class weights for weighted loss / weighted sampler

gistfile1.txt

def get_class_weights(label_freq, mu=1., return_log=False):
    total = np.sum(list(label_freq.values()))
    keys = label_freq.keys()
    class_weight = dict()
    class_weight_log = dict()

    for key in keys:
        score = total / float(label_freq[key])
        score_log = np.log(mu * score)
        class_weight[key] = round(score, 2) if score > 1.0 else 1.0
        class_weight_log[key] = round(score_log, 2) if score_log > 1.0 else 1.0

    if return_log:
        return(class_weight_log)
    else:
        return(class_weight)

def get_sample_weights(labels,class_weights,splitter=' '):
  sample_weights = []
  for label in labels:
      max_weight = np.max([class_weights[i] for i in str(label).split(splitter)])
      sample_weights.append(max_weight)
  return(sample_weights)