| """
|
| Training utilities — class weighting and label encoding helpers.
|
|
|
| Class weighting is critical: with ~20-25% positives in the training set,
|
| the model will bias toward predicting ``"safe"`` without it.
|
| """
|
|
|
| import numpy as np
|
| from sklearn.utils.class_weight import compute_class_weight
|
|
|
| from app.ml.cnn.architecture import LABEL_NAMES
|
| from app.core.logging import get_logger
|
|
|
| logger = get_logger(__name__)
|
|
|
|
|
| def get_class_weights(labels_onehot: np.ndarray) -> dict[int, float]:
|
| """Compute balanced class weights from one-hot encoded labels.
|
|
|
| Uses ``sklearn.utils.class_weight.compute_class_weight`` with
|
| ``class_weight="balanced"`` to inversely weight classes by frequency.
|
|
|
| Args:
|
| labels_onehot: One-hot encoded labels, shape ``(N, 3)``.
|
|
|
| Returns:
|
| Dict mapping class index → weight, suitable for
|
| ``model.fit(..., class_weight={"label": weights})``.
|
| """
|
|
|
| y_int = np.argmax(labels_onehot, axis=1)
|
| classes = np.arange(len(LABEL_NAMES))
|
|
|
|
|
| total_samples = len(y_int)
|
| num_classes = len(classes)
|
| weight_dict = {}
|
|
|
| for cls in classes:
|
| cls_count = np.sum(y_int == cls)
|
| if cls_count > 0:
|
| weight = total_samples / (num_classes * cls_count)
|
| else:
|
| weight = 1.0
|
| weight_dict[int(cls)] = float(weight)
|
|
|
| logger.info(
|
| "Class weights: %s",
|
| {LABEL_NAMES[k]: f"{v:.3f}" for k, v in weight_dict.items()},
|
| )
|
| return weight_dict
|
|
|
