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Always use partition based categorical splits. (#7857)

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This commit is contained in:
Jiaming Yuan
2022-05-03 22:30:32 +08:00
committed by GitHub
parent 90cce38236
commit 317d7be6ee
13 changed files with 79 additions and 104 deletions
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10
src/tree/hist/evaluate_splits.h
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@@ -11,7 +11,6 @@
#include <utility>
#include <vector>
#include "xgboost/task.h"
#include "../param.h"
#include "../constraints.h"
#include "../split_evaluator.h"
@@ -39,7 +38,6 @@ template <typename GradientSumT, typename ExpandEntry> class HistEvaluator {
int32_t n_threads_ {0};
FeatureInteractionConstraintHost interaction_constraints_;
std::vector<NodeEntry> snode_;
ObjInfo task_;
// if sum of statistics for non-missing values in the node
// is equal to sum of statistics for all values:
@@ -244,7 +242,7 @@ template <typename GradientSumT, typename ExpandEntry> class HistEvaluator {
}
if (is_cat) {
auto n_bins = cut_ptrs.at(fidx + 1) - cut_ptrs[fidx];
if (common::UseOneHot(n_bins, param_.max_cat_to_onehot, task_)) {
if (common::UseOneHot(n_bins, param_.max_cat_to_onehot)) {
EnumerateSplit<+1, kOneHot>(cut, {}, histogram, fidx, nidx, evaluator, best);
EnumerateSplit<-1, kOneHot>(cut, {}, histogram, fidx, nidx, evaluator, best);
} else {
@@ -345,7 +343,6 @@ template <typename GradientSumT, typename ExpandEntry> class HistEvaluator {
auto Evaluator() const { return tree_evaluator_.GetEvaluator(); }
auto const& Stats() const { return snode_; }
auto Task() const { return task_; }
float InitRoot(GradStats const& root_sum) {
snode_.resize(1);
@@ -363,12 +360,11 @@ template <typename GradientSumT, typename ExpandEntry> class HistEvaluator {
// The column sampler must be constructed by caller since we need to preserve the rng
// for the entire training session.
explicit HistEvaluator(TrainParam const &param, MetaInfo const &info, int32_t n_threads,
std::shared_ptr<common::ColumnSampler> sampler, ObjInfo task)
std::shared_ptr<common::ColumnSampler> sampler)
: param_{param},
column_sampler_{std::move(sampler)},
tree_evaluator_{param, static_cast<bst_feature_t>(info.num_col_), GenericParameter::kCpuId},
n_threads_{n_threads},
task_{task} {
n_threads_{n_threads} {
interaction_constraints_.Configure(param, info.num_col_);
column_sampler_->Init(info.num_col_, info.feature_weights.HostVector(), param_.colsample_bynode,
param_.colsample_bylevel, param_.colsample_bytree);