replacing the deprecated append method with the concat method in pandas

pyFTS/models/multivariate/cmvfts.py

@@ -146,7 +146,7 @@ class ClusteredMVFTS(mvfts.MVFTS):
 
             new_data_point[self.target_variable.data_label] = tmp.expected_value()
 
-            sample = sample.append(new_data_point, ignore_index=True)
+            sample = pd.concat([sample, pd.DataFrame([new_data_point])], ignore_index=True)
 
         return ret[-steps:]
 
@@ -199,7 +199,7 @@ class ClusteredMVFTS(mvfts.MVFTS):
         for k in np.arange(0, steps):
             sample = ret.iloc[k:self.order+k]
             tmp = self.forecast_multivariate(sample, **kwargs)
-            ret = ret.append(tmp, ignore_index=True)
+            ret = pd.concat([ret, pd.DataFrame([tmp])], ignore_index=True)
 
         return ret
 

pyFTS/models/multivariate/mvfts.py

@@ -211,7 +211,7 @@ class MVFTS(fts.FTS):
 
             new_data_point[self.target_variable.data_label] = tmp
 
-            ndata = ndata.append(new_data_point, ignore_index=True)
+            ndata = pd.concat([ndata, pd.DataFrame([new_data_point])], ignore_index=True)
 
         return ret[-steps:]
 
@@ -307,8 +307,8 @@ class MVFTS(fts.FTS):
             new_data_point_lo[self.target_variable.data_label] = min(tmp_lo)
             new_data_point_up[self.target_variable.data_label] = max(tmp_up)
 
-            lo = lo.append(new_data_point_lo, ignore_index=True)
-            up = up.append(new_data_point_up, ignore_index=True)
+            lo = pd.concat([lo, pd.DataFrame([new_data_point_lo])], ignore_index=True)
+            up = pd.concat([up, pd.DataFrame([new_data_point_up])], ignore_index=True)
 
         return ret[-steps:]
 

pyFTS/partitioners/Huarng.py

@@ -19,13 +19,13 @@ class HuarngPartitioner(partitioner.Partitioner):
     def build(self, data):
         diff = Transformations.Differential(1)
         data2 = diff.apply(data)
-        davg = np.abs( np.mean(data2) / 2 )
+        divs = np.abs( np.mean(data2) / 2 )
 
-        if davg <= 1.0:
+        if divs <= 1.0:
             base = 0.1
-        elif 1 < davg <= 10:
+        elif 1 < divs <= 10:
             base = 1.0
-        elif 10 < davg <= 100:
+        elif 10 < divs <= 100:
             base = 10
         else:
             base = 100

pyFTS/partitioners/partitioner.py

@@ -47,7 +47,7 @@ class Partitioner(object):
 
             data = kwargs.get('data',[None])
             
-            if len(data.shape) > 1:
+            if isinstance(data, np.ndarray) and len(data.shape) > 1:
                 warnings.warn(f"An ndarray of dimension greater than 1 is used. shape.len(): {len(data.shape)}")
 
             if self.indexer is not None: