def evaluate(best_processed_path, model):
x_test_char, x_test_type, y_test = prepare_feature(best_processed_path, option='<STR_LIT:test>')<EOL>y_predict = model.predict([x_test_char, x_test_type])<EOL>y_predict = (y_predict.ravel() > <NUM_LIT:0.5>).astype(int)<EOL>f1score = f1_score(y_test, y_predict)<EOL>precision = precision_score(y_test, y_predict)<EOL>recall = recall_score(y_test, y_predict)<EOL>return f1score, precision, recall<EOL>
Evaluate model on splitted 10 percent testing set