a5a58102e5
This PR replaces the original RABIT implementation with a new one, which has already been partially merged into XGBoost. The new one features: - Federated learning for both CPU and GPU. - NCCL. - More data types. - A unified interface for all the underlying implementations. - Improved timeout handling for both tracker and workers. - Exhausted tests with metrics (fixed a couple of bugs along the way). - A reusable tracker for Python and JVM packages.
90 lines
3.0 KiB
C++
90 lines
3.0 KiB
C++
// Copyright by Contributors
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#include <xgboost/metric.h>
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#include <string>
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#include "../helpers.h"
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namespace xgboost {
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namespace metric {
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inline void CheckDeterministicMetricMultiClass(StringView name, int32_t device) {
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auto ctx = MakeCUDACtx(device);
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std::unique_ptr<Metric> metric{Metric::Create(name.c_str(), &ctx)};
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HostDeviceVector<float> predts;
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auto p_fmat = EmptyDMatrix();
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MetaInfo& info = p_fmat->Info();
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auto &h_predts = predts.HostVector();
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SimpleLCG lcg;
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size_t n_samples = 2048, n_classes = 4;
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info.labels.Reshape(n_samples);
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auto &h_labels = info.labels.Data()->HostVector();
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h_predts.resize(n_samples * n_classes);
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{
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SimpleRealUniformDistribution<float> dist{0.0f, static_cast<float>(n_classes)};
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for (size_t i = 0; i < n_samples; ++i) {
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h_labels[i] = dist(&lcg);
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}
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}
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{
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SimpleRealUniformDistribution<float> dist{0.0f, 1.0f};
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for (size_t i = 0; i < n_samples * n_classes; ++i) {
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h_predts[i] = dist(&lcg);
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}
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}
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auto result = metric->Evaluate(predts, p_fmat);
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for (size_t i = 0; i < 8; ++i) {
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ASSERT_EQ(metric->Evaluate(predts, p_fmat), result);
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}
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}
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inline void TestMultiClassError(DataSplitMode data_split_mode, DeviceOrd device) {
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auto ctx = MakeCUDACtx(device.ordinal);
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xgboost::Metric * metric = xgboost::Metric::Create("merror", &ctx);
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metric->Configure({});
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ASSERT_STREQ(metric->Name(), "merror");
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EXPECT_ANY_THROW(GetMetricEval(metric, {0}, {0, 0}, {}, {}, data_split_mode));
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EXPECT_NEAR(GetMetricEval(
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metric, {1, 0, 0, 0, 1, 0, 0, 0, 1}, {0, 1, 2}, {}, {}, data_split_mode), 0, 1e-10);
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EXPECT_NEAR(GetMetricEval(metric,
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{0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f},
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{0, 1, 2}, {}, {}, data_split_mode),
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0.666f, 0.001f);
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delete metric;
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}
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inline void VerifyMultiClassError(DataSplitMode data_split_mode, DeviceOrd device) {
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TestMultiClassError(data_split_mode, device);
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CheckDeterministicMetricMultiClass(StringView{"merror"}, device.ordinal);
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}
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inline void TestMultiClassLogLoss(DataSplitMode data_split_mode, DeviceOrd device) {
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auto ctx = MakeCUDACtx(device.ordinal);
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xgboost::Metric * metric = xgboost::Metric::Create("mlogloss", &ctx);
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metric->Configure({});
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ASSERT_STREQ(metric->Name(), "mlogloss");
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EXPECT_ANY_THROW(GetMetricEval(metric, {0}, {0, 0}, {}, {}, data_split_mode));
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EXPECT_NEAR(GetMetricEval(
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metric, {1, 0, 0, 0, 1, 0, 0, 0, 1}, {0, 1, 2}, {}, {}, data_split_mode), 0, 1e-10);
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EXPECT_NEAR(GetMetricEval(metric,
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{0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f},
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{0, 1, 2}, {}, {}, data_split_mode),
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2.302f, 0.001f);
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delete metric;
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}
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inline void VerifyMultiClassLogLoss(DataSplitMode data_split_mode, DeviceOrd device) {
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TestMultiClassLogLoss(data_split_mode, device);
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CheckDeterministicMetricMultiClass(StringView{"mlogloss"}, device.ordinal);
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}
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} // namespace metric
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} // namespace xgboost
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