dataset training code model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+data.json filter=lfs diff=lfs merge=lfs -text

data.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1b85606f9c7ada0074db507f62b451765566a24d50c8710e9f324bfff94923b1
+size 19234021

model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d2ef9ae383a6463e2d69d5f434756f64550ce1f413aafb13f97e3321a69cbb49
+size 13501

train.py

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+import json
+import torch
+import torch.nn as nn
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import r2_score
+
+with open('./data.json', 'r') as f:
+    data = json.load(f)
+
+X = np.array([[v['area'], v['dis'], v['type'], v['middle_point'][0], v['middle_point'][1]] for v in data.values()])
+y = np.array([v['price'] for v in data.values()])
+
+scaler = StandardScaler()
+X = scaler.fit_transform(X)
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
+
+class Net(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.net = nn.Sequential(nn.Linear(5, 64), nn.ReLU(), nn.Linear(64, 32), nn.ReLU(), nn.Linear(32, 1))
+    def forward(self, x):
+        return self.net(x)
+
+model = Net()
+X_train_tensor = torch.FloatTensor(X_train)
+y_train_tensor = torch.FloatTensor(y_train)
+X_test_tensor = torch.FloatTensor(X_test)
+y_test_tensor = torch.FloatTensor(y_test)
+optimizer = torch.optim.Adam(model.parameters(), lr=0.5)
+
+for epoch in range(2000):
+    pred = model(X_train_tensor).squeeze()
+    loss = nn.MSELoss()(pred, y_train_tensor)
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    
+    if epoch % 100 == 0:
+        with torch.no_grad():
+            train_pred = model(X_train_tensor).squeeze().numpy()
+            test_pred = model(X_test_tensor).squeeze().numpy()
+            train_r2 = r2_score(y_train, train_pred)
+            test_r2 = r2_score(y_test, test_pred)
+            print(f"Epoch {epoch}: Train R²: {train_r2:.4f}, Test R²: {test_r2:.4f}")
+
+with torch.no_grad():
+    train_pred = model(X_train_tensor).squeeze().numpy()
+    test_pred = model(X_test_tensor).squeeze().numpy()
+
+train_r2 = r2_score(y_train, train_pred)
+test_r2 = r2_score(y_test, test_pred)
+print(f"Final - Train R²: {train_r2:.4f}, Test R²: {test_r2:.4f}")
+
+torch.save({'model': model.state_dict(), 'scaler': scaler}, 'model.pth')
+print("Model saved!")