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Prevent empty quantiles in fast hist (#4155)

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* Prevent empty quantiles

* Revise and improve unit tests for quantile hist

* Remove unnecessary comment

* Add #2943 as a test case

* Skip test if no sklearn

* Revise misleading comments
This commit is contained in:
Philip Hyunsu Cho 2019-02-17 16:01:07 -08:00 committed by GitHub
parent e1240413c9
commit 549c8d6ae9
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3 changed files with 179 additions and 39 deletions
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21
src/common/hist_util.cc
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@ -148,14 +148,17 @@ void HistCutMatrix::Init
}
}
// push a value that is greater than anything
if (a.size != 0) {
bst_float cpt = a.data[a.size - 1].value;
// this must be bigger than last value in a scale
bst_float last = cpt + (fabs(cpt) + 1e-5);
cut.push_back(last);
}
const bst_float cpt
= (a.size > 0) ? a.data[a.size - 1].value : this->min_val[fid];
// this must be bigger than last value in a scale
const bst_float last = cpt + (fabs(cpt) + 1e-5);
cut.push_back(last);
row_ptr.push_back(static_cast<bst_uint>(cut.size()));
// Ensure that every feature gets at least one quantile point
CHECK_LE(cut.size(), std::numeric_limits<uint32_t>::max());
auto cut_size = static_cast<uint32_t>(cut.size());
CHECK_GT(cut_size, row_ptr.back());
row_ptr.push_back(cut_size);
}
}
@ -165,7 +168,9 @@ uint32_t HistCutMatrix::GetBinIdx(const Entry& e) {
auto cend = cut.begin() + row_ptr[fid + 1];
CHECK(cbegin != cend);
auto it = std::upper_bound(cbegin, cend, e.fvalue);
if (it == cend) it = cend - 1;
if (it == cend) {
it = cend - 1;
}
uint32_t idx = static_cast<uint32_t>(it - cut.begin());
return idx;
}

182
tests/cpp/tree/test_quantile_hist.cc
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@ -4,11 +4,13 @@
#include "../helpers.h"
#include "../../../src/tree/param.h"
#include "../../../src/tree/updater_quantile_hist.h"
#include "../../../src/tree/split_evaluator.h"
#include "../../../src/common/host_device_vector.h"
#include <xgboost/tree_updater.h>
#include <gtest/gtest.h>
#include <algorithm>
#include <vector>
#include <string>
@ -22,43 +24,105 @@ class QuantileHistMock : public QuantileHistMaker {
using RealImpl = QuantileHistMaker::Builder;
BuilderMock(const TrainParam& param,
std::unique_ptr<TreeUpdater> pruner,
std::unique_ptr<SplitEvaluator> spliteval)
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);
const std::vector<GradientPair>& gpair,
DMatrix* p_fmat,
const RegTree& tree) {
RealImpl::InitData(gmat, gpair, *p_fmat, tree);
ASSERT_EQ(data_layout_, kSparseData);
/* The creation of HistCutMatrix and GHistIndexMatrix are not technically
* part of QuantileHist updater logic, but we include it here because
* QuantileHist updater object currently stores GHistIndexMatrix
* internally. According to https://github.com/dmlc/xgboost/pull/3803,
* we should eventually move GHistIndexMatrix out of the QuantileHist
* updater. */
const size_t num_row = p_fmat->Info().num_row_;
const size_t num_col = p_fmat->Info().num_col_;
/* Validate HistCutMatrix */
ASSERT_EQ(gmat.cut.row_ptr.size(), num_col + 1);
for (size_t fid = 0; fid < num_col; ++fid) {
// Each feature must have at least one quantile point (cut)
const size_t ibegin = gmat.cut.row_ptr[fid];
const size_t iend = gmat.cut.row_ptr[fid + 1];
ASSERT_LT(ibegin, iend);
for (size_t i = ibegin; i < iend - 1; ++i) {
// Quantile points must be sorted in ascending order
// No duplicates allowed
ASSERT_LT(gmat.cut.cut[i], gmat.cut.cut[i + 1]);
}
}
/* Validate GHistIndexMatrix */
ASSERT_EQ(gmat.row_ptr.size(), num_row + 1);
ASSERT_LT(*std::max_element(gmat.index.begin(), gmat.index.end()),
gmat.cut.row_ptr.back());
for (const auto& batch : p_fmat->GetRowBatches()) {
for (size_t i = 0; i < batch.Size(); ++i) {
const size_t rid = batch.base_rowid + i;
ASSERT_LT(rid, num_row);
const size_t gmat_row_offset = gmat.row_ptr[rid];
ASSERT_LT(gmat_row_offset, gmat.index.size());
SparsePage::Inst inst = batch[i];
ASSERT_EQ(gmat.row_ptr[rid] + inst.size(), gmat.row_ptr[rid + 1]);
for (size_t j = 0; j < inst.size(); ++j) {
// Each entry of GHistIndexMatrix represents a bin ID
const size_t bin_id = gmat.index[gmat_row_offset + j];
const size_t fid = inst[j].index;
// The bin ID must correspond to correct feature
ASSERT_GE(bin_id, gmat.cut.row_ptr[fid]);
ASSERT_LT(bin_id, gmat.cut.row_ptr[fid + 1]);
// The bin ID must correspond to a region between two
// suitable quantile points
ASSERT_LT(inst[j].fvalue, gmat.cut.cut[bin_id]);
if (bin_id > gmat.cut.row_ptr[fid]) {
ASSERT_GE(inst[j].fvalue, gmat.cut.cut[bin_id - 1]);
} else {
ASSERT_GE(inst[j].fvalue, gmat.cut.min_val[fid]);
}
}
}
}
}
void TestBuildHist(int nid,
const GHistIndexMatrix& gmat,
const DMatrix& fmat,
const RegTree& tree) {
std::vector<GradientPair> gpair =
const 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;
GHistIndexBlockMatrix dummy;
hist_.AddHistRow(nid);
BuildHist(gpair, row_set_collection_[nid],
gmat, quantile_index_block, hist_[nid], false);
std::vector<GradientPairPrecise> solution {
{0.27f, 0.29f}, {0.27f, 0.29f}, {0.47f, 0.49f},
{0.27f, 0.29f}, {0.57f, 0.59f}, {0.26f, 0.27f},
{0.37f, 0.39f}, {0.23f, 0.24f}, {0.37f, 0.39f},
{0.27f, 0.28f}, {0.27f, 0.29f}, {0.37f, 0.39f},
{0.26f, 0.27f}, {0.23f, 0.24f}, {0.57f, 0.59f},
{0.47f, 0.49f}, {0.47f, 0.49f}, {0.37f, 0.39f},
{0.26f, 0.27f}, {0.23f, 0.24f}, {0.27f, 0.28f},
{0.57f, 0.59f}, {0.23f, 0.24f}, {0.47f, 0.49f}};
gmat, dummy, hist_[nid], false);
// Check if number of histogram bins is correct
ASSERT_EQ(hist_[nid].size(), gmat.cut.row_ptr.back());
std::vector<GradientPairPrecise> histogram_expected(hist_[nid].size());
// Compute the correct histogram (histogram_expected)
const size_t num_row = fmat.Info().num_row_;
CHECK_EQ(gpair.size(), num_row);
for (size_t rid = 0; rid < num_row; ++rid) {
const size_t ibegin = gmat.row_ptr[rid];
const size_t iend = gmat.row_ptr[rid + 1];
for (size_t i = ibegin; i < iend; ++i) {
const size_t bin_id = gmat.index[i];
histogram_expected[bin_id] += GradientPairPrecise(gpair[rid]);
}
}
// Now validate the computed histogram returned by BuildHist
for (size_t i = 0; i < hist_[nid].size(); ++i) {
GradientPairPrecise sol = solution[i];
GradientPairPrecise sol = histogram_expected[i];
ASSERT_NEAR(sol.GetGrad(), hist_[nid][i].GetGrad(), kEps);
ASSERT_NEAR(sol.GetHess(), hist_[nid][i].GetHess(), kEps);
}
@ -67,10 +131,11 @@ class QuantileHistMock : public QuantileHistMaker {
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} };
{ {1.23f, 0.24f}, {0.24f, 0.25f}, {0.26f, 0.27f}, {2.27f, 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
auto dmat = CreateDMatrix(n_rows, n_cols, 0, 3);
// dense, no missing values
common::GHistIndexMatrix gmat;
gmat.Init((*dmat).get(), max_bins);
@ -82,14 +147,67 @@ class QuantileHistMock : public QuantileHistMaker {
gmat, quantile_index_block, hist_[0], false);
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);
/* Compute correct split (best_split) using the computed histogram */
const size_t num_row = dmat->get()->Info().num_row_;
const size_t num_feature = dmat->get()->Info().num_col_;
CHECK_EQ(num_row, row_gpairs.size());
// Compute total gradient for all data points
GradientPairPrecise total_gpair;
for (const auto& e : row_gpairs) {
total_gpair += GradientPairPrecise(e);
}
// Initialize split evaluator
std::unique_ptr<SplitEvaluator> evaluator(SplitEvaluator::Create("elastic_net"));
evaluator->Init({});
// Now enumerate all feature*threshold combination to get best split
// To simplify logic, we make some assumptions:
// 1) no missing values in data
// 2) no regularization, i.e. set min_child_weight, reg_lambda, reg_alpha,
// and max_delta_step to 0.
bst_float best_split_gain = 0.0f;
size_t best_split_threshold, best_split_feature;
// Enumerate all features
for (size_t fid = 0; fid < num_feature; ++fid) {
const size_t bin_id_min = gmat.cut.row_ptr[fid];
const size_t bin_id_max = gmat.cut.row_ptr[fid + 1];
// Enumerate all bin ID in [bin_id_min, bin_id_max), i.e. every possible
// choice of thresholds for feature fid
for (size_t split_thresh = bin_id_min;
split_thresh < bin_id_max; ++split_thresh) {
// left_sum, right_sum: Gradient sums for data points whose feature
// value is left/right side of the split threshold
GradientPairPrecise left_sum, right_sum;
for (size_t rid = 0; rid < num_row; ++rid) {
for (size_t offset = gmat.row_ptr[rid];
offset < gmat.row_ptr[rid + 1]; ++offset) {
const size_t bin_id = gmat.index[offset];
if (bin_id >= bin_id_min && bin_id < bin_id_max) {
if (bin_id <= split_thresh) {
left_sum += GradientPairPrecise(row_gpairs[rid]);
} else {
right_sum += GradientPairPrecise(row_gpairs[rid]);
}
}
}
}
// Now compute gain (change in loss)
const auto split_gain
= evaluator->ComputeSplitScore(0, fid, GradStats(left_sum),
GradStats(right_sum));
if (split_gain > best_split_gain) {
best_split_gain = split_gain;
best_split_feature = fid;
best_split_threshold = split_thresh;
}
}
}
/* Now compare against result given by EvaluateSplit() */
RealImpl::EvaluateSplit(0, gmat, hist_, *(*dmat), tree);
ASSERT_EQ(snode_[0].best.SplitIndex(), best_split_feature);
ASSERT_EQ(snode_[0].best.split_value, gmat.cut.cut[best_split_threshold]);
delete dmat;
}
@ -128,7 +246,7 @@ class QuantileHistMock : public QuantileHistMaker {
{ {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);
builder_->TestInitData(gmat, gpair, dmat->get(), tree);
}
void TestBuildHist() {
@ -169,7 +287,9 @@ TEST(Updater, QuantileHist_BuildHist) {
TEST(Updater, QuantileHist_EvalSplits) {
std::vector<std::pair<std::string, std::string>> cfg
{{"num_feature", std::to_string(QuantileHistMock::GetNumColumns())},
{"split_evaluator", "elastic_net"}};
{"split_evaluator", "elastic_net"},
{"reg_lambda", "0"}, {"reg_alpha", "0"}, {"max_delta_step", "0"},
{"min_child_weight", "0"}};
QuantileHistMock maker(cfg);
maker.TestEvaluateSplit();
}

15
tests/python/test_updaters.py
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@ -2,6 +2,7 @@ import testing as tm
import unittest
import pytest
import xgboost as xgb
import numpy as np
try:
from regression_test_utilities import run_suite, parameter_combinations, \
@ -59,3 +60,17 @@ class TestUpdaters(unittest.TestCase):
evals_result=exact_res)
assert hist_res['train']['auc'] == exact_res['train']['auc']
assert hist_res['test']['auc'] == exact_res['test']['auc']
@pytest.mark.skipif(**tm.no_sklearn())
def test_fast_histmaker_degenerate_case(self):
# Test a degenerate case where the quantile sketcher won't return any
# quantile points for a particular feature (the second feature in
# this example). Source: https://github.com/dmlc/xgboost/issues/2943
nan = np.nan
param = {'missing': nan, 'tree_method': 'hist'}
model = xgb.XGBRegressor(**param)
X = [[6.18827160e+05, 1.73000000e+02], [6.37345679e+05, nan],
[6.38888889e+05, nan], [6.28086420e+05, nan]]
y = [1000000., 0., 0., 500000.]
w = [0, 0, 1, 0]
model.fit(X, y, sample_weight=w)