core/graph_mamba.py

#!/usr/bin/env python3
"""
🚨 EMERGENCY OVERFITTING FIX 🚨
Tiny GraphMamba designed specifically for 140 training samples
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
from torch_geometric.datasets import Planetoid
from torch_geometric.transforms import NormalizeFeatures
from torch_geometric.utils import to_undirected, add_self_loops
import torch.optim as optim
import time

def get_device():
    if torch.cuda.is_available():
        device = torch.device('cuda')
        print(f"🚀 Using GPU: {torch.cuda.get_device_name()}")
        torch.cuda.empty_cache()
    else:
        device = torch.device('cpu')
        print("💻 Using CPU")
    return device

class EmergencyTinyMamba(nn.Module):
    """Emergency ultra-tiny model for 140 samples"""
    def __init__(self, input_dim=1433, hidden_dim=8, num_classes=7):
        super().__init__()
        
        # TINY feature extraction
        self.feature_reduce = nn.Sequential(
            nn.Linear(input_dim, 32),
            nn.ReLU(),
            nn.Dropout(0.9),  # Extreme dropout
            nn.Linear(32, hidden_dim)
        )
        
        # Single GCN layer
        self.gcn = GCNConv(hidden_dim, hidden_dim)
        
        # Tiny "Mamba-inspired" temporal processing
        self.temporal = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim),
            nn.Tanh(),  # Bounded activation
            nn.Dropout(0.9)
        )
        
        # Direct classifier
        self.classifier = nn.Sequential(
            nn.Dropout(0.95),  # Extreme dropout before classification
            nn.Linear(hidden_dim, num_classes)
        )
        
        print(f"🦾 Emergency Model - Parameters: {sum(p.numel() for p in self.parameters()):,}")
        
    def forward(self, x, edge_index):
        # Feature reduction
        h = self.feature_reduce(x)
        
        # Graph convolution
        h_gcn = F.relu(self.gcn(h, edge_index))
        
        # Temporal processing (Mamba-inspired)
        h_temporal = self.temporal(h_gcn)
        
        # Small residual connection
        h = h + h_temporal * 0.1  # Very small update
        
        # Classification
        return self.classifier(h)

class MicroMamba(nn.Module):
    """Even smaller model"""
    def __init__(self, input_dim=1433, hidden_dim=4, num_classes=7):
        super().__init__()
        
        # Ultra-compressed feature extraction
        self.features = nn.Sequential(
            nn.Linear(input_dim, 16),
            nn.ReLU(),
            nn.Dropout(0.95),
            nn.Linear(16, hidden_dim)
        )
        
        # Minimal processing
        self.process = nn.Sequential(
            GCNConv(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Dropout(0.9)
        )
        
        # Direct classification
        self.classify = nn.Sequential(
            nn.Dropout(0.95),
            nn.Linear(hidden_dim, num_classes)
        )
        
        print(f"🤏 Micro Model - Parameters: {sum(p.numel() for p in self.parameters()):,}")
        
    def forward(self, x, edge_index):
        h = self.features(x)
        h = self.process[0](h, edge_index)  # GCN
        h = self.process[1](h)  # ReLU
        h = self.process[2](h)  # Dropout
        return self.classify(h)

class NanoMamba(nn.Module):
    """Absolutely minimal model"""
    def __init__(self, input_dim=1433, num_classes=7):
        super().__init__()
        
        # Direct path - no hidden layers
        self.direct = nn.Sequential(
            nn.Linear(input_dim, num_classes),
            nn.Dropout(0.8)
        )
        
        # GCN path
        self.gcn_path = nn.Sequential(
            nn.Linear(input_dim, 8),
            nn.Dropout(0.9)
        )
        self.gcn = GCNConv(8, num_classes)
        
        print(f"⚛️ Nano Model - Parameters: {sum(p.numel() for p in self.parameters()):,}")
        
    def forward(self, x, edge_index):
        # Direct classification
        direct_out = self.direct(x)
        
        # GCN path
        h = self.gcn_path(x)
        gcn_out = self.gcn(h, edge_index)
        
        # Minimal combination
        return direct_out * 0.7 + gcn_out * 0.3

def emergency_train(model, data, device, epochs=2000):
    """Emergency training with extreme regularization"""
    model = model.to(device)
    data = data.to(device)
    
    # Very conservative optimizer
    optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=0.5)
    
    # Label smoothing cross entropy
    criterion = nn.CrossEntropyLoss(label_smoothing=0.5)
    
    print(f"🚨 Emergency Training Protocol")
    print(f"   Parameters: {sum(p.numel() for p in model.parameters()):,}")
    print(f"   Per sample: {sum(p.numel() for p in model.parameters())/140:.1f}")
    print(f"   Epochs: {epochs}")
    print(f"   Learning rate: 0.001")
    print(f"   Weight decay: 0.5")
    print(f"   Label smoothing: 0.5")
    
    best_val_acc = 0
    patience = 0
    
    for epoch in range(epochs):
        # Training
        model.train()
        optimizer.zero_grad()
        
        out = model(data.x, data.edge_index)
        loss = criterion(out[data.train_mask], data.y[data.train_mask])
        
        loss.backward()
        torch.nn.utils.clip_grad_norm_(model.parameters(), 0.1)  # Tiny gradients
        optimizer.step()
        
        # Evaluation
        if (epoch + 1) % 100 == 0:
            model.eval()
            with torch.no_grad():
                out = model(data.x, data.edge_index)
                
                train_pred = out[data.train_mask].argmax(dim=1)
                train_acc = (train_pred == data.y[data.train_mask]).float().mean().item()
                
                val_pred = out[data.val_mask].argmax(dim=1)
                val_acc = (val_pred == data.y[data.val_mask]).float().mean().item()
                
                test_pred = out[data.test_mask].argmax(dim=1)
                test_acc = (test_pred == data.y[data.test_mask]).float().mean().item()
                
                gap = train_acc - val_acc
                
                print(f"   Epoch {epoch+1:4d}: Train {train_acc:.3f} | Val {val_acc:.3f} | "
                      f"Test {test_acc:.3f} | Gap {gap:.3f}")
                
                if val_acc > best_val_acc:
                    best_val_acc = val_acc
                    patience = 0
                else:
                    patience += 100
                    
                if patience >= 500:  # Stop if no improvement
                    print(f"   Early stopping at epoch {epoch+1}")
                    break
    
    # Final evaluation
    model.eval()
    with torch.no_grad():
        out = model(data.x, data.edge_index)
        
        train_pred = out[data.train_mask].argmax(dim=1)
        train_acc = (train_pred == data.y[data.train_mask]).float().mean().item()
        
        val_pred = out[data.val_mask].argmax(dim=1)
        val_acc = (val_pred == data.y[data.val_mask]).float().mean().item()
        
        test_pred = out[data.test_mask].argmax(dim=1)
        test_acc = (test_pred == data.y[data.test_mask]).float().mean().item()
        
        gap = train_acc - val_acc
    
    return {
        'train_acc': train_acc,
        'val_acc': val_acc,
        'test_acc': test_acc,
        'gap': gap
    }

def run_emergency_fix():
    """Emergency overfitting fix"""
    print("🚨🚨🚨 EMERGENCY OVERFITTING FIX 🚨🚨🚨")
    print("🩹 Ultra-Tiny Models for 140 Training Samples")
    print("=" * 60)
    
    device = get_device()
    
    # Load data
    print("\n📊 Loading Cora dataset...")
    dataset = Planetoid(root='/tmp/Cora', name='Cora', transform=NormalizeFeatures())
    data = dataset[0].to(device)
    data.edge_index = to_undirected(data.edge_index)
    data.edge_index, _ = add_self_loops(data.edge_index, num_nodes=data.x.size(0))
    
    print(f"✅ Dataset: {data.num_nodes} nodes, Train: {data.train_mask.sum()} samples")
    print(f"🎯 Target: <50 parameters per sample = <7,000 total parameters")
    
    # Test emergency models
    models = {
        'Emergency Tiny (8D)': EmergencyTinyMamba(hidden_dim=8),
        'Micro (4D)': MicroMamba(hidden_dim=4),
        'Nano (Direct)': NanoMamba()
    }
    
    results = {}
    
    for name, model in models.items():
        print(f"\n🏗️ Testing {name}...")
        
        total_params = sum(p.numel() for p in model.parameters())
        params_per_sample = total_params / 140
        
        print(f"   Parameters: {total_params:,} ({params_per_sample:.1f} per sample)")
        
        if params_per_sample < 50:
            print(f"   ✅ EXCELLENT parameter ratio!")
        elif params_per_sample < 100:
            print(f"   👍 Good parameter ratio!")
        else:
            print(f"   ⚠️ Still might overfit")
        
        # Test forward pass
        with torch.no_grad():
            out = model(data.x, data.edge_index)
            print(f"   Forward: {data.x.shape} -> {out.shape} ✅")
        
        try:
            # Emergency training
            result = emergency_train(model, data, device)
            results[name] = result
            
            print(f"   🎯 Final Results:")
            print(f"      Test Accuracy: {result['test_acc']:.3f} ({result['test_acc']*100:.1f}%)")
            print(f"      Train Accuracy: {result['train_acc']:.3f}")
            print(f"      Overfitting Gap: {result['gap']:.3f}")
            
            if result['gap'] < 0.1:
                print(f"      🎉 OVERFITTING SOLVED!")
            elif result['gap'] < 0.2:
                print(f"      👍 Much better generalization!")
            elif result['gap'] < 0.3:
                print(f"      📈 Improved generalization")
            else:
                print(f"      ⚠️ Still overfitting")
                
        except Exception as e:
            print(f"   ❌ Training failed: {e}")
    
    # Emergency summary
    print(f"\n{'='*60}")
    print("🚨 EMERGENCY RESULTS SUMMARY")
    print(f"{'='*60}")
    
    best_gap = float('inf')
    best_model = None
    
    for name, result in results.items():
        print(f"📊 {name}:")
        print(f"   Test: {result['test_acc']:.3f} | Gap: {result['gap']:.3f}")
        
        if result['gap'] < best_gap:
            best_gap = result['gap']
            best_model = name
    
    if best_model:
        print(f"\n🏆 Best Generalization: {best_model} (Gap: {best_gap:.3f})")
        
        if best_gap < 0.1:
            print(f"🎉 MISSION ACCOMPLISHED! Overfitting crisis resolved!")
        elif best_gap < 0.2:
            print(f"👍 Significant improvement in generalization!")
        else:
            print(f"📈 Progress made, but still work to do...")
    
    # Comparison with your current model
    print(f"\n📈 Comparison:")
    print(f"   Your model: 194K params, Gap ~0.5")
    if best_model and best_gap < 0.3:
        improvement = 0.5 - best_gap
        print(f"   Best tiny model: Gap {best_gap:.3f} (Improvement: {improvement:.3f})")
        print(f"   🎯 {improvement/0.5*100:.0f}% reduction in overfitting!")
    
    print(f"\n💡 Key Lesson: With only 140 samples, bigger ≠ better!")
    print(f"🧠 Tiny models can achieve competitive performance with much better generalization.")
    
    return results

if __name__ == "__main__":
    results = run_emergency_fix()
    
    print(f"\n🌐 Emergency fix complete. Process staying alive...")
    try:
        while True:
            time.sleep(60)
    except KeyboardInterrupt:
        print("\n👋 Emergency protocol terminated.")