diff --git a/hics/contrast_meassure.py b/hics/contrast_meassure.py
index 4c5d637..dbfd51f 100644
--- a/hics/contrast_meassure.py
+++ b/hics/contrast_meassure.py
@@ -31,14 +31,14 @@ def __init__(self, data, alpha, iterations, continuous_divergence = KS, categori
 			else:
 				self.types[column] = 'continuous'
 
-	def values(self, feature):
+	def get_values(self, feature):
 		if not feature in self.values:
 			return False
 
 		else:
 			return self.values[feature]
 
-	def type(self, feature):
+	def get_type(self, feature):
 		if not feature in self.types:
 			return False
 
@@ -102,7 +102,7 @@ def output_slices(self, score, conditions, slices):
 			ft = condition['feature']
 			
 			if self.types[ft] == 'categorical':
-				to_append = [1*(value in condition['values']) for value in self.values(ft)]
+				to_append = [1*(value in condition['values']) for value in self.get_values(ft)]
 				if ft in slices['features']:
 					slices['features'][ft].append(to_append)
 				else:
diff --git a/hics/incremental_correlation.py b/hics/incremental_correlation.py
index e34add9..bf33f01 100644
--- a/hics/incremental_correlation.py
+++ b/hics/incremental_correlation.py
@@ -15,7 +15,7 @@ def __init__(self, data, target, result_storage, iterations = 10, alpha = 0.1, d
 		self.features = [str(ft) for ft in data.columns.values if str(ft) != target]
 
 		if drop_discrete:
-			self.features = [ft for ft in self.features if self.subspace_contrast.types[ft] != 'discrete']
+			self.features = [ft for ft in self.features if self.subspace_contrast.get_type(ft) != 'discrete']
 
 		self.result_storage = result_storage
 
@@ -63,8 +63,8 @@ def _relevancy_dict_to_df(self, new_scores):
 	def _add_slices_to_dict(self, subspace, slices, slices_store):
 		subspace_tuple = tuple(sorted(subspace))
 		if not subspace_tuple in slices_store:
-			categorical = [{'name' : ft, 'values' : self.subspace_contrast.values(ft)} for ft in subspace if self.subspace_contrast.type(ft) == 'categorical']
-			continuous = [ft for ft in subspace if self.subspace_contrast.type(ft) == 'continuous']
+			categorical = [{'name' : ft, 'values' : self.subspace_contrast.get_values(ft)} for ft in subspace if self.subspace_contrast.get_type(ft) == 'categorical']
+			continuous = [ft for ft in subspace if self.subspace_contrast.get_type(ft) == 'continuous']
 			slices_store[subspace_tuple] = ScoredSlices(categorical, continuous)
 
 		slices_store[subspace_tuple].add_slices(slices)