/
c_api_utils.h
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/
c_api_utils.h
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/*!
* Copyright (c) 2021 by XGBoost Contributors
*/
#ifndef XGBOOST_C_API_C_API_UTILS_H_
#define XGBOOST_C_API_C_API_UTILS_H_
#include <algorithm>
#include <functional>
#include <vector>
#include <memory>
#include <string>
#include "xgboost/logging.h"
#include "xgboost/json.h"
#include "xgboost/learner.h"
#include "xgboost/c_api.h"
namespace xgboost {
/* \brief Determine the output shape of prediction.
*
* \param strict_shape Whether should we reshape the output with consideration of groups
* and forest.
* \param type Prediction type
* \param rows Input samples
* \param cols Input features
* \param chunksize Total elements of output / rows
* \param groups Number of output groups from Learner
* \param rounds end_iteration - beg_iteration
* \param out_shape Output shape
* \param out_dim Output dimension
*/
inline void CalcPredictShape(bool strict_shape, PredictionType type, size_t rows, size_t cols,
size_t chunksize, size_t groups, size_t rounds,
std::vector<bst_ulong> *out_shape,
xgboost::bst_ulong *out_dim) {
auto &shape = *out_shape;
if (type == PredictionType::kMargin && rows != 0) {
// When kValue is used, softmax can change the chunksize.
CHECK_EQ(chunksize, groups);
}
switch (type) {
case PredictionType::kValue:
case PredictionType::kMargin: {
if (chunksize == 1 && !strict_shape) {
*out_dim = 1;
shape.resize(*out_dim);
shape.front() = rows;
} else {
*out_dim = 2;
shape.resize(*out_dim);
shape.front() = rows;
shape.back() = std::min(groups, chunksize);
}
break;
}
case PredictionType::kApproxContribution:
case PredictionType::kContribution: {
if (groups == 1 && !strict_shape) {
*out_dim = 2;
shape.resize(*out_dim);
shape.front() = rows;
shape.back() = cols + 1;
} else {
*out_dim = 3;
shape.resize(*out_dim);
shape[0] = rows;
shape[1] = groups;
shape[2] = cols + 1;
}
break;
}
case PredictionType::kApproxInteraction:
case PredictionType::kInteraction: {
if (groups == 1 && !strict_shape) {
*out_dim = 3;
shape.resize(*out_dim);
shape[0] = rows;
shape[1] = cols + 1;
shape[2] = cols + 1;
} else {
*out_dim = 4;
shape.resize(*out_dim);
shape[0] = rows;
shape[1] = groups;
shape[2] = cols + 1;
shape[3] = cols + 1;
}
break;
}
case PredictionType::kLeaf: {
if (strict_shape) {
shape.resize(4);
shape[0] = rows;
shape[1] = rounds;
shape[2] = groups;
auto forest = chunksize / (shape[1] * shape[2]);
forest = std::max(static_cast<decltype(forest)>(1), forest);
shape[3] = forest;
*out_dim = shape.size();
} else if (chunksize == 1) {
*out_dim = 1;
shape.resize(*out_dim);
shape.front() = rows;
} else {
*out_dim = 2;
shape.resize(*out_dim);
shape.front() = rows;
shape.back() = chunksize;
}
break;
}
default: {
LOG(FATAL) << "Unknown prediction type:" << static_cast<int>(type);
}
}
CHECK_EQ(
std::accumulate(shape.cbegin(), shape.cend(), 1, std::multiplies<>{}),
chunksize * rows);
}
// Reverse the ntree_limit in old prediction API.
inline uint32_t GetIterationFromTreeLimit(uint32_t ntree_limit, Learner *learner) {
// On Python and R, `best_ntree_limit` is set to `best_iteration * num_parallel_tree`.
// To reverse it we just divide it by `num_parallel_tree`.
if (ntree_limit != 0) {
learner->Configure();
uint32_t num_parallel_tree = 0;
Json config{Object()};
learner->SaveConfig(&config);
auto const &booster =
get<String const>(config["learner"]["gradient_booster"]["name"]);
if (booster == "gblinear") {
num_parallel_tree = 0;
} else if (booster == "dart") {
num_parallel_tree = std::stoi(
get<String const>(config["learner"]["gradient_booster"]["gbtree"]
["gbtree_train_param"]["num_parallel_tree"]));
} else if (booster == "gbtree") {
num_parallel_tree = std::stoi(get<String const>(
(config["learner"]["gradient_booster"]["gbtree_train_param"]
["num_parallel_tree"])));
} else {
LOG(FATAL) << "Unknown booster:" << booster;
}
ntree_limit /= std::max(num_parallel_tree, 1u);
}
return ntree_limit;
}
inline float GetMissing(Json const &config) {
float missing;
auto const& j_missing = config["missing"];
if (IsA<Number const>(j_missing)) {
missing = get<Number const>(j_missing);
} else if (IsA<Integer const>(j_missing)) {
missing = get<Integer const>(j_missing);
} else {
missing = nan("");
LOG(FATAL) << "Invalid missing value: " << j_missing;
}
return missing;
}
// Safe guard some global variables from being changed by XGBoost.
class XGBoostAPIGuard {
int32_t device_id_ {0};
#if defined(XGBOOST_USE_CUDA)
void SetGPUAttribute();
void RestoreGPUAttribute();
#else
void SetGPUAttribute() {}
void RestoreGPUAttribute() {}
#endif
public:
XGBoostAPIGuard() {
SetGPUAttribute();
}
~XGBoostAPIGuard() {
RestoreGPUAttribute();
}
};
inline FeatureMap LoadFeatureMap(std::string const& uri) {
FeatureMap feat;
if (uri.size() != 0) {
std::unique_ptr<dmlc::Stream> fs(dmlc::Stream::Create(uri.c_str(), "r"));
dmlc::istream is(fs.get());
feat.LoadText(is);
}
return feat;
}
// FIXME(jiamingy): Use this for model dump.
inline void GenerateFeatureMap(Learner const *learner,
size_t n_features, FeatureMap *out_feature_map) {
auto &feature_map = *out_feature_map;
auto maybe = [&](std::vector<std::string> const &values, size_t i,
std::string const &dft) {
return values.empty() ? dft : values[i];
};
if (feature_map.Size() == 0) {
// Use the feature names and types from booster.
std::vector<std::string> feature_names;
learner->GetFeatureNames(&feature_names);
if (!feature_names.empty()) {
CHECK_EQ(feature_names.size(), n_features) << "Incorrect number of feature names.";
}
std::vector<std::string> feature_types;
learner->GetFeatureTypes(&feature_types);
if (!feature_types.empty()) {
CHECK_EQ(feature_types.size(), n_features) << "Incorrect number of feature types.";
}
for (size_t i = 0; i < n_features; ++i) {
feature_map.PushBack(
i,
maybe(feature_names, i, "f" + std::to_string(i)).data(),
maybe(feature_types, i, "q").data());
}
}
CHECK_EQ(feature_map.Size(), n_features);
}
} // namespace xgboost
#endif // XGBOOST_C_API_C_API_UTILS_H_