fdf533f2b9
Support adaptive tree, a feature supported by both sklearn and lightgbm. The tree leaf is recomputed based on residue of labels and predictions after construction. For l1 error, the optimal value is the median (50 percentile). This is marked as experimental support for the following reasons: - The value is not well defined for distributed training, where we might have empty leaves for local workers. Right now I just use the original leaf value for computing the average with other workers, which might cause significant errors. - Some follow-ups are required, for exact, pruner, and optimization for quantile function. Also, we need to calculate the initial estimation.
494 lines
19 KiB
C++
494 lines
19 KiB
C++
/*!
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* Copyright 2017-2022 XGBoost contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/generic_parameters.h>
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#include <xgboost/json.h>
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#include <xgboost/objective.h>
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#include "../../../src/objective/adaptive.h"
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#include "../helpers.h"
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namespace xgboost {
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TEST(Objective, DeclareUnifiedTest(LinearRegressionGPair)) {
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GenericParameter tparam = CreateEmptyGenericParam(GPUIDX);
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std::vector<std::pair<std::string, std::string>> args;
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std::unique_ptr<ObjFunction> obj {
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ObjFunction::Create("reg:squarederror", &tparam)
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};
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obj->Configure(args);
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CheckObjFunction(obj,
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{0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{0, 0, 0, 0, 1, 1, 1, 1},
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{1, 1, 1, 1, 1, 1, 1, 1},
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{0, 0.1f, 0.9f, 1.0f, -1.0f, -0.9f, -0.1f, 0},
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{1, 1, 1, 1, 1, 1, 1, 1});
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CheckObjFunction(obj,
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{0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{0, 0, 0, 0, 1, 1, 1, 1},
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{}, // empty weight
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{0, 0.1f, 0.9f, 1.0f, -1.0f, -0.9f, -0.1f, 0},
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{1, 1, 1, 1, 1, 1, 1, 1});
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ASSERT_NO_THROW(obj->DefaultEvalMetric());
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}
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TEST(Objective, DeclareUnifiedTest(SquaredLog)) {
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GenericParameter tparam = CreateEmptyGenericParam(GPUIDX);
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std::vector<std::pair<std::string, std::string>> args;
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std::unique_ptr<ObjFunction> obj { ObjFunction::Create("reg:squaredlogerror", &tparam) };
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obj->Configure(args);
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CheckConfigReload(obj, "reg:squaredlogerror");
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CheckObjFunction(obj,
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{0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
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{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
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{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // weights
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{-0.5435f, -0.4257f, -0.25475f, -0.05855f, 0.1009f},
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{ 1.3205f, 1.0492f, 0.69215f, 0.34115f, 0.1091f});
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CheckObjFunction(obj,
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{0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
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{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
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{}, // empty weights
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{-0.5435f, -0.4257f, -0.25475f, -0.05855f, 0.1009f},
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{ 1.3205f, 1.0492f, 0.69215f, 0.34115f, 0.1091f});
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ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"rmsle"});
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}
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TEST(Objective, DeclareUnifiedTest(PseudoHuber)) {
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GenericParameter tparam = CreateEmptyGenericParam(GPUIDX);
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Args args;
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std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:pseudohubererror", &tparam)};
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obj->Configure(args);
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CheckConfigReload(obj, "reg:pseudohubererror");
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CheckObjFunction(obj, {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
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{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
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{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // weights
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{-0.668965f, -0.624695f, -0.514496f, -0.196116f, 0.514496f}, // out_grad
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{0.410660f, 0.476140f, 0.630510f, 0.9428660f, 0.630510f}); // out_hess
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CheckObjFunction(obj, {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
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{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
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{}, // empty weights
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{-0.668965f, -0.624695f, -0.514496f, -0.196116f, 0.514496f}, // out_grad
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{0.410660f, 0.476140f, 0.630510f, 0.9428660f, 0.630510f}); // out_hess
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ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"mphe"});
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obj->Configure({{"huber_slope", "0.1"}});
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CheckConfigReload(obj, "reg:pseudohubererror");
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CheckObjFunction(obj, {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
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{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
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{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // weights
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{-0.099388f, -0.099228f, -0.098639f, -0.089443f, 0.098639f}, // out_grad
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{0.0013467f, 0.001908f, 0.004443f, 0.089443f, 0.004443f}); // out_hess
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}
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TEST(Objective, DeclareUnifiedTest(LogisticRegressionGPair)) {
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GenericParameter tparam = CreateEmptyGenericParam(GPUIDX);
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std::vector<std::pair<std::string, std::string>> args;
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std::unique_ptr<ObjFunction> obj { ObjFunction::Create("reg:logistic", &tparam) };
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obj->Configure(args);
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CheckConfigReload(obj, "reg:logistic");
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CheckObjFunction(obj,
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{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1}, // preds
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{ 0, 0, 0, 0, 1, 1, 1, 1}, // labels
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{ 1, 1, 1, 1, 1, 1, 1, 1}, // weights
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{ 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f}, // out_grad
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{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f}); // out_hess
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}
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TEST(Objective, DeclareUnifiedTest(LogisticRegressionBasic)) {
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GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
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std::vector<std::pair<std::string, std::string>> args;
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std::unique_ptr<ObjFunction> obj {
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ObjFunction::Create("reg:logistic", &lparam)
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};
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obj->Configure(args);
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CheckConfigReload(obj, "reg:logistic");
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// test label validation
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EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {10}, {1}, {0}, {0}))
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<< "Expected error when label not in range [0,1f] for LogisticRegression";
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// test ProbToMargin
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EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.197f, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.5f), 0, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.9f), 2.197f, 0.01f);
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EXPECT_ANY_THROW(obj->ProbToMargin(10))
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<< "Expected error when base_score not in range [0,1f] for LogisticRegression";
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// test PredTransform
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HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
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std::vector<bst_float> out_preds = {0.5f, 0.524f, 0.622f, 0.710f, 0.731f};
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obj->PredTransform(&io_preds);
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auto& preds = io_preds.HostVector();
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for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
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EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
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}
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}
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TEST(Objective, DeclareUnifiedTest(LogisticRawGPair)) {
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GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
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std::vector<std::pair<std::string, std::string>> args;
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std::unique_ptr<ObjFunction> obj {
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ObjFunction::Create("binary:logitraw", &lparam)
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};
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obj->Configure(args);
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CheckObjFunction(obj,
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{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{ 0, 0, 0, 0, 1, 1, 1, 1},
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{ 1, 1, 1, 1, 1, 1, 1, 1},
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{ 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f},
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{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f});
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}
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TEST(Objective, DeclareUnifiedTest(PoissonRegressionGPair)) {
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GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
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std::vector<std::pair<std::string, std::string>> args;
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std::unique_ptr<ObjFunction> obj {
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ObjFunction::Create("count:poisson", &lparam)
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};
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args.emplace_back(std::make_pair("max_delta_step", "0.1f"));
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obj->Configure(args);
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CheckObjFunction(obj,
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{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{ 0, 0, 0, 0, 1, 1, 1, 1},
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{ 1, 1, 1, 1, 1, 1, 1, 1},
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{ 1, 1.10f, 2.45f, 2.71f, 0, 0.10f, 1.45f, 1.71f},
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{1.10f, 1.22f, 2.71f, 3.00f, 1.10f, 1.22f, 2.71f, 3.00f});
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CheckObjFunction(obj,
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{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{ 0, 0, 0, 0, 1, 1, 1, 1},
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{}, // Empty weight
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{ 1, 1.10f, 2.45f, 2.71f, 0, 0.10f, 1.45f, 1.71f},
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{1.10f, 1.22f, 2.71f, 3.00f, 1.10f, 1.22f, 2.71f, 3.00f});
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}
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TEST(Objective, DeclareUnifiedTest(PoissonRegressionBasic)) {
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GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
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std::vector<std::pair<std::string, std::string>> args;
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std::unique_ptr<ObjFunction> obj {
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ObjFunction::Create("count:poisson", &lparam)
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};
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obj->Configure(args);
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CheckConfigReload(obj, "count:poisson");
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// test label validation
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EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {-1}, {1}, {0}, {0}))
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<< "Expected error when label < 0 for PoissonRegression";
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// test ProbToMargin
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EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.30f, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.5f), -0.69f, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.9f), -0.10f, 0.01f);
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// test PredTransform
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HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
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std::vector<bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
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obj->PredTransform(&io_preds);
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auto& preds = io_preds.HostVector();
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for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
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EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
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}
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}
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TEST(Objective, DeclareUnifiedTest(GammaRegressionGPair)) {
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GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
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std::vector<std::pair<std::string, std::string>> args;
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std::unique_ptr<ObjFunction> obj {
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ObjFunction::Create("reg:gamma", &lparam)
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};
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obj->Configure(args);
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CheckObjFunction(obj,
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{0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{2, 2, 2, 2, 1, 1, 1, 1},
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{1, 1, 1, 1, 1, 1, 1, 1},
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{-1, -0.809, 0.187, 0.264, 0, 0.09f, 0.59f, 0.63f},
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{2, 1.809, 0.813, 0.735, 1, 0.90f, 0.40f, 0.36f});
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CheckObjFunction(obj,
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{0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{2, 2, 2, 2, 1, 1, 1, 1},
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{}, // Empty weight
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{-1, -0.809, 0.187, 0.264, 0, 0.09f, 0.59f, 0.63f},
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{2, 1.809, 0.813, 0.735, 1, 0.90f, 0.40f, 0.36f});
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}
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TEST(Objective, DeclareUnifiedTest(GammaRegressionBasic)) {
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GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
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std::vector<std::pair<std::string, std::string>> args;
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std::unique_ptr<ObjFunction> obj {
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ObjFunction::Create("reg:gamma", &lparam)
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};
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obj->Configure(args);
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CheckConfigReload(obj, "reg:gamma");
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// test label validation
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EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {0}, {1}, {0}, {0}))
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<< "Expected error when label = 0 for GammaRegression";
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EXPECT_ANY_THROW(CheckObjFunction(obj, {-1}, {-1}, {1}, {-1}, {-3}))
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<< "Expected error when label < 0 for GammaRegression";
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// test ProbToMargin
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EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.30f, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.5f), -0.69f, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.9f), -0.10f, 0.01f);
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// test PredTransform
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HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
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std::vector<bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
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obj->PredTransform(&io_preds);
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auto& preds = io_preds.HostVector();
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for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
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EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
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}
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}
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TEST(Objective, DeclareUnifiedTest(TweedieRegressionGPair)) {
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GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
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std::vector<std::pair<std::string, std::string>> args;
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std::unique_ptr<ObjFunction> obj {
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ObjFunction::Create("reg:tweedie", &lparam)
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};
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args.emplace_back(std::make_pair("tweedie_variance_power", "1.1f"));
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obj->Configure(args);
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CheckObjFunction(obj,
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{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{ 0, 0, 0, 0, 1, 1, 1, 1},
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{ 1, 1, 1, 1, 1, 1, 1, 1},
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{ 1, 1.09f, 2.24f, 2.45f, 0, 0.10f, 1.33f, 1.55f},
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{0.89f, 0.98f, 2.02f, 2.21f, 1, 1.08f, 2.11f, 2.30f});
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CheckObjFunction(obj,
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{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{ 0, 0, 0, 0, 1, 1, 1, 1},
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{}, // Empty weight.
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{ 1, 1.09f, 2.24f, 2.45f, 0, 0.10f, 1.33f, 1.55f},
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{0.89f, 0.98f, 2.02f, 2.21f, 1, 1.08f, 2.11f, 2.30f});
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ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"tweedie-nloglik@1.1"});
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}
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#if defined(__CUDACC__)
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TEST(Objective, CPU_vs_CUDA) {
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GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
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ObjFunction * obj =
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ObjFunction::Create("reg:squarederror", &lparam);
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HostDeviceVector<GradientPair> cpu_out_preds;
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HostDeviceVector<GradientPair> cuda_out_preds;
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constexpr size_t kRows = 400;
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constexpr size_t kCols = 100;
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auto pdmat = RandomDataGenerator(kRows, kCols, 0).Seed(0).GenerateDMatrix();
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HostDeviceVector<float> preds;
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preds.Resize(kRows);
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auto& h_preds = preds.HostVector();
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for (size_t i = 0; i < h_preds.size(); ++i) {
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h_preds[i] = static_cast<float>(i);
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}
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auto& info = pdmat->Info();
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info.labels.Reshape(kRows);
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auto& h_labels = info.labels.Data()->HostVector();
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for (size_t i = 0; i < h_labels.size(); ++i) {
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h_labels[i] = 1 / (float)(i+1);
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}
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{
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// CPU
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lparam.gpu_id = -1;
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obj->GetGradient(preds, info, 0, &cpu_out_preds);
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}
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{
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// CUDA
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lparam.gpu_id = 0;
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obj->GetGradient(preds, info, 0, &cuda_out_preds);
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}
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auto& h_cpu_out = cpu_out_preds.HostVector();
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auto& h_cuda_out = cuda_out_preds.HostVector();
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float sgrad = 0;
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float shess = 0;
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for (size_t i = 0; i < kRows; ++i) {
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sgrad += std::pow(h_cpu_out[i].GetGrad() - h_cuda_out[i].GetGrad(), 2);
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shess += std::pow(h_cpu_out[i].GetHess() - h_cuda_out[i].GetHess(), 2);
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}
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ASSERT_NEAR(sgrad, 0.0f, kRtEps);
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ASSERT_NEAR(shess, 0.0f, kRtEps);
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delete obj;
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}
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#endif
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TEST(Objective, DeclareUnifiedTest(TweedieRegressionBasic)) {
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GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
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std::vector<std::pair<std::string, std::string>> args;
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std::unique_ptr<ObjFunction> obj {
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ObjFunction::Create("reg:tweedie", &lparam)
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};
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obj->Configure(args);
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CheckConfigReload(obj, "reg:tweedie");
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// test label validation
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EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {-1}, {1}, {0}, {0}))
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<< "Expected error when label < 0 for TweedieRegression";
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// test ProbToMargin
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EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.30f, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.5f), -0.69f, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.9f), -0.10f, 0.01f);
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// test PredTransform
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HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
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std::vector<bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
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obj->PredTransform(&io_preds);
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auto& preds = io_preds.HostVector();
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for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
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EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
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}
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}
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// CoxRegression not implemented in GPU code, no need for testing.
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#if !defined(__CUDACC__)
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TEST(Objective, CoxRegressionGPair) {
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GenericParameter lparam = CreateEmptyGenericParam(GPUIDX);
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std::vector<std::pair<std::string, std::string>> args;
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std::unique_ptr<ObjFunction> obj {
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ObjFunction::Create("survival:cox", &lparam)
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};
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obj->Configure(args);
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CheckObjFunction(obj,
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{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{ 0, -2, -2, 2, 3, 5, -10, 100},
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{ 1, 1, 1, 1, 1, 1, 1, 1},
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{ 0, 0, 0, -0.799f, -0.788f, -0.590f, 0.910f, 1.006f},
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{ 0, 0, 0, 0.160f, 0.186f, 0.348f, 0.610f, 0.639f});
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}
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#endif
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|
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TEST(Objective, DeclareUnifiedTest(AbsoluteError)) {
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Context ctx = CreateEmptyGenericParam(GPUIDX);
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std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:absoluteerror", &ctx)};
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obj->Configure({});
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CheckConfigReload(obj, "reg:absoluteerror");
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|
|
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MetaInfo info;
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std::vector<float> labels{0.f, 3.f, 2.f, 5.f, 4.f, 7.f};
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info.labels.Reshape(6, 1);
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info.labels.Data()->HostVector() = labels;
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info.num_row_ = labels.size();
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HostDeviceVector<float> predt{1.f, 2.f, 3.f, 4.f, 5.f, 6.f};
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info.weights_.HostVector() = {1.f, 1.f, 1.f, 1.f, 1.f, 1.f};
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|
|
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CheckObjFunction(obj, predt.HostVector(), labels, info.weights_.HostVector(),
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{1.f, -1.f, 1.f, -1.f, 1.f, -1.f}, info.weights_.HostVector());
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|
|
|
RegTree tree;
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tree.ExpandNode(0, /*split_index=*/1, 2, true, 0.0f, 2.f, 3.f, 4.f, 2.f, 1.f, 1.f);
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|
|
|
HostDeviceVector<bst_node_t> position(labels.size(), 0);
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|
auto& h_position = position.HostVector();
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for (size_t i = 0; i < labels.size(); ++i) {
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|
if (i < labels.size() / 2) {
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h_position[i] = 1; // left
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} else {
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h_position[i] = 2; // right
|
|
}
|
|
}
|
|
|
|
auto& h_predt = predt.HostVector();
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|
for (size_t i = 0; i < h_predt.size(); ++i) {
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|
h_predt[i] = labels[i] + i;
|
|
}
|
|
|
|
obj->UpdateTreeLeaf(position, info, predt, &tree);
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|
ASSERT_EQ(tree[1].LeafValue(), -1);
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|
ASSERT_EQ(tree[2].LeafValue(), -4);
|
|
}
|
|
|
|
TEST(Objective, DeclareUnifiedTest(AbsoluteErrorLeaf)) {
|
|
Context ctx = CreateEmptyGenericParam(GPUIDX);
|
|
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:absoluteerror", &ctx)};
|
|
obj->Configure({});
|
|
|
|
MetaInfo info;
|
|
info.labels.Reshape(16, 1);
|
|
info.num_row_ = info.labels.Size();
|
|
CHECK_EQ(info.num_row_, 16);
|
|
auto h_labels = info.labels.HostView().Values();
|
|
std::iota(h_labels.begin(), h_labels.end(), 0);
|
|
HostDeviceVector<float> predt(h_labels.size());
|
|
auto& h_predt = predt.HostVector();
|
|
for (size_t i = 0; i < h_predt.size(); ++i) {
|
|
h_predt[i] = h_labels[i] + i;
|
|
}
|
|
|
|
HostDeviceVector<bst_node_t> position(info.labels.Size(), 0);
|
|
auto& h_position = position.HostVector();
|
|
for (int32_t i = 0; i < 3; ++i) {
|
|
h_position[i] = ~i; // negation for sampled nodes.
|
|
}
|
|
for (size_t i = 3; i < 8; ++i) {
|
|
h_position[i] = 3;
|
|
}
|
|
// empty leaf for node 4
|
|
for (size_t i = 8; i < 13; ++i) {
|
|
h_position[i] = 5;
|
|
}
|
|
for (size_t i = 13; i < h_labels.size(); ++i) {
|
|
h_position[i] = 6;
|
|
}
|
|
|
|
RegTree tree;
|
|
tree.ExpandNode(0, /*split_index=*/1, 2, true, 0.0f, 2.f, 3.f, 4.f, 2.f, 1.f, 1.f);
|
|
tree.ExpandNode(1, /*split_index=*/1, 2, true, 0.0f, 2.f, 3.f, 4.f, 2.f, 1.f, 1.f);
|
|
tree.ExpandNode(2, /*split_index=*/1, 2, true, 0.0f, 2.f, 3.f, 4.f, 2.f, 1.f, 1.f);
|
|
ASSERT_EQ(tree.GetNumLeaves(), 4);
|
|
|
|
auto empty_leaf = tree[4].LeafValue();
|
|
obj->UpdateTreeLeaf(position, info, predt, &tree);
|
|
ASSERT_EQ(tree[3].LeafValue(), -5);
|
|
ASSERT_EQ(tree[4].LeafValue(), empty_leaf);
|
|
ASSERT_EQ(tree[5].LeafValue(), -10);
|
|
ASSERT_EQ(tree[6].LeafValue(), -14);
|
|
}
|
|
|
|
TEST(Adaptive, DeclareUnifiedTest(MissingLeaf)) {
|
|
std::vector<bst_node_t> missing{1, 3};
|
|
|
|
std::vector<bst_node_t> h_nidx = {2, 4, 5};
|
|
std::vector<size_t> h_nptr = {0, 4, 8, 16};
|
|
|
|
obj::detail::FillMissingLeaf(missing, &h_nidx, &h_nptr);
|
|
|
|
ASSERT_EQ(h_nidx[0], missing[0]);
|
|
ASSERT_EQ(h_nidx[2], missing[1]);
|
|
ASSERT_EQ(h_nidx[1], 2);
|
|
ASSERT_EQ(h_nidx[3], 4);
|
|
ASSERT_EQ(h_nidx[4], 5);
|
|
|
|
ASSERT_EQ(h_nptr[0], 0);
|
|
ASSERT_EQ(h_nptr[1], 0); // empty
|
|
ASSERT_EQ(h_nptr[2], 4);
|
|
ASSERT_EQ(h_nptr[3], 4); // empty
|
|
ASSERT_EQ(h_nptr[4], 8);
|
|
ASSERT_EQ(h_nptr[5], 16);
|
|
}
|
|
} // namespace xgboost
|