e8c5c53e2f
* Use normal predictor for dart booster. * Implement `inplace_predict` for dart. * Enable `dart` for dask interface now that it's thread-safe. * categorical data should be working out of box for dart now. The implementation is not very efficient as it has to pull back the data and apply weight for each tree, but still a significant improvement over previous implementation as now we no longer binary search for each sample. * Fix output prediction shape on dataframe.
72 lines
2.6 KiB
C++
72 lines
2.6 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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gbm::GBTreeModel model = CreateTestModel(¶m, 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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} // namespace xgboost
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#endif // XGBOOST_TEST_PREDICTOR_H_
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