Make sure metrics work with federated learning (#9037)
This commit is contained in:
Rong Ou
GitHub
@@ -2,109 +2,13 @@
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* Copyright (c) by Contributors 2020
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*/
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#include <gtest/gtest.h>
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#include <cmath>
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#include "test_survival_metric.h"
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#include "xgboost/metric.h"
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#include "../helpers.h"
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#include "../../../src/common/survival_util.h"
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/** Tests for Survival metrics that should run both on CPU and GPU **/
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namespace xgboost {
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namespace common {
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namespace {
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inline void CheckDeterministicMetricElementWise(StringView name, int32_t device) {
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auto ctx = CreateEmptyGenericParam(device);
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std::unique_ptr<Metric> metric{Metric::Create(name.c_str(), &ctx)};
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metric->Configure(Args{});
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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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SimpleRealUniformDistribution<float> dist{0.0f, 1.0f};
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size_t n_samples = 2048;
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h_predts.resize(n_samples);
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for (size_t i = 0; i < n_samples; ++i) {
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h_predts[i] = dist(&lcg);
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}
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auto &h_upper = info.labels_upper_bound_.HostVector();
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auto &h_lower = info.labels_lower_bound_.HostVector();
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h_lower.resize(n_samples);
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h_upper.resize(n_samples);
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for (size_t i = 0; i < n_samples; ++i) {
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h_lower[i] = 1;
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h_upper[i] = 10;
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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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void VerifyAFTNegLogLik(DataSplitMode data_split_mode = DataSplitMode::kRow) {
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auto ctx = xgboost::CreateEmptyGenericParam(GPUIDX);
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/**
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* Test aggregate output from the AFT metric over a small test data set.
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* This is unlike AFTLoss.* tests, which verify metric values over individual data points.
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**/
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auto p_fmat = EmptyDMatrix();
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MetaInfo& info = p_fmat->Info();
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info.num_row_ = 4;
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info.labels_lower_bound_.HostVector()
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= { 100.0f, 0.0f, 60.0f, 16.0f };
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info.labels_upper_bound_.HostVector()
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= { 100.0f, 20.0f, std::numeric_limits<bst_float>::infinity(), 200.0f };
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info.weights_.HostVector() = std::vector<bst_float>();
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info.data_split_mode = data_split_mode;
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HostDeviceVector<bst_float> preds(4, std::log(64));
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struct TestCase {
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std::string dist_type;
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bst_float reference_value;
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};
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for (const auto& test_case : std::vector<TestCase>{ {"normal", 2.1508f}, {"logistic", 2.1804f},
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{"extreme", 2.0706f} }) {
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std::unique_ptr<Metric> metric(Metric::Create("aft-nloglik", &ctx));
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metric->Configure({ {"aft_loss_distribution", test_case.dist_type},
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{"aft_loss_distribution_scale", "1.0"} });
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EXPECT_NEAR(metric->Evaluate(preds, p_fmat), test_case.reference_value, 1e-4);
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}
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}
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void VerifyIntervalRegressionAccuracy(DataSplitMode data_split_mode = DataSplitMode::kRow) {
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auto ctx = xgboost::CreateEmptyGenericParam(GPUIDX);
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auto p_fmat = EmptyDMatrix();
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MetaInfo& info = p_fmat->Info();
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info.num_row_ = 4;
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info.labels_lower_bound_.HostVector() = { 20.0f, 0.0f, 60.0f, 16.0f };
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info.labels_upper_bound_.HostVector() = { 80.0f, 20.0f, 80.0f, 200.0f };
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info.weights_.HostVector() = std::vector<bst_float>();
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info.data_split_mode = data_split_mode;
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HostDeviceVector<bst_float> preds(4, std::log(60.0f));
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std::unique_ptr<Metric> metric(Metric::Create("interval-regression-accuracy", &ctx));
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EXPECT_FLOAT_EQ(metric->Evaluate(preds, p_fmat), 0.75f);
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info.labels_lower_bound_.HostVector()[2] = 70.0f;
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EXPECT_FLOAT_EQ(metric->Evaluate(preds, p_fmat), 0.50f);
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info.labels_upper_bound_.HostVector()[2] = std::numeric_limits<bst_float>::infinity();
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EXPECT_FLOAT_EQ(metric->Evaluate(preds, p_fmat), 0.50f);
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info.labels_upper_bound_.HostVector()[3] = std::numeric_limits<bst_float>::infinity();
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EXPECT_FLOAT_EQ(metric->Evaluate(preds, p_fmat), 0.50f);
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info.labels_lower_bound_.HostVector()[0] = 70.0f;
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EXPECT_FLOAT_EQ(metric->Evaluate(preds, p_fmat), 0.25f);
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CheckDeterministicMetricElementWise(StringView{"interval-regression-accuracy"}, GPUIDX);
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}
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} // anonymous namespace
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TEST(Metric, DeclareUnifiedTest(AFTNegLogLik)) { VerifyAFTNegLogLik(); }
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TEST_F(DeclareUnifiedDistributedTest(MetricTest), AFTNegLogLikRowSplit) {
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@@ -140,6 +44,5 @@ TEST(AFTNegLogLikMetric, DeclareUnifiedTest(Configuration)) {
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CheckDeterministicMetricElementWise(StringView{"aft-nloglik"}, GPUIDX);
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}
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} // namespace common
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} // namespace xgboost
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107
tests/cpp/metric/test_survival_metric.h
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107
tests/cpp/metric/test_survival_metric.h
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@@ -0,0 +1,107 @@
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/**
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* Copyright 2020-2023 by XGBoost Contributors
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*/
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#pragma once
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#include <gtest/gtest.h>
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#include <cmath>
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#include "../../../src/common/survival_util.h"
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#include "../helpers.h"
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#include "xgboost/metric.h"
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namespace xgboost {
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namespace common {
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inline void CheckDeterministicMetricElementWise(StringView name, int32_t device) {
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auto ctx = CreateEmptyGenericParam(device);
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std::unique_ptr<Metric> metric{Metric::Create(name.c_str(), &ctx)};
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metric->Configure(Args{});
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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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SimpleRealUniformDistribution<float> dist{0.0f, 1.0f};
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size_t n_samples = 2048;
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h_predts.resize(n_samples);
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for (size_t i = 0; i < n_samples; ++i) {
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h_predts[i] = dist(&lcg);
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}
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auto &h_upper = info.labels_upper_bound_.HostVector();
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auto &h_lower = info.labels_lower_bound_.HostVector();
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h_lower.resize(n_samples);
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h_upper.resize(n_samples);
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for (size_t i = 0; i < n_samples; ++i) {
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h_lower[i] = 1;
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h_upper[i] = 10;
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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 VerifyAFTNegLogLik(DataSplitMode data_split_mode = DataSplitMode::kRow) {
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auto ctx = xgboost::CreateEmptyGenericParam(GPUIDX);
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/**
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* Test aggregate output from the AFT metric over a small test data set.
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* This is unlike AFTLoss.* tests, which verify metric values over individual data points.
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**/
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auto p_fmat = EmptyDMatrix();
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MetaInfo& info = p_fmat->Info();
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info.num_row_ = 4;
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info.labels_lower_bound_.HostVector()
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= { 100.0f, 0.0f, 60.0f, 16.0f };
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info.labels_upper_bound_.HostVector()
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= { 100.0f, 20.0f, std::numeric_limits<bst_float>::infinity(), 200.0f };
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info.weights_.HostVector() = std::vector<bst_float>();
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info.data_split_mode = data_split_mode;
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HostDeviceVector<bst_float> preds(4, std::log(64));
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struct TestCase {
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std::string dist_type;
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bst_float reference_value;
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};
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for (const auto& test_case : std::vector<TestCase>{ {"normal", 2.1508f}, {"logistic", 2.1804f},
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{"extreme", 2.0706f} }) {
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std::unique_ptr<Metric> metric(Metric::Create("aft-nloglik", &ctx));
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metric->Configure({ {"aft_loss_distribution", test_case.dist_type},
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{"aft_loss_distribution_scale", "1.0"} });
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EXPECT_NEAR(metric->Evaluate(preds, p_fmat), test_case.reference_value, 1e-4);
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}
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}
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inline void VerifyIntervalRegressionAccuracy(DataSplitMode data_split_mode = DataSplitMode::kRow) {
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auto ctx = xgboost::CreateEmptyGenericParam(GPUIDX);
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auto p_fmat = EmptyDMatrix();
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MetaInfo& info = p_fmat->Info();
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info.num_row_ = 4;
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info.labels_lower_bound_.HostVector() = { 20.0f, 0.0f, 60.0f, 16.0f };
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info.labels_upper_bound_.HostVector() = { 80.0f, 20.0f, 80.0f, 200.0f };
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info.weights_.HostVector() = std::vector<bst_float>();
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info.data_split_mode = data_split_mode;
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HostDeviceVector<bst_float> preds(4, std::log(60.0f));
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std::unique_ptr<Metric> metric(Metric::Create("interval-regression-accuracy", &ctx));
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EXPECT_FLOAT_EQ(metric->Evaluate(preds, p_fmat), 0.75f);
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info.labels_lower_bound_.HostVector()[2] = 70.0f;
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EXPECT_FLOAT_EQ(metric->Evaluate(preds, p_fmat), 0.50f);
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info.labels_upper_bound_.HostVector()[2] = std::numeric_limits<bst_float>::infinity();
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EXPECT_FLOAT_EQ(metric->Evaluate(preds, p_fmat), 0.50f);
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info.labels_upper_bound_.HostVector()[3] = std::numeric_limits<bst_float>::infinity();
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EXPECT_FLOAT_EQ(metric->Evaluate(preds, p_fmat), 0.50f);
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info.labels_lower_bound_.HostVector()[0] = 70.0f;
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EXPECT_FLOAT_EQ(metric->Evaluate(preds, p_fmat), 0.25f);
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CheckDeterministicMetricElementWise(StringView{"interval-regression-accuracy"}, GPUIDX);
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}
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} // namespace common
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} // namespace xgboost
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