83a66b4994
* Extract partitioner from hist. * Implement categorical data support by passing the gradient index directly into the partitioner. * Organize/update document. * Remove code for negative hessian.
382 lines
15 KiB
C++
382 lines
15 KiB
C++
/*!
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* Copyright 2021-2022 by XGBoost Contributors
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*/
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#ifndef XGBOOST_TREE_HIST_EVALUATE_SPLITS_H_
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#define XGBOOST_TREE_HIST_EVALUATE_SPLITS_H_
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#include <algorithm>
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#include <memory>
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#include <numeric>
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#include <limits>
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#include <utility>
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#include <vector>
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#include "xgboost/task.h"
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#include "../param.h"
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#include "../constraints.h"
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#include "../split_evaluator.h"
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#include "../../common/categorical.h"
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#include "../../common/random.h"
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#include "../../common/hist_util.h"
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#include "../../data/gradient_index.h"
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namespace xgboost {
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namespace tree {
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template <typename GradientSumT, typename ExpandEntry> class HistEvaluator {
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private:
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struct NodeEntry {
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/*! \brief statics for node entry */
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GradStats stats;
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/*! \brief loss of this node, without split */
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bst_float root_gain{0.0f};
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};
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private:
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TrainParam param_;
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std::shared_ptr<common::ColumnSampler> column_sampler_;
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TreeEvaluator tree_evaluator_;
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int32_t n_threads_ {0};
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FeatureInteractionConstraintHost interaction_constraints_;
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std::vector<NodeEntry> snode_;
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ObjInfo task_;
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// if sum of statistics for non-missing values in the node
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// is equal to sum of statistics for all values:
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// then - there are no missing values
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// else - there are missing values
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bool static SplitContainsMissingValues(const GradStats e, const NodeEntry &snode) {
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if (e.GetGrad() == snode.stats.GetGrad() &&
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e.GetHess() == snode.stats.GetHess()) {
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return false;
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} else {
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return true;
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}
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}
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// Enumerate/Scan the split values of specific feature
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// Returns the sum of gradients corresponding to the data points that contains
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// a non-missing value for the particular feature fid.
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template <int d_step, SplitType split_type>
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GradStats EnumerateSplit(common::HistogramCuts const &cut, common::Span<size_t const> sorted_idx,
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const common::GHistRow<GradientSumT> &hist, bst_feature_t fidx,
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bst_node_t nidx,
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TreeEvaluator::SplitEvaluator<TrainParam> const &evaluator,
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SplitEntry *p_best) const {
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static_assert(d_step == +1 || d_step == -1, "Invalid step.");
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// aliases
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const std::vector<uint32_t> &cut_ptr = cut.Ptrs();
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const std::vector<bst_float> &cut_val = cut.Values();
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auto const &parent = snode_[nidx];
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int32_t n_bins{static_cast<int32_t>(cut_ptr.at(fidx + 1) - cut_ptr[fidx])};
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auto f_hist = hist.subspan(cut_ptr[fidx], n_bins);
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// statistics on both sides of split
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GradStats left_sum;
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GradStats right_sum;
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// best split so far
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SplitEntry best;
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// bin boundaries
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CHECK_LE(cut_ptr[fidx], static_cast<uint32_t>(std::numeric_limits<int32_t>::max()));
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CHECK_LE(cut_ptr[fidx + 1], static_cast<uint32_t>(std::numeric_limits<int32_t>::max()));
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// imin: index (offset) of the minimum value for feature fid need this for backward
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// enumeration
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const auto imin = static_cast<int32_t>(cut_ptr[fidx]);
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// ibegin, iend: smallest/largest cut points for feature fid use int to allow for
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// value -1
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int32_t ibegin, iend;
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if (d_step > 0) {
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ibegin = static_cast<int32_t>(cut_ptr[fidx]);
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iend = static_cast<int32_t>(cut_ptr.at(fidx + 1));
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} else {
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ibegin = static_cast<int32_t>(cut_ptr[fidx + 1]) - 1;
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iend = static_cast<int32_t>(cut_ptr[fidx]) - 1;
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}
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auto calc_bin_value = [&](auto i) {
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switch (split_type) {
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case kNum: {
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left_sum.Add(hist[i].GetGrad(), hist[i].GetHess());
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right_sum.SetSubstract(parent.stats, left_sum);
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break;
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}
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case kOneHot: {
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// not-chosen categories go to left
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right_sum = GradStats{hist[i]};
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left_sum.SetSubstract(parent.stats, right_sum);
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break;
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}
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case kPart: {
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auto j = d_step == 1 ? (i - ibegin) : (ibegin - i);
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right_sum.Add(f_hist[sorted_idx[j]].GetGrad(), f_hist[sorted_idx[j]].GetHess());
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left_sum.SetSubstract(parent.stats, right_sum);
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break;
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}
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}
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};
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int32_t best_thresh{-1};
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for (int32_t i = ibegin; i != iend; i += d_step) {
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// start working
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// try to find a split
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calc_bin_value(i);
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bool improved{false};
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if (left_sum.GetHess() >= param_.min_child_weight &&
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right_sum.GetHess() >= param_.min_child_weight) {
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bst_float loss_chg;
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bst_float split_pt;
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if (d_step > 0) {
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// forward enumeration: split at right bound of each bin
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loss_chg =
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static_cast<float>(evaluator.CalcSplitGain(param_, nidx, fidx, GradStats{left_sum},
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GradStats{right_sum}) -
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parent.root_gain);
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split_pt = cut_val[i]; // not used for partition based
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improved = best.Update(loss_chg, fidx, split_pt, d_step == -1, split_type != kNum,
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left_sum, right_sum);
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} else {
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// backward enumeration: split at left bound of each bin
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loss_chg =
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static_cast<float>(evaluator.CalcSplitGain(param_, nidx, fidx, GradStats{right_sum},
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GradStats{left_sum}) -
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parent.root_gain);
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switch (split_type) {
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case kNum: {
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if (i == imin) {
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split_pt = cut.MinValues()[fidx];
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} else {
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split_pt = cut_val[i - 1];
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}
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break;
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}
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case kOneHot: {
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split_pt = cut_val[i];
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break;
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}
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case kPart: {
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split_pt = cut_val[i];
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break;
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}
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}
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improved = best.Update(loss_chg, fidx, split_pt, d_step == -1, split_type != kNum,
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right_sum, left_sum);
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}
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if (improved) {
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best_thresh = i;
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}
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}
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}
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if (split_type == kPart && best_thresh != -1) {
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auto n = common::CatBitField::ComputeStorageSize(n_bins);
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best.cat_bits.resize(n, 0);
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common::CatBitField cat_bits{best.cat_bits};
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if (d_step == 1) {
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std::for_each(sorted_idx.begin(), sorted_idx.begin() + (best_thresh - ibegin + 1),
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[&](size_t c) { cat_bits.Set(cut_val[c + ibegin]); });
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} else {
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std::for_each(sorted_idx.rbegin(), sorted_idx.rbegin() + (ibegin - best_thresh),
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[&](size_t c) { cat_bits.Set(cut_val[c + cut_ptr[fidx]]); });
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}
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}
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p_best->Update(best);
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switch (split_type) {
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case kNum:
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// Normal, accumulated to left
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return left_sum;
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case kOneHot:
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// Doesn't matter, not accumulating.
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return {};
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case kPart:
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// Accumulated to right due to chosen cats go to right.
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return right_sum;
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}
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return left_sum;
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}
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public:
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void EvaluateSplits(const common::HistCollection<GradientSumT> &hist,
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common::HistogramCuts const &cut,
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common::Span<FeatureType const> feature_types,
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const RegTree &tree,
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std::vector<ExpandEntry> *p_entries) {
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auto& entries = *p_entries;
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// All nodes are on the same level, so we can store the shared ptr.
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std::vector<std::shared_ptr<HostDeviceVector<bst_feature_t>>> features(
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entries.size());
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for (size_t nidx_in_set = 0; nidx_in_set < entries.size(); ++nidx_in_set) {
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auto nidx = entries[nidx_in_set].nid;
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features[nidx_in_set] =
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column_sampler_->GetFeatureSet(tree.GetDepth(nidx));
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}
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CHECK(!features.empty());
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const size_t grain_size =
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std::max<size_t>(1, features.front()->Size() / n_threads_);
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common::BlockedSpace2d space(entries.size(), [&](size_t nidx_in_set) {
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return features[nidx_in_set]->Size();
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}, grain_size);
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std::vector<ExpandEntry> tloc_candidates(n_threads_ * entries.size());
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for (size_t i = 0; i < entries.size(); ++i) {
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for (decltype(n_threads_) j = 0; j < n_threads_; ++j) {
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tloc_candidates[i * n_threads_ + j] = entries[i];
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}
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}
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auto evaluator = tree_evaluator_.GetEvaluator();
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auto const& cut_ptrs = cut.Ptrs();
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common::ParallelFor2d(space, n_threads_, [&](size_t nidx_in_set, common::Range1d r) {
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auto tidx = omp_get_thread_num();
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auto entry = &tloc_candidates[n_threads_ * nidx_in_set + tidx];
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auto best = &entry->split;
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auto nidx = entry->nid;
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auto histogram = hist[nidx];
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auto features_set = features[nidx_in_set]->ConstHostSpan();
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for (auto fidx_in_set = r.begin(); fidx_in_set < r.end(); fidx_in_set++) {
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auto fidx = features_set[fidx_in_set];
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bool is_cat = common::IsCat(feature_types, fidx);
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if (!interaction_constraints_.Query(nidx, fidx)) {
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continue;
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}
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if (is_cat) {
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auto n_bins = cut_ptrs.at(fidx + 1) - cut_ptrs[fidx];
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if (common::UseOneHot(n_bins, param_.max_cat_to_onehot, task_)) {
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EnumerateSplit<+1, kOneHot>(cut, {}, histogram, fidx, nidx, evaluator, best);
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EnumerateSplit<-1, kOneHot>(cut, {}, histogram, fidx, nidx, evaluator, best);
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} else {
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std::vector<size_t> sorted_idx(n_bins);
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std::iota(sorted_idx.begin(), sorted_idx.end(), 0);
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auto feat_hist = histogram.subspan(cut_ptrs[fidx], n_bins);
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// Sort the histogram to get contiguous partitions.
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std::stable_sort(sorted_idx.begin(), sorted_idx.end(), [&](size_t l, size_t r) {
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auto ret = evaluator.CalcWeightCat(param_, feat_hist[l]) <
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evaluator.CalcWeightCat(param_, feat_hist[r]);
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return ret;
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});
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EnumerateSplit<+1, kPart>(cut, sorted_idx, histogram, fidx, nidx, evaluator, best);
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EnumerateSplit<-1, kPart>(cut, sorted_idx, histogram, fidx, nidx, evaluator, best);
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}
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} else {
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auto grad_stats =
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EnumerateSplit<+1, kNum>(cut, {}, histogram, fidx, nidx, evaluator, best);
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if (SplitContainsMissingValues(grad_stats, snode_[nidx])) {
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EnumerateSplit<-1, kNum>(cut, {}, histogram, fidx, nidx, evaluator, best);
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}
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}
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}
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});
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for (unsigned nidx_in_set = 0; nidx_in_set < entries.size();
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++nidx_in_set) {
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for (auto tidx = 0; tidx < n_threads_; ++tidx) {
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entries[nidx_in_set].split.Update(
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tloc_candidates[n_threads_ * nidx_in_set + tidx].split);
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}
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}
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}
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// Add splits to tree, handles all statistic
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void ApplyTreeSplit(ExpandEntry const& candidate, RegTree *p_tree) {
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auto evaluator = tree_evaluator_.GetEvaluator();
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RegTree &tree = *p_tree;
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GradStats parent_sum = candidate.split.left_sum;
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parent_sum.Add(candidate.split.right_sum);
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auto base_weight =
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evaluator.CalcWeight(candidate.nid, param_, GradStats{parent_sum});
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auto left_weight =
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evaluator.CalcWeight(candidate.nid, param_, GradStats{candidate.split.left_sum});
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auto right_weight =
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evaluator.CalcWeight(candidate.nid, param_, GradStats{candidate.split.right_sum});
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if (candidate.split.is_cat) {
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std::vector<uint32_t> split_cats;
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if (candidate.split.cat_bits.empty()) {
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if (common::InvalidCat(candidate.split.split_value)) {
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common::InvalidCategory();
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}
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auto cat = common::AsCat(candidate.split.split_value);
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split_cats.resize(LBitField32::ComputeStorageSize(std::max(cat + 1, 1)), 0);
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LBitField32 cat_bits;
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cat_bits = LBitField32(split_cats);
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cat_bits.Set(cat);
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} else {
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split_cats = candidate.split.cat_bits;
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common::CatBitField cat_bits{split_cats};
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}
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tree.ExpandCategorical(
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candidate.nid, candidate.split.SplitIndex(), split_cats, candidate.split.DefaultLeft(),
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base_weight, left_weight * param_.learning_rate, right_weight * param_.learning_rate,
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candidate.split.loss_chg, parent_sum.GetHess(), candidate.split.left_sum.GetHess(),
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candidate.split.right_sum.GetHess());
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} else {
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tree.ExpandNode(candidate.nid, candidate.split.SplitIndex(), candidate.split.split_value,
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candidate.split.DefaultLeft(), base_weight,
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left_weight * param_.learning_rate, right_weight * param_.learning_rate,
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candidate.split.loss_chg, parent_sum.GetHess(),
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candidate.split.left_sum.GetHess(), candidate.split.right_sum.GetHess());
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}
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// Set up child constraints
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auto left_child = tree[candidate.nid].LeftChild();
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auto right_child = tree[candidate.nid].RightChild();
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tree_evaluator_.AddSplit(candidate.nid, left_child, right_child,
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tree[candidate.nid].SplitIndex(), left_weight,
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right_weight);
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auto max_node = std::max(left_child, tree[candidate.nid].RightChild());
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max_node = std::max(candidate.nid, max_node);
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snode_.resize(tree.GetNodes().size());
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snode_.at(left_child).stats = candidate.split.left_sum;
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snode_.at(left_child).root_gain = evaluator.CalcGain(
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candidate.nid, param_, GradStats{candidate.split.left_sum});
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snode_.at(right_child).stats = candidate.split.right_sum;
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snode_.at(right_child).root_gain = evaluator.CalcGain(
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candidate.nid, param_, GradStats{candidate.split.right_sum});
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interaction_constraints_.Split(candidate.nid,
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tree[candidate.nid].SplitIndex(), left_child,
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right_child);
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}
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auto Evaluator() const { return tree_evaluator_.GetEvaluator(); }
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auto const& Stats() const { return snode_; }
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auto Task() const { return task_; }
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float InitRoot(GradStats const& root_sum) {
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snode_.resize(1);
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auto root_evaluator = tree_evaluator_.GetEvaluator();
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snode_[0].stats = GradStats{root_sum.GetGrad(), root_sum.GetHess()};
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snode_[0].root_gain = root_evaluator.CalcGain(RegTree::kRoot, param_,
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GradStats{snode_[0].stats});
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auto weight = root_evaluator.CalcWeight(RegTree::kRoot, param_,
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GradStats{snode_[0].stats});
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return weight;
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}
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public:
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// The column sampler must be constructed by caller since we need to preserve the rng
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// for the entire training session.
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explicit HistEvaluator(TrainParam const ¶m, MetaInfo const &info, int32_t n_threads,
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std::shared_ptr<common::ColumnSampler> sampler, ObjInfo task,
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bool skip_0_index = false)
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: param_{param},
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column_sampler_{std::move(sampler)},
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tree_evaluator_{param, static_cast<bst_feature_t>(info.num_col_), GenericParameter::kCpuId},
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n_threads_{n_threads},
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task_{task} {
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interaction_constraints_.Configure(param, info.num_col_);
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column_sampler_->Init(info.num_col_, info.feature_weights.HostVector(),
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param_.colsample_bynode, param_.colsample_bylevel,
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param_.colsample_bytree, skip_0_index);
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}
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};
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} // namespace tree
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} // namespace xgboost
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#endif // XGBOOST_TREE_HIST_EVALUATE_SPLITS_H_
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