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Pass pointer to model parameters. (#5101)

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* 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.
This commit is contained in:
Jiaming Yuan
2019-12-10 12:11:22 +08:00
committed by GitHub
parent 979f74d51a
commit e089e16e3d
33 changed files with 623 additions and 404 deletions
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44
tests/cpp/helpers.cc
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@@ -1,15 +1,17 @@
/*!
* Copyright 2016-2018 XGBoost contributors
* Copyright 2016-2019 XGBoost contributors
*/
#include <dmlc/filesystem.h>
#include <xgboost/logging.h>
#include <xgboost/gbm.h>
#include <xgboost/json.h>
#include <gtest/gtest.h>
#include <algorithm>
#include <random>
#include <cinttypes>
#include "./helpers.h"
#include "helpers.h"
#include "xgboost/c_api.h"
#include "../../src/data/simple_csr_source.h"
@@ -113,7 +115,6 @@ void CheckRankingObjFunction(std::unique_ptr<xgboost::ObjFunction> const& obj,
CheckObjFunctionImpl(obj, preds, labels, weights, info, out_grad, out_hess);
}
xgboost::bst_float GetMetricEval(xgboost::Metric * metric,
xgboost::HostDeviceVector<xgboost::bst_float> preds,
std::vector<xgboost::bst_float> labels,
@@ -192,6 +193,7 @@ std::unique_ptr<DMatrix> CreateSparsePageDMatrix(
return dmat;
}
std::unique_ptr<DMatrix> CreateSparsePageDMatrixWithRC(
size_t n_rows, size_t n_cols, size_t page_size, bool deterministic,
const dmlc::TemporaryDirectory& tempdir) {
@@ -257,16 +259,42 @@ std::unique_ptr<DMatrix> CreateSparsePageDMatrixWithRC(
}
}
gbm::GBTreeModel CreateTestModel() {
gbm::GBTreeModel CreateTestModel(LearnerModelParam const* param) {
std::vector<std::unique_ptr<RegTree>> trees;
trees.push_back(std::unique_ptr<RegTree>(new RegTree));
(*trees.back())[0].SetLeaf(1.5f);
(*trees.back()).Stat(0).sum_hess = 1.0f;
gbm::GBTreeModel model(0.5);
gbm::GBTreeModel model(param);
model.CommitModel(std::move(trees), 0);
model.param.num_output_group = 1;
model.base_margin = 0;
return model;
}
std::unique_ptr<GradientBooster> CreateTrainedGBM(
std::string name, Args kwargs, size_t kRows, size_t kCols,
LearnerModelParam const* learner_model_param,
GenericParameter const* generic_param) {
std::unique_ptr<GradientBooster> gbm {
GradientBooster::Create(name, generic_param, learner_model_param, {})};
gbm->Configure(kwargs);
auto pp_dmat = CreateDMatrix(kRows, kCols, 0);
auto p_dmat = *pp_dmat;
std::vector<float> labels(kRows);
for (size_t i = 0; i < kRows; ++i) {
labels[i] = i;
}
p_dmat->Info().labels_.HostVector() = labels;
HostDeviceVector<GradientPair> gpair;
auto& h_gpair = gpair.HostVector();
h_gpair.resize(kRows);
for (size_t i = 0; i < kRows; ++i) {
h_gpair[i] = {static_cast<float>(i), 1};
}
gbm->DoBoost(p_dmat.get(), &gpair, nullptr);
delete pp_dmat;
return gbm;
}
} // namespace xgboost