weights.py

import numpy as np


def get_class_weights(total_counts, class_positive_counts, multiply):
    """
    Calculate class_weight used in training

    Arguments:
    total_counts - int
    class_positive_counts - dict of int, ex: {"Effusion": 300, "Infiltration": 500 ...}
    multiply - int, positve weighting multiply
    use_class_balancing - boolean 

    Returns:
    class_weight - dict of dict, ex: {"Effusion": { 0: 0.01, 1: 0.99 }, ... }
    """
    def get_single_class_weight(pos_counts, total_counts):
        denominator = (total_counts - pos_counts) * multiply + pos_counts
        return {
            0: pos_counts / denominator,
            1: (denominator - pos_counts) / denominator,
        }

    class_names = list(class_positive_counts.keys())
    label_counts = np.array(list(class_positive_counts.values()))
    class_weights = []
    for i, class_name in enumerate(class_names):
        class_weights.append(get_single_class_weight(label_counts[i], total_counts))

    return class_weights