| 146 | return selected_model, my_history |
| 147 | |
| 148 | def summary_statistics(X, Y, set, task, main_model, main_params, out_dir): |
| 149 | pred = main_model.predict(X, batch_size=main_params['batch_size']) # predict |
| 150 | if task =="Dev": |
| 151 | i=0 |
| 152 | if task =="Hk": |
| 153 | i=1 |
| 154 | print(set + ' MSE ' + task + ' = ' + str("{0:0.2f}".format(mean_squared_error(Y, pred[i].squeeze())))) |
| 155 | print(set + ' PCC ' + task + ' = ' + str("{0:0.2f}".format(stats.pearsonr(Y, pred[i].squeeze())[0]))) |
| 156 | print(set + ' SCC ' + task + ' = ' + str("{0:0.2f}".format(stats.spearmanr(Y, pred[i].squeeze())[0]))) |
| 157 | return str("{0:0.2f}".format(stats.pearsonr(Y, pred[i].squeeze())[0])) |
| 158 | |
| 159 | def main(config, indir, out_dir, label): |
| 160 | data = pd.read_table(indir) |