290 lines
12 KiB
Plaintext
290 lines
12 KiB
Plaintext
/*!
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* Copyright 2020-2022 by XGBoost contributors
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*/
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#include <gtest/gtest.h>
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#include "../../../../src/tree/gpu_hist/evaluate_splits.cuh"
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#include "../../helpers.h"
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#include "../../histogram_helpers.h"
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#include "../test_evaluate_splits.h" // TestPartitionBasedSplit
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namespace xgboost {
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namespace tree {
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namespace {
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auto ZeroParam() {
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auto args = Args{{"min_child_weight", "0"},
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{"lambda", "0"}};
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TrainParam tparam;
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tparam.UpdateAllowUnknown(args);
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return tparam;
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}
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} // anonymous namespace
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void TestEvaluateSingleSplit(bool is_categorical) {
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GradientPairPrecise parent_sum(0.0, 1.0);
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TrainParam tparam = ZeroParam();
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GPUTrainingParam param{tparam};
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common::HistogramCuts cuts;
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cuts.cut_values_.HostVector() = std::vector<float>{1.0, 2.0, 11.0, 12.0};
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cuts.cut_ptrs_.HostVector() = std::vector<uint32_t>{0, 2, 4};
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cuts.min_vals_.HostVector() = std::vector<float>{0.0, 0.0};
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cuts.cut_ptrs_.SetDevice(0);
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cuts.cut_values_.SetDevice(0);
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cuts.min_vals_.SetDevice(0);
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thrust::device_vector<bst_feature_t> feature_set =
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std::vector<bst_feature_t>{0, 1};
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// Setup gradients so that second feature gets higher gain
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thrust::device_vector<GradientPair> feature_histogram =
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std::vector<GradientPair>{
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{-0.5, 0.5}, {0.5, 0.5}, {-1.0, 0.5}, {1.0, 0.5}};
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thrust::device_vector<int> monotonic_constraints(feature_set.size(), 0);
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dh::device_vector<FeatureType> feature_types(feature_set.size(),
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FeatureType::kCategorical);
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common::Span<FeatureType> d_feature_types;
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if (is_categorical) {
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auto max_cat = *std::max_element(cuts.cut_values_.HostVector().begin(),
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cuts.cut_values_.HostVector().end());
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cuts.SetCategorical(true, max_cat);
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d_feature_types = dh::ToSpan(feature_types);
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}
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EvaluateSplitInputs<GradientPair> input{1,
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parent_sum,
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param,
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dh::ToSpan(feature_set),
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d_feature_types,
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cuts.cut_ptrs_.ConstDeviceSpan(),
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cuts.cut_values_.ConstDeviceSpan(),
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cuts.min_vals_.ConstDeviceSpan(),
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dh::ToSpan(feature_histogram)};
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GPUHistEvaluator<GradientPair> evaluator{
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tparam, static_cast<bst_feature_t>(feature_set.size()), 0};
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evaluator.Reset(cuts, dh::ToSpan(feature_types), feature_set.size(), tparam, 0);
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DeviceSplitCandidate result =
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evaluator.EvaluateSingleSplit(input, 0).split;
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EXPECT_EQ(result.findex, 1);
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EXPECT_EQ(result.fvalue, 11.0);
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EXPECT_FLOAT_EQ(result.left_sum.GetGrad() + result.right_sum.GetGrad(),
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parent_sum.GetGrad());
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EXPECT_FLOAT_EQ(result.left_sum.GetHess() + result.right_sum.GetHess(),
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parent_sum.GetHess());
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}
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TEST(GpuHist, EvaluateSingleSplit) {
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TestEvaluateSingleSplit(false);
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}
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TEST(GpuHist, EvaluateCategoricalSplit) {
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TestEvaluateSingleSplit(true);
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}
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TEST(GpuHist, EvaluateSingleSplitMissing) {
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GradientPairPrecise parent_sum(1.0, 1.5);
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TrainParam tparam = ZeroParam();
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GPUTrainingParam param{tparam};
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thrust::device_vector<bst_feature_t> feature_set =
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std::vector<bst_feature_t>{0};
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thrust::device_vector<uint32_t> feature_segments =
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std::vector<bst_row_t>{0, 2};
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thrust::device_vector<float> feature_values = std::vector<float>{1.0, 2.0};
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thrust::device_vector<float> feature_min_values = std::vector<float>{0.0};
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thrust::device_vector<GradientPair> feature_histogram =
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std::vector<GradientPair>{{-0.5, 0.5}, {0.5, 0.5}};
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thrust::device_vector<int> monotonic_constraints(feature_set.size(), 0);
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EvaluateSplitInputs<GradientPair> input{1,
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parent_sum,
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param,
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dh::ToSpan(feature_set),
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{},
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dh::ToSpan(feature_segments),
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dh::ToSpan(feature_values),
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dh::ToSpan(feature_min_values),
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dh::ToSpan(feature_histogram)};
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GPUHistEvaluator<GradientPair> evaluator(tparam, feature_set.size(), 0);
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DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, 0).split;
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EXPECT_EQ(result.findex, 0);
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EXPECT_EQ(result.fvalue, 1.0);
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EXPECT_EQ(result.dir, kRightDir);
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EXPECT_EQ(result.left_sum, GradientPairPrecise(-0.5, 0.5));
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EXPECT_EQ(result.right_sum, GradientPairPrecise(1.5, 1.0));
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}
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TEST(GpuHist, EvaluateSingleSplitEmpty) {
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TrainParam tparam = ZeroParam();
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GPUHistEvaluator<GradientPair> evaluator(tparam, 1, 0);
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DeviceSplitCandidate result =
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evaluator.EvaluateSingleSplit(EvaluateSplitInputs<GradientPair>{}, 0).split;
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EXPECT_EQ(result.findex, -1);
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EXPECT_LT(result.loss_chg, 0.0f);
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}
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// Feature 0 has a better split, but the algorithm must select feature 1
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TEST(GpuHist, EvaluateSingleSplitFeatureSampling) {
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GradientPairPrecise parent_sum(0.0, 1.0);
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TrainParam tparam = ZeroParam();
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tparam.UpdateAllowUnknown(Args{});
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GPUTrainingParam param{tparam};
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thrust::device_vector<bst_feature_t> feature_set =
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std::vector<bst_feature_t>{1};
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thrust::device_vector<uint32_t> feature_segments =
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std::vector<bst_row_t>{0, 2, 4};
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thrust::device_vector<float> feature_values =
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std::vector<float>{1.0, 2.0, 11.0, 12.0};
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thrust::device_vector<float> feature_min_values =
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std::vector<float>{0.0, 10.0};
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thrust::device_vector<GradientPair> feature_histogram =
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std::vector<GradientPair>{
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{-10.0, 0.5}, {10.0, 0.5}, {-0.5, 0.5}, {0.5, 0.5}};
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thrust::device_vector<int> monotonic_constraints(2, 0);
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EvaluateSplitInputs<GradientPair> input{1,
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parent_sum,
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param,
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dh::ToSpan(feature_set),
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{},
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dh::ToSpan(feature_segments),
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dh::ToSpan(feature_values),
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dh::ToSpan(feature_min_values),
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dh::ToSpan(feature_histogram)};
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GPUHistEvaluator<GradientPair> evaluator(tparam, feature_min_values.size(), 0);
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DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, 0).split;
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EXPECT_EQ(result.findex, 1);
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EXPECT_EQ(result.fvalue, 11.0);
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EXPECT_EQ(result.left_sum, GradientPairPrecise(-0.5, 0.5));
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EXPECT_EQ(result.right_sum, GradientPairPrecise(0.5, 0.5));
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}
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// Features 0 and 1 have identical gain, the algorithm must select 0
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TEST(GpuHist, EvaluateSingleSplitBreakTies) {
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GradientPairPrecise parent_sum(0.0, 1.0);
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TrainParam tparam = ZeroParam();
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tparam.UpdateAllowUnknown(Args{});
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GPUTrainingParam param{tparam};
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thrust::device_vector<bst_feature_t> feature_set =
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std::vector<bst_feature_t>{0, 1};
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thrust::device_vector<uint32_t> feature_segments =
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std::vector<bst_row_t>{0, 2, 4};
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thrust::device_vector<float> feature_values =
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std::vector<float>{1.0, 2.0, 11.0, 12.0};
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thrust::device_vector<float> feature_min_values =
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std::vector<float>{0.0, 10.0};
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thrust::device_vector<GradientPair> feature_histogram =
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std::vector<GradientPair>{
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{-0.5, 0.5}, {0.5, 0.5}, {-0.5, 0.5}, {0.5, 0.5}};
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thrust::device_vector<int> monotonic_constraints(2, 0);
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EvaluateSplitInputs<GradientPair> input{1,
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parent_sum,
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param,
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dh::ToSpan(feature_set),
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{},
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dh::ToSpan(feature_segments),
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dh::ToSpan(feature_values),
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dh::ToSpan(feature_min_values),
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dh::ToSpan(feature_histogram)};
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GPUHistEvaluator<GradientPair> evaluator(tparam, feature_min_values.size(), 0);
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DeviceSplitCandidate result = evaluator.EvaluateSingleSplit(input, 0).split;
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EXPECT_EQ(result.findex, 0);
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EXPECT_EQ(result.fvalue, 1.0);
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}
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TEST(GpuHist, EvaluateSplits) {
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thrust::device_vector<DeviceSplitCandidate> out_splits(2);
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GradientPairPrecise parent_sum(0.0, 1.0);
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TrainParam tparam = ZeroParam();
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tparam.UpdateAllowUnknown(Args{});
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GPUTrainingParam param{tparam};
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thrust::device_vector<bst_feature_t> feature_set =
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std::vector<bst_feature_t>{0, 1};
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thrust::device_vector<uint32_t> feature_segments =
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std::vector<bst_row_t>{0, 2, 4};
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thrust::device_vector<float> feature_values =
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std::vector<float>{1.0, 2.0, 11.0, 12.0};
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thrust::device_vector<float> feature_min_values =
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std::vector<float>{0.0, 0.0};
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thrust::device_vector<GradientPair> feature_histogram_left =
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std::vector<GradientPair>{
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{-0.5, 0.5}, {0.5, 0.5}, {-1.0, 0.5}, {1.0, 0.5}};
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thrust::device_vector<GradientPair> feature_histogram_right =
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std::vector<GradientPair>{
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{-1.0, 0.5}, {1.0, 0.5}, {-0.5, 0.5}, {0.5, 0.5}};
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thrust::device_vector<int> monotonic_constraints(feature_set.size(), 0);
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EvaluateSplitInputs<GradientPair> input_left{
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1,
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parent_sum,
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param,
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dh::ToSpan(feature_set),
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{},
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dh::ToSpan(feature_segments),
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dh::ToSpan(feature_values),
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dh::ToSpan(feature_min_values),
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dh::ToSpan(feature_histogram_left)};
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EvaluateSplitInputs<GradientPair> input_right{
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2,
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parent_sum,
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param,
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dh::ToSpan(feature_set),
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{},
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dh::ToSpan(feature_segments),
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dh::ToSpan(feature_values),
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dh::ToSpan(feature_min_values),
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dh::ToSpan(feature_histogram_right)};
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GPUHistEvaluator<GradientPair> evaluator{
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tparam, static_cast<bst_feature_t>(feature_min_values.size()), 0};
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evaluator.EvaluateSplits(input_left, input_right, evaluator.GetEvaluator(),
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dh::ToSpan(out_splits));
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DeviceSplitCandidate result_left = out_splits[0];
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EXPECT_EQ(result_left.findex, 1);
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EXPECT_EQ(result_left.fvalue, 11.0);
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DeviceSplitCandidate result_right = out_splits[1];
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EXPECT_EQ(result_right.findex, 0);
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EXPECT_EQ(result_right.fvalue, 1.0);
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}
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TEST_F(TestPartitionBasedSplit, GpuHist) {
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dh::device_vector<FeatureType> ft{std::vector<FeatureType>{FeatureType::kCategorical}};
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GPUHistEvaluator<GradientPairPrecise> evaluator{param_,
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static_cast<bst_feature_t>(info_.num_col_), 0};
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cuts_.cut_ptrs_.SetDevice(0);
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cuts_.cut_values_.SetDevice(0);
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cuts_.min_vals_.SetDevice(0);
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evaluator.Reset(cuts_, dh::ToSpan(ft), info_.num_col_, param_, 0);
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dh::device_vector<GradientPairPrecise> d_hist(hist_[0].size());
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auto node_hist = hist_[0];
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dh::safe_cuda(cudaMemcpy(d_hist.data().get(), node_hist.data(), node_hist.size_bytes(),
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cudaMemcpyHostToDevice));
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dh::device_vector<bst_feature_t> feature_set{std::vector<bst_feature_t>{0}};
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EvaluateSplitInputs<GradientPairPrecise> input{0,
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total_gpair_,
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GPUTrainingParam{param_},
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dh::ToSpan(feature_set),
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dh::ToSpan(ft),
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cuts_.cut_ptrs_.ConstDeviceSpan(),
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cuts_.cut_values_.ConstDeviceSpan(),
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cuts_.min_vals_.ConstDeviceSpan(),
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dh::ToSpan(d_hist)};
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auto split = evaluator.EvaluateSingleSplit(input, 0).split;
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ASSERT_NEAR(split.loss_chg, best_score_, 1e-16);
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}
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} // namespace tree
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} // namespace xgboost
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