ClickHouse/dbms/src/Interpreters/CatBoostModel.cpp
2019-08-09 23:58:16 +03:00

565 lines
21 KiB
C++

#include "CatBoostModel.h"
#include <Common/FieldVisitors.h>
#include <mutex>
#include <Columns/ColumnString.h>
#include <Columns/ColumnFixedString.h>
#include <Columns/ColumnVector.h>
#include <Columns/ColumnTuple.h>
#include <Common/typeid_cast.h>
#include <IO/WriteBufferFromString.h>
#include <IO/Operators.h>
#include <Common/PODArray.h>
#include <Common/SharedLibrary.h>
#include <DataTypes/DataTypesNumber.h>
#include <DataTypes/DataTypeTuple.h>
namespace DB
{
namespace ErrorCodes
{
extern const int LOGICAL_ERROR;
extern const int BAD_ARGUMENTS;
extern const int CANNOT_LOAD_CATBOOST_MODEL;
extern const int CANNOT_APPLY_CATBOOST_MODEL;
}
/// CatBoost wrapper interface functions.
struct CatBoostWrapperAPI
{
typedef void ModelCalcerHandle;
ModelCalcerHandle * (* ModelCalcerCreate)();
void (* ModelCalcerDelete)(ModelCalcerHandle * calcer);
const char * (* GetErrorString)();
bool (* LoadFullModelFromFile)(ModelCalcerHandle * calcer, const char * filename);
bool (* CalcModelPredictionFlat)(ModelCalcerHandle * calcer, size_t docCount,
const float ** floatFeatures, size_t floatFeaturesSize,
double * result, size_t resultSize);
bool (* CalcModelPrediction)(ModelCalcerHandle * calcer, size_t docCount,
const float ** floatFeatures, size_t floatFeaturesSize,
const char *** catFeatures, size_t catFeaturesSize,
double * result, size_t resultSize);
bool (* CalcModelPredictionWithHashedCatFeatures)(ModelCalcerHandle * calcer, size_t docCount,
const float ** floatFeatures, size_t floatFeaturesSize,
const int ** catFeatures, size_t catFeaturesSize,
double * result, size_t resultSize);
int (* GetStringCatFeatureHash)(const char * data, size_t size);
int (* GetIntegerCatFeatureHash)(long long val);
size_t (* GetFloatFeaturesCount)(ModelCalcerHandle* calcer);
size_t (* GetCatFeaturesCount)(ModelCalcerHandle* calcer);
size_t (* GetTreeCount)(ModelCalcerHandle* modelHandle);
size_t (* GetDimensionsCount)(ModelCalcerHandle* modelHandle);
bool (* CheckModelMetadataHasKey)(ModelCalcerHandle* modelHandle, const char* keyPtr, size_t keySize);
size_t (*GetModelInfoValueSize)(ModelCalcerHandle* modelHandle, const char* keyPtr, size_t keySize);
const char* (*GetModelInfoValue)(ModelCalcerHandle* modelHandle, const char* keyPtr, size_t keySize);
};
namespace
{
class CatBoostModelHolder
{
private:
CatBoostWrapperAPI::ModelCalcerHandle * handle;
const CatBoostWrapperAPI * api;
public:
explicit CatBoostModelHolder(const CatBoostWrapperAPI * api_) : api(api_) { handle = api->ModelCalcerCreate(); }
~CatBoostModelHolder() { api->ModelCalcerDelete(handle); }
CatBoostWrapperAPI::ModelCalcerHandle * get() { return handle; }
};
class CatBoostModelImpl : public ICatBoostModel
{
public:
CatBoostModelImpl(const CatBoostWrapperAPI * api_, const std::string & model_path) : api(api_)
{
auto handle_ = std::make_unique<CatBoostModelHolder>(api);
if (!handle_)
{
std::string msg = "Cannot create CatBoost model: ";
throw Exception(msg + api->GetErrorString(), ErrorCodes::CANNOT_LOAD_CATBOOST_MODEL);
}
if (!api->LoadFullModelFromFile(handle_->get(), model_path.c_str()))
{
std::string msg = "Cannot load CatBoost model: ";
throw Exception(msg + api->GetErrorString(), ErrorCodes::CANNOT_LOAD_CATBOOST_MODEL);
}
float_features_count = api->GetFloatFeaturesCount(handle_->get());
cat_features_count = api->GetCatFeaturesCount(handle_->get());
tree_count = 1;
if (api->GetDimensionsCount)
tree_count = api->GetDimensionsCount(handle_->get());
handle = std::move(handle_);
}
ColumnPtr evaluate(const ColumnRawPtrs & columns) const override
{
if (columns.empty())
throw Exception("Got empty columns list for CatBoost model.", ErrorCodes::BAD_ARGUMENTS);
if (columns.size() != float_features_count + cat_features_count)
{
std::string msg;
{
WriteBufferFromString buffer(msg);
buffer << "Number of columns is different with number of features: ";
buffer << columns.size() << " vs " << float_features_count << " + " << cat_features_count;
}
throw Exception(msg, ErrorCodes::BAD_ARGUMENTS);
}
for (size_t i = 0; i < float_features_count; ++i)
{
if (!columns[i]->isNumeric())
{
std::string msg;
{
WriteBufferFromString buffer(msg);
buffer << "Column " << i << " should be numeric to make float feature.";
}
throw Exception(msg, ErrorCodes::BAD_ARGUMENTS);
}
}
bool cat_features_are_strings = true;
for (size_t i = float_features_count; i < float_features_count + cat_features_count; ++i)
{
auto column = columns[i];
if (column->isNumeric())
cat_features_are_strings = false;
else if (!(typeid_cast<const ColumnString *>(column)
|| typeid_cast<const ColumnFixedString *>(column)))
{
std::string msg;
{
WriteBufferFromString buffer(msg);
buffer << "Column " << i << " should be numeric or string.";
}
throw Exception(msg, ErrorCodes::BAD_ARGUMENTS);
}
}
auto result = evalImpl(columns, cat_features_are_strings);
if (tree_count == 1)
return result;
size_t column_size = columns.front()->size();
auto result_buf = result->getData().data();
/// Multiple trees case. Copy data to several columns.
MutableColumns mutable_columns(tree_count);
std::vector<Float64 *> column_ptrs(tree_count);
for (size_t i = 0; i < tree_count; ++i)
{
auto col = ColumnFloat64::create(column_size);
column_ptrs[i] = col->getData().data();
mutable_columns[i] = std::move(col);
}
Float64 * data = result_buf;
for (size_t row = 0; row < column_size; ++row)
{
for (size_t i = 0; i < tree_count; ++i)
{
*column_ptrs[i] = *data;
++column_ptrs[i];
++data;
}
}
return ColumnTuple::create(std::move(mutable_columns));
}
size_t getFloatFeaturesCount() const override { return float_features_count; }
size_t getCatFeaturesCount() const override { return cat_features_count; }
size_t getTreeCount() const override { return tree_count; }
private:
std::unique_ptr<CatBoostModelHolder> handle;
const CatBoostWrapperAPI * api;
size_t float_features_count;
size_t cat_features_count;
size_t tree_count;
/// Buffer should be allocated with features_count * column->size() elements.
/// Place column elements in positions buffer[0], buffer[features_count], ... , buffer[size * features_count]
template <typename T>
void placeColumnAsNumber(const IColumn * column, T * buffer, size_t features_count) const
{
size_t size = column->size();
FieldVisitorConvertToNumber<T> visitor;
for (size_t i = 0; i < size; ++i)
{
/// TODO: Replace with column visitor.
Field field;
column->get(i, field);
*buffer = applyVisitor(visitor, field);
buffer += features_count;
}
}
/// Buffer should be allocated with features_count * column->size() elements.
/// Place string pointers in positions buffer[0], buffer[features_count], ... , buffer[size * features_count]
void placeStringColumn(const ColumnString & column, const char ** buffer, size_t features_count) const
{
size_t size = column.size();
for (size_t i = 0; i < size; ++i)
{
*buffer = const_cast<char *>(column.getDataAtWithTerminatingZero(i).data);
buffer += features_count;
}
}
/// Buffer should be allocated with features_count * column->size() elements.
/// Place string pointers in positions buffer[0], buffer[features_count], ... , buffer[size * features_count]
/// Returns PODArray which holds data (because ColumnFixedString doesn't store terminating zero).
PODArray<char> placeFixedStringColumn(
const ColumnFixedString & column, const char ** buffer, size_t features_count) const
{
size_t size = column.size();
size_t str_size = column.getN();
PODArray<char> data(size * (str_size + 1));
char * data_ptr = data.data();
for (size_t i = 0; i < size; ++i)
{
auto ref = column.getDataAt(i);
memcpy(data_ptr, ref.data, ref.size);
data_ptr[ref.size] = 0;
*buffer = data_ptr;
data_ptr += ref.size + 1;
buffer += features_count;
}
return data;
}
/// Place columns into buffer, returns column which holds placed data. Buffer should contains column->size() values.
template <typename T>
ColumnPtr placeNumericColumns(const ColumnRawPtrs & columns,
size_t offset, size_t size, const T** buffer) const
{
if (size == 0)
return nullptr;
size_t column_size = columns[offset]->size();
auto data_column = ColumnVector<T>::create(size * column_size);
T * data = data_column->getData().data();
for (size_t i = 0; i < size; ++i)
{
auto column = columns[offset + i];
if (column->isNumeric())
placeColumnAsNumber(column, data + i, size);
}
for (size_t i = 0; i < column_size; ++i)
{
*buffer = data;
++buffer;
data += size;
}
return data_column;
}
/// Place columns into buffer, returns data which was used for fixed string columns.
/// Buffer should contains column->size() values, each value contains size strings.
std::vector<PODArray<char>> placeStringColumns(
const ColumnRawPtrs & columns, size_t offset, size_t size, const char ** buffer) const
{
if (size == 0)
return {};
std::vector<PODArray<char>> data;
for (size_t i = 0; i < size; ++i)
{
auto column = columns[offset + i];
if (auto column_string = typeid_cast<const ColumnString *>(column))
placeStringColumn(*column_string, buffer + i, size);
else if (auto column_fixed_string = typeid_cast<const ColumnFixedString *>(column))
data.push_back(placeFixedStringColumn(*column_fixed_string, buffer + i, size));
else
throw Exception("Cannot place string column.", ErrorCodes::LOGICAL_ERROR);
}
return data;
}
/// Calc hash for string cat feature at ps positions.
template <typename Column>
void calcStringHashes(const Column * column, size_t ps, const int ** buffer) const
{
size_t column_size = column->size();
for (size_t j = 0; j < column_size; ++j)
{
auto ref = column->getDataAt(j);
const_cast<int *>(*buffer)[ps] = api->GetStringCatFeatureHash(ref.data, ref.size);
++buffer;
}
}
/// Calc hash for int cat feature at ps position. Buffer at positions ps should contains unhashed values.
void calcIntHashes(size_t column_size, size_t ps, const int ** buffer) const
{
for (size_t j = 0; j < column_size; ++j)
{
const_cast<int *>(*buffer)[ps] = api->GetIntegerCatFeatureHash((*buffer)[ps]);
++buffer;
}
}
/// buffer contains column->size() rows and size columns.
/// For int cat features calc hash inplace.
/// For string cat features calc hash from column rows.
void calcHashes(const ColumnRawPtrs & columns, size_t offset, size_t size, const int ** buffer) const
{
if (size == 0)
return;
size_t column_size = columns[offset]->size();
std::vector<PODArray<char>> data;
for (size_t i = 0; i < size; ++i)
{
auto column = columns[offset + i];
if (auto column_string = typeid_cast<const ColumnString *>(column))
calcStringHashes(column_string, i, buffer);
else if (auto column_fixed_string = typeid_cast<const ColumnFixedString *>(column))
calcStringHashes(column_fixed_string, i, buffer);
else
calcIntHashes(column_size, i, buffer);
}
}
/// buffer[column_size * cat_features_count] -> char * => cat_features[column_size][cat_features_count] -> char *
void fillCatFeaturesBuffer(const char *** cat_features, const char ** buffer,
size_t column_size) const
{
for (size_t i = 0; i < column_size; ++i)
{
*cat_features = buffer;
++cat_features;
buffer += cat_features_count;
}
}
/// Convert values to row-oriented format and call evaluation function from CatBoost wrapper api.
/// * CalcModelPredictionFlat if no cat features
/// * CalcModelPrediction if all cat features are strings
/// * CalcModelPredictionWithHashedCatFeatures if has int cat features.
ColumnFloat64::MutablePtr evalImpl(
const ColumnRawPtrs & columns,
bool cat_features_are_strings) const
{
std::string error_msg = "Error occurred while applying CatBoost model: ";
size_t column_size = columns.front()->size();
auto result = ColumnFloat64::create(column_size * tree_count);
auto result_buf = result->getData().data();
if (!column_size)
return result;
/// Prepare float features.
PODArray<const float *> float_features(column_size);
auto float_features_buf = float_features.data();
/// Store all float data into single column. float_features is a list of pointers to it.
auto float_features_col = placeNumericColumns<float>(columns, 0, float_features_count, float_features_buf);
if (cat_features_count == 0)
{
if (!api->CalcModelPredictionFlat(handle->get(), column_size,
float_features_buf, float_features_count,
result_buf, column_size * tree_count))
{
throw Exception(error_msg + api->GetErrorString(), ErrorCodes::CANNOT_APPLY_CATBOOST_MODEL);
}
return result;
}
/// Prepare cat features.
if (cat_features_are_strings)
{
/// cat_features_holder stores pointers to ColumnString data or fixed_strings_data.
PODArray<const char *> cat_features_holder(cat_features_count * column_size);
PODArray<const char **> cat_features(column_size);
auto cat_features_buf = cat_features.data();
fillCatFeaturesBuffer(cat_features_buf, cat_features_holder.data(), column_size);
/// Fixed strings are stored without termination zero, so have to copy data into fixed_strings_data.
auto fixed_strings_data = placeStringColumns(columns, float_features_count,
cat_features_count, cat_features_holder.data());
if (!api->CalcModelPrediction(handle->get(), column_size,
float_features_buf, float_features_count,
cat_features_buf, cat_features_count,
result_buf, column_size * tree_count))
{
throw Exception(error_msg + api->GetErrorString(), ErrorCodes::CANNOT_APPLY_CATBOOST_MODEL);
}
}
else
{
PODArray<const int *> cat_features(column_size);
auto cat_features_buf = cat_features.data();
auto cat_features_col = placeNumericColumns<int>(columns, float_features_count,
cat_features_count, cat_features_buf);
calcHashes(columns, float_features_count, cat_features_count, cat_features_buf);
if (!api->CalcModelPredictionWithHashedCatFeatures(
handle->get(), column_size,
float_features_buf, float_features_count,
cat_features_buf, cat_features_count,
result_buf, column_size * tree_count))
{
throw Exception(error_msg + api->GetErrorString(), ErrorCodes::CANNOT_APPLY_CATBOOST_MODEL);
}
}
return result;
}
};
/// Holds CatBoost wrapper library and provides wrapper interface.
class CatBoostLibHolder: public CatBoostWrapperAPIProvider
{
public:
explicit CatBoostLibHolder(std::string lib_path_) : lib_path(std::move(lib_path_)), lib(lib_path) { initAPI(); }
const CatBoostWrapperAPI & getAPI() const override { return api; }
const std::string & getCurrentPath() const { return lib_path; }
private:
CatBoostWrapperAPI api;
std::string lib_path;
SharedLibrary lib;
void initAPI();
template <typename T>
void load(T& func, const std::string & name) { func = lib.get<T>(name); }
template <typename T>
void tryLoad(T& func, const std::string & name) { func = lib.tryGet<T>(name); }
};
void CatBoostLibHolder::initAPI()
{
load(api.ModelCalcerCreate, "ModelCalcerCreate");
load(api.ModelCalcerDelete, "ModelCalcerDelete");
load(api.GetErrorString, "GetErrorString");
load(api.LoadFullModelFromFile, "LoadFullModelFromFile");
load(api.CalcModelPredictionFlat, "CalcModelPredictionFlat");
load(api.CalcModelPrediction, "CalcModelPrediction");
load(api.CalcModelPredictionWithHashedCatFeatures, "CalcModelPredictionWithHashedCatFeatures");
load(api.GetStringCatFeatureHash, "GetStringCatFeatureHash");
load(api.GetIntegerCatFeatureHash, "GetIntegerCatFeatureHash");
load(api.GetFloatFeaturesCount, "GetFloatFeaturesCount");
load(api.GetCatFeaturesCount, "GetCatFeaturesCount");
tryLoad(api.CheckModelMetadataHasKey, "CheckModelMetadataHasKey");
tryLoad(api.GetModelInfoValueSize, "GetModelInfoValueSize");
tryLoad(api.GetModelInfoValue, "GetModelInfoValue");
tryLoad(api.GetTreeCount, "GetTreeCount");
tryLoad(api.GetDimensionsCount, "GetDimensionsCount");
}
std::shared_ptr<CatBoostLibHolder> getCatBoostWrapperHolder(const std::string & lib_path)
{
static std::weak_ptr<CatBoostLibHolder> ptr;
static std::mutex mutex;
std::lock_guard lock(mutex);
auto result = ptr.lock();
if (!result || result->getCurrentPath() != lib_path)
{
result = std::make_shared<CatBoostLibHolder>(lib_path);
/// This assignment is not atomic, which prevents from creating lock only inside 'if'.
ptr = result;
}
return result;
}
}
CatBoostModel::CatBoostModel(std::string name_, std::string model_path_, std::string lib_path_,
const ExternalLoadableLifetime & lifetime_)
: name(std::move(name_)), model_path(std::move(model_path_)), lib_path(std::move(lib_path_)), lifetime(lifetime_)
{
api_provider = getCatBoostWrapperHolder(lib_path);
api = &api_provider->getAPI();
model = std::make_unique<CatBoostModelImpl>(api, model_path);
float_features_count = model->getFloatFeaturesCount();
cat_features_count = model->getCatFeaturesCount();
tree_count = model->getTreeCount();
}
const ExternalLoadableLifetime & CatBoostModel::getLifetime() const
{
return lifetime;
}
bool CatBoostModel::isModified() const
{
return true;
}
std::shared_ptr<const IExternalLoadable> CatBoostModel::clone() const
{
return std::make_shared<CatBoostModel>(name, model_path, lib_path, lifetime);
}
size_t CatBoostModel::getFloatFeaturesCount() const
{
return float_features_count;
}
size_t CatBoostModel::getCatFeaturesCount() const
{
return cat_features_count;
}
size_t CatBoostModel::getTreeCount() const
{
return tree_count;
}
DataTypePtr CatBoostModel::getReturnType() const
{
auto type = std::make_shared<DataTypeFloat64>();
if (tree_count == 1)
return type;
DataTypes types(tree_count, type);
return std::make_shared<DataTypeTuple>(types);
}
ColumnPtr CatBoostModel::evaluate(const ColumnRawPtrs & columns) const
{
if (!model)
throw Exception("CatBoost model was not loaded.", ErrorCodes::LOGICAL_ERROR);
return model->evaluate(columns);
}
}