81 lines
2.4 KiB
C++
81 lines
2.4 KiB
C++
/*!
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* Copyright 2020 by Contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/predictor.h>
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#include <xgboost/data.h>
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#include <xgboost/host_device_vector.h>
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#include <xgboost/generic_parameters.h>
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#include "test_predictor.h"
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#include "../helpers.h"
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#include "../../../src/common/io.h"
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namespace xgboost {
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TEST(Predictor, PredictionCache) {
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size_t constexpr kRows = 16, kCols = 4;
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PredictionContainer container;
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DMatrix* m;
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// Add a cache that is immediately expired.
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auto add_cache = [&]() {
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auto p_dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatix();
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container.Cache(p_dmat, GenericParameter::kCpuId);
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m = p_dmat.get();
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};
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add_cache();
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ASSERT_EQ(container.Container().size(), 0);
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add_cache();
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EXPECT_ANY_THROW(container.Entry(m));
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}
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// Only run this test when CUDA is enabled.
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void TestTrainingPrediction(size_t rows, std::string tree_method) {
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size_t constexpr kCols = 16;
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size_t constexpr kClasses = 3;
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size_t constexpr kIters = 3;
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std::unique_ptr<Learner> learner;
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auto train = [&](std::string predictor, HostDeviceVector<float>* out) {
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auto p_m = RandomDataGenerator(rows, kCols, 0).GenerateDMatix();
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auto &h_label = p_m->Info().labels_.HostVector();
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h_label.resize(rows);
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for (size_t i = 0; i < rows; ++i) {
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h_label[i] = i % kClasses;
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}
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learner.reset(Learner::Create({}));
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learner->SetParam("tree_method", tree_method);
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learner->SetParam("objective", "multi:softprob");
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learner->SetParam("predictor", predictor);
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learner->SetParam("num_feature", std::to_string(kCols));
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learner->SetParam("num_class", std::to_string(kClasses));
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learner->Configure();
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for (size_t i = 0; i < kIters; ++i) {
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learner->UpdateOneIter(i, p_m);
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}
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learner->Predict(p_m, false, out);
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};
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// Alternate the predictor, CPU predictor can not use ellpack while GPU predictor can
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// not use CPU histogram index. So it's guaranteed one of the following is not
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// predicting from histogram index. Note: As of writing only GPU supports predicting
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// from gradient index, the test is written for future portability.
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HostDeviceVector<float> predictions_0;
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train("cpu_predictor", &predictions_0);
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HostDeviceVector<float> predictions_1;
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train("gpu_predictor", &predictions_1);
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for (size_t i = 0; i < rows; ++i) {
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EXPECT_NEAR(predictions_1.ConstHostVector()[i],
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predictions_0.ConstHostVector()[i], kRtEps);
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}
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}
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} // namespace xgboost
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