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c_api.cc
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c_api.cc
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// Copyright (c) 2014-2021 by Contributors
#include <rabit/rabit.h>
#include <rabit/c_api.h>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <algorithm>
#include <vector>
#include <string>
#include <memory>
#include "xgboost/base.h"
#include "xgboost/data.h"
#include "xgboost/host_device_vector.h"
#include "xgboost/learner.h"
#include "xgboost/c_api.h"
#include "xgboost/logging.h"
#include "xgboost/version_config.h"
#include "xgboost/json.h"
#include "xgboost/global_config.h"
#include "c_api_error.h"
#include "c_api_utils.h"
#include "../common/io.h"
#include "../common/charconv.h"
#include "../data/adapter.h"
#include "../data/simple_dmatrix.h"
#include "../data/proxy_dmatrix.h"
using namespace xgboost; // NOLINT(*);
XGB_DLL void XGBoostVersion(int* major, int* minor, int* patch) {
if (major) {
*major = XGBOOST_VER_MAJOR;
}
if (minor) {
*minor = XGBOOST_VER_MINOR;
}
if (patch) {
*patch = XGBOOST_VER_PATCH;
}
}
XGB_DLL int XGBRegisterLogCallback(void (*callback)(const char*)) {
API_BEGIN_UNGUARD();
LogCallbackRegistry* registry = LogCallbackRegistryStore::Get();
registry->Register(callback);
API_END();
}
XGB_DLL int XGBSetGlobalConfig(const char* json_str) {
API_BEGIN();
Json config{Json::Load(StringView{json_str})};
for (auto& items : get<Object>(config)) {
switch (items.second.GetValue().Type()) {
case xgboost::Value::ValueKind::kInteger: {
items.second = String{std::to_string(get<Integer const>(items.second))};
break;
}
case xgboost::Value::ValueKind::kBoolean: {
if (get<Boolean const>(items.second)) {
items.second = String{"true"};
} else {
items.second = String{"false"};
}
break;
}
case xgboost::Value::ValueKind::kNumber: {
auto n = get<Number const>(items.second);
char chars[NumericLimits<float>::kToCharsSize];
auto ec = to_chars(chars, chars + sizeof(chars), n).ec;
CHECK(ec == std::errc());
items.second = String{chars};
break;
}
default:
break;
}
}
auto unknown = FromJson(config, GlobalConfigThreadLocalStore::Get());
if (!unknown.empty()) {
std::stringstream ss;
ss << "Unknown global parameters: { ";
size_t i = 0;
for (auto const& item : unknown) {
ss << item.first;
i++;
if (i != unknown.size()) {
ss << ", ";
}
}
LOG(FATAL) << ss.str() << " }";
}
API_END();
}
using GlobalConfigAPIThreadLocalStore = dmlc::ThreadLocalStore<XGBAPIThreadLocalEntry>;
XGB_DLL int XGBGetGlobalConfig(const char** json_str) {
API_BEGIN();
auto const& global_config = *GlobalConfigThreadLocalStore::Get();
Json config {ToJson(global_config)};
auto const* mgr = global_config.__MANAGER__();
for (auto& item : get<Object>(config)) {
auto const &str = get<String const>(item.second);
auto const &name = item.first;
auto e = mgr->Find(name);
CHECK(e);
if (dynamic_cast<dmlc::parameter::FieldEntry<int32_t> const*>(e) ||
dynamic_cast<dmlc::parameter::FieldEntry<int64_t> const*>(e) ||
dynamic_cast<dmlc::parameter::FieldEntry<uint32_t> const*>(e) ||
dynamic_cast<dmlc::parameter::FieldEntry<uint64_t> const*>(e)) {
auto i = std::strtoimax(str.data(), nullptr, 10);
CHECK_LE(i, static_cast<intmax_t>(std::numeric_limits<int64_t>::max()));
item.second = Integer(static_cast<int64_t>(i));
} else if (dynamic_cast<dmlc::parameter::FieldEntry<float> const *>(e) ||
dynamic_cast<dmlc::parameter::FieldEntry<double> const *>(e)) {
float f;
auto ec = from_chars(str.data(), str.data() + str.size(), f).ec;
CHECK(ec == std::errc());
item.second = Number(f);
} else if (dynamic_cast<dmlc::parameter::FieldEntry<bool> const *>(e)) {
item.second = Boolean(str != "0");
}
}
auto& local = *GlobalConfigAPIThreadLocalStore::Get();
Json::Dump(config, &local.ret_str);
*json_str = local.ret_str.c_str();
API_END();
}
XGB_DLL int XGDMatrixCreateFromFile(const char *fname,
int silent,
DMatrixHandle *out) {
API_BEGIN();
bool load_row_split = false;
if (rabit::IsDistributed()) {
LOG(CONSOLE) << "XGBoost distributed mode detected, "
<< "will split data among workers";
load_row_split = true;
}
*out = new std::shared_ptr<DMatrix>(DMatrix::Load(fname, silent != 0, load_row_split));
API_END();
}
XGB_DLL int XGDMatrixCreateFromDataIter(
void *data_handle, // a Java iterator
XGBCallbackDataIterNext *callback, // C++ callback defined in xgboost4j.cpp
const char *cache_info, DMatrixHandle *out) {
API_BEGIN();
std::string scache;
if (cache_info != nullptr) {
scache = cache_info;
}
xgboost::data::IteratorAdapter<DataIterHandle, XGBCallbackDataIterNext,
XGBoostBatchCSR> adapter(data_handle, callback);
*out = new std::shared_ptr<DMatrix> {
DMatrix::Create(
&adapter, std::numeric_limits<float>::quiet_NaN(),
1, scache
)
};
API_END();
}
#ifndef XGBOOST_USE_CUDA
XGB_DLL int XGDMatrixCreateFromArrayInterfaceColumns(char const* c_json_strs,
bst_float missing,
int nthread,
DMatrixHandle* out) {
API_BEGIN();
common::AssertGPUSupport();
API_END();
}
XGB_DLL int XGDMatrixCreateFromArrayInterface(char const* c_json_strs,
bst_float missing,
int nthread,
DMatrixHandle* out) {
API_BEGIN();
common::AssertGPUSupport();
API_END();
}
#endif
// Create from data iterator
XGB_DLL int XGProxyDMatrixCreate(DMatrixHandle* out) {
API_BEGIN();
*out = new std::shared_ptr<xgboost::DMatrix>(new xgboost::data::DMatrixProxy);;
API_END();
}
XGB_DLL int
XGProxyDMatrixSetDataCudaArrayInterface(DMatrixHandle handle,
char const *c_interface_str) {
API_BEGIN();
CHECK_HANDLE();
auto p_m = static_cast<std::shared_ptr<xgboost::DMatrix> *>(handle);
CHECK(p_m);
auto m = static_cast<xgboost::data::DMatrixProxy*>(p_m->get());
CHECK(m) << "Current DMatrix type does not support set data.";
m->SetData(c_interface_str);
API_END();
}
XGB_DLL int XGProxyDMatrixSetDataCudaColumnar(DMatrixHandle handle,
char const *c_interface_str) {
API_BEGIN();
CHECK_HANDLE();
auto p_m = static_cast<std::shared_ptr<xgboost::DMatrix> *>(handle);
CHECK(p_m);
auto m = static_cast<xgboost::data::DMatrixProxy*>(p_m->get());
CHECK(m) << "Current DMatrix type does not support set data.";
m->SetData(c_interface_str);
API_END();
}
XGB_DLL int XGDeviceQuantileDMatrixCreateFromCallback(
DataIterHandle iter, DMatrixHandle proxy, DataIterResetCallback *reset,
XGDMatrixCallbackNext *next, float missing, int nthread,
int max_bin, DMatrixHandle *out) {
API_BEGIN();
*out = new std::shared_ptr<xgboost::DMatrix>{
xgboost::DMatrix::Create(iter, proxy, reset, next, missing, nthread, max_bin)};
API_END();
}
// End Create from data iterator
XGB_DLL int XGDMatrixCreateFromCSREx(const size_t* indptr,
const unsigned* indices,
const bst_float* data,
size_t nindptr,
size_t nelem,
size_t num_col,
DMatrixHandle* out) {
API_BEGIN();
data::CSRAdapter adapter(indptr, indices, data, nindptr - 1, nelem, num_col);
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, std::nan(""), 1));
API_END();
}
XGB_DLL int XGDMatrixCreateFromCSR(char const *indptr,
char const *indices, char const *data,
xgboost::bst_ulong ncol,
char const* c_json_config,
DMatrixHandle* out) {
API_BEGIN();
data::CSRArrayAdapter adapter(StringView{indptr}, StringView{indices},
StringView{data}, ncol);
auto config = Json::Load(StringView{c_json_config});
float missing = GetMissing(config);
auto nthread = get<Integer const>(config["nthread"]);
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, missing, nthread));
API_END();
}
XGB_DLL int XGDMatrixCreateFromDense(char const *data,
char const *c_json_config,
DMatrixHandle *out) {
API_BEGIN();
xgboost::data::ArrayAdapter adapter{
xgboost::data::ArrayAdapter(StringView{data})};
auto config = Json::Load(StringView{c_json_config});
float missing = GetMissing(config);
auto nthread = get<Integer const>(config["nthread"]);
*out =
new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, missing, nthread));
API_END();
}
XGB_DLL int XGDMatrixCreateFromCSCEx(const size_t* col_ptr,
const unsigned* indices,
const bst_float* data,
size_t nindptr,
size_t,
size_t num_row,
DMatrixHandle* out) {
API_BEGIN();
data::CSCAdapter adapter(col_ptr, indices, data, nindptr - 1, num_row);
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, std::nan(""), 1));
API_END();
}
XGB_DLL int XGDMatrixCreateFromMat(const bst_float* data,
xgboost::bst_ulong nrow,
xgboost::bst_ulong ncol, bst_float missing,
DMatrixHandle* out) {
API_BEGIN();
data::DenseAdapter adapter(data, nrow, ncol);
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, missing, 1));
API_END();
}
XGB_DLL int XGDMatrixCreateFromMat_omp(const bst_float* data, // NOLINT
xgboost::bst_ulong nrow,
xgboost::bst_ulong ncol,
bst_float missing, DMatrixHandle* out,
int nthread) {
API_BEGIN();
data::DenseAdapter adapter(data, nrow, ncol);
*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, missing, nthread));
API_END();
}
XGB_DLL int XGDMatrixCreateFromDT(void** data, const char** feature_stypes,
xgboost::bst_ulong nrow,
xgboost::bst_ulong ncol, DMatrixHandle* out,
int nthread) {
API_BEGIN();
data::DataTableAdapter adapter(data, feature_stypes, nrow, ncol);
*out = new std::shared_ptr<DMatrix>(
DMatrix::Create(&adapter, std::nan(""), nthread));
API_END();
}
XGB_DLL int XGDMatrixSliceDMatrix(DMatrixHandle handle,
const int* idxset,
xgboost::bst_ulong len,
DMatrixHandle* out) {
return XGDMatrixSliceDMatrixEx(handle, idxset, len, out, 0);
}
XGB_DLL int XGDMatrixSliceDMatrixEx(DMatrixHandle handle,
const int* idxset,
xgboost::bst_ulong len,
DMatrixHandle* out,
int allow_groups) {
API_BEGIN();
CHECK_HANDLE();
if (!allow_groups) {
CHECK_EQ(static_cast<std::shared_ptr<DMatrix>*>(handle)
->get()
->Info()
.group_ptr_.size(),
0U)
<< "slice does not support group structure";
}
DMatrix* dmat = static_cast<std::shared_ptr<DMatrix>*>(handle)->get();
*out = new std::shared_ptr<DMatrix>(
dmat->Slice({idxset, static_cast<std::size_t>(len)}));
API_END();
}
XGB_DLL int XGDMatrixFree(DMatrixHandle handle) {
API_BEGIN();
CHECK_HANDLE();
delete static_cast<std::shared_ptr<DMatrix>*>(handle);
API_END();
}
XGB_DLL int XGDMatrixSaveBinary(DMatrixHandle handle, const char* fname,
int) {
API_BEGIN();
CHECK_HANDLE();
auto dmat = static_cast<std::shared_ptr<DMatrix>*>(handle)->get();
if (data::SimpleDMatrix* derived = dynamic_cast<data::SimpleDMatrix*>(dmat)) {
derived->SaveToLocalFile(fname);
} else {
LOG(FATAL) << "binary saving only supported by SimpleDMatrix";
}
API_END();
}
XGB_DLL int XGDMatrixSetFloatInfo(DMatrixHandle handle,
const char* field,
const bst_float* info,
xgboost::bst_ulong len) {
API_BEGIN();
CHECK_HANDLE();
static_cast<std::shared_ptr<DMatrix>*>(handle)
->get()->Info().SetInfo(field, info, xgboost::DataType::kFloat32, len);
API_END();
}
XGB_DLL int XGDMatrixSetInfoFromInterface(DMatrixHandle handle,
char const* field,
char const* interface_c_str) {
API_BEGIN();
CHECK_HANDLE();
static_cast<std::shared_ptr<DMatrix>*>(handle)
->get()->Info().SetInfo(field, interface_c_str);
API_END();
}
XGB_DLL int XGDMatrixSetUIntInfo(DMatrixHandle handle,
const char* field,
const unsigned* info,
xgboost::bst_ulong len) {
API_BEGIN();
CHECK_HANDLE();
static_cast<std::shared_ptr<DMatrix>*>(handle)
->get()->Info().SetInfo(field, info, xgboost::DataType::kUInt32, len);
API_END();
}
XGB_DLL int XGDMatrixSetStrFeatureInfo(DMatrixHandle handle, const char *field,
const char **c_info,
const xgboost::bst_ulong size) {
API_BEGIN();
CHECK_HANDLE();
auto &info = static_cast<std::shared_ptr<DMatrix> *>(handle)->get()->Info();
info.SetFeatureInfo(field, c_info, size);
API_END();
}
XGB_DLL int XGDMatrixGetStrFeatureInfo(DMatrixHandle handle, const char *field,
xgboost::bst_ulong *len,
const char ***out_features) {
API_BEGIN();
CHECK_HANDLE();
auto m = *static_cast<std::shared_ptr<DMatrix>*>(handle);
auto &info = static_cast<std::shared_ptr<DMatrix> *>(handle)->get()->Info();
std::vector<const char *> &charp_vecs = m->GetThreadLocal().ret_vec_charp;
std::vector<std::string> &str_vecs = m->GetThreadLocal().ret_vec_str;
info.GetFeatureInfo(field, &str_vecs);
charp_vecs.resize(str_vecs.size());
for (size_t i = 0; i < str_vecs.size(); ++i) {
charp_vecs[i] = str_vecs[i].c_str();
}
*out_features = dmlc::BeginPtr(charp_vecs);
*len = static_cast<xgboost::bst_ulong>(charp_vecs.size());
API_END();
}
XGB_DLL int XGDMatrixSetDenseInfo(DMatrixHandle handle, const char *field,
void const *data, xgboost::bst_ulong size,
int type) {
API_BEGIN();
CHECK_HANDLE();
auto &info = static_cast<std::shared_ptr<DMatrix> *>(handle)->get()->Info();
CHECK(type >= 1 && type <= 4);
info.SetInfo(field, data, static_cast<DataType>(type), size);
API_END();
}
XGB_DLL int XGDMatrixSetGroup(DMatrixHandle handle,
const unsigned* group,
xgboost::bst_ulong len) {
API_BEGIN();
CHECK_HANDLE();
LOG(WARNING) << "XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead.";
static_cast<std::shared_ptr<DMatrix>*>(handle)
->get()->Info().SetInfo("group", group, xgboost::DataType::kUInt32, len);
API_END();
}
XGB_DLL int XGDMatrixGetFloatInfo(const DMatrixHandle handle,
const char* field,
xgboost::bst_ulong* out_len,
const bst_float** out_dptr) {
API_BEGIN();
CHECK_HANDLE();
const MetaInfo& info = static_cast<std::shared_ptr<DMatrix>*>(handle)->get()->Info();
info.GetInfo(field, out_len, DataType::kFloat32, reinterpret_cast<void const**>(out_dptr));
API_END();
}
XGB_DLL int XGDMatrixGetUIntInfo(const DMatrixHandle handle,
const char *field,
xgboost::bst_ulong *out_len,
const unsigned **out_dptr) {
API_BEGIN();
CHECK_HANDLE();
const MetaInfo& info = static_cast<std::shared_ptr<DMatrix>*>(handle)->get()->Info();
info.GetInfo(field, out_len, DataType::kUInt32, reinterpret_cast<void const**>(out_dptr));
API_END();
}
XGB_DLL int XGDMatrixNumRow(const DMatrixHandle handle,
xgboost::bst_ulong *out) {
API_BEGIN();
CHECK_HANDLE();
*out = static_cast<xgboost::bst_ulong>(
static_cast<std::shared_ptr<DMatrix>*>(handle)->get()->Info().num_row_);
API_END();
}
XGB_DLL int XGDMatrixNumCol(const DMatrixHandle handle,
xgboost::bst_ulong *out) {
API_BEGIN();
CHECK_HANDLE();
*out = static_cast<xgboost::bst_ulong>(
static_cast<std::shared_ptr<DMatrix>*>(handle)->get()->Info().num_col_);
API_END();
}
// xgboost implementation
XGB_DLL int XGBoosterCreate(const DMatrixHandle dmats[],
xgboost::bst_ulong len,
BoosterHandle *out) {
API_BEGIN();
std::vector<std::shared_ptr<DMatrix> > mats;
for (xgboost::bst_ulong i = 0; i < len; ++i) {
mats.push_back(*static_cast<std::shared_ptr<DMatrix>*>(dmats[i]));
}
*out = Learner::Create(mats);
API_END();
}
XGB_DLL int XGBoosterFree(BoosterHandle handle) {
API_BEGIN();
CHECK_HANDLE();
delete static_cast<Learner*>(handle);
API_END();
}
XGB_DLL int XGBoosterSetParam(BoosterHandle handle,
const char *name,
const char *value) {
API_BEGIN();
CHECK_HANDLE();
static_cast<Learner*>(handle)->SetParam(name, value);
API_END();
}
XGB_DLL int XGBoosterGetNumFeature(BoosterHandle handle,
xgboost::bst_ulong *out) {
API_BEGIN();
CHECK_HANDLE();
static_cast<Learner*>(handle)->Configure();
*out = static_cast<Learner*>(handle)->GetNumFeature();
API_END();
}
XGB_DLL int XGBoosterBoostedRounds(BoosterHandle handle, int* out) {
API_BEGIN();
CHECK_HANDLE();
static_cast<Learner*>(handle)->Configure();
*out = static_cast<Learner*>(handle)->BoostedRounds();
API_END();
}
XGB_DLL int XGBoosterLoadJsonConfig(BoosterHandle handle, char const* json_parameters) {
API_BEGIN();
CHECK_HANDLE();
Json config { Json::Load(StringView{json_parameters}) };
static_cast<Learner*>(handle)->LoadConfig(config);
API_END();
}
XGB_DLL int XGBoosterSaveJsonConfig(BoosterHandle handle,
xgboost::bst_ulong *out_len,
char const** out_str) {
API_BEGIN();
CHECK_HANDLE();
Json config { Object() };
auto* learner = static_cast<Learner*>(handle);
learner->Configure();
learner->SaveConfig(&config);
std::string& raw_str = learner->GetThreadLocal().ret_str;
Json::Dump(config, &raw_str);
*out_str = raw_str.c_str();
*out_len = static_cast<xgboost::bst_ulong>(raw_str.length());
API_END();
}
XGB_DLL int XGBoosterUpdateOneIter(BoosterHandle handle,
int iter,
DMatrixHandle dtrain) {
API_BEGIN();
CHECK_HANDLE();
auto* bst = static_cast<Learner*>(handle);
auto *dtr =
static_cast<std::shared_ptr<DMatrix>*>(dtrain);
bst->UpdateOneIter(iter, *dtr);
API_END();
}
XGB_DLL int XGBoosterBoostOneIter(BoosterHandle handle,
DMatrixHandle dtrain,
bst_float *grad,
bst_float *hess,
xgboost::bst_ulong len) {
API_BEGIN();
CHECK_HANDLE();
HostDeviceVector<GradientPair> tmp_gpair;
auto* bst = static_cast<Learner*>(handle);
auto* dtr =
static_cast<std::shared_ptr<DMatrix>*>(dtrain);
tmp_gpair.Resize(len);
std::vector<GradientPair>& tmp_gpair_h = tmp_gpair.HostVector();
for (xgboost::bst_ulong i = 0; i < len; ++i) {
tmp_gpair_h[i] = GradientPair(grad[i], hess[i]);
}
bst->BoostOneIter(0, *dtr, &tmp_gpair);
API_END();
}
XGB_DLL int XGBoosterEvalOneIter(BoosterHandle handle,
int iter,
DMatrixHandle dmats[],
const char* evnames[],
xgboost::bst_ulong len,
const char** out_str) {
API_BEGIN();
CHECK_HANDLE();
auto* bst = static_cast<Learner*>(handle);
std::string& eval_str = bst->GetThreadLocal().ret_str;
std::vector<std::shared_ptr<DMatrix>> data_sets;
std::vector<std::string> data_names;
for (xgboost::bst_ulong i = 0; i < len; ++i) {
data_sets.push_back(*static_cast<std::shared_ptr<DMatrix>*>(dmats[i]));
data_names.emplace_back(evnames[i]);
}
eval_str = bst->EvalOneIter(iter, data_sets, data_names);
*out_str = eval_str.c_str();
API_END();
}
XGB_DLL int XGBoosterPredict(BoosterHandle handle,
DMatrixHandle dmat,
int option_mask,
unsigned ntree_limit,
int training,
xgboost::bst_ulong *len,
const bst_float **out_result) {
API_BEGIN();
CHECK_HANDLE();
auto *learner = static_cast<Learner*>(handle);
auto& entry = learner->GetThreadLocal().prediction_entry;
auto iteration_end = GetIterationFromTreeLimit(ntree_limit, learner);
learner->Predict(*static_cast<std::shared_ptr<DMatrix> *>(dmat),
(option_mask & 1) != 0, &entry.predictions, 0, iteration_end,
static_cast<bool>(training), (option_mask & 2) != 0,
(option_mask & 4) != 0, (option_mask & 8) != 0,
(option_mask & 16) != 0);
*out_result = dmlc::BeginPtr(entry.predictions.ConstHostVector());
*len = static_cast<xgboost::bst_ulong>(entry.predictions.Size());
API_END();
}
XGB_DLL int XGBoosterPredictFromDMatrix(BoosterHandle handle,
DMatrixHandle dmat,
char const* c_json_config,
xgboost::bst_ulong const **out_shape,
xgboost::bst_ulong *out_dim,
bst_float const **out_result) {
API_BEGIN();
if (handle == nullptr) {
LOG(FATAL) << "Booster has not been initialized or has already been disposed.";
}
if (dmat == nullptr) {
LOG(FATAL) << "DMatrix has not been initialized or has already been disposed.";
}
auto config = Json::Load(StringView{c_json_config});
auto *learner = static_cast<Learner*>(handle);
auto& entry = learner->GetThreadLocal().prediction_entry;
auto p_m = *static_cast<std::shared_ptr<DMatrix> *>(dmat);
auto const& j_config = get<Object const>(config);
auto type = PredictionType(get<Integer const>(j_config.at("type")));
auto iteration_begin = get<Integer const>(j_config.at("iteration_begin"));
auto iteration_end = get<Integer const>(j_config.at("iteration_end"));
auto ntree_limit_it = j_config.find("ntree_limit");
if (ntree_limit_it != j_config.cend() && !IsA<Null>(ntree_limit_it->second) &&
get<Integer const>(ntree_limit_it->second) != 0) {
CHECK(iteration_end == 0) <<
"Only one of the `ntree_limit` or `iteration_range` can be specified.";
LOG(WARNING) << "`ntree_limit` is deprecated, use `iteration_range` instead.";
iteration_end = GetIterationFromTreeLimit(get<Integer const>(ntree_limit_it->second), learner);
}
bool approximate = type == PredictionType::kApproxContribution ||
type == PredictionType::kApproxInteraction;
bool contribs = type == PredictionType::kContribution ||
type == PredictionType::kApproxContribution;
bool interactions = type == PredictionType::kInteraction ||
type == PredictionType::kApproxInteraction;
bool training = get<Boolean const>(config["training"]);
learner->Predict(p_m, type == PredictionType::kMargin, &entry.predictions,
iteration_begin, iteration_end, training,
type == PredictionType::kLeaf, contribs, approximate,
interactions);
*out_result = dmlc::BeginPtr(entry.predictions.ConstHostVector());
auto &shape = learner->GetThreadLocal().prediction_shape;
auto chunksize = p_m->Info().num_row_ == 0 ? 0 : entry.predictions.Size() / p_m->Info().num_row_;
auto rounds = iteration_end - iteration_begin;
rounds = rounds == 0 ? learner->BoostedRounds() : rounds;
// Determine shape
bool strict_shape = get<Boolean const>(config["strict_shape"]);
CalcPredictShape(strict_shape, type, p_m->Info().num_row_,
p_m->Info().num_col_, chunksize, learner->Groups(), rounds,
&shape, out_dim);
*out_shape = dmlc::BeginPtr(shape);
API_END();
}
template <typename T>
void InplacePredictImpl(std::shared_ptr<T> x, std::shared_ptr<DMatrix> p_m,
char const *c_json_config, Learner *learner,
size_t n_rows, size_t n_cols,
xgboost::bst_ulong const **out_shape,
xgboost::bst_ulong *out_dim, const float **out_result) {
auto config = Json::Load(StringView{c_json_config});
CHECK_EQ(get<Integer const>(config["cache_id"]), 0) << "Cache ID is not supported yet";
HostDeviceVector<float>* p_predt { nullptr };
auto type = PredictionType(get<Integer const>(config["type"]));
float missing = GetMissing(config);
learner->InplacePredict(x, p_m, type, missing, &p_predt,
get<Integer const>(config["iteration_begin"]),
get<Integer const>(config["iteration_end"]));
CHECK(p_predt);
auto &shape = learner->GetThreadLocal().prediction_shape;
auto chunksize = n_rows == 0 ? 0 : p_predt->Size() / n_rows;
bool strict_shape = get<Boolean const>(config["strict_shape"]);
CalcPredictShape(strict_shape, type, n_rows, n_cols, chunksize, learner->Groups(),
learner->BoostedRounds(), &shape, out_dim);
*out_result = dmlc::BeginPtr(p_predt->HostVector());
*out_shape = dmlc::BeginPtr(shape);
}
// A hidden API as cache id is not being supported yet.
XGB_DLL int XGBoosterPredictFromDense(BoosterHandle handle,
char const *array_interface,
char const *c_json_config,
DMatrixHandle m,
xgboost::bst_ulong const **out_shape,
xgboost::bst_ulong *out_dim,
const float **out_result) {
API_BEGIN();
CHECK_HANDLE();
std::shared_ptr<xgboost::data::ArrayAdapter> x{
new xgboost::data::ArrayAdapter(StringView{array_interface})};
std::shared_ptr<DMatrix> p_m {nullptr};
if (m) {
p_m = *static_cast<std::shared_ptr<DMatrix> *>(m);
}
auto *learner = static_cast<xgboost::Learner *>(handle);
InplacePredictImpl(x, p_m, c_json_config, learner, x->NumRows(),
x->NumColumns(), out_shape, out_dim, out_result);
API_END();
}
// A hidden API as cache id is not being supported yet.
XGB_DLL int XGBoosterPredictFromCSR(BoosterHandle handle, char const *indptr,
char const *indices, char const *data,
xgboost::bst_ulong cols,
char const *c_json_config, DMatrixHandle m,
xgboost::bst_ulong const **out_shape,
xgboost::bst_ulong *out_dim,
const float **out_result) {
API_BEGIN();
CHECK_HANDLE();
std::shared_ptr<xgboost::data::CSRArrayAdapter> x{
new xgboost::data::CSRArrayAdapter{StringView{indptr},
StringView{indices}, StringView{data},
static_cast<size_t>(cols)}};
std::shared_ptr<DMatrix> p_m {nullptr};
if (m) {
p_m = *static_cast<std::shared_ptr<DMatrix> *>(m);
}
auto *learner = static_cast<xgboost::Learner *>(handle);
InplacePredictImpl(x, p_m, c_json_config, learner, x->NumRows(),
x->NumColumns(), out_shape, out_dim, out_result);
API_END();
}
#if !defined(XGBOOST_USE_CUDA)
XGB_DLL int XGBoosterPredictFromCUDAArray(
BoosterHandle handle, char const *c_json_strs, char const *c_json_config,
DMatrixHandle m, xgboost::bst_ulong const **out_shape, xgboost::bst_ulong *out_dim,
const float **out_result) {
API_BEGIN();
CHECK_HANDLE();
common::AssertGPUSupport();
API_END();
}
XGB_DLL int XGBoosterPredictFromCUDAColumnar(
BoosterHandle handle, char const *c_json_strs, char const *c_json_config,
DMatrixHandle m, xgboost::bst_ulong const **out_shape, xgboost::bst_ulong *out_dim,
const float **out_result) {
API_BEGIN();
CHECK_HANDLE();
common::AssertGPUSupport();
API_END();
}
#endif // !defined(XGBOOST_USE_CUDA)
XGB_DLL int XGBoosterLoadModel(BoosterHandle handle, const char* fname) {
API_BEGIN();
CHECK_HANDLE();
if (common::FileExtension(fname) == "json") {
auto str = common::LoadSequentialFile(fname);
CHECK_GT(str.size(), 2);
CHECK_EQ(str[0], '{');
Json in { Json::Load({str.c_str(), str.size()}) };
static_cast<Learner*>(handle)->LoadModel(in);
} else {
std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname, "r"));
static_cast<Learner*>(handle)->LoadModel(fi.get());
}
API_END();
}
XGB_DLL int XGBoosterSaveModel(BoosterHandle handle, const char* c_fname) {
API_BEGIN();
CHECK_HANDLE();
std::unique_ptr<dmlc::Stream> fo(dmlc::Stream::Create(c_fname, "w"));
auto *learner = static_cast<Learner *>(handle);
learner->Configure();
if (common::FileExtension(c_fname) == "json") {
Json out { Object() };
learner->SaveModel(&out);
std::string str;
Json::Dump(out, &str);
fo->Write(str.c_str(), str.size());
} else {
auto *bst = static_cast<Learner*>(handle);
bst->SaveModel(fo.get());
}
API_END();
}
XGB_DLL int XGBoosterLoadModelFromBuffer(BoosterHandle handle,
const void* buf,
xgboost::bst_ulong len) {
API_BEGIN();
CHECK_HANDLE();
common::MemoryFixSizeBuffer fs((void*)buf, len); // NOLINT(*)
static_cast<Learner*>(handle)->LoadModel(&fs);
API_END();
}
XGB_DLL int XGBoosterGetModelRaw(BoosterHandle handle,
xgboost::bst_ulong* out_len,
const char** out_dptr) {
API_BEGIN();
CHECK_HANDLE();
auto *learner = static_cast<Learner*>(handle);
std::string& raw_str = learner->GetThreadLocal().ret_str;
raw_str.resize(0);
common::MemoryBufferStream fo(&raw_str);
learner->Configure();
learner->SaveModel(&fo);
*out_dptr = dmlc::BeginPtr(raw_str);
*out_len = static_cast<xgboost::bst_ulong>(raw_str.length());
API_END();
}
// The following two functions are `Load` and `Save` for memory based
// serialization methods. E.g. Python pickle.
XGB_DLL int XGBoosterSerializeToBuffer(BoosterHandle handle,
xgboost::bst_ulong *out_len,
const char **out_dptr) {
API_BEGIN();
CHECK_HANDLE();
auto *learner = static_cast<Learner*>(handle);
std::string &raw_str = learner->GetThreadLocal().ret_str;
raw_str.resize(0);
common::MemoryBufferStream fo(&raw_str);
learner->Configure();
learner->Save(&fo);
*out_dptr = dmlc::BeginPtr(raw_str);
*out_len = static_cast<xgboost::bst_ulong>(raw_str.length());
API_END();
}
XGB_DLL int XGBoosterUnserializeFromBuffer(BoosterHandle handle,
const void *buf,
xgboost::bst_ulong len) {
API_BEGIN();
CHECK_HANDLE();
common::MemoryFixSizeBuffer fs((void*)buf, len); // NOLINT(*)
static_cast<Learner*>(handle)->Load(&fs);
API_END();
}
XGB_DLL int XGBoosterLoadRabitCheckpoint(BoosterHandle handle,
int* version) {
API_BEGIN();
CHECK_HANDLE();
auto* bst = static_cast<Learner*>(handle);
*version = rabit::LoadCheckPoint(bst);
if (*version != 0) {
bst->Configure();
}
API_END();
}
XGB_DLL int XGBoosterSaveRabitCheckpoint(BoosterHandle handle) {
API_BEGIN();
CHECK_HANDLE();
auto* learner = static_cast<Learner*>(handle);
learner->Configure();
if (learner->AllowLazyCheckPoint()) {
rabit::LazyCheckPoint(learner);
} else {
rabit::CheckPoint(learner);
}
API_END();
}
XGB_DLL int XGBoosterSlice(BoosterHandle handle, int begin_layer,
int end_layer, int step,
BoosterHandle *out) {
API_BEGIN();
CHECK_HANDLE();
auto* learner = static_cast<Learner*>(handle);
bool out_of_bound = false;
auto p_out = learner->Slice(begin_layer, end_layer, step, &out_of_bound);
if (out_of_bound) {
return -2;
}
CHECK(p_out);
*out = p_out;
API_END();
}
inline void XGBoostDumpModelImpl(BoosterHandle handle, FeatureMap* fmap,
int with_stats, const char *format,
xgboost::bst_ulong *len,
const char ***out_models) {
auto *bst = static_cast<Learner*>(handle);
bst->Configure();
GenerateFeatureMap(bst, {}, bst->GetNumFeature(), fmap);
std::vector<std::string>& str_vecs = bst->GetThreadLocal().ret_vec_str;
std::vector<const char*>& charp_vecs = bst->GetThreadLocal().ret_vec_charp;
str_vecs = bst->DumpModel(*fmap, with_stats != 0, format);
charp_vecs.resize(str_vecs.size());
for (size_t i = 0; i < str_vecs.size(); ++i) {
charp_vecs[i] = str_vecs[i].c_str();
}
*out_models = dmlc::BeginPtr(charp_vecs);
*len = static_cast<xgboost::bst_ulong>(charp_vecs.size());
}
XGB_DLL int XGBoosterDumpModel(BoosterHandle handle,
const char* fmap,
int with_stats,
xgboost::bst_ulong* len,
const char*** out_models) {
API_BEGIN();
CHECK_HANDLE();
return XGBoosterDumpModelEx(handle, fmap, with_stats, "text", len, out_models);
API_END();
}
XGB_DLL int XGBoosterDumpModelEx(BoosterHandle handle,
const char* fmap,
int with_stats,
const char *format,
xgboost::bst_ulong* len,
const char*** out_models) {
API_BEGIN();
CHECK_HANDLE();
std::string uri{fmap};
FeatureMap featmap = LoadFeatureMap(uri);
XGBoostDumpModelImpl(handle, &featmap, with_stats, format, len, out_models);
API_END();
}
XGB_DLL int XGBoosterDumpModelWithFeatures(BoosterHandle handle,
int fnum,
const char** fname,
const char** ftype,
int with_stats,
xgboost::bst_ulong* len,
const char*** out_models) {
return XGBoosterDumpModelExWithFeatures(handle, fnum, fname, ftype,
with_stats, "text", len, out_models);
}
XGB_DLL int XGBoosterDumpModelExWithFeatures(BoosterHandle handle,
int fnum,
const char** fname,
const char** ftype,
int with_stats,
const char *format,
xgboost::bst_ulong* len,
const char*** out_models) {
API_BEGIN();
CHECK_HANDLE();
FeatureMap featmap;
for (int i = 0; i < fnum; ++i) {
featmap.PushBack(i, fname[i], ftype[i]);
}
XGBoostDumpModelImpl(handle, &featmap, with_stats, format, len, out_models);
API_END();
}