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Dmatrix refactor stage 1 (#3301)

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* Use sparse page as singular CSR matrix representation

* Simplify dmatrix methods

* Reduce statefullness of batch iterators

* BREAKING CHANGE: Remove prob_buffer_row parameter. Users are instead recommended to sample their dataset as a preprocessing step before using XGBoost.
This commit is contained in:
Rory Mitchell
2018-06-07 10:25:58 +12:00
committed by GitHub
parent 286dccb8e8
commit a96039141a
47 changed files with 650 additions and 1036 deletions
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30
src/linear/coordinate_common.h
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@@ -65,10 +65,10 @@ inline std::pair<double, double> GetGradient(int group_idx, int num_group, int f
const std::vector<GradientPair> &gpair,
DMatrix *p_fmat) {
double sum_grad = 0.0, sum_hess = 0.0;
dmlc::DataIter<ColBatch> *iter = p_fmat->ColIterator({static_cast<bst_uint>(fidx)});
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
const ColBatch &batch = iter->Value();
ColBatch::Inst col = batch[0];
auto batch = iter->Value();
auto col = batch[fidx];
const auto ndata = static_cast<bst_omp_uint>(col.length);
for (bst_omp_uint j = 0; j < ndata; ++j) {
const bst_float v = col[j].fvalue;
@@ -96,10 +96,10 @@ inline std::pair<double, double> GetGradientParallel(int group_idx, int num_grou
const std::vector<GradientPair> &gpair,
DMatrix *p_fmat) {
double sum_grad = 0.0, sum_hess = 0.0;
dmlc::DataIter<ColBatch> *iter = p_fmat->ColIterator({static_cast<bst_uint>(fidx)});
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
const ColBatch &batch = iter->Value();
ColBatch::Inst col = batch[0];
auto batch = iter->Value();
auto col = batch[fidx];
const auto ndata = static_cast<bst_omp_uint>(col.length);
#pragma omp parallel for schedule(static) reduction(+ : sum_grad, sum_hess)
for (bst_omp_uint j = 0; j < ndata; ++j) {
@@ -154,10 +154,10 @@ inline void UpdateResidualParallel(int fidx, int group_idx, int num_group,
float dw, std::vector<GradientPair> *in_gpair,
DMatrix *p_fmat) {
if (dw == 0.0f) return;
dmlc::DataIter<ColBatch> *iter = p_fmat->ColIterator({static_cast<bst_uint>(fidx)});
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
const ColBatch &batch = iter->Value();
ColBatch::Inst col = batch[0];
auto batch = iter->Value();
auto col = batch[fidx];
// update grad value
const auto num_row = static_cast<bst_omp_uint>(col.length);
#pragma omp parallel for schedule(static)
@@ -325,12 +325,12 @@ class GreedyFeatureSelector : public FeatureSelector {
const bst_omp_uint nfeat = model.param.num_feature;
// Calculate univariate gradient sums
std::fill(gpair_sums_.begin(), gpair_sums_.end(), std::make_pair(0., 0.));
dmlc::DataIter<ColBatch> *iter = p_fmat->ColIterator();
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
const ColBatch &batch = iter->Value();
auto batch = iter->Value();
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nfeat; ++i) {
const ColBatch::Inst col = batch[i];
const auto col = batch[i];
const bst_uint ndata = col.length;
auto &sums = gpair_sums_[group_idx * nfeat + i];
for (bst_uint j = 0u; j < ndata; ++j) {
@@ -392,13 +392,13 @@ class ThriftyFeatureSelector : public FeatureSelector {
}
// Calculate univariate gradient sums
std::fill(gpair_sums_.begin(), gpair_sums_.end(), std::make_pair(0., 0.));
dmlc::DataIter<ColBatch> *iter = p_fmat->ColIterator();
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
const ColBatch &batch = iter->Value();
auto batch = iter->Value();
// column-parallel is usually faster than row-parallel
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nfeat; ++i) {
const ColBatch::Inst col = batch[i];
const auto col = batch[i];
const bst_uint ndata = col.length;
for (bst_uint gid = 0u; gid < ngroup; ++gid) {
auto &sums = gpair_sums_[gid * nfeat + i];