189 lines
6.6 KiB
C
189 lines
6.6 KiB
C
#ifndef XGBOOST_PYTHON_H
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#define XGBOOST_PYTHON_H
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/*!
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* \file xgboost_regrank_data.h
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* \brief python wrapper for xgboost, using ctypes,
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* hides everything behind functions
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* use c style interface
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*/
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#include "../booster/xgboost_data.h"
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extern "C"{
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/*! \brief type of row entry */
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typedef xgboost::booster::FMatrixS::REntry XGEntry;
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/*!
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* \brief create a data matrix
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* \return a new data matrix
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*/
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void* XGDMatrixCreate(void);
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/*!
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* \brief free space in data matrix
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*/
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void XGDMatrixFree(void *handle);
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/*!
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* \brief load a data matrix from text file or buffer(if exists)
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* \param handle a instance of data matrix
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* \param fname file name
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* \param silent print statistics when loading
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*/
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void XGDMatrixLoad(void *handle, const char *fname, int silent);
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/*!
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* \brief load a data matrix into binary file
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* \param handle a instance of data matrix
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* \param fname file name
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* \param silent print statistics when saving
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*/
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void XGDMatrixSaveBinary(void *handle, const char *fname, int silent);
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/*!
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* \brief set matrix content from csr format
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* \param handle a instance of data matrix
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* \param indptr pointer to row headers
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* \param indices findex
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* \param data fvalue
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* \param nindptr number of rows in the matix + 1
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* \param nelem number of nonzero elements in the matrix
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*/
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void XGDMatrixParseCSR( void *handle,
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const size_t *indptr,
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const unsigned *indices,
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const float *data,
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size_t nindptr,
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size_t nelem );
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/*!
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* \brief set label of the training matrix
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* \param handle a instance of data matrix
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* \param label pointer to label
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* \param len length of array
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*/
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void XGDMatrixSetLabel( void *handle, const float *label, size_t len );
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/*!
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* \brief set label of the training matrix
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* \param handle a instance of data matrix
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* \param group pointer to group size
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* \param len length of array
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*/
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void XGDMatrixSetGroup( void *handle, const unsigned *group, size_t len );
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/*!
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* \brief set weight of each instacne
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* \param handle a instance of data matrix
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* \param weight data pointer to weights
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* \param len length of array
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*/
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void XGDMatrixSetWeight( void *handle, const float *weight, size_t len );
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/*!
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* \brief get label set from matrix
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* \param handle a instance of data matrix
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* \param len used to set result length
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* \return pointer to the row
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*/
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const float* XGDMatrixGetLabel( const void *handle, size_t* len );
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/*!
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* \brief clear all the records, including feature matrix and label
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* \param handle a instance of data matrix
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*/
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void XGDMatrixClear(void *handle);
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/*!
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* \brief return number of rows
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*/
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size_t XGDMatrixNumRow(const void *handle);
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/*!
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* \brief add row
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* \param handle a instance of data matrix
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* \param data array of row content
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* \param len length of array
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*/
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void XGDMatrixAddRow(void *handle, const XGEntry *data, size_t len);
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/*!
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* \brief get ridx-th row of sparse matrix
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* \param handle handle
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* \param ridx row index
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* \param len used to set result length
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* \reurn pointer to the row
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*/
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const XGEntry* XGDMatrixGetRow(void *handle, unsigned ridx, size_t* len);
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// --- start XGBoost class
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/*!
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* \brief create xgboost learner
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* \param dmats matrices that are set to be cached
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* \param create a booster
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*/
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void *XGBoosterCreate( void* dmats[], size_t len );
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/*!
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* \brief free obj in handle
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* \param handle handle to be freed
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*/
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void XGBoosterFree( void* handle );
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/*!
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* \brief set parameters
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* \param handle handle
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* \param name parameter name
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* \param val value of parameter
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*/
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void XGBoosterSetParam( void *handle, const char *name, const char *value );
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/*!
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* \brief update the model in one round using dtrain
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* \param handle handle
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* \param dtrain training data
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*/
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void XGBoosterUpdateOneIter( void *handle, void *dtrain );
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/*!
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* \brief update the model, by directly specify gradient and second order gradient,
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* this can be used to replace UpdateOneIter, to support customized loss function
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* \param handle handle
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* \param dtrain training data
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* \param grad gradient statistics
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* \param hess second order gradient statistics
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* \param len length of grad/hess array
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* \param bst_group boost group we are working at, default = -1
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*/
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void XGBoosterBoostOneIter( void *handle, void *dtrain,
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float *grad, float *hess, size_t len, int bst_group );
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/*!
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* \brief print evaluation statistics to stdout for xgboost
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* \param handle handle
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* \param iter current iteration rounds
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* \param dmats pointers to data to be evaluated
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* \param evnames pointers to names of each data
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* \param len length of dmats
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*/
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void XGBoosterEvalOneIter( void *handle, int iter, void *dmats[], const char *evnames[], size_t len );
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/*!
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* \brief make prediction based on dmat
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* \param handle handle
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* \param dmat data matrix
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* \param len used to store length of returning result
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* \param bst_group booster group, if model contains multiple booster group, default = -1 means predict for all groups
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*/
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const float *XGBoosterPredict( void *handle, void *dmat, size_t *len, int bst_group );
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/*!
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* \brief load model from existing file
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* \param handle handle
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* \param fname file name
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*/
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void XGBoosterLoadModel( void *handle, const char *fname );
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/*!
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* \brief save model into existing file
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* \param handle handle
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* \param fname file name
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*/
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void XGBoosterSaveModel( const void *handle, const char *fname );
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/*!
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* \brief dump model into text file
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* \param handle handle
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* \param fname file name
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* \param fmap name to fmap can be empty string
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*/
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void XGBoosterDumpModel( void *handle, const char *fname, const char *fmap );
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/*!
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* \brief interactively update model: beta
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* \param handle handle
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* \param dtrain training data
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* \param action action name
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*/
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void XGBoosterUpdateInteract( void *handle, void *dtrain, const char* action );
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};
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#endif
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