Crate catboost_portable_sys

Structs

• max_align_t

Constants

• CATBOOST_APPLIER_FIX
• CATBOOST_APPLIER_MAJOR
• CATBOOST_APPLIER_MINOR
• EApiPredictionType_APT_CLASS
• EApiPredictionType_APT_EXPONENT
• EApiPredictionType_APT_PROBABILITY
• EApiPredictionType_APT_RAW_FORMULA_VAL
• EApiPredictionType_APT_RMSE_WITH_UNCERTAINTY
• __bool_true_false_are_defined
• false_
• true_

Functions

• AddCatFeatures^⚠
• AddFloatFeatures^⚠
• AddTextFeatures^⚠
• BuildDataProvider^⚠
• CalcModelPrediction^⚠
  Calculate raw model predictions on float features and string categorical feature values @param calcer model handle @param docCount object count @param floatFeatures array of array of float (first dimension is object index, second is feature index) @param floatFeaturesSize float feature count @param catFeatures array of array of char* categorical value pointers. String pointer should point to zero terminated string. @param catFeaturesSize categorical feature count @param result pointer to user allocated results vector @param resultSize result size should be equal to modelApproxDimension * docCount (e.g. for non multiclass models should be equal to docCount) @return false if error occured
• CalcModelPredictionFlat^⚠
  Use this method only if you really understand what you want. Calculate raw model predictions on flat feature vectors Flat here means that float features and categorical feature are in the same float array. @param calcer model handle @param docCount number of objects @param floatFeatures array of array of float (first dimension is object index, second if feature index) @param floatFeaturesSize float values array size @param result pointer to user allocated results vector @param resultSize Result size should be equal to modelApproxDimension * docCount (e.g. for non multiclass models should be equal to docCount) @return false if error occured
• CalcModelPredictionSingle^⚠
  Calculate raw model prediction on float features and string categorical feature values for single object @param calcer model handle @param floatFeatures array of float features @param floatFeaturesSize float feature count @param catFeatures array of char* categorical feature value pointers. Each string pointer should point to zero terminated string. @param catFeaturesSize categorical feature count @param result pointer to user allocated results vector (or single double) @param resultSize result size should be equal to modelApproxDimension (e.g. for non multiclass models should be equal to 1) @return false if error occured
• CalcModelPredictionText^⚠
  Calculate raw model predictions on float features and string categorical feature values @param calcer model handle @param docCount object count @param floatFeatures array of array of float (first dimension is object index, second is feature index) @param floatFeaturesSize float feature count @param catFeatures array of array of char* categorical value pointers. String pointer should point to zero terminated string. @param catFeaturesSize categorical feature count @param textFeatures array of array of char* text value pointers. String pointer should point to zero terminated string. @param textFeaturesSize text feature count @param result pointer to user allocated results vector @param resultSize result size should be equal to modelApproxDimension * docCount (e.g. for non multiclass models should be equal to docCount) @return false if error occured
• CalcModelPredictionWithHashedCatFeatures^⚠
  Calculate raw model predictions on float features and hashed categorical feature values @param calcer model handle @param docCount object count @param floatFeatures array of array of float (first dimension is object index, second if feature index) @param floatFeaturesSize float feature count @param catFeatures array of array of integers - hashed categorical feature values. @param catFeaturesSize categorical feature count @param result pointer to user allocated results vector @param resultSize result size should be equal to modelApproxDimension * docCount (e.g. for non multiclass models should be equal to docCount) @return false if error occured
• CalcModelPredictionWithHashedCatFeaturesAndTextFeatures^⚠
• CheckModelMetadataHasKey^⚠
  Check if model metadata holds some value for provided key @param calcer model handle
• DataWrapperCreate^⚠
  Create empty data wrapper @return
• DataWrapperDelete^⚠
• EnableGPUEvaluation^⚠
  Use CUDA gpu device for model evaluation
• GetCatFeaturesCount^⚠
  Get expected categorical feature count for model @param calcer model handle
• GetDimensionsCount^⚠
  Get number of dimensions in model @param calcer model handle
• GetErrorString^⚠
  If error occured will return stored exception message. If no error occured, will return invalid pointer @return
• GetFloatFeaturesCount^⚠
  Get expected float feature count for model @param calcer model handle
• GetIntegerCatFeatureHash^⚠
  Special case for hash calculation - integer hash. Internally we cast value to string and then calulcate string hash function. Used in ClickHouse for catboost model evaluation on integer cat features. @param val integer cat feature value @return hash value
• GetModelInfoValue^⚠
  Get model metainfo for some key. Returns const char* pointer to inner string. If key is missing in model metainfo storage this method will return nullptr @param calcer model handle
• GetModelInfoValueSize^⚠
  Get model metainfo value size for some key. Returns 0 both if key is missing in model metadata and if it is really missing @param calcer model handle
• GetPredictionDimensionsCount^⚠
  Get number of dimensions for current prediction For default APT_RAW_FORMULA_VAL, APT_EXPONENT, APT_PROBABILITY, APT_CLASS prediction type GetPredictionDimensionsCount == GetDimensionsCount For APT_RMSE_WITH_UNCERTAINTY - returns 2 (value prediction and predicted uncertainty) @param calcer model handle
• GetStringCatFeatureHash^⚠
  Get hash for given string value @param data we don’t expect data to be zero terminated, so pass correct size @param size string length @return hash value
• GetTreeCount^⚠
  Get number of trees in model @param calcer model handle
• LoadFullModelFromBuffer^⚠
  Load model from memory buffer into given model handle @param calcer @param binaryBuffer pointer to a memory buffer where model file is mapped @param binaryBufferSize size of the buffer in bytes @return false if error occured
• LoadFullModelFromFile^⚠
  Load model from file into given model handle @param calcer @param filename @return false if error occured
• ModelCalcerCreate^⚠
  Create empty model handle @return
• ModelCalcerDelete^⚠
  Delete model handle @param calcer
• PredictSpecificClass^⚠
• PredictSpecificClassFlat^⚠
  Methods equivalent to the methods above only returning a prediction for the specific class @param classId number of the class should be in [0, modelApproxDimension - 1] @param resultSize result size should be equal to docCount
• PredictSpecificClassSingle^⚠
• PredictSpecificClassText^⚠
• PredictSpecificClassWithHashedCatFeatures^⚠
• PredictSpecificClassWithHashedCatFeaturesAndTextFeatures^⚠
• SetPredictionType^⚠
  Set prediction type for model evaluation
• SetPredictionTypeString^⚠
  Set prediction type for model evaluation with string constant

Type Definitions

• DataProviderHandle
• DataWrapperHandle
• EApiPredictionType
• ModelCalcerHandle
• size_t
• wchar_t