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dataset.cpp
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dataset.cpp
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/*!
* Copyright (c) 2016 Microsoft Corporation. All rights reserved.
* Licensed under the MIT License. See LICENSE file in the project root for
* license information.
*/
#include <LightGBM/dataset.h>
#include <LightGBM/feature_group.h>
#include <LightGBM/pairwise_ranking_feature_group.h>
#include <LightGBM/cuda/vector_cudahost.h>
#include <LightGBM/utils/array_args.h>
#include <LightGBM/utils/openmp_wrapper.h>
#include <LightGBM/utils/threading.h>
#include <chrono>
#include <cstdio>
#include <limits>
#include <sstream>
#include <unordered_map>
namespace LightGBM {
const int Dataset::kSerializedReferenceVersionLength = 2;
const char* Dataset::serialized_reference_version = "v1";
const char* Dataset::binary_file_token =
"______LightGBM_Binary_File_Token______\n";
const char* Dataset::binary_serialized_reference_token =
"______LightGBM_Binary_Serialized_Token______\n";
Dataset::Dataset() {
data_filename_ = "noname";
num_data_ = 0;
is_finish_load_ = false;
wait_for_manual_finish_ = false;
has_raw_ = false;
}
Dataset::Dataset(data_size_t num_data) {
CHECK_GT(num_data, 0);
data_filename_ = "noname";
num_data_ = num_data;
metadata_.Init(num_data_, NO_SPECIFIC, NO_SPECIFIC);
is_finish_load_ = false;
wait_for_manual_finish_ = false;
group_bin_boundaries_.push_back(0);
has_raw_ = false;
}
Dataset::~Dataset() {}
std::vector<std::vector<int>> OneFeaturePerGroup(const std::vector<int>& used_features) {
std::vector<std::vector<int>> features_in_group;
features_in_group.resize(used_features.size());
for (size_t i = 0; i < used_features.size(); ++i) {
features_in_group[i].emplace_back(used_features[i]);
}
return features_in_group;
}
int GetConflictCount(const std::vector<bool>& mark, const int* indices,
int num_indices, data_size_t max_cnt) {
int ret = 0;
for (int i = 0; i < num_indices; ++i) {
if (mark[indices[i]]) {
++ret;
}
if (ret > max_cnt) {
return -1;
}
}
return ret;
}
void MarkUsed(std::vector<bool>* mark, const int* indices,
data_size_t num_indices) {
auto& ref_mark = *mark;
for (int i = 0; i < num_indices; ++i) {
ref_mark[indices[i]] = true;
}
}
std::vector<int> FixSampleIndices(const BinMapper* bin_mapper,
int num_total_samples, int num_indices,
const int* sample_indices,
const double* sample_values) {
std::vector<int> ret;
if (bin_mapper->GetDefaultBin() == bin_mapper->GetMostFreqBin()) {
return ret;
}
int i = 0, j = 0;
while (i < num_total_samples) {
if (j < num_indices && sample_indices[j] < i) {
++j;
} else if (j < num_indices && sample_indices[j] == i) {
if (bin_mapper->ValueToBin(sample_values[j]) !=
bin_mapper->GetMostFreqBin()) {
ret.push_back(i);
}
++i;
} else {
ret.push_back(i++);
}
}
return ret;
}
std::vector<std::vector<int>> FindGroups(
const std::vector<std::unique_ptr<BinMapper>>& bin_mappers,
const std::vector<int>& find_order, int** sample_indices,
const int* num_per_col, int num_sample_col, data_size_t total_sample_cnt,
data_size_t num_data, bool is_use_gpu, bool is_sparse,
std::vector<int8_t>* multi_val_group) {
const int max_search_group = 100;
const int max_bin_per_group = 256;
const data_size_t single_val_max_conflict_cnt =
static_cast<data_size_t>(total_sample_cnt / 10000);
multi_val_group->clear();
Random rand(num_data);
std::vector<std::vector<int>> features_in_group;
std::vector<std::vector<bool>> conflict_marks;
std::vector<data_size_t> group_used_row_cnt;
std::vector<data_size_t> group_total_data_cnt;
std::vector<int> group_num_bin;
// first round: fill the single val group
for (auto fidx : find_order) {
bool is_filtered_feature = fidx >= num_sample_col;
const data_size_t cur_non_zero_cnt =
is_filtered_feature ? 0 : num_per_col[fidx];
std::vector<int> available_groups;
for (int gid = 0; gid < static_cast<int>(features_in_group.size()); ++gid) {
auto cur_num_bin = group_num_bin[gid] + bin_mappers[fidx]->num_bin() +
(bin_mappers[fidx]->GetDefaultBin() == 0 ? -1 : 0);
if (group_total_data_cnt[gid] + cur_non_zero_cnt <=
total_sample_cnt + single_val_max_conflict_cnt) {
if (!is_use_gpu || cur_num_bin <= max_bin_per_group) {
available_groups.push_back(gid);
}
}
}
std::vector<int> search_groups;
if (!available_groups.empty()) {
int last = static_cast<int>(available_groups.size()) - 1;
auto indices = rand.Sample(last, std::min(last, max_search_group - 1));
// always push the last group
search_groups.push_back(available_groups.back());
for (auto idx : indices) {
search_groups.push_back(available_groups[idx]);
}
}
int best_gid = -1;
int best_conflict_cnt = -1;
for (auto gid : search_groups) {
const data_size_t rest_max_cnt = single_val_max_conflict_cnt -
group_total_data_cnt[gid] +
group_used_row_cnt[gid];
const data_size_t cnt =
is_filtered_feature
? 0
: GetConflictCount(conflict_marks[gid], sample_indices[fidx],
num_per_col[fidx], rest_max_cnt);
if (cnt >= 0 && cnt <= rest_max_cnt && cnt <= cur_non_zero_cnt / 2) {
best_gid = gid;
best_conflict_cnt = cnt;
break;
}
}
if (best_gid >= 0) {
features_in_group[best_gid].push_back(fidx);
group_total_data_cnt[best_gid] += cur_non_zero_cnt;
group_used_row_cnt[best_gid] += cur_non_zero_cnt - best_conflict_cnt;
if (!is_filtered_feature) {
MarkUsed(&conflict_marks[best_gid], sample_indices[fidx],
num_per_col[fidx]);
}
group_num_bin[best_gid] +=
bin_mappers[fidx]->num_bin() +
(bin_mappers[fidx]->GetDefaultBin() == 0 ? -1 : 0);
} else {
features_in_group.emplace_back();
features_in_group.back().push_back(fidx);
conflict_marks.emplace_back(total_sample_cnt, false);
if (!is_filtered_feature) {
MarkUsed(&(conflict_marks.back()), sample_indices[fidx],
num_per_col[fidx]);
}
group_total_data_cnt.emplace_back(cur_non_zero_cnt);
group_used_row_cnt.emplace_back(cur_non_zero_cnt);
group_num_bin.push_back(
1 + bin_mappers[fidx]->num_bin() +
(bin_mappers[fidx]->GetDefaultBin() == 0 ? -1 : 0));
}
}
if (!is_sparse) {
multi_val_group->resize(features_in_group.size(), false);
return features_in_group;
}
std::vector<int> second_round_features;
std::vector<std::vector<int>> features_in_group2;
std::vector<std::vector<bool>> conflict_marks2;
const double dense_threshold = 0.4;
for (int gid = 0; gid < static_cast<int>(features_in_group.size()); ++gid) {
const double dense_rate =
static_cast<double>(group_used_row_cnt[gid]) / total_sample_cnt;
if (dense_rate >= dense_threshold) {
features_in_group2.push_back(std::move(features_in_group[gid]));
conflict_marks2.push_back(std::move(conflict_marks[gid]));
} else {
for (auto fidx : features_in_group[gid]) {
second_round_features.push_back(fidx);
}
}
}
features_in_group = features_in_group2;
conflict_marks = conflict_marks2;
multi_val_group->resize(features_in_group.size(), false);
if (!second_round_features.empty()) {
features_in_group.emplace_back();
conflict_marks.emplace_back(total_sample_cnt, false);
bool is_multi_val = is_use_gpu ? true : false;
int conflict_cnt = 0;
for (auto fidx : second_round_features) {
features_in_group.back().push_back(fidx);
if (!is_multi_val) {
const int rest_max_cnt = single_val_max_conflict_cnt - conflict_cnt;
const auto cnt =
GetConflictCount(conflict_marks.back(), sample_indices[fidx],
num_per_col[fidx], rest_max_cnt);
conflict_cnt += cnt;
if (cnt < 0 || conflict_cnt > single_val_max_conflict_cnt) {
is_multi_val = true;
continue;
}
MarkUsed(&(conflict_marks.back()), sample_indices[fidx],
num_per_col[fidx]);
}
}
multi_val_group->push_back(is_multi_val);
}
return features_in_group;
}
std::vector<std::vector<int>> FastFeatureBundling(
const std::vector<std::unique_ptr<BinMapper>>& bin_mappers,
int** sample_indices, double** sample_values, const int* num_per_col,
int num_sample_col, data_size_t total_sample_cnt,
const std::vector<int>& used_features, data_size_t num_data,
bool is_use_gpu, bool is_sparse, std::vector<int8_t>* multi_val_group) {
Common::FunctionTimer fun_timer("Dataset::FastFeatureBundling", global_timer);
std::vector<size_t> feature_non_zero_cnt;
feature_non_zero_cnt.reserve(used_features.size());
// put dense feature first
for (auto fidx : used_features) {
if (fidx < num_sample_col) {
feature_non_zero_cnt.emplace_back(num_per_col[fidx]);
} else {
feature_non_zero_cnt.emplace_back(0);
}
}
// sort by non zero cnt
std::vector<int> sorted_idx;
sorted_idx.reserve(used_features.size());
for (int i = 0; i < static_cast<int>(used_features.size()); ++i) {
sorted_idx.emplace_back(i);
}
// sort by non zero cnt, bigger first
std::stable_sort(sorted_idx.begin(), sorted_idx.end(),
[&feature_non_zero_cnt](int a, int b) {
return feature_non_zero_cnt[a] > feature_non_zero_cnt[b];
});
std::vector<int> feature_order_by_cnt;
feature_order_by_cnt.reserve(sorted_idx.size());
for (auto sidx : sorted_idx) {
feature_order_by_cnt.push_back(used_features[sidx]);
}
std::vector<std::vector<int>> tmp_indices;
std::vector<int> tmp_num_per_col(num_sample_col, 0);
for (auto fidx : used_features) {
if (fidx >= num_sample_col) {
continue;
}
auto ret = FixSampleIndices(
bin_mappers[fidx].get(), static_cast<int>(total_sample_cnt),
num_per_col[fidx], sample_indices[fidx], sample_values[fidx]);
if (!ret.empty()) {
tmp_indices.push_back(ret);
tmp_num_per_col[fidx] = static_cast<int>(ret.size());
sample_indices[fidx] = tmp_indices.back().data();
} else {
tmp_num_per_col[fidx] = num_per_col[fidx];
}
}
std::vector<int8_t> group_is_multi_val, group_is_multi_val2;
auto features_in_group =
FindGroups(bin_mappers, used_features, sample_indices,
tmp_num_per_col.data(), num_sample_col, total_sample_cnt,
num_data, is_use_gpu, is_sparse, &group_is_multi_val);
auto group2 =
FindGroups(bin_mappers, feature_order_by_cnt, sample_indices,
tmp_num_per_col.data(), num_sample_col, total_sample_cnt,
num_data, is_use_gpu, is_sparse, &group_is_multi_val2);
if (features_in_group.size() > group2.size()) {
features_in_group = group2;
group_is_multi_val = group_is_multi_val2;
}
// shuffle groups
int num_group = static_cast<int>(features_in_group.size());
Random tmp_rand(num_data);
for (int i = 0; i < num_group - 1; ++i) {
int j = tmp_rand.NextShort(i + 1, num_group);
std::swap(features_in_group[i], features_in_group[j]);
// Using std::swap for vector<bool> will cause the wrong result.
std::swap(group_is_multi_val[i], group_is_multi_val[j]);
}
*multi_val_group = group_is_multi_val;
return features_in_group;
}
void Dataset::Construct(std::vector<std::unique_ptr<BinMapper>>* bin_mappers,
int num_total_features,
const std::vector<std::vector<double>>& forced_bins,
int** sample_non_zero_indices,
double** sample_values,
const int* num_per_col,
int num_sample_col,
size_t total_sample_cnt,
const Config& io_config) {
num_total_features_ = num_total_features;
CHECK_EQ(num_total_features_, static_cast<int>(bin_mappers->size()));
// get num_features
std::vector<int> used_features;
auto& ref_bin_mappers = *bin_mappers;
for (int i = 0; i < static_cast<int>(bin_mappers->size()); ++i) {
if (ref_bin_mappers[i] != nullptr && !ref_bin_mappers[i]->is_trivial()) {
used_features.emplace_back(i);
}
}
if (used_features.empty()) {
Log::Warning(
"There are no meaningful features which satisfy the provided configuration. "
"Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing "
"Dataset might resolve this warning.");
}
auto features_in_group = OneFeaturePerGroup(used_features);
auto is_sparse = io_config.is_enable_sparse;
if (io_config.device_type == std::string("cuda")) {
LGBM_config_::current_device = lgbm_device_cuda;
if (is_sparse) {
Log::Warning("Using sparse features with CUDA is currently not supported.");
is_sparse = false;
}
} else if ((io_config.objective == std::string("pairwise_lambdarank")) && is_sparse) {
Log::Warning("Using sparse features with pairwise_lambdarank is currently not supported.");
is_sparse = false;
}
std::vector<int8_t> group_is_multi_val(used_features.size(), 0);
if (io_config.enable_bundle && !used_features.empty()) {
bool lgbm_is_gpu_used = io_config.device_type == std::string("gpu") || io_config.device_type == std::string("cuda");
features_in_group = FastFeatureBundling(
*bin_mappers, sample_non_zero_indices, sample_values, num_per_col,
num_sample_col, static_cast<data_size_t>(total_sample_cnt),
used_features, num_data_, lgbm_is_gpu_used,
is_sparse, &group_is_multi_val);
}
num_features_ = 0;
for (const auto& fs : features_in_group) {
num_features_ += static_cast<int>(fs.size());
}
int cur_fidx = 0;
used_feature_map_ = std::vector<int>(num_total_features_, -1);
num_groups_ = static_cast<int>(features_in_group.size());
real_feature_idx_.resize(num_features_);
feature2group_.resize(num_features_);
feature2subfeature_.resize(num_features_);
feature_need_push_zeros_.clear();
group_bin_boundaries_.clear();
uint64_t num_total_bin = 0;
group_bin_boundaries_.push_back(num_total_bin);
group_feature_start_.resize(num_groups_);
group_feature_cnt_.resize(num_groups_);
for (int i = 0; i < num_groups_; ++i) {
auto cur_features = features_in_group[i];
int cur_cnt_features = static_cast<int>(cur_features.size());
group_feature_start_[i] = cur_fidx;
group_feature_cnt_[i] = cur_cnt_features;
// get bin_mappers
std::vector<std::unique_ptr<BinMapper>> cur_bin_mappers;
for (int j = 0; j < cur_cnt_features; ++j) {
int real_fidx = cur_features[j];
used_feature_map_[real_fidx] = cur_fidx;
real_feature_idx_[cur_fidx] = real_fidx;
feature2group_[cur_fidx] = i;
feature2subfeature_[cur_fidx] = j;
cur_bin_mappers.emplace_back(ref_bin_mappers[real_fidx].release());
if (cur_bin_mappers.back()->GetDefaultBin() !=
cur_bin_mappers.back()->GetMostFreqBin()) {
feature_need_push_zeros_.push_back(cur_fidx);
}
++cur_fidx;
}
feature_groups_.emplace_back(std::unique_ptr<FeatureGroup>(
new FeatureGroup(cur_cnt_features, group_is_multi_val[i], &cur_bin_mappers, num_data_, i)));
num_total_bin += feature_groups_[i]->num_total_bin_;
group_bin_boundaries_.push_back(num_total_bin);
}
if (!io_config.max_bin_by_feature.empty()) {
CHECK_EQ(static_cast<size_t>(num_total_features_),
io_config.max_bin_by_feature.size());
CHECK_GT(*(std::min_element(io_config.max_bin_by_feature.begin(),
io_config.max_bin_by_feature.end())), 1);
max_bin_by_feature_.resize(num_total_features_);
max_bin_by_feature_.assign(io_config.max_bin_by_feature.begin(),
io_config.max_bin_by_feature.end());
}
forced_bin_bounds_ = forced_bins;
max_bin_ = io_config.max_bin;
min_data_in_bin_ = io_config.min_data_in_bin;
bin_construct_sample_cnt_ = io_config.bin_construct_sample_cnt;
use_missing_ = io_config.use_missing;
zero_as_missing_ = io_config.zero_as_missing;
has_raw_ = false;
if (io_config.linear_tree) {
has_raw_ = true;
}
numeric_feature_map_ = std::vector<int>(num_features_, -1);
num_numeric_features_ = 0;
for (int i = 0; i < num_features_; ++i) {
if (FeatureBinMapper(i)->bin_type() == BinType::NumericalBin) {
numeric_feature_map_[i] = num_numeric_features_;
++num_numeric_features_;
}
}
device_type_ = io_config.device_type;
gpu_device_id_ = io_config.gpu_device_id;
}
void Dataset::FinishLoad() {
if (is_finish_load_) {
return;
}
if (num_groups_ > 0) {
for (int i = 0; i < num_groups_; ++i) {
feature_groups_[i]->FinishLoad();
}
}
metadata_.FinishLoad();
#ifdef USE_CUDA
if (device_type_ == std::string("cuda")) {
CreateCUDAColumnData();
metadata_.CreateCUDAMetadata(gpu_device_id_);
} else {
cuda_column_data_.reset(nullptr);
}
#endif // USE_CUDA
is_finish_load_ = true;
}
void PushDataToMultiValBin(
data_size_t num_data, const std::vector<uint32_t> most_freq_bins,
const std::vector<uint32_t> offsets,
std::vector<std::vector<std::unique_ptr<BinIterator>>>* iters,
MultiValBin* ret) {
Common::FunctionTimer fun_time("Dataset::PushDataToMultiValBin",
global_timer);
if (ret->IsSparse()) {
Threading::For<data_size_t>(
0, num_data, 1024, [&](int tid, data_size_t start, data_size_t end) {
std::vector<uint32_t> cur_data;
cur_data.reserve(most_freq_bins.size());
for (size_t j = 0; j < most_freq_bins.size(); ++j) {
(*iters)[tid][j]->Reset(start);
}
for (data_size_t i = start; i < end; ++i) {
cur_data.clear();
for (size_t j = 0; j < most_freq_bins.size(); ++j) {
// for sparse multi value bin, we store the feature bin values with offset added
auto cur_bin = (*iters)[tid][j]->Get(i);
if (cur_bin == most_freq_bins[j]) {
continue;
}
cur_bin += offsets[j];
if (most_freq_bins[j] == 0) {
cur_bin -= 1;
}
cur_data.push_back(cur_bin);
}
ret->PushOneRow(tid, i, cur_data);
}
});
} else {
Threading::For<data_size_t>(
0, num_data, 1024, [&](int tid, data_size_t start, data_size_t end) {
std::vector<uint32_t> cur_data(most_freq_bins.size(), 0);
for (size_t j = 0; j < most_freq_bins.size(); ++j) {
(*iters)[tid][j]->Reset(start);
}
for (data_size_t i = start; i < end; ++i) {
for (size_t j = 0; j < most_freq_bins.size(); ++j) {
// for dense multi value bin, the feature bin values without offsets are used
auto cur_bin = (*iters)[tid][j]->Get(i);
cur_data[j] = cur_bin;
}
ret->PushOneRow(tid, i, cur_data);
}
});
}
}
MultiValBin* Dataset::GetMultiBinFromSparseFeatures(const std::vector<uint32_t>& offsets) const {
Common::FunctionTimer fun_time("Dataset::GetMultiBinFromSparseFeatures",
global_timer);
int multi_group_id = -1;
for (int i = 0; i < num_groups_; ++i) {
if (feature_groups_[i]->is_multi_val_) {
if (multi_group_id < 0) {
multi_group_id = i;
} else {
Log::Fatal("Bug. There should be only one multi-val group.");
}
}
}
if (multi_group_id < 0) {
return nullptr;
}
const int num_feature = feature_groups_[multi_group_id]->num_feature_;
int num_threads = OMP_NUM_THREADS();
std::vector<std::vector<std::unique_ptr<BinIterator>>> iters(num_threads);
std::vector<uint32_t> most_freq_bins;
double sum_sparse_rate = 0;
for (int i = 0; i < num_feature; ++i) {
#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1)
for (int tid = 0; tid < num_threads; ++tid) {
iters[tid].emplace_back(
feature_groups_[multi_group_id]->SubFeatureIterator(i));
}
most_freq_bins.push_back(
feature_groups_[multi_group_id]->bin_mappers_[i]->GetMostFreqBin());
sum_sparse_rate +=
feature_groups_[multi_group_id]->bin_mappers_[i]->sparse_rate();
}
sum_sparse_rate /= num_feature;
Log::Debug("Dataset::GetMultiBinFromSparseFeatures: sparse rate %f",
sum_sparse_rate);
std::unique_ptr<MultiValBin> ret;
ret.reset(MultiValBin::CreateMultiValBin(num_data_, offsets.back(),
num_feature, sum_sparse_rate, offsets));
PushDataToMultiValBin(num_data_, most_freq_bins, offsets, &iters, ret.get());
ret->FinishLoad();
return ret.release();
}
MultiValBin* Dataset::GetMultiBinFromAllFeatures(const std::vector<uint32_t>& offsets) const {
Common::FunctionTimer fun_time("Dataset::GetMultiBinFromAllFeatures",
global_timer);
int num_threads = OMP_NUM_THREADS();
double sum_dense_ratio = 0;
std::unique_ptr<MultiValBin> ret;
std::vector<std::vector<std::unique_ptr<BinIterator>>> iters(num_threads);
std::vector<uint32_t> most_freq_bins;
int ncol = 0;
for (int gid = 0; gid < num_groups_; ++gid) {
if (feature_groups_[gid]->is_multi_val_) {
ncol += feature_groups_[gid]->num_feature_;
} else {
++ncol;
}
for (int fid = 0; fid < feature_groups_[gid]->num_feature_; ++fid) {
const auto& bin_mapper = feature_groups_[gid]->bin_mappers_[fid];
sum_dense_ratio += 1.0f - bin_mapper->sparse_rate();
}
}
sum_dense_ratio /= ncol;
for (int gid = 0; gid < num_groups_; ++gid) {
if (feature_groups_[gid]->is_multi_val_) {
for (int fid = 0; fid < feature_groups_[gid]->num_feature_; ++fid) {
const auto& bin_mapper = feature_groups_[gid]->bin_mappers_[fid];
most_freq_bins.push_back(bin_mapper->GetMostFreqBin());
#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1)
for (int tid = 0; tid < num_threads; ++tid) {
iters[tid].emplace_back(
feature_groups_[gid]->SubFeatureIterator(fid));
}
}
} else {
most_freq_bins.push_back(0);
for (int tid = 0; tid < num_threads; ++tid) {
iters[tid].emplace_back(feature_groups_[gid]->FeatureGroupIterator());
}
}
}
CHECK(static_cast<int>(most_freq_bins.size()) == ncol);
Log::Debug("Dataset::GetMultiBinFromAllFeatures: sparse rate %f",
1.0 - sum_dense_ratio);
ret.reset(MultiValBin::CreateMultiValBin(
num_data_, offsets.back(), static_cast<int>(most_freq_bins.size()),
1.0 - sum_dense_ratio, offsets));
PushDataToMultiValBin(num_data_, most_freq_bins, offsets, &iters, ret.get());
ret->FinishLoad();
return ret.release();
}
template <bool USE_QUANT_GRAD, int HIST_BITS>
TrainingShareStates* Dataset::GetShareStates(
score_t* gradients, score_t* hessians,
const std::vector<int8_t>& is_feature_used, bool is_constant_hessian,
bool force_col_wise, bool force_row_wise,
const int num_grad_quant_bins) const {
Common::FunctionTimer fun_timer("Dataset::TestMultiThreadingMethod",
global_timer);
if (force_col_wise && force_row_wise) {
Log::Fatal(
"Cannot set both of `force_col_wise` and `force_row_wise` to `true` at "
"the same time");
}
if (num_groups_ <= 0) {
TrainingShareStates* share_state = new TrainingShareStates();
share_state->is_col_wise = true;
share_state->is_constant_hessian = is_constant_hessian;
return share_state;
}
if (force_col_wise) {
TrainingShareStates* share_state = new TrainingShareStates();
std::vector<uint32_t> offsets;
share_state->CalcBinOffsets(
feature_groups_, &offsets, true);
share_state->SetMultiValBin(GetMultiBinFromSparseFeatures(offsets),
num_data_, feature_groups_, false, true, num_grad_quant_bins);
share_state->is_col_wise = true;
share_state->is_constant_hessian = is_constant_hessian;
return share_state;
} else if (force_row_wise) {
TrainingShareStates* share_state = new TrainingShareStates();
std::vector<uint32_t> offsets;
share_state->CalcBinOffsets(
feature_groups_, &offsets, false);
share_state->SetMultiValBin(GetMultiBinFromAllFeatures(offsets), num_data_,
feature_groups_, false, false, num_grad_quant_bins);
share_state->is_col_wise = false;
share_state->is_constant_hessian = is_constant_hessian;
return share_state;
} else {
std::unique_ptr<MultiValBin> sparse_bin;
std::unique_ptr<MultiValBin> all_bin;
std::unique_ptr<TrainingShareStates> col_wise_state;
std::unique_ptr<TrainingShareStates> row_wise_state;
col_wise_state.reset(new TrainingShareStates());
row_wise_state.reset(new TrainingShareStates());
std::chrono::duration<double, std::milli> col_wise_init_time, row_wise_init_time;
auto start_time = std::chrono::steady_clock::now();
std::vector<uint32_t> col_wise_offsets;
col_wise_state->CalcBinOffsets(feature_groups_, &col_wise_offsets, true);
col_wise_state->SetMultiValBin(GetMultiBinFromSparseFeatures(col_wise_offsets), num_data_,
feature_groups_, false, true, num_grad_quant_bins);
col_wise_init_time = std::chrono::steady_clock::now() - start_time;
start_time = std::chrono::steady_clock::now();
std::vector<uint32_t> row_wise_offsets;
row_wise_state->CalcBinOffsets(feature_groups_, &row_wise_offsets, false);
row_wise_state->SetMultiValBin(GetMultiBinFromAllFeatures(row_wise_offsets), num_data_,
feature_groups_, false, false, num_grad_quant_bins);
row_wise_init_time = std::chrono::steady_clock::now() - start_time;
uint64_t max_total_bin = std::max<uint64_t>(row_wise_state->num_hist_total_bin(),
col_wise_state->num_hist_total_bin());
std::vector<hist_t, Common::AlignmentAllocator<hist_t, kAlignedSize>>
hist_data(max_total_bin * 2);
Log::Debug(
"init for col-wise cost %f seconds, init for row-wise cost %f seconds",
col_wise_init_time * 1e-3, row_wise_init_time * 1e-3);
col_wise_state->is_col_wise = true;
col_wise_state->is_constant_hessian = is_constant_hessian;
InitTrain(is_feature_used, col_wise_state.get());
row_wise_state->is_col_wise = false;
row_wise_state->is_constant_hessian = is_constant_hessian;
InitTrain(is_feature_used, row_wise_state.get());
std::chrono::duration<double, std::milli> col_wise_time, row_wise_time;
start_time = std::chrono::steady_clock::now();
ConstructHistograms<USE_QUANT_GRAD, HIST_BITS>(is_feature_used, nullptr, num_data_, gradients,
hessians, gradients, hessians, col_wise_state.get(),
hist_data.data());
col_wise_time = std::chrono::steady_clock::now() - start_time;
start_time = std::chrono::steady_clock::now();
ConstructHistograms<USE_QUANT_GRAD, HIST_BITS>(is_feature_used, nullptr, num_data_, gradients,
hessians, gradients, hessians, row_wise_state.get(),
hist_data.data());
row_wise_time = std::chrono::steady_clock::now() - start_time;
if (col_wise_time < row_wise_time) {
auto overhead_cost = row_wise_init_time + row_wise_time + col_wise_time;
Log::Info(
"Auto-choosing col-wise multi-threading, the overhead of testing was "
"%f seconds.\n"
"You can set `force_col_wise=true` to remove the overhead.",
overhead_cost * 1e-3);
return col_wise_state.release();
} else {
auto overhead_cost = col_wise_init_time + row_wise_time + col_wise_time;
Log::Info(
"Auto-choosing row-wise multi-threading, the overhead of testing was "
"%f seconds.\n"
"You can set `force_row_wise=true` to remove the overhead.\n"
"And if memory is not enough, you can set `force_col_wise=true`.",
overhead_cost * 1e-3);
if (row_wise_state->IsSparseRowwise()) {
Log::Debug("Using Sparse Multi-Val Bin");
} else {
Log::Debug("Using Dense Multi-Val Bin");
}
return row_wise_state.release();
}
}
}
template TrainingShareStates* Dataset::GetShareStates<false, 0>(
score_t* gradients, score_t* hessians,
const std::vector<int8_t>& is_feature_used, bool is_constant_hessian,
bool force_col_wise, bool force_row_wise,
const int num_grad_quant_bins) const;
template TrainingShareStates* Dataset::GetShareStates<true, 16>(
score_t* gradients, score_t* hessians,
const std::vector<int8_t>& is_feature_used, bool is_constant_hessian,
bool force_col_wise, bool force_row_wise,
const int num_grad_quant_bins) const;
template TrainingShareStates* Dataset::GetShareStates<true, 32>(
score_t* gradients, score_t* hessians,
const std::vector<int8_t>& is_feature_used, bool is_constant_hessian,
bool force_col_wise, bool force_row_wise,
const int num_grad_quant_bins) const;
void Dataset::CopyFeatureMapperFrom(const Dataset* dataset) {
feature_groups_.clear();
num_features_ = dataset->num_features_;
num_groups_ = dataset->num_groups_;
has_raw_ = dataset->has_raw();
// copy feature bin mapper data
for (int i = 0; i < num_groups_; ++i) {
feature_groups_.emplace_back(
new FeatureGroup(*dataset->feature_groups_[i], num_data_));
}
feature_groups_.shrink_to_fit();
used_feature_map_ = dataset->used_feature_map_;
num_total_features_ = dataset->num_total_features_;
feature_names_ = dataset->feature_names_;
label_idx_ = dataset->label_idx_;
real_feature_idx_ = dataset->real_feature_idx_;
feature2group_ = dataset->feature2group_;
feature2subfeature_ = dataset->feature2subfeature_;
group_bin_boundaries_ = dataset->group_bin_boundaries_;
group_feature_start_ = dataset->group_feature_start_;
group_feature_cnt_ = dataset->group_feature_cnt_;
forced_bin_bounds_ = dataset->forced_bin_bounds_;
feature_need_push_zeros_ = dataset->feature_need_push_zeros_;
max_bin_ = dataset->max_bin_;
min_data_in_bin_ = dataset->min_data_in_bin_;
bin_construct_sample_cnt_ = dataset->bin_construct_sample_cnt_;
use_missing_ = dataset->use_missing_;
zero_as_missing_ = dataset->zero_as_missing_;
}
void Dataset::CreateValid(const Dataset* dataset) {
feature_groups_.clear();
num_features_ = dataset->num_features_;
num_groups_ = num_features_;
max_bin_ = dataset->max_bin_;
min_data_in_bin_ = dataset->min_data_in_bin_;
bin_construct_sample_cnt_ = dataset->bin_construct_sample_cnt_;
use_missing_ = dataset->use_missing_;
zero_as_missing_ = dataset->zero_as_missing_;
feature2group_.clear();
feature2subfeature_.clear();
has_raw_ = dataset->has_raw();
numeric_feature_map_ = dataset->numeric_feature_map_;
num_numeric_features_ = dataset->num_numeric_features_;
// copy feature bin mapper data
feature_need_push_zeros_.clear();
group_bin_boundaries_.clear();
uint64_t num_total_bin = 0;
group_bin_boundaries_.push_back(num_total_bin);
group_feature_start_.resize(num_groups_);
group_feature_cnt_.resize(num_groups_);
for (int i = 0; i < num_features_; ++i) {
std::vector<std::unique_ptr<BinMapper>> bin_mappers;
bin_mappers.emplace_back(new BinMapper(*(dataset->FeatureBinMapper(i))));
if (bin_mappers.back()->GetDefaultBin() !=
bin_mappers.back()->GetMostFreqBin()) {
feature_need_push_zeros_.push_back(i);
}
feature_groups_.emplace_back(new FeatureGroup(&bin_mappers, num_data_));
feature2group_.push_back(i);
feature2subfeature_.push_back(0);
num_total_bin += feature_groups_[i]->num_total_bin_;
group_bin_boundaries_.push_back(num_total_bin);
group_feature_start_[i] = i;
group_feature_cnt_[i] = 1;
}
feature_groups_.shrink_to_fit();
used_feature_map_ = dataset->used_feature_map_;
num_total_features_ = dataset->num_total_features_;
feature_names_ = dataset->feature_names_;
label_idx_ = dataset->label_idx_;
real_feature_idx_ = dataset->real_feature_idx_;
forced_bin_bounds_ = dataset->forced_bin_bounds_;
device_type_ = dataset->device_type_;
gpu_device_id_ = dataset->gpu_device_id_;
}
void Dataset::CreatePairWiseRankingData(const Dataset* dataset) {
num_data_ = metadata_.BuildPairwiseFeatureRanking(dataset->metadata());
feature_groups_.clear();
num_features_ = dataset->num_features_ * 2;
num_groups_ = dataset->num_groups_ * 2;
max_bin_ = dataset->max_bin_;
min_data_in_bin_ = dataset->min_data_in_bin_;
bin_construct_sample_cnt_ = dataset->bin_construct_sample_cnt_;
use_missing_ = dataset->use_missing_;
zero_as_missing_ = dataset->zero_as_missing_;
feature2group_.clear();
feature2subfeature_.clear();
has_raw_ = dataset->has_raw();
numeric_feature_map_ = dataset->numeric_feature_map_;
for (const int feature_index : dataset->numeric_feature_map_) {
if (feature_index != -1) {
numeric_feature_map_.push_back(feature_index + dataset->num_features_);
} else {
numeric_feature_map_.push_back(-1);
}
}
num_numeric_features_ = dataset->num_numeric_features_ * 2;
// copy feature bin mapper data
feature_need_push_zeros_.clear();
group_bin_boundaries_.clear();
uint64_t num_total_bin = 0;
group_bin_boundaries_.push_back(num_total_bin);
group_feature_start_.resize(num_groups_);
group_feature_cnt_.resize(num_groups_);
int cur_feature_index = 0;
for (int i = 0; i < num_groups_; ++i) {
int original_group_index = i % dataset->num_groups_;
int original_group_feature_start = dataset->group_feature_start_[original_group_index];
const int is_first_or_second_in_pairing = i / dataset->num_groups_; // 0 for first, 1 for second
group_feature_start_[i] = cur_feature_index;
for (int feature_index_in_group = 0; feature_index_in_group < dataset->group_feature_cnt_[original_group_index]; ++feature_index_in_group) {
const BinMapper* feature_bin_mapper = dataset->FeatureBinMapper(original_group_feature_start + feature_index_in_group);
if (feature_bin_mapper->GetDefaultBin() != feature_bin_mapper->GetMostFreqBin()) {
feature_need_push_zeros_.push_back(cur_feature_index);
}
feature2group_.push_back(i);
feature2subfeature_.push_back(dataset->feature2subfeature_[original_group_feature_start + feature_index_in_group]);
cur_feature_index += 1;
}
feature_groups_.emplace_back(new PairwiseRankingFeatureGroup(*dataset->feature_groups_[original_group_index].get(), dataset->num_data(), is_first_or_second_in_pairing, metadata_.paired_ranking_item_index_map_size(), metadata_.paired_ranking_item_index_map()));
num_total_bin += dataset->FeatureGroupNumBin(original_group_index);
group_bin_boundaries_.push_back(num_total_bin);
group_feature_cnt_[i] = dataset->group_feature_cnt_[original_group_index];
}
feature_groups_.shrink_to_fit();
used_feature_map_.clear();
used_feature_map_.reserve(2 * dataset->used_feature_map_.size());
used_feature_map_.insert(used_feature_map_.begin(), dataset->used_feature_map_.begin(), dataset->used_feature_map_.end());
for (int i = 0; i < dataset->num_total_features_; ++i) {
if (dataset->used_feature_map_[i] != -1) {
used_feature_map_.push_back(dataset->used_feature_map_[i] + dataset->num_features_);
} else {
used_feature_map_.push_back(-1);
}
}
feature_names_.clear();
for (const std::string& feature_name : dataset->feature_names_) {
feature_names_.push_back(feature_name + std::string("_i"));
}
for (const std::string& feature_name : dataset->feature_names_) {
feature_names_.push_back(feature_name + std::string("_j"));
}
real_feature_idx_.clear();
for (const int idx : dataset->real_feature_idx_) {
real_feature_idx_.push_back(idx);
}
for (const int idx : dataset->real_feature_idx_) {
real_feature_idx_.push_back(idx + dataset->num_total_features_);
}
forced_bin_bounds_.clear();
forced_bin_bounds_.reserve(dataset->forced_bin_bounds_.size() * 2);
forced_bin_bounds_.insert(forced_bin_bounds_.begin(), dataset->forced_bin_bounds_.begin(), dataset->forced_bin_bounds_.end());
forced_bin_bounds_.insert(forced_bin_bounds_.begin() + dataset->forced_bin_bounds_.size(), dataset->forced_bin_bounds_.begin(), dataset->forced_bin_bounds_.end());
num_total_features_ = dataset->num_total_features_ * 2;
label_idx_ = dataset->label_idx_;
device_type_ = dataset->device_type_;
gpu_device_id_ = dataset->gpu_device_id_;
}
void Dataset::ReSize(data_size_t num_data) {
if (num_data_ != num_data) {
num_data_ = num_data;
OMP_INIT_EX();
#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)
for (int group = 0; group < num_groups_; ++group) {
OMP_LOOP_EX_BEGIN();
feature_groups_[group]->ReSize(num_data_);
OMP_LOOP_EX_END();
}
OMP_THROW_EX();
}
}
void Dataset::CopySubrow(const Dataset* fullset,
const data_size_t* used_indices,
data_size_t num_used_indices, bool need_meta_data) {
CHECK_EQ(num_used_indices, num_data_);
std::vector<int> group_ids, subfeature_ids;
group_ids.reserve(num_features_);
subfeature_ids.reserve(num_features_);
for (int group = 0; group < num_groups_; ++group) {
if (fullset->IsMultiGroup(group)) {
for (int sub_feature = 0; sub_feature <
fullset->feature_groups_[group]->num_feature_; ++sub_feature) {
group_ids.emplace_back(group);
subfeature_ids.emplace_back(sub_feature);
}
} else {
group_ids.emplace_back(group);
subfeature_ids.emplace_back(-1);
}
}
int num_copy_tasks = static_cast<int>(group_ids.size());
OMP_INIT_EX();
#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(dynamic)
for (int task_id = 0; task_id < num_copy_tasks; ++task_id) {
OMP_LOOP_EX_BEGIN();
int group = group_ids[task_id];
int subfeature = subfeature_ids[task_id];
feature_groups_[group]->CopySubrowByCol(fullset->feature_groups_[group].get(),
used_indices, num_used_indices, subfeature);
OMP_LOOP_EX_END();
}
OMP_THROW_EX();
if (need_meta_data) {
metadata_.Init(fullset->metadata_, used_indices, num_used_indices);
}
is_finish_load_ = true;
numeric_feature_map_ = fullset->numeric_feature_map_;
num_numeric_features_ = fullset->num_numeric_features_;
if (has_raw_) {
ResizeRaw(num_used_indices);
#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)
for (int i = 0; i < num_used_indices; ++i) {
for (int j = 0; j < num_numeric_features_; ++j) {
raw_data_[j][i] = fullset->raw_data_[j][used_indices[i]];
}
}
}
// update CUDA storage for column data and metadata
device_type_ = fullset->device_type_;
gpu_device_id_ = fullset->gpu_device_id_;
#ifdef USE_CUDA
if (device_type_ == std::string("cuda")) {
if (cuda_column_data_ == nullptr) {
cuda_column_data_.reset(new CUDAColumnData(fullset->num_data(), gpu_device_id_));
metadata_.CreateCUDAMetadata(gpu_device_id_);
}
cuda_column_data_->CopySubrow(fullset->cuda_column_data(), used_indices, num_used_indices);
}
#endif // USE_CUDA
}
bool Dataset::SetFieldFromArrow(const char* field_name, const ArrowChunkedArray &ca) {
std::string name(field_name);
name = Common::Trim(name);
if (name == std::string("label") || name == std::string("target")) {