267 lines
11 KiB
Plaintext
267 lines
11 KiB
Plaintext
#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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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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thrust::device_vector<DeviceSplitCandidate> out_splits(1);
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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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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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// 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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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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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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TreeEvaluator tree_evaluator(tparam, feature_min_values.size(), 0);
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auto evaluator = tree_evaluator.GetEvaluator<GPUTrainingParam>();
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EvaluateSingleSplit(dh::ToSpan(out_splits), evaluator, input);
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DeviceSplitCandidate result = out_splits[0];
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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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thrust::device_vector<DeviceSplitCandidate> out_splits(1);
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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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TreeEvaluator tree_evaluator(tparam, feature_set.size(), 0);
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auto evaluator = tree_evaluator.GetEvaluator<GPUTrainingParam>();
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EvaluateSingleSplit(dh::ToSpan(out_splits), evaluator, input);
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DeviceSplitCandidate result = out_splits[0];
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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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DeviceSplitCandidate nonzeroed;
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nonzeroed.findex = 1;
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nonzeroed.loss_chg = 1.0;
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thrust::device_vector<DeviceSplitCandidate> out_split(1);
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out_split[0] = nonzeroed;
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TrainParam tparam = ZeroParam();
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TreeEvaluator tree_evaluator(tparam, 1, 0);
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auto evaluator = tree_evaluator.GetEvaluator<GPUTrainingParam>();
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EvaluateSingleSplit(dh::ToSpan(out_split), evaluator,
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EvaluateSplitInputs<GradientPair>{});
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DeviceSplitCandidate result = out_split[0];
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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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thrust::device_vector<DeviceSplitCandidate> out_splits(1);
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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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TreeEvaluator tree_evaluator(tparam, feature_min_values.size(), 0);
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auto evaluator = tree_evaluator.GetEvaluator<GPUTrainingParam>();
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EvaluateSingleSplit(dh::ToSpan(out_splits), evaluator, input);
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DeviceSplitCandidate result = out_splits[0];
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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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thrust::device_vector<DeviceSplitCandidate> out_splits(1);
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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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TreeEvaluator tree_evaluator(tparam, feature_min_values.size(), 0);
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auto evaluator = tree_evaluator.GetEvaluator<GPUTrainingParam>();
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EvaluateSingleSplit(dh::ToSpan(out_splits), evaluator, input);
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DeviceSplitCandidate result = out_splits[0];
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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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TreeEvaluator tree_evaluator(tparam, feature_min_values.size(), 0);
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auto evaluator = tree_evaluator.GetEvaluator<GPUTrainingParam>();
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EvaluateSplits(dh::ToSpan(out_splits), evaluator, input_left, input_right);
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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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} // namespace tree
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} // namespace xgboost
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