e089e16e3d
* Pass pointer to model parameters. This PR de-duplicates most of the model parameters except the one in `tree_model.h`. One difficulty is `base_score` is a model property but can be changed at runtime by objective function. Hence when performing model IO, we need to save the one provided by users, instead of the one transformed by objective. Here we created an immutable version of `LearnerModelParam` that represents the value of model parameter after configuration.
36 lines
996 B
Plaintext
36 lines
996 B
Plaintext
// Copyright by Contributors
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#include <xgboost/linear_updater.h>
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#include <xgboost/gbm.h>
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#include "../helpers.h"
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#include "../../../src/gbm/gblinear_model.h"
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namespace xgboost {
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TEST(Linear, GPUCoordinate) {
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size_t constexpr kRows = 10;
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size_t constexpr kCols = 10;
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auto mat = xgboost::CreateDMatrix(kRows, kCols, 0);
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auto lparam = CreateEmptyGenericParam(GPUIDX);
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LearnerModelParam mparam;
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mparam.num_feature = kCols;
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mparam.num_output_group = 1;
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mparam.base_score = 0.5;
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auto updater = std::unique_ptr<xgboost::LinearUpdater>(
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xgboost::LinearUpdater::Create("gpu_coord_descent", &lparam));
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updater->Configure({{"eta", "1."}});
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xgboost::HostDeviceVector<xgboost::GradientPair> gpair(
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(*mat)->Info().num_row_, xgboost::GradientPair(-5, 1.0));
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xgboost::gbm::GBLinearModel model{&mparam};
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model.LazyInitModel();
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updater->Update(&gpair, (*mat).get(), &model, gpair.Size());
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ASSERT_EQ(model.bias()[0], 5.0f);
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delete mat;
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}
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} // namespace xgboost |