config.h

/// desc and descl2 fields must be written in reStructuredText format

#ifndef LIGHTGBM_CONFIG_H_
#define LIGHTGBM_CONFIG_H_

#include <LightGBM/utils/common.h>
#include <LightGBM/utils/log.h>

#include <LightGBM/meta.h>
#include <LightGBM/export.h>

#include <vector>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <algorithm>
#include <memory>

namespace LightGBM {

/*! \brief Types of tasks */
enum TaskType {
  kTrain, kPredict, kConvertModel, KRefitTree
};
const int kDefaultNumLeaves = 31;

struct Config {
public:
  std::string ToString() const;
  /*!
  * \brief Get string value by specific name of key
  * \param params Store the key and value for params
  * \param name Name of key
  * \param out Value will assign to out if key exists
  * \return True if key exists
  */
  inline static bool GetString(
    const std::unordered_map<std::string, std::string>& params,
    const std::string& name, std::string* out);

  /*!
  * \brief Get int value by specific name of key
  * \param params Store the key and value for params
  * \param name Name of key
  * \param out Value will assign to out if key exists
  * \return True if key exists
  */
  inline static bool GetInt(
    const std::unordered_map<std::string, std::string>& params,
    const std::string& name, int* out);

  /*!
  * \brief Get double value by specific name of key
  * \param params Store the key and value for params
  * \param name Name of key
  * \param out Value will assign to out if key exists
  * \return True if key exists
  */
  inline static bool GetDouble(
    const std::unordered_map<std::string, std::string>& params,
    const std::string& name, double* out);

  /*!
  * \brief Get bool value by specific name of key
  * \param params Store the key and value for params
  * \param name Name of key
  * \param out Value will assign to out if key exists
  * \return True if key exists
  */
  inline static bool GetBool(
    const std::unordered_map<std::string, std::string>& params,
    const std::string& name, bool* out);

  static void KV2Map(std::unordered_map<std::string, std::string>& params, const char* kv);
  static std::unordered_map<std::string, std::string> Str2Map(const char* parameters);

  #pragma region Parameters

  #pragma region Core Parameters

  // [doc-only]
  // alias = config_file
  // desc = path of config file
  // desc = **Note**: can be used only in CLI version
  std::string config = "";

  // [doc-only]
  // type = enum
  // default = train
  // options = train, predict, convert_model, refit
  // alias = task_type
  // desc = ``train``, for training, aliases: ``training``
  // desc = ``predict``, for prediction, aliases: ``prediction``, ``test``
  // desc = ``convert_model``, for converting model file into if-else format, see more information in `IO Parameters <#io-parameters>`__
  // desc = ``refit``, for refitting existing models with new data, aliases: ``refit_tree``
  // desc = **Note**: can be used only in CLI version
  TaskType task = TaskType::kTrain;

  // [doc-only]
  // type = enum
  // options = regression, regression_l1, huber, fair, poisson, quantile, mape, gammma, tweedie, binary, multiclass, multiclassova, xentropy, xentlambda, lambdarank
  // alias = objective_type, app, application
  // desc = regression application
  // descl2 = ``regression_l2``, L2 loss, aliases: ``regression``, ``mean_squared_error``, ``mse``, ``l2_root``, ``root_mean_squared_error``, ``rmse``
  // descl2 = ``regression_l1``, L1 loss, aliases: ``mean_absolute_error``, ``mae``
  // descl2 = ``huber``, `Huber loss <https://en.wikipedia.org/wiki/Huber_loss>`__
  // descl2 = ``fair``, `Fair loss <https://www.kaggle.com/c/allstate-claims-severity/discussion/24520>`__
  // descl2 = ``poisson``, `Poisson regression <https://en.wikipedia.org/wiki/Poisson_regression>`__
  // descl2 = ``quantile``, `Quantile regression <https://en.wikipedia.org/wiki/Quantile_regression>`__
  // descl2 = ``mape``, `MAPE loss <https://en.wikipedia.org/wiki/Mean_absolute_percentage_error>`__, aliases: ``mean_absolute_percentage_error``
  // descl2 = ``gamma``, Gamma regression with log-link. It might be useful, e.g., for modeling insurance claims severity, or for any target that might be `gamma-distributed <https://en.wikipedia.org/wiki/Gamma_distribution#Applications>`__
  // descl2 = ``tweedie``, Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any target that might be `tweedie-distributed <https://en.wikipedia.org/wiki/Tweedie_distribution#Applications>`__
  // desc = ``binary``, binary `log loss <https://en.wikipedia.org/wiki/Cross_entropy>`__ classification (or logistic regression). Requires labels in {0, 1}; see ``xentropy`` for general probability labels in [0, 1]
  // desc = multi-class classification application
  // descl2 = ``multiclass``, `softmax <https://en.wikipedia.org/wiki/Softmax_function>`__ objective function, aliases: ``softmax``
  // descl2 = ``multiclassova``, `One-vs-All <https://en.wikipedia.org/wiki/Multiclass_classification#One-vs.-rest>`__ binary objective function, aliases: ``multiclass_ova``, ``ova``, ``ovr``
  // descl2 = ``num_class`` should be set as well
  // desc = cross-entropy application
  // descl2 = ``xentropy``, objective function for cross-entropy (with optional linear weights), aliases: ``cross_entropy``
  // descl2 = ``xentlambda``, alternative parameterization of cross-entropy, aliases: ``cross_entropy_lambda``
  // descl2 = label is anything in interval [0, 1]
  // desc = ``lambdarank``, `lambdarank <https://papers.nips.cc/paper/2971-learning-to-rank-with-nonsmooth-cost-functions.pdf>`__ application
  // descl2 = label should be ``int`` type in lambdarank tasks, and larger number represents the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect)
  // descl2 = `label_gain <#objective-parameters>`__ can be used to set the gain (weight) of ``int`` label
  // descl2 = all values in ``label`` must be smaller than number of elements in ``label_gain``
  std::string objective = "regression";

  // [doc-only]
  // type = enum
  // alias = boosting_type, boost
  // options = gbdt, gbrt, rf, random_forest, dart, goss
  // desc = ``gbdt``, traditional Gradient Boosting Decision Tree, aliases: ``gbrt``
  // desc = ``rf``, Random Forest, aliases: ``random_forest``
  // desc = ``dart``, `Dropouts meet Multiple Additive Regression Trees <https://arxiv.org/abs/1505.01866>`__
  // desc = ``goss``, Gradient-based One-Side Sampling
  std::string boosting = "gbdt";

  // alias = train, train_data, data_filename
  // desc = training data, LightGBM will train from this data
  std::string data = "";

  // alias = test, valid_data, valid_data_file, test_data, valid_filenames
  // default = ""
  // desc = validation/test data, LightGBM will output metrics for these data
  // desc = support multiple validation data, separated by ``,``
  std::vector<std::string> valid;

  // alias = num_iteration, num_tree, num_trees, num_round, num_rounds, num_boost_round, n_estimators
  // check = >=0
  // desc = number of boosting iterations
  // desc = **Note**: for Python/R-package, **this parameter is ignored**, use ``num_boost_round`` (Python) or ``nrounds`` (R) input arguments of ``train`` and ``cv`` methods instead
  // desc = **Note**: internally, LightGBM constructs ``num_class * num_iterations`` trees for multi-class classification problems
  int num_iterations = 100;

  // alias = shrinkage_rate
  // check = >0.0
  // desc = shrinkage rate
  // desc = in ``dart``, it also affects on normalization weights of dropped trees
  double learning_rate = 0.1;

  // default = 31
  // alias = num_leaf
  // check = >1
  // desc = max number of leaves in one tree
  int num_leaves = kDefaultNumLeaves;

  // [doc-only]
  // type = enum
  // options = serial, feature, data, voting
  // alias = tree, tree_learner_type
  // desc = ``serial``, single machine tree learner
  // desc = ``feature``, feature parallel tree learner, aliases: ``feature_parallel``
  // desc = ``data``, data parallel tree learner, aliases: ``data_parallel``
  // desc = ``voting``, voting parallel tree learner, aliases: ``voting_parallel``
  // desc = refer to `Parallel Learning Guide <./Parallel-Learning-Guide.rst>`__ to get more details
  std::string tree_learner = "serial";

  // alias = num_thread, nthread, nthreads
  // desc = number of threads for LightGBM
  // desc = ``0`` means default number of threads in OpenMP
  // desc = for the best speed, set this to the number of **real CPU cores**, not the number of threads (most CPUs use `hyper-threading <https://en.wikipedia.org/wiki/Hyper-threading>`__ to generate 2 threads per CPU core)
  // desc = do not set it too large if your dataset is small (for instance, do not use 64 threads for a dataset with 10,000 rows)
  // desc = be aware a task manager or any similar CPU monitoring tool might report that cores not being fully utilized. **This is normal**
  // desc = for parallel learning, do not use all CPU cores because this will cause poor performance for the network communication
  int num_threads = 0;

  // [doc-only]
  // type = enum
  // options = cpu, gpu
  // alias = device
  // desc = device for the tree learning, you can use GPU to achieve the faster learning
  // desc = **Note**: it is recommended to use the smaller ``max_bin`` (e.g. 63) to get the better speed up
  // desc = **Note**: for the faster speed, GPU uses 32-bit float point to sum up by default, so this may affect the accuracy for some tasks. You can set ``gpu_use_dp=true`` to enable 64-bit float point, but it will slow down the training
  // desc = **Note**: refer to `Installation Guide <./Installation-Guide.rst#build-gpu-version>`__ to build LightGBM with GPU support
  std::string device_type = "cpu";

  // [doc-only]
  // alias = random_seed
  // desc = this seed is used to generate other seeds, e.g. ``data_random_seed``, ``feature_fraction_seed``
  // desc = will be overridden, if you set other seeds
  int seed = 0;

  #pragma endregion

  #pragma region Learning Control Parameters

  // desc = limit the max depth for tree model. This is used to deal with over-fitting when ``#data`` is small. Tree still grows leaf-wise
  // desc = ``< 0`` means no limit
  int max_depth = -1;

  // alias = min_data_per_leaf, min_data, min_child_samples
  // check = >=0
  // desc = minimal number of data in one leaf. Can be used to deal with over-fitting
  int min_data_in_leaf = 20;

  // alias = min_sum_hessian_per_leaf, min_sum_hessian, min_hessian, min_child_weight
  // check = >=0.0
  // desc = minimal sum hessian in one leaf. Like ``min_data_in_leaf``, it can be used to deal with over-fitting
  double min_sum_hessian_in_leaf = 1e-3;

  // alias = sub_row, subsample, bagging
  // check = >0.0
  // check = <=1.0
  // desc = like ``feature_fraction``, but this will randomly select part of data without resampling
  // desc = can be used to speed up training
  // desc = can be used to deal with over-fitting
  // desc = **Note**: to enable bagging, ``bagging_freq`` should be set to a non zero value as well
  double bagging_fraction = 1.0;

  // alias = subsample_freq
  // desc = frequency for bagging
  // desc = ``0`` means disable bagging; ``k`` means perform bagging at every ``k`` iteration
  // desc = **Note**: to enable bagging, ``bagging_fraction`` should be set to value smaller than ``1.0`` as well
  int bagging_freq = 0;

  // alias = bagging_fraction_seed
  // desc = random seed for bagging
  int bagging_seed = 3;

  // alias = sub_feature, colsample_bytree
  // check = >0.0
  // check = <=1.0
  // desc = LightGBM will randomly select part of features on each iteration if ``feature_fraction`` smaller than ``1.0``. For example, if you set it to ``0.8``, LightGBM will select 80% of features before training each tree
  // desc = can be used to speed up training
  // desc = can be used to deal with over-fitting
  double feature_fraction = 1.0;

  // desc = random seed for ``feature_fraction``
  int feature_fraction_seed = 2;

  // alias = early_stopping_rounds, early_stopping
  // desc = will stop training if one metric of one validation data doesn't improve in last ``early_stopping_round`` rounds
  // desc = ``<= 0`` means disable
  int early_stopping_round = 0;

  // alias = max_tree_output, max_leaf_output
  // desc = used to limit the max output of tree leaves
  // desc = ``<= 0`` means no constraint
  // desc = the final max output of leaves is ``learning_rate * max_delta_step``
  double max_delta_step = 0.0;

  // alias = reg_alpha
  // check = >=0.0
  // desc = L1 regularization
  double lambda_l1 = 0.0;

  // alias = reg_lambda
  // check = >=0.0
  // desc = L2 regularization
  double lambda_l2 = 0.0;

  // alias = min_split_gain
  // check = >=0.0
  // desc = the minimal gain to perform split
  double min_gain_to_split = 0.0;

  // check = >=0.0
  // check = <=1.0
  // desc = used only in ``dart``
  // desc = dropout rate
  double drop_rate = 0.1;

  // desc = used only in ``dart``
  // desc = max number of dropped trees on one iteration
  // desc = ``<=0`` means no limit
  int max_drop = 50;

  // check = >=0.0
  // check = <=1.0
  // desc = used only in ``dart``
  // desc = probability of skipping drop
  double skip_drop = 0.5;

  // desc = used only in ``dart``
  // desc = set this to ``true``, if you want to use xgboost dart mode
  bool xgboost_dart_mode = false;

  // desc = used only in ``dart``
  // desc = set this to ``true``, if you want to use uniform drop
  bool uniform_drop = false;

  // desc = used only in ``dart``
  // desc = random seed to choose dropping models
  int drop_seed = 4;

  // check = >=0.0
  // check = <=1.0
  // desc = used only in ``goss``
  // desc = the retain ratio of large gradient data
  double top_rate = 0.2;

  // check = >=0.0
  // check = <=1.0
  // desc = used only in ``goss``
  // desc = the retain ratio of small gradient data
  double other_rate = 0.1;

  // check = >0
  // desc = minimal number of data per categorical group
  int min_data_per_group = 100;

  // check = >0
  // desc = used for the categorical features
  // desc = limit the max threshold points in categorical features
  int max_cat_threshold = 32;

  // check = >=0.0
  // desc = used for the categorical features
  // desc = L2 regularization in categorcial split
  double cat_l2 = 10.0;

  // check = >=0.0
  // desc = used for the categorical features
  // desc = this can reduce the effect of noises in categorical features, especially for categories with few data
  double cat_smooth = 10.0;
  
  // check = >0
  // desc = when number of categories of one feature smaller than or equal to ``max_cat_to_onehot``, one-vs-other split algorithm will be used
  int max_cat_to_onehot = 4;

  // alias = topk
  // check = >0
  // desc = used in `Voting parallel <./Parallel-Learning-Guide.rst#choose-appropriate-parallel-algorithm>`__
  // desc = set this to larger value for more accurate result, but it will slow down the training speed
  int top_k = 20;

  // type = multi-int
  // alias = mc, monotone_constraint
  // default = None
  // desc = used for constraints of monotonic features
  // desc = ``1`` means increasing, ``-1`` means decreasing, ``0`` means non-constraint
  // desc = you need to specify all features in order. For example, ``mc=-1,0,1`` means decreasing for 1st feature, non-constraint for 2nd feature and increasing for the 3rd feature
  std::vector<int8_t> monotone_constraints;
  
  // alias = fs, forced_splits_filename, forced_splits_file, forced_splits
  // desc = path to a ``.json`` file that specifies splits to force at the top of every decision tree before best-first learning commences
  // desc = ``.json`` file can be arbitrarily nested, and each split contains ``feature``, ``threshold`` fields, as well as ``left`` and ``right`` fields representing subsplits
  // desc = categorical splits are forced in a one-hot fashion, with ``left`` representing the split containing the feature value and ``right`` representing other values
  // desc = see `this file <https://github.com/Microsoft/LightGBM/tree/master/examples/binary_classification/forced_splits.json>`__ as an example
  std::string forcedsplits_filename = "";

  #pragma endregion

  #pragma region IO Parameters

  // alias = verbose
  // desc = controls the level of LightGBM's verbosity
  // desc = ``< 0``: Fatal, ``= 0``: Error (Warn), ``> 0``: Info
  int verbosity = 1;

  // check = >1
  // desc = max number of bins that feature values will be bucketed in
  // desc = small number of bins may reduce training accuracy but may increase general power (deal with over-fitting)
  // desc = LightGBM will auto compress memory according to ``max_bin``. For example, LightGBM will use ``uint8_t`` for feature value if ``max_bin=255``
  int max_bin = 255;

  // check = >0
  // desc = minimal number of data inside one bin
  // desc = use this to avoid one-data-one-bin (potential over-fitting)
  int min_data_in_bin = 3;

  // alias = subsample_for_bin
  // check = >0
  // desc = number of data that sampled to construct histogram bins
  // desc = setting this to larger value will give better training result, but will increase data loading time
  // desc = set this to larger value if data is very sparse
  int bin_construct_sample_cnt = 200000;

  // desc = max cache size in MB for historical histogram
  // desc = ``< 0`` means no limit
  double histogram_pool_size = -1.0;

  // desc = random seed for data partition in parallel learning (excluding the ``feature_parallel`` mode)
  int data_random_seed = 1;

  // alias = model_output, model_out
  // desc = filename of output model in training
  std::string output_model = "LightGBM_model.txt";

  // desc = frequency of saving model file snapshot
  // desc = set this to positive value to enable this function. For example, the model file will be snapshotted at each iteration if ``snapshot_freq=1``
  int snapshot_freq = -1;

  // alias = model_input, model_in
  // desc = filename of input model
  // desc = for ``prediction`` task, this model will be applied to prediction data
  // desc = for ``train`` task, training will be continued from this model
  // desc = **Note**: can be used only in CLI version
  std::string input_model = "";

  // alias = predict_result, prediction_result
  // desc = filename of prediction result in ``prediction`` task
  std::string output_result = "LightGBM_predict_result.txt";

  // alias = init_score_filename, init_score_file, init_score, input_init_score
  // desc = path of file with training initial score
  // desc = if ``""``, will use ``train_data_file`` + ``.init`` (if exists)
  std::string initscore_filename = "";

  // alias = valid_data_init_scores, valid_init_score_file, valid_init_score
  // default = ""
  // desc = path(s) of file(s) with validation initial score(s)
  // desc = if ``""``, will use ``valid_data_file`` + ``.init`` (if exists)
  // desc = separate by ``,`` for multi-validation data
  std::vector<std::string> valid_data_initscores;

  // alias = is_pre_partition
  // desc = used for parallel learning (excluding the ``feature_parallel`` mode)
  // desc = ``true`` if training data are pre-partitioned, and different machines use different partitions
  bool pre_partition = false;

  // alias = is_enable_bundle, bundle
  // desc = set this to ``false`` to disable Exclusive Feature Bundling (EFB), which is described in `LightGBM: A Highly Efficient Gradient Boosting Decision Tree <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree>`__
  // desc = **Note**: disabling this may cause the slow training speed for sparse datasets
  bool enable_bundle = true;

  // check = >=0.0
  // check = <1.0
  // desc = max conflict rate for bundles in EFB
  // desc = set this to ``0.0`` to disallow the conflict and provide more accurate results
  // desc = set this to a larger value to achieve faster speed
  double max_conflict_rate = 0.0;

  // alias = is_sparse, enable_sparse, sparse
  // desc = used to enable/disable sparse optimization
  bool is_enable_sparse = true;

  // check = >0.0
  // check = <=1.0
  // desc = the threshold of zero elements precentage for treating a feature as a sparse one
  double sparse_threshold = 0.8;

  // desc = set this to ``false`` to disable the special handle of missing value
  bool use_missing = true;

  // desc = set this to ``true`` to treat all zero as missing values (including the unshown values in libsvm/sparse matrics)
  // desc = set this to ``false`` to use ``na`` for representing missing values
  bool zero_as_missing = false;

  // alias = two_round_loading, use_two_round_loading
  // desc = set this to ``true`` if data file is too big to fit in memory
  // desc = by default, LightGBM will map data file to memory and load features from memory. This will provide faster data loading speed, but may cause run out of memory error when the data file is very big
  bool two_round = false;

  // alias = is_save_binary, is_save_binary_file
  // desc = if ``true``, LightGBM will save the dataset (including validation data) to a binary file. This speed ups the data loading for the next time
  bool save_binary = false;

  // alias = load_from_binary_file, binary_load, load_binary
  // desc = set this to ``true`` to enable autoloading from previous saved binary datasets
  // desc = set this to ``false`` to ignore binary datasets
  bool enable_load_from_binary_file = true;

  // alias = has_header
  // desc = set this to ``true`` if input data has header
  bool header = false;

  // type = int or string
  // alias = label
  // desc = used to specify the label column
  // desc = use number for index, e.g. ``label=0`` means column\_0 is the label
  // desc = add a prefix ``name:`` for column name, e.g. ``label=name:is_click``
  std::string label_column = "";

  // type = int or string
  // alias = weight
  // desc = used to specify the weight column
  // desc = use number for index, e.g. ``weight=0`` means column\_0 is the weight
  // desc = add a prefix ``name:`` for column name, e.g. ``weight=name:weight``
  // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``, e.g. when label is column\_0, and weight is column\_1, the correct parameter is ``weight=0``
  std::string weight_column = "";

  // type = int or string
  // alias = group, group_id, query_column, query, query_id
  // desc = used to specify the query/group id column
  // desc = use number for index, e.g. ``query=0`` means column\_0 is the query id
  // desc = add a prefix ``name:`` for column name, e.g. ``query=name:query_id``
  // desc = **Note**: data should be grouped by query\_id
  // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``, e.g. when label is column\_0 and query\_id is column\_1, the correct parameter is ``query=0``
  std::string group_column = "";

  // type = multi-int or string
  // alias = ignore_feature, blacklist
  // desc = used to specify some ignoring columns in training
  // desc = use number for index, e.g. ``ignore_column=0,1,2`` means column\_0, column\_1 and column\_2 will be ignored
  // desc = add a prefix ``name:`` for column name, e.g. ``ignore_column=name:c1,c2,c3`` means c1, c2 and c3 will be ignored
  // desc = **Note**: works only in case of loading data directly from file
  // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``
  std::string ignore_column = "";

  // type = multi-int or string
  // alias = cat_feature, categorical_column, cat_column
  // desc = used to specify categorical features
  // desc = use number for index, e.g. ``categorical_feature=0,1,2`` means column\_0, column\_1 and column\_2 are categorical features
  // desc = add a prefix ``name:`` for column name, e.g. ``categorical_feature=name:c1,c2,c3`` means c1, c2 and c3 are categorical features
  // desc = **Note**: only supports categorical with ``int`` type
  // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``
  // desc = **Note**: all values should be less than ``Int32.MaxValue`` (2147483647)
  // desc = **Note**: the negative values will be treated as **missing values**
  std::string categorical_feature = "";

  // alias = is_predict_raw_score, predict_rawscore, raw_score
  // desc = used only in ``prediction`` task
  // desc = set this to ``true`` to predict only the raw scores
  // desc = set this to ``false`` to predict transformed scores
  bool predict_raw_score = false;

  // alias = is_predict_leaf_index, leaf_index
  // desc = used only in ``prediction`` task
  // desc = set this to ``true`` to predict with leaf index of all trees
  bool predict_leaf_index = false;

  // alias = is_predict_contrib, contrib
  // desc = used only in ``prediction`` task
  // desc = set this to ``true`` to estimate `SHAP values <https://arxiv.org/abs/1706.06060>`__, which represent how each feature contributs to each prediction
  // desc = produces ``#features + 1`` values where the last value is the expected value of the model output over the training data
  bool predict_contrib = false;

  // desc = used only in ``prediction`` task
  // desc = used to specify how many trained iterations will be used in prediction
  // desc = ``<= 0`` means no limit
  int num_iteration_predict = -1;

  // desc = used only in ``prediction`` task
  // desc = if ``true``, will use early-stopping to speed up the prediction. May affect the accuracy
  bool pred_early_stop = false;

  // desc = used only in ``prediction`` task
  // desc = the frequency of checking early-stopping prediction
  int pred_early_stop_freq = 10;

  // desc = used only in ``prediction`` task
  // desc = the threshold of margin in early-stopping prediction
  double pred_early_stop_margin = 10.0;

  // desc = used only in ``convert_model`` task
  // desc = only ``cpp`` is supported yet
  // desc = if ``convert_model_language`` is set and ``task=train``, the model will be also converted
  std::string convert_model_language = "";

  // alias = convert_model_file
  // desc = used only in ``convert_model`` task
  // desc = output filename of converted model
  std::string convert_model = "gbdt_prediction.cpp";

  #pragma endregion

  #pragma region Objective Parameters
  
  // alias=num_classes
  // desc=need to specify this in multi-class classification
  int num_class = 1;

  // check=>0
  // desc=parameter for sigmoid function. Will be used in binary and multiclassova classification and in lambdarank
  double sigmoid = 1.0;

  // desc=parameter for `Huber loss`_ and `Quantile regression`_. Will be used in regression task
  double alpha = 0.9;

  // desc=parameter for `Fair loss`_. Will be used in regression task
  double fair_c = 1.0;

  // desc=parameter for `Poisson regression`_ to safeguard optimization
  double poisson_max_delta_step = 0.7;

  // desc=only used in regression task
  // desc=adjust initial score to the mean of labels for faster convergence
  bool boost_from_average = true;

  // alias=unbalanced_sets
  // desc=used in binary classification
  // desc=set this to true if training data are unbalance
  bool is_unbalance = false;

  // check=>0
  // desc=weight of positive class in binary classification task
  double scale_pos_weight = 1.0;

  // desc=only used in regression, usually works better for the large-range of labels
  // desc=will fit sqrt(label) instead and prediction result will be also automatically converted to pow2(prediction)
  bool reg_sqrt = false;

  // desc=only used in tweedie regression
  // desc=controls the variance of the tweedie distribution
  // desc=set closer to 2 to shift towards a gamma distribution
  // desc=set closer to 1 to shift towards a poisson distribution
  double tweedie_variance_power = 1.5;

  // default = 0, 1, 3, 7, 15, 31, 63, ..., 2 ^ 30 - 1
  // desc=used in lambdarank
  // desc=relevant gain for labels. For example,the gain of label 2 is 3 if using default label gains
  // desc=separate by ,
  std::vector<double> label_gain;

  // check=>0
  // desc=used in lambdarank
  // desc=will optimize `NDCG`_ at this position
  int max_position = 20;

  #pragma endregion

  #pragma region Metric Parameters
  
  // [doc-only]
  // alias=metric_types
  // default=''
  // type=multi-enum
  // desc=metric to be evaluated on the evaluation sets **in addition** to what is provided in the training arguments
  // descl2='' (empty string or not specific),metric corresponding to specified objective will be used (this is possible only for pre - defined objective functions, otherwise no evaluation metric will be added)
  // descl2='None' (string,**not** a None value),no metric registered,alias=na
  // descl2=l1,absolute loss,alias=mean_absolute_error,mae,regression_l1
  // descl2=l2,square loss,alias=mean_squared_error,mse,regression_l2,regression
  // descl2=l2_root,root square loss,alias=root_mean_squared_error,rmse
  // descl2=quantile,`Quantile regression`_
  // descl2=mape,`MAPE loss`_,alias=mean_absolute_percentage_error
  // descl2=huber,`Huber loss`_
  // descl2=fair,`Fair loss`_
  // descl2=poisson,negative log-likelihood for `Poisson regression`_
  // descl2=gamma,negative log-likelihood for Gamma regression
  // descl2=gamma_deviance,residual deviance for Gamma regression
  // descl2=tweedie,negative log-likelihood for Tweedie regression
  // descl2=ndcg,`NDCG`_
  // descl2=map,`MAP`_,alias=mean_average_precision
  // descl2=auc,`AUC`_
  // descl2=binary_logloss,`log loss`_,alias=binary
  // descl2=binary_error,for one sample: 0 for correct classification,1 for error classification
  // descl2=multi_logloss,log loss for mulit-class classification,alias=multiclass,softmax,multiclassova,multiclass_ova,ova,ovr
  // descl2=multi_error,error rate for mulit-class classification
  // descl2=xentropy,cross-entropy (with optional linear weights),alias=cross_entropy
  // descl2=xentlambda,"intensity-weighted" cross-entropy,alias=cross_entropy_lambda
  // descl2=kldiv,`Kullback-Leibler divergence`_,alias=kullback_leibler
  // desc=support multiple metrics,separated by ,
  std::vector<std::string> metric;

  // check=>0
  // alias = output_freq
  // desc = frequency for metric output
  int metric_freq = 1;

  // alias=training_metric,is_training_metric,train_metric
  // desc=set this to true if you need to output metric result over training dataset
  bool is_provide_training_metric = false;

  // default=1,2,3,4,5
  // alias=ndcg_eval_at,ndcg_at
  // desc=`NDCG`_ evaluation positions,separated by ,
  std::vector<int> eval_at;

  #pragma endregion

  #pragma region Network Parameters

  // alias=num_machine
  // desc=used for parallel learning,the number of machines for parallel learning application
  // desc=need to set this in both socket and mpi versions
  int num_machines = 1;

  // alias = local_port
  // desc=TCP listen port for local machines
  // desc=you should allow this port in firewall settings before training
  int local_listen_port = 12400;

  // desc=socket time-out in minutes
  int time_out = 120;  // in minutes

  // alias=mlist
  // desc=file that lists machines for this parallel learning application
  // desc=each line contains one IP and one port for one machine. The format is ip port,separate by space
  std::string machine_list_filename = "";

  // alias=works,nodes
  // desc=list of machines, format: ip1:port1,ip2:port2
  std::string machines = "";

  #pragma endregion

  #pragma region GPU Parameters

  // desc=OpenCL platform ID. Usually each GPU vendor exposes one OpenCL platform
  // desc=default value is -1,means the system-wide default platform
  int gpu_platform_id = -1;

  // desc=OpenCL device ID in the specified platform. Each GPU in the selected platform has a unique device ID
  // desc=default value is -1,means the default device in the selected platform
  int gpu_device_id = -1;

  // desc=set to true to use double precision math on GPU (default using single precision)
  bool gpu_use_dp = false;

  #pragma endregion

  #pragma endregion

  bool is_parallel = false;
  bool is_parallel_find_bin = false;
  LIGHTGBM_EXPORT void Set(const std::unordered_map<std::string, std::string>& params);
  static std::unordered_map<std::string, std::string> alias_table;
  static std::unordered_set<std::string> parameter_set;
private:
  void CheckParamConflict();
  void GetMembersFromString(const std::unordered_map<std::string, std::string>& params);
  std::string SaveMembersToString() const;
};

inline bool Config::GetString(
  const std::unordered_map<std::string, std::string>& params,
  const std::string& name, std::string* out) {
  if (params.count(name) > 0) {
    *out = params.at(name);
    return true;
  }
  return false;
}

inline bool Config::GetInt(
  const std::unordered_map<std::string, std::string>& params,
  const std::string& name, int* out) {
  if (params.count(name) > 0) {
    if (!Common::AtoiAndCheck(params.at(name).c_str(), out)) {
      Log::Fatal("Parameter %s should be of type int, got \"%s\"",
                 name.c_str(), params.at(name).c_str());
    }
    return true;
  }
  return false;
}

inline bool Config::GetDouble(
  const std::unordered_map<std::string, std::string>& params,
  const std::string& name, double* out) {
  if (params.count(name) > 0) {
    if (!Common::AtofAndCheck(params.at(name).c_str(), out)) {
      Log::Fatal("Parameter %s should be of type double, got \"%s\"",
                 name.c_str(), params.at(name).c_str());
    }
    return true;
  }
  return false;
}

inline bool Config::GetBool(
  const std::unordered_map<std::string, std::string>& params,
  const std::string& name, bool* out) {
  if (params.count(name) > 0) {
    std::string value = params.at(name);
    std::transform(value.begin(), value.end(), value.begin(), Common::tolower);
    if (value == std::string("false") || value == std::string("-")) {
      *out = false;
    } else if (value == std::string("true") || value == std::string("+")) {
      *out = true;
    } else {
      Log::Fatal("Parameter %s should be \"true\"/\"+\" or \"false\"/\"-\", got \"%s\"",
                 name.c_str(), params.at(name).c_str());
    }
    return true;
  }
  return false;
}

struct ParameterAlias {
  static void KeyAliasTransform(std::unordered_map<std::string, std::string>* params) {
    std::unordered_map<std::string, std::string> tmp_map;
    for (const auto& pair : *params) {
      auto alias = Config::alias_table.find(pair.first);
      if (alias != Config::alias_table.end()) { // found alias
        auto alias_set = tmp_map.find(alias->second);
        if (alias_set != tmp_map.end()) { // alias already set
                                          // set priority by length & alphabetically to ensure reproducible behavior
          if (alias_set->second.size() < pair.first.size() ||
            (alias_set->second.size() == pair.first.size() && alias_set->second < pair.first)) {
            Log::Warning("%s is set with %s=%s, %s=%s will be ignored. Current value: %s=%s",
                         alias->second.c_str(), alias_set->second.c_str(), params->at(alias_set->second).c_str(),
                         pair.first.c_str(), pair.second.c_str(), alias->second.c_str(), params->at(alias_set->second).c_str());
          } else {
            Log::Warning("%s is set with %s=%s, will be overridden by %s=%s. Current value: %s=%s",
                         alias->second.c_str(), alias_set->second.c_str(), params->at(alias_set->second).c_str(),
                         pair.first.c_str(), pair.second.c_str(), alias->second.c_str(), pair.second.c_str());
            tmp_map[alias->second] = pair.first;
          }
        } else { // alias not set
          tmp_map.emplace(alias->second, pair.first);
        }
      } else if (Config::parameter_set.find(pair.first) == Config::parameter_set.end()) {
        Log::Warning("Unknown parameter: %s", pair.first.c_str());
      }
    }
    for (const auto& pair : tmp_map) {
      auto alias = params->find(pair.first);
      if (alias == params->end()) { // not find
        params->emplace(pair.first, params->at(pair.second));
        params->erase(pair.second);
      } else {
        Log::Warning("%s is set=%s, %s=%s will be ignored. Current value: %s=%s",
                     pair.first.c_str(), alias->second.c_str(), pair.second.c_str(), params->at(pair.second).c_str(),
                     pair.first.c_str(), alias->second.c_str());
      }
    }
  }
};

}   // namespace LightGBM

#endif   // LightGBM_CONFIG_H_