diff --git a/pyFTS/benchmarks/benchmarks.py b/pyFTS/benchmarks/benchmarks.py
index bd566aa..ef0859b 100644
--- a/pyFTS/benchmarks/benchmarks.py
+++ b/pyFTS/benchmarks/benchmarks.py
@@ -280,8 +280,6 @@ def sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs):
                         if progress:
                             progressbar.update(1)
                         try:
-                            print(model.shortname, model.order, partitioner.name,
-                                  partitioner.partitions, str(partitioner.transformation))
                             job = experiment_method(deepcopy(model), deepcopy(partitioner), train, test, **kwargs)
                             synthesis_method(dataset, tag, job, conn)
                         except Exception as ex:
diff --git a/pyFTS/models/nonstationary/cvfts.py b/pyFTS/models/nonstationary/cvfts.py
index d64cc9a..c93afd2 100644
--- a/pyFTS/models/nonstationary/cvfts.py
+++ b/pyFTS/models/nonstationary/cvfts.py
@@ -101,7 +101,7 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
 
         _range = (_max - _min)/2
 
-        translate = np.linspace(_min, _max, self.partitioner.partitions)
+        translate = np.linspace(_min, _max, len(self.partitioner.sets))
 
         var = np.std(self.residuals)
 
@@ -111,7 +111,7 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
 
         location = [_range + w + loc + k for k in np.linspace(-var,var) for w in translate]
 
-        perturb = [[location[k], var] for k in np.arange(0, self.partitioner.partitions)]
+        perturb = [[location[k], var] for k in np.arange(len(self.partitioner.sets))]
 
         return perturb
 
@@ -138,11 +138,14 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
 
         return perturb
 
+    def _fsset_key(self, ix):
+        return self.partitioner.ordered_sets[ix]
+
     def _affected_sets(self, sample, perturb):
 
-        affected_sets = [[ct, self.sets[key].membership(sample, perturb[ct])]
-                         for ct, key in enumerate(self.partitioner.ordered_sets)
-                         if self.sets[key].membership(sample, perturb[ct]) > 0.0]
+        affected_sets = [[ct, self.sets[self._fsset_key(ct)].membership(sample, perturb[ct])]
+                         for ct in np.arange(self.partitioner.partitions)
+                         if self.sets[self._fsset_key(ct)].membership(sample, perturb[ct]) > 0.0]
 
         if len(affected_sets) == 0:
             if sample < self.partitioner.lower_set().get_lower(perturb[0]):
@@ -167,7 +170,7 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
             if not no_update:
                 perturb = self.perturbation_factors(sample)
             else:
-                perturb = [[0, 1] for k in np.arange(0, self.partitioner.partitions)]
+                perturb = [[0, 1] for k in np.arange(len(self.partitioner.sets))]
 
             affected_sets = self._affected_sets(sample, perturb)
 
diff --git a/pyFTS/models/nonstationary/partitioners.py b/pyFTS/models/nonstationary/partitioners.py
index 55f5eb1..edb75d8 100644
--- a/pyFTS/models/nonstationary/partitioners.py
+++ b/pyFTS/models/nonstationary/partitioners.py
@@ -134,7 +134,6 @@ class SimpleNonStationaryPartitioner(partitioner.Partitioner):
         return {}
 
 
-
 def simplenonstationary_gridpartitioner_builder(data, npart, transformation):
     from pyFTS.partitioners import Grid
     from pyFTS.models.nonstationary import perturbation, partitioners
diff --git a/pyFTS/partitioners/partitioner.py b/pyFTS/partitioners/partitioner.py
index 656ab28..08060f3 100644
--- a/pyFTS/partitioners/partitioner.py
+++ b/pyFTS/partitioners/partitioner.py
@@ -53,16 +53,10 @@ class Partitioner(object):
                 ndata[ndata == -np.inf] = 0
                 _min = np.nanmin(ndata)
 
-            if _min < 0:
-                self.min = _min * 1.1
-            else:
-                self.min = _min * 0.9
+            self.min = float(_min * 1.1 if _min < 0 else _min * 0.9)
 
             _max = max(ndata)
-            if _max > 0:
-                self.max = _max * 1.1
-            else:
-                self.max = _max * 0.9
+            self.max = float(_max * 1.1 if _max > 0 else _max * 0.9)
 
             self.sets = self.build(ndata)
 
diff --git a/pyFTS/tests/nonstationary.py b/pyFTS/tests/nonstationary.py
index b4e74cf..18e4731 100644
--- a/pyFTS/tests/nonstationary.py
+++ b/pyFTS/tests/nonstationary.py
@@ -11,17 +11,17 @@ import pandas as pd
 from pyFTS.data import TAIEX, NASDAQ, SP500, artificial
 
 datasets = {
-    "TAIEX": TAIEX.get_data()[:4000],
+    #"TAIEX": TAIEX.get_data()[:4000],
     "SP500": SP500.get_data()[10000:14000],
-    "NASDAQ": NASDAQ.get_data()[:4000],
+    #"NASDAQ": NASDAQ.get_data()[:4000],
     # Incremental Mean and Incremental Variance
-    "IMIV": artificial.generate_gaussian_linear(1,0.2,0.2,0.05,it=100, num=40),
+    #"IMIV": artificial.generate_gaussian_linear(1,0.2,0.2,0.05,it=100, num=40),
     # Incremental Mean and Incremental Variance, lower bound equals to 0
-    "IMIV0": artificial.generate_gaussian_linear(1,0.2,0.,0.05, vmin=0,it=100, num=40),
+    #"IMIV0": artificial.generate_gaussian_linear(1,0.2,0.,0.05, vmin=0,it=100, num=40),
     # Constant Mean and Incremental Variance
-    "CMIV": artificial.generate_gaussian_linear(5,0.1,0,0.02,it=100, num=40),
+    #"CMIV": artificial.generate_gaussian_linear(5,0.1,0,0.02,it=100, num=40),
     # Incremental Mean and Constant Variance
-    "IMCV": artificial.generate_gaussian_linear(1,0.6,0.1,0,it=100, num=40)
+    #"IMCV": artificial.generate_gaussian_linear(1,0.6,0.1,0,it=100, num=40)
 }
 
 train_split = 2000
@@ -34,9 +34,9 @@ tdiff = Transformations.Differential(1)
 boxcox = Transformations.BoxCox(0)
 
 transformations = {
-    'None': None,
+    #'None': None,
     'Differential(1)': tdiff,
-    'BoxCox(0)': boxcox
+    #'BoxCox(0)': boxcox
 }
 
 from pyFTS.partitioners import Grid, Util as pUtil
@@ -78,6 +78,8 @@ for ds in datasets.keys():
 for ds in datasets.keys():
     dataset = datasets[ds]
 
+    print(ds)
+
     for tf in transformations.keys():
         transformation = transformations[tf]