972730cde0
- Use the `linalg::Matrix` for storing gradients. - New API for the custom objective. - Custom objective for multi-class/multi-target is now required to return the correct shape. - Custom objective for Python can accept arrays with any strides. (row-major, column-major)
72 lines
2.0 KiB
C++
72 lines
2.0 KiB
C++
/*!
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* Copyright 2018-2019 by Contributors
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*/
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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 "test_json_io.h"
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#include "../../../src/gbm/gblinear_model.h"
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#include "xgboost/base.h"
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namespace xgboost {
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TEST(Linear, Shotgun) {
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size_t constexpr kRows = 10;
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size_t constexpr kCols = 10;
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auto p_fmat = xgboost::RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
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auto ctx = MakeCUDACtx(GPUIDX);
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LearnerModelParam mparam{MakeMP(kCols, .5, 1)};
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{
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auto updater =
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std::unique_ptr<xgboost::LinearUpdater>(xgboost::LinearUpdater::Create("shotgun", &ctx));
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updater->Configure({{"eta", "1."}});
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linalg::Matrix<xgboost::GradientPair> gpair{
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linalg::Constant(&ctx, xgboost::GradientPair(-5, 1.0), p_fmat->Info().num_row_, 1)};
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xgboost::gbm::GBLinearModel model{&mparam};
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model.LazyInitModel();
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updater->Update(&gpair, p_fmat.get(), &model, gpair.Size());
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ASSERT_EQ(model.Bias()[0], 5.0f);
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}
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{
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auto updater = std::unique_ptr<xgboost::LinearUpdater>(
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xgboost::LinearUpdater::Create("shotgun", &ctx));
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EXPECT_ANY_THROW(updater->Configure({{"feature_selector", "random"}}));
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}
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}
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TEST(Shotgun, JsonIO) {
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TestUpdaterJsonIO("shotgun");
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}
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TEST(Linear, coordinate) {
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size_t constexpr kRows = 10;
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size_t constexpr kCols = 10;
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auto p_fmat = xgboost::RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
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auto ctx = MakeCUDACtx(GPUIDX);
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LearnerModelParam mparam{MakeMP(kCols, .5, 1)};
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auto updater = std::unique_ptr<xgboost::LinearUpdater>(
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xgboost::LinearUpdater::Create("coord_descent", &ctx));
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updater->Configure({{"eta", "1."}});
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linalg::Matrix<xgboost::GradientPair> gpair{
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linalg::Constant(&ctx, xgboost::GradientPair(-5, 1.0), p_fmat->Info().num_row_, 1)};
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xgboost::gbm::GBLinearModel model{&mparam};
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model.LazyInitModel();
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updater->Update(&gpair, p_fmat.get(), &model, gpair.Size());
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ASSERT_EQ(model.Bias()[0], 5.0f);
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}
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TEST(Coordinate, JsonIO){
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TestUpdaterJsonIO("coord_descent");
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}
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} // namespace xgboost
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