utils/metrics.py

import torch
import torch.nn.functional as F
from sklearn.metrics import accuracy_score, f1_score, roc_auc_score
import numpy as np

class GraphMetrics:
    """Production-ready evaluation metrics"""
    
    @staticmethod
    def accuracy(pred, target):
        """Classification accuracy"""
        pred_labels = pred.argmax(dim=1)
        return (pred_labels == target).float().mean().item()
    
    @staticmethod 
    def f1_score_macro(pred, target):
        """Macro F1 score"""
        pred_labels = pred.argmax(dim=1).cpu().numpy()
        target_labels = target.cpu().numpy()
        return f1_score(target_labels, pred_labels, average='macro')
    
    @staticmethod
    def f1_score_micro(pred, target):
        """Micro F1 score"""
        pred_labels = pred.argmax(dim=1).cpu().numpy() 
        target_labels = target.cpu().numpy()
        return f1_score(target_labels, pred_labels, average='micro')
    
    @staticmethod
    def roc_auc(pred, target, num_classes):
        """ROC AUC for multi-class"""
        if num_classes == 2:
            # Binary classification
            pred_probs = F.softmax(pred, dim=1)[:, 1].cpu().numpy()
            target_labels = target.cpu().numpy()
            return roc_auc_score(target_labels, pred_probs)
        else:
            # Multi-class
            pred_probs = F.softmax(pred, dim=1).cpu().numpy()
            target_onehot = F.one_hot(target, num_classes).cpu().numpy()
            return roc_auc_score(target_onehot, pred_probs, multi_class='ovr', average='macro')
    
    @staticmethod
    def evaluate_node_classification(model, data, mask, device):
        """Comprehensive node classification evaluation"""
        model.eval()
        
        with torch.no_grad():
            data = data.to(device)
            h = model(data.x, data.edge_index)
            
            # Assuming a classification head exists
            if hasattr(model, 'classifier'):
                pred = model.classifier(h)
            else:
                # If no classifier, return embeddings
                return {'embeddings': h[mask].cpu()}
            
            pred_masked = pred[mask]
            target_masked = data.y[mask]
            
            metrics = {
                'accuracy': GraphMetrics.accuracy(pred_masked, target_masked),
                'f1_macro': GraphMetrics.f1_score_macro(pred_masked, target_masked),
                'f1_micro': GraphMetrics.f1_score_micro(pred_masked, target_masked),
            }
            
            # Add ROC AUC if binary/multi-class
            try:
                num_classes = pred.size(1)
                metrics['roc_auc'] = GraphMetrics.roc_auc(pred_masked, target_masked, num_classes)
            except:
                pass
                
        return metrics
    
    @staticmethod
    def evaluate_graph_classification(model, dataloader, device):
        """Comprehensive graph classification evaluation"""
        model.eval()
        
        all_preds = []
        all_targets = []
        
        with torch.no_grad():
            for batch in dataloader:
                batch = batch.to(device)
                h = model(batch.x, batch.edge_index, batch.batch)
                
                # Graph-level prediction
                graph_h = model.get_graph_embedding(h, batch.batch)
                
                if hasattr(model, 'classifier'):
                    pred = model.classifier(graph_h)
                    all_preds.append(pred)
                    all_targets.append(batch.y)
        
        if all_preds:
            all_preds = torch.cat(all_preds, dim=0)
            all_targets = torch.cat(all_targets, dim=0)
            
            metrics = {
                'accuracy': GraphMetrics.accuracy(all_preds, all_targets),
                'f1_macro': GraphMetrics.f1_score_macro(all_preds, all_targets),
                'f1_micro': GraphMetrics.f1_score_micro(all_preds, all_targets),
            }
            
            try:
                num_classes = all_preds.size(1)
                metrics['roc_auc'] = GraphMetrics.roc_auc(all_preds, all_targets, num_classes)
            except:
                pass
                
            return metrics
        
        return {'error': 'No predictions generated'}