mirror of
https://github.com/ClickHouse/ClickHouse.git
synced 2024-12-18 04:12:19 +00:00
fac1be9700
- This commit restores statements "SYSTEM RELOAD MODEL(S)" which provide a mechanism to update a model explicitly. It also saves potentially unnecessary reloads of a model from disk after it's initial load. To keep the complexity low, the semantics of "SYSTEM RELOAD MODEL(S) was changed from eager to lazy. This means that both statements previously immedately reloaded the specified/all models, whereas now the statements only trigger an unload and the first call to catboostEvaluate() does the actual load. - Monitoring view SYSTEM.MODELS is also restored but with some obsolete fields removed. The view was not documented in the past and for now it remains undocumented. The commit is thus not considered a breach of ClickHouse's public interface.
390 lines
15 KiB
C++
390 lines
15 KiB
C++
#include "CatBoostLibraryHandler.h"
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#include <Columns/ColumnTuple.h>
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#include <Common/FieldVisitorConvertToNumber.h>
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namespace DB
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{
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namespace ErrorCodes
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{
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extern const int BAD_ARGUMENTS;
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extern const int CANNOT_APPLY_CATBOOST_MODEL;
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extern const int CANNOT_LOAD_CATBOOST_MODEL;
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extern const int LOGICAL_ERROR;
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}
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CatBoostLibraryHandler::APIHolder::APIHolder(SharedLibrary & lib)
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{
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ModelCalcerCreate = lib.get<CatBoostLibraryAPI::ModelCalcerCreateFunc>(CatBoostLibraryAPI::ModelCalcerCreateName);
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ModelCalcerDelete = lib.get<CatBoostLibraryAPI::ModelCalcerDeleteFunc>(CatBoostLibraryAPI::ModelCalcerDeleteName);
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GetErrorString = lib.get<CatBoostLibraryAPI::GetErrorStringFunc>(CatBoostLibraryAPI::GetErrorStringName);
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LoadFullModelFromFile = lib.get<CatBoostLibraryAPI::LoadFullModelFromFileFunc>(CatBoostLibraryAPI::LoadFullModelFromFileName);
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CalcModelPredictionFlat = lib.get<CatBoostLibraryAPI::CalcModelPredictionFlatFunc>(CatBoostLibraryAPI::CalcModelPredictionFlatName);
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CalcModelPrediction = lib.get<CatBoostLibraryAPI::CalcModelPredictionFunc>(CatBoostLibraryAPI::CalcModelPredictionName);
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CalcModelPredictionWithHashedCatFeatures = lib.get<CatBoostLibraryAPI::CalcModelPredictionWithHashedCatFeaturesFunc>(CatBoostLibraryAPI::CalcModelPredictionWithHashedCatFeaturesName);
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GetStringCatFeatureHash = lib.get<CatBoostLibraryAPI::GetStringCatFeatureHashFunc>(CatBoostLibraryAPI::GetStringCatFeatureHashName);
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GetIntegerCatFeatureHash = lib.get<CatBoostLibraryAPI::GetIntegerCatFeatureHashFunc>(CatBoostLibraryAPI::GetIntegerCatFeatureHashName);
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GetFloatFeaturesCount = lib.get<CatBoostLibraryAPI::GetFloatFeaturesCountFunc>(CatBoostLibraryAPI::GetFloatFeaturesCountName);
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GetCatFeaturesCount = lib.get<CatBoostLibraryAPI::GetCatFeaturesCountFunc>(CatBoostLibraryAPI::GetCatFeaturesCountName);
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GetTreeCount = lib.tryGet<CatBoostLibraryAPI::GetTreeCountFunc>(CatBoostLibraryAPI::GetTreeCountName);
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GetDimensionsCount = lib.tryGet<CatBoostLibraryAPI::GetDimensionsCountFunc>(CatBoostLibraryAPI::GetDimensionsCountName);
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}
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CatBoostLibraryHandler::CatBoostLibraryHandler(
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const std::string & library_path,
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const std::string & model_path)
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: loading_start_time(std::chrono::system_clock::now())
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, library(std::make_shared<SharedLibrary>(library_path))
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, api(*library)
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{
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model_calcer_handle = api.ModelCalcerCreate();
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if (!api.LoadFullModelFromFile(model_calcer_handle, model_path.c_str()))
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{
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throw Exception(ErrorCodes::CANNOT_LOAD_CATBOOST_MODEL,
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"Cannot load CatBoost model: {}", api.GetErrorString());
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}
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float_features_count = api.GetFloatFeaturesCount(model_calcer_handle);
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cat_features_count = api.GetCatFeaturesCount(model_calcer_handle);
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tree_count = 1;
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if (api.GetDimensionsCount)
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tree_count = api.GetDimensionsCount(model_calcer_handle);
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loading_duration = std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::system_clock::now() - loading_start_time);
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}
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CatBoostLibraryHandler::~CatBoostLibraryHandler()
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{
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api.ModelCalcerDelete(model_calcer_handle);
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}
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std::chrono::system_clock::time_point CatBoostLibraryHandler::getLoadingStartTime() const
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{
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return loading_start_time;
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}
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std::chrono::milliseconds CatBoostLibraryHandler::getLoadingDuration() const
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{
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return loading_duration;
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}
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namespace
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{
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/// Buffer should be allocated with features_count * column->size() elements.
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/// Place column elements in positions buffer[0], buffer[features_count], ... , buffer[size * features_count]
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template <typename T>
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void placeColumnAsNumber(const IColumn * column, T * buffer, size_t features_count)
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{
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size_t size = column->size();
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FieldVisitorConvertToNumber<T> visitor;
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for (size_t i = 0; i < size; ++i)
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{
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/// TODO: Replace with column visitor.
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Field field;
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column->get(i, field);
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*buffer = applyVisitor(visitor, field);
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buffer += features_count;
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}
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}
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/// Buffer should be allocated with features_count * column->size() elements.
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/// Place string pointers in positions buffer[0], buffer[features_count], ... , buffer[size * features_count]
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void placeStringColumn(const ColumnString & column, const char ** buffer, size_t features_count)
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{
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size_t size = column.size();
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for (size_t i = 0; i < size; ++i)
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{
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*buffer = const_cast<char *>(column.getDataAtWithTerminatingZero(i).data);
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buffer += features_count;
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}
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}
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/// Buffer should be allocated with features_count * column->size() elements.
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/// Place string pointers in positions buffer[0], buffer[features_count], ... , buffer[size * features_count]
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/// Returns PODArray which holds data (because ColumnFixedString doesn't store terminating zero).
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PODArray<char> placeFixedStringColumn(const ColumnFixedString & column, const char ** buffer, size_t features_count)
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{
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size_t size = column.size();
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size_t str_size = column.getN();
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PODArray<char> data(size * (str_size + 1));
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char * data_ptr = data.data();
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for (size_t i = 0; i < size; ++i)
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{
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auto ref = column.getDataAt(i);
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memcpy(data_ptr, ref.data, ref.size);
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data_ptr[ref.size] = 0;
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*buffer = data_ptr;
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data_ptr += ref.size + 1;
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buffer += features_count;
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}
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return data;
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}
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/// Place columns into buffer, returns column which holds placed data. Buffer should contains column->size() values.
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template <typename T>
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ColumnPtr placeNumericColumns(const ColumnRawPtrs & columns, size_t offset, size_t size, const T** buffer)
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{
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if (size == 0)
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return nullptr;
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size_t column_size = columns[offset]->size();
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auto data_column = ColumnVector<T>::create(size * column_size);
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T * data = data_column->getData().data();
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for (size_t i = 0; i < size; ++i)
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{
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const auto * column = columns[offset + i];
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if (column->isNumeric())
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placeColumnAsNumber(column, data + i, size);
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}
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for (size_t i = 0; i < column_size; ++i)
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{
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*buffer = data;
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++buffer;
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data += size;
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}
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return data_column;
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}
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/// Place columns into buffer, returns data which was used for fixed string columns.
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/// Buffer should contains column->size() values, each value contains size strings.
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std::vector<PODArray<char>> placeStringColumns(const ColumnRawPtrs & columns, size_t offset, size_t size, const char ** buffer)
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{
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if (size == 0)
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return {};
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std::vector<PODArray<char>> data;
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for (size_t i = 0; i < size; ++i)
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{
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const auto * column = columns[offset + i];
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if (const auto * column_string = typeid_cast<const ColumnString *>(column))
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placeStringColumn(*column_string, buffer + i, size);
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else if (const auto * column_fixed_string = typeid_cast<const ColumnFixedString *>(column))
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data.push_back(placeFixedStringColumn(*column_fixed_string, buffer + i, size));
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else
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throw Exception("Cannot place string column.", ErrorCodes::LOGICAL_ERROR);
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}
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return data;
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}
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/// buffer[column_size * cat_features_count] -> char * => cat_features[column_size][cat_features_count] -> char *
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void fillCatFeaturesBuffer(
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const char *** cat_features, const char ** buffer,
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size_t column_size, size_t cat_features_count)
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{
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for (size_t i = 0; i < column_size; ++i)
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{
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*cat_features = buffer;
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++cat_features;
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buffer += cat_features_count;
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}
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}
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/// Calc hash for string cat feature at ps positions.
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template <typename Column>
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void calcStringHashes(const Column * column, size_t ps, const int ** buffer, const CatBoostLibraryHandler::APIHolder & api)
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{
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size_t column_size = column->size();
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for (size_t j = 0; j < column_size; ++j)
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{
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auto ref = column->getDataAt(j);
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const_cast<int *>(*buffer)[ps] = api.GetStringCatFeatureHash(ref.data, ref.size);
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++buffer;
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}
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}
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/// Calc hash for int cat feature at ps position. Buffer at positions ps should contains unhashed values.
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void calcIntHashes(size_t column_size, size_t ps, const int ** buffer, const CatBoostLibraryHandler::APIHolder & api)
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{
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for (size_t j = 0; j < column_size; ++j)
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{
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const_cast<int *>(*buffer)[ps] = api.GetIntegerCatFeatureHash((*buffer)[ps]);
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++buffer;
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}
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}
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/// buffer contains column->size() rows and size columns.
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/// For int cat features calc hash inplace.
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/// For string cat features calc hash from column rows.
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void calcHashes(const ColumnRawPtrs & columns, size_t offset, size_t size, const int ** buffer, const CatBoostLibraryHandler::APIHolder & api)
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{
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if (size == 0)
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return;
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size_t column_size = columns[offset]->size();
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std::vector<PODArray<char>> data;
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for (size_t i = 0; i < size; ++i)
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{
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const auto * column = columns[offset + i];
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if (const auto * column_string = typeid_cast<const ColumnString *>(column))
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calcStringHashes(column_string, i, buffer, api);
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else if (const auto * column_fixed_string = typeid_cast<const ColumnFixedString *>(column))
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calcStringHashes(column_fixed_string, i, buffer, api);
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else
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calcIntHashes(column_size, i, buffer, api);
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}
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}
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}
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/// Convert values to row-oriented format and call evaluation function from CatBoost wrapper api.
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/// * CalcModelPredictionFlat if no cat features
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/// * CalcModelPrediction if all cat features are strings
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/// * CalcModelPredictionWithHashedCatFeatures if has int cat features.
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ColumnFloat64::MutablePtr CatBoostLibraryHandler::evalImpl(
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const ColumnRawPtrs & columns,
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bool cat_features_are_strings) const
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{
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std::string error_msg = "Error occurred while applying CatBoost model: ";
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size_t column_size = columns.front()->size();
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auto result = ColumnFloat64::create(column_size * tree_count);
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auto * result_buf = result->getData().data();
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if (!column_size)
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return result;
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/// Prepare float features.
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PODArray<const float *> float_features(column_size);
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auto * float_features_buf = float_features.data();
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/// Store all float data into single column. float_features is a list of pointers to it.
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auto float_features_col = placeNumericColumns<float>(columns, 0, float_features_count, float_features_buf);
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if (cat_features_count == 0)
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{
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if (!api.CalcModelPredictionFlat(model_calcer_handle, column_size,
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float_features_buf, float_features_count,
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result_buf, column_size * tree_count))
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{
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throw Exception(error_msg + api.GetErrorString(), ErrorCodes::CANNOT_APPLY_CATBOOST_MODEL);
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}
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return result;
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}
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/// Prepare cat features.
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if (cat_features_are_strings)
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{
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/// cat_features_holder stores pointers to ColumnString data or fixed_strings_data.
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PODArray<const char *> cat_features_holder(cat_features_count * column_size);
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PODArray<const char **> cat_features(column_size);
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auto * cat_features_buf = cat_features.data();
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fillCatFeaturesBuffer(cat_features_buf, cat_features_holder.data(), column_size, cat_features_count);
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/// Fixed strings are stored without termination zero, so have to copy data into fixed_strings_data.
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auto fixed_strings_data = placeStringColumns(columns, float_features_count,
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cat_features_count, cat_features_holder.data());
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if (!api.CalcModelPrediction(model_calcer_handle, column_size,
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float_features_buf, float_features_count,
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cat_features_buf, cat_features_count,
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result_buf, column_size * tree_count))
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{
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throw Exception(error_msg + api.GetErrorString(), ErrorCodes::CANNOT_APPLY_CATBOOST_MODEL);
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}
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}
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else
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{
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PODArray<const int *> cat_features(column_size);
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auto * cat_features_buf = cat_features.data();
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auto cat_features_col = placeNumericColumns<int>(columns, float_features_count,
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cat_features_count, cat_features_buf);
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calcHashes(columns, float_features_count, cat_features_count, cat_features_buf, api);
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if (!api.CalcModelPredictionWithHashedCatFeatures(
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model_calcer_handle, column_size,
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float_features_buf, float_features_count,
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cat_features_buf, cat_features_count,
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result_buf, column_size * tree_count))
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{
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throw Exception(error_msg + api.GetErrorString(), ErrorCodes::CANNOT_APPLY_CATBOOST_MODEL);
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}
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}
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return result;
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}
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size_t CatBoostLibraryHandler::getTreeCount() const
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{
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std::lock_guard lock(mutex);
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return tree_count;
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}
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ColumnPtr CatBoostLibraryHandler::evaluate(const ColumnRawPtrs & columns) const
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{
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std::lock_guard lock(mutex);
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if (columns.empty())
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throw Exception(ErrorCodes::BAD_ARGUMENTS, "Got empty columns list for CatBoost model.");
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if (columns.size() != float_features_count + cat_features_count)
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throw Exception(ErrorCodes::BAD_ARGUMENTS,
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"Number of columns is different with number of features: columns size {} float features size {} + cat features size {}",
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columns.size(),
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float_features_count,
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cat_features_count);
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for (size_t i = 0; i < float_features_count; ++i)
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{
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if (!columns[i]->isNumeric())
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{
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throw Exception(ErrorCodes::BAD_ARGUMENTS, "Column {} should be numeric to make float feature.", i);
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}
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}
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bool cat_features_are_strings = true;
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for (size_t i = float_features_count; i < float_features_count + cat_features_count; ++i)
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{
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const auto * column = columns[i];
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if (column->isNumeric())
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{
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cat_features_are_strings = false;
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}
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else if (!(typeid_cast<const ColumnString *>(column)
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|| typeid_cast<const ColumnFixedString *>(column)))
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{
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throw Exception(ErrorCodes::BAD_ARGUMENTS, "Column {} should be numeric or string.", i);
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}
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}
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auto result = evalImpl(columns, cat_features_are_strings);
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if (tree_count == 1)
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return result;
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size_t column_size = columns.front()->size();
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auto * result_buf = result->getData().data();
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/// Multiple trees case. Copy data to several columns.
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MutableColumns mutable_columns(tree_count);
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std::vector<Float64 *> column_ptrs(tree_count);
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for (size_t i = 0; i < tree_count; ++i)
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{
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auto col = ColumnFloat64::create(column_size);
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column_ptrs[i] = col->getData().data();
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mutable_columns[i] = std::move(col);
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}
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Float64 * data = result_buf;
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for (size_t row = 0; row < column_size; ++row)
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{
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for (size_t i = 0; i < tree_count; ++i)
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{
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*column_ptrs[i] = *data;
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++column_ptrs[i];
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++data;
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}
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}
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return ColumnTuple::create(std::move(mutable_columns));
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}
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}
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