c_api.h

/*!
 * \file c_api.h
 * \copyright Copyright (c) 2016 Microsoft Corporation. All rights reserved.
 *            Licensed under the MIT License. See LICENSE file in the project root for license information.
 * \note
 * To avoid type conversion on large data, the most of our exposed interface supports both float32 and float64,
 * except the following:
 * 1. gradient and Hessian;
 * 2. current score for training and validation data.
 * .
 * The reason is that they are called frequently, and the type conversion on them may be time-cost.
 */
#ifndef LIGHTGBM_C_API_H_
#define LIGHTGBM_C_API_H_

#include <LightGBM/export.h>

#ifdef __cplusplus
#include <cstdint>
#include <cstdio>
#include <cstring>
#else
#include <stdint.h>
#include <stdio.h>
#include <string.h>
#endif


typedef void* DatasetHandle;  /*!< \brief Handle of dataset. */
typedef void* BoosterHandle;  /*!< \brief Handle of booster. */
typedef void* FastConfigHandle; /*!< \brief Handle of FastConfig. */

#define C_API_DTYPE_FLOAT32 (0)  /*!< \brief float32 (single precision float). */
#define C_API_DTYPE_FLOAT64 (1)  /*!< \brief float64 (double precision float). */
#define C_API_DTYPE_INT32   (2)  /*!< \brief int32. */
#define C_API_DTYPE_INT64   (3)  /*!< \brief int64. */

#define C_API_PREDICT_NORMAL     (0)  /*!< \brief Normal prediction, with transform (if needed). */
#define C_API_PREDICT_RAW_SCORE  (1)  /*!< \brief Predict raw score. */
#define C_API_PREDICT_LEAF_INDEX (2)  /*!< \brief Predict leaf index. */
#define C_API_PREDICT_CONTRIB    (3)  /*!< \brief Predict feature contributions (SHAP values). */

#define C_API_MATRIX_TYPE_CSR (0)  /*!< \brief CSR sparse matrix type. */
#define C_API_MATRIX_TYPE_CSC (1)  /*!< \brief CSC sparse matrix type. */

#define C_API_FEATURE_IMPORTANCE_SPLIT (0)  /*!< \brief Split type of feature importance. */
#define C_API_FEATURE_IMPORTANCE_GAIN  (1)  /*!< \brief Gain type of feature importance. */

/*!
 * \brief Get string message of the last error.
 * \return Error information
 */
LIGHTGBM_C_EXPORT const char* LGBM_GetLastError();

/*!
 * \brief Dump all parameter names with their aliases to JSON.
 * \param buffer_len String buffer length, if ``buffer_len < out_len``, you should re-allocate buffer
 * \param[out] out_len Actual output length
 * \param[out] out_str JSON format string of parameters, should pre-allocate memory
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DumpParamAliases(int64_t buffer_len,
                                            int64_t* out_len,
                                            char* out_str);

/*!
 * \brief Register a callback function for log redirecting.
 * \param callback The callback function to register
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_RegisterLogCallback(void (*callback)(const char*));

/*!
 * \brief Get number of samples based on parameters and total number of rows of data.
 * \param num_total_row Number of total rows
 * \param parameters Additional parameters, namely, ``bin_construct_sample_cnt`` is used to calculate returned value
 * \param[out] out Number of samples. This value is used to pre-allocate memory to hold sample indices when calling ``LGBM_SampleIndices``
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_GetSampleCount(int32_t num_total_row,
                                          const char* parameters,
                                          int* out);

/*!
 * \brief Create sample indices for total number of rows.
 * \note
 * You should pre-allocate memory for ``out``, you can get its length by ``LGBM_GetSampleCount``.
 * \param num_total_row Number of total rows
 * \param parameters Additional parameters, namely, ``bin_construct_sample_cnt`` and ``data_random_seed`` are used to produce the output
 * \param[out] out Created indices, type is int32_t
 * \param[out] out_len Number of indices
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_SampleIndices(int32_t num_total_row,
                                         const char* parameters,
                                         void* out,
                                         int32_t* out_len);

/* --- start Dataset interface */

/*!
 * \brief Load dataset from file (like LightGBM CLI version does).
 * \param filename The name of the file
 * \param parameters Additional parameters
 * \param reference Used to align bin mapper with other dataset, nullptr means isn't used
 * \param[out] out A loaded dataset
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromFile(const char* filename,
                                                 const char* parameters,
                                                 const DatasetHandle reference,
                                                 DatasetHandle* out);

/*!
 * \brief Allocate the space for dataset and bucket feature bins according to sampled data.
 * \param sample_data Sampled data, grouped by the column
 * \param sample_indices Indices of sampled data
 * \param ncol Number of columns
 * \param num_per_col Size of each sampling column
 * \param num_sample_row Number of sampled rows
 * \param num_local_row Total number of rows local to machine
 * \param num_dist_row Number of total distributed rows
 * \param parameters Additional parameters
 * \param[out] out Created dataset
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromSampledColumn(double** sample_data,
                                                          int** sample_indices,
                                                          int32_t ncol,
                                                          const int* num_per_col,
                                                          int32_t num_sample_row,
                                                          int32_t num_local_row,
                                                          int64_t num_dist_row,
                                                          const char* parameters,
                                                          DatasetHandle* out);

/*!
 * \brief Allocate the space for dataset and bucket feature bins according to reference dataset.
 * \param reference Used to align bin mapper with other dataset
 * \param num_total_row Number of total rows
 * \param[out] out Created dataset
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetCreateByReference(const DatasetHandle reference,
                                                    int64_t num_total_row,
                                                    DatasetHandle* out);

/*!
 * \brief Push data to existing dataset, if ``nrow + start_row == num_total_row``, will call ``dataset->FinishLoad``.
 * \param dataset Handle of dataset
 * \param data Pointer to the data space
 * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \param nrow Number of rows
 * \param ncol Number of columns
 * \param start_row Row start index
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetPushRows(DatasetHandle dataset,
                                           const void* data,
                                           int data_type,
                                           int32_t nrow,
                                           int32_t ncol,
                                           int32_t start_row);

/*!
 * \brief Push data to existing dataset, if ``nrow + start_row == num_total_row``, will call ``dataset->FinishLoad``.
 * \param dataset Handle of dataset
 * \param indptr Pointer to row headers
 * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``
 * \param indices Pointer to column indices
 * \param data Pointer to the data space
 * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \param nindptr Number of rows in the matrix + 1
 * \param nelem Number of nonzero elements in the matrix
 * \param num_col Number of columns
 * \param start_row Row start index
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetPushRowsByCSR(DatasetHandle dataset,
                                                const void* indptr,
                                                int indptr_type,
                                                const int32_t* indices,
                                                const void* data,
                                                int data_type,
                                                int64_t nindptr,
                                                int64_t nelem,
                                                int64_t num_col,
                                                int64_t start_row);

/*!
 * \brief Create a dataset from CSR format.
 * \param indptr Pointer to row headers
 * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``
 * \param indices Pointer to column indices
 * \param data Pointer to the data space
 * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \param nindptr Number of rows in the matrix + 1
 * \param nelem Number of nonzero elements in the matrix
 * \param num_col Number of columns
 * \param parameters Additional parameters
 * \param reference Used to align bin mapper with other dataset, nullptr means isn't used
 * \param[out] out Created dataset
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromCSR(const void* indptr,
                                                int indptr_type,
                                                const int32_t* indices,
                                                const void* data,
                                                int data_type,
                                                int64_t nindptr,
                                                int64_t nelem,
                                                int64_t num_col,
                                                const char* parameters,
                                                const DatasetHandle reference,
                                                DatasetHandle* out);

/*!
 * \brief Create a dataset from CSR format through callbacks.
 * \param get_row_funptr Pointer to ``std::function<void(int idx, std::vector<std::pair<int, double>>& ret)>``
 *                       (called for every row and expected to clear and fill ``ret``)
 * \param num_rows Number of rows
 * \param num_col Number of columns
 * \param parameters Additional parameters
 * \param reference Used to align bin mapper with other dataset, nullptr means isn't used
 * \param[out] out Created dataset
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromCSRFunc(void* get_row_funptr,
                                                    int num_rows,
                                                    int64_t num_col,
                                                    const char* parameters,
                                                    const DatasetHandle reference,
                                                    DatasetHandle* out);

/*!
 * \brief Create a dataset from CSC format.
 * \param col_ptr Pointer to column headers
 * \param col_ptr_type Type of ``col_ptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``
 * \param indices Pointer to row indices
 * \param data Pointer to the data space
 * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \param ncol_ptr Number of columns in the matrix + 1
 * \param nelem Number of nonzero elements in the matrix
 * \param num_row Number of rows
 * \param parameters Additional parameters
 * \param reference Used to align bin mapper with other dataset, nullptr means isn't used
 * \param[out] out Created dataset
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromCSC(const void* col_ptr,
                                                int col_ptr_type,
                                                const int32_t* indices,
                                                const void* data,
                                                int data_type,
                                                int64_t ncol_ptr,
                                                int64_t nelem,
                                                int64_t num_row,
                                                const char* parameters,
                                                const DatasetHandle reference,
                                                DatasetHandle* out);

/*!
 * \brief Create dataset from dense matrix.
 * \param data Pointer to the data space
 * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \param nrow Number of rows
 * \param ncol Number of columns
 * \param is_row_major 1 for row-major, 0 for column-major
 * \param parameters Additional parameters
 * \param reference Used to align bin mapper with other dataset, nullptr means isn't used
 * \param[out] out Created dataset
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromMat(const void* data,
                                                int data_type,
                                                int32_t nrow,
                                                int32_t ncol,
                                                int is_row_major,
                                                const char* parameters,
                                                const DatasetHandle reference,
                                                DatasetHandle* out);

/*!
 * \brief Create dataset from array of dense matrices.
 * \param nmat Number of dense matrices
 * \param data Pointer to the data space
 * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \param nrow Number of rows
 * \param ncol Number of columns
 * \param is_row_major 1 for row-major, 0 for column-major
 * \param parameters Additional parameters
 * \param reference Used to align bin mapper with other dataset, nullptr means isn't used
 * \param[out] out Created dataset
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromMats(int32_t nmat,
                                                 const void** data,
                                                 int data_type,
                                                 int32_t* nrow,
                                                 int32_t ncol,
                                                 int is_row_major,
                                                 const char* parameters,
                                                 const DatasetHandle reference,
                                                 DatasetHandle* out);

/*!
 * \brief Create subset of a data.
 * \param handle Handle of full dataset
 * \param used_row_indices Indices used in subset
 * \param num_used_row_indices Length of ``used_row_indices``
 * \param parameters Additional parameters
 * \param[out] out Subset of data
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetGetSubset(const DatasetHandle handle,
                                            const int32_t* used_row_indices,
                                            int32_t num_used_row_indices,
                                            const char* parameters,
                                            DatasetHandle* out);

/*!
 * \brief Save feature names to dataset.
 * \param handle Handle of dataset
 * \param feature_names Feature names
 * \param num_feature_names Number of feature names
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetSetFeatureNames(DatasetHandle handle,
                                                  const char** feature_names,
                                                  int num_feature_names);

/*!
 * \brief Get feature names of dataset.
 * \param handle Handle of dataset
 * \param len Number of ``char*`` pointers stored at ``out_strs``.
 *            If smaller than the max size, only this many strings are copied
 * \param[out] num_feature_names Number of feature names
 * \param buffer_len Size of pre-allocated strings.
 *                   Content is copied up to ``buffer_len - 1`` and null-terminated
 * \param[out] out_buffer_len String sizes required to do the full string copies
 * \param[out] feature_names Feature names, should pre-allocate memory
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetGetFeatureNames(DatasetHandle handle,
                                                  const int len,
                                                  int* num_feature_names,
                                                  const size_t buffer_len,
                                                  size_t* out_buffer_len,
                                                  char** feature_names);

/*!
 * \brief Free space for dataset.
 * \param handle Handle of dataset to be freed
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetFree(DatasetHandle handle);

/*!
 * \brief Save dataset to binary file.
 * \param handle Handle of dataset
 * \param filename The name of the file
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetSaveBinary(DatasetHandle handle,
                                             const char* filename);

/*!
 * \brief Save dataset to text file, intended for debugging use only.
 * \param handle Handle of dataset
 * \param filename The name of the file
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetDumpText(DatasetHandle handle,
                                           const char* filename);

/*!
 * \brief Set vector to a content in info.
 * \note
 * - \a group only works for ``C_API_DTYPE_INT32``;
 * - \a label and \a weight only work for ``C_API_DTYPE_FLOAT32``;
 * - \a init_score only works for ``C_API_DTYPE_FLOAT64``.
 * \param handle Handle of dataset
 * \param field_name Field name, can be \a label, \a weight, \a init_score, \a group
 * \param field_data Pointer to data vector
 * \param num_element Number of elements in ``field_data``
 * \param type Type of ``field_data`` pointer, can be ``C_API_DTYPE_INT32``, ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetSetField(DatasetHandle handle,
                                           const char* field_name,
                                           const void* field_data,
                                           int num_element,
                                           int type);

/*!
 * \brief Get info vector from dataset.
 * \param handle Handle of dataset
 * \param field_name Field name
 * \param[out] out_len Used to set result length
 * \param[out] out_ptr Pointer to the result
 * \param[out] out_type Type of result pointer, can be ``C_API_DTYPE_INT32``, ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetGetField(DatasetHandle handle,
                                           const char* field_name,
                                           int* out_len,
                                           const void** out_ptr,
                                           int* out_type);

/*!
 * \brief Raise errors for attempts to update dataset parameters.
 * \param old_parameters Current dataset parameters
 * \param new_parameters New dataset parameters
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetUpdateParamChecking(const char* old_parameters,
                                                      const char* new_parameters);

/*!
 * \brief Get number of data points.
 * \param handle Handle of dataset
 * \param[out] out The address to hold number of data points
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetGetNumData(DatasetHandle handle,
                                             int* out);

/*!
 * \brief Get number of features.
 * \param handle Handle of dataset
 * \param[out] out The address to hold number of features
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetGetNumFeature(DatasetHandle handle,
                                                int* out);

/*!
 * \brief Get number of bins for feature.
 * \param handle Handle of dataset
 * \param feature Index of the feature
 * \param[out] out The address to hold number of bins
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetGetFeatureNumBin(DatasetHandle handle,
                                                   int feature,
                                                   int* out);

/*!
 * \brief Add features from ``source`` to ``target``.
 * \param target The handle of the dataset to add features to
 * \param source The handle of the dataset to take features from
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_DatasetAddFeaturesFrom(DatasetHandle target,
                                                  DatasetHandle source);

/* --- start Booster interfaces */

/*!
* \brief Get int representing whether booster is fitting linear trees.
* \param handle Handle of booster
* \param[out] out The address to hold linear trees indicator
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetLinear(BoosterHandle handle, int* out);

/*!
 * \brief Create a new boosting learner.
 * \param train_data Training dataset
 * \param parameters Parameters in format 'key1=value1 key2=value2'
 * \param[out] out Handle of created booster
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterCreate(const DatasetHandle train_data,
                                         const char* parameters,
                                         BoosterHandle* out);

/*!
 * \brief Load an existing booster from model file.
 * \param filename Filename of model
 * \param[out] out_num_iterations Number of iterations of this booster
 * \param[out] out Handle of created booster
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterCreateFromModelfile(const char* filename,
                                                      int* out_num_iterations,
                                                      BoosterHandle* out);

/*!
 * \brief Load an existing booster from string.
 * \param model_str Model string
 * \param[out] out_num_iterations Number of iterations of this booster
 * \param[out] out Handle of created booster
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterLoadModelFromString(const char* model_str,
                                                      int* out_num_iterations,
                                                      BoosterHandle* out);

/*!
 * \brief Free space for booster.
 * \param handle Handle of booster to be freed
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterFree(BoosterHandle handle);

/*!
 * \brief Shuffle models.
 * \param handle Handle of booster
 * \param start_iter The first iteration that will be shuffled
 * \param end_iter The last iteration that will be shuffled
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterShuffleModels(BoosterHandle handle,
                                                int start_iter,
                                                int end_iter);

/*!
 * \brief Merge model from ``other_handle`` into ``handle``.
 * \param handle Handle of booster, will merge another booster into this one
 * \param other_handle Other handle of booster
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterMerge(BoosterHandle handle,
                                        BoosterHandle other_handle);

/*!
 * \brief Add new validation data to booster.
 * \param handle Handle of booster
 * \param valid_data Validation dataset
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterAddValidData(BoosterHandle handle,
                                               const DatasetHandle valid_data);

/*!
 * \brief Reset training data for booster.
 * \param handle Handle of booster
 * \param train_data Training dataset
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterResetTrainingData(BoosterHandle handle,
                                                    const DatasetHandle train_data);

/*!
 * \brief Reset config for booster.
 * \param handle Handle of booster
 * \param parameters Parameters in format 'key1=value1 key2=value2'
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterResetParameter(BoosterHandle handle,
                                                 const char* parameters);

/*!
 * \brief Get number of classes.
 * \param handle Handle of booster
 * \param[out] out_len Number of classes
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumClasses(BoosterHandle handle,
                                                int* out_len);

/*!
 * \brief Update the model for one iteration.
 * \param handle Handle of booster
 * \param[out] is_finished 1 means the update was successfully finished (cannot split any more), 0 indicates failure
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterUpdateOneIter(BoosterHandle handle,
                                                int* is_finished);

/*!
 * \brief Refit the tree model using the new data (online learning).
 * \param handle Handle of booster
 * \param leaf_preds Pointer to predicted leaf indices
 * \param nrow Number of rows of ``leaf_preds``
 * \param ncol Number of columns of ``leaf_preds``
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterRefit(BoosterHandle handle,
                                        const int32_t* leaf_preds,
                                        int32_t nrow,
                                        int32_t ncol);

/*!
 * \brief Update the model by specifying gradient and Hessian directly
 *        (this can be used to support customized loss functions).
 * \note
 * The length of the arrays referenced by ``grad`` and ``hess`` must be equal to
 * ``num_class * num_train_data``, this is not verified by the library, the caller must ensure this.
 * \param handle Handle of booster
 * \param grad The first order derivative (gradient) statistics
 * \param hess The second order derivative (Hessian) statistics
 * \param[out] is_finished 1 means the update was successfully finished (cannot split any more), 0 indicates failure
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterUpdateOneIterCustom(BoosterHandle handle,
                                                      const float* grad,
                                                      const float* hess,
                                                      int* is_finished);

/*!
 * \brief Rollback one iteration.
 * \param handle Handle of booster
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterRollbackOneIter(BoosterHandle handle);

/*!
 * \brief Get index of the current boosting iteration.
 * \param handle Handle of booster
 * \param[out] out_iteration Index of the current boosting iteration
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterGetCurrentIteration(BoosterHandle handle,
                                                      int* out_iteration);

/*!
 * \brief Get number of trees per iteration.
 * \param handle Handle of booster
 * \param[out] out_tree_per_iteration Number of trees per iteration
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterNumModelPerIteration(BoosterHandle handle,
                                                       int* out_tree_per_iteration);

/*!
 * \brief Get number of weak sub-models.
 * \param handle Handle of booster
 * \param[out] out_models Number of weak sub-models
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterNumberOfTotalModel(BoosterHandle handle,
                                                     int* out_models);

/*!
 * \brief Get number of evaluation metrics.
 * \param handle Handle of booster
 * \param[out] out_len Total number of evaluation metrics
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalCounts(BoosterHandle handle,
                                                int* out_len);

/*!
 * \brief Get names of evaluation metrics.
 * \param handle Handle of booster
 * \param len Number of ``char*`` pointers stored at ``out_strs``.
 *            If smaller than the max size, only this many strings are copied
 * \param[out] out_len Total number of evaluation metrics
 * \param buffer_len Size of pre-allocated strings.
 *                   Content is copied up to ``buffer_len - 1`` and null-terminated
 * \param[out] out_buffer_len String sizes required to do the full string copies
 * \param[out] out_strs Names of evaluation metrics, should pre-allocate memory
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalNames(BoosterHandle handle,
                                               const int len,
                                               int* out_len,
                                               const size_t buffer_len,
                                               size_t* out_buffer_len,
                                               char** out_strs);

/*!
 * \brief Get names of features.
 * \param handle Handle of booster
 * \param len Number of ``char*`` pointers stored at ``out_strs``.
 *            If smaller than the max size, only this many strings are copied
 * \param[out] out_len Total number of features
 * \param buffer_len Size of pre-allocated strings.
 *                   Content is copied up to ``buffer_len - 1`` and null-terminated
 * \param[out] out_buffer_len String sizes required to do the full string copies
 * \param[out] out_strs Names of features, should pre-allocate memory
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterGetFeatureNames(BoosterHandle handle,
                                                  const int len,
                                                  int* out_len,
                                                  const size_t buffer_len,
                                                  size_t* out_buffer_len,
                                                  char** out_strs);

/*!
 * \brief Check that the feature names of the data match the ones used to train the booster.
 * \param handle Handle of booster
 * \param data_names Array with the feature names in the data
 * \param data_num_features Number of features in the data
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterValidateFeatureNames(BoosterHandle handle,
                                                       const char** data_names,
                                                       int data_num_features);

/*!
 * \brief Get number of features.
 * \param handle Handle of booster
 * \param[out] out_len Total number of features
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumFeature(BoosterHandle handle,
                                                int* out_len);

/*!
 * \brief Get evaluation for training data and validation data.
 * \note
 *   1. You should call ``LGBM_BoosterGetEvalNames`` first to get the names of evaluation metrics.
 *   2. You should pre-allocate memory for ``out_results``, you can get its length by ``LGBM_BoosterGetEvalCounts``.
 * \param handle Handle of booster
 * \param data_idx Index of data, 0: training data, 1: 1st validation data, 2: 2nd validation data and so on
 * \param[out] out_len Length of output result
 * \param[out] out_results Array with evaluation results
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterGetEval(BoosterHandle handle,
                                          int data_idx,
                                          int* out_len,
                                          double* out_results);

/*!
 * \brief Get number of predictions for training data and validation data
 *        (this can be used to support customized evaluation functions).
 * \param handle Handle of booster
 * \param data_idx Index of data, 0: training data, 1: 1st validation data, 2: 2nd validation data and so on
 * \param[out] out_len Number of predictions
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumPredict(BoosterHandle handle,
                                                int data_idx,
                                                int64_t* out_len);

/*!
 * \brief Get prediction for training data and validation data.
 * \note
 * You should pre-allocate memory for ``out_result``, its length is equal to ``num_class * num_data``.
 * \param handle Handle of booster
 * \param data_idx Index of data, 0: training data, 1: 1st validation data, 2: 2nd validation data and so on
 * \param[out] out_len Length of output result
 * \param[out] out_result Pointer to array with predictions
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterGetPredict(BoosterHandle handle,
                                             int data_idx,
                                             int64_t* out_len,
                                             double* out_result);

/*!
 * \brief Make prediction for file.
 * \param handle Handle of booster
 * \param data_filename Filename of file with data
 * \param data_has_header Whether file has header or not
 * \param predict_type What should be predicted
 *   - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
 *   - ``C_API_PREDICT_RAW_SCORE``: raw score;
 *   - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
 *   - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
 * \param start_iteration Start index of the iteration to predict
 * \param num_iteration Number of iterations for prediction, <= 0 means no limit
 * \param parameter Other parameters for prediction, e.g. early stopping for prediction
 * \param result_filename Filename of result file in which predictions will be written
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForFile(BoosterHandle handle,
                                                 const char* data_filename,
                                                 int data_has_header,
                                                 int predict_type,
                                                 int start_iteration,
                                                 int num_iteration,
                                                 const char* parameter,
                                                 const char* result_filename);

/*!
 * \brief Get number of predictions.
 * \param handle Handle of booster
 * \param num_row Number of rows
 * \param predict_type What should be predicted
 *   - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
 *   - ``C_API_PREDICT_RAW_SCORE``: raw score;
 *   - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
 *   - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
 * \param start_iteration Start index of the iteration to predict
 * \param num_iteration Number of iterations for prediction, <= 0 means no limit
 * \param[out] out_len Length of prediction
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterCalcNumPredict(BoosterHandle handle,
                                                 int num_row,
                                                 int predict_type,
                                                 int start_iteration,
                                                 int num_iteration,
                                                 int64_t* out_len);

/*!
 * \brief Release FastConfig object.
 *
 * \param fastConfig Handle to the FastConfig object acquired with a ``*FastInit()`` method.
 * \return 0 when it succeeds, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_FastConfigFree(FastConfigHandle fastConfig);

/*!
 * \brief Make prediction for a new dataset in CSR format.
 * \note
 * You should pre-allocate memory for ``out_result``:
 *   - for normal and raw score, its length is equal to ``num_class * num_data``;
 *   - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;
 *   - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.
 * \param handle Handle of booster
 * \param indptr Pointer to row headers
 * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``
 * \param indices Pointer to column indices
 * \param data Pointer to the data space
 * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \param nindptr Number of rows in the matrix + 1
 * \param nelem Number of nonzero elements in the matrix
 * \param num_col Number of columns
 * \param predict_type What should be predicted
 *   - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
 *   - ``C_API_PREDICT_RAW_SCORE``: raw score;
 *   - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
 *   - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
 * \param start_iteration Start index of the iteration to predict
 * \param num_iteration Number of iterations for prediction, <= 0 means no limit
 * \param parameter Other parameters for prediction, e.g. early stopping for prediction
 * \param[out] out_len Length of output result
 * \param[out] out_result Pointer to array with predictions
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSR(BoosterHandle handle,
                                                const void* indptr,
                                                int indptr_type,
                                                const int32_t* indices,
                                                const void* data,
                                                int data_type,
                                                int64_t nindptr,
                                                int64_t nelem,
                                                int64_t num_col,
                                                int predict_type,
                                                int start_iteration,
                                                int num_iteration,
                                                const char* parameter,
                                                int64_t* out_len,
                                                double* out_result);

/*!
 * \brief Make sparse prediction for a new dataset in CSR or CSC format. Currently only used for feature contributions.
 * \note
 * The outputs are pre-allocated, as they can vary for each invocation, but the shape should be the same:
 *   - for feature contributions, the shape of sparse matrix will be ``num_class * num_data * (num_feature + 1)``.
 * The output indptr_type for the sparse matrix will be the same as the given input indptr_type.
 * Call ``LGBM_BoosterFreePredictSparse`` to deallocate resources.
 * \param handle Handle of booster
 * \param indptr Pointer to row headers for CSR or column headers for CSC
 * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``
 * \param indices Pointer to column indices for CSR or row indices for CSC
 * \param data Pointer to the data space
 * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \param nindptr Number of entries in ``indptr``
 * \param nelem Number of nonzero elements in the matrix
 * \param num_col_or_row Number of columns for CSR or number of rows for CSC
 * \param predict_type What should be predicted, only feature contributions supported currently
 *   - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
 * \param start_iteration Start index of the iteration to predict
 * \param num_iteration Number of iterations for prediction, <= 0 means no limit
 * \param parameter Other parameters for prediction, e.g. early stopping for prediction
 * \param matrix_type Type of matrix input and output, can be ``C_API_MATRIX_TYPE_CSR`` or ``C_API_MATRIX_TYPE_CSC``
 * \param[out] out_len Length of output data and output indptr (pointer to an array with two entries where to write them)
 * \param[out] out_indptr Pointer to output row headers for CSR or column headers for CSC
 * \param[out] out_indices Pointer to sparse column indices for CSR or row indices for CSC
 * \param[out] out_data Pointer to sparse data space
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictSparseOutput(BoosterHandle handle,
                                                      const void* indptr,
                                                      int indptr_type,
                                                      const int32_t* indices,
                                                      const void* data,
                                                      int data_type,
                                                      int64_t nindptr,
                                                      int64_t nelem,
                                                      int64_t num_col_or_row,
                                                      int predict_type,
                                                      int start_iteration,
                                                      int num_iteration,
                                                      const char* parameter,
                                                      int matrix_type,
                                                      int64_t* out_len,
                                                      void** out_indptr,
                                                      int32_t** out_indices,
                                                      void** out_data);

/*!
 * \brief Method corresponding to ``LGBM_BoosterPredictSparseOutput`` to free the allocated data.
 * \param indptr Pointer to output row headers or column headers to be deallocated
 * \param indices Pointer to sparse indices to be deallocated
 * \param data Pointer to sparse data space to be deallocated
 * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``
 * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterFreePredictSparse(void* indptr, int32_t* indices, void* data, int indptr_type, int data_type);

/*!
 * \brief Make prediction for a new dataset in CSR format. This method re-uses the internal predictor structure
 *        from previous calls and is optimized for single row invocation.
 * \note
 * You should pre-allocate memory for ``out_result``:
 *   - for normal and raw score, its length is equal to ``num_class * num_data``;
 *   - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;
 *   - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.
 * \param handle Handle of booster
 * \param indptr Pointer to row headers
 * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``
 * \param indices Pointer to column indices
 * \param data Pointer to the data space
 * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \param nindptr Number of rows in the matrix + 1
 * \param nelem Number of nonzero elements in the matrix
 * \param num_col Number of columns
 * \param predict_type What should be predicted
 *   - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
 *   - ``C_API_PREDICT_RAW_SCORE``: raw score;
 *   - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
 *   - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
 * \param start_iteration Start index of the iteration to predict
 * \param num_iteration Number of iterations for prediction, <= 0 means no limit
 * \param parameter Other parameters for prediction, e.g. early stopping for prediction
 * \param[out] out_len Length of output result
 * \param[out] out_result Pointer to array with predictions
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSRSingleRow(BoosterHandle handle,
                                                         const void* indptr,
                                                         int indptr_type,
                                                         const int32_t* indices,
                                                         const void* data,
                                                         int data_type,
                                                         int64_t nindptr,
                                                         int64_t nelem,
                                                         int64_t num_col,
                                                         int predict_type,
                                                         int start_iteration,
                                                         int num_iteration,
                                                         const char* parameter,
                                                         int64_t* out_len,
                                                         double* out_result);

/*!
 * \brief Initialize and return a ``FastConfigHandle`` for use with ``LGBM_BoosterPredictForCSRSingleRowFast``.
 *
 * Release the ``FastConfig`` by passing its handle to ``LGBM_FastConfigFree`` when no longer needed.
 *
 * \param handle Booster handle
 * \param predict_type What should be predicted
 *   - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
 *   - ``C_API_PREDICT_RAW_SCORE``: raw score;
 *   - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
 *   - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
 * \param start_iteration Start index of the iteration to predict
 * \param num_iteration Number of iterations for prediction, <= 0 means no limit
 * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \param num_col Number of columns
 * \param parameter Other parameters for prediction, e.g. early stopping for prediction
 * \param[out] out_fastConfig FastConfig object with which you can call ``LGBM_BoosterPredictForCSRSingleRowFast``
 * \return 0 when it succeeds, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSRSingleRowFastInit(BoosterHandle handle,
                                                                 const int predict_type,
                                                                 const int start_iteration,
                                                                 const int num_iteration,
                                                                 const int data_type,
                                                                 const int64_t num_col,
                                                                 const char* parameter,
                                                                 FastConfigHandle *out_fastConfig);

/*!
 * \brief Faster variant of ``LGBM_BoosterPredictForCSRSingleRow``.
 *
 * Score single rows after setup with ``LGBM_BoosterPredictForCSRSingleRowFastInit``.
 *
 * By removing the setup steps from this call extra optimizations can be made like
 * initializing the config only once, instead of once per call.
 *
 * \note
 *   Setting up the number of threads is only done once at ``LGBM_BoosterPredictForCSRSingleRowFastInit``
 *   instead of at each prediction.
 *   If you use a different number of threads in other calls, you need to start the setup process over,
 *   or that number of threads will be used for these calls as well.
 *
 * \note
 * You should pre-allocate memory for ``out_result``:
 *   - for normal and raw score, its length is equal to ``num_class * num_data``;
 *   - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;
 *   - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.
 *
 * \param fastConfig_handle FastConfig object handle returned by ``LGBM_BoosterPredictForCSRSingleRowFastInit``
 * \param indptr Pointer to row headers
 * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``
 * \param indices Pointer to column indices
 * \param data Pointer to the data space
 * \param nindptr Number of rows in the matrix + 1
 * \param nelem Number of nonzero elements in the matrix
 * \param[out] out_len Length of output result
 * \param[out] out_result Pointer to array with predictions
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSRSingleRowFast(FastConfigHandle fastConfig_handle,
                                                             const void* indptr,
                                                             const int indptr_type,
                                                             const int32_t* indices,
                                                             const void* data,
                                                             const int64_t nindptr,
                                                             const int64_t nelem,
                                                             int64_t* out_len,
                                                             double* out_result);

/*!
 * \brief Make prediction for a new dataset in CSC format.
 * \note
 * You should pre-allocate memory for ``out_result``:
 *   - for normal and raw score, its length is equal to ``num_class * num_data``;
 *   - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;
 *   - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.
 * \param handle Handle of booster
 * \param col_ptr Pointer to column headers
 * \param col_ptr_type Type of ``col_ptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``
 * \param indices Pointer to row indices
 * \param data Pointer to the data space
 * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \param ncol_ptr Number of columns in the matrix + 1
 * \param nelem Number of nonzero elements in the matrix
 * \param num_row Number of rows
 * \param predict_type What should be predicted
 *   - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
 *   - ``C_API_PREDICT_RAW_SCORE``: raw score;
 *   - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
 *   - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
 * \param start_iteration Start index of the iteration to predict
 * \param num_iteration Number of iteration for prediction, <= 0 means no limit
 * \param parameter Other parameters for prediction, e.g. early stopping for prediction
 * \param[out] out_len Length of output result
 * \param[out] out_result Pointer to array with predictions
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSC(BoosterHandle handle,
                                                const void* col_ptr,
                                                int col_ptr_type,
                                                const int32_t* indices,
                                                const void* data,
                                                int data_type,
                                                int64_t ncol_ptr,
                                                int64_t nelem,
                                                int64_t num_row,
                                                int predict_type,
                                                int start_iteration,
                                                int num_iteration,
                                                const char* parameter,
                                                int64_t* out_len,
                                                double* out_result);

/*!
 * \brief Make prediction for a new dataset.
 * \note
 * You should pre-allocate memory for ``out_result``:
 *   - for normal and raw score, its length is equal to ``num_class * num_data``;
 *   - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;
 *   - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.
 * \param handle Handle of booster
 * \param data Pointer to the data space
 * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \param nrow Number of rows
 * \param ncol Number of columns
 * \param is_row_major 1 for row-major, 0 for column-major
 * \param predict_type What should be predicted
 *   - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
 *   - ``C_API_PREDICT_RAW_SCORE``: raw score;
 *   - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
 *   - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
 * \param start_iteration Start index of the iteration to predict
 * \param num_iteration Number of iteration for prediction, <= 0 means no limit
 * \param parameter Other parameters for prediction, e.g. early stopping for prediction
 * \param[out] out_len Length of output result
 * \param[out] out_result Pointer to array with predictions
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMat(BoosterHandle handle,
                                                const void* data,
                                                int data_type,
                                                int32_t nrow,
                                                int32_t ncol,
                                                int is_row_major,
                                                int predict_type,
                                                int start_iteration,
                                                int num_iteration,
                                                const char* parameter,
                                                int64_t* out_len,
                                                double* out_result);

/*!
 * \brief Make prediction for a new dataset. This method re-uses the internal predictor structure
 *        from previous calls and is optimized for single row invocation.
 * \note
 * You should pre-allocate memory for ``out_result``:
 *   - for normal and raw score, its length is equal to ``num_class * num_data``;
 *   - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;
 *   - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.
 * \param handle Handle of booster
 * \param data Pointer to the data space
 * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \param ncol Number columns
 * \param is_row_major 1 for row-major, 0 for column-major
 * \param predict_type What should be predicted
 *   - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
 *   - ``C_API_PREDICT_RAW_SCORE``: raw score;
 *   - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
 *   - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
 * \param start_iteration Start index of the iteration to predict
 * \param num_iteration Number of iteration for prediction, <= 0 means no limit
 * \param parameter Other parameters for prediction, e.g. early stopping for prediction
 * \param[out] out_len Length of output result
 * \param[out] out_result Pointer to array with predictions
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMatSingleRow(BoosterHandle handle,
                                                         const void* data,
                                                         int data_type,
                                                         int ncol,
                                                         int is_row_major,
                                                         int predict_type,
                                                         int start_iteration,
                                                         int num_iteration,
                                                         const char* parameter,
                                                         int64_t* out_len,
                                                         double* out_result);

/*!
 * \brief Initialize and return a ``FastConfigHandle`` for use with ``LGBM_BoosterPredictForMatSingleRowFast``.
 *
 * Release the ``FastConfig`` by passing its handle to ``LGBM_FastConfigFree`` when no longer needed.
 *
 * \param handle Booster handle
 * \param predict_type What should be predicted
 *   - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
 *   - ``C_API_PREDICT_RAW_SCORE``: raw score;
 *   - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
 *   - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
 * \param start_iteration Start index of the iteration to predict
 * \param num_iteration Number of iterations for prediction, <= 0 means no limit
 * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \param ncol Number of columns
 * \param parameter Other parameters for prediction, e.g. early stopping for prediction
 * \param[out] out_fastConfig FastConfig object with which you can call ``LGBM_BoosterPredictForMatSingleRowFast``
 * \return 0 when it succeeds, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMatSingleRowFastInit(BoosterHandle handle,
                                                                 const int predict_type,
                                                                 const int start_iteration,
                                                                 const int num_iteration,
                                                                 const int data_type,
                                                                 const int32_t ncol,
                                                                 const char* parameter,
                                                                 FastConfigHandle *out_fastConfig);

/*!
 * \brief Faster variant of ``LGBM_BoosterPredictForMatSingleRow``.
 *
 * Score a single row after setup with ``LGBM_BoosterPredictForMatSingleRowFastInit``.
 *
 * By removing the setup steps from this call extra optimizations can be made like
 * initializing the config only once, instead of once per call.
 *
 * \note
 *   Setting up the number of threads is only done once at ``LGBM_BoosterPredictForMatSingleRowFastInit``
 *   instead of at each prediction.
 *   If you use a different number of threads in other calls, you need to start the setup process over,
 *   or that number of threads will be used for these calls as well.
 *
 * \param fastConfig_handle FastConfig object handle returned by ``LGBM_BoosterPredictForMatSingleRowFastInit``
 * \param data Single-row array data (no other way than row-major form).
 * \param[out] out_len Length of output result
 * \param[out] out_result Pointer to array with predictions
 * \return 0 when it succeeds, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMatSingleRowFast(FastConfigHandle fastConfig_handle,
                                                             const void* data,
                                                             int64_t* out_len,
                                                             double* out_result);

/*!
 * \brief Make prediction for a new dataset presented in a form of array of pointers to rows.
 * \note
 * You should pre-allocate memory for ``out_result``:
 *   - for normal and raw score, its length is equal to ``num_class * num_data``;
 *   - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;
 *   - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.
 * \param handle Handle of booster
 * \param data Pointer to the data space
 * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``
 * \param nrow Number of rows
 * \param ncol Number columns
 * \param predict_type What should be predicted
 *   - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);
 *   - ``C_API_PREDICT_RAW_SCORE``: raw score;
 *   - ``C_API_PREDICT_LEAF_INDEX``: leaf index;
 *   - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)
 * \param start_iteration Start index of the iteration to predict
 * \param num_iteration Number of iteration for prediction, <= 0 means no limit
 * \param parameter Other parameters for prediction, e.g. early stopping for prediction
 * \param[out] out_len Length of output result
 * \param[out] out_result Pointer to array with predictions
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMats(BoosterHandle handle,
                                                 const void** data,
                                                 int data_type,
                                                 int32_t nrow,
                                                 int32_t ncol,
                                                 int predict_type,
                                                 int start_iteration,
                                                 int num_iteration,
                                                 const char* parameter,
                                                 int64_t* out_len,
                                                 double* out_result);

/*!
 * \brief Save model into file.
 * \param handle Handle of booster
 * \param start_iteration Start index of the iteration that should be saved
 * \param num_iteration Index of the iteration that should be saved, <= 0 means save all
 * \param feature_importance_type Type of feature importance, can be ``C_API_FEATURE_IMPORTANCE_SPLIT`` or ``C_API_FEATURE_IMPORTANCE_GAIN``
 * \param filename The name of the file
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterSaveModel(BoosterHandle handle,
                                            int start_iteration,
                                            int num_iteration,
                                            int feature_importance_type,
                                            const char* filename);

/*!
 * \brief Save model to string.
 * \param handle Handle of booster
 * \param start_iteration Start index of the iteration that should be saved
 * \param num_iteration Index of the iteration that should be saved, <= 0 means save all
 * \param feature_importance_type Type of feature importance, can be ``C_API_FEATURE_IMPORTANCE_SPLIT`` or ``C_API_FEATURE_IMPORTANCE_GAIN``
 * \param buffer_len String buffer length, if ``buffer_len < out_len``, you should re-allocate buffer
 * \param[out] out_len Actual output length
 * \param[out] out_str String of model, should pre-allocate memory
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterSaveModelToString(BoosterHandle handle,
                                                    int start_iteration,
                                                    int num_iteration,
                                                    int feature_importance_type,
                                                    int64_t buffer_len,
                                                    int64_t* out_len,
                                                    char* out_str);

/*!
 * \brief Dump model to JSON.
 * \param handle Handle of booster
 * \param start_iteration Start index of the iteration that should be dumped
 * \param num_iteration Index of the iteration that should be dumped, <= 0 means dump all
 * \param feature_importance_type Type of feature importance, can be ``C_API_FEATURE_IMPORTANCE_SPLIT`` or ``C_API_FEATURE_IMPORTANCE_GAIN``
 * \param buffer_len String buffer length, if ``buffer_len < out_len``, you should re-allocate buffer
 * \param[out] out_len Actual output length
 * \param[out] out_str JSON format string of model, should pre-allocate memory
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterDumpModel(BoosterHandle handle,
                                            int start_iteration,
                                            int num_iteration,
                                            int feature_importance_type,
                                            int64_t buffer_len,
                                            int64_t* out_len,
                                            char* out_str);

/*!
 * \brief Get leaf value.
 * \param handle Handle of booster
 * \param tree_idx Index of tree
 * \param leaf_idx Index of leaf
 * \param[out] out_val Output result from the specified leaf
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterGetLeafValue(BoosterHandle handle,
                                               int tree_idx,
                                               int leaf_idx,
                                               double* out_val);

/*!
 * \brief Set leaf value.
 * \param handle Handle of booster
 * \param tree_idx Index of tree
 * \param leaf_idx Index of leaf
 * \param val Leaf value
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterSetLeafValue(BoosterHandle handle,
                                               int tree_idx,
                                               int leaf_idx,
                                               double val);

/*!
 * \brief Get model feature importance.
 * \param handle Handle of booster
 * \param num_iteration Number of iterations for which feature importance is calculated, <= 0 means use all
 * \param importance_type Method of importance calculation:
 *   - ``C_API_FEATURE_IMPORTANCE_SPLIT``: result contains numbers of times the feature is used in a model;
 *   - ``C_API_FEATURE_IMPORTANCE_GAIN``: result contains total gains of splits which use the feature
 * \param[out] out_results Result array with feature importance
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterFeatureImportance(BoosterHandle handle,
                                                    int num_iteration,
                                                    int importance_type,
                                                    double* out_results);

/*!
 * \brief Get model upper bound value.
 * \param handle Handle of booster
 * \param[out] out_results Result pointing to max value
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterGetUpperBoundValue(BoosterHandle handle,
                                                     double* out_results);

/*!
 * \brief Get model lower bound value.
 * \param handle Handle of booster
 * \param[out] out_results Result pointing to min value
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_BoosterGetLowerBoundValue(BoosterHandle handle,
                                                     double* out_results);

/*!
 * \brief Initialize the network.
 * \param machines List of machines in format 'ip1:port1,ip2:port2'
 * \param local_listen_port TCP listen port for local machines
 * \param listen_time_out Socket time-out in minutes
 * \param num_machines Total number of machines
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_NetworkInit(const char* machines,
                                       int local_listen_port,
                                       int listen_time_out,
                                       int num_machines);

/*!
 * \brief Finalize the network.
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_NetworkFree();

/*!
 * \brief Initialize the network with external collective functions.
 * \param num_machines Total number of machines
 * \param rank Rank of local machine
 * \param reduce_scatter_ext_fun The external reduce-scatter function
 * \param allgather_ext_fun The external allgather function
 * \return 0 when succeed, -1 when failure happens
 */
LIGHTGBM_C_EXPORT int LGBM_NetworkInitWithFunctions(int num_machines,
                                                    int rank,
                                                    void* reduce_scatter_ext_fun,
                                                    void* allgather_ext_fun);

#if !defined(__cplusplus) && (!defined(__STDC__) || (__STDC_VERSION__ < 199901L))
/*! \brief Inline specifier no-op in C using standards before C99. */
#define INLINE_FUNCTION
#else
/*! \brief Inline specifier. */
#define INLINE_FUNCTION inline
#endif

#if !defined(__cplusplus) && (!defined(__STDC__) || (__STDC_VERSION__ < 201112L))
/*! \brief Thread local specifier no-op in C using standards before C11. */
#define THREAD_LOCAL
#elif !defined(__cplusplus)
/*! \brief Thread local specifier. */
#define THREAD_LOCAL _Thread_local
#elif defined(_MSC_VER)
/*! \brief Thread local specifier. */
#define THREAD_LOCAL __declspec(thread)
#else
/*! \brief Thread local specifier. */
#define THREAD_LOCAL thread_local
#endif

/*!
 * \brief Handle of error message.
 * \return Error message
 */
static char* LastErrorMsg() { static THREAD_LOCAL char err_msg[512] = "Everything is fine"; return err_msg; }

#ifdef _MSC_VER
  #pragma warning(disable : 4996)
#endif
/*!
 * \brief Set string message of the last error.
 * \note
 * This will call unsafe ``sprintf`` when compiled using C standards before C99.
 * \param msg Error message
 */
INLINE_FUNCTION void LGBM_SetLastError(const char* msg) {
#if !defined(__cplusplus) && (!defined(__STDC__) || (__STDC_VERSION__ < 199901L))
  sprintf(LastErrorMsg(), "%s", msg);  /* NOLINT(runtime/printf) */
#else
  const int err_buf_len = 512;
  snprintf(LastErrorMsg(), err_buf_len, "%s", msg);
#endif
}

#endif  /* LIGHTGBM_C_API_H_ */