1203 lines
43 KiB
C++
1203 lines
43 KiB
C++
// Copyright (c) 2014-2021 by Contributors
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#include <rabit/rabit.h>
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#include <rabit/c_api.h>
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#include <cstdio>
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#include <cstring>
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#include <fstream>
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#include <algorithm>
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#include <vector>
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#include <string>
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#include <memory>
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#include "xgboost/base.h"
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#include "xgboost/data.h"
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#include "xgboost/host_device_vector.h"
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#include "xgboost/learner.h"
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#include "xgboost/c_api.h"
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#include "xgboost/logging.h"
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#include "xgboost/version_config.h"
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#include "xgboost/json.h"
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#include "xgboost/global_config.h"
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#include "c_api_error.h"
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#include "c_api_utils.h"
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#include "../common/io.h"
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#include "../common/charconv.h"
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#include "../data/adapter.h"
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#include "../data/simple_dmatrix.h"
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#include "../data/proxy_dmatrix.h"
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using namespace xgboost; // NOLINT(*);
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XGB_DLL void XGBoostVersion(int* major, int* minor, int* patch) {
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if (major) {
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*major = XGBOOST_VER_MAJOR;
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}
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if (minor) {
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*minor = XGBOOST_VER_MINOR;
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}
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if (patch) {
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*patch = XGBOOST_VER_PATCH;
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}
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}
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XGB_DLL int XGBRegisterLogCallback(void (*callback)(const char*)) {
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API_BEGIN_UNGUARD();
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LogCallbackRegistry* registry = LogCallbackRegistryStore::Get();
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registry->Register(callback);
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API_END();
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}
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XGB_DLL int XGBSetGlobalConfig(const char* json_str) {
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API_BEGIN();
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Json config{Json::Load(StringView{json_str})};
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for (auto& items : get<Object>(config)) {
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switch (items.second.GetValue().Type()) {
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case xgboost::Value::ValueKind::kInteger: {
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items.second = String{std::to_string(get<Integer const>(items.second))};
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break;
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}
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case xgboost::Value::ValueKind::kBoolean: {
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if (get<Boolean const>(items.second)) {
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items.second = String{"true"};
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} else {
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items.second = String{"false"};
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}
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break;
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}
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case xgboost::Value::ValueKind::kNumber: {
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auto n = get<Number const>(items.second);
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char chars[NumericLimits<float>::kToCharsSize];
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auto ec = to_chars(chars, chars + sizeof(chars), n).ec;
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CHECK(ec == std::errc());
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items.second = String{chars};
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break;
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}
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default:
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break;
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}
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}
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auto unknown = FromJson(config, GlobalConfigThreadLocalStore::Get());
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if (!unknown.empty()) {
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std::stringstream ss;
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ss << "Unknown global parameters: { ";
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size_t i = 0;
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for (auto const& item : unknown) {
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ss << item.first;
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i++;
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if (i != unknown.size()) {
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ss << ", ";
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}
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}
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LOG(FATAL) << ss.str() << " }";
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}
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API_END();
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}
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using GlobalConfigAPIThreadLocalStore = dmlc::ThreadLocalStore<XGBAPIThreadLocalEntry>;
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XGB_DLL int XGBGetGlobalConfig(const char** json_str) {
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API_BEGIN();
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auto const& global_config = *GlobalConfigThreadLocalStore::Get();
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Json config {ToJson(global_config)};
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auto const* mgr = global_config.__MANAGER__();
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for (auto& item : get<Object>(config)) {
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auto const &str = get<String const>(item.second);
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auto const &name = item.first;
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auto e = mgr->Find(name);
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CHECK(e);
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if (dynamic_cast<dmlc::parameter::FieldEntry<int32_t> const*>(e) ||
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dynamic_cast<dmlc::parameter::FieldEntry<int64_t> const*>(e) ||
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dynamic_cast<dmlc::parameter::FieldEntry<uint32_t> const*>(e) ||
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dynamic_cast<dmlc::parameter::FieldEntry<uint64_t> const*>(e)) {
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auto i = std::strtoimax(str.data(), nullptr, 10);
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CHECK_LE(i, static_cast<intmax_t>(std::numeric_limits<int64_t>::max()));
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item.second = Integer(static_cast<int64_t>(i));
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} else if (dynamic_cast<dmlc::parameter::FieldEntry<float> const *>(e) ||
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dynamic_cast<dmlc::parameter::FieldEntry<double> const *>(e)) {
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float f;
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auto ec = from_chars(str.data(), str.data() + str.size(), f).ec;
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CHECK(ec == std::errc());
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item.second = Number(f);
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} else if (dynamic_cast<dmlc::parameter::FieldEntry<bool> const *>(e)) {
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item.second = Boolean(str != "0");
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}
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}
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auto& local = *GlobalConfigAPIThreadLocalStore::Get();
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Json::Dump(config, &local.ret_str);
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*json_str = local.ret_str.c_str();
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API_END();
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}
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XGB_DLL int XGDMatrixCreateFromFile(const char *fname,
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int silent,
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DMatrixHandle *out) {
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API_BEGIN();
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bool load_row_split = false;
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if (rabit::IsDistributed()) {
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LOG(CONSOLE) << "XGBoost distributed mode detected, "
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<< "will split data among workers";
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load_row_split = true;
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}
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*out = new std::shared_ptr<DMatrix>(DMatrix::Load(fname, silent != 0, load_row_split));
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API_END();
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}
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XGB_DLL int XGDMatrixCreateFromDataIter(
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void *data_handle, // a Java iterator
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XGBCallbackDataIterNext *callback, // C++ callback defined in xgboost4j.cpp
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const char *cache_info, DMatrixHandle *out) {
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API_BEGIN();
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std::string scache;
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if (cache_info != nullptr) {
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scache = cache_info;
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}
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xgboost::data::IteratorAdapter<DataIterHandle, XGBCallbackDataIterNext,
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XGBoostBatchCSR> adapter(data_handle, callback);
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*out = new std::shared_ptr<DMatrix> {
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DMatrix::Create(
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&adapter, std::numeric_limits<float>::quiet_NaN(),
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1, scache
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)
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};
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API_END();
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}
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#ifndef XGBOOST_USE_CUDA
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XGB_DLL int XGDMatrixCreateFromCudaColumnar(char const *data,
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char const* c_json_config,
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DMatrixHandle *out) {
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API_BEGIN();
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common::AssertGPUSupport();
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API_END();
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}
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XGB_DLL int XGDMatrixCreateFromCudaArrayInterface(char const *data,
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char const* c_json_config,
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DMatrixHandle *out) {
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API_BEGIN();
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common::AssertGPUSupport();
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API_END();
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}
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#endif
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// Create from data iterator
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XGB_DLL int XGDMatrixCreateFromCallback(DataIterHandle iter,
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DMatrixHandle proxy,
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DataIterResetCallback *reset,
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XGDMatrixCallbackNext *next,
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char const* c_json_config,
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DMatrixHandle *out) {
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API_BEGIN();
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auto config = Json::Load(StringView{c_json_config});
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float missing = get<Number const>(config["missing"]);
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std::string cache = get<String const>(config["cache_prefix"]);
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int32_t n_threads = omp_get_max_threads();
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if (!IsA<Null>(config["nthread"])) {
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n_threads = get<Integer const>(config["nthread"]);
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}
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*out = new std::shared_ptr<xgboost::DMatrix>{xgboost::DMatrix::Create(
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iter, proxy, reset, next, missing, n_threads, cache)};
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API_END();
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}
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XGB_DLL int XGDeviceQuantileDMatrixCreateFromCallback(
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DataIterHandle iter, DMatrixHandle proxy, DataIterResetCallback *reset,
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XGDMatrixCallbackNext *next, float missing, int nthread,
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int max_bin, DMatrixHandle *out) {
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API_BEGIN();
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*out = new std::shared_ptr<xgboost::DMatrix>{
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xgboost::DMatrix::Create(iter, proxy, reset, next, missing, nthread, max_bin)};
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API_END();
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}
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XGB_DLL int XGProxyDMatrixCreate(DMatrixHandle* out) {
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API_BEGIN();
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*out = new std::shared_ptr<xgboost::DMatrix>(new xgboost::data::DMatrixProxy);;
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API_END();
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}
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XGB_DLL int
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XGProxyDMatrixSetDataCudaArrayInterface(DMatrixHandle handle,
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char const *c_interface_str) {
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API_BEGIN();
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CHECK_HANDLE();
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auto p_m = static_cast<std::shared_ptr<xgboost::DMatrix> *>(handle);
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CHECK(p_m);
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auto m = static_cast<xgboost::data::DMatrixProxy*>(p_m->get());
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CHECK(m) << "Current DMatrix type does not support set data.";
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m->SetData(c_interface_str);
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API_END();
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}
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XGB_DLL int XGProxyDMatrixSetDataCudaColumnar(DMatrixHandle handle,
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char const *c_interface_str) {
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API_BEGIN();
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CHECK_HANDLE();
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auto p_m = static_cast<std::shared_ptr<xgboost::DMatrix> *>(handle);
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CHECK(p_m);
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auto m = static_cast<xgboost::data::DMatrixProxy*>(p_m->get());
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CHECK(m) << "Current DMatrix type does not support set data.";
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m->SetData(c_interface_str);
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API_END();
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}
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XGB_DLL int XGProxyDMatrixSetDataDense(DMatrixHandle handle,
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char const *c_interface_str) {
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API_BEGIN();
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CHECK_HANDLE();
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auto p_m = static_cast<std::shared_ptr<xgboost::DMatrix> *>(handle);
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CHECK(p_m);
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auto m = static_cast<xgboost::data::DMatrixProxy*>(p_m->get());
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CHECK(m) << "Current DMatrix type does not support set data.";
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m->SetArrayData(c_interface_str);
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API_END();
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}
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XGB_DLL int XGProxyDMatrixSetDataCSR(DMatrixHandle handle, char const *indptr,
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char const *indices, char const *data,
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xgboost::bst_ulong ncol) {
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API_BEGIN();
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CHECK_HANDLE();
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auto p_m = static_cast<std::shared_ptr<xgboost::DMatrix> *>(handle);
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CHECK(p_m);
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auto m = static_cast<xgboost::data::DMatrixProxy*>(p_m->get());
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CHECK(m) << "Current DMatrix type does not support set data.";
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m->SetCSRData(indptr, indices, data, ncol, true);
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API_END();
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}
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// End Create from data iterator
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XGB_DLL int XGDMatrixCreateFromCSREx(const size_t* indptr,
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const unsigned* indices,
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const bst_float* data,
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size_t nindptr,
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size_t nelem,
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size_t num_col,
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DMatrixHandle* out) {
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API_BEGIN();
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data::CSRAdapter adapter(indptr, indices, data, nindptr - 1, nelem, num_col);
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*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, std::nan(""), 1));
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API_END();
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}
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XGB_DLL int XGDMatrixCreateFromCSR(char const *indptr,
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char const *indices, char const *data,
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xgboost::bst_ulong ncol,
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char const* c_json_config,
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DMatrixHandle* out) {
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API_BEGIN();
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data::CSRArrayAdapter adapter(StringView{indptr}, StringView{indices},
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StringView{data}, ncol);
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auto config = Json::Load(StringView{c_json_config});
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float missing = GetMissing(config);
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auto nthread = get<Integer const>(config["nthread"]);
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*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, missing, nthread));
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API_END();
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}
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XGB_DLL int XGDMatrixCreateFromDense(char const *data,
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char const *c_json_config,
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DMatrixHandle *out) {
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API_BEGIN();
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xgboost::data::ArrayAdapter adapter{
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xgboost::data::ArrayAdapter(StringView{data})};
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auto config = Json::Load(StringView{c_json_config});
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float missing = GetMissing(config);
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auto nthread = get<Integer const>(config["nthread"]);
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*out =
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new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, missing, nthread));
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API_END();
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}
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XGB_DLL int XGDMatrixCreateFromCSCEx(const size_t* col_ptr,
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const unsigned* indices,
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const bst_float* data,
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size_t nindptr,
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size_t,
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size_t num_row,
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DMatrixHandle* out) {
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API_BEGIN();
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data::CSCAdapter adapter(col_ptr, indices, data, nindptr - 1, num_row);
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*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, std::nan(""), 1));
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API_END();
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}
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XGB_DLL int XGDMatrixCreateFromMat(const bst_float* data,
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xgboost::bst_ulong nrow,
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xgboost::bst_ulong ncol, bst_float missing,
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DMatrixHandle* out) {
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API_BEGIN();
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data::DenseAdapter adapter(data, nrow, ncol);
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*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, missing, 1));
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API_END();
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}
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XGB_DLL int XGDMatrixCreateFromMat_omp(const bst_float* data, // NOLINT
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xgboost::bst_ulong nrow,
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xgboost::bst_ulong ncol,
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bst_float missing, DMatrixHandle* out,
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int nthread) {
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API_BEGIN();
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data::DenseAdapter adapter(data, nrow, ncol);
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*out = new std::shared_ptr<DMatrix>(DMatrix::Create(&adapter, missing, nthread));
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API_END();
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}
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XGB_DLL int XGDMatrixCreateFromDT(void** data, const char** feature_stypes,
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xgboost::bst_ulong nrow,
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xgboost::bst_ulong ncol, DMatrixHandle* out,
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int nthread) {
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API_BEGIN();
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data::DataTableAdapter adapter(data, feature_stypes, nrow, ncol);
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*out = new std::shared_ptr<DMatrix>(
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DMatrix::Create(&adapter, std::nan(""), nthread));
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API_END();
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}
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XGB_DLL int XGDMatrixSliceDMatrix(DMatrixHandle handle,
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const int* idxset,
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xgboost::bst_ulong len,
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DMatrixHandle* out) {
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return XGDMatrixSliceDMatrixEx(handle, idxset, len, out, 0);
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}
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XGB_DLL int XGDMatrixSliceDMatrixEx(DMatrixHandle handle,
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const int* idxset,
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xgboost::bst_ulong len,
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DMatrixHandle* out,
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int allow_groups) {
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API_BEGIN();
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CHECK_HANDLE();
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if (!allow_groups) {
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CHECK_EQ(static_cast<std::shared_ptr<DMatrix>*>(handle)
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->get()
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->Info()
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.group_ptr_.size(),
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0U)
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<< "slice does not support group structure";
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}
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DMatrix* dmat = static_cast<std::shared_ptr<DMatrix>*>(handle)->get();
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*out = new std::shared_ptr<DMatrix>(
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dmat->Slice({idxset, static_cast<std::size_t>(len)}));
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API_END();
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}
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XGB_DLL int XGDMatrixFree(DMatrixHandle handle) {
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API_BEGIN();
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CHECK_HANDLE();
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delete static_cast<std::shared_ptr<DMatrix>*>(handle);
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API_END();
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}
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XGB_DLL int XGDMatrixSaveBinary(DMatrixHandle handle, const char* fname,
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int) {
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API_BEGIN();
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CHECK_HANDLE();
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auto dmat = static_cast<std::shared_ptr<DMatrix>*>(handle)->get();
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if (data::SimpleDMatrix* derived = dynamic_cast<data::SimpleDMatrix*>(dmat)) {
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derived->SaveToLocalFile(fname);
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} else {
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LOG(FATAL) << "binary saving only supported by SimpleDMatrix";
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}
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API_END();
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}
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XGB_DLL int XGDMatrixSetFloatInfo(DMatrixHandle handle,
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const char* field,
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const bst_float* info,
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xgboost::bst_ulong len) {
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API_BEGIN();
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CHECK_HANDLE();
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static_cast<std::shared_ptr<DMatrix>*>(handle)
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->get()->Info().SetInfo(field, info, xgboost::DataType::kFloat32, len);
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API_END();
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}
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XGB_DLL int XGDMatrixSetInfoFromInterface(DMatrixHandle handle,
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char const* field,
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char const* interface_c_str) {
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API_BEGIN();
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CHECK_HANDLE();
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static_cast<std::shared_ptr<DMatrix>*>(handle)
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->get()->Info().SetInfo(field, interface_c_str);
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API_END();
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}
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XGB_DLL int XGDMatrixSetUIntInfo(DMatrixHandle handle,
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const char* field,
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const unsigned* info,
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xgboost::bst_ulong len) {
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API_BEGIN();
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CHECK_HANDLE();
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static_cast<std::shared_ptr<DMatrix>*>(handle)
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->get()->Info().SetInfo(field, info, xgboost::DataType::kUInt32, len);
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API_END();
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}
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XGB_DLL int XGDMatrixSetStrFeatureInfo(DMatrixHandle handle, const char *field,
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const char **c_info,
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const xgboost::bst_ulong size) {
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API_BEGIN();
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CHECK_HANDLE();
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auto &info = static_cast<std::shared_ptr<DMatrix> *>(handle)->get()->Info();
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info.SetFeatureInfo(field, c_info, size);
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API_END();
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}
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XGB_DLL int XGDMatrixGetStrFeatureInfo(DMatrixHandle handle, const char *field,
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xgboost::bst_ulong *len,
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const char ***out_features) {
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API_BEGIN();
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CHECK_HANDLE();
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auto m = *static_cast<std::shared_ptr<DMatrix>*>(handle);
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auto &info = static_cast<std::shared_ptr<DMatrix> *>(handle)->get()->Info();
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std::vector<const char *> &charp_vecs = m->GetThreadLocal().ret_vec_charp;
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std::vector<std::string> &str_vecs = m->GetThreadLocal().ret_vec_str;
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info.GetFeatureInfo(field, &str_vecs);
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charp_vecs.resize(str_vecs.size());
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for (size_t i = 0; i < str_vecs.size(); ++i) {
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charp_vecs[i] = str_vecs[i].c_str();
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}
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*out_features = dmlc::BeginPtr(charp_vecs);
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*len = static_cast<xgboost::bst_ulong>(charp_vecs.size());
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API_END();
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}
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|
|
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();
|
|
}
|
|
|
|
XGB_DLL int XGBoosterGetAttr(BoosterHandle handle,
|
|
const char* key,
|
|
const char** out,
|
|
int* success) {
|
|
auto* bst = static_cast<Learner*>(handle);
|
|
std::string& ret_str = bst->GetThreadLocal().ret_str;
|
|
API_BEGIN();
|
|
CHECK_HANDLE();
|
|
if (bst->GetAttr(key, &ret_str)) {
|
|
*out = ret_str.c_str();
|
|
*success = 1;
|
|
} else {
|
|
*out = nullptr;
|
|
*success = 0;
|
|
}
|
|
API_END();
|
|
}
|
|
|
|
XGB_DLL int XGBoosterSetAttr(BoosterHandle handle,
|
|
const char* key,
|
|
const char* value) {
|
|
API_BEGIN();
|
|
CHECK_HANDLE();
|
|
auto* bst = static_cast<Learner*>(handle);
|
|
if (value == nullptr) {
|
|
bst->DelAttr(key);
|
|
} else {
|
|
bst->SetAttr(key, value);
|
|
}
|
|
API_END();
|
|
}
|
|
|
|
XGB_DLL int XGBoosterGetAttrNames(BoosterHandle handle,
|
|
xgboost::bst_ulong* out_len,
|
|
const char*** out) {
|
|
API_BEGIN();
|
|
CHECK_HANDLE();
|
|
auto *learner = static_cast<Learner *>(handle);
|
|
std::vector<std::string> &str_vecs = learner->GetThreadLocal().ret_vec_str;
|
|
std::vector<const char *> &charp_vecs =
|
|
learner->GetThreadLocal().ret_vec_charp;
|
|
str_vecs = learner->GetAttrNames();
|
|
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 = dmlc::BeginPtr(charp_vecs);
|
|
*out_len = static_cast<xgboost::bst_ulong>(charp_vecs.size());
|
|
API_END();
|
|
}
|
|
|
|
XGB_DLL int XGBoosterSetStrFeatureInfo(BoosterHandle handle, const char *field,
|
|
const char **features,
|
|
const xgboost::bst_ulong size) {
|
|
API_BEGIN();
|
|
CHECK_HANDLE();
|
|
auto *learner = static_cast<Learner *>(handle);
|
|
std::vector<std::string> feature_info;
|
|
for (size_t i = 0; i < size; ++i) {
|
|
feature_info.emplace_back(features[i]);
|
|
}
|
|
if (!std::strcmp(field, "feature_name")) {
|
|
learner->SetFeatureNames(feature_info);
|
|
} else if (!std::strcmp(field, "feature_type")) {
|
|
learner->SetFeatureTypes(feature_info);
|
|
} else {
|
|
LOG(FATAL) << "Unknown field for Booster feature info:" << field;
|
|
}
|
|
API_END();
|
|
}
|
|
|
|
XGB_DLL int XGBoosterGetStrFeatureInfo(BoosterHandle handle, const char *field,
|
|
xgboost::bst_ulong *len,
|
|
const char ***out_features) {
|
|
API_BEGIN();
|
|
CHECK_HANDLE();
|
|
auto const *learner = static_cast<Learner const *>(handle);
|
|
std::vector<const char *> &charp_vecs =
|
|
learner->GetThreadLocal().ret_vec_charp;
|
|
std::vector<std::string> &str_vecs = learner->GetThreadLocal().ret_vec_str;
|
|
if (!std::strcmp(field, "feature_name")) {
|
|
learner->GetFeatureNames(&str_vecs);
|
|
} else if (!std::strcmp(field, "feature_type")) {
|
|
learner->GetFeatureTypes(&str_vecs);
|
|
} else {
|
|
LOG(FATAL) << "Unknown field for Booster feature info:" << field;
|
|
}
|
|
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 XGBoosterFeatureScore(BoosterHandle handle, char const *json_config,
|
|
xgboost::bst_ulong *out_n_features,
|
|
char const ***out_features,
|
|
bst_ulong *out_dim,
|
|
bst_ulong const **out_shape,
|
|
float const **out_scores) {
|
|
API_BEGIN();
|
|
CHECK_HANDLE();
|
|
auto *learner = static_cast<Learner *>(handle);
|
|
auto config = Json::Load(StringView{json_config});
|
|
auto importance = get<String const>(config["importance_type"]);
|
|
std::string feature_map_uri;
|
|
if (!IsA<Null>(config["feature_map"])) {
|
|
feature_map_uri = get<String const>(config["feature_map"]);
|
|
}
|
|
FeatureMap feature_map = LoadFeatureMap(feature_map_uri);
|
|
std::vector<Json> custom_feature_names;
|
|
if (!IsA<Null>(config["feature_names"])) {
|
|
custom_feature_names = get<Array const>(config["feature_names"]);
|
|
}
|
|
|
|
auto& scores = learner->GetThreadLocal().ret_vec_float;
|
|
std::vector<bst_feature_t> features;
|
|
learner->CalcFeatureScore(importance, &features, &scores);
|
|
|
|
auto n_features = learner->GetNumFeature();
|
|
GenerateFeatureMap(learner, custom_feature_names, n_features, &feature_map);
|
|
|
|
auto& feature_names = learner->GetThreadLocal().ret_vec_str;
|
|
feature_names.resize(features.size());
|
|
auto& feature_names_c = learner->GetThreadLocal().ret_vec_charp;
|
|
feature_names_c.resize(features.size());
|
|
|
|
for (bst_feature_t i = 0; i < features.size(); ++i) {
|
|
feature_names[i] = feature_map.Name(features[i]);
|
|
feature_names_c[i] = feature_names[i].data();
|
|
}
|
|
*out_n_features = feature_names.size();
|
|
|
|
CHECK_LE(features.size(), scores.size());
|
|
auto &shape = learner->GetThreadLocal().prediction_shape;
|
|
if (scores.size() > features.size()) {
|
|
// Linear model multi-class model
|
|
CHECK_EQ(scores.size() % features.size(), 0ul);
|
|
auto n_classes = scores.size() / features.size();
|
|
*out_dim = 2;
|
|
shape = {n_features, n_classes};
|
|
} else {
|
|
CHECK_EQ(features.size(), scores.size());
|
|
*out_dim = 1;
|
|
shape.resize(1);
|
|
shape.front() = scores.size();
|
|
}
|
|
|
|
*out_shape = dmlc::BeginPtr(shape);
|
|
*out_scores = scores.data();
|
|
*out_features = dmlc::BeginPtr(feature_names_c);
|
|
API_END();
|
|
}
|
|
|
|
// force link rabit
|
|
static DMLC_ATTRIBUTE_UNUSED int XGBOOST_LINK_RABIT_C_API_ = RabitLinkTag();
|