight', SebastianiCAP(h, acc_fn, ACC, alpha=0)
     #yield 'PabCAP', PabloCAP(h, acc_fn, ACC)
-    #yield 'PabCAP-SLD-median', PabloCAP(h, acc_fn, EMQ, aggr='median')
+    yield 'PabCAP-SLD-median', PabloCAP(h, acc_fn, EMQ, aggr='median')
     yield 'ATC-MC', ATC(h, acc_fn, scoring_fn='maxconf')
     #yield 'ATC-NE', ATC(h, acc_fn, scoring_fn='neg_entropy')
     yield 'DoC', DoC(h, acc_fn, sample_size=qp.environ['SAMPLE_SIZE'])
@@ -288,7 +300,7 @@ def get_dataset_stats(path, test_prot, L, V):
 
 
 def gen_tables(basedir, datasets):
-    from tabular import Table
+
 
     mock_h = LogisticRegression(),
     methods = [method for method, _ in gen_CAP(mock_h, None)] + [method for method, _ in gen_CAP_cont_table(mock_h)]
@@ -313,7 +325,7 @@ def gen_tables(basedir, datasets):
     classifier = classifiers[0]
     for metric in [measure for measure, _ in gen_acc_measure()]:
 
-        table = Table(datasets, methods)
+        table = Table(datasets, methods, prec_mean=5, clean_zero=True)
         for method, dataset in itertools.product(methods, datasets):
             path = getpath(basedir, classifier, metric, dataset, method)
             if not os.path.exists(path):
diff --git a/ClassifierAccuracy/util/plotting.py b/ClassifierAccuracy/util/plotting.py
index 18ee82e..7771edc 100644
--- a/ClassifierAccuracy/util/plotting.py
+++ b/ClassifierAccuracy/util/plotting.py
@@ -16,7 +16,7 @@ def plot_diagonal(basedir, cls_name, measure_name, dataset_name='*'):
         xs.append(results[method_name]['true_acc'])
         ys.append(results[method_name]['estim_acc'])
     plotsubdir = 'all' if dataset_name=='*' else dataset_name
-    save_path = join('plots', basedir, plotsubdir, 'diagonal.png')
+    save_path = join('plots', basedir, measure_name, plotsubdir, 'diagonal.png')
     _plot_diagonal(methods, xs, ys, save_path, measure_name)
 
 
@@ -31,7 +31,7 @@ def _plot_diagonal(methods_names, true_xs, estim_ys, save_path, measure_name, ti
     plt.plot([0, 1], [0, 1], color='black', linestyle='--')
 
     for (method_name, xs, ys) in zip(methods_names, true_xs, estim_ys):
-        plt.scatter(xs, ys, label=f'{method_name}', alpha=0.6)
+        plt.scatter(xs, ys, label=f'{method_name}', alpha=0.5, linewidths=0)
 
     plt.legend()
 
diff --git a/examples/custom_quantifier.py b/examples/custom_quantifier.py
index fa014de..9c89714 100644
--- a/examples/custom_quantifier.py
+++ b/examples/custom_quantifier.py
@@ -1,33 +1,79 @@
 import quapy as qp
 from quapy.data import LabelledCollection
-from quapy.method.base import BinaryQuantifier
+from quapy.method.base import BinaryQuantifier, BaseQuantifier
 from quapy.model_selection import GridSearchQ
 from quapy.method.aggregative import AggregativeSoftQuantifier
 from quapy.protocol import APP
 import numpy as np
 from sklearn.linear_model import LogisticRegression
+from time import time
 
 
 # Define a custom quantifier: for this example, we will consider a new quantification algorithm that uses a
 # logistic regressor for generating posterior probabilities, and then applies a custom threshold value to the
 # posteriors. Since the quantifier internally uses a classifier, it is an aggregative quantifier; and since it
-# relies on posterior probabilities, it is a probabilistic-aggregative quantifier. Note also it has an
-# internal hyperparameter (let say, alpha) which is the decision threshold. Let's also assume the quantifier
-# is binary, for simplicity.
+# relies on posterior probabilities, it is a probabilistic-aggregative quantifier (aka AggregativeSoftQuantifier).
+# Note also it has an internal hyperparameter (let say, alpha) which is the decision threshold.
+#
+# Let's also assume the quantifier is binary, for simplicity. Any quantifier (i.e., any subclass of BaseQuantifier)
+# is required to implement the "fit" and "quantify" methods. Aggregative quantifiers are special subtypes of base
+# quantifiers, i.e., are quantifiers that undertake a classification-phase followed by an aggregation-phase. QuaPy
+# already implements most common functionality, and requires the developer to simply implement the "aggregation_fit"
+# and the "aggregation" methods.
+#
+# We are providing two implementations of the same method to illustrate this characteristic of QuaPy. Let us begin
+# with the general case, in which we implement a (base) quantifier
+
+class MyQuantifier(BaseQuantifier):
 
-class MyQuantifier(AggregativeSoftQuantifier, BinaryQuantifier):
     def __init__(self, classifier, alpha=0.5):
         self.alpha = alpha
-        # aggregative quantifiers have an internal self.classifier attribute
         self.classifier = classifier
 
-    def fit(self, data: LabelledCollection, fit_classifier=True):
-        assert fit_classifier, 'this quantifier needs to fit the classifier!'
+    # in general, we would need to implement the method fit(self, data: LabelledCollection, fit_classifier=True,
+    # val_split=None); this would amount to:
+    def fit(self, data: LabelledCollection):
+        assert data.n_classes==2, \
+            'this quantifier is only valid for binary problems [abort]'
         self.classifier.fit(*data.Xy)
         return self
 
-    # in general, we would need to implement the method quantify(self, instances) but, since this method is of
-    # type aggregative, we can simply implement the method aggregate, which has the following interface
+    # in general, we would need to implement the method quantify(self, instances); this would amount to:
+    def quantify(self, instances):
+        assert hasattr(self.classifier, 'predict_proba'), \
+            'the underlying classifier is not probabilistic! [abort]'
+        posterior_probabilities = self.classifier.predict_proba(instances)
+        positive_probabilities = posterior_probabilities[:, 1]
+        crisp_decisions = positive_probabilities > self.alpha
+        pos_prev = crisp_decisions.mean()
+        neg_prev = 1 - pos_prev
+        return np.asarray([neg_prev, pos_prev])
+
+
+# Note that the above implementation contains a lot of boilerplate code. Many parts can be omitted since QuaPy
+# provides implementations for them. Some of these routines (like, for example, training a classifier and generating
+# posterior probabilities) are often carried out in a k-fold cross-validation manner. These, along with many other
+# common routines are already provided by highly-optimized routines in QuaPy. Let's see a much better implementation
+# of the method, now adhering to the AggregativeSoftQuantifier:
+
+class MyAggregativeSoftQuantifier(AggregativeSoftQuantifier, BinaryQuantifier):
+    def __init__(self, classifier, alpha=0.5):
+        # aggregative quantifiers have an internal attribute called self.classifier
+        self.classifier = classifier
+        self.alpha = alpha
+
+    # since this method is of type aggregative, we can simply implement the method aggregation_fit, which
+    # assumes the classifier has already been fitted properly and the predictions for the training set required
+    # to train the aggregation function have been properly generated (i.e., on a validation split, or using a
+    # k-fold cross validation strategy). What remains ahead is to learn an aggregation function. In our case
+    # this amounts to doing... nothing, since our method was pretty basic. BinaryQuantifier also add some
+    # basic functionality for checking binary consistency.
+    def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
+        pass
+
+    # since this method is of type aggregative, we can simply implement the method aggregate (i.e., we should
+    # only describe what to do with the classifier predictions --which in this case are posterior probabilities
+    # because we are inheriting from the "Soft" subtype). This comes down to:
     def aggregate(self, classif_predictions: np.ndarray):
         # the posterior probabilities have already been generated by the quantify method; we only need to
         # specify what to do with them
@@ -38,31 +84,68 @@ class MyQuantifier(AggregativeSoftQuantifier, BinaryQuantifier):
         return np.asarray([neg_prev, pos_prev])
 
 
+# a small example using these two implementations of our method
+
 if __name__ == '__main__':
 
-    qp.environ['SAMPLE_SIZE'] = 100
-
-    # define an instance of our custom quantifier
-    quantifier = MyQuantifier(LogisticRegression(), alpha=0.5)
+    qp.environ['SAMPLE_SIZE'] = 250
 
     # load the IMDb dataset
     train, test = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=5).train_test
+    train, val = train.split_stratified(train_prop=0.75)  # let's create a validation set for optimizing hyperparams
 
-    # model selection
-    # let us assume we want to explore our hyperparameter alpha along with one hyperparameter of the classifier
-    train, val = train.split_stratified(train_prop=0.75)
-    param_grid = {
-        'alpha': np.linspace(0, 1, 11),         # quantifier-dependent hyperparameter
-        'classifier__C': np.logspace(-2, 2, 5)  # classifier-dependent hyperparameter
-    }
-    quantifier = GridSearchQ(quantifier, param_grid, protocol=APP(val), n_jobs=-1, verbose=True).fit(train)
+    def test_implementation(quantifier):
+        class_name = quantifier.__class__.__name__
+        print(f'\ntesting implementation {class_name}...')
+        # model selection
+        # let us assume we want to explore our hyperparameter alpha along with one hyperparameter of the classifier
+        tinit = time()
+        param_grid = {
+            'alpha': np.linspace(0, 1, 11),         # quantifier-dependent hyperparameter
+            'classifier__C': np.logspace(-2, 2, 5)  # classifier-dependent hyperparameter
+        }
+        gridsearch = GridSearchQ(quantifier, param_grid, protocol=APP(val), n_jobs=-1, verbose=False).fit(train)
+        t_modsel = time() - tinit
+        print(f'\tmodel selection took {t_modsel:.2f}s', flush=True)
 
-    # evaluation
-    mae = qp.evaluation.evaluate(quantifier, protocol=APP(test), error_metric='mae')
+        # evaluation
+        optimized_model = gridsearch.best_model_
+        mae = qp.evaluation.evaluate(
+            optimized_model,
+            protocol=APP(test, repeats=5000, sanity_check=None),  # disable the check, we want to generate many tests!
+            error_metric='mae',
+            verbose=True)
 
-    print(f'MAE = {mae:.4f}')
+        t_eval = time() - t_modsel - tinit
+        print(f'\tevaluation took {t_eval:.2f}s [MAE = {mae:.4f}]')
 
-    # final remarks: this method is only for demonstration purposes and makes little sense in general. The method relies
+    # define an instance of our custom quantifier and test it!
+    quantifier = MyQuantifier(LogisticRegression(), alpha=0.5)
+    test_implementation(quantifier)
+
+    # define an instance of our custom quantifier, with the second implementation, and test it!
+    quantifier = MyAggregativeSoftQuantifier(LogisticRegression(), alpha=0.5)
+    test_implementation(quantifier)
+
+    # the output should look like this:
+    """
+    testing implementation MyQuantifier...
+        model selection took 12.86s
+    predicting: 100%|██████████| 105000/105000 [00:22<00:00, 4626.30it/s]
+        evaluation took 22.75s [MAE = 0.0630]
+    
+    testing implementation MyAggregativeSoftQuantifier...
+        model selection took 3.10s
+    speeding up the prediction for the aggregative quantifier, total classifications 25000 instead of 26250000
+    predicting: 100%|██████████| 105000/105000 [00:04<00:00, 22779.62it/s]
+        evaluation took 4.66s [MAE = 0.0630]
+    """
+    # Note that the first implementation is much slower, both in terms of grid-search optimization and in terms of
+    # evaluation. The reason why is that QuaPy is highly optimized for aggregative quantifiers (by far, the most
+    # popular type of quantification methods), thus significantly speeding up model selection and test routines.
+    # Furthermore, it is simpler to extend an aggregation type since QuaPy implements boilerplate functions for you.
+
+    # Final remarks: this method is only for demonstration purposes and makes little sense in general. The method relies
     # on an hyperparameter alpha for binarizing the posterior probabilities. A much better way for fulfilling this
     # goal would be to calibrate the classifier (LogisticRegression is already reasonably well calibrated) and then
     # simply cut at 0.5.