diff --git a/NewMethods/ClassWeightQuantification.py b/NewMethods/ClassWeightQuantification.py
index 1b75f3e..456f2e8 100644
--- a/NewMethods/ClassWeightQuantification.py
+++ b/NewMethods/ClassWeightQuantification.py
@@ -16,6 +16,17 @@ import os
 from scipy.stats import ttest_rel
 
 
+"""
+The idea of this method is to make a first guess of the test class distribution (maybe with PACC) and then
+train a method without adjustment (maybe PCC) setting the class_weight param in such a way that best compensates
+for the positive and negative contribution wrt the guessed distribution. The method can be iterative, though I 
+have not seen any major inprovements (if at all) in doing more than 1 iteration.
+This file is the proof of concept with artificial data and nice plots. The quantifier is implemented in file
+class_weight_model.py. 
+So far, it looks like for artificial datasets works, for UCI (without model selection for now) works better than PACC.
+For reviews it does not improve over PACC though. 
+"""
+
 x_min, x_max = 0, 11
 y_min, y_max = 0, x_max
 center0 = (2*x_max/5,2*x_max/5)
diff --git a/NewMethods/quantification_stumps.py b/NewMethods/quantification_stumps.py
new file mode 100644
index 0000000..a2256b5
--- /dev/null
+++ b/NewMethods/quantification_stumps.py
@@ -0,0 +1,87 @@
+from sklearn.base import BaseEstimator
+import numpy as np
+import quapy as qp
+import quapy.functional as F
+from data import LabelledCollection
+from method.aggregative import ACC
+from method.base import BaseQuantifier
+from tqdm import tqdm
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+
+data = qp.datasets.fetch_reviews('kindle', tfidf=True, min_df=10)
+
+class DecisionStump(BaseEstimator):
+    def __init__(self, feat_id):
+        self.feat_id = feat_id
+        self.classes_ = np.asarray([0,1], dtype=int)
+
+    def fit(self, X, y):
+        return self
+
+    def predict(self, X):
+        return (X[:,self.feat_id].toarray().flatten()>0).astype(int)
+
+
+class QuantificationStump(BaseQuantifier):
+    def __init__(self, feat_id):
+        self.feat_id = feat_id
+
+    def fit(self, data: LabelledCollection):
+        self.qs = ACC(DecisionStump(self.feat_id))
+        self.qs.fit(data, fit_learner=False, val_split=data)
+        self.classes = data.classes_
+        return self
+
+    def quantify(self, instances):
+        return self.qs.quantify(instances)
+
+    def set_params(self, **parameters):
+        raise NotImplemented()
+
+    def get_params(self, deep=True):
+        raise NotImplemented()
+
+    @property
+    def classes_(self):
+        return self.classes
+
+
+train, dev = data.training.split_stratified()
+test = data.test.sampling(1000, 0.3, 0.7)
+
+print(f'test prevalence = {F.strprev(test.prevalence())}')
+
+nF = train.instances.shape[1]
+
+qs_scores = []
+qs = np.asarray([QuantificationStump(i).fit(train) for i in tqdm(range(nF))])
+scores = np.zeros(shape=(nF, 11*5))
+for j, dev_sample in tqdm(enumerate(dev.artificial_sampling_generator(500, n_prevalences=11, repeats=5)), total=11*5):
+    sample_prev = dev_sample.prevalence()
+    for i, qs_i in enumerate(qs):
+        estim_prev = qs_i.quantify(dev.instances)
+        error = qp.error.ae(sample_prev, estim_prev)
+        scores[i,j] = error
+
+k=250
+scores = scores.mean(axis=1)
+order = np.argsort(scores)
+qs = qs[order][:k]
+
+prevs = np.asarray([qs_i.quantify(test.instances)[1] for qs_i in tqdm(qs)])
+
+print(f'test estimation mean {prevs.mean():.3f}, median = {np.median(prevs)}')
+
+# sns.histplot(data=prevs, binwidth=3)
+# An "interface" to matplotlib.axes.Axes.hist() method
+# n, bins, patches = plt.hist(x=prevs, bins='auto', alpha=0.7)
+# plt.grid(axis='y', alpha=0.75)
+# plt.xlabel('Value')
+# plt.ylabel('Frequency')
+# plt.title('My Very Own Histogram')
+# maxfreq = n.max()
+# Set a clean upper y-axis limit.
+# plt.ylim(ymax=np.ceil(maxfreq / 10) * 10 if maxfreq % 10 else maxfreq + 10)
+# plt.show()
\ No newline at end of file