71b0528a2f
* 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
116 lines
5.4 KiB
C++
116 lines
5.4 KiB
C++
/*!
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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 <memory>
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#include <cmath>
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#include "xgboost/logging.h"
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#include "../../../src/common/probability_distribution.h"
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namespace xgboost {
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namespace common {
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template <typename Distribution>
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void RunDistributionGenericTest() {
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double integral_of_pdf = Distribution::CDF(-2.0);
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double integral_of_grad_pdf = Distribution::PDF(-2.0);
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double integral_of_hess_pdf = Distribution::GradPDF(-2.0);
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// Perform numerical differentiation and integration
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// Enumerate 4000 grid points in range [-2, 2]
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for (int i = 0; i <= 4000; ++i) {
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const double x = static_cast<double>(i) / 1000.0 - 2.0;
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// Numerical differentiation (p. 246, Numerical Analysis 2nd ed. by Timothy Sauer)
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EXPECT_NEAR((Distribution::CDF(x + 1e-5) - Distribution::CDF(x - 1e-5)) / 2e-5,
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Distribution::PDF(x), 6e-11);
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EXPECT_NEAR((Distribution::PDF(x + 1e-5) - Distribution::PDF(x - 1e-5)) / 2e-5,
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Distribution::GradPDF(x), 6e-11);
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EXPECT_NEAR((Distribution::GradPDF(x + 1e-5) - Distribution::GradPDF(x - 1e-5)) / 2e-5,
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Distribution::HessPDF(x), 6e-11);
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// Numerical integration using Trapezoid Rule (p. 257, Sauer)
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integral_of_pdf += 5e-4 * (Distribution::PDF(x - 1e-3) + Distribution::PDF(x));
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integral_of_grad_pdf += 5e-4 * (Distribution::GradPDF(x - 1e-3) + Distribution::GradPDF(x));
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integral_of_hess_pdf += 5e-4 * (Distribution::HessPDF(x - 1e-3) + Distribution::HessPDF(x));
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EXPECT_NEAR(integral_of_pdf, Distribution::CDF(x), 2e-4);
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EXPECT_NEAR(integral_of_grad_pdf, Distribution::PDF(x), 2e-4);
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EXPECT_NEAR(integral_of_hess_pdf, Distribution::GradPDF(x), 2e-4);
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}
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}
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TEST(ProbabilityDistribution, DistributionGeneric) {
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// Assert d/dx CDF = PDF, d/dx PDF = GradPDF, d/dx GradPDF = HessPDF
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// Do this for every distribution type
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RunDistributionGenericTest<NormalDistribution>();
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RunDistributionGenericTest<LogisticDistribution>();
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RunDistributionGenericTest<ExtremeDistribution>();
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}
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TEST(ProbabilityDistribution, NormalDist) {
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// "Three-sigma rule" (https://en.wikipedia.org/wiki/68–95–99.7_rule)
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// 68% of values are within 1 standard deviation away from the mean
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// 95% of values are within 2 standard deviation away from the mean
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// 99.7% of values are within 3 standard deviation away from the mean
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EXPECT_NEAR(NormalDistribution::CDF(0.5) - NormalDistribution::CDF(-0.5), 0.3829, 0.00005);
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EXPECT_NEAR(NormalDistribution::CDF(1.0) - NormalDistribution::CDF(-1.0), 0.6827, 0.00005);
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EXPECT_NEAR(NormalDistribution::CDF(1.5) - NormalDistribution::CDF(-1.5), 0.8664, 0.00005);
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EXPECT_NEAR(NormalDistribution::CDF(2.0) - NormalDistribution::CDF(-2.0), 0.9545, 0.00005);
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EXPECT_NEAR(NormalDistribution::CDF(2.5) - NormalDistribution::CDF(-2.5), 0.9876, 0.00005);
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EXPECT_NEAR(NormalDistribution::CDF(3.0) - NormalDistribution::CDF(-3.0), 0.9973, 0.00005);
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EXPECT_NEAR(NormalDistribution::CDF(3.5) - NormalDistribution::CDF(-3.5), 0.9995, 0.00005);
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EXPECT_NEAR(NormalDistribution::CDF(4.0) - NormalDistribution::CDF(-4.0), 0.9999, 0.00005);
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}
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TEST(ProbabilityDistribution, LogisticDist) {
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/**
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* Enforce known properties of the logistic distribution.
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* (https://en.wikipedia.org/wiki/Logistic_distribution)
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**/
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// Enumerate 4000 grid points in range [-2, 2]
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for (int i = 0; i <= 4000; ++i) {
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const double x = static_cast<double>(i) / 1000.0 - 2.0;
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// PDF = 1/4 * sech(x/2)**2
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const double sech_x = 1.0 / std::cosh(x * 0.5); // hyperbolic secant at x/2
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EXPECT_NEAR(0.25 * sech_x * sech_x, LogisticDistribution::PDF(x), 1e-15);
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// CDF = 1/2 + 1/2 * tanh(x/2)
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EXPECT_NEAR(0.5 + 0.5 * std::tanh(x * 0.5), LogisticDistribution::CDF(x), 1e-15);
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}
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}
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TEST(ProbabilityDistribution, ExtremeDist) {
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/**
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* Enforce known properties of the extreme distribution (also known as Gumbel distribution).
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* The mean is the negative of the Euler-Mascheroni constant.
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* The variance is 1/6 * pi**2. (https://mathworld.wolfram.com/GumbelDistribution.html)
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**/
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// Enumerate 25000 grid points in range [-20, 5].
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// Compute the mean (expected value) of the distribution using numerical integration.
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// Nearly all mass of the extreme distribution is concentrated between -20 and 5,
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// so numerically integrating x*PDF(x) over [-20, 5] gives good estimate of the mean.
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double mean = 0.0;
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for (int i = 0; i <= 25000; ++i) {
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const double x = static_cast<double>(i) / 1000.0 - 20.0;
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// Numerical integration using Trapezoid Rule (p. 257, Sauer)
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mean +=
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5e-4 * ((x - 1e-3) * ExtremeDistribution::PDF(x - 1e-3) + x * ExtremeDistribution::PDF(x));
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}
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EXPECT_NEAR(mean, -kEulerMascheroni, 1e-7);
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// Enumerate 25000 grid points in range [-20, 5].
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// Compute the variance of the distribution using numerical integration.
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// Nearly all mass of the extreme distribution is concentrated between -20 and 5,
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// so numerically integrating (x-mean)*PDF(x) over [-20, 5] gives good estimate of the variance.
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double variance = 0.0;
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for (int i = 0; i <= 25000; ++i) {
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const double x = static_cast<double>(i) / 1000.0 - 20.0;
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// Numerical integration using Trapezoid Rule (p. 257, Sauer)
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variance += 5e-4 * ((x - 1e-3 - mean) * (x - 1e-3 - mean) * ExtremeDistribution::PDF(x - 1e-3)
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+ (x - mean) * (x - mean) * ExtremeDistribution::PDF(x));
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}
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EXPECT_NEAR(variance, kPI * kPI / 6.0, 1e-6);
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}
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} // namespace common
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} // namespace xgboost
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