286dccb8e8
* GPU binning and compression. - binning and index compression are done inside the DeviceShard constructor - in case of a DMatrix with multiple row batches, it is first converted into a single row batch
96 lines
3.0 KiB
C++
96 lines
3.0 KiB
C++
#include "./helpers.h"
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#include "xgboost/c_api.h"
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#include <random>
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std::string TempFileName() {
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return std::tmpnam(nullptr);
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}
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bool FileExists(const std::string name) {
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struct stat st;
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return stat(name.c_str(), &st) == 0;
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}
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long GetFileSize(const std::string filename) {
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struct stat st;
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stat(filename.c_str(), &st);
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return st.st_size;
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}
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std::string CreateSimpleTestData() {
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return CreateBigTestData(6);
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}
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std::string CreateBigTestData(size_t n_entries) {
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std::string tmp_file = TempFileName();
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std::ofstream fo;
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fo.open(tmp_file);
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const size_t entries_per_row = 3;
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size_t n_rows = (n_entries + entries_per_row - 1) / entries_per_row;
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for (size_t i = 0; i < n_rows; ++i) {
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const char* row = i % 2 == 0 ? " 0:0 1:10 2:20\n" : " 0:0 3:30 4:40\n";
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fo << i << row;
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}
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fo.close();
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return tmp_file;
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}
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void CheckObjFunction(xgboost::ObjFunction * obj,
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std::vector<xgboost::bst_float> preds,
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std::vector<xgboost::bst_float> labels,
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std::vector<xgboost::bst_float> weights,
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std::vector<xgboost::bst_float> out_grad,
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std::vector<xgboost::bst_float> out_hess) {
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xgboost::MetaInfo info;
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info.num_row_ = labels.size();
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info.labels_ = labels;
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info.weights_ = weights;
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xgboost::HostDeviceVector<xgboost::bst_float> in_preds(preds);
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xgboost::HostDeviceVector<xgboost::GradientPair> out_gpair;
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obj->GetGradient(&in_preds, info, 1, &out_gpair);
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std::vector<xgboost::GradientPair>& gpair = out_gpair.HostVector();
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ASSERT_EQ(gpair.size(), in_preds.Size());
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for (int i = 0; i < static_cast<int>(gpair.size()); ++i) {
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EXPECT_NEAR(gpair[i].GetGrad(), out_grad[i], 0.01)
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<< "Unexpected grad for pred=" << preds[i] << " label=" << labels[i]
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<< " weight=" << weights[i];
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EXPECT_NEAR(gpair[i].GetHess(), out_hess[i], 0.01)
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<< "Unexpected hess for pred=" << preds[i] << " label=" << labels[i]
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<< " weight=" << weights[i];
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}
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}
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xgboost::bst_float GetMetricEval(xgboost::Metric * metric,
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std::vector<xgboost::bst_float> preds,
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std::vector<xgboost::bst_float> labels,
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std::vector<xgboost::bst_float> weights) {
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xgboost::MetaInfo info;
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info.num_row_ = labels.size();
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info.labels_ = labels;
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info.weights_ = weights;
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return metric->Eval(preds, info, false);
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}
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std::shared_ptr<xgboost::DMatrix> CreateDMatrix(int rows, int columns,
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float sparsity, int seed) {
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const float missing_value = -1;
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std::vector<float> test_data(rows * columns);
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std::mt19937 gen(seed);
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std::uniform_real_distribution<float> dis(0.0f, 1.0f);
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for (auto &e : test_data) {
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if (dis(gen) < sparsity) {
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e = missing_value;
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} else {
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e = dis(gen);
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}
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}
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DMatrixHandle handle;
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XGDMatrixCreateFromMat(test_data.data(), rows, columns, missing_value,
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&handle);
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return *static_cast<std::shared_ptr<xgboost::DMatrix> *>(handle);
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}
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