Fix CPU hist init for sparse dataset. (#4625)
* Fix CPU hist init for sparse dataset. * Implement sparse histogram cut. * Allow empty features. * Fix windows build, don't use sparse in distributed environment. * Comments. * Smaller threshold. * Fix windows omp. * Fix msvc lambda capture. * Fix MSVC macro. * Fix MSVC initialization list. * Fix MSVC initialization list x2. * Preserve categorical feature behavior. * Rename matrix to sparse cuts. * Reuse UseGroup. * Check for categorical data when adding cut. Co-Authored-By: Philip Hyunsu Cho <chohyu01@cs.washington.edu> * Sanity check. * Fix comments. * Fix comment.
This commit is contained in:
Jiaming Yuan
committed by
Philip Hyunsu Cho
Philip Hyunsu Cho
parent
b7a1f22d24
commit
d9a47794a5
@@ -1,5 +1,5 @@
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/*!
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* Copyright 2018 by Contributors
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* Copyright 2018-2019 by Contributors
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*/
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#include "../helpers.h"
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#include "../../../src/tree/param.h"
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@@ -46,23 +46,25 @@ class QuantileHistMock : public QuantileHistMaker {
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const size_t num_row = p_fmat->Info().num_row_;
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const size_t num_col = p_fmat->Info().num_col_;
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/* Validate HistCutMatrix */
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ASSERT_EQ(gmat.cut.row_ptr.size(), num_col + 1);
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ASSERT_EQ(gmat.cut.Ptrs().size(), num_col + 1);
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for (size_t fid = 0; fid < num_col; ++fid) {
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// Each feature must have at least one quantile point (cut)
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const size_t ibegin = gmat.cut.row_ptr[fid];
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const size_t iend = gmat.cut.row_ptr[fid + 1];
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ASSERT_LT(ibegin, iend);
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const size_t ibegin = gmat.cut.Ptrs()[fid];
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const size_t iend = gmat.cut.Ptrs()[fid + 1];
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// Ordered, but empty feature is allowed.
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ASSERT_LE(ibegin, iend);
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for (size_t i = ibegin; i < iend - 1; ++i) {
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// Quantile points must be sorted in ascending order
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// No duplicates allowed
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ASSERT_LT(gmat.cut.cut[i], gmat.cut.cut[i + 1]);
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ASSERT_LT(gmat.cut.Values()[i], gmat.cut.Values()[i + 1])
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<< "ibegin: " << ibegin << ", "
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<< "iend: " << iend;
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}
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}
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/* Validate GHistIndexMatrix */
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ASSERT_EQ(gmat.row_ptr.size(), num_row + 1);
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ASSERT_LT(*std::max_element(gmat.index.begin(), gmat.index.end()),
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gmat.cut.row_ptr.back());
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gmat.cut.Ptrs().back());
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for (const auto& batch : p_fmat->GetRowBatches()) {
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for (size_t i = 0; i < batch.Size(); ++i) {
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const size_t rid = batch.base_rowid + i;
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@@ -71,20 +73,20 @@ class QuantileHistMock : public QuantileHistMaker {
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ASSERT_LT(gmat_row_offset, gmat.index.size());
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SparsePage::Inst inst = batch[i];
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ASSERT_EQ(gmat.row_ptr[rid] + inst.size(), gmat.row_ptr[rid + 1]);
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for (size_t j = 0; j < inst.size(); ++j) {
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for (int64_t j = 0; j < inst.size(); ++j) {
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// Each entry of GHistIndexMatrix represents a bin ID
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const size_t bin_id = gmat.index[gmat_row_offset + j];
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const size_t fid = inst[j].index;
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// The bin ID must correspond to correct feature
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ASSERT_GE(bin_id, gmat.cut.row_ptr[fid]);
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ASSERT_LT(bin_id, gmat.cut.row_ptr[fid + 1]);
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ASSERT_GE(bin_id, gmat.cut.Ptrs()[fid]);
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ASSERT_LT(bin_id, gmat.cut.Ptrs()[fid + 1]);
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// The bin ID must correspond to a region between two
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// suitable quantile points
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ASSERT_LT(inst[j].fvalue, gmat.cut.cut[bin_id]);
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if (bin_id > gmat.cut.row_ptr[fid]) {
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ASSERT_GE(inst[j].fvalue, gmat.cut.cut[bin_id - 1]);
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ASSERT_LT(inst[j].fvalue, gmat.cut.Values()[bin_id]);
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if (bin_id > gmat.cut.Ptrs()[fid]) {
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ASSERT_GE(inst[j].fvalue, gmat.cut.Values()[bin_id - 1]);
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} else {
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ASSERT_GE(inst[j].fvalue, gmat.cut.min_val[fid]);
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ASSERT_GE(inst[j].fvalue, gmat.cut.MinValues()[fid]);
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}
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}
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}
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@@ -106,11 +108,12 @@ class QuantileHistMock : public QuantileHistMaker {
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std::vector<std::vector<uint8_t>> hist_is_init;
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std::vector<ExpandEntry> nodes = {ExpandEntry(nid, -1, -1, tree.GetDepth(0), 0.0, 0)};
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BuildHistsBatch(nodes, const_cast<RegTree*>(&tree), gmat, gpair, &hist_buffers, &hist_is_init);
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RealImpl::InitNewNode(nid, gmat, gpair, fmat, const_cast<RegTree*>(&tree), &snode_[0], tree[0].Parent());
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RealImpl::InitNewNode(nid, gmat, gpair, fmat,
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const_cast<RegTree*>(&tree), &snode_[0], tree[0].Parent());
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EvaluateSplitsBatch(nodes, gmat, fmat, hist_is_init, hist_buffers);
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// Check if number of histogram bins is correct
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ASSERT_EQ(hist_[nid].size(), gmat.cut.row_ptr.back());
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ASSERT_EQ(hist_[nid].size(), gmat.cut.Ptrs().back());
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std::vector<GradientPairPrecise> histogram_expected(hist_[nid].size());
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// Compute the correct histogram (histogram_expected)
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@@ -126,7 +129,7 @@ class QuantileHistMock : public QuantileHistMaker {
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}
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// Now validate the computed histogram returned by BuildHist
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for (size_t i = 0; i < hist_[nid].size(); ++i) {
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for (int64_t i = 0; i < hist_[nid].size(); ++i) {
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GradientPairPrecise sol = histogram_expected[i];
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ASSERT_NEAR(sol.GetGrad(), hist_[nid][i].GetGrad(), kEps);
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ASSERT_NEAR(sol.GetHess(), hist_[nid][i].GetHess(), kEps);
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@@ -140,7 +143,7 @@ class QuantileHistMock : public QuantileHistMaker {
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{0.27f, 0.29f}, {0.37f, 0.39f}, {-0.47f, 0.49f}, {0.57f, 0.59f} };
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size_t constexpr kMaxBins = 4;
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auto dmat = CreateDMatrix(kNRows, kNCols, 0, 3);
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// dense, no missing values
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// dense, no missing values
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common::GHistIndexMatrix gmat;
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gmat.Init((*dmat).get(), kMaxBins);
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@@ -152,7 +155,8 @@ class QuantileHistMock : public QuantileHistMaker {
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std::vector<std::vector<float*>> hist_buffers;
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std::vector<std::vector<uint8_t>> hist_is_init;
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BuildHistsBatch(nodes, const_cast<RegTree*>(&tree), gmat, row_gpairs, &hist_buffers, &hist_is_init);
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RealImpl::InitNewNode(0, gmat, row_gpairs, *(*dmat), const_cast<RegTree*>(&tree), &snode_[0], tree[0].Parent());
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RealImpl::InitNewNode(0, gmat, row_gpairs, *(*dmat),
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const_cast<RegTree*>(&tree), &snode_[0], tree[0].Parent());
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EvaluateSplitsBatch(nodes, gmat, **dmat, hist_is_init, hist_buffers);
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/* Compute correct split (best_split) using the computed histogram */
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@@ -178,8 +182,8 @@ class QuantileHistMock : public QuantileHistMaker {
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size_t best_split_feature = std::numeric_limits<size_t>::max();
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// Enumerate all features
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for (size_t fid = 0; fid < num_feature; ++fid) {
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const size_t bin_id_min = gmat.cut.row_ptr[fid];
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const size_t bin_id_max = gmat.cut.row_ptr[fid + 1];
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const size_t bin_id_min = gmat.cut.Ptrs()[fid];
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const size_t bin_id_max = gmat.cut.Ptrs()[fid + 1];
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// Enumerate all bin ID in [bin_id_min, bin_id_max), i.e. every possible
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// choice of thresholds for feature fid
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for (size_t split_thresh = bin_id_min;
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@@ -217,7 +221,7 @@ class QuantileHistMock : public QuantileHistMaker {
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EvaluateSplitsBatch(nodes, gmat, **dmat, hist_is_init, hist_buffers);
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ASSERT_EQ(snode_[0].best.SplitIndex(), best_split_feature);
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ASSERT_EQ(snode_[0].best.split_value, gmat.cut.cut[best_split_threshold]);
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ASSERT_EQ(snode_[0].best.split_value, gmat.cut.Values()[best_split_threshold]);
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delete dmat;
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}
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