tensorFlowNetworkMultiTask.py 文件源码

def getOverallResults(self, average_over_tasks=False):
        if average_over_tasks:
            accs = [0] * len(self.optimize_labels)
            aucs = [0] * len(self.optimize_labels)
            f1s = [0] * len(self.optimize_labels)
            precisions = [0] * len(self.optimize_labels)
            recalls = [0] * len(self.optimize_labels)

            for i in range(len(self.optimize_labels)):
                accs[i] = self.training_val_results_per_task['acc'][self.optimize_labels[i]][-1]
                aucs[i] = self.training_val_results_per_task['auc'][self.optimize_labels[i]][-1]
                f1s[i] = self.training_val_results_per_task['f1'][self.optimize_labels[i]][-1]
                precisions[i] = self.training_val_results_per_task['precision'][self.optimize_labels[i]][-1]
                recalls[i] = self.training_val_results_per_task['recall'][self.optimize_labels[i]][-1]
            return np.nanmean(accs), np.nanmean(aucs), np.nanmean(f1s), np.nanmean(precisions), np.nanmean(recalls)
        else:
            acc = self.training_val_results['acc'][-1]
            auc = self.training_val_results['auc'][-1]
            f1 = self.training_val_results['f1'][-1]
            precision = self.training_val_results['precision'][-1]
            recall = self.training_val_results['recall'][-1]

        return acc, auc, f1, precision, recall