plots

seq2seq_time/data/dataset.py

@@ -31,6 +31,7 @@ class Seq2SeqDataSet(torch.utils.data.Dataset):
         assert df.index.freq is not None, 'should have freq'
         assert_no_objects(df)
 
+        self.freq = self.df.index.freq
         self.df = df.dropna(subset=columns_target).ffill()
 
         self.window_past = window_past
@@ -93,7 +94,8 @@ class Seq2SeqDataSet(torch.utils.data.Dataset):
         y_future = pd.DataFrame(y_future, columns=self.columns_target, index=t_future)
         return x_past, y_past, x_future, y_future
 
-
+    def show_batches(self, i=0):
+        raise Exception('not implemented')
         
     def __len__(self):
         return len(self._x) - (self.window_past + self.window_future)

seq2seq_time/predict.py

@@ -49,6 +49,7 @@ def predict(model, ds_test, batch_size, device='cpu', scaler=None):
                 "y_true": (["t_source", "t_ahead",], y_future),
             },
             coords={"t_source": t_source, "t_ahead": t_ahead, "t_behind": t_behind},
+            attrs={'freq': ds_test.freq, "model": str(model), "targets": ds_test.columns_target}
         )
         xrs.append(xr_out)