config_auto.cpp

/*!
 * Copyright (c) 2018 Microsoft Corporation. All rights reserved.
 * Licensed under the MIT License. See LICENSE file in the project root for license information.
 *
 * \note
 * This file is auto generated by LightGBM\.ci\parameter-generator.py from LightGBM\include\LightGBM\config.h file.
 */
#include<LightGBM/config.h>
namespace LightGBM {
const std::unordered_map<std::string, std::string>& Config::alias_table() {
  static std::unordered_map<std::string, std::string> aliases({
  {"config_file", "config"},
  {"task_type", "task"},
  {"objective_type", "objective"},
  {"app", "objective"},
  {"application", "objective"},
  {"loss", "objective"},
  {"boosting_type", "boosting"},
  {"boost", "boosting"},
  {"train", "data"},
  {"train_data", "data"},
  {"train_data_file", "data"},
  {"data_filename", "data"},
  {"test", "valid"},
  {"valid_data", "valid"},
  {"valid_data_file", "valid"},
  {"test_data", "valid"},
  {"test_data_file", "valid"},
  {"valid_filenames", "valid"},
  {"num_iteration", "num_iterations"},
  {"n_iter", "num_iterations"},
  {"num_tree", "num_iterations"},
  {"num_trees", "num_iterations"},
  {"num_round", "num_iterations"},
  {"num_rounds", "num_iterations"},
  {"nrounds", "num_iterations"},
  {"num_boost_round", "num_iterations"},
  {"n_estimators", "num_iterations"},
  {"max_iter", "num_iterations"},
  {"shrinkage_rate", "learning_rate"},
  {"eta", "learning_rate"},
  {"num_leaf", "num_leaves"},
  {"max_leaves", "num_leaves"},
  {"max_leaf", "num_leaves"},
  {"max_leaf_nodes", "num_leaves"},
  {"tree", "tree_learner"},
  {"tree_type", "tree_learner"},
  {"tree_learner_type", "tree_learner"},
  {"num_thread", "num_threads"},
  {"nthread", "num_threads"},
  {"nthreads", "num_threads"},
  {"n_jobs", "num_threads"},
  {"device", "device_type"},
  {"random_seed", "seed"},
  {"random_state", "seed"},
  {"hist_pool_size", "histogram_pool_size"},
  {"min_data_per_leaf", "min_data_in_leaf"},
  {"min_data", "min_data_in_leaf"},
  {"min_child_samples", "min_data_in_leaf"},
  {"min_samples_leaf", "min_data_in_leaf"},
  {"min_sum_hessian_per_leaf", "min_sum_hessian_in_leaf"},
  {"min_sum_hessian", "min_sum_hessian_in_leaf"},
  {"min_hessian", "min_sum_hessian_in_leaf"},
  {"min_child_weight", "min_sum_hessian_in_leaf"},
  {"sub_row", "bagging_fraction"},
  {"subsample", "bagging_fraction"},
  {"bagging", "bagging_fraction"},
  {"pos_sub_row", "pos_bagging_fraction"},
  {"pos_subsample", "pos_bagging_fraction"},
  {"pos_bagging", "pos_bagging_fraction"},
  {"neg_sub_row", "neg_bagging_fraction"},
  {"neg_subsample", "neg_bagging_fraction"},
  {"neg_bagging", "neg_bagging_fraction"},
  {"subsample_freq", "bagging_freq"},
  {"bagging_fraction_seed", "bagging_seed"},
  {"sub_feature", "feature_fraction"},
  {"colsample_bytree", "feature_fraction"},
  {"sub_feature_bynode", "feature_fraction_bynode"},
  {"colsample_bynode", "feature_fraction_bynode"},
  {"extra_tree", "extra_trees"},
  {"early_stopping_rounds", "early_stopping_round"},
  {"early_stopping", "early_stopping_round"},
  {"n_iter_no_change", "early_stopping_round"},
  {"max_tree_output", "max_delta_step"},
  {"max_leaf_output", "max_delta_step"},
  {"reg_alpha", "lambda_l1"},
  {"l1_regularization", "lambda_l1"},
  {"reg_lambda", "lambda_l2"},
  {"lambda", "lambda_l2"},
  {"l2_regularization", "lambda_l2"},
  {"min_split_gain", "min_gain_to_split"},
  {"rate_drop", "drop_rate"},
  {"topk", "top_k"},
  {"mc", "monotone_constraints"},
  {"monotone_constraint", "monotone_constraints"},
  {"monotonic_cst", "monotone_constraints"},
  {"monotone_constraining_method", "monotone_constraints_method"},
  {"mc_method", "monotone_constraints_method"},
  {"monotone_splits_penalty", "monotone_penalty"},
  {"ms_penalty", "monotone_penalty"},
  {"mc_penalty", "monotone_penalty"},
  {"feature_contrib", "feature_contri"},
  {"fc", "feature_contri"},
  {"fp", "feature_contri"},
  {"feature_penalty", "feature_contri"},
  {"fs", "forcedsplits_filename"},
  {"forced_splits_filename", "forcedsplits_filename"},
  {"forced_splits_file", "forcedsplits_filename"},
  {"forced_splits", "forcedsplits_filename"},
  {"verbose", "verbosity"},
  {"model_input", "input_model"},
  {"model_in", "input_model"},
  {"model_output", "output_model"},
  {"model_out", "output_model"},
  {"save_period", "snapshot_freq"},
  {"linear_trees", "linear_tree"},
  {"max_bins", "max_bin"},
  {"subsample_for_bin", "bin_construct_sample_cnt"},
  {"data_seed", "data_random_seed"},
  {"is_sparse", "is_enable_sparse"},
  {"enable_sparse", "is_enable_sparse"},
  {"sparse", "is_enable_sparse"},
  {"is_enable_bundle", "enable_bundle"},
  {"bundle", "enable_bundle"},
  {"is_pre_partition", "pre_partition"},
  {"two_round_loading", "two_round"},
  {"use_two_round_loading", "two_round"},
  {"has_header", "header"},
  {"label", "label_column"},
  {"weight", "weight_column"},
  {"group", "group_column"},
  {"group_id", "group_column"},
  {"query_column", "group_column"},
  {"query", "group_column"},
  {"query_id", "group_column"},
  {"ignore_feature", "ignore_column"},
  {"blacklist", "ignore_column"},
  {"cat_feature", "categorical_feature"},
  {"categorical_column", "categorical_feature"},
  {"cat_column", "categorical_feature"},
  {"categorical_features", "categorical_feature"},
  {"is_save_binary", "save_binary"},
  {"is_save_binary_file", "save_binary"},
  {"is_predict_raw_score", "predict_raw_score"},
  {"predict_rawscore", "predict_raw_score"},
  {"raw_score", "predict_raw_score"},
  {"is_predict_leaf_index", "predict_leaf_index"},
  {"leaf_index", "predict_leaf_index"},
  {"is_predict_contrib", "predict_contrib"},
  {"contrib", "predict_contrib"},
  {"predict_result", "output_result"},
  {"prediction_result", "output_result"},
  {"predict_name", "output_result"},
  {"prediction_name", "output_result"},
  {"pred_name", "output_result"},
  {"name_pred", "output_result"},
  {"convert_model_file", "convert_model"},
  {"num_classes", "num_class"},
  {"unbalance", "is_unbalance"},
  {"unbalanced_sets", "is_unbalance"},
  {"metrics", "metric"},
  {"metric_types", "metric"},
  {"output_freq", "metric_freq"},
  {"training_metric", "is_provide_training_metric"},
  {"is_training_metric", "is_provide_training_metric"},
  {"train_metric", "is_provide_training_metric"},
  {"ndcg_eval_at", "eval_at"},
  {"ndcg_at", "eval_at"},
  {"map_eval_at", "eval_at"},
  {"map_at", "eval_at"},
  {"num_machine", "num_machines"},
  {"local_port", "local_listen_port"},
  {"port", "local_listen_port"},
  {"machine_list_file", "machine_list_filename"},
  {"machine_list", "machine_list_filename"},
  {"mlist", "machine_list_filename"},
  {"workers", "machines"},
  {"nodes", "machines"},
  });
  return aliases;
}

const std::unordered_set<std::string>& Config::parameter_set() {
  static std::unordered_set<std::string> params({
  "config",
  "task",
  "objective",
  "boosting",
  "data_sample_strategy",
  "data",
  "valid",
  "num_iterations",
  "learning_rate",
  "num_leaves",
  "tree_learner",
  "num_threads",
  "device_type",
  "seed",
  "deterministic",
  "force_col_wise",
  "force_row_wise",
  "histogram_pool_size",
  "max_depth",
  "min_data_in_leaf",
  "min_sum_hessian_in_leaf",
  "bagging_fraction",
  "pos_bagging_fraction",
  "neg_bagging_fraction",
  "bagging_freq",
  "bagging_seed",
  "bagging_by_query",
  "feature_fraction",
  "feature_fraction_bynode",
  "feature_fraction_seed",
  "extra_trees",
  "extra_seed",
  "early_stopping_round",
  "early_stopping_min_delta",
  "first_metric_only",
  "max_delta_step",
  "lambda_l1",
  "lambda_l2",
  "linear_lambda",
  "min_gain_to_split",
  "drop_rate",
  "max_drop",
  "skip_drop",
  "xgboost_dart_mode",
  "uniform_drop",
  "drop_seed",
  "top_rate",
  "other_rate",
  "min_data_per_group",
  "max_cat_threshold",
  "cat_l2",
  "cat_smooth",
  "max_cat_to_onehot",
  "top_k",
  "monotone_constraints",
  "monotone_constraints_method",
  "monotone_penalty",
  "feature_contri",
  "forcedsplits_filename",
  "refit_decay_rate",
  "cegb_tradeoff",
  "cegb_penalty_split",
  "cegb_penalty_feature_lazy",
  "cegb_penalty_feature_coupled",
  "path_smooth",
  "interaction_constraints",
  "verbosity",
  "input_model",
  "output_model",
  "saved_feature_importance_type",
  "snapshot_freq",
  "use_quantized_grad",
  "num_grad_quant_bins",
  "quant_train_renew_leaf",
  "stochastic_rounding",
  "linear_tree",
  "max_bin",
  "max_bin_by_feature",
  "min_data_in_bin",
  "bin_construct_sample_cnt",
  "data_random_seed",
  "is_enable_sparse",
  "enable_bundle",
  "use_missing",
  "zero_as_missing",
  "feature_pre_filter",
  "pre_partition",
  "two_round",
  "header",
  "label_column",
  "weight_column",
  "group_column",
  "ignore_column",
  "categorical_feature",
  "forcedbins_filename",
  "save_binary",
  "precise_float_parser",
  "parser_config_file",
  "start_iteration_predict",
  "num_iteration_predict",
  "predict_raw_score",
  "predict_leaf_index",
  "predict_contrib",
  "predict_disable_shape_check",
  "pred_early_stop",
  "pred_early_stop_freq",
  "pred_early_stop_margin",
  "output_result",
  "convert_model_language",
  "convert_model",
  "objective_seed",
  "num_class",
  "is_unbalance",
  "scale_pos_weight",
  "sigmoid",
  "boost_from_average",
  "reg_sqrt",
  "alpha",
  "fair_c",
  "poisson_max_delta_step",
  "tweedie_variance_power",
  "lambdarank_truncation_level",
  "lambdarank_norm",
  "label_gain",
  "lambdarank_position_bias_regularization",
  "metric",
  "metric_freq",
  "is_provide_training_metric",
  "eval_at",
  "multi_error_top_k",
  "auc_mu_weights",
  "num_machines",
  "local_listen_port",
  "time_out",
  "machine_list_filename",
  "machines",
  "gpu_platform_id",
  "gpu_device_id",
  "gpu_use_dp",
  "num_gpu",
  });
  return params;
}

void Config::GetMembersFromString(const std::unordered_map<std::string, std::string>& params) {
  std::string tmp_str = "";
  GetString(params, "data", &data);

  if (GetString(params, "valid", &tmp_str)) {
    valid = Common::Split(tmp_str.c_str(), ',');
  }

  GetInt(params, "num_iterations", &num_iterations);
  CHECK_GE(num_iterations, 0);

  GetDouble(params, "learning_rate", &learning_rate);
  CHECK_GT(learning_rate, 0.0);

  GetInt(params, "num_leaves", &num_leaves);
  CHECK_GT(num_leaves, 1);
  CHECK_LE(num_leaves, 131072);

  GetInt(params, "num_threads", &num_threads);

  GetBool(params, "deterministic", &deterministic);

  GetBool(params, "force_col_wise", &force_col_wise);

  GetBool(params, "force_row_wise", &force_row_wise);

  GetDouble(params, "histogram_pool_size", &histogram_pool_size);

  GetInt(params, "max_depth", &max_depth);

  GetInt(params, "min_data_in_leaf", &min_data_in_leaf);
  CHECK_GE(min_data_in_leaf, 0);

  GetDouble(params, "min_sum_hessian_in_leaf", &min_sum_hessian_in_leaf);
  CHECK_GE(min_sum_hessian_in_leaf, 0.0);

  GetDouble(params, "bagging_fraction", &bagging_fraction);
  CHECK_GT(bagging_fraction, 0.0);
  CHECK_LE(bagging_fraction, 1.0);

  GetDouble(params, "pos_bagging_fraction", &pos_bagging_fraction);
  CHECK_GT(pos_bagging_fraction, 0.0);
  CHECK_LE(pos_bagging_fraction, 1.0);

  GetDouble(params, "neg_bagging_fraction", &neg_bagging_fraction);
  CHECK_GT(neg_bagging_fraction, 0.0);
  CHECK_LE(neg_bagging_fraction, 1.0);

  GetInt(params, "bagging_freq", &bagging_freq);

  GetInt(params, "bagging_seed", &bagging_seed);

  GetBool(params, "bagging_by_query", &bagging_by_query);

  GetDouble(params, "feature_fraction", &feature_fraction);
  CHECK_GT(feature_fraction, 0.0);
  CHECK_LE(feature_fraction, 1.0);

  GetDouble(params, "feature_fraction_bynode", &feature_fraction_bynode);
  CHECK_GT(feature_fraction_bynode, 0.0);
  CHECK_LE(feature_fraction_bynode, 1.0);

  GetInt(params, "feature_fraction_seed", &feature_fraction_seed);

  GetBool(params, "extra_trees", &extra_trees);

  GetInt(params, "extra_seed", &extra_seed);

  GetInt(params, "early_stopping_round", &early_stopping_round);

  GetDouble(params, "early_stopping_min_delta", &early_stopping_min_delta);
  CHECK_GE(early_stopping_min_delta, 0.0);

  GetBool(params, "first_metric_only", &first_metric_only);

  GetDouble(params, "max_delta_step", &max_delta_step);

  GetDouble(params, "lambda_l1", &lambda_l1);
  CHECK_GE(lambda_l1, 0.0);

  GetDouble(params, "lambda_l2", &lambda_l2);
  CHECK_GE(lambda_l2, 0.0);

  GetDouble(params, "linear_lambda", &linear_lambda);
  CHECK_GE(linear_lambda, 0.0);

  GetDouble(params, "min_gain_to_split", &min_gain_to_split);
  CHECK_GE(min_gain_to_split, 0.0);

  GetDouble(params, "drop_rate", &drop_rate);
  CHECK_GE(drop_rate, 0.0);
  CHECK_LE(drop_rate, 1.0);

  GetInt(params, "max_drop", &max_drop);

  GetDouble(params, "skip_drop", &skip_drop);
  CHECK_GE(skip_drop, 0.0);
  CHECK_LE(skip_drop, 1.0);

  GetBool(params, "xgboost_dart_mode", &xgboost_dart_mode);

  GetBool(params, "uniform_drop", &uniform_drop);

  GetInt(params, "drop_seed", &drop_seed);

  GetDouble(params, "top_rate", &top_rate);
  CHECK_GE(top_rate, 0.0);
  CHECK_LE(top_rate, 1.0);

  GetDouble(params, "other_rate", &other_rate);
  CHECK_GE(other_rate, 0.0);
  CHECK_LE(other_rate, 1.0);

  GetInt(params, "min_data_per_group", &min_data_per_group);
  CHECK_GT(min_data_per_group, 0);

  GetInt(params, "max_cat_threshold", &max_cat_threshold);
  CHECK_GT(max_cat_threshold, 0);

  GetDouble(params, "cat_l2", &cat_l2);
  CHECK_GE(cat_l2, 0.0);

  GetDouble(params, "cat_smooth", &cat_smooth);
  CHECK_GE(cat_smooth, 0.0);

  GetInt(params, "max_cat_to_onehot", &max_cat_to_onehot);
  CHECK_GT(max_cat_to_onehot, 0);

  GetInt(params, "top_k", &top_k);
  CHECK_GT(top_k, 0);

  if (GetString(params, "monotone_constraints", &tmp_str)) {
    monotone_constraints = Common::StringToArray<int8_t>(tmp_str, ',');
  }

  GetString(params, "monotone_constraints_method", &monotone_constraints_method);

  GetDouble(params, "monotone_penalty", &monotone_penalty);
  CHECK_GE(monotone_penalty, 0.0);

  if (GetString(params, "feature_contri", &tmp_str)) {
    feature_contri = Common::StringToArray<double>(tmp_str, ',');
  }

  GetString(params, "forcedsplits_filename", &forcedsplits_filename);

  GetDouble(params, "refit_decay_rate", &refit_decay_rate);
  CHECK_GE(refit_decay_rate, 0.0);
  CHECK_LE(refit_decay_rate, 1.0);

  GetDouble(params, "cegb_tradeoff", &cegb_tradeoff);
  CHECK_GE(cegb_tradeoff, 0.0);

  GetDouble(params, "cegb_penalty_split", &cegb_penalty_split);
  CHECK_GE(cegb_penalty_split, 0.0);

  if (GetString(params, "cegb_penalty_feature_lazy", &tmp_str)) {
    cegb_penalty_feature_lazy = Common::StringToArray<double>(tmp_str, ',');
  }

  if (GetString(params, "cegb_penalty_feature_coupled", &tmp_str)) {
    cegb_penalty_feature_coupled = Common::StringToArray<double>(tmp_str, ',');
  }

  GetDouble(params, "path_smooth", &path_smooth);
  CHECK_GE(path_smooth,  0.0);

  GetString(params, "interaction_constraints", &interaction_constraints);

  GetInt(params, "verbosity", &verbosity);

  GetString(params, "input_model", &input_model);

  GetString(params, "output_model", &output_model);

  GetInt(params, "saved_feature_importance_type", &saved_feature_importance_type);

  GetInt(params, "snapshot_freq", &snapshot_freq);

  GetBool(params, "use_quantized_grad", &use_quantized_grad);

  GetInt(params, "num_grad_quant_bins", &num_grad_quant_bins);

  GetBool(params, "quant_train_renew_leaf", &quant_train_renew_leaf);

  GetBool(params, "stochastic_rounding", &stochastic_rounding);

  GetBool(params, "linear_tree", &linear_tree);

  GetInt(params, "max_bin", &max_bin);
  CHECK_GT(max_bin, 1);

  if (GetString(params, "max_bin_by_feature", &tmp_str)) {
    max_bin_by_feature = Common::StringToArray<int32_t>(tmp_str, ',');
  }

  GetInt(params, "min_data_in_bin", &min_data_in_bin);
  CHECK_GT(min_data_in_bin, 0);

  GetInt(params, "bin_construct_sample_cnt", &bin_construct_sample_cnt);
  CHECK_GT(bin_construct_sample_cnt, 0);

  GetInt(params, "data_random_seed", &data_random_seed);

  GetBool(params, "is_enable_sparse", &is_enable_sparse);

  GetBool(params, "enable_bundle", &enable_bundle);

  GetBool(params, "use_missing", &use_missing);

  GetBool(params, "zero_as_missing", &zero_as_missing);

  GetBool(params, "feature_pre_filter", &feature_pre_filter);

  GetBool(params, "pre_partition", &pre_partition);

  GetBool(params, "two_round", &two_round);

  GetBool(params, "header", &header);

  GetString(params, "label_column", &label_column);

  GetString(params, "weight_column", &weight_column);

  GetString(params, "group_column", &group_column);

  GetString(params, "ignore_column", &ignore_column);

  GetString(params, "categorical_feature", &categorical_feature);

  GetString(params, "forcedbins_filename", &forcedbins_filename);

  GetBool(params, "save_binary", &save_binary);

  GetBool(params, "precise_float_parser", &precise_float_parser);

  GetString(params, "parser_config_file", &parser_config_file);

  GetInt(params, "start_iteration_predict", &start_iteration_predict);

  GetInt(params, "num_iteration_predict", &num_iteration_predict);

  GetBool(params, "predict_raw_score", &predict_raw_score);

  GetBool(params, "predict_leaf_index", &predict_leaf_index);

  GetBool(params, "predict_contrib", &predict_contrib);

  GetBool(params, "predict_disable_shape_check", &predict_disable_shape_check);

  GetBool(params, "pred_early_stop", &pred_early_stop);

  GetInt(params, "pred_early_stop_freq", &pred_early_stop_freq);

  GetDouble(params, "pred_early_stop_margin", &pred_early_stop_margin);

  GetString(params, "output_result", &output_result);

  GetString(params, "convert_model_language", &convert_model_language);

  GetString(params, "convert_model", &convert_model);

  GetInt(params, "objective_seed", &objective_seed);

  GetInt(params, "num_class", &num_class);
  CHECK_GT(num_class, 0);

  GetBool(params, "is_unbalance", &is_unbalance);

  GetDouble(params, "scale_pos_weight", &scale_pos_weight);
  CHECK_GT(scale_pos_weight, 0.0);

  GetDouble(params, "sigmoid", &sigmoid);
  CHECK_GT(sigmoid, 0.0);

  GetBool(params, "boost_from_average", &boost_from_average);

  GetBool(params, "reg_sqrt", &reg_sqrt);

  GetDouble(params, "alpha", &alpha);
  CHECK_GT(alpha, 0.0);

  GetDouble(params, "fair_c", &fair_c);
  CHECK_GT(fair_c, 0.0);

  GetDouble(params, "poisson_max_delta_step", &poisson_max_delta_step);
  CHECK_GT(poisson_max_delta_step, 0.0);

  GetDouble(params, "tweedie_variance_power", &tweedie_variance_power);
  CHECK_GE(tweedie_variance_power, 1.0);
  CHECK_LT(tweedie_variance_power, 2.0);

  GetInt(params, "lambdarank_truncation_level", &lambdarank_truncation_level);
  CHECK_GT(lambdarank_truncation_level, 0);

  GetBool(params, "lambdarank_norm", &lambdarank_norm);

  if (GetString(params, "label_gain", &tmp_str)) {
    label_gain = Common::StringToArray<double>(tmp_str, ',');
  }

  GetDouble(params, "lambdarank_position_bias_regularization", &lambdarank_position_bias_regularization);
  CHECK_GE(lambdarank_position_bias_regularization, 0.0);

  GetInt(params, "metric_freq", &metric_freq);
  CHECK_GT(metric_freq, 0);

  GetBool(params, "is_provide_training_metric", &is_provide_training_metric);

  if (GetString(params, "eval_at", &tmp_str)) {
    eval_at = Common::StringToArray<int>(tmp_str, ',');
  }

  GetInt(params, "multi_error_top_k", &multi_error_top_k);
  CHECK_GT(multi_error_top_k, 0);

  if (GetString(params, "auc_mu_weights", &tmp_str)) {
    auc_mu_weights = Common::StringToArray<double>(tmp_str, ',');
  }

  GetInt(params, "num_machines", &num_machines);
  CHECK_GT(num_machines, 0);

  GetInt(params, "local_listen_port", &local_listen_port);
  CHECK_GT(local_listen_port, 0);

  GetInt(params, "time_out", &time_out);
  CHECK_GT(time_out, 0);

  GetString(params, "machine_list_filename", &machine_list_filename);

  GetString(params, "machines", &machines);

  GetInt(params, "gpu_platform_id", &gpu_platform_id);

  GetInt(params, "gpu_device_id", &gpu_device_id);

  GetBool(params, "gpu_use_dp", &gpu_use_dp);

  GetInt(params, "num_gpu", &num_gpu);
  CHECK_GT(num_gpu, 0);
}

std::string Config::SaveMembersToString() const {
  std::stringstream str_buf;
  str_buf << "[data_sample_strategy: " << data_sample_strategy << "]\n";
  str_buf << "[data: " << data << "]\n";
  str_buf << "[valid: " << Common::Join(valid, ",") << "]\n";
  str_buf << "[num_iterations: " << num_iterations << "]\n";
  str_buf << "[learning_rate: " << learning_rate << "]\n";
  str_buf << "[num_leaves: " << num_leaves << "]\n";
  str_buf << "[num_threads: " << num_threads << "]\n";
  str_buf << "[seed: " << seed << "]\n";
  str_buf << "[deterministic: " << deterministic << "]\n";
  str_buf << "[force_col_wise: " << force_col_wise << "]\n";
  str_buf << "[force_row_wise: " << force_row_wise << "]\n";
  str_buf << "[histogram_pool_size: " << histogram_pool_size << "]\n";
  str_buf << "[max_depth: " << max_depth << "]\n";
  str_buf << "[min_data_in_leaf: " << min_data_in_leaf << "]\n";
  str_buf << "[min_sum_hessian_in_leaf: " << min_sum_hessian_in_leaf << "]\n";
  str_buf << "[bagging_fraction: " << bagging_fraction << "]\n";
  str_buf << "[pos_bagging_fraction: " << pos_bagging_fraction << "]\n";
  str_buf << "[neg_bagging_fraction: " << neg_bagging_fraction << "]\n";
  str_buf << "[bagging_freq: " << bagging_freq << "]\n";
  str_buf << "[bagging_seed: " << bagging_seed << "]\n";
  str_buf << "[bagging_by_query: " << bagging_by_query << "]\n";
  str_buf << "[feature_fraction: " << feature_fraction << "]\n";
  str_buf << "[feature_fraction_bynode: " << feature_fraction_bynode << "]\n";
  str_buf << "[feature_fraction_seed: " << feature_fraction_seed << "]\n";
  str_buf << "[extra_trees: " << extra_trees << "]\n";
  str_buf << "[extra_seed: " << extra_seed << "]\n";
  str_buf << "[early_stopping_round: " << early_stopping_round << "]\n";
  str_buf << "[early_stopping_min_delta: " << early_stopping_min_delta << "]\n";
  str_buf << "[first_metric_only: " << first_metric_only << "]\n";
  str_buf << "[max_delta_step: " << max_delta_step << "]\n";
  str_buf << "[lambda_l1: " << lambda_l1 << "]\n";
  str_buf << "[lambda_l2: " << lambda_l2 << "]\n";
  str_buf << "[linear_lambda: " << linear_lambda << "]\n";
  str_buf << "[min_gain_to_split: " << min_gain_to_split << "]\n";
  str_buf << "[drop_rate: " << drop_rate << "]\n";
  str_buf << "[max_drop: " << max_drop << "]\n";
  str_buf << "[skip_drop: " << skip_drop << "]\n";
  str_buf << "[xgboost_dart_mode: " << xgboost_dart_mode << "]\n";
  str_buf << "[uniform_drop: " << uniform_drop << "]\n";
  str_buf << "[drop_seed: " << drop_seed << "]\n";
  str_buf << "[top_rate: " << top_rate << "]\n";
  str_buf << "[other_rate: " << other_rate << "]\n";
  str_buf << "[min_data_per_group: " << min_data_per_group << "]\n";
  str_buf << "[max_cat_threshold: " << max_cat_threshold << "]\n";
  str_buf << "[cat_l2: " << cat_l2 << "]\n";
  str_buf << "[cat_smooth: " << cat_smooth << "]\n";
  str_buf << "[max_cat_to_onehot: " << max_cat_to_onehot << "]\n";
  str_buf << "[top_k: " << top_k << "]\n";
  str_buf << "[monotone_constraints: " << Common::Join(Common::ArrayCast<int8_t, int>(monotone_constraints), ",") << "]\n";
  str_buf << "[monotone_constraints_method: " << monotone_constraints_method << "]\n";
  str_buf << "[monotone_penalty: " << monotone_penalty << "]\n";
  str_buf << "[feature_contri: " << Common::Join(feature_contri, ",") << "]\n";
  str_buf << "[forcedsplits_filename: " << forcedsplits_filename << "]\n";
  str_buf << "[refit_decay_rate: " << refit_decay_rate << "]\n";
  str_buf << "[cegb_tradeoff: " << cegb_tradeoff << "]\n";
  str_buf << "[cegb_penalty_split: " << cegb_penalty_split << "]\n";
  str_buf << "[cegb_penalty_feature_lazy: " << Common::Join(cegb_penalty_feature_lazy, ",") << "]\n";
  str_buf << "[cegb_penalty_feature_coupled: " << Common::Join(cegb_penalty_feature_coupled, ",") << "]\n";
  str_buf << "[path_smooth: " << path_smooth << "]\n";
  str_buf << "[interaction_constraints: " << interaction_constraints << "]\n";
  str_buf << "[verbosity: " << verbosity << "]\n";
  str_buf << "[saved_feature_importance_type: " << saved_feature_importance_type << "]\n";
  str_buf << "[use_quantized_grad: " << use_quantized_grad << "]\n";
  str_buf << "[num_grad_quant_bins: " << num_grad_quant_bins << "]\n";
  str_buf << "[quant_train_renew_leaf: " << quant_train_renew_leaf << "]\n";
  str_buf << "[stochastic_rounding: " << stochastic_rounding << "]\n";
  str_buf << "[linear_tree: " << linear_tree << "]\n";
  str_buf << "[max_bin: " << max_bin << "]\n";
  str_buf << "[max_bin_by_feature: " << Common::Join(max_bin_by_feature, ",") << "]\n";
  str_buf << "[min_data_in_bin: " << min_data_in_bin << "]\n";
  str_buf << "[bin_construct_sample_cnt: " << bin_construct_sample_cnt << "]\n";
  str_buf << "[data_random_seed: " << data_random_seed << "]\n";
  str_buf << "[is_enable_sparse: " << is_enable_sparse << "]\n";
  str_buf << "[enable_bundle: " << enable_bundle << "]\n";
  str_buf << "[use_missing: " << use_missing << "]\n";
  str_buf << "[zero_as_missing: " << zero_as_missing << "]\n";
  str_buf << "[feature_pre_filter: " << feature_pre_filter << "]\n";
  str_buf << "[pre_partition: " << pre_partition << "]\n";
  str_buf << "[two_round: " << two_round << "]\n";
  str_buf << "[header: " << header << "]\n";
  str_buf << "[label_column: " << label_column << "]\n";
  str_buf << "[weight_column: " << weight_column << "]\n";
  str_buf << "[group_column: " << group_column << "]\n";
  str_buf << "[ignore_column: " << ignore_column << "]\n";
  str_buf << "[categorical_feature: " << categorical_feature << "]\n";
  str_buf << "[forcedbins_filename: " << forcedbins_filename << "]\n";
  str_buf << "[precise_float_parser: " << precise_float_parser << "]\n";
  str_buf << "[parser_config_file: " << parser_config_file << "]\n";
  str_buf << "[objective_seed: " << objective_seed << "]\n";
  str_buf << "[num_class: " << num_class << "]\n";
  str_buf << "[is_unbalance: " << is_unbalance << "]\n";
  str_buf << "[scale_pos_weight: " << scale_pos_weight << "]\n";
  str_buf << "[sigmoid: " << sigmoid << "]\n";
  str_buf << "[boost_from_average: " << boost_from_average << "]\n";
  str_buf << "[reg_sqrt: " << reg_sqrt << "]\n";
  str_buf << "[alpha: " << alpha << "]\n";
  str_buf << "[fair_c: " << fair_c << "]\n";
  str_buf << "[poisson_max_delta_step: " << poisson_max_delta_step << "]\n";
  str_buf << "[tweedie_variance_power: " << tweedie_variance_power << "]\n";
  str_buf << "[lambdarank_truncation_level: " << lambdarank_truncation_level << "]\n";
  str_buf << "[lambdarank_norm: " << lambdarank_norm << "]\n";
  str_buf << "[label_gain: " << Common::Join(label_gain, ",") << "]\n";
  str_buf << "[lambdarank_position_bias_regularization: " << lambdarank_position_bias_regularization << "]\n";
  str_buf << "[eval_at: " << Common::Join(eval_at, ",") << "]\n";
  str_buf << "[multi_error_top_k: " << multi_error_top_k << "]\n";
  str_buf << "[auc_mu_weights: " << Common::Join(auc_mu_weights, ",") << "]\n";
  str_buf << "[num_machines: " << num_machines << "]\n";
  str_buf << "[local_listen_port: " << local_listen_port << "]\n";
  str_buf << "[time_out: " << time_out << "]\n";
  str_buf << "[machine_list_filename: " << machine_list_filename << "]\n";
  str_buf << "[machines: " << machines << "]\n";
  str_buf << "[gpu_platform_id: " << gpu_platform_id << "]\n";
  str_buf << "[gpu_device_id: " << gpu_device_id << "]\n";
  str_buf << "[gpu_use_dp: " << gpu_use_dp << "]\n";
  str_buf << "[num_gpu: " << num_gpu << "]\n";
  return str_buf.str();
}

const std::unordered_map<std::string, std::vector<std::string>>& Config::parameter2aliases() {
  static std::unordered_map<std::string, std::vector<std::string>> map({
    {"config", {"config_file"}},
    {"task", {"task_type"}},
    {"objective", {"objective_type", "app", "application", "loss"}},
    {"boosting", {"boosting_type", "boost"}},
    {"data_sample_strategy", {}},
    {"data", {"train", "train_data", "train_data_file", "data_filename"}},
    {"valid", {"test", "valid_data", "valid_data_file", "test_data", "test_data_file", "valid_filenames"}},
    {"num_iterations", {"num_iteration", "n_iter", "num_tree", "num_trees", "num_round", "num_rounds", "nrounds", "num_boost_round", "n_estimators", "max_iter"}},
    {"learning_rate", {"shrinkage_rate", "eta"}},
    {"num_leaves", {"num_leaf", "max_leaves", "max_leaf", "max_leaf_nodes"}},
    {"tree_learner", {"tree", "tree_type", "tree_learner_type"}},
    {"num_threads", {"num_thread", "nthread", "nthreads", "n_jobs"}},
    {"device_type", {"device"}},
    {"seed", {"random_seed", "random_state"}},
    {"deterministic", {}},
    {"force_col_wise", {}},
    {"force_row_wise", {}},
    {"histogram_pool_size", {"hist_pool_size"}},
    {"max_depth", {}},
    {"min_data_in_leaf", {"min_data_per_leaf", "min_data", "min_child_samples", "min_samples_leaf"}},
    {"min_sum_hessian_in_leaf", {"min_sum_hessian_per_leaf", "min_sum_hessian", "min_hessian", "min_child_weight"}},
    {"bagging_fraction", {"sub_row", "subsample", "bagging"}},
    {"pos_bagging_fraction", {"pos_sub_row", "pos_subsample", "pos_bagging"}},
    {"neg_bagging_fraction", {"neg_sub_row", "neg_subsample", "neg_bagging"}},
    {"bagging_freq", {"subsample_freq"}},
    {"bagging_seed", {"bagging_fraction_seed"}},
    {"bagging_by_query", {}},
    {"feature_fraction", {"sub_feature", "colsample_bytree"}},
    {"feature_fraction_bynode", {"sub_feature_bynode", "colsample_bynode"}},
    {"feature_fraction_seed", {}},
    {"extra_trees", {"extra_tree"}},
    {"extra_seed", {}},
    {"early_stopping_round", {"early_stopping_rounds", "early_stopping", "n_iter_no_change"}},
    {"early_stopping_min_delta", {}},
    {"first_metric_only", {}},
    {"max_delta_step", {"max_tree_output", "max_leaf_output"}},
    {"lambda_l1", {"reg_alpha", "l1_regularization"}},
    {"lambda_l2", {"reg_lambda", "lambda", "l2_regularization"}},
    {"linear_lambda", {}},
    {"min_gain_to_split", {"min_split_gain"}},
    {"drop_rate", {"rate_drop"}},
    {"max_drop", {}},
    {"skip_drop", {}},
    {"xgboost_dart_mode", {}},
    {"uniform_drop", {}},
    {"drop_seed", {}},
    {"top_rate", {}},
    {"other_rate", {}},
    {"min_data_per_group", {}},
    {"max_cat_threshold", {}},
    {"cat_l2", {}},
    {"cat_smooth", {}},
    {"max_cat_to_onehot", {}},
    {"top_k", {"topk"}},
    {"monotone_constraints", {"mc", "monotone_constraint", "monotonic_cst"}},
    {"monotone_constraints_method", {"monotone_constraining_method", "mc_method"}},
    {"monotone_penalty", {"monotone_splits_penalty", "ms_penalty", "mc_penalty"}},
    {"feature_contri", {"feature_contrib", "fc", "fp", "feature_penalty"}},
    {"forcedsplits_filename", {"fs", "forced_splits_filename", "forced_splits_file", "forced_splits"}},
    {"refit_decay_rate", {}},
    {"cegb_tradeoff", {}},
    {"cegb_penalty_split", {}},
    {"cegb_penalty_feature_lazy", {}},
    {"cegb_penalty_feature_coupled", {}},
    {"path_smooth", {}},
    {"interaction_constraints", {}},
    {"verbosity", {"verbose"}},
    {"input_model", {"model_input", "model_in"}},
    {"output_model", {"model_output", "model_out"}},
    {"saved_feature_importance_type", {}},
    {"snapshot_freq", {"save_period"}},
    {"use_quantized_grad", {}},
    {"num_grad_quant_bins", {}},
    {"quant_train_renew_leaf", {}},
    {"stochastic_rounding", {}},
    {"linear_tree", {"linear_trees"}},
    {"max_bin", {"max_bins"}},
    {"max_bin_by_feature", {}},
    {"min_data_in_bin", {}},
    {"bin_construct_sample_cnt", {"subsample_for_bin"}},
    {"data_random_seed", {"data_seed"}},
    {"is_enable_sparse", {"is_sparse", "enable_sparse", "sparse"}},
    {"enable_bundle", {"is_enable_bundle", "bundle"}},
    {"use_missing", {}},
    {"zero_as_missing", {}},
    {"feature_pre_filter", {}},
    {"pre_partition", {"is_pre_partition"}},
    {"two_round", {"two_round_loading", "use_two_round_loading"}},
    {"header", {"has_header"}},
    {"label_column", {"label"}},
    {"weight_column", {"weight"}},
    {"group_column", {"group", "group_id", "query_column", "query", "query_id"}},
    {"ignore_column", {"ignore_feature", "blacklist"}},
    {"categorical_feature", {"cat_feature", "categorical_column", "cat_column", "categorical_features"}},
    {"forcedbins_filename", {}},
    {"save_binary", {"is_save_binary", "is_save_binary_file"}},
    {"precise_float_parser", {}},
    {"parser_config_file", {}},
    {"start_iteration_predict", {}},
    {"num_iteration_predict", {}},
    {"predict_raw_score", {"is_predict_raw_score", "predict_rawscore", "raw_score"}},
    {"predict_leaf_index", {"is_predict_leaf_index", "leaf_index"}},
    {"predict_contrib", {"is_predict_contrib", "contrib"}},
    {"predict_disable_shape_check", {}},
    {"pred_early_stop", {}},
    {"pred_early_stop_freq", {}},
    {"pred_early_stop_margin", {}},
    {"output_result", {"predict_result", "prediction_result", "predict_name", "prediction_name", "pred_name", "name_pred"}},
    {"convert_model_language", {}},
    {"convert_model", {"convert_model_file"}},
    {"objective_seed", {}},
    {"num_class", {"num_classes"}},
    {"is_unbalance", {"unbalance", "unbalanced_sets"}},
    {"scale_pos_weight", {}},
    {"sigmoid", {}},
    {"boost_from_average", {}},
    {"reg_sqrt", {}},
    {"alpha", {}},
    {"fair_c", {}},
    {"poisson_max_delta_step", {}},
    {"tweedie_variance_power", {}},
    {"lambdarank_truncation_level", {}},
    {"lambdarank_norm", {}},
    {"label_gain", {}},
    {"lambdarank_position_bias_regularization", {}},
    {"metric", {"metrics", "metric_types"}},
    {"metric_freq", {"output_freq"}},
    {"is_provide_training_metric", {"training_metric", "is_training_metric", "train_metric"}},
    {"eval_at", {"ndcg_eval_at", "ndcg_at", "map_eval_at", "map_at"}},
    {"multi_error_top_k", {}},
    {"auc_mu_weights", {}},
    {"num_machines", {"num_machine"}},
    {"local_listen_port", {"local_port", "port"}},
    {"time_out", {}},
    {"machine_list_filename", {"machine_list_file", "machine_list", "mlist"}},
    {"machines", {"workers", "nodes"}},
    {"gpu_platform_id", {}},
    {"gpu_device_id", {}},
    {"gpu_use_dp", {}},
    {"num_gpu", {}},
  });
  return map;
}

const std::unordered_map<std::string, std::string>& Config::ParameterTypes() {
  static std::unordered_map<std::string, std::string> map({
    {"config", "string"},
    {"objective", "string"},
    {"boosting", "string"},
    {"data_sample_strategy", "string"},
    {"data", "string"},
    {"valid", "vector<string>"},
    {"num_iterations", "int"},
    {"learning_rate", "double"},
    {"num_leaves", "int"},
    {"tree_learner", "string"},
    {"num_threads", "int"},
    {"device_type", "string"},
    {"seed", "int"},
    {"deterministic", "bool"},
    {"force_col_wise", "bool"},
    {"force_row_wise", "bool"},
    {"histogram_pool_size", "double"},
    {"max_depth", "int"},
    {"min_data_in_leaf", "int"},
    {"min_sum_hessian_in_leaf", "double"},
    {"bagging_fraction", "double"},
    {"pos_bagging_fraction", "double"},
    {"neg_bagging_fraction", "double"},
    {"bagging_freq", "int"},
    {"bagging_seed", "int"},
    {"bagging_by_query", "bool"},
    {"feature_fraction", "double"},
    {"feature_fraction_bynode", "double"},
    {"feature_fraction_seed", "int"},
    {"extra_trees", "bool"},
    {"extra_seed", "int"},
    {"early_stopping_round", "int"},
    {"early_stopping_min_delta", "double"},
    {"first_metric_only", "bool"},
    {"max_delta_step", "double"},
    {"lambda_l1", "double"},
    {"lambda_l2", "double"},
    {"linear_lambda", "double"},
    {"min_gain_to_split", "double"},
    {"drop_rate", "double"},
    {"max_drop", "int"},
    {"skip_drop", "double"},
    {"xgboost_dart_mode", "bool"},
    {"uniform_drop", "bool"},
    {"drop_seed", "int"},
    {"top_rate", "double"},
    {"other_rate", "double"},
    {"min_data_per_group", "int"},
    {"max_cat_threshold", "int"},
    {"cat_l2", "double"},
    {"cat_smooth", "double"},
    {"max_cat_to_onehot", "int"},
    {"top_k", "int"},
    {"monotone_constraints", "vector<int>"},
    {"monotone_constraints_method", "string"},
    {"monotone_penalty", "double"},
    {"feature_contri", "vector<double>"},
    {"forcedsplits_filename", "string"},
    {"refit_decay_rate", "double"},
    {"cegb_tradeoff", "double"},
    {"cegb_penalty_split", "double"},
    {"cegb_penalty_feature_lazy", "vector<double>"},
    {"cegb_penalty_feature_coupled", "vector<double>"},
    {"path_smooth", "double"},
    {"interaction_constraints", "vector<vector<int>>"},
    {"verbosity", "int"},
    {"input_model", "string"},
    {"output_model", "string"},
    {"saved_feature_importance_type", "int"},
    {"snapshot_freq", "int"},
    {"use_quantized_grad", "bool"},
    {"num_grad_quant_bins", "int"},
    {"quant_train_renew_leaf", "bool"},
    {"stochastic_rounding", "bool"},
    {"linear_tree", "bool"},
    {"max_bin", "int"},
    {"max_bin_by_feature", "vector<int>"},
    {"min_data_in_bin", "int"},
    {"bin_construct_sample_cnt", "int"},
    {"data_random_seed", "int"},
    {"is_enable_sparse", "bool"},
    {"enable_bundle", "bool"},
    {"use_missing", "bool"},
    {"zero_as_missing", "bool"},
    {"feature_pre_filter", "bool"},
    {"pre_partition", "bool"},
    {"two_round", "bool"},
    {"header", "bool"},
    {"label_column", "string"},
    {"weight_column", "string"},
    {"group_column", "string"},
    {"ignore_column", "vector<int>"},
    {"categorical_feature", "vector<int>"},
    {"forcedbins_filename", "string"},
    {"save_binary", "bool"},
    {"precise_float_parser", "bool"},
    {"parser_config_file", "string"},
    {"start_iteration_predict", "int"},
    {"num_iteration_predict", "int"},
    {"predict_raw_score", "bool"},
    {"predict_leaf_index", "bool"},
    {"predict_contrib", "bool"},
    {"predict_disable_shape_check", "bool"},
    {"pred_early_stop", "bool"},
    {"pred_early_stop_freq", "int"},
    {"pred_early_stop_margin", "double"},
    {"output_result", "string"},
    {"convert_model_language", "string"},
    {"convert_model", "string"},
    {"objective_seed", "int"},
    {"num_class", "int"},
    {"is_unbalance", "bool"},
    {"scale_pos_weight", "double"},
    {"sigmoid", "double"},
    {"boost_from_average", "bool"},
    {"reg_sqrt", "bool"},
    {"alpha", "double"},
    {"fair_c", "double"},
    {"poisson_max_delta_step", "double"},
    {"tweedie_variance_power", "double"},
    {"lambdarank_truncation_level", "int"},
    {"lambdarank_norm", "bool"},
    {"label_gain", "vector<double>"},
    {"lambdarank_position_bias_regularization", "double"},
    {"metric", "vector<string>"},
    {"metric_freq", "int"},
    {"is_provide_training_metric", "bool"},
    {"eval_at", "vector<int>"},
    {"multi_error_top_k", "int"},
    {"auc_mu_weights", "vector<double>"},
    {"num_machines", "int"},
    {"local_listen_port", "int"},
    {"time_out", "int"},
    {"machine_list_filename", "string"},
    {"machines", "string"},
    {"gpu_platform_id", "int"},
    {"gpu_device_id", "int"},
    {"gpu_use_dp", "bool"},
    {"num_gpu", "int"},
  });
  return map;
}

}  // namespace LightGBM