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/*!
* \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>
#include <cstdint>
#include <cstring>
typedef void* DatasetHandle; /*!< \brief Handle of dataset. */
typedef void* BoosterHandle; /*!< \brief Handle of booster. */
#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_DTYPE_INT8 (4) /*!< \brief int8. */
#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). */
/*!
* \brief Get string message of the last error.
* \return Error information
*/
LIGHTGBM_C_EXPORT const char* LGBM_GetLastError();
// --- 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_total_row Number of total 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_total_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[out] feature_names Feature names, should pre-allocate memory
* \param[out] num_feature_names Number of feature names
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetGetFeatureNames(DatasetHandle handle,
char** feature_names,
int* num_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_INT8``, ``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 Update parameters for a dataset.
* \param handle Handle of dataset
* \param parameters Parameters
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetUpdateParam(DatasetHandle handle,
const char* 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 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 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).
* \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 datasets.
* \param handle Handle of booster
* \param[out] out_len Total number of evaluation datasets
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalCounts(BoosterHandle handle,
int* out_len);
/*!
* \brief Get names of evaluation datasets.
* \param handle Handle of booster
* \param[out] out_len Total number of evaluation datasets
* \param[out] out_strs Names of evaluation datasets, should pre-allocate memory
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalNames(BoosterHandle handle,
int* out_len,
char** out_strs);
/*!
* \brief Get names of features.
* \param handle Handle of booster
* \param[out] out_len Total number of features
* \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,
int* out_len,
char** out_strs);
/*!
* \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 datasets.
* 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 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 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 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 num_iteration,
int64_t* out_len);
/*!
* \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 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 num_iteration,
const char* parameter,
int64_t* out_len,
double* out_result);
/*!
* \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 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 num_iteration,
const char* parameter,
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 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 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 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 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 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 num_iteration,
const char* parameter,
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 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 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 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,
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 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,
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 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,
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:
* - 0 for split, result contains numbers of times the feature is used in a model;
* - 1 for 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 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(_MSC_VER)
#define THREAD_LOCAL __declspec(thread) /*!< \brief Thread local specifier. */
#else
#define THREAD_LOCAL thread_local /*!< \brief Thread local specifier. */
#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; }
#pragma warning(disable : 4996)
/*!
* \brief Set string message of the last error.
* \param msg Error message
*/
inline void LGBM_SetLastError(const char* msg) {
std::strcpy(LastErrorMsg(), msg);
}
#endif // LIGHTGBM_C_API_H_
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