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GPU implementation of AFT survival objective and metric (#5714)

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* Add interval accuracy

* De-virtualize AFT functions

* Lint

* Refactor AFT metric using GPU-CPU reducer

* Fix R build

* Fix build on Windows

* Fix copyright header

* Clang-tidy

* Fix crashing demo

* Fix typos in comment; explain GPU ID

* Remove unnecessary #include

* Add C++ test for interval accuracy

* Fix a bug in accuracy metric: use log pred

* Refactor AFT objective using GPU-CPU Transform

* Lint

* Fix lint

* Use Ninja to speed up build

* Use time, not /usr/bin/time

* Add cpu_build worker class, with concurrency = 1

* Use concurrency = 1 only for CUDA build

* concurrency = 1 for clang-tidy

* Address reviewer's feedback

* Update link to AFT paper
This commit is contained in:
Philip Hyunsu Cho
2020-07-17 01:18:13 -07:00
committed by GitHub
parent 7c2686146e
commit 71b0528a2f
20 changed files with 1050 additions and 822 deletions
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31
tests/cpp/common/test_survival_util.cc
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@@ -8,36 +8,37 @@
namespace xgboost {
namespace common {
inline static void RobustTestSuite(ProbabilityDistributionType dist_type,
double y_lower, double y_upper, double sigma) {
AFTLoss loss(dist_type);
template <typename Distribution>
inline static void RobustTestSuite(double y_lower, double y_upper, double sigma) {
for (int i = 50; i >= -50; --i) {
const double y_pred = std::pow(10.0, static_cast<double>(i));
const double z = (std::log(y_lower) - std::log(y_pred)) / sigma;
const double gradient = loss.Gradient(y_lower, y_upper, std::log(y_pred), sigma);
const double hessian = loss.Hessian(y_lower, y_upper, std::log(y_pred), sigma);
const double gradient
= AFTLoss<Distribution>::Gradient(y_lower, y_upper, std::log(y_pred), sigma);
const double hessian
= AFTLoss<Distribution>::Hessian(y_lower, y_upper, std::log(y_pred), sigma);
ASSERT_FALSE(std::isnan(gradient)) << "z = " << z << ", y \\in ["
<< y_lower << ", " << y_upper << "], y_pred = " << y_pred
<< ", dist = " << static_cast<int>(dist_type);
<< ", dist = " << static_cast<int>(Distribution::Type());
ASSERT_FALSE(std::isinf(gradient)) << "z = " << z << ", y \\in ["
<< y_lower << ", " << y_upper << "], y_pred = " << y_pred
<< ", dist = " << static_cast<int>(dist_type);
<< ", dist = " << static_cast<int>(Distribution::Type());
ASSERT_FALSE(std::isnan(hessian)) << "z = " << z << ", y \\in ["
<< y_lower << ", " << y_upper << "], y_pred = " << y_pred
<< ", dist = " << static_cast<int>(dist_type);
<< ", dist = " << static_cast<int>(Distribution::Type());
ASSERT_FALSE(std::isinf(hessian)) << "z = " << z << ", y \\in ["
<< y_lower << ", " << y_upper << "], y_pred = " << y_pred
<< ", dist = " << static_cast<int>(dist_type);
<< ", dist = " << static_cast<int>(Distribution::Type());
}
}
TEST(AFTLoss, RobustGradientPair) { // Ensure that INF and NAN don't show up in gradient pair
RobustTestSuite(ProbabilityDistributionType::kNormal, 16.0, 200.0, 2.0);
RobustTestSuite(ProbabilityDistributionType::kLogistic, 16.0, 200.0, 2.0);
RobustTestSuite(ProbabilityDistributionType::kExtreme, 16.0, 200.0, 2.0);
RobustTestSuite(ProbabilityDistributionType::kNormal, 100.0, 100.0, 2.0);
RobustTestSuite(ProbabilityDistributionType::kLogistic, 100.0, 100.0, 2.0);
RobustTestSuite(ProbabilityDistributionType::kExtreme, 100.0, 100.0, 2.0);
RobustTestSuite<NormalDistribution>(16.0, 200.0, 2.0);
RobustTestSuite<LogisticDistribution>(16.0, 200.0, 2.0);
RobustTestSuite<ExtremeDistribution>(16.0, 200.0, 2.0);
RobustTestSuite<NormalDistribution>(100.0, 100.0, 2.0);
RobustTestSuite<LogisticDistribution>(100.0, 100.0, 2.0);
RobustTestSuite<ExtremeDistribution>(100.0, 100.0, 2.0);
}
} // namespace common