feat: add one hot code data

dataloader.py

@@ -78,8 +78,6 @@ def one_hot_encode(ids, num_classes):
 class HousePriceTrainDataset(Dataset):
     def __init__(self, dataframe, target_column, normalize_columns=None):
         # Load area data
-        area_file_path = 'data/area.csv'
-        self.area_df = pd.read_csv(area_file_path)
         self.dataframe = dataframe.copy()  # Creating a copy to avoid modifying the original dataframe
 
         feature_list=[]

inference.py

@@ -63,11 +63,13 @@ def inference():
     with torch.no_grad():
         for batch in data_loader:
             if gpu:
-                features = batch['features'].cuda()
+                data = batch['features']
+                data[0] = data[0].cuda()
+                data[1] = data[1].cuda()
             else:
-                features = batch['features']
+                data = batch['features']
 
-            predictions = model(features).cpu().numpy().flatten()
+            predictions = model(data).cpu().numpy().flatten()
 
     # Create a DataFrame to hold the IDs and predicted prices
     ids = [f"PU-{i}" for i in range(1, len(predictions) + 1)]  # Adjust ID format as needed

train.py

@@ -70,7 +70,9 @@ def main():
             # print(batch['features'].shape)
             # print(batch['target'].shape)
             if gpu:
-                data = batch['features'].cuda()
+                data = batch['features']
+                data[0] = data[0].cuda()
+                data[1] = data[1].cuda()
                 targets = batch['target'].cuda()
             else:
                 data = batch['features']