Added LSTM model

src/data/dataset.py

@@ -12,10 +12,12 @@ class NrvDataset(Dataset):
         full_day_skip: bool = False,
         sequence_length=96,
         predict_sequence_length=96,
+        lstm: bool = False,
     ):
         self.data_config = data_config
         self.dataframe = dataframe
         self.full_day_skip = full_day_skip
+        self.lstm = lstm
 
         # reset dataframe index
         self.dataframe.reset_index(drop=True, inplace=True)
@@ -107,19 +109,26 @@ class NrvDataset(Dataset):
         history_features = history_df[self.history_features].values
 
         # combine the history features to one tensor (first one feature, then the next one, etc.)
-        history_features = torch.tensor(history_features).reshape(-1)
+        history_features = torch.tensor(history_features)
 
         # get forecast features
         forecast_features = forecast_df[self.forecast_features].values
-        forecast_features = torch.tensor(forecast_features).view(-1)
+        forecast_features = torch.tensor(forecast_features)
 
         # add last time feature of the history
         time_feature = history_df["time_feature"].iloc[-1]
 
         ## all features
-        all_features = torch.cat(
-            [nrv_features, history_features, forecast_features, torch.tensor([time_feature])], dim=0
-        )
+        if not self.lstm:
+            all_features = torch.cat(
+                [nrv_features, history_features.reshape(-1), forecast_features.reshape(-1), torch.tensor([time_feature])], dim=0
+            )
+        else:
+            time_features = torch.tensor(history_df["time_feature"].values).reshape(-1, 1)
+            # combine (96, ) and (96, 2) to  (96, 3)
+            all_features = torch.cat(
+                [nrv_features.unsqueeze(1), time_features], dim=1
+            )
 
         # Target sequence, flattened if necessary
         nrv_target = forecast_df["nrv"].values
@@ -133,7 +142,7 @@ class NrvDataset(Dataset):
         # all features and target to float
         all_features = all_features.float()
 
-        # to tensors
+        # to tens&éazzaéaz"ezéors
         nrv_target = torch.tensor(nrv_target).float()
         return all_features, nrv_target, idx
 

src/data/preprocessing.py

@@ -36,9 +36,10 @@ class DataConfig:
 
 
 class DataProcessor:
-    def __init__(self, data_config: DataConfig, path:str="./"):
+    def __init__(self, data_config: DataConfig, lstm: bool = False, path:str="./"):
         self.batch_size = 2048
         self.path = path
+        self.lstm = lstm
 
         self.train_range = (
             -np.inf,
@@ -204,6 +205,7 @@ class DataProcessor:
             data_config=self.data_config,
             full_day_skip=self.full_day_skip,
             predict_sequence_length=predict_sequence_length,
+            lstm=self.lstm,
         )
         return self.get_dataloader(train_dataset, shuffle=shuffle)
 
@@ -234,6 +236,7 @@ class DataProcessor:
             data_config=self.data_config,
             full_day_skip=self.full_day_skip,
             predict_sequence_length=predict_sequence_length,
+            lstm=self.lstm,
         )
         return self.get_dataloader(test_dataset, shuffle=False)
 
@@ -274,7 +277,7 @@ class DataProcessor:
             predict_sequence_length=self.output_size
         )
         input, _, _ = next(iter(data_loader))
-        return input.shape[-1]
+        return input.shape
 
     def get_time_feature_size(self):
         time_feature_size = 1