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Move feature names and types of DMatrix from Python to C++. (#5858)

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* Add thread local return entry for DMatrix.
* Save feature name and feature type in binary file.

Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
This commit is contained in:
Jiaming Yuan
2020-07-07 09:40:13 +08:00
committed by GitHub
parent 4b0852ee41
commit 93c44a9a64
12 changed files with 451 additions and 84 deletions
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134
src/data/data.cc
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@@ -11,6 +11,7 @@
#include "xgboost/host_device_vector.h"
#include "xgboost/logging.h"
#include "xgboost/version_config.h"
#include "xgboost/learner.h"
#include "sparse_page_writer.h"
#include "simple_dmatrix.h"
@@ -148,8 +149,10 @@ void MetaInfo::Clear() {
* | group_ptr | kUInt32 | False | ${size} | 1 | ${group_ptr_} |
* | weights | kFloat32 | False | ${size} | 1 | ${weights_} |
* | base_margin | kFloat32 | False | ${size} | 1 | ${base_margin_} |
* | labels_lower_bound | kFloat32 | False | ${size} | 1 | ${labels_lower_bound__} |
* | labels_upper_bound | kFloat32 | False | ${size} | 1 | ${labels_upper_bound__} |
* | labels_lower_bound | kFloat32 | False | ${size} | 1 | ${labels_lower_bound_} |
* | labels_upper_bound | kFloat32 | False | ${size} | 1 | ${labels_upper_bound_} |
* | feature_names | kStr | False | ${size} | 1 | ${feature_names} |
* | feature_types | kStr | False | ${size} | 1 | ${feature_types} |
*
* Note that the scalar fields (is_scalar=True) will have num_row and num_col missing.
* Also notice the difference between the saved name and the name used in `SetInfo':
@@ -177,9 +180,31 @@ void MetaInfo::SaveBinary(dmlc::Stream *fo) const {
SaveVectorField(fo, u8"labels_upper_bound", DataType::kFloat32,
{labels_upper_bound_.Size(), 1}, labels_upper_bound_); ++field_cnt;
SaveVectorField(fo, u8"feature_names", DataType::kStr,
{feature_names.size(), 1}, feature_names); ++field_cnt;
SaveVectorField(fo, u8"feature_types", DataType::kStr,
{feature_type_names.size(), 1}, feature_type_names); ++field_cnt;
CHECK_EQ(field_cnt, kNumField) << "Wrong number of fields";
}
void LoadFeatureType(std::vector<std::string>const& type_names, std::vector<FeatureType>* types) {
types->clear();
for (auto const &elem : type_names) {
if (elem == "int") {
types->emplace_back(FeatureType::kNumerical);
} else if (elem == "float") {
types->emplace_back(FeatureType::kNumerical);
} else if (elem == "i") {
types->emplace_back(FeatureType::kNumerical);
} else if (elem == "q") {
types->emplace_back(FeatureType::kNumerical);
} else {
LOG(FATAL) << "All feature_types must be {int, float, i, q}";
}
}
}
void MetaInfo::LoadBinary(dmlc::Stream *fi) {
auto version = Version::Load(fi);
auto major = std::get<0>(version);
@@ -193,11 +218,20 @@ void MetaInfo::LoadBinary(dmlc::Stream *fi) {
const uint64_t expected_num_field = kNumField;
uint64_t num_field { 0 };
CHECK(fi->Read(&num_field)) << "MetaInfo: invalid format";
CHECK_GE(num_field, expected_num_field)
<< "MetaInfo: insufficient number of fields (expected at least " << expected_num_field
<< " fields, but the binary file only contains " << num_field << "fields.)";
size_t expected = 0;
if (major == 1 && std::get<1>(version) < 2) {
// feature names and types are added in 1.2
expected = expected_num_field - 2;
} else {
expected = expected_num_field;
}
CHECK_GE(num_field, expected)
<< "MetaInfo: insufficient number of fields (expected at least "
<< expected << " fields, but the binary file only contains " << num_field
<< "fields.)";
if (num_field > expected_num_field) {
LOG(WARNING) << "MetaInfo: the given binary file contains extra fields which will be ignored.";
LOG(WARNING) << "MetaInfo: the given binary file contains extra fields "
"which will be ignored.";
}
LoadScalarField(fi, u8"num_row", DataType::kUInt64, &num_row_);
@@ -209,6 +243,10 @@ void MetaInfo::LoadBinary(dmlc::Stream *fi) {
LoadVectorField(fi, u8"base_margin", DataType::kFloat32, &base_margin_);
LoadVectorField(fi, u8"labels_lower_bound", DataType::kFloat32, &labels_lower_bound_);
LoadVectorField(fi, u8"labels_upper_bound", DataType::kFloat32, &labels_upper_bound_);
LoadVectorField(fi, u8"feature_names", DataType::kStr, &feature_names);
LoadVectorField(fi, u8"feature_types", DataType::kStr, &feature_type_names);
LoadFeatureType(feature_type_names, &feature_types.HostVector());
}
template <typename T>
@@ -344,6 +382,76 @@ void MetaInfo::SetInfo(const char* key, const void* dptr, DataType dtype, size_t
}
}
void MetaInfo::GetInfo(char const *key, bst_ulong *out_len, DataType dtype,
const void **out_dptr) const {
if (dtype == DataType::kFloat32) {
const std::vector<bst_float>* vec = nullptr;
if (!std::strcmp(key, "label")) {
vec = &this->labels_.HostVector();
} else if (!std::strcmp(key, "weight")) {
vec = &this->weights_.HostVector();
} else if (!std::strcmp(key, "base_margin")) {
vec = &this->base_margin_.HostVector();
} else if (!std::strcmp(key, "label_lower_bound")) {
vec = &this->labels_lower_bound_.HostVector();
} else if (!std::strcmp(key, "label_upper_bound")) {
vec = &this->labels_upper_bound_.HostVector();
} else {
LOG(FATAL) << "Unknown float field name: " << key;
}
*out_len = static_cast<xgboost::bst_ulong>(vec->size()); // NOLINT
*reinterpret_cast<float const**>(out_dptr) = dmlc::BeginPtr(*vec);
} else if (dtype == DataType::kUInt32) {
const std::vector<unsigned> *vec = nullptr;
if (!std::strcmp(key, "group_ptr")) {
vec = &this->group_ptr_;
} else {
LOG(FATAL) << "Unknown uint32 field name: " << key;
}
*out_len = static_cast<xgboost::bst_ulong>(vec->size());
*reinterpret_cast<unsigned const**>(out_dptr) = dmlc::BeginPtr(*vec);
} else {
LOG(FATAL) << "Unknown data type for getting meta info.";
}
}
void MetaInfo::SetFeatureInfo(const char* key, const char **info, const bst_ulong size) {
if (size != 0) {
CHECK_EQ(size, this->num_col_)
<< "Length of " << key << " must be equal to number of columns.";
}
if (!std::strcmp(key, "feature_type")) {
feature_type_names.clear();
auto& h_feature_types = feature_types.HostVector();
for (size_t i = 0; i < size; ++i) {
auto elem = info[i];
feature_type_names.emplace_back(elem);
}
LoadFeatureType(feature_type_names, &h_feature_types);
} else if (!std::strcmp(key, "feature_name")) {
feature_names.clear();
for (size_t i = 0; i < size; ++i) {
feature_names.emplace_back(info[i]);
}
} else {
LOG(FATAL) << "Unknown feature info name: " << key;
}
}
void MetaInfo::GetFeatureInfo(const char *field,
std::vector<std::string> *out_str_vecs) const {
auto &str_vecs = *out_str_vecs;
if (!std::strcmp(field, "feature_type")) {
str_vecs.resize(feature_type_names.size());
std::copy(feature_type_names.cbegin(), feature_type_names.cend(), str_vecs.begin());
} else if (!strcmp(field, "feature_name")) {
str_vecs.resize(feature_names.size());
std::copy(feature_names.begin(), feature_names.end(), str_vecs.begin());
} else {
LOG(FATAL) << "Unknown feature info: " << field;
}
}
void MetaInfo::Extend(MetaInfo const& that, bool accumulate_rows) {
if (accumulate_rows) {
this->num_row_ += that.num_row_;
@@ -441,6 +549,20 @@ void MetaInfo::SetInfo(const char * c_key, std::string const& interface_str) {
}
#endif // !defined(XGBOOST_USE_CUDA)
using DMatrixThreadLocal =
dmlc::ThreadLocalStore<std::map<DMatrix const *, XGBAPIThreadLocalEntry>>;
XGBAPIThreadLocalEntry& DMatrix::GetThreadLocal() const {
return (*DMatrixThreadLocal::Get())[this];
}
DMatrix::~DMatrix() {
auto local_map = DMatrixThreadLocal::Get();
if (local_map->find(this) != local_map->cend()) {
local_map->erase(this);
}
}
DMatrix* DMatrix::Load(const std::string& uri,
bool silent,
bool load_row_split,