def compute_stats(self, confm_list, train_loss):
TP_list, TN_list, FP_list, FN_list = extract_stats_from_confm(
confm_list)
mean_accuracy = compute_accuracy(TP_list, TN_list, FP_list,
FN_list)
mean_precision = compute_precision(TP_list, FP_list)
mean_recall = compute_recall(TP_list, FN_list)
mean_f1score = compute_f1score(TP_list, FP_list, FN_list)
self.stats.train.acc = np.nanmean(mean_accuracy)
self.stats.train.recall = np.nanmean(mean_recall)
self.stats.train.precision = np.nanmean(mean_precision)
self.stats.train.f1score = np.nanmean(mean_f1score)
if train_loss is not None:
self.stats.train.loss = train_loss.avg
def compute_stats(self, confm_list, val_loss):
TP_list, TN_list, FP_list, FN_list = extract_stats_from_confm(
confm_list)
mean_accuracy = compute_accuracy(TP_list, TN_list, FP_list,
FN_list)
mean_precision = compute_precision(TP_list, FP_list)
mean_recall = compute_recall(TP_list, FN_list)
# self.stats.val.acc = np.nanmean(mean_accuracy)
self.stats.val.acc = np.sum(TP_list) / (np.sum(FP_list) +
np.sum(TP_list))
self.stats.val.acc = np.nanmean(mean_accuracy)
self.stats.val.recall = np.nanmean(mean_recall)
self.stats.val.precision = np.nanmean(mean_precision)
self.stats.val.f1score = np.nanmean(mean_f1score)
if val_loss is not None:
self.stats.val.loss = val_loss.avg