fix a bug in the definition of source data shape (#335)

rdagent/scenarios/kaggle/experiment/playground-series-s4e8_template/model/model_nn.py

@@ -1,76 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.utils.data import DataLoader, TensorDataset
-from tqdm import tqdm
-
-# Check if a GPU is available
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-
-# Restored three-layer model structure
-class FeatureInteractionModel(nn.Module):
-    def __init__(self, num_features):
-        super(FeatureInteractionModel, self).__init__()
-        self.fc1 = nn.Linear(num_features, 128)
-        self.bn1 = nn.BatchNorm1d(128)
-        self.fc2 = nn.Linear(128, 64)
-        self.bn2 = nn.BatchNorm1d(64)
-        self.fc3 = nn.Linear(64, 1)
-        self.dropout = nn.Dropout(0.3)
-
-    def forward(self, x):
-        x = F.relu(self.bn1(self.fc1(x)))
-        x = F.relu(self.bn2(self.fc2(x)))
-        x = self.dropout(x)
-        x = torch.sigmoid(self.fc3(x))
-        return x
-
-
-# Training function
-def fit(X_train, y_train, X_valid, y_valid):
-    num_features = X_train.shape[1]
-    model = FeatureInteractionModel(num_features).to(device)
-    criterion = nn.BCELoss()  # Binary classification problem
-    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
-
-    # Convert to TensorDataset and create DataLoader
-    train_dataset = TensorDataset(
-        torch.tensor(X_train.to_numpy(), dtype=torch.float32), torch.tensor(y_train.reshape(-1), dtype=torch.float32)
-    )
-    valid_dataset = TensorDataset(
-        torch.tensor(X_valid.to_numpy(), dtype=torch.float32), torch.tensor(y_valid.reshape(-1), dtype=torch.float32)
-    )
-    train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
-    valid_loader = DataLoader(valid_dataset, batch_size=32, shuffle=False)
-
-    # Train the model
-    model.train()
-    for epoch in range(5):
-        print(f"Epoch {epoch + 1}/5")
-        epoch_loss = 0
-        for X_batch, y_batch in tqdm(train_loader, desc="Training", leave=False):
-            X_batch, y_batch = X_batch.to(device), y_batch.to(device)  # Move data to the device
-            optimizer.zero_grad()
-            outputs = model(X_batch).squeeze(1)  # Reshape outputs to [32]
-            loss = criterion(outputs, y_batch)  # Adjust target shape
-            loss.backward()
-            optimizer.step()
-            epoch_loss += loss.item()
-        print(f"End of epoch {epoch + 1}, Avg Loss: {epoch_loss / len(train_loader):.4f}")
-
-    return model
-
-
-# Prediction function
-def predict(model, X):
-    model.eval()
-    predictions = []
-    with torch.no_grad():
-        X_tensor = torch.tensor(X.values, dtype=torch.float32).to(device)  # Move data to the device
-        for i in tqdm(range(0, len(X_tensor), 32), desc="Predicting", leave=False):
-            batch = X_tensor[i : i + 32]  # Predict in batches
-            pred = model(batch).squeeze().cpu().numpy()  # Move results back to CPU
-            predictions.extend(pred)
-    return np.array(predictions)  # Return boolean predictions

rdagent/scenarios/kaggle/experiment/scenario.py

@@ -24,6 +24,7 @@ class KGScenario(Scenario):
         super().__init__()
         self.competition = competition
         self.competition_descriptions = crawl_descriptions(competition)
+        self.input_shape = None
         self._source_data = self.source_data
 
         self.competition_type = None
@@ -121,6 +122,7 @@ class KGScenario(Scenario):
             buffer = io.StringIO()
             X_valid.info(verbose=True, buf=buffer, show_counts=True)
             data_info = buffer.getvalue()
+            self.input_shape = X_valid.shape
             return data_info
 
         preprocess_experiment = KGFactorExperiment([])
@@ -141,7 +143,7 @@ class KGScenario(Scenario):
         pickle.dump(X_test, open(data_folder / "X_test.pkl", "wb"))
         pickle.dump(others, open(data_folder / "others.pkl", "wb"))
 
-        self.input_shape = X_train.shape
+        self.input_shape = X_valid.shape
 
         buffer = io.StringIO()
         X_valid.info(verbose=True, buf=buffer, show_counts=True)