def get_eval_string(true_prediction):
"""
Given a list of (gold, prediction)s, generate output string.
"""
count, pred_count, avg_pred_count, p, r, f1 = eval_metric.micro(true_prediction)
_, _, _, ma_p, ma_r, ma_f1 = eval_metric.macro(true_prediction)
output_str = "Eval: {0} {1} {2:.3f} P:{3:.3f} R:{4:.3f} F1:{5:.3f} Ma_P:{6:.3f} Ma_R:{7:.3f} Ma_F1:{8:.3f}".format(
count, pred_count, avg_pred_count, p, r, f1, ma_p, ma_r, ma_f1)
accuracy = sum([set(y) == set(yp) for y, yp in true_prediction]) * 1.0 / len(true_prediction)
output_str += '\t Dev accuracy: {0:.1f}%'.format(accuracy * 100)
return output_str
def metric_dicts(true_prediction):
count, pred_count, avg_pred_count, p, r, f1 = eval_metric.micro(
true_prediction)
_, _, _, ma_p, ma_r, ma_f1 = eval_metric.macro(true_prediction)
output_str = "Eval: {0} {1} {2:.3f} P:{3:.3f} R:{4:.3f} F1:{5:.3f} Ma_P:{6:.3f} Ma_R:{7:.3f} Ma_F1:{8:.3f}".format(
count, pred_count, avg_pred_count, p, r, f1, ma_p, ma_r, ma_f1)
accuracy = sum([set(y) == set(yp)
for y, yp in true_prediction]) * 1.0 / len(true_prediction)
output_str += '\t Dev accuracy: {0:.1f}%'.format(accuracy * 100)
result = {
"precision": p,
"recall": r,
'f1': f1,
"ma_precision": ma_p,
"ma_recall": ma_r,
"ma_f1": ma_f1,
"accu": accuracy
}
return result, output_str