src/evaluation.py

import pandas as pd
import numpy as np
from sklearn.metrics import roc_auc_score

def compute_roc_auc_from_csv(preds_csv: str, labels_csv: str, valid_mask):
    """
    Compute ROC AUC per class and overall mean, similar to the PyTorch-style function.
    Handles missing labels (NaN) like y_mask.
    """
    preds = pd.read_csv(preds_csv)
    labels = pd.read_csv(labels_csv)

    smiles_cols = [c for c in preds.columns if "smiles" in c.lower()]
    if smiles_cols:
        print(f"🧪 Dropping SMILES columns: {smiles_cols}")
        preds = preds.drop(columns=smiles_cols, errors="ignore")
        labels = labels.drop(columns=smiles_cols, errors="ignore")

    shared_cols = [c for c in preds.columns if c in labels.columns]
    preds = preds[shared_cols].apply(pd.to_numeric, errors="coerce")
    labels = labels[shared_cols].apply(pd.to_numeric, errors="coerce")

    y_pred_clean = preds.to_numpy(dtype=float)
    y_true = labels.to_numpy(dtype=float)
    valid_mask = valid_mask[-y_true.shape[0]:]
    #Re-expand to original size
    y_pred = np.full((len(valid_mask), y_pred_clean.shape[1]), 0.5, dtype=float)
    y_pred[valid_mask] = y_pred_clean

    y_mask = ~np.isnan(y_true)  

    auc_list = []
    for i in range(y_true.shape[1]):
        mask_i = y_mask[:, i]
        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 
        else:
            auc = np.nan
        auc_list.append(auc)

    auc_array = np.array(auc_list, dtype=np.float32)
    mean_auc = np.nanmean(auc_array)  

    return auc_array, mean_auc