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This commit is contained in:
tqchen
2014-08-29 18:35:26 -07:00
parent 551b3b70f1
commit ce2d34ecd4
4 changed files with 177 additions and 26 deletions
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2
src/io/page_dmatrix-inl.hpp
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@@ -18,7 +18,7 @@ struct RowBatchPage {
utils::Assert(data_ != NULL, "fail to allocate row batch page");
this->Clear();
}
~RowBatchPage(void) {
~BinaryPage(void) {
if (data_ != NULL) delete [] data_;
}
/*!

6
src/tree/param.h
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@@ -37,7 +37,9 @@ struct TrainParam{
// speed optimization for dense column
float opt_dense_col;
// leaf vector size
int size_leaf_vector;
int size_leaf_vector;
// option for parallelization
int parallel_option;
// number of threads to be used for tree construction,
// if OpenMP is enabled, if equals 0, use system default
int nthread;
@@ -55,6 +57,7 @@ struct TrainParam{
opt_dense_col = 1.0f;
nthread = 0;
size_leaf_vector = 0;
parallel_option = 0;
}
/*!
* \brief set parameters from outside
@@ -79,6 +82,7 @@ struct TrainParam{
if (!strcmp(name, "size_leaf_vector")) size_leaf_vector = atoi(val);
if (!strcmp(name, "max_depth")) max_depth = atoi(val);
if (!strcmp(name, "nthread")) nthread = atoi(val);
if (!strcmp(name, "parallel_option")) parallel_option = atoi(val);
if (!strcmp(name, "default_direction")) {
if (!strcmp(val, "learn")) default_direction = 0;
if (!strcmp(val, "left")) default_direction = 1;

194
src/tree/updater_colmaker-inl.hpp
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@@ -45,15 +45,19 @@ class ColMaker: public IUpdater {
// data structure
/*! \brief per thread x per node entry to store tmp data */
struct ThreadEntry {
/*! \brief statistics of data*/
/*! \brief statistics of data */
TStats stats;
/*! \brief extra statistics of data */
TStats stats_extra;
/*! \brief last feature value scanned */
float last_fvalue;
/*! \brief first feature value scanned */
float first_fvalue;
/*! \brief current best solution */
SplitEntry best;
// constructor
explicit ThreadEntry(const TrainParam &param)
: stats(param) {
: stats(param), stats_extra(param) {
}
};
struct NodeEntry {
@@ -219,7 +223,137 @@ class ColMaker: public IUpdater {
}
// use new nodes for qexpand
qexpand = newnodes;
}
}
// parallel find the best split of current fid
// this function does not support nested functions
inline void ParallelFindSplit(const ColBatch::Inst &col,
bst_uint fid,
const IFMatrix &fmat,
const std::vector<bst_gpair> &gpair,
const BoosterInfo &info) {
bool need_forward = param.need_forward_search(fmat.GetColDensity(fid));
bool need_backward = param.need_backward_search(fmat.GetColDensity(fid));
int nthread;
#pragma omp parallel
{
const int tid = omp_get_thread_num();
std::vector<ThreadEntry> &temp = stemp[tid];
// cleanup temp statistics
for (size_t j = 0; j < qexpand.size(); ++j) {
temp[qexpand[j]].stats.Clear();
}
nthread = omp_get_num_threads();
bst_uint step = (col.length + nthread - 1) / nthread;
bst_uint end = std::min(col.length, step * (tid + 1));
for (bst_uint i = tid * step; i < end; ++i) {
const bst_uint ridx = col[i].index;
const int nid = position[ridx];
if (nid < 0) continue;
const float fvalue = col[i].fvalue;
if (temp[nid].stats.Empty()) {
temp[nid].first_fvalue = fvalue;
}
temp[nid].stats.Add(gpair, info, ridx);
temp[nid].last_fvalue = fvalue;
}
}
// start collecting the partial sum statistics
bst_omp_uint nnode = static_cast<bst_omp_uint>(qexpand.size());
#pragma omp parallel for schedule(static)
for (bst_omp_uint j = 0; j < nnode; ++j) {
const int nid = qexpand[j];
TStats sum(param), tmp(param), c(param);
for (int tid = 0; tid < nthread; ++tid) {
tmp = stemp[tid][nid].stats;
stemp[tid][nid].stats = sum;
sum.Add(tmp);
if (tid != 0) {
std::swap(stemp[tid - 1][nid].last_fvalue, stemp[tid][nid].first_fvalue);
}
}
for (int tid = 0; tid < nthread; ++tid) {
stemp[tid][nid].stats_extra = sum;
ThreadEntry &e = stemp[tid][nid];
float fsplit;
if (tid != 0) {
if(fabsf(stemp[tid - 1][nid].last_fvalue - e.first_fvalue) > rt_2eps) {
fsplit = (stemp[tid - 1][nid].last_fvalue - e.first_fvalue) * 0.5f;
} else {
continue;
}
} else {
fsplit = e.first_fvalue - rt_eps;
}
if (need_forward && tid != 0) {
c.SetSubstract(snode[nid].stats, e.stats);
if (c.sum_hess >= param.min_child_weight && e.stats.sum_hess >= param.min_child_weight) {
bst_float loss_chg = static_cast<bst_float>(e.stats.CalcGain(param) + c.CalcGain(param) - snode[nid].root_gain);
e.best.Update(loss_chg, fid, fsplit, false);
}
}
if (need_backward) {
tmp.SetSubstract(sum, e.stats);
c.SetSubstract(snode[nid].stats, tmp);
if (c.sum_hess >= param.min_child_weight && tmp.sum_hess >= param.min_child_weight) {
bst_float loss_chg = static_cast<bst_float>(tmp.CalcGain(param) + c.CalcGain(param) - snode[nid].root_gain);
e.best.Update(loss_chg, fid, fsplit, true);
}
}
}
if (need_backward) {
tmp = sum;
ThreadEntry &e = stemp[nthread-1][nid];
c.SetSubstract(snode[nid].stats, tmp);
if (c.sum_hess >= param.min_child_weight && tmp.sum_hess >= param.min_child_weight) {
bst_float loss_chg = static_cast<bst_float>(tmp.CalcGain(param) + c.CalcGain(param) - snode[nid].root_gain);
e.best.Update(loss_chg, fid, e.last_fvalue + rt_eps, true);
}
}
}
// rescan, generate candidate split
#pragma omp parallel
{
TStats c(param), cright(param);
const int tid = omp_get_thread_num();
std::vector<ThreadEntry> &temp = stemp[tid];
nthread = static_cast<bst_uint>(omp_get_num_threads());
bst_uint step = (col.length + nthread - 1) / nthread;
bst_uint end = std::min(col.length, step * (tid + 1));
for (bst_uint i = tid * step; i < end; ++i) {
const bst_uint ridx = col[i].index;
const int nid = position[ridx];
if (nid < 0) continue;
const float fvalue = col[i].fvalue;
// get the statistics of nid
ThreadEntry &e = temp[nid];
if (e.stats.Empty()) {
e.stats.Add(gpair, info, ridx);
e.first_fvalue = fvalue;
} else {
// forward default right
if (fabsf(fvalue - e.first_fvalue) > rt_2eps){
if (need_forward) {
c.SetSubstract(snode[nid].stats, e.stats);
if (c.sum_hess >= param.min_child_weight && e.stats.sum_hess >= param.min_child_weight) {
bst_float loss_chg = static_cast<bst_float>(e.stats.CalcGain(param) + c.CalcGain(param) - snode[nid].root_gain);
e.best.Update(loss_chg, fid, (fvalue + e.first_fvalue) * 0.5f, false);
}
}
if (need_backward) {
cright.SetSubstract(e.stats_extra, e.stats);
c.SetSubstract(snode[nid].stats, cright);
if (c.sum_hess >= param.min_child_weight && cright.sum_hess >= param.min_child_weight) {
bst_float loss_chg = static_cast<bst_float>(cright.CalcGain(param) + c.CalcGain(param) - snode[nid].root_gain);
e.best.Update(loss_chg, fid, (fvalue + e.first_fvalue) * 0.5f, true);
}
}
}
e.stats.Add(gpair, info, ridx);
e.first_fvalue = fvalue;
}
}
}
}
// enumerate the split values of specific feature
inline void EnumerateSplit(const ColBatch::Entry *begin,
const ColBatch::Entry *end,
@@ -272,6 +406,38 @@ class ColMaker: public IUpdater {
}
}
}
// update the solution candidate
virtual void UpdateSolution(const ColBatch &batch,
const std::vector<bst_gpair> &gpair,
const IFMatrix &fmat,
const BoosterInfo &info) {
// start enumeration
const bst_omp_uint nsize = static_cast<bst_omp_uint>(batch.size);
#if defined(_OPENMP)
const int batch_size = std::max(static_cast<int>(nsize / this->nthread / 32), 1);
#endif
if (param.parallel_option == 0) {
#pragma omp parallel for schedule(dynamic, batch_size)
for (bst_omp_uint i = 0; i < nsize; ++i) {
const bst_uint fid = batch.col_index[i];
const int tid = omp_get_thread_num();
const ColBatch::Inst c = batch[i];
if (param.need_forward_search(fmat.GetColDensity(fid))) {
this->EnumerateSplit(c.data, c.data + c.length, +1,
fid, gpair, info, stemp[tid]);
}
if (param.need_backward_search(fmat.GetColDensity(fid))) {
this->EnumerateSplit(c.data + c.length - 1, c.data - 1, -1,
fid, gpair, info, stemp[tid]);
}
}
} else {
for (bst_omp_uint i = 0; i < nsize; ++i) {
this->ParallelFindSplit(batch[i], batch.col_index[i],
fmat, gpair, info);
}
}
}
// find splits at current level, do split per level
inline void FindSplit(int depth,
const std::vector<int> &qexpand,
@@ -288,26 +454,7 @@ class ColMaker: public IUpdater {
}
utils::IIterator<ColBatch> *iter = p_fmat->ColIterator(feat_set);
while (iter->Next()) {
const ColBatch &batch = iter->Value();
// start enumeration
const bst_omp_uint nsize = static_cast<bst_omp_uint>(batch.size);
#if defined(_OPENMP)
const int batch_size = std::max(static_cast<int>(nsize / this->nthread / 32), 1);
#endif
#pragma omp parallel for schedule(dynamic, batch_size)
for (bst_omp_uint i = 0; i < nsize; ++i) {
const bst_uint fid = batch.col_index[i];
const int tid = omp_get_thread_num();
const ColBatch::Inst c = batch[i];
if (param.need_forward_search(p_fmat->GetColDensity(fid))) {
this->EnumerateSplit(c.data, c.data + c.length, +1,
fid, gpair, info, stemp[tid]);
}
if (param.need_backward_search(p_fmat->GetColDensity(fid))) {
this->EnumerateSplit(c.data + c.length - 1, c.data - 1, -1,
fid, gpair, info, stemp[tid]);
}
}
this->UpdateSolution(iter->Value(), gpair, *p_fmat, info);
}
// after this each thread's stemp will get the best candidates, aggregate results
for (size_t i = 0; i < qexpand.size(); ++i) {
@@ -325,6 +472,7 @@ class ColMaker: public IUpdater {
}
}
}
// reset position of each data points after split is created in the tree
inline void ResetPosition(const std::vector<int> &qexpand, IFMatrix *p_fmat, const RegTree &tree) {
const std::vector<bst_uint> &rowset = p_fmat->buffered_rowset();

1
src/utils/io.h
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@@ -100,7 +100,6 @@ class ISeekStream: public IStream {
/*! \brief implementation of file i/o stream */
class FileStream : public ISeekStream {
public:
explicit FileStream(void) {}
explicit FileStream(FILE *fp) {
this->fp = fp;
}