#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Timestamp: "2025-10-02 (ywatanabe)"
# File: /home/ywatanabe/proj/scitex_repo/src/scitex/ml/metrics/_calc_bacc.py
"""Calculate balanced accuracy metric."""
__FILE__ = __file__
from typing import Any, Dict, List, Optional
import numpy as np
from sklearn.metrics import balanced_accuracy_score
from ._normalize_labels import normalize_labels
[docs]
def calc_bacc(
y_true: np.ndarray,
y_pred: np.ndarray,
labels: Optional[List] = None,
fold: Optional[int] = None,
) -> Dict[str, Any]:
"""
Calculate balanced accuracy with robust label handling.
Parameters
----------
y_true : np.ndarray
True labels (can be str or int)
y_pred : np.ndarray
Predicted labels (can be str or int)
labels : List, optional
Expected label list
fold : int, optional
Fold number for tracking
Returns
-------
Dict[str, Any]
{'metric': 'balanced_accuracy', 'value': float, 'fold': int}
"""
try:
y_true_norm, y_pred_norm, label_names, _ = normalize_labels(
y_true, y_pred, labels
)
value = balanced_accuracy_score(y_true_norm, y_pred_norm)
return {
"metric": "balanced_accuracy",
"value": float(value),
"fold": fold,
"labels": label_names,
}
except Exception as e:
return {
"metric": "balanced_accuracy",
"value": np.nan,
"fold": fold,
"error": str(e),
}
# EOF