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Refactor fast-hist, add tests for some updaters. (#3836)

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Add unittest for prune.

Add unittest for refresh.

Refactor fast_hist.

* Remove fast_hist_param.
* Rename to quantile_hist.

Add unittests for QuantileHist.

* Refactor QuantileHist into .h and .cc file.
* Remove sync.h.
* Remove MGPU_mock test.

Rename fast hist method to quantile hist.
This commit is contained in:
Jiaming Yuan 2018-11-07 21:15:07 +13:00 committed by GitHub
parent 2b045aa805
commit 19ee0a3579
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GPG Key ID: 4AEE18F83AFDEB23
30 changed files with 1366 additions and 983 deletions
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2
amalgamation/xgboost-all0.cc
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@ -48,7 +48,7 @@
#include "../src/tree/tree_model.cc"
#include "../src/tree/tree_updater.cc"
#include "../src/tree/updater_colmaker.cc"
#include "../src/tree/updater_fast_hist.cc"
#include "../src/tree/updater_quantile_hist.cc"
#include "../src/tree/updater_prune.cc"
#include "../src/tree/updater_refresh.cc"
#include "../src/tree/updater_sync.cc"

1
src/cli_main.cc
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@ -19,7 +19,6 @@
#include <cstdio>
#include <cstring>
#include <vector>
#include "./common/sync.h"
#include "./common/config.h"

9
src/common/hist_util.cc
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@ -4,10 +4,11 @@
* \brief Utilities to store histograms
* \author Philip Cho, Tianqi Chen
*/
#include <rabit/rabit.h>
#include <dmlc/omp.h>
#include <numeric>
#include <vector>
#include "./sync.h"
#include "./random.h"
#include "./column_matrix.h"
#include "./hist_util.h"
@ -216,7 +217,7 @@ FindGroups(const std::vector<unsigned>& feature_list,
const std::vector<size_t>& feature_nnz,
const ColumnMatrix& colmat,
size_t nrow,
const FastHistParam& param) {
const tree::TrainParam& param) {
/* Goal: Bundle features together that has little or no "overlap", i.e.
only a few data points should have nonzero values for
member features.
@ -278,7 +279,7 @@ FindGroups(const std::vector<unsigned>& feature_list,
inline std::vector<std::vector<unsigned>>
FastFeatureGrouping(const GHistIndexMatrix& gmat,
const ColumnMatrix& colmat,
const FastHistParam& param) {
const tree::TrainParam& param) {
const size_t nrow = gmat.row_ptr.size() - 1;
const size_t nfeature = gmat.cut.row_ptr.size() - 1;
@ -332,7 +333,7 @@ FastFeatureGrouping(const GHistIndexMatrix& gmat,
void GHistIndexBlockMatrix::Init(const GHistIndexMatrix& gmat,
const ColumnMatrix& colmat,
const FastHistParam& param) {
const tree::TrainParam& param) {
cut_ = &gmat.cut;
const size_t nrow = gmat.row_ptr.size() - 1;

5
src/common/hist_util.h
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@ -11,7 +11,6 @@
#include <limits>
#include <vector>
#include "row_set.h"
#include "../tree/fast_hist_param.h"
#include "../tree/param.h"
#include "./quantile.h"
@ -19,8 +18,6 @@ namespace xgboost {
namespace common {
using tree::FastHistParam;
/*! \brief sums of gradient statistics corresponding to a histogram bin */
struct GHistEntry {
/*! \brief sum of first-order gradient statistics */
@ -145,7 +142,7 @@ class GHistIndexBlockMatrix {
public:
void Init(const GHistIndexMatrix& gmat,
const ColumnMatrix& colmat,
const FastHistParam& param);
const tree::TrainParam& param);
inline GHistIndexBlock operator[](size_t i) const {
return {blocks_[i].row_ptr_begin, blocks_[i].index_begin};

2
src/common/io.h
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@ -9,9 +9,9 @@
#define XGBOOST_COMMON_IO_H_
#include <dmlc/io.h>
#include <rabit/rabit.h>
#include <string>
#include <cstring>
#include "./sync.h"
namespace xgboost {
namespace common {

13
src/common/sync.h
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@ -1,13 +0,0 @@
/*!
* Copyright 2014 by Contributors
* \file sync.h
* \brief the synchronization module of rabit
* redirects to rabit header
* \author Tianqi Chen
*/
#ifndef XGBOOST_COMMON_SYNC_H_
#define XGBOOST_COMMON_SYNC_H_
#include <rabit/rabit.h>
#endif // XGBOOST_COMMON_SYNC_H_

4
src/learner.cc
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@ -211,8 +211,8 @@ class LearnerImpl : public Learner {
break;
case TreeMethod::kHist:
LOG(CONSOLE) << "Tree method is selected to be 'hist', which uses a "
"single updater grow_fast_histmaker.";
cfg_["updater"] = "grow_fast_histmaker";
"single updater grow_quantile_histmaker.";
cfg_["updater"] = "grow_quantile_histmaker";
break;
case TreeMethod::kGPUExact:
this->AssertGPUSupport();

2
src/logging.cc
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@ -4,9 +4,9 @@
* \brief Implementation of loggers.
* \author Tianqi Chen
*/
#include <rabit/rabit.h>
#include <xgboost/logging.h>
#include <iostream>
#include "./common/sync.h"
#if !defined(XGBOOST_STRICT_R_MODE) || XGBOOST_STRICT_R_MODE == 0
// Override logging mechanism for non-R interfaces

2
src/metric/elementwise_metric.cc
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@ -4,11 +4,11 @@
* \brief evaluation metrics for elementwise binary or regression.
* \author Kailong Chen, Tianqi Chen
*/
#include <rabit/rabit.h>
#include <xgboost/metric.h>
#include <dmlc/registry.h>
#include <cmath>
#include "../common/math.h"
#include "../common/sync.h"
namespace xgboost {
namespace metric {

2
src/metric/multiclass_metric.cc
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@ -4,9 +4,9 @@
* \brief evaluation metrics for multiclass classification.
* \author Kailong Chen, Tianqi Chen
*/
#include <rabit/rabit.h>
#include <xgboost/metric.h>
#include <cmath>
#include "../common/sync.h"
#include "../common/math.h"
namespace xgboost {

2
src/metric/rank_metric.cc
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@ -4,10 +4,10 @@
* \brief prediction rank based metrics.
* \author Kailong Chen, Tianqi Chen
*/
#include <rabit/rabit.h>
#include <xgboost/metric.h>
#include <dmlc/registry.h>
#include <cmath>
#include "../common/sync.h"
#include "../common/math.h"
namespace xgboost {

54
src/tree/fast_hist_param.h
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@ -1,54 +0,0 @@
/*!
* Copyright 2017 by Contributors
* \file updater_fast_hist.h
* \brief parameters for histogram-based training
* \author Philip Cho, Tianqi Chen
*/
#ifndef XGBOOST_TREE_FAST_HIST_PARAM_H_
#define XGBOOST_TREE_FAST_HIST_PARAM_H_
namespace xgboost {
namespace tree {
/*! \brief training parameters for histogram-based training */
struct FastHistParam : public dmlc::Parameter<FastHistParam> {
int colmat_dtype;
// percentage threshold for treating a feature as sparse
// e.g. 0.2 indicates a feature with fewer than 20% nonzeros is considered sparse
double sparse_threshold;
// use feature grouping? (default yes)
int enable_feature_grouping;
// when grouping features, how many "conflicts" to allow.
// conflict is when an instance has nonzero values for two or more features
// default is 0, meaning features should be strictly complementary
double max_conflict_rate;
// when grouping features, how much effort to expend to prevent singleton groups
// we'll try to insert each feature into existing groups before creating a new group
// for that feature; to save time, only up to (max_search_group) of existing groups
// will be considered. If set to zero, ALL existing groups will be examined
unsigned max_search_group;
// declare the parameters
DMLC_DECLARE_PARAMETER(FastHistParam) {
DMLC_DECLARE_FIELD(sparse_threshold).set_range(0, 1.0).set_default(0.2)
.describe("percentage threshold for treating a feature as sparse");
DMLC_DECLARE_FIELD(enable_feature_grouping).set_lower_bound(0).set_default(0)
.describe("if >0, enable feature grouping to ameliorate work imbalance "
"among worker threads");
DMLC_DECLARE_FIELD(max_conflict_rate).set_range(0, 1.0).set_default(0)
.describe("when grouping features, how many \"conflicts\" to allow."
"conflict is when an instance has nonzero values for two or more features."
"default is 0, meaning features should be strictly complementary.");
DMLC_DECLARE_FIELD(max_search_group).set_lower_bound(0).set_default(100)
.describe("when grouping features, how much effort to expend to prevent "
"singleton groups. We'll try to insert each feature into existing "
"groups before creating a new group for that feature; to save time, "
"only up to (max_search_group) of existing groups will be "
"considered. If set to zero, ALL existing groups will be examined.");
}
};
} // namespace tree
} // namespace xgboost
#endif // XGBOOST_TREE_FAST_HIST_PARAM_H_

35
src/tree/param.h
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@ -81,6 +81,23 @@ struct TrainParam : public dmlc::Parameter<TrainParam> {
int gpu_batch_nrows;
// the criteria to use for ranking splits
std::string split_evaluator;
// ------ From cpu quantile histogram -------.
// percentage threshold for treating a feature as sparse
// e.g. 0.2 indicates a feature with fewer than 20% nonzeros is considered sparse
double sparse_threshold;
// use feature grouping? (default yes)
int enable_feature_grouping;
// when grouping features, how many "conflicts" to allow.
// conflict is when an instance has nonzero values for two or more features
// default is 0, meaning features should be strictly complementary
double max_conflict_rate;
// when grouping features, how much effort to expend to prevent singleton groups
// we'll try to insert each feature into existing groups before creating a new group
// for that feature; to save time, only up to (max_search_group) of existing groups
// will be considered. If set to zero, ALL existing groups will be examined
unsigned max_search_group;
// declare the parameters
DMLC_DECLARE_PARAMETER(TrainParam) {
DMLC_DECLARE_FIELD(learning_rate)
@ -196,6 +213,24 @@ struct TrainParam : public dmlc::Parameter<TrainParam> {
DMLC_DECLARE_FIELD(split_evaluator)
.set_default("elastic_net,monotonic,interaction")
.describe("The criteria to use for ranking splits");
// ------ From cpu quantile histogram -------.
DMLC_DECLARE_FIELD(sparse_threshold).set_range(0, 1.0).set_default(0.2)
.describe("percentage threshold for treating a feature as sparse");
DMLC_DECLARE_FIELD(enable_feature_grouping).set_lower_bound(0).set_default(0)
.describe("if >0, enable feature grouping to ameliorate work imbalance "
"among worker threads");
DMLC_DECLARE_FIELD(max_conflict_rate).set_range(0, 1.0).set_default(0)
.describe("when grouping features, how many \"conflicts\" to allow."
"conflict is when an instance has nonzero values for two or more features."
"default is 0, meaning features should be strictly complementary.");
DMLC_DECLARE_FIELD(max_search_group).set_lower_bound(0).set_default(100)
.describe("when grouping features, how much effort to expend to prevent "
"singleton groups. We'll try to insert each feature into existing "
"groups before creating a new group for that feature; to save time, "
"only up to (max_search_group) of existing groups will be "
"considered. If set to zero, ALL existing groups will be examined.");
// add alias of parameters
DMLC_DECLARE_ALIAS(reg_lambda, lambda);
DMLC_DECLARE_ALIAS(reg_alpha, alpha);

2
src/tree/tree_updater.cc
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@ -31,7 +31,7 @@ DMLC_REGISTRY_LINK_TAG(updater_colmaker);
DMLC_REGISTRY_LINK_TAG(updater_skmaker);
DMLC_REGISTRY_LINK_TAG(updater_refresh);
DMLC_REGISTRY_LINK_TAG(updater_prune);
DMLC_REGISTRY_LINK_TAG(updater_fast_hist);
DMLC_REGISTRY_LINK_TAG(updater_quantile_hist);
DMLC_REGISTRY_LINK_TAG(updater_histmaker);
DMLC_REGISTRY_LINK_TAG(updater_sync);
#ifdef XGBOOST_USE_CUDA

4
src/tree/updater_basemaker-inl.h
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@ -7,6 +7,8 @@
#ifndef XGBOOST_TREE_UPDATER_BASEMAKER_INL_H_
#define XGBOOST_TREE_UPDATER_BASEMAKER_INL_H_
#include <rabit/rabit.h>
#include <xgboost/base.h>
#include <xgboost/tree_updater.h>
#include <vector>
@ -14,8 +16,8 @@
#include <string>
#include <limits>
#include <utility>
#include "./param.h"
#include "../common/sync.h"
#include "../common/io.h"
#include "../common/random.h"
#include "../common/quantile.h"

3
src/tree/updater_colmaker.cc
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@ -4,15 +4,16 @@
* \brief use columnwise update to construct a tree
* \author Tianqi Chen
*/
#include <rabit/rabit.h>
#include <xgboost/tree_updater.h>
#include <memory>
#include <vector>
#include <cmath>
#include <algorithm>
#include "./param.h"
#include "../common/random.h"
#include "../common/bitmap.h"
#include "../common/sync.h"
#include "split_evaluator.h"
namespace xgboost {

873
src/tree/updater_fast_hist.cc
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@ -1,873 +0,0 @@
/*!
* Copyright 2017 by Contributors
* \file updater_fast_hist.cc
* \brief use quantized feature values to construct a tree
* \author Philip Cho, Tianqi Checn
*/
#include <dmlc/timer.h>
#include <xgboost/tree_updater.h>
#include <cmath>
#include <memory>
#include <vector>
#include <algorithm>
#include <queue>
#include <iomanip>
#include <numeric>
#include "./param.h"
#include "./fast_hist_param.h"
#include "./split_evaluator.h"
#include "../common/random.h"
#include "../common/bitmap.h"
#include "../common/sync.h"
#include "../common/hist_util.h"
#include "../common/row_set.h"
#include "../common/column_matrix.h"
namespace xgboost {
namespace tree {
using xgboost::common::HistCutMatrix;
using xgboost::common::GHistIndexMatrix;
using xgboost::common::GHistIndexBlockMatrix;
using xgboost::common::GHistIndexRow;
using xgboost::common::GHistEntry;
using xgboost::common::HistCollection;
using xgboost::common::RowSetCollection;
using xgboost::common::GHistRow;
using xgboost::common::GHistBuilder;
using xgboost::common::ColumnMatrix;
using xgboost::common::Column;
DMLC_REGISTRY_FILE_TAG(updater_fast_hist);
DMLC_REGISTER_PARAMETER(FastHistParam);
/*! \brief construct a tree using quantized feature values */
class FastHistMaker: public TreeUpdater {
public:
void Init(const std::vector<std::pair<std::string, std::string> >& args) override {
// initialize pruner
if (!pruner_) {
pruner_.reset(TreeUpdater::Create("prune"));
}
pruner_->Init(args);
param_.InitAllowUnknown(args);
fhparam_.InitAllowUnknown(args);
is_gmat_initialized_ = false;
// initialise the split evaluator
if (!spliteval_) {
spliteval_.reset(SplitEvaluator::Create(param_.split_evaluator));
}
spliteval_->Init(args);
}
void Update(HostDeviceVector<GradientPair>* gpair,
DMatrix* dmat,
const std::vector<RegTree*>& trees) override {
GradStats::CheckInfo(dmat->Info());
if (is_gmat_initialized_ == false) {
double tstart = dmlc::GetTime();
gmat_.Init(dmat, static_cast<uint32_t>(param_.max_bin));
column_matrix_.Init(gmat_, fhparam_.sparse_threshold);
if (fhparam_.enable_feature_grouping > 0) {
gmatb_.Init(gmat_, column_matrix_, fhparam_);
}
is_gmat_initialized_ = true;
if (param_.debug_verbose > 0) {
LOG(INFO) << "Generating gmat: " << dmlc::GetTime() - tstart << " sec";
}
}
// rescale learning rate according to size of trees
float lr = param_.learning_rate;
param_.learning_rate = lr / trees.size();
// build tree
if (!builder_) {
builder_.reset(new Builder(
param_,
fhparam_,
std::move(pruner_),
std::unique_ptr<SplitEvaluator>(spliteval_->GetHostClone())));
}
for (auto tree : trees) {
builder_->Update
(gmat_, gmatb_, column_matrix_, gpair, dmat, tree);
}
param_.learning_rate = lr;
}
bool UpdatePredictionCache(const DMatrix* data,
HostDeviceVector<bst_float>* out_preds) override {
if (!builder_ || param_.subsample < 1.0f) {
return false;
} else {
return builder_->UpdatePredictionCache(data, out_preds);
}
}
protected:
// training parameter
TrainParam param_;
FastHistParam fhparam_;
// quantized data matrix
GHistIndexMatrix gmat_;
// (optional) data matrix with feature grouping
GHistIndexBlockMatrix gmatb_;
// column accessor
ColumnMatrix column_matrix_;
bool is_gmat_initialized_;
// data structure
struct NodeEntry {
/*! \brief statics for node entry */
GradStats stats;
/*! \brief loss of this node, without split */
bst_float root_gain;
/*! \brief weight calculated related to current data */
float weight;
/*! \brief current best solution */
SplitEntry best;
// constructor
explicit NodeEntry(const TrainParam& param)
: stats(param), root_gain(0.0f), weight(0.0f) {
}
};
// actual builder that runs the algorithm
struct Builder {
public:
// constructor
explicit Builder(const TrainParam& param,
const FastHistParam& fhparam,
std::unique_ptr<TreeUpdater> pruner,
std::unique_ptr<SplitEvaluator> spliteval)
: param_(param), fhparam_(fhparam), pruner_(std::move(pruner)),
spliteval_(std::move(spliteval)), p_last_tree_(nullptr),
p_last_fmat_(nullptr) {}
// update one tree, growing
virtual void Update(const GHistIndexMatrix& gmat,
const GHistIndexBlockMatrix& gmatb,
const ColumnMatrix& column_matrix,
HostDeviceVector<GradientPair>* gpair,
DMatrix* p_fmat,
RegTree* p_tree) {
double gstart = dmlc::GetTime();
int num_leaves = 0;
unsigned timestamp = 0;
double tstart;
double time_init_data = 0;
double time_init_new_node = 0;
double time_build_hist = 0;
double time_evaluate_split = 0;
double time_apply_split = 0;
const std::vector<GradientPair>& gpair_h = gpair->ConstHostVector();
spliteval_->Reset();
tstart = dmlc::GetTime();
this->InitData(gmat, gpair_h, *p_fmat, *p_tree);
time_init_data = dmlc::GetTime() - tstart;
// FIXME(hcho3): this code is broken when param.num_roots > 1. Please fix it
CHECK_EQ(p_tree->param.num_roots, 1)
<< "tree_method=hist does not support multiple roots at this moment";
for (int nid = 0; nid < p_tree->param.num_roots; ++nid) {
tstart = dmlc::GetTime();
hist_.AddHistRow(nid);
BuildHist(gpair_h, row_set_collection_[nid], gmat, gmatb, hist_[nid]);
time_build_hist += dmlc::GetTime() - tstart;
tstart = dmlc::GetTime();
this->InitNewNode(nid, gmat, gpair_h, *p_fmat, *p_tree);
time_init_new_node += dmlc::GetTime() - tstart;
tstart = dmlc::GetTime();
this->EvaluateSplit(nid, gmat, hist_, *p_fmat, *p_tree);
time_evaluate_split += dmlc::GetTime() - tstart;
qexpand_->push(ExpandEntry(nid, p_tree->GetDepth(nid),
snode_[nid].best.loss_chg,
timestamp++));
++num_leaves;
}
while (!qexpand_->empty()) {
const ExpandEntry candidate = qexpand_->top();
const int nid = candidate.nid;
qexpand_->pop();
if (candidate.loss_chg <= kRtEps
|| (param_.max_depth > 0 && candidate.depth == param_.max_depth)
|| (param_.max_leaves > 0 && num_leaves == param_.max_leaves) ) {
(*p_tree)[nid].SetLeaf(snode_[nid].weight * param_.learning_rate);
} else {
tstart = dmlc::GetTime();
this->ApplySplit(nid, gmat, column_matrix, hist_, *p_fmat, p_tree);
time_apply_split += dmlc::GetTime() - tstart;
tstart = dmlc::GetTime();
const int cleft = (*p_tree)[nid].LeftChild();
const int cright = (*p_tree)[nid].RightChild();
hist_.AddHistRow(cleft);
hist_.AddHistRow(cright);
if (row_set_collection_[cleft].Size() < row_set_collection_[cright].Size()) {
BuildHist(gpair_h, row_set_collection_[cleft], gmat, gmatb, hist_[cleft]);
SubtractionTrick(hist_[cright], hist_[cleft], hist_[nid]);
} else {
BuildHist(gpair_h, row_set_collection_[cright], gmat, gmatb, hist_[cright]);
SubtractionTrick(hist_[cleft], hist_[cright], hist_[nid]);
}
time_build_hist += dmlc::GetTime() - tstart;
tstart = dmlc::GetTime();
this->InitNewNode(cleft, gmat, gpair_h, *p_fmat, *p_tree);
this->InitNewNode(cright, gmat, gpair_h, *p_fmat, *p_tree);
bst_uint featureid = snode_[nid].best.SplitIndex();
spliteval_->AddSplit(nid, cleft, cright, featureid,
snode_[cleft].weight, snode_[cright].weight);
time_init_new_node += dmlc::GetTime() - tstart;
tstart = dmlc::GetTime();
this->EvaluateSplit(cleft, gmat, hist_, *p_fmat, *p_tree);
this->EvaluateSplit(cright, gmat, hist_, *p_fmat, *p_tree);
time_evaluate_split += dmlc::GetTime() - tstart;
qexpand_->push(ExpandEntry(cleft, p_tree->GetDepth(cleft),
snode_[cleft].best.loss_chg,
timestamp++));
qexpand_->push(ExpandEntry(cright, p_tree->GetDepth(cright),
snode_[cright].best.loss_chg,
timestamp++));
++num_leaves; // give two and take one, as parent is no longer a leaf
}
}
// set all the rest expanding nodes to leaf
// This post condition is not needed in current code, but may be necessary
// when there are stopping rule that leaves qexpand non-empty
while (!qexpand_->empty()) {
const int nid = qexpand_->top().nid;
qexpand_->pop();
(*p_tree)[nid].SetLeaf(snode_[nid].weight * param_.learning_rate);
}
// remember auxiliary statistics in the tree node
for (int nid = 0; nid < p_tree->param.num_nodes; ++nid) {
p_tree->Stat(nid).loss_chg = snode_[nid].best.loss_chg;
p_tree->Stat(nid).base_weight = snode_[nid].weight;
p_tree->Stat(nid).sum_hess = static_cast<float>(snode_[nid].stats.sum_hess);
snode_[nid].stats.SetLeafVec(param_, p_tree->Leafvec(nid));
}
pruner_->Update(gpair, p_fmat, std::vector<RegTree*>{p_tree});
if (param_.debug_verbose > 0) {
double total_time = dmlc::GetTime() - gstart;
LOG(INFO) << "\nInitData: "
<< std::fixed << std::setw(6) << std::setprecision(4) << time_init_data
<< " (" << std::fixed << std::setw(5) << std::setprecision(2)
<< time_init_data / total_time * 100 << "%)\n"
<< "InitNewNode: "
<< std::fixed << std::setw(6) << std::setprecision(4) << time_init_new_node
<< " (" << std::fixed << std::setw(5) << std::setprecision(2)
<< time_init_new_node / total_time * 100 << "%)\n"
<< "BuildHist: "
<< std::fixed << std::setw(6) << std::setprecision(4) << time_build_hist
<< " (" << std::fixed << std::setw(5) << std::setprecision(2)
<< time_build_hist / total_time * 100 << "%)\n"
<< "EvaluateSplit: "
<< std::fixed << std::setw(6) << std::setprecision(4) << time_evaluate_split
<< " (" << std::fixed << std::setw(5) << std::setprecision(2)
<< time_evaluate_split / total_time * 100 << "%)\n"
<< "ApplySplit: "
<< std::fixed << std::setw(6) << std::setprecision(4) << time_apply_split
<< " (" << std::fixed << std::setw(5) << std::setprecision(2)
<< time_apply_split / total_time * 100 << "%)\n"
<< "========================================\n"
<< "Total: "
<< std::fixed << std::setw(6) << std::setprecision(4) << total_time;
}
}
inline void BuildHist(const std::vector<GradientPair>& gpair,
const RowSetCollection::Elem row_indices,
const GHistIndexMatrix& gmat,
const GHistIndexBlockMatrix& gmatb,
GHistRow hist) {
if (fhparam_.enable_feature_grouping > 0) {
hist_builder_.BuildBlockHist(gpair, row_indices, gmatb, hist);
} else {
hist_builder_.BuildHist(gpair, row_indices, gmat, hist);
}
}
inline void SubtractionTrick(GHistRow self, GHistRow sibling, GHistRow parent) {
hist_builder_.SubtractionTrick(self, sibling, parent);
}
inline bool UpdatePredictionCache(const DMatrix* data,
HostDeviceVector<bst_float>* p_out_preds) {
std::vector<bst_float>& out_preds = p_out_preds->HostVector();
// p_last_fmat_ is a valid pointer as long as UpdatePredictionCache() is called in
// conjunction with Update().
if (!p_last_fmat_ || !p_last_tree_ || data != p_last_fmat_) {
return false;
}
if (leaf_value_cache_.empty()) {
leaf_value_cache_.resize(p_last_tree_->param.num_nodes,
std::numeric_limits<float>::infinity());
}
CHECK_GT(out_preds.size(), 0U);
for (const RowSetCollection::Elem rowset : row_set_collection_) {
if (rowset.begin != nullptr && rowset.end != nullptr) {
int nid = rowset.node_id;
bst_float leaf_value;
// if a node is marked as deleted by the pruner, traverse upward to locate
// a non-deleted leaf.
if ((*p_last_tree_)[nid].IsDeleted()) {
while ((*p_last_tree_)[nid].IsDeleted()) {
nid = (*p_last_tree_)[nid].Parent();
}
CHECK((*p_last_tree_)[nid].IsLeaf());
}
leaf_value = (*p_last_tree_)[nid].LeafValue();
for (const size_t* it = rowset.begin; it < rowset.end; ++it) {
out_preds[*it] += leaf_value;
}
}
}
return true;
}
protected:
// initialize temp data structure
inline void InitData(const GHistIndexMatrix& gmat,
const std::vector<GradientPair>& gpair,
const DMatrix& fmat,
const RegTree& tree) {
CHECK_EQ(tree.param.num_nodes, tree.param.num_roots)
<< "ColMakerHist: can only grow new tree";
CHECK((param_.max_depth > 0 || param_.max_leaves > 0))
<< "max_depth or max_leaves cannot be both 0 (unlimited); "
<< "at least one should be a positive quantity.";
if (param_.grow_policy == TrainParam::kDepthWise) {
CHECK(param_.max_depth > 0) << "max_depth cannot be 0 (unlimited) "
<< "when grow_policy is depthwise.";
}
const auto& info = fmat.Info();
{
// initialize the row set
row_set_collection_.Clear();
// clear local prediction cache
leaf_value_cache_.clear();
// initialize histogram collection
uint32_t nbins = gmat.cut.row_ptr.back();
hist_.Init(nbins);
// initialize histogram builder
#pragma omp parallel
{
this->nthread_ = omp_get_num_threads();
}
hist_builder_.Init(this->nthread_, nbins);
CHECK_EQ(info.root_index_.size(), 0U);
std::vector<size_t>& row_indices = row_set_collection_.row_indices_;
// mark subsample and build list of member rows
if (param_.subsample < 1.0f) {
std::bernoulli_distribution coin_flip(param_.subsample);
auto& rnd = common::GlobalRandom();
for (size_t i = 0; i < info.num_row_; ++i) {
if (gpair[i].GetHess() >= 0.0f && coin_flip(rnd)) {
row_indices.push_back(i);
}
}
} else {
for (size_t i = 0; i < info.num_row_; ++i) {
if (gpair[i].GetHess() >= 0.0f) {
row_indices.push_back(i);
}
}
}
row_set_collection_.Init();
}
{
/* determine layout of data */
const size_t nrow = info.num_row_;
const size_t ncol = info.num_col_;
const size_t nnz = info.num_nonzero_;
// number of discrete bins for feature 0
const uint32_t nbins_f0 = gmat.cut.row_ptr[1] - gmat.cut.row_ptr[0];
if (nrow * ncol == nnz) {
// dense data with zero-based indexing
data_layout_ = kDenseDataZeroBased;
} else if (nbins_f0 == 0 && nrow * (ncol - 1) == nnz) {
// dense data with one-based indexing
data_layout_ = kDenseDataOneBased;
} else {
// sparse data
data_layout_ = kSparseData;
}
}
{
// store a pointer to the tree
p_last_tree_ = &tree;
// store a pointer to training data
p_last_fmat_ = &fmat;
// initialize feature index
if (data_layout_ == kDenseDataOneBased) {
column_sampler_.Init(info.num_col_, param_.colsample_bylevel,
param_.colsample_bytree, true);
} else {
column_sampler_.Init(info.num_col_, param_.colsample_bylevel,
param_.colsample_bytree, false);
}
}
if (data_layout_ == kDenseDataZeroBased || data_layout_ == kDenseDataOneBased) {
/* specialized code for dense data:
choose the column that has a least positive number of discrete bins.
For dense data (with no missing value),
the sum of gradient histogram is equal to snode[nid] */
const std::vector<uint32_t>& row_ptr = gmat.cut.row_ptr;
const auto nfeature = static_cast<bst_uint>(row_ptr.size() - 1);
uint32_t min_nbins_per_feature = 0;
for (bst_uint i = 0; i < nfeature; ++i) {
const uint32_t nbins = row_ptr[i + 1] - row_ptr[i];
if (nbins > 0) {
if (min_nbins_per_feature == 0 || min_nbins_per_feature > nbins) {
min_nbins_per_feature = nbins;
fid_least_bins_ = i;
}
}
}
CHECK_GT(min_nbins_per_feature, 0U);
}
{
snode_.reserve(256);
snode_.clear();
}
{
if (param_.grow_policy == TrainParam::kLossGuide) {
qexpand_.reset(new ExpandQueue(LossGuide));
} else {
qexpand_.reset(new ExpandQueue(DepthWise));
}
}
}
inline void EvaluateSplit(int nid,
const GHistIndexMatrix& gmat,
const HistCollection& hist,
const DMatrix& fmat,
const RegTree& tree) {
// start enumeration
const MetaInfo& info = fmat.Info();
const auto& feature_set = column_sampler_.GetFeatureSet(tree.GetDepth(nid)).HostVector();
const auto nfeature = static_cast<bst_uint>(feature_set.size());
const auto nthread = static_cast<bst_omp_uint>(this->nthread_);
best_split_tloc_.resize(nthread);
#pragma omp parallel for schedule(static) num_threads(nthread)
for (bst_omp_uint tid = 0; tid < nthread; ++tid) {
best_split_tloc_[tid] = snode_[nid].best;
}
#pragma omp parallel for schedule(dynamic) num_threads(nthread)
for (bst_omp_uint i = 0; i < nfeature; ++i) {
const bst_uint fid = feature_set[i];
const unsigned tid = omp_get_thread_num();
this->EnumerateSplit(-1, gmat, hist[nid], snode_[nid], info,
&best_split_tloc_[tid], fid, nid);
this->EnumerateSplit(+1, gmat, hist[nid], snode_[nid], info,
&best_split_tloc_[tid], fid, nid);
}
for (unsigned tid = 0; tid < nthread; ++tid) {
snode_[nid].best.Update(best_split_tloc_[tid]);
}
}
inline void ApplySplit(int nid,
const GHistIndexMatrix& gmat,
const ColumnMatrix& column_matrix,
const HistCollection& hist,
const DMatrix& fmat,
RegTree* p_tree) {
// TODO(hcho3): support feature sampling by levels
/* 1. Create child nodes */
NodeEntry& e = snode_[nid];
p_tree->AddChilds(nid);
(*p_tree)[nid].SetSplit(e.best.SplitIndex(), e.best.split_value, e.best.DefaultLeft());
// mark right child as 0, to indicate fresh leaf
int cleft = (*p_tree)[nid].LeftChild();
int cright = (*p_tree)[nid].RightChild();
(*p_tree)[cleft].SetLeaf(0.0f, 0);
(*p_tree)[cright].SetLeaf(0.0f, 0);
/* 2. Categorize member rows */
const auto nthread = static_cast<bst_omp_uint>(this->nthread_);
row_split_tloc_.resize(nthread);
for (bst_omp_uint i = 0; i < nthread; ++i) {
row_split_tloc_[i].left.clear();
row_split_tloc_[i].right.clear();
}
const bool default_left = (*p_tree)[nid].DefaultLeft();
const bst_uint fid = (*p_tree)[nid].SplitIndex();
const bst_float split_pt = (*p_tree)[nid].SplitCond();
const uint32_t lower_bound = gmat.cut.row_ptr[fid];
const uint32_t upper_bound = gmat.cut.row_ptr[fid + 1];
int32_t split_cond = -1;
// convert floating-point split_pt into corresponding bin_id
// split_cond = -1 indicates that split_pt is less than all known cut points
CHECK_LT(upper_bound,
static_cast<uint32_t>(std::numeric_limits<int32_t>::max()));
for (uint32_t i = lower_bound; i < upper_bound; ++i) {
if (split_pt == gmat.cut.cut[i]) {
split_cond = static_cast<int32_t>(i);
}
}
const auto& rowset = row_set_collection_[nid];
Column column = column_matrix.GetColumn(fid);
if (column.GetType() == xgboost::common::kDenseColumn) {
ApplySplitDenseData(rowset, gmat, &row_split_tloc_, column, split_cond,
default_left);
} else {
ApplySplitSparseData(rowset, gmat, &row_split_tloc_, column, lower_bound,
upper_bound, split_cond, default_left);
}
row_set_collection_.AddSplit(
nid, row_split_tloc_, (*p_tree)[nid].LeftChild(), (*p_tree)[nid].RightChild());
}
inline void ApplySplitDenseData(const RowSetCollection::Elem rowset,
const GHistIndexMatrix& gmat,
std::vector<RowSetCollection::Split>* p_row_split_tloc,
const Column& column,
bst_int split_cond,
bool default_left) {
std::vector<RowSetCollection::Split>& row_split_tloc = *p_row_split_tloc;
constexpr int kUnroll = 8; // loop unrolling factor
const size_t nrows = rowset.end - rowset.begin;
const size_t rest = nrows % kUnroll;
#pragma omp parallel for num_threads(nthread_) schedule(static)
for (bst_omp_uint i = 0; i < nrows - rest; i += kUnroll) {
const bst_uint tid = omp_get_thread_num();
auto& left = row_split_tloc[tid].left;
auto& right = row_split_tloc[tid].right;
size_t rid[kUnroll];
uint32_t rbin[kUnroll];
for (int k = 0; k < kUnroll; ++k) {
rid[k] = rowset.begin[i + k];
}
for (int k = 0; k < kUnroll; ++k) {
rbin[k] = column.GetFeatureBinIdx(rid[k]);
}
for (int k = 0; k < kUnroll; ++k) { // NOLINT
if (rbin[k] == std::numeric_limits<uint32_t>::max()) { // missing value
if (default_left) {
left.push_back(rid[k]);
} else {
right.push_back(rid[k]);
}
} else {
if (static_cast<int32_t>(rbin[k] + column.GetBaseIdx()) <= split_cond) {
left.push_back(rid[k]);
} else {
right.push_back(rid[k]);
}
}
}
}
for (size_t i = nrows - rest; i < nrows; ++i) {
auto& left = row_split_tloc[nthread_-1].left;
auto& right = row_split_tloc[nthread_-1].right;
const size_t rid = rowset.begin[i];
const uint32_t rbin = column.GetFeatureBinIdx(rid);
if (rbin == std::numeric_limits<uint32_t>::max()) { // missing value
if (default_left) {
left.push_back(rid);
} else {
right.push_back(rid);
}
} else {
if (static_cast<int32_t>(rbin + column.GetBaseIdx()) <= split_cond) {
left.push_back(rid);
} else {
right.push_back(rid);
}
}
}
}
inline void ApplySplitSparseData(const RowSetCollection::Elem rowset,
const GHistIndexMatrix& gmat,
std::vector<RowSetCollection::Split>* p_row_split_tloc,
const Column& column,
bst_uint lower_bound,
bst_uint upper_bound,
bst_int split_cond,
bool default_left) {
std::vector<RowSetCollection::Split>& row_split_tloc = *p_row_split_tloc;
const size_t nrows = rowset.end - rowset.begin;
#pragma omp parallel num_threads(nthread_)
{
const auto tid = static_cast<size_t>(omp_get_thread_num());
const size_t ibegin = tid * nrows / nthread_;
const size_t iend = (tid + 1) * nrows / nthread_;
if (ibegin < iend) { // ensure that [ibegin, iend) is nonempty range
// search first nonzero row with index >= rowset[ibegin]
const size_t* p = std::lower_bound(column.GetRowData(),
column.GetRowData() + column.Size(),
rowset.begin[ibegin]);
auto& left = row_split_tloc[tid].left;
auto& right = row_split_tloc[tid].right;
if (p != column.GetRowData() + column.Size() && *p <= rowset.begin[iend - 1]) {
size_t cursor = p - column.GetRowData();
for (size_t i = ibegin; i < iend; ++i) {
const size_t rid = rowset.begin[i];
while (cursor < column.Size()
&& column.GetRowIdx(cursor) < rid
&& column.GetRowIdx(cursor) <= rowset.begin[iend - 1]) {
++cursor;
}
if (cursor < column.Size() && column.GetRowIdx(cursor) == rid) {
const uint32_t rbin = column.GetFeatureBinIdx(cursor);
if (static_cast<int32_t>(rbin + column.GetBaseIdx()) <= split_cond) {
left.push_back(rid);
} else {
right.push_back(rid);
}
++cursor;
} else {
// missing value
if (default_left) {
left.push_back(rid);
} else {
right.push_back(rid);
}
}
}
} else { // all rows in [ibegin, iend) have missing values
if (default_left) {
for (size_t i = ibegin; i < iend; ++i) {
const size_t rid = rowset.begin[i];
left.push_back(rid);
}
} else {
for (size_t i = ibegin; i < iend; ++i) {
const size_t rid = rowset.begin[i];
right.push_back(rid);
}
}
}
}
}
}
inline void InitNewNode(int nid,
const GHistIndexMatrix& gmat,
const std::vector<GradientPair>& gpair,
const DMatrix& fmat,
const RegTree& tree) {
{
snode_.resize(tree.param.num_nodes, NodeEntry(param_));
}
{
auto& stats = snode_[nid].stats;
if (data_layout_ == kDenseDataZeroBased || data_layout_ == kDenseDataOneBased) {
/* specialized code for dense data
For dense data (with no missing value),
the sum of gradient histogram is equal to snode[nid] */
GHistRow hist = hist_[nid];
const std::vector<uint32_t>& row_ptr = gmat.cut.row_ptr;
const uint32_t ibegin = row_ptr[fid_least_bins_];
const uint32_t iend = row_ptr[fid_least_bins_ + 1];
for (uint32_t i = ibegin; i < iend; ++i) {
const GHistEntry et = hist.begin[i];
stats.Add(et.sum_grad, et.sum_hess);
}
} else {
const RowSetCollection::Elem e = row_set_collection_[nid];
for (const size_t* it = e.begin; it < e.end; ++it) {
stats.Add(gpair[*it]);
}
}
}
// calculating the weights
{
bst_uint parentid = tree[nid].Parent();
snode_[nid].weight = static_cast<float>(
spliteval_->ComputeWeight(parentid, snode_[nid].stats));
snode_[nid].root_gain = static_cast<float>(
spliteval_->ComputeScore(parentid, snode_[nid].stats, snode_[nid].weight));
}
}
// enumerate the split values of specific feature
inline void EnumerateSplit(int d_step,
const GHistIndexMatrix& gmat,
const GHistRow& hist,
const NodeEntry& snode,
const MetaInfo& info,
SplitEntry* p_best,
bst_uint fid,
bst_uint nodeID) {
CHECK(d_step == +1 || d_step == -1);
// aliases
const std::vector<uint32_t>& cut_ptr = gmat.cut.row_ptr;
const std::vector<bst_float>& cut_val = gmat.cut.cut;
// statistics on both sides of split
GradStats c(param_);
GradStats e(param_);
// best split so far
SplitEntry best;
// bin boundaries
CHECK_LE(cut_ptr[fid],
static_cast<uint32_t>(std::numeric_limits<int32_t>::max()));
CHECK_LE(cut_ptr[fid + 1],
static_cast<uint32_t>(std::numeric_limits<int32_t>::max()));
// imin: index (offset) of the minimum value for feature fid
// need this for backward enumeration
const auto imin = static_cast<int32_t>(cut_ptr[fid]);
// ibegin, iend: smallest/largest cut points for feature fid
// use int to allow for value -1
int32_t ibegin, iend;
if (d_step > 0) {
ibegin = static_cast<int32_t>(cut_ptr[fid]);
iend = static_cast<int32_t>(cut_ptr[fid + 1]);
} else {
ibegin = static_cast<int32_t>(cut_ptr[fid + 1]) - 1;
iend = static_cast<int32_t>(cut_ptr[fid]) - 1;
}
for (int32_t i = ibegin; i != iend; i += d_step) {
// start working
// try to find a split
e.Add(hist.begin[i].sum_grad, hist.begin[i].sum_hess);
if (e.sum_hess >= param_.min_child_weight) {
c.SetSubstract(snode.stats, e);
if (c.sum_hess >= param_.min_child_weight) {
bst_float loss_chg;
bst_float split_pt;
if (d_step > 0) {
// forward enumeration: split at right bound of each bin
loss_chg = static_cast<bst_float>(
spliteval_->ComputeSplitScore(nodeID, fid, e, c) -
snode.root_gain);
split_pt = cut_val[i];
} else {
// backward enumeration: split at left bound of each bin
loss_chg = static_cast<bst_float>(
spliteval_->ComputeSplitScore(nodeID, fid, c, e) -
snode.root_gain);
if (i == imin) {
// for leftmost bin, left bound is the smallest feature value
split_pt = gmat.cut.min_val[fid];
} else {
split_pt = cut_val[i - 1];
}
}
best.Update(loss_chg, fid, split_pt, d_step == -1);
}
}
}
p_best->Update(best);
}
/* tree growing policies */
struct ExpandEntry {
int nid;
int depth;
bst_float loss_chg;
unsigned timestamp;
ExpandEntry(int nid, int depth, bst_float loss_chg, unsigned tstmp)
: nid(nid), depth(depth), loss_chg(loss_chg), timestamp(tstmp) {}
};
inline static bool DepthWise(ExpandEntry lhs, ExpandEntry rhs) {
if (lhs.depth == rhs.depth) {
return lhs.timestamp > rhs.timestamp; // favor small timestamp
} else {
return lhs.depth > rhs.depth; // favor small depth
}
}
inline static bool LossGuide(ExpandEntry lhs, ExpandEntry rhs) {
if (lhs.loss_chg == rhs.loss_chg) {
return lhs.timestamp > rhs.timestamp; // favor small timestamp
} else {
return lhs.loss_chg < rhs.loss_chg; // favor large loss_chg
}
}
// --data fields--
const TrainParam& param_;
const FastHistParam& fhparam_;
// number of omp thread used during training
int nthread_;
common::ColumnSampler column_sampler_;
// the internal row sets
RowSetCollection row_set_collection_;
// the temp space for split
std::vector<RowSetCollection::Split> row_split_tloc_;
std::vector<SplitEntry> best_split_tloc_;
/*! \brief TreeNode Data: statistics for each constructed node */
std::vector<NodeEntry> snode_;
/*! \brief culmulative histogram of gradients. */
HistCollection hist_;
/*! \brief feature with least # of bins. to be used for dense specialization
of InitNewNode() */
uint32_t fid_least_bins_;
/*! \brief local prediction cache; maps node id to leaf value */
std::vector<float> leaf_value_cache_;
GHistBuilder hist_builder_;
std::unique_ptr<TreeUpdater> pruner_;
std::unique_ptr<SplitEvaluator> spliteval_;
// back pointers to tree and data matrix
const RegTree* p_last_tree_;
const DMatrix* p_last_fmat_;
using ExpandQueue =
std::priority_queue<ExpandEntry, std::vector<ExpandEntry>,
std::function<bool(ExpandEntry, ExpandEntry)>>;
std::unique_ptr<ExpandQueue> qexpand_;
enum DataLayout { kDenseDataZeroBased, kDenseDataOneBased, kSparseData };
DataLayout data_layout_;
};
std::unique_ptr<Builder> builder_;
std::unique_ptr<TreeUpdater> pruner_;
std::unique_ptr<SplitEvaluator> spliteval_;
};
XGBOOST_REGISTER_TREE_UPDATER(FastHistMaker, "grow_fast_histmaker")
.describe("Grow tree using quantized histogram.")
.set_body([]() {
return new FastHistMaker();
});
} // namespace tree
} // namespace xgboost

3
src/tree/updater_histmaker.cc
View File

@ -4,11 +4,12 @@
* \brief use histogram counting to construct a tree
* \author Tianqi Chen
*/
#include <rabit/rabit.h>
#include <xgboost/base.h>
#include <xgboost/tree_updater.h>
#include <vector>
#include <algorithm>
#include "../common/sync.h"
#include "../common/quantile.h"
#include "../common/group_data.h"
#include "./updater_basemaker-inl.h"

5
src/tree/updater_prune.cc
View File

@ -4,12 +4,13 @@
* \brief prune a tree given the statistics
* \author Tianqi Chen
*/
#include <rabit/rabit.h>
#include <xgboost/tree_updater.h>
#include <string>
#include <memory>
#include "./param.h"
#include "../common/sync.h"
#include "../common/io.h"
namespace xgboost {

748
src/tree/updater_quantile_hist.cc Normal file
View File

@ -0,0 +1,748 @@
/*!
* Copyright 2017-2018 by Contributors
* \file updater_quantile_hist.cc
* \brief use quantized feature values to construct a tree
* \author Philip Cho, Tianqi Checn
*/
#include <dmlc/timer.h>
#include <rabit/rabit.h>
#include <xgboost/tree_updater.h>
#include <cmath>
#include <memory>
#include <vector>
#include <algorithm>
#include <queue>
#include <iomanip>
#include <numeric>
#include <string>
#include <utility>
#include "./param.h"
#include "./updater_quantile_hist.h"
#include "./split_evaluator.h"
#include "../common/random.h"
#include "../common/hist_util.h"
#include "../common/row_set.h"
#include "../common/column_matrix.h"
namespace xgboost {
namespace tree {
DMLC_REGISTRY_FILE_TAG(updater_quantile_hist);
void QuantileHistMaker::Init(const std::vector<std::pair<std::string, std::string> >& args) {
// initialize pruner
if (!pruner_) {
pruner_.reset(TreeUpdater::Create("prune"));
}
pruner_->Init(args);
param_.InitAllowUnknown(args);
is_gmat_initialized_ = false;
// initialise the split evaluator
if (!spliteval_) {
spliteval_.reset(SplitEvaluator::Create(param_.split_evaluator));
}
spliteval_->Init(args);
}
void QuantileHistMaker::Update(HostDeviceVector<GradientPair> *gpair,
DMatrix *dmat,
const std::vector<RegTree *> &trees) {
GradStats::CheckInfo(dmat->Info());
if (is_gmat_initialized_ == false) {
double tstart = dmlc::GetTime();
gmat_.Init(dmat, static_cast<uint32_t>(param_.max_bin));
column_matrix_.Init(gmat_, param_.sparse_threshold);
if (param_.enable_feature_grouping > 0) {
gmatb_.Init(gmat_, column_matrix_, param_);
}
is_gmat_initialized_ = true;
if (param_.debug_verbose > 0) {
LOG(INFO) << "Generating gmat: " << dmlc::GetTime() - tstart << " sec";
}
}
// rescale learning rate according to size of trees
float lr = param_.learning_rate;
param_.learning_rate = lr / trees.size();
// build tree
if (!builder_) {
builder_.reset(new Builder(
param_,
std::move(pruner_),
std::unique_ptr<SplitEvaluator>(spliteval_->GetHostClone())));
}
for (auto tree : trees) {
builder_->Update
(gmat_, gmatb_, column_matrix_, gpair, dmat, tree);
}
param_.learning_rate = lr;
}
bool QuantileHistMaker::UpdatePredictionCache(
const DMatrix* data,
HostDeviceVector<bst_float>* out_preds) {
if (!builder_ || param_.subsample < 1.0f) {
return false;
} else {
return builder_->UpdatePredictionCache(data, out_preds);
}
}
void QuantileHistMaker::Builder::Update(const GHistIndexMatrix& gmat,
const GHistIndexBlockMatrix& gmatb,
const ColumnMatrix& column_matrix,
HostDeviceVector<GradientPair>* gpair,
DMatrix* p_fmat,
RegTree* p_tree) {
double gstart = dmlc::GetTime();
int num_leaves = 0;
unsigned timestamp = 0;
double tstart;
double time_init_data = 0;
double time_init_new_node = 0;
double time_build_hist = 0;
double time_evaluate_split = 0;
double time_apply_split = 0;
const std::vector<GradientPair>& gpair_h = gpair->ConstHostVector();
spliteval_->Reset();
tstart = dmlc::GetTime();
this->InitData(gmat, gpair_h, *p_fmat, *p_tree);
time_init_data = dmlc::GetTime() - tstart;
// FIXME(hcho3): this code is broken when param.num_roots > 1. Please fix it
CHECK_EQ(p_tree->param.num_roots, 1)
<< "tree_method=hist does not support multiple roots at this moment";
for (int nid = 0; nid < p_tree->param.num_roots; ++nid) {
tstart = dmlc::GetTime();
hist_.AddHistRow(nid);
BuildHist(gpair_h, row_set_collection_[nid], gmat, gmatb, hist_[nid]);
time_build_hist += dmlc::GetTime() - tstart;
tstart = dmlc::GetTime();
this->InitNewNode(nid, gmat, gpair_h, *p_fmat, *p_tree);
time_init_new_node += dmlc::GetTime() - tstart;
tstart = dmlc::GetTime();
this->EvaluateSplit(nid, gmat, hist_, *p_fmat, *p_tree);
time_evaluate_split += dmlc::GetTime() - tstart;
qexpand_->push(ExpandEntry(nid, p_tree->GetDepth(nid),
snode_[nid].best.loss_chg,
timestamp++));
++num_leaves;
}
while (!qexpand_->empty()) {
const ExpandEntry candidate = qexpand_->top();
const int nid = candidate.nid;
qexpand_->pop();
if (candidate.loss_chg <= kRtEps
|| (param_.max_depth > 0 && candidate.depth == param_.max_depth)
|| (param_.max_leaves > 0 && num_leaves == param_.max_leaves) ) {
(*p_tree)[nid].SetLeaf(snode_[nid].weight * param_.learning_rate);
} else {
tstart = dmlc::GetTime();
this->ApplySplit(nid, gmat, column_matrix, hist_, *p_fmat, p_tree);
time_apply_split += dmlc::GetTime() - tstart;
tstart = dmlc::GetTime();
const int cleft = (*p_tree)[nid].LeftChild();
const int cright = (*p_tree)[nid].RightChild();
hist_.AddHistRow(cleft);
hist_.AddHistRow(cright);
if (row_set_collection_[cleft].Size() < row_set_collection_[cright].Size()) {
BuildHist(gpair_h, row_set_collection_[cleft], gmat, gmatb, hist_[cleft]);
SubtractionTrick(hist_[cright], hist_[cleft], hist_[nid]);
} else {
BuildHist(gpair_h, row_set_collection_[cright], gmat, gmatb, hist_[cright]);
SubtractionTrick(hist_[cleft], hist_[cright], hist_[nid]);
}
time_build_hist += dmlc::GetTime() - tstart;
tstart = dmlc::GetTime();
this->InitNewNode(cleft, gmat, gpair_h, *p_fmat, *p_tree);
this->InitNewNode(cright, gmat, gpair_h, *p_fmat, *p_tree);
bst_uint featureid = snode_[nid].best.SplitIndex();
spliteval_->AddSplit(nid, cleft, cright, featureid,
snode_[cleft].weight, snode_[cright].weight);
time_init_new_node += dmlc::GetTime() - tstart;
tstart = dmlc::GetTime();
this->EvaluateSplit(cleft, gmat, hist_, *p_fmat, *p_tree);
this->EvaluateSplit(cright, gmat, hist_, *p_fmat, *p_tree);
time_evaluate_split += dmlc::GetTime() - tstart;
qexpand_->push(ExpandEntry(cleft, p_tree->GetDepth(cleft),
snode_[cleft].best.loss_chg,
timestamp++));
qexpand_->push(ExpandEntry(cright, p_tree->GetDepth(cright),
snode_[cright].best.loss_chg,
timestamp++));
++num_leaves; // give two and take one, as parent is no longer a leaf
}
}
// set all the rest expanding nodes to leaf
// This post condition is not needed in current code, but may be necessary
// when there are stopping rule that leaves qexpand non-empty
while (!qexpand_->empty()) {
const int nid = qexpand_->top().nid;
qexpand_->pop();
(*p_tree)[nid].SetLeaf(snode_[nid].weight * param_.learning_rate);
}
// remember auxiliary statistics in the tree node
for (int nid = 0; nid < p_tree->param.num_nodes; ++nid) {
p_tree->Stat(nid).loss_chg = snode_[nid].best.loss_chg;
p_tree->Stat(nid).base_weight = snode_[nid].weight;
p_tree->Stat(nid).sum_hess = static_cast<float>(snode_[nid].stats.sum_hess);
snode_[nid].stats.SetLeafVec(param_, p_tree->Leafvec(nid));
}
pruner_->Update(gpair, p_fmat, std::vector<RegTree*>{p_tree});
if (param_.debug_verbose > 0) {
double total_time = dmlc::GetTime() - gstart;
LOG(INFO) << "\nInitData: "
<< std::fixed << std::setw(6) << std::setprecision(4) << time_init_data
<< " (" << std::fixed << std::setw(5) << std::setprecision(2)
<< time_init_data / total_time * 100 << "%)\n"
<< "InitNewNode: "
<< std::fixed << std::setw(6) << std::setprecision(4) << time_init_new_node
<< " (" << std::fixed << std::setw(5) << std::setprecision(2)
<< time_init_new_node / total_time * 100 << "%)\n"
<< "BuildHist: "
<< std::fixed << std::setw(6) << std::setprecision(4) << time_build_hist
<< " (" << std::fixed << std::setw(5) << std::setprecision(2)
<< time_build_hist / total_time * 100 << "%)\n"
<< "EvaluateSplit: "
<< std::fixed << std::setw(6) << std::setprecision(4) << time_evaluate_split
<< " (" << std::fixed << std::setw(5) << std::setprecision(2)
<< time_evaluate_split / total_time * 100 << "%)\n"
<< "ApplySplit: "
<< std::fixed << std::setw(6) << std::setprecision(4) << time_apply_split
<< " (" << std::fixed << std::setw(5) << std::setprecision(2)
<< time_apply_split / total_time * 100 << "%)\n"
<< "========================================\n"
<< "Total: "
<< std::fixed << std::setw(6) << std::setprecision(4) << total_time;
}
}
bool QuantileHistMaker::Builder::UpdatePredictionCache(
const DMatrix* data,
HostDeviceVector<bst_float>* p_out_preds) {
std::vector<bst_float>& out_preds = p_out_preds->HostVector();
// p_last_fmat_ is a valid pointer as long as UpdatePredictionCache() is called in
// conjunction with Update().
if (!p_last_fmat_ || !p_last_tree_ || data != p_last_fmat_) {
return false;
}
if (leaf_value_cache_.empty()) {
leaf_value_cache_.resize(p_last_tree_->param.num_nodes,
std::numeric_limits<float>::infinity());
}
CHECK_GT(out_preds.size(), 0U);
for (const RowSetCollection::Elem rowset : row_set_collection_) {
if (rowset.begin != nullptr && rowset.end != nullptr) {
int nid = rowset.node_id;
bst_float leaf_value;
// if a node is marked as deleted by the pruner, traverse upward to locate
// a non-deleted leaf.
if ((*p_last_tree_)[nid].IsDeleted()) {
while ((*p_last_tree_)[nid].IsDeleted()) {
nid = (*p_last_tree_)[nid].Parent();
}
CHECK((*p_last_tree_)[nid].IsLeaf());
}
leaf_value = (*p_last_tree_)[nid].LeafValue();
for (const size_t* it = rowset.begin; it < rowset.end; ++it) {
out_preds[*it] += leaf_value;
}
}
}
return true;
}
void QuantileHistMaker::Builder::InitData(const GHistIndexMatrix& gmat,
const std::vector<GradientPair>& gpair,
const DMatrix& fmat,
const RegTree& tree) {
CHECK_EQ(tree.param.num_nodes, tree.param.num_roots)
<< "ColMakerHist: can only grow new tree";
CHECK((param_.max_depth > 0 || param_.max_leaves > 0))
<< "max_depth or max_leaves cannot be both 0 (unlimited); "
<< "at least one should be a positive quantity.";
if (param_.grow_policy == TrainParam::kDepthWise) {
CHECK(param_.max_depth > 0) << "max_depth cannot be 0 (unlimited) "
<< "when grow_policy is depthwise.";
}
const auto& info = fmat.Info();
{
// initialize the row set
row_set_collection_.Clear();
// clear local prediction cache
leaf_value_cache_.clear();
// initialize histogram collection
uint32_t nbins = gmat.cut.row_ptr.back();
hist_.Init(nbins);
// initialize histogram builder
#pragma omp parallel
{
this->nthread_ = omp_get_num_threads();
}
hist_builder_.Init(this->nthread_, nbins);
CHECK_EQ(info.root_index_.size(), 0U);
std::vector<size_t>& row_indices = row_set_collection_.row_indices_;
// mark subsample and build list of member rows
if (param_.subsample < 1.0f) {
std::bernoulli_distribution coin_flip(param_.subsample);
auto& rnd = common::GlobalRandom();
for (size_t i = 0; i < info.num_row_; ++i) {
if (gpair[i].GetHess() >= 0.0f && coin_flip(rnd)) {
row_indices.push_back(i);
}
}
} else {
for (size_t i = 0; i < info.num_row_; ++i) {
if (gpair[i].GetHess() >= 0.0f) {
row_indices.push_back(i);
}
}
}
row_set_collection_.Init();
}
{
/* determine layout of data */
const size_t nrow = info.num_row_;
const size_t ncol = info.num_col_;
const size_t nnz = info.num_nonzero_;
// number of discrete bins for feature 0
const uint32_t nbins_f0 = gmat.cut.row_ptr[1] - gmat.cut.row_ptr[0];
if (nrow * ncol == nnz) {
// dense data with zero-based indexing
data_layout_ = kDenseDataZeroBased;
} else if (nbins_f0 == 0 && nrow * (ncol - 1) == nnz) {
// dense data with one-based indexing
data_layout_ = kDenseDataOneBased;
} else {
// sparse data
data_layout_ = kSparseData;
}
}
{
// store a pointer to the tree
p_last_tree_ = &tree;
// store a pointer to training data
p_last_fmat_ = &fmat;
// initialize feature index
if (data_layout_ == kDenseDataOneBased) {
column_sampler_.Init(info.num_col_, param_.colsample_bylevel,
param_.colsample_bytree, true);
} else {
column_sampler_.Init(info.num_col_, param_.colsample_bylevel,
param_.colsample_bytree, false);
}
}
if (data_layout_ == kDenseDataZeroBased || data_layout_ == kDenseDataOneBased) {
/* specialized code for dense data:
choose the column that has a least positive number of discrete bins.
For dense data (with no missing value),
the sum of gradient histogram is equal to snode[nid] */
const std::vector<uint32_t>& row_ptr = gmat.cut.row_ptr;
const auto nfeature = static_cast<bst_uint>(row_ptr.size() - 1);
uint32_t min_nbins_per_feature = 0;
for (bst_uint i = 0; i < nfeature; ++i) {
const uint32_t nbins = row_ptr[i + 1] - row_ptr[i];
if (nbins > 0) {
if (min_nbins_per_feature == 0 || min_nbins_per_feature > nbins) {
min_nbins_per_feature = nbins;
fid_least_bins_ = i;
}
}
}
CHECK_GT(min_nbins_per_feature, 0U);
}
{
snode_.reserve(256);
snode_.clear();
}
{
if (param_.grow_policy == TrainParam::kLossGuide) {
qexpand_.reset(new ExpandQueue(LossGuide));
} else {
qexpand_.reset(new ExpandQueue(DepthWise));
}
}
}
void QuantileHistMaker::Builder::EvaluateSplit(int nid,
const GHistIndexMatrix& gmat,
const HistCollection& hist,
const DMatrix& fmat,
const RegTree& tree) {
// start enumeration
const MetaInfo& info = fmat.Info();
const auto& feature_set = column_sampler_.GetFeatureSet(
tree.GetDepth(nid)).HostVector();
const auto nfeature = static_cast<bst_uint>(feature_set.size());
const auto nthread = static_cast<bst_omp_uint>(this->nthread_);
best_split_tloc_.resize(nthread);
#pragma omp parallel for schedule(static) num_threads(nthread)
for (bst_omp_uint tid = 0; tid < nthread; ++tid) {
best_split_tloc_[tid] = snode_[nid].best;
}
#pragma omp parallel for schedule(dynamic) num_threads(nthread)
for (bst_omp_uint i = 0; i < nfeature; ++i) {
const bst_uint fid = feature_set[i];
const unsigned tid = omp_get_thread_num();
this->EnumerateSplit(-1, gmat, hist[nid], snode_[nid], info,
&best_split_tloc_[tid], fid, nid);
this->EnumerateSplit(+1, gmat, hist[nid], snode_[nid], info,
&best_split_tloc_[tid], fid, nid);
}
for (unsigned tid = 0; tid < nthread; ++tid) {
snode_[nid].best.Update(best_split_tloc_[tid]);
}
}
void QuantileHistMaker::Builder::ApplySplit(int nid,
const GHistIndexMatrix& gmat,
const ColumnMatrix& column_matrix,
const HistCollection& hist,
const DMatrix& fmat,
RegTree* p_tree) {
// TODO(hcho3): support feature sampling by levels
/* 1. Create child nodes */
NodeEntry& e = snode_[nid];
p_tree->AddChilds(nid);
(*p_tree)[nid].SetSplit(e.best.SplitIndex(), e.best.split_value, e.best.DefaultLeft());
// mark right child as 0, to indicate fresh leaf
int cleft = (*p_tree)[nid].LeftChild();
int cright = (*p_tree)[nid].RightChild();
(*p_tree)[cleft].SetLeaf(0.0f, 0);
(*p_tree)[cright].SetLeaf(0.0f, 0);
/* 2. Categorize member rows */
const auto nthread = static_cast<bst_omp_uint>(this->nthread_);
row_split_tloc_.resize(nthread);
for (bst_omp_uint i = 0; i < nthread; ++i) {
row_split_tloc_[i].left.clear();
row_split_tloc_[i].right.clear();
}
const bool default_left = (*p_tree)[nid].DefaultLeft();
const bst_uint fid = (*p_tree)[nid].SplitIndex();
const bst_float split_pt = (*p_tree)[nid].SplitCond();
const uint32_t lower_bound = gmat.cut.row_ptr[fid];
const uint32_t upper_bound = gmat.cut.row_ptr[fid + 1];
int32_t split_cond = -1;
// convert floating-point split_pt into corresponding bin_id
// split_cond = -1 indicates that split_pt is less than all known cut points
CHECK_LT(upper_bound,
static_cast<uint32_t>(std::numeric_limits<int32_t>::max()));
for (uint32_t i = lower_bound; i < upper_bound; ++i) {
if (split_pt == gmat.cut.cut[i]) {
split_cond = static_cast<int32_t>(i);
}
}
const auto& rowset = row_set_collection_[nid];
Column column = column_matrix.GetColumn(fid);
if (column.GetType() == xgboost::common::kDenseColumn) {
ApplySplitDenseData(rowset, gmat, &row_split_tloc_, column, split_cond,
default_left);
} else {
ApplySplitSparseData(rowset, gmat, &row_split_tloc_, column, lower_bound,
upper_bound, split_cond, default_left);
}
row_set_collection_.AddSplit(
nid, row_split_tloc_, (*p_tree)[nid].LeftChild(), (*p_tree)[nid].RightChild());
}
void QuantileHistMaker::Builder::ApplySplitDenseData(
const RowSetCollection::Elem rowset,
const GHistIndexMatrix& gmat,
std::vector<RowSetCollection::Split>* p_row_split_tloc,
const Column& column,
bst_int split_cond,
bool default_left) {
std::vector<RowSetCollection::Split>& row_split_tloc = *p_row_split_tloc;
constexpr int kUnroll = 8; // loop unrolling factor
const size_t nrows = rowset.end - rowset.begin;
const size_t rest = nrows % kUnroll;
#pragma omp parallel for num_threads(nthread_) schedule(static)
for (bst_omp_uint i = 0; i < nrows - rest; i += kUnroll) {
const bst_uint tid = omp_get_thread_num();
auto& left = row_split_tloc[tid].left;
auto& right = row_split_tloc[tid].right;
size_t rid[kUnroll];
uint32_t rbin[kUnroll];
for (int k = 0; k < kUnroll; ++k) {
rid[k] = rowset.begin[i + k];
}
for (int k = 0; k < kUnroll; ++k) {
rbin[k] = column.GetFeatureBinIdx(rid[k]);
}
for (int k = 0; k < kUnroll; ++k) { // NOLINT
if (rbin[k] == std::numeric_limits<uint32_t>::max()) { // missing value
if (default_left) {
left.push_back(rid[k]);
} else {
right.push_back(rid[k]);
}
} else {
if (static_cast<int32_t>(rbin[k] + column.GetBaseIdx()) <= split_cond) {
left.push_back(rid[k]);
} else {
right.push_back(rid[k]);
}
}
}
}
for (size_t i = nrows - rest; i < nrows; ++i) {
auto& left = row_split_tloc[nthread_-1].left;
auto& right = row_split_tloc[nthread_-1].right;
const size_t rid = rowset.begin[i];
const uint32_t rbin = column.GetFeatureBinIdx(rid);
if (rbin == std::numeric_limits<uint32_t>::max()) { // missing value
if (default_left) {
left.push_back(rid);
} else {
right.push_back(rid);
}
} else {
if (static_cast<int32_t>(rbin + column.GetBaseIdx()) <= split_cond) {
left.push_back(rid);
} else {
right.push_back(rid);
}
}
}
}
void QuantileHistMaker::Builder::ApplySplitSparseData(
const RowSetCollection::Elem rowset,
const GHistIndexMatrix& gmat,
std::vector<RowSetCollection::Split>* p_row_split_tloc,
const Column& column,
bst_uint lower_bound,
bst_uint upper_bound,
bst_int split_cond,
bool default_left) {
std::vector<RowSetCollection::Split>& row_split_tloc = *p_row_split_tloc;
const size_t nrows = rowset.end - rowset.begin;
#pragma omp parallel num_threads(nthread_)
{
const auto tid = static_cast<size_t>(omp_get_thread_num());
const size_t ibegin = tid * nrows / nthread_;
const size_t iend = (tid + 1) * nrows / nthread_;
if (ibegin < iend) { // ensure that [ibegin, iend) is nonempty range
// search first nonzero row with index >= rowset[ibegin]
const size_t* p = std::lower_bound(column.GetRowData(),
column.GetRowData() + column.Size(),
rowset.begin[ibegin]);
auto& left = row_split_tloc[tid].left;
auto& right = row_split_tloc[tid].right;
if (p != column.GetRowData() + column.Size() && *p <= rowset.begin[iend - 1]) {
size_t cursor = p - column.GetRowData();
for (size_t i = ibegin; i < iend; ++i) {
const size_t rid = rowset.begin[i];
while (cursor < column.Size()
&& column.GetRowIdx(cursor) < rid
&& column.GetRowIdx(cursor) <= rowset.begin[iend - 1]) {
++cursor;
}
if (cursor < column.Size() && column.GetRowIdx(cursor) == rid) {
const uint32_t rbin = column.GetFeatureBinIdx(cursor);
if (static_cast<int32_t>(rbin + column.GetBaseIdx()) <= split_cond) {
left.push_back(rid);
} else {
right.push_back(rid);
}
++cursor;
} else {
// missing value
if (default_left) {
left.push_back(rid);
} else {
right.push_back(rid);
}
}
}
} else { // all rows in [ibegin, iend) have missing values
if (default_left) {
for (size_t i = ibegin; i < iend; ++i) {
const size_t rid = rowset.begin[i];
left.push_back(rid);
}
} else {
for (size_t i = ibegin; i < iend; ++i) {
const size_t rid = rowset.begin[i];
right.push_back(rid);
}
}
}
}
}
}
void QuantileHistMaker::Builder::InitNewNode(int nid,
const GHistIndexMatrix& gmat,
const std::vector<GradientPair>& gpair,
const DMatrix& fmat,
const RegTree& tree) {
{
snode_.resize(tree.param.num_nodes, NodeEntry(param_));
}
{
auto& stats = snode_[nid].stats;
if (data_layout_ == kDenseDataZeroBased || data_layout_ == kDenseDataOneBased) {
/* specialized code for dense data
For dense data (with no missing value),
the sum of gradient histogram is equal to snode[nid] */
GHistRow hist = hist_[nid];
const std::vector<uint32_t>& row_ptr = gmat.cut.row_ptr;
const uint32_t ibegin = row_ptr[fid_least_bins_];
const uint32_t iend = row_ptr[fid_least_bins_ + 1];
for (uint32_t i = ibegin; i < iend; ++i) {
const GHistEntry et = hist.begin[i];
stats.Add(et.sum_grad, et.sum_hess);
}
} else {
const RowSetCollection::Elem e = row_set_collection_[nid];
for (const size_t* it = e.begin; it < e.end; ++it) {
stats.Add(gpair[*it]);
}
}
}
// calculating the weights
{
bst_uint parentid = tree[nid].Parent();
snode_[nid].weight = static_cast<float>(
spliteval_->ComputeWeight(parentid, snode_[nid].stats));
snode_[nid].root_gain = static_cast<float>(
spliteval_->ComputeScore(parentid, snode_[nid].stats, snode_[nid].weight));
}
}
// enumerate the split values of specific feature
void QuantileHistMaker::Builder::EnumerateSplit(int d_step,
const GHistIndexMatrix& gmat,
const GHistRow& hist,
const NodeEntry& snode,
const MetaInfo& info,
SplitEntry* p_best,
bst_uint fid,
bst_uint nodeID) {
CHECK(d_step == +1 || d_step == -1);
// aliases
const std::vector<uint32_t>& cut_ptr = gmat.cut.row_ptr;
const std::vector<bst_float>& cut_val = gmat.cut.cut;
// statistics on both sides of split
GradStats c(param_);
GradStats e(param_);
// best split so far
SplitEntry best;
// bin boundaries
CHECK_LE(cut_ptr[fid],
static_cast<uint32_t>(std::numeric_limits<int32_t>::max()));
CHECK_LE(cut_ptr[fid + 1],
static_cast<uint32_t>(std::numeric_limits<int32_t>::max()));
// imin: index (offset) of the minimum value for feature fid
// need this for backward enumeration
const auto imin = static_cast<int32_t>(cut_ptr[fid]);
// ibegin, iend: smallest/largest cut points for feature fid
// use int to allow for value -1
int32_t ibegin, iend;
if (d_step > 0) {
ibegin = static_cast<int32_t>(cut_ptr[fid]);
iend = static_cast<int32_t>(cut_ptr[fid + 1]);
} else {
ibegin = static_cast<int32_t>(cut_ptr[fid + 1]) - 1;
iend = static_cast<int32_t>(cut_ptr[fid]) - 1;
}
for (int32_t i = ibegin; i != iend; i += d_step) {
// start working
// try to find a split
e.Add(hist.begin[i].sum_grad, hist.begin[i].sum_hess);
if (e.sum_hess >= param_.min_child_weight) {
c.SetSubstract(snode.stats, e);
if (c.sum_hess >= param_.min_child_weight) {
bst_float loss_chg;
bst_float split_pt;
if (d_step > 0) {
// forward enumeration: split at right bound of each bin
loss_chg = static_cast<bst_float>(
spliteval_->ComputeSplitScore(nodeID, fid, e, c) -
snode.root_gain);
split_pt = cut_val[i];
} else {
// backward enumeration: split at left bound of each bin
loss_chg = static_cast<bst_float>(
spliteval_->ComputeSplitScore(nodeID, fid, c, e) -
snode.root_gain);
if (i == imin) {
// for leftmost bin, left bound is the smallest feature value
split_pt = gmat.cut.min_val[fid];
} else {
split_pt = cut_val[i - 1];
}
}
best.Update(loss_chg, fid, split_pt, d_step == -1);
}
}
}
p_best->Update(best);
}
XGBOOST_REGISTER_TREE_UPDATER(FastHistMaker, "grow_fast_histmaker")
.describe("(Deprecated, use grow_quantile_histmaker instead.)"
" Grow tree using quantized histogram.")
.set_body(
[]() {
LOG(WARNING) << "grow_fast_histmaker is deprecated, "
<< "use grow_quantile_histmaker instead.";
return new QuantileHistMaker();
});
XGBOOST_REGISTER_TREE_UPDATER(QuantileHistMaker, "grow_quantile_histmaker")
.describe("Grow tree using quantized histogram.")
.set_body(
[]() {
return new QuantileHistMaker();
});
} // namespace tree
} // namespace xgboost

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/*!
* Copyright 2017-2018 by Contributors
* \file updater_quantile_hist.h
* \brief use quantized feature values to construct a tree
* \author Philip Cho, Tianqi Chen
*/
#ifndef XGBOOST_TREE_UPDATER_QUANTILE_HIST_H_
#define XGBOOST_TREE_UPDATER_QUANTILE_HIST_H_
#include <rabit/rabit.h>
#include <xgboost/tree_updater.h>
#include <memory>
#include <vector>
#include <string>
#include <queue>
#include <utility>
#include "./param.h"
#include "./split_evaluator.h"
#include "../common/random.h"
#include "../common/hist_util.h"
#include "../common/row_set.h"
#include "../common/column_matrix.h"
namespace xgboost {
namespace tree {
using xgboost::common::HistCutMatrix;
using xgboost::common::GHistIndexMatrix;
using xgboost::common::GHistIndexBlockMatrix;
using xgboost::common::GHistIndexRow;
using xgboost::common::GHistEntry;
using xgboost::common::HistCollection;
using xgboost::common::RowSetCollection;
using xgboost::common::GHistRow;
using xgboost::common::GHistBuilder;
using xgboost::common::ColumnMatrix;
using xgboost::common::Column;
/*! \brief construct a tree using quantized feature values */
class QuantileHistMaker: public TreeUpdater {
public:
void Init(const std::vector<std::pair<std::string, std::string> >& args) override;
void Update(HostDeviceVector<GradientPair>* gpair,
DMatrix* dmat,
const std::vector<RegTree*>& trees) override;
bool UpdatePredictionCache(const DMatrix* data,
HostDeviceVector<bst_float>* out_preds) override;
protected:
// training parameter
TrainParam param_;
// quantized data matrix
GHistIndexMatrix gmat_;
// (optional) data matrix with feature grouping
GHistIndexBlockMatrix gmatb_;
// column accessor
ColumnMatrix column_matrix_;
bool is_gmat_initialized_;
// data structure
struct NodeEntry {
/*! \brief statics for node entry */
GradStats stats;
/*! \brief loss of this node, without split */
bst_float root_gain;
/*! \brief weight calculated related to current data */
float weight;
/*! \brief current best solution */
SplitEntry best;
// constructor
explicit NodeEntry(const TrainParam& param)
: stats(param), root_gain(0.0f), weight(0.0f) {
}
};
// actual builder that runs the algorithm
struct Builder {
public:
// constructor
explicit Builder(const TrainParam& param,
std::unique_ptr<TreeUpdater> pruner,
std::unique_ptr<SplitEvaluator> spliteval)
: param_(param), pruner_(std::move(pruner)),
spliteval_(std::move(spliteval)), p_last_tree_(nullptr),
p_last_fmat_(nullptr) {}
// update one tree, growing
virtual void Update(const GHistIndexMatrix& gmat,
const GHistIndexBlockMatrix& gmatb,
const ColumnMatrix& column_matrix,
HostDeviceVector<GradientPair>* gpair,
DMatrix* p_fmat,
RegTree* p_tree);
inline void BuildHist(const std::vector<GradientPair>& gpair,
const RowSetCollection::Elem row_indices,
const GHistIndexMatrix& gmat,
const GHistIndexBlockMatrix& gmatb,
GHistRow hist) {
if (param_.enable_feature_grouping > 0) {
hist_builder_.BuildBlockHist(gpair, row_indices, gmatb, hist);
} else {
hist_builder_.BuildHist(gpair, row_indices, gmat, hist);
}
}
inline void SubtractionTrick(GHistRow self, GHistRow sibling, GHistRow parent) {
hist_builder_.SubtractionTrick(self, sibling, parent);
}
bool UpdatePredictionCache(const DMatrix* data,
HostDeviceVector<bst_float>* p_out_preds);
protected:
// initialize temp data structure
void InitData(const GHistIndexMatrix& gmat,
const std::vector<GradientPair>& gpair,
const DMatrix& fmat,
const RegTree& tree);
void EvaluateSplit(int nid,
const GHistIndexMatrix& gmat,
const HistCollection& hist,
const DMatrix& fmat,
const RegTree& tree);
void ApplySplit(int nid,
const GHistIndexMatrix& gmat,
const ColumnMatrix& column_matrix,
const HistCollection& hist,
const DMatrix& fmat,
RegTree* p_tree);
void ApplySplitDenseData(const RowSetCollection::Elem rowset,
const GHistIndexMatrix& gmat,
std::vector<RowSetCollection::Split>* p_row_split_tloc,
const Column& column,
bst_int split_cond,
bool default_left);
void ApplySplitSparseData(const RowSetCollection::Elem rowset,
const GHistIndexMatrix& gmat,
std::vector<RowSetCollection::Split>* p_row_split_tloc,
const Column& column,
bst_uint lower_bound,
bst_uint upper_bound,
bst_int split_cond,
bool default_left);
void InitNewNode(int nid,
const GHistIndexMatrix& gmat,
const std::vector<GradientPair>& gpair,
const DMatrix& fmat,
const RegTree& tree);
// enumerate the split values of specific feature
void EnumerateSplit(int d_step,
const GHistIndexMatrix& gmat,
const GHistRow& hist,
const NodeEntry& snode,
const MetaInfo& info,
SplitEntry* p_best,
bst_uint fid,
bst_uint nodeID);
/* tree growing policies */
struct ExpandEntry {
int nid;
int depth;
bst_float loss_chg;
unsigned timestamp;
ExpandEntry(int nid, int depth, bst_float loss_chg, unsigned tstmp)
: nid(nid), depth(depth), loss_chg(loss_chg), timestamp(tstmp) {}
};
inline static bool DepthWise(ExpandEntry lhs, ExpandEntry rhs) {
if (lhs.depth == rhs.depth) {
return lhs.timestamp > rhs.timestamp; // favor small timestamp
} else {
return lhs.depth > rhs.depth; // favor small depth
}
}
inline static bool LossGuide(ExpandEntry lhs, ExpandEntry rhs) {
if (lhs.loss_chg == rhs.loss_chg) {
return lhs.timestamp > rhs.timestamp; // favor small timestamp
} else {
return lhs.loss_chg < rhs.loss_chg; // favor large loss_chg
}
}
// --data fields--
const TrainParam& param_;
// number of omp thread used during training
int nthread_;
common::ColumnSampler column_sampler_;
// the internal row sets
RowSetCollection row_set_collection_;
// the temp space for split
std::vector<RowSetCollection::Split> row_split_tloc_;
std::vector<SplitEntry> best_split_tloc_;
/*! \brief TreeNode Data: statistics for each constructed node */
std::vector<NodeEntry> snode_;
/*! \brief culmulative histogram of gradients. */
HistCollection hist_;
/*! \brief feature with least # of bins. to be used for dense specialization
of InitNewNode() */
uint32_t fid_least_bins_;
/*! \brief local prediction cache; maps node id to leaf value */
std::vector<float> leaf_value_cache_;
GHistBuilder hist_builder_;
std::unique_ptr<TreeUpdater> pruner_;
std::unique_ptr<SplitEvaluator> spliteval_;
// back pointers to tree and data matrix
const RegTree* p_last_tree_;
const DMatrix* p_last_fmat_;
using ExpandQueue =
std::priority_queue<ExpandEntry, std::vector<ExpandEntry>,
std::function<bool(ExpandEntry, ExpandEntry)>>;
std::unique_ptr<ExpandQueue> qexpand_;
enum DataLayout { kDenseDataZeroBased, kDenseDataOneBased, kSparseData };
DataLayout data_layout_;
};
std::unique_ptr<Builder> builder_;
std::unique_ptr<TreeUpdater> pruner_;
std::unique_ptr<SplitEvaluator> spliteval_;
};
} // namespace tree
} // namespace xgboost
#endif // XGBOOST_TREE_UPDATER_QUANTILE_HIST_H_

5
src/tree/updater_refresh.cc
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@ -4,12 +4,13 @@
* \brief refresh the statistics and leaf value on the tree on the dataset
* \author Tianqi Chen
*/
#include <rabit/rabit.h>
#include <xgboost/tree_updater.h>
#include <vector>
#include <limits>
#include "./param.h"
#include "../common/sync.h"
#include "../common/io.h"
namespace xgboost {

4
src/tree/updater_skmaker.cc
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@ -5,12 +5,12 @@
a refresh is needed to make the statistics exactly correct
* \author Tianqi Chen
*/
#include <rabit/rabit.h>
#include <xgboost/base.h>
#include <xgboost/tree_updater.h>
#include <vector>
#include <algorithm>
#include "../common/sync.h"
#include "../common/quantile.h"
#include "../common/group_data.h"
#include "./updater_basemaker-inl.h"

1
src/tree/updater_sync.cc
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@ -7,7 +7,6 @@
#include <vector>
#include <string>
#include <limits>
#include "../common/sync.h"
#include "../common/io.h"
namespace xgboost {

2
tests/cpp/test_learner.cc
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@ -32,7 +32,7 @@ TEST(learner, SelectTreeMethod) {
"grow_colmaker,prune");
learner->Configure({arg("tree_method", "hist")});
ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
"grow_fast_histmaker");
"grow_quantile_histmaker");
#ifdef XGBOOST_USE_CUDA
learner->Configure({arg("tree_method", "gpu_exact")});
ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),

12
tests/cpp/tree/test_gpu_hist.cu
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@ -328,8 +328,8 @@ TEST(GpuHist, ApplySplit) {
shard->row_stride = n_cols;
thrust::sequence(shard->ridx.CurrentDVec().tbegin(),
shard->ridx.CurrentDVec().tend());
// Free inside DeviceShard
dh::safe_cuda(cudaMallocHost(&(shard->tmp_pinned), sizeof(int64_t)));
// Initialize GPUHistMaker
hist_maker.param_ = param;
RegTree tree;
@ -390,15 +390,5 @@ TEST(GpuHist, ApplySplit) {
ASSERT_EQ(shard->ridx_segments[right_nidx].end, 16);
}
TEST(GpuHist, MGPU_mock) {
// Attempt to choose multiple GPU devices
int ngpu;
dh::safe_cuda(cudaGetDeviceCount(&ngpu));
CHECK_GT(ngpu, 1);
for (int i = 0; i < ngpu; ++i) {
dh::safe_cuda(cudaSetDevice(i));
}
}
} // namespace tree
} // namespace xgboost

2
tests/cpp/tree/test_param.cc
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@ -1,7 +1,7 @@
// Copyright by Contributors
#include "../../../src/tree/param.h"
#include "../helpers.h"
#include <gtest/gtest.h>
TEST(Param, VectorIOStream) {
std::vector<int> vals = {3, 2, 1};

72
tests/cpp/tree/test_prune.cc Normal file
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@ -0,0 +1,72 @@
/*!
* Copyright 2018 by Contributors
*/
#include "../helpers.h"
#include "../../../src/common/host_device_vector.h"
#include <xgboost/tree_updater.h>
#include <gtest/gtest.h>
#include <vector>
#include <string>
#include <memory>
namespace xgboost {
namespace tree {
TEST(Updater, Prune) {
int constexpr n_rows = 32, n_cols = 16;
std::vector<std::pair<std::string, std::string>> cfg;
cfg.push_back(std::pair<std::string, std::string>(
"num_feature", std::to_string(n_cols)));
cfg.push_back(std::pair<std::string, std::string>(
"min_split_loss", "10"));
cfg.push_back(std::pair<std::string, std::string>(
"silent", "1"));
// These data are just place holders.
HostDeviceVector<GradientPair> gpair =
{ {0.50f, 0.25f}, {0.50f, 0.25f}, {0.50f, 0.25f}, {0.50f, 0.25f},
{0.25f, 0.24f}, {0.25f, 0.24f}, {0.25f, 0.24f}, {0.25f, 0.24f} };
auto dmat = CreateDMatrix(32, 16, 0.4, 3);
// prepare tree
RegTree tree = RegTree();
tree.InitModel();
tree.param.InitAllowUnknown(cfg);
std::vector<RegTree*> trees {&tree};
// prepare pruner
std::unique_ptr<TreeUpdater> pruner(TreeUpdater::Create("prune"));
pruner->Init(cfg);
// loss_chg < min_split_loss;
tree.AddChilds(0);
int cleft = tree[0].LeftChild();
int cright = tree[0].RightChild();
tree[cleft].SetLeaf(0.3f, 0);
tree[cright].SetLeaf(0.4f, 0);
pruner->Update(&gpair, dmat->get(), trees);
ASSERT_EQ(tree.NumExtraNodes(), 0);
// loss_chg > min_split_loss;
tree.AddChilds(0);
cleft = tree[0].LeftChild();
cright = tree[0].RightChild();
tree[cleft].SetLeaf(0.3f, 0);
tree[cright].SetLeaf(0.4f, 0);
tree.Stat(0).loss_chg = 11;
pruner->Update(&gpair, dmat->get(), trees);
ASSERT_EQ(tree.NumExtraNodes(), 2);
// loss_chg == min_split_loss;
tree.Stat(0).loss_chg = 10;
pruner->Update(&gpair, dmat->get(), trees);
ASSERT_EQ(tree.NumExtraNodes(), 2);
delete dmat;
}
} // namespace tree
} // namespace xgboost

181
tests/cpp/tree/test_quantile_hist.cc Normal file
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@ -0,0 +1,181 @@
/*!
* Copyright 2018 by Contributors
*/
#include "../helpers.h"
#include "../../../src/tree/param.h"
#include "../../../src/tree/updater_quantile_hist.h"
#include "../../../src/common/host_device_vector.h"
#include <xgboost/tree_updater.h>
#include <gtest/gtest.h>
#include <vector>
#include <string>
namespace xgboost {
namespace tree {
class QuantileHistMock : public QuantileHistMaker {
static double constexpr kEps = 1e-6;
struct BuilderMock : public QuantileHistMaker::Builder {
using RealImpl = QuantileHistMaker::Builder;
BuilderMock(const TrainParam& param,
std::unique_ptr<TreeUpdater> pruner,
std::unique_ptr<SplitEvaluator> spliteval)
: RealImpl(param, std::move(pruner), std::move(spliteval)) {}
public:
void TestInitData(const GHistIndexMatrix& gmat,
const std::vector<GradientPair>& gpair,
const DMatrix& fmat,
const RegTree& tree) {
RealImpl::InitData(gmat, gpair, fmat, tree);
ASSERT_EQ(data_layout_, kSparseData);
}
void TestBuildHist(int nid,
const GHistIndexMatrix& gmat,
const DMatrix& fmat,
const RegTree& tree) {
std::vector<GradientPair> gpair =
{ {0.23f, 0.24f}, {0.24f, 0.25f}, {0.26f, 0.27f}, {0.27f, 0.28f},
{0.27f, 0.29f}, {0.37f, 0.39f}, {0.47f, 0.49f}, {0.57f, 0.59f} };
RealImpl::InitData(gmat, gpair, fmat, tree);
GHistIndexBlockMatrix quantile_index_block;
hist_.AddHistRow(nid);
BuildHist(gpair, row_set_collection_[nid],
gmat, quantile_index_block, hist_[nid]);
std::vector<GradientPairPrecise> solution {
{0.27, 0.29}, {0.27, 0.29}, {0.47, 0.49},
{0.27, 0.29}, {0.57, 0.59}, {0.26, 0.27},
{0.37, 0.39}, {0.23, 0.24}, {0.37, 0.39},
{0.27, 0.28}, {0.27, 0.29}, {0.37, 0.39},
{0.26, 0.27}, {0.23, 0.24}, {0.57, 0.59},
{0.47, 0.49}, {0.47, 0.49}, {0.37, 0.39},
{0.26, 0.27}, {0.23, 0.24}, {0.27, 0.28},
{0.57, 0.59}, {0.23, 0.24}, {0.47, 0.49}};
for (size_t i = 0; i < hist_[nid].size; ++i) {
GradientPairPrecise sol = solution[i];
ASSERT_NEAR(sol.GetGrad(), hist_[nid].begin[i].sum_grad, kEps);
ASSERT_NEAR(sol.GetHess(), hist_[nid].begin[i].sum_hess, kEps);
}
}
void TestEvaluateSplit(const GHistIndexBlockMatrix& quantile_index_block,
const RegTree& tree) {
std::vector<GradientPair> row_gpairs =
{ {0.23f, 0.24f}, {0.24f, 0.25f}, {0.26f, 0.27f}, {0.27f, 0.28f},
{0.27f, 0.29f}, {0.37f, 0.39f}, {0.47f, 0.49f}, {0.57f, 0.59f} };
size_t constexpr max_bins = 4;
auto dmat = CreateDMatrix(n_rows, n_cols, 0, 3); // dense
common::GHistIndexMatrix gmat;
gmat.Init((*dmat).get(), max_bins);
RealImpl::InitData(gmat, row_gpairs, *(*dmat), tree);
hist_.AddHistRow(0);
BuildHist(row_gpairs, row_set_collection_[0],
gmat, quantile_index_block, hist_[0]);
RealImpl::InitNewNode(0, gmat, row_gpairs, *(*dmat), tree);
// Manipulate the root_gain so that I don't have to invent an actual
// split. Yes, I'm cheating.
snode_[0].root_gain = 0.8;
RealImpl::EvaluateSplit(0, gmat, hist_, *(*dmat), tree);
ASSERT_NEAR(snode_.at(0).best.loss_chg, 0.7128048, kEps);
ASSERT_EQ(snode_.at(0).best.SplitIndex(), 10);
ASSERT_NEAR(snode_.at(0).best.split_value, 0.182258, kEps);
delete dmat;
}
};
int static constexpr n_rows = 8, n_cols = 16;
std::shared_ptr<xgboost::DMatrix> *dmat;
const std::vector<std::pair<std::string, std::string> > cfg;
std::shared_ptr<BuilderMock> builder_;
public:
explicit QuantileHistMock(
const std::vector<std::pair<std::string, std::string> >& args) :
cfg{args} {
QuantileHistMaker::Init(args);
builder_.reset(
new BuilderMock(
param_,
std::move(pruner_),
std::unique_ptr<SplitEvaluator>(spliteval_->GetHostClone())));
dmat = CreateDMatrix(n_rows, n_cols, 0.8, 3);
}
~QuantileHistMock() { delete dmat; }
static size_t GetNumColumns() { return n_cols; }
void TestInitData() {
size_t constexpr max_bins = 4;
common::GHistIndexMatrix gmat;
gmat.Init((*dmat).get(), max_bins);
RegTree tree = RegTree();
tree.InitModel();
tree.param.InitAllowUnknown(cfg);
std::vector<GradientPair> gpair =
{ {0.23f, 0.24f}, {0.23f, 0.24f}, {0.23f, 0.24f}, {0.23f, 0.24f},
{0.27f, 0.29f}, {0.27f, 0.29f}, {0.27f, 0.29f}, {0.27f, 0.29f} };
builder_->TestInitData(gmat, gpair, *(*dmat), tree);
}
void TestBuildHist() {
RegTree tree = RegTree();
tree.InitModel();
tree.param.InitAllowUnknown(cfg);
size_t constexpr max_bins = 4;
common::GHistIndexMatrix gmat;
gmat.Init((*dmat).get(), max_bins);
builder_->TestBuildHist(0, gmat, *(*dmat).get(), tree);
}
void TestEvaluateSplit() {
RegTree tree = RegTree();
tree.InitModel();
tree.param.InitAllowUnknown(cfg);
builder_->TestEvaluateSplit(gmatb_, tree);
}
};
TEST(Updater, QuantileHist_InitData) {
std::vector<std::pair<std::string, std::string>> cfg
{{"num_feature", std::to_string(QuantileHistMock::GetNumColumns())}};
QuantileHistMock maker(cfg);
maker.TestInitData();
}
TEST(Updater, QuantileHist_BuildHist) {
// Don't enable feature grouping
std::vector<std::pair<std::string, std::string>> cfg
{{"num_feature", std::to_string(QuantileHistMock::GetNumColumns())},
{"enable_feature_grouping", std::to_string(0)}};
QuantileHistMock maker(cfg);
maker.TestBuildHist();
}
TEST(Updater, QuantileHist_EvalSplits) {
std::vector<std::pair<std::string, std::string>> cfg
{{"num_feature", std::to_string(QuantileHistMock::GetNumColumns())},
{"split_evaluator", "elastic_net"}};
QuantileHistMock maker(cfg);
maker.TestEvaluateSplit();
}
} // namespace tree
} // namespace xgboost

57
tests/cpp/tree/test_refresh.cc Normal file
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@ -0,0 +1,57 @@
/*!
* Copyright 2018 by Contributors
*/
#include "../helpers.h"
#include "../../../src/common/host_device_vector.h"
#include <xgboost/tree_updater.h>
#include <gtest/gtest.h>
#include <vector>
#include <string>
#include <memory>
namespace xgboost {
namespace tree {
TEST(Updater, Refresh) {
int constexpr n_rows = 8, n_cols = 16;
HostDeviceVector<GradientPair> gpair =
{ {0.23f, 0.24f}, {0.23f, 0.24f}, {0.23f, 0.24f}, {0.23f, 0.24f},
{0.27f, 0.29f}, {0.27f, 0.29f}, {0.27f, 0.29f}, {0.27f, 0.29f} };
auto dmat = CreateDMatrix(n_rows, n_cols, 0.4, 3);
std::vector<std::pair<std::string, std::string>> cfg {
{"reg_alpha", "0.0"},
{"num_feature", std::to_string(n_cols)},
{"reg_lambda", "1"}};
RegTree tree = RegTree();
tree.InitModel();
tree.param.InitAllowUnknown(cfg);
std::vector<RegTree*> trees {&tree};
std::unique_ptr<TreeUpdater> refresher(TreeUpdater::Create("refresh"));
tree.AddChilds(0);
int cleft = tree[0].LeftChild();
int cright = tree[0].RightChild();
tree[cleft].SetLeaf(0.2f, 0);
tree[cright].SetLeaf(0.8f, 0);
tree[0].SetSplit(2, 0.2f);
tree.Stat(cleft).base_weight = 1.2;
tree.Stat(cright).base_weight = 1.3;
refresher->Init(cfg);
refresher->Update(&gpair, dmat->get(), trees);
bst_float constexpr kEps = 1e-6;
ASSERT_NEAR(-0.183392, tree[cright].LeafValue(), kEps);
ASSERT_NEAR(-0.224489, tree.Stat(0).loss_chg, kEps);
ASSERT_NEAR(0, tree.Stat(cleft).loss_chg, kEps);
ASSERT_NEAR(0, tree.Stat(1).loss_chg, kEps);
ASSERT_NEAR(0, tree.Stat(2).loss_chg, kEps);
delete dmat;
}
} // namespace tree
} // namespace xgboost