81210420c6
This is the one last PR for removing omp global variable. * Add context object to the `DMatrix`. This bridges `DMatrix` with https://github.com/dmlc/xgboost/issues/7308 . * Require context to be available at the construction time of booster. * Add `n_threads` support for R csc DMatrix constructor. * Remove `omp_get_max_threads` in R glue code. * Remove threading utilities that rely on omp global variable.
80 lines
2.8 KiB
C++
80 lines
2.8 KiB
C++
#ifndef XGBOOST_TEST_PREDICTOR_H_
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#define XGBOOST_TEST_PREDICTOR_H_
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#include <xgboost/predictor.h>
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#include <string>
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#include <cstddef>
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#include "../helpers.h"
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namespace xgboost {
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template <typename Page>
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void TestPredictionFromGradientIndex(std::string name, size_t rows, size_t cols,
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std::shared_ptr<DMatrix> p_hist) {
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constexpr size_t kClasses { 3 };
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LearnerModelParam param;
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param.num_feature = cols;
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param.num_output_group = kClasses;
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param.base_score = 0.5;
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auto lparam = CreateEmptyGenericParam(0);
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std::unique_ptr<Predictor> predictor =
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std::unique_ptr<Predictor>(Predictor::Create(name, &lparam));
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predictor->Configure({});
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GenericParameter ctx;
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ctx.UpdateAllowUnknown(Args{});
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gbm::GBTreeModel model = CreateTestModel(¶m, &ctx, kClasses);
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{
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auto p_precise = RandomDataGenerator(rows, cols, 0).GenerateDMatrix();
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PredictionCacheEntry approx_out_predictions;
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predictor->InitOutPredictions(p_hist->Info(), &approx_out_predictions.predictions, model);
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predictor->PredictBatch(p_hist.get(), &approx_out_predictions, model, 0);
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PredictionCacheEntry precise_out_predictions;
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predictor->InitOutPredictions(p_precise->Info(), &precise_out_predictions.predictions, model);
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predictor->PredictBatch(p_precise.get(), &precise_out_predictions, model, 0);
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for (size_t i = 0; i < rows; ++i) {
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CHECK_EQ(approx_out_predictions.predictions.HostVector()[i],
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precise_out_predictions.predictions.HostVector()[i]);
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}
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}
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{
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// Predictor should never try to create the histogram index by itself. As only
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// histogram index from training data is valid and predictor doesn't known which
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// matrix is used for training.
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auto p_dmat = RandomDataGenerator(rows, cols, 0).GenerateDMatrix();
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PredictionCacheEntry precise_out_predictions;
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predictor->InitOutPredictions(p_dmat->Info(), &precise_out_predictions.predictions, model);
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predictor->PredictBatch(p_dmat.get(), &precise_out_predictions, model, 0);
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ASSERT_FALSE(p_dmat->PageExists<Page>());
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}
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}
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// p_full and p_hist should come from the same data set.
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void TestTrainingPrediction(size_t rows, size_t bins, std::string tree_method,
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std::shared_ptr<DMatrix> p_full,
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std::shared_ptr<DMatrix> p_hist);
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void TestInplacePrediction(dmlc::any x, std::string predictor,
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bst_row_t rows, bst_feature_t cols,
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int32_t device = -1);
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void TestPredictionWithLesserFeatures(std::string preditor_name);
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void TestCategoricalPrediction(std::string name);
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void TestCategoricalPredictLeaf(StringView name);
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void TestIterationRange(std::string name);
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void TestSparsePrediction(float sparsity, std::string predictor);
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} // namespace xgboost
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#endif // XGBOOST_TEST_PREDICTOR_H_
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