utils/train_evaluate.py

import torch
import torch.nn as nn
import numpy as np
from sklearn.metrics import roc_auc_score

def masked_bce_loss(logits, labels, mask):
    """
    logits: [batch_size, num_classes] (raw outputs)
    labels: [batch_size, num_classes] (0/1 with filler)
    mask:   [batch_size, num_classes] (True if label is valid)
    """
    criterion = nn.BCEWithLogitsLoss(reduction="none")
    loss_raw = criterion(logits, labels)
    loss = (loss_raw * mask.float()).sum() / mask.float().sum()
    return loss

def train_model(model, loader, optimizer, device):
    model.train()
    total_loss = 0
    for batch in loader:
        batch = batch.to(device)

        optimizer.zero_grad()
        out = model(batch.x, batch.edge_index, batch.batch)  # [num_graphs, num_classes]

        loss = masked_bce_loss(out, batch.y, batch.mask)
        loss.backward()
        optimizer.step()

        total_loss += loss.item() * batch.num_graphs
    return total_loss / len(loader.dataset)


@torch.no_grad()
def evaluate(model, loader, device):
    model.eval()
    total_loss = 0
    for batch in loader:
        batch = batch.to(device)
        out = model(batch.x, batch.edge_index, batch.batch)
        loss = masked_bce_loss(out, batch.y, batch.mask)
        total_loss += loss.item() * batch.num_graphs
    return total_loss / len(loader.dataset)


@torch.no_grad()
def compute_roc_auc(model, loader, device):
    model.eval()
    y_true, y_pred, y_mask = [], [], []

    for batch in loader:
        batch = batch.to(device)
        out = model(batch.x, batch.edge_index, batch.batch)

        # Store predictions (sigmoid → probabilities)
        y_pred.append(torch.sigmoid(out).cpu())
        y_true.append(batch.y.cpu())
        y_mask.append(batch.mask.cpu())

    # Concatenate across all batches
    y_true = torch.cat(y_true, dim=0).numpy()
    y_pred = torch.cat(y_pred, dim=0).numpy()
    y_mask = torch.cat(y_mask, dim=0).numpy()

    auc_list = []
    for i in range(y_true.shape[1]):  # per label
        mask_i = y_mask[:, i].astype(bool)
        if mask_i.sum() > 0:  # at least one valid label
            try:
                auc = roc_auc_score(y_true[mask_i, i], y_pred[mask_i, i])
                auc_list.append(auc)
            except ValueError:
                # happens if only one class present (all 0 or all 1)
                pass

    return np.mean(auc_list) if len(auc_list) > 0 else float("nan")

@torch.no_grad()
def compute_roc_auc_avg_and_per_class(model, loader, device):
    model.eval()
    y_true, y_pred, y_mask = [], [], []

    with torch.no_grad():
        for batch in loader:
            batch = batch.to(device)
            out = model(batch.x, batch.edge_index, batch.batch)

            # Store predictions (sigmoid → probabilities)
            y_pred.append(torch.sigmoid(out).cpu())
            y_true.append(batch.y.cpu())
            y_mask.append(batch.mask.cpu())

    # Concatenate across all batches
    y_true = torch.cat(y_true, dim=0).numpy()
    y_pred = torch.cat(y_pred, dim=0).numpy()
    y_mask = torch.cat(y_mask, dim=0).numpy()

    # Compute AUC per class
    auc_list = []
    for i in range(y_true.shape[1]):
        mask_i = y_mask[:, i].astype(bool)
        if mask_i.sum() > 0:
            try:
                auc = roc_auc_score(y_true[mask_i, i], y_pred[mask_i, i])
            except ValueError:
                auc = np.nan  # in case only one class present
        else:
            auc = np.nan
        auc_list.append(auc)

    # Convert to numpy array for easier manipulation
    auc_array = np.array(auc_list, dtype=np.float32)
    mean_auc = np.nanmean(auc_array)  # overall mean ignoring NaNs

    # Return both per-class and mean
    return auc_array, mean_auc