feat: Add output denormailze function

inference.py

@@ -5,35 +5,53 @@
 import torch.nn.functional as F
 from torch.utils.data import Dataset, DataLoader
 from model import HousePriceModel
-from data import  HousePriceTestDataset
-
-# Load Data
-data_path = 'data/public_dataset.csv'  # Update with the path of your data file
-data = pd.read_csv(data_path)
-normalize_columns = {'橫坐標': 'min-max', '縱坐標': 'min-max'}
-selected_features = ['橫坐標', '縱坐標']
-target_column = '單價'
-dataset = HousePriceTestDataset(data, selected_features, normalize_columns)
-data_loader = DataLoader(dataset, batch_size=len(dataset), shuffle=False)
-
-# Load Model
-model_path = 'model.pth'  # Update with the path of your trained model file
-input_dim = len(selected_features)
-model = HousePriceModel(input_dim)
-model.load_state_dict(torch.load(model_path))
-model.eval()
-
-# Inference
-with torch.no_grad():
-    for batch in data_loader:
-        features = batch['features']
-        predictions = model(features).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
-predicted_prices_df = pd.DataFrame({"ID": ids, "predicted_price": predictions})
-
-# Save to CSV
-output_csv_path = 'predicted_prices.csv'  # Update with the desired output path
-predicted_prices_df.to_csv(output_csv_path, index=False)
-print(f"Predictions saved to {output_csv_path}")
+from dataloader import  HousePriceTestDataset, min_max_denormalize, z_score_denormalize
+import platform
+
+
+
+def inference():
+    # Load Data
+    data_path = 'data/public_dataset.csv'  # Update with the path of your data file
+    data = pd.read_csv(data_path)
+    normalize_columns = {'橫坐標': 'min-max', '縱坐標': 'min-max'}
+    selected_features = ['橫坐標', '縱坐標']
+    if platform.system() == "Linux":
+        gpu = True
+    elif platform.system() == "Darwin":
+        gpu = False
+
+    dataset = HousePriceTestDataset(data, selected_features, normalize_columns)
+    data_loader = DataLoader(dataset, batch_size=len(dataset), shuffle=False)
+
+    # Load Model
+    model_path = 'model.pth'  # Update with the path of your trained model file
+    input_dim = len(selected_features)
+    if gpu:
+        model = HousePriceModel(input_dim).cuda()
+    else:
+        model = HousePriceModel(input_dim)
+    model.load_state_dict(torch.load(model_path))
+    model.eval()
+
+    # Inference
+    with torch.no_grad():
+        for batch in data_loader:
+            if gpu:
+                features = batch['features'].cuda()
+            else:
+                features = batch['features']
+
+            predictions = model(features).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
+    predicted_prices_df = pd.DataFrame({"ID": ids, "predicted_price": predictions})
+    predicted_prices_df = min_max_denormalize(predicted_prices_df, ["predicted_price"],scaler_path="label_min_max_normalize_data.pkl")
+    # Save to CSV
+    output_csv_path = 'predicted_prices.csv'  # Update with the desired output path
+    predicted_prices_df.to_csv(output_csv_path, index=False)
+    print(f"Predictions saved to {output_csv_path}")
+
+if __name__ == "__main__":
+    inference()