c589eff941
* Only define `gpu_id` and `n_gpus` in `LearnerTrainParam` * Pass LearnerTrainParam through XGBoost vid factory method. * Disable all GPU usage when GPU related parameters are not specified (fixes XGBoost choosing GPU over aggressively). * Test learner train param io. * Fix gpu pickling.
100 lines
3.3 KiB
C++
100 lines
3.3 KiB
C++
// Copyright by Contributors
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#include <dmlc/filesystem.h>
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#include <gtest/gtest.h>
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#include <xgboost/predictor.h>
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#include "../helpers.h"
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namespace xgboost {
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TEST(cpu_predictor, Test) {
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auto lparam = CreateEmptyGenericParam(0, 0);
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std::unique_ptr<Predictor> cpu_predictor =
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std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor", &lparam));
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gbm::GBTreeModel model = CreateTestModel();
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int n_row = 5;
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int n_col = 5;
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auto dmat = CreateDMatrix(n_row, n_col, 0);
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// Test predict batch
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HostDeviceVector<float> out_predictions;
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cpu_predictor->PredictBatch((*dmat).get(), &out_predictions, model, 0);
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std::vector<float>& out_predictions_h = out_predictions.HostVector();
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for (int i = 0; i < out_predictions.Size(); i++) {
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ASSERT_EQ(out_predictions_h[i], 1.5);
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}
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// Test predict instance
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auto &batch = *(*dmat)->GetRowBatches().begin();
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for (int i = 0; i < batch.Size(); i++) {
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std::vector<float> instance_out_predictions;
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cpu_predictor->PredictInstance(batch[i], &instance_out_predictions, model);
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ASSERT_EQ(instance_out_predictions[0], 1.5);
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}
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// Test predict leaf
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std::vector<float> leaf_out_predictions;
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cpu_predictor->PredictLeaf((*dmat).get(), &leaf_out_predictions, model);
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for (auto v : leaf_out_predictions) {
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ASSERT_EQ(v, 0);
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}
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// Test predict contribution
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std::vector<float> out_contribution;
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cpu_predictor->PredictContribution((*dmat).get(), &out_contribution, model);
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for (auto const& contri : out_contribution) {
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ASSERT_EQ(contri, 1.5);
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}
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// Test predict contribution (approximate method)
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cpu_predictor->PredictContribution((*dmat).get(), &out_contribution, model, true);
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for (auto const& contri : out_contribution) {
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ASSERT_EQ(contri, 1.5);
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}
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delete dmat;
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}
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TEST(cpu_predictor, ExternalMemoryTest) {
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std::unique_ptr<DMatrix> dmat = CreateSparsePageDMatrix(12, 64);
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auto lparam = CreateEmptyGenericParam(0, 0);
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std::unique_ptr<Predictor> cpu_predictor =
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std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor", &lparam));
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gbm::GBTreeModel model = CreateTestModel();
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// Test predict batch
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HostDeviceVector<float> out_predictions;
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cpu_predictor->PredictBatch(dmat.get(), &out_predictions, model, 0);
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std::vector<float> &out_predictions_h = out_predictions.HostVector();
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EXPECT_EQ(out_predictions.Size(), dmat->Info().num_row_);
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for (const auto& v : out_predictions_h) {
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ASSERT_EQ(v, 1.5);
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}
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// Test predict leaf
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std::vector<float> leaf_out_predictions;
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cpu_predictor->PredictLeaf(dmat.get(), &leaf_out_predictions, model);
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EXPECT_EQ(leaf_out_predictions.size(), dmat->Info().num_row_);
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for (const auto& v : leaf_out_predictions) {
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ASSERT_EQ(v, 0);
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}
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// Test predict contribution
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std::vector<float> out_contribution;
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cpu_predictor->PredictContribution(dmat.get(), &out_contribution, model);
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EXPECT_EQ(out_contribution.size(), dmat->Info().num_row_);
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for (const auto& v : out_contribution) {
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ASSERT_EQ(v, 1.5);
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}
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// Test predict contribution (approximate method)
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std::vector<float> out_contribution_approximate;
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cpu_predictor->PredictContribution(dmat.get(), &out_contribution_approximate, model, true);
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EXPECT_EQ(out_contribution_approximate.size(), dmat->Info().num_row_);
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for (const auto& v : out_contribution_approximate) {
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ASSERT_EQ(v, 1.5);
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}
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}
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} // namespace xgboost
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