5fc5ec539d
* Robust regularization of AFT gradient and hessian * Fix AFT doc; expose it to tutorial TOC * Apply robust regularization to uncensored case too * Revise unit test slightly * Fix lint * Update test_survival.py * Use GradientPairPrecise * Remove unused variables
265 lines
9.7 KiB
C++
265 lines
9.7 KiB
C++
/*!
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* Copyright 2019 by Contributors
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* \file survival_util.cc
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* \brief Utility functions, useful for implementing objective and metric functions for survival
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* analysis
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* \author Avinash Barnwal, Hyunsu Cho and Toby Hocking
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*/
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#include <dmlc/registry.h>
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#include <algorithm>
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#include <cmath>
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#include "survival_util.h"
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/*
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- Formulas are motivated from document -
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http://members.cbio.mines-paristech.fr/~thocking/survival.pdf
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- Detailed Derivation of Loss/Gradient/Hessian -
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https://github.com/avinashbarnwal/GSOC-2019/blob/master/doc/Accelerated_Failure_Time.pdf
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*/
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namespace {
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// Allowable range for gradient and hessian. Used for regularization
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constexpr double kMinGradient = -15.0;
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constexpr double kMaxGradient = 15.0;
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constexpr double kMinHessian = 1e-16; // Ensure that no data point gets zero hessian
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constexpr double kMaxHessian = 15.0;
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constexpr double kEps = 1e-12; // A denomitor in a fraction should not be too small
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// Clip (limit) x to fit range [x_min, x_max].
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// If x < x_min, return x_min; if x > x_max, return x_max; if x_min <= x <= x_max, return x.
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// This function assumes x_min < x_max; behavior is undefined if this assumption does not hold.
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inline double Clip(double x, double x_min, double x_max) {
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if (x < x_min) {
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return x_min;
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}
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if (x > x_max) {
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return x_max;
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}
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return x;
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}
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using xgboost::common::ProbabilityDistributionType;
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enum class CensoringType : uint8_t {
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kUncensored, kRightCensored, kLeftCensored, kIntervalCensored
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};
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using xgboost::GradientPairPrecise;
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inline GradientPairPrecise GetLimitAtInfPred(ProbabilityDistributionType dist_type,
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CensoringType censor_type,
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double sign, double sigma) {
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switch (censor_type) {
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case CensoringType::kUncensored:
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switch (dist_type) {
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case ProbabilityDistributionType::kNormal:
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return sign ? GradientPairPrecise{ kMinGradient, 1.0 / (sigma * sigma) }
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: GradientPairPrecise{ kMaxGradient, 1.0 / (sigma * sigma) };
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case ProbabilityDistributionType::kLogistic:
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return sign ? GradientPairPrecise{ -1.0 / sigma, kMinHessian }
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: GradientPairPrecise{ 1.0 / sigma, kMinHessian };
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case ProbabilityDistributionType::kExtreme:
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return sign ? GradientPairPrecise{ kMinGradient, kMaxHessian }
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: GradientPairPrecise{ 1.0 / sigma, kMinHessian };
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default:
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LOG(FATAL) << "Unknown distribution type";
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}
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case CensoringType::kRightCensored:
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switch (dist_type) {
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case ProbabilityDistributionType::kNormal:
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return sign ? GradientPairPrecise{ kMinGradient, 1.0 / (sigma * sigma) }
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: GradientPairPrecise{ 0.0, kMinHessian };
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case ProbabilityDistributionType::kLogistic:
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return sign ? GradientPairPrecise{ -1.0 / sigma, kMinHessian }
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: GradientPairPrecise{ 0.0, kMinHessian };
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case ProbabilityDistributionType::kExtreme:
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return sign ? GradientPairPrecise{ kMinGradient, kMaxHessian }
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: GradientPairPrecise{ 0.0, kMinHessian };
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default:
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LOG(FATAL) << "Unknown distribution type";
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}
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case CensoringType::kLeftCensored:
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switch (dist_type) {
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case ProbabilityDistributionType::kNormal:
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return sign ? GradientPairPrecise{ 0.0, kMinHessian }
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: GradientPairPrecise{ kMaxGradient, 1.0 / (sigma * sigma) };
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case ProbabilityDistributionType::kLogistic:
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return sign ? GradientPairPrecise{ 0.0, kMinHessian }
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: GradientPairPrecise{ 1.0 / sigma, kMinHessian };
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case ProbabilityDistributionType::kExtreme:
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return sign ? GradientPairPrecise{ 0.0, kMinHessian }
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: GradientPairPrecise{ 1.0 / sigma, kMinHessian };
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default:
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LOG(FATAL) << "Unknown distribution type";
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}
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case CensoringType::kIntervalCensored:
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switch (dist_type) {
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case ProbabilityDistributionType::kNormal:
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return sign ? GradientPairPrecise{ kMinGradient, 1.0 / (sigma * sigma) }
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: GradientPairPrecise{ kMaxGradient, 1.0 / (sigma * sigma) };
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case ProbabilityDistributionType::kLogistic:
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return sign ? GradientPairPrecise{ -1.0 / sigma, kMinHessian }
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: GradientPairPrecise{ 1.0 / sigma, kMinHessian };
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case ProbabilityDistributionType::kExtreme:
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return sign ? GradientPairPrecise{ kMinGradient, kMaxHessian }
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: GradientPairPrecise{ 1.0 / sigma, kMinHessian };
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default:
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LOG(FATAL) << "Unknown distribution type";
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}
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default:
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LOG(FATAL) << "Unknown censoring type";
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}
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return { 0.0, 0.0 };
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}
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} // anonymous namespace
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namespace xgboost {
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namespace common {
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DMLC_REGISTER_PARAMETER(AFTParam);
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double AFTLoss::Loss(double y_lower, double y_upper, double y_pred, double sigma) {
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const double log_y_lower = std::log(y_lower);
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const double log_y_upper = std::log(y_upper);
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double cost;
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if (y_lower == y_upper) { // uncensored
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const double z = (log_y_lower - y_pred) / sigma;
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const double pdf = dist_->PDF(z);
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// Regularize the denominator with eps, to avoid INF or NAN
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cost = -std::log(std::max(pdf / (sigma * y_lower), kEps));
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} else { // censored; now check what type of censorship we have
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double z_u, z_l, cdf_u, cdf_l;
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if (std::isinf(y_upper)) { // right-censored
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cdf_u = 1;
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} else { // left-censored or interval-censored
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z_u = (log_y_upper - y_pred) / sigma;
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cdf_u = dist_->CDF(z_u);
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}
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if (std::isinf(y_lower)) { // left-censored
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cdf_l = 0;
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} else { // right-censored or interval-censored
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z_l = (log_y_lower - y_pred) / sigma;
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cdf_l = dist_->CDF(z_l);
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}
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// Regularize the denominator with eps, to avoid INF or NAN
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cost = -std::log(std::max(cdf_u - cdf_l, kEps));
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}
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return cost;
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}
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double AFTLoss::Gradient(double y_lower, double y_upper, double y_pred, double sigma) {
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const double log_y_lower = std::log(y_lower);
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const double log_y_upper = std::log(y_upper);
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double numerator, denominator, gradient; // numerator and denominator of gradient
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CensoringType censor_type;
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bool z_sign; // sign of z-score
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if (y_lower == y_upper) { // uncensored
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const double z = (log_y_lower - y_pred) / sigma;
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const double pdf = dist_->PDF(z);
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const double grad_pdf = dist_->GradPDF(z);
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censor_type = CensoringType::kUncensored;
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numerator = grad_pdf;
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denominator = sigma * pdf;
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z_sign = (z > 0);
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} else { // censored; now check what type of censorship we have
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double z_u = 0.0, z_l = 0.0, pdf_u, pdf_l, cdf_u, cdf_l;
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censor_type = CensoringType::kIntervalCensored;
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if (std::isinf(y_upper)) { // right-censored
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pdf_u = 0;
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cdf_u = 1;
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censor_type = CensoringType::kRightCensored;
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} else { // interval-censored or left-censored
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z_u = (log_y_upper - y_pred) / sigma;
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pdf_u = dist_->PDF(z_u);
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cdf_u = dist_->CDF(z_u);
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}
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if (std::isinf(y_lower)) { // left-censored
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pdf_l = 0;
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cdf_l = 0;
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censor_type = CensoringType::kLeftCensored;
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} else { // interval-censored or right-censored
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z_l = (log_y_lower - y_pred) / sigma;
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pdf_l = dist_->PDF(z_l);
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cdf_l = dist_->CDF(z_l);
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}
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z_sign = (z_u > 0 || z_l > 0);
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numerator = pdf_u - pdf_l;
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denominator = sigma * (cdf_u - cdf_l);
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}
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gradient = numerator / denominator;
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if (denominator < kEps && (std::isnan(gradient) || std::isinf(gradient))) {
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gradient = GetLimitAtInfPred(dist_type_, censor_type, z_sign, sigma).GetGrad();
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}
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return Clip(gradient, kMinGradient, kMaxGradient);
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}
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double AFTLoss::Hessian(double y_lower, double y_upper, double y_pred, double sigma) {
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const double log_y_lower = std::log(y_lower);
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const double log_y_upper = std::log(y_upper);
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double numerator, denominator, hessian; // numerator and denominator of hessian
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CensoringType censor_type;
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bool z_sign; // sign of z-score
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if (y_lower == y_upper) { // uncensored
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const double z = (log_y_lower - y_pred) / sigma;
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const double pdf = dist_->PDF(z);
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const double grad_pdf = dist_->GradPDF(z);
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const double hess_pdf = dist_->HessPDF(z);
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censor_type = CensoringType::kUncensored;
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numerator = -(pdf * hess_pdf - grad_pdf * grad_pdf);
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denominator = sigma * sigma * pdf * pdf;
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z_sign = (z > 0);
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} else { // censored; now check what type of censorship we have
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double z_u = 0.0, z_l = 0.0, grad_pdf_u, grad_pdf_l, pdf_u, pdf_l, cdf_u, cdf_l;
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censor_type = CensoringType::kIntervalCensored;
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if (std::isinf(y_upper)) { // right-censored
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pdf_u = 0;
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cdf_u = 1;
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grad_pdf_u = 0;
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censor_type = CensoringType::kRightCensored;
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} else { // interval-censored or left-censored
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z_u = (log_y_upper - y_pred) / sigma;
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pdf_u = dist_->PDF(z_u);
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cdf_u = dist_->CDF(z_u);
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grad_pdf_u = dist_->GradPDF(z_u);
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}
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if (std::isinf(y_lower)) { // left-censored
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pdf_l = 0;
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cdf_l = 0;
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grad_pdf_l = 0;
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censor_type = CensoringType::kLeftCensored;
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} else { // interval-censored or right-censored
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z_l = (log_y_lower - y_pred) / sigma;
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pdf_l = dist_->PDF(z_l);
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cdf_l = dist_->CDF(z_l);
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grad_pdf_l = dist_->GradPDF(z_l);
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}
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const double cdf_diff = cdf_u - cdf_l;
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const double pdf_diff = pdf_u - pdf_l;
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const double grad_diff = grad_pdf_u - grad_pdf_l;
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const double sqrt_denominator = sigma * cdf_diff;
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z_sign = (z_u > 0 || z_l > 0);
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numerator = -(cdf_diff * grad_diff - pdf_diff * pdf_diff);
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denominator = sqrt_denominator * sqrt_denominator;
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}
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hessian = numerator / denominator;
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if (denominator < kEps && (std::isnan(hessian) || std::isinf(hessian))) {
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hessian = GetLimitAtInfPred(dist_type_, censor_type, z_sign, sigma).GetHess();
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}
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return Clip(hessian, kMinHessian, kMaxHessian);
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}
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} // namespace common
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} // namespace xgboost
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