a96039141a
* Use sparse page as singular CSR matrix representation * Simplify dmatrix methods * Reduce statefullness of batch iterators * BREAKING CHANGE: Remove prob_buffer_row parameter. Users are instead recommended to sample their dataset as a preprocessing step before using XGBoost.
63 lines
2.0 KiB
C++
63 lines
2.0 KiB
C++
// Copyright by Contributors
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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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std::unique_ptr<Predictor> cpu_predictor =
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std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor"));
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std::vector<std::unique_ptr<RegTree>> trees;
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trees.push_back(std::unique_ptr<RegTree>(new RegTree));
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trees.back()->InitModel();
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(*trees.back())[0].SetLeaf(1.5f);
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(*trees.back()).Stat(0).sum_hess = 1.0f;
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gbm::GBTreeModel model(0.5);
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model.CommitModel(std::move(trees), 0);
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model.param.num_output_group = 1;
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model.base_margin = 0;
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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->RowIterator()->Value();
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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 (int i = 0; i < leaf_out_predictions.size(); i++) {
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ASSERT_EQ(leaf_out_predictions[i], 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 (int i = 0; i < out_contribution.size(); i++) {
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ASSERT_EQ(out_contribution[i], 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 (int i = 0; i < out_contribution.size(); i++) {
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ASSERT_EQ(out_contribution[i], 1.5);
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}
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}
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} // namespace xgboost
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