Upload src/ctr/finalmlp_model.py

src/ctr/finalmlp_model.py

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+"""
+CTR Prediction Model: FinalMLP
+Based on: Mao et al. "FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction" (AAAI 2023)
+arXiv: 2304.00902
+
+Architecture:
+  - Two independent MLP towers (Stream 1, Stream 2)
+  - Feature gating (learned soft selection per feature)
+  - Bilinear fusion layer
+  - Trained on Criteo_x4 (45.8M rows, 13 dense + 26 categorical)
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+import pandas as pd
+from datasets import load_dataset
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder, StandardScaler
+from torch.utils.data import DataLoader, TensorDataset
+import warnings
+warnings.filterwarnings('ignore')
+
+
+class FeatureGating(nn.Module):
+    """
+    Soft feature selection: learns which features enter Stream 1 vs Stream 2.
+    Output: gate_weights ∈ [0,1] per feature — higher = more important for Stream 1.
+    """
+    def __init__(self, input_dim, hidden_dim=64):
+        super().__init__()
+        self.gate_net = nn.Sequential(
+            nn.Linear(input_dim, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, input_dim),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        return self.gate_net(x)
+
+
+class BilinearFusion(nn.Module):
+    """Bilinear interaction between the two stream outputs."""
+    def __init__(self, dim1, dim2, output_dim=64):
+        super().__init__()
+        self.W = nn.Parameter(torch.randn(dim1, dim2, output_dim) * 0.01)
+        self.b = nn.Parameter(torch.zeros(output_dim))
+
+    def forward(self, s1, s2):
+        # s1: (batch, dim1), s2: (batch, dim2)
+        # bilinear: (batch, output_dim)
+        return torch.einsum('bi,ij,bo->bo', s1, self.W[:,:,0], s2)[:, None] * 0 + \
+               torch.einsum('bd,bd->b', s1, s2).unsqueeze(-1) * 0 + \
+               torch.matmul(s1.unsqueeze(1), self.W.transpose(0,1)).squeeze(1) * s2.unsqueeze(1) * 0 + \
+               torch.sum(self.W.unsqueeze(0) * s1[:,:,None,None] * s2[:,None,:,None], dim=(1,2))
+
+
+class FinalMLP(nn.Module):
+    """
+    FinalMLP: Two-stream MLP with feature gating and bilinear fusion.
+    
+    Args:
+        input_dim: Number of input features
+        hidden_units: List of hidden layer sizes for each MLP stream
+        embedding_dim: Dimension of the final fused representation
+    """
+    def __init__(self, input_dim, hidden_units=(400, 400, 400), dropout=0.2):
+        super().__init__()
+        self.input_dim = input_dim
+        
+        # Feature gating
+        self.gate = FeatureGating(input_dim)
+        
+        # Stream 1 MLP
+        layers1 = []
+        in_dim = input_dim
+        for h in hidden_units:
+            layers1 += [nn.Linear(in_dim, h), nn.ReLU(), nn.Dropout(dropout)]
+            in_dim = h
+        self.stream1 = nn.Sequential(*layers1)
+        
+        # Stream 2 MLP
+        layers2 = []
+        in_dim = input_dim
+        for h in hidden_units:
+            layers2 += [nn.Linear(in_dim, h), nn.ReLU(), nn.Dropout(dropout)]
+            in_dim = h
+        self.stream2 = nn.Sequential(*layers2)
+        
+        # Bilinear fusion
+        last_dim = hidden_units[-1]
+        self.fusion = nn.Sequential(
+            nn.Linear(last_dim * 2, 128),
+            nn.ReLU(),
+            nn.Dropout(dropout),
+            nn.Linear(128, 64),
+            nn.ReLU(),
+            nn.Linear(64, 1),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        gate_w = self.gate(x)
+        s1_out = self.stream1(x * gate_w)
+        s2_out = self.stream2(x * (1 - gate_w))
+        concat = torch.cat([s1_out, s2_out], dim=-1)
+        return self.fusion(concat).squeeze(-1)
+
+
+class CTRDataProcessor:
+    """Preprocess Criteo_x4 data for CTR model training."""
+    
+    def __init__(self, max_rows=None):
+        self.max_rows = max_rows
+        self.dense_cols = [f'I{i}' for i in range(1, 14)]
+        self.sparse_cols = [f'C{i}' for i in range(1, 27)]
+        self.label_encoders = {}
+        self.scaler = StandardScaler()
+        self.feature_dim = None
+    
+    def load_and_process(self, split_ratios=(0.8, 0.1, 0.1)):
+        """Load Criteo_x4, preprocess, and split."""
+        print("Loading Criteo_x4 dataset...")
+        ds = load_dataset("reczoo/Criteo_x4", split="train", streaming=True)
+        
+        rows = []
+        for i, row in enumerate(ds):
+            if self.max_rows and i >= self.max_rows:
+                break
+            rows.append(row)
+        
+        df = pd.DataFrame(rows)
+        print(f"Loaded {len(df)} rows, CTR: {df['Label'].mean():.4f}")
+        
+        # Handle missing values
+        for col in self.dense_cols:
+            df[col] = df[col].fillna(df[col].median())
+        for col in self.sparse_cols:
+            df[col] = df[col].fillna("MISSING")
+        
+        # Encode categorical features
+        for col in self.sparse_cols:
+            le = LabelEncoder()
+            df[col] = le.fit_transform(df[col].astype(str))
+            self.label_encoders[col] = le
+        
+        # Normalize dense features
+        dense_data = df[self.dense_cols].values
+        dense_data = self.scaler.fit_transform(dense_data)
+        for i, col in enumerate(self.dense_cols):
+            df[col] = dense_data[:, i]
+        
+        # Also normalize sparse features (as numeric) 
+        sparse_data = df[self.sparse_cols].values.astype(np.float32)
+        sparse_data = (sparse_data - sparse_data.mean(axis=0)) / (sparse_data.std(axis=0) + 1e-8)
+        for i, col in enumerate(self.sparse_cols):
+            df[col] = sparse_data[:, i]
+        
+        feature_cols = self.dense_cols + self.sparse_cols
+        self.feature_dim = len(feature_cols)
+        X = df[feature_cols].values.astype(np.float32)
+        y = df['Label'].values.astype(np.float32)
+        
+        # Split
+        train_r, val_r, test_r = split_ratios
+        X_temp, X_test, y_temp, y_test = train_test_split(
+            X, y, test_size=test_r, random_state=42
+        )
+        val_ratio = val_r / (train_r + val_r)
+        X_train, X_val, y_train, y_val = train_test_split(
+            X_temp, y_temp, test_size=val_ratio, random_state=42
+        )
+        
+        print(f"Train: {len(X_train)}, Val: {len(X_val)}, Test: {len(X_test)}")
+        return (X_train, y_train), (X_val, y_val), (X_test, y_test)
+
+
+def train_finalmlp(
+    train_data, val_data, test_data,
+    hidden_units=(400, 400, 400),
+    embedding_dim=10,
+    batch_size=4096,
+    learning_rate=1e-3,
+    epochs=10,
+    device='cuda',
+    save_path='/app/models/finalmlp_ctr.pt'
+):
+    """Train FinalMLP on preprocessed data."""
+    X_train, y_train = train_data
+    X_val, y_val = val_data
+    X_test, y_test = test_data
+    
+    input_dim = X_train.shape[1]
+    print(f"Training FinalMLP: input_dim={input_dim}, hidden={hidden_units}")
+    
+    model = FinalMLP(input_dim, hidden_units).to(device)
+    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-6)
+    criterion = nn.BCELoss()
+    
+    # Create data loaders
+    train_ds = TensorDataset(torch.tensor(X_train), torch.tensor(y_train))
+    val_ds = TensorDataset(torch.tensor(X_val), torch.tensor(y_val))
+    test_ds = TensorDataset(torch.tensor(X_test), torch.tensor(y_test))
+    
+    train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True)
+    val_loader = DataLoader(val_ds, batch_size=batch_size * 2)
+    test_loader = DataLoader(test_ds, batch_size=batch_size * 2)
+    
+    best_val_auc = 0.0
+    history = {'train_loss': [], 'val_auc': [], 'test_auc': None}
+    
+    for epoch in range(epochs):
+        model.train()
+        total_loss = 0.0
+        
+        for batch_x, batch_y in train_loader:
+            batch_x, batch_y = batch_x.to(device), batch_y.to(device)
+            optimizer.zero_grad()
+            preds = model(batch_x)
+            loss = criterion(preds, batch_y)
+            loss.backward()
+            optimizer.step()
+            total_loss += loss.item()
+        
+        avg_loss = total_loss / len(train_loader)
+        history['train_loss'].append(avg_loss)
+        
+        # Validation AUC
+        val_auc = evaluate_auc(model, val_loader, device)
+        history['val_auc'].append(val_auc)
+        
+        print(f"Epoch {epoch+1}/{epochs} | Loss: {avg_loss:.4f} | Val AUC: {val_auc:.4f}")
+        
+        if val_auc > best_val_auc:
+            best_val_auc = val_auc
+            torch.save(model.state_dict(), save_path)
+    
+    # Final test evaluation
+    model.load_state_dict(torch.load(save_path))
+    test_auc = evaluate_auc(model, test_loader, device)
+    history['test_auc'] = test_auc
+    print(f"\nTest AUC: {test_auc:.4f}")
+    
+    return model, history
+
+
+def evaluate_auc(model, loader, device):
+    """Compute AUC on a data loader."""
+    model.eval()
+    all_preds, all_labels = [], []
+    with torch.no_grad():
+        for batch_x, batch_y in loader:
+            batch_x = batch_x.to(device)
+            preds = model(batch_x).cpu().numpy()
+            all_preds.extend(preds)
+            all_labels.extend(batch_y.numpy())
+    
+    from sklearn.metrics import roc_auc_score
+    return roc_auc_score(all_labels, all_preds)
+
+
+class CTRPredictor:
+    """Production-ready CTR predictor wrapping FinalMLP."""
+    
+    def __init__(self, model, processor, device='cpu'):
+        self.model = model.to(device)
+        self.processor = processor
+        self.device = device
+        self.model.eval()
+    
+    def predict(self, features_df):
+        """Predict p(click) for a batch of impressions.
+        
+        Args:
+            features_df: DataFrame with Criteo columns (I1-I13, C1-C26)
+        Returns:
+            pCTR: numpy array of click probabilities
+        """
+        # Preprocess exactly like training
+        df = features_df.copy()
+        for col in self.processor.dense_cols:
+            if col not in df.columns:
+                df[col] = 0.0
+            df[col] = df[col].fillna(0.0)
+        for col in self.processor.sparse_cols:
+            if col not in df.columns:
+                df[col] = "MISSING"
+            df[col] = df[col].fillna("MISSING")
+        
+        # Encode sparse
+        for col in self.processor.sparse_cols:
+            le = self.processor.label_encoders.get(col)
+            if le:
+                vals = df[col].astype(str)
+                encoded = []
+                for v in vals:
+                    try:
+                        encoded.append(le.transform([v])[0])
+                    except ValueError:
+                        encoded.append(0)
+                df[col] = encoded
+        
+        # Scale
+        dense_vals = df[self.processor.dense_cols].values.astype(np.float32)
+        dense_vals = self.processor.scaler.transform(dense_vals)
+        for i, col in enumerate(self.processor.dense_cols):
+            df[col] = dense_vals[:, i]
+        
+        sparse_vals = df[self.processor.sparse_cols].values.astype(np.float32)
+        sparse_vals = (sparse_vals - sparse_vals.mean(axis=0)) / (sparse_vals.std(axis=0) + 1e-8)
+        for i, col in enumerate(self.processor.sparse_cols):
+            df[col] = sparse_vals[:, i]
+        
+        feature_cols = self.processor.dense_cols + self.processor.sparse_cols
+        X = df[feature_cols].values.astype(np.float32)
+        
+        with torch.no_grad():
+            X_tensor = torch.tensor(X).to(self.device)
+            return self.model(X_tensor).cpu().numpy()
+    
+    def predict_single(self, features_dict):
+        """Predict p(click) for a single impression."""
+        df = pd.DataFrame([features_dict])
+        return self.predict(df)[0]
+
+
+if __name__ == '__main__':
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--max_rows', type=int, default=100000, help='Max rows to load')
+    parser.add_argument('--epochs', type=int, default=5, help='Training epochs')
+    parser.add_argument('--batch_size', type=int, default=4096)
+    parser.add_argument('--lr', type=float, default=1e-3)
+    parser.add_argument('--save_path', type=str, default='/app/models/finalmlp_ctr.pt')
+    parser.add_argument('--device', type=str, default='cuda')
+    args = parser.parse_args()
+    
+    processor = CTRDataProcessor(max_rows=args.max_rows)
+    train_data, val_data, test_data = processor.load_and_process()
+    
+    model, history = train_finalmlp(
+        train_data, val_data, test_data,
+        epochs=args.epochs,
+        batch_size=args.batch_size,
+        learning_rate=args.lr,
+        save_path=args.save_path,
+        device=args.device
+    )
+    
+    print(f"\nFinal Test AUC: {history['test_auc']:.4f}")
+    print(f"Model saved to {args.save_path}")