From 1c558a54e99c0081df1402b5384e1921ff74ebdf Mon Sep 17 00:00:00 2001
From: James Lamb <jaylamb20@gmail.com>
Date: Thu, 7 Oct 2021 22:04:20 -0500
Subject: [PATCH] fix possible precision loss in xentropy and fair loss
 objectives (#4651)

---
 src/metric/regression_metric.hpp     | 2 +-
 src/metric/xentropy_metric.hpp       | 4 ++--
 src/objective/xentropy_objective.hpp | 6 +++---
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/metric/regression_metric.hpp b/src/metric/regression_metric.hpp
index d9631811d780..379c36c46aca 100644
--- a/src/metric/regression_metric.hpp
+++ b/src/metric/regression_metric.hpp
@@ -212,7 +212,7 @@ class FairLossMetric: public RegressionMetric<FairLossMetric> {
   inline static double LossOnPoint(label_t label, double score, const Config& config) {
     const double x = std::fabs(score - label);
     const double c = config.fair_c;
-    return c * x - c * c * std::log(1.0f + x / c);
+    return c * x - c * c * std::log1p(x / c);
   }
 
   inline static const char* Name() {
diff --git a/src/metric/xentropy_metric.hpp b/src/metric/xentropy_metric.hpp
index 241b0a856efe..29d4984c64b3 100644
--- a/src/metric/xentropy_metric.hpp
+++ b/src/metric/xentropy_metric.hpp
@@ -194,13 +194,13 @@ class CrossEntropyLambdaMetric : public Metric {
       if (weights_ == nullptr) {
         #pragma omp parallel for schedule(static) reduction(+:sum_loss)
         for (data_size_t i = 0; i < num_data_; ++i) {
-          double hhat = std::log(1.0f + std::exp(score[i]));  // auto-convert
+          double hhat = std::log1p(std::exp(score[i]));  // auto-convert
           sum_loss += XentLambdaLoss(label_[i], 1.0f, hhat);
         }
       } else {
         #pragma omp parallel for schedule(static) reduction(+:sum_loss)
         for (data_size_t i = 0; i < num_data_; ++i) {
-          double hhat = std::log(1.0f + std::exp(score[i]));  // auto-convert
+          double hhat = std::log1p(std::exp(score[i]));  // auto-convert
           sum_loss += XentLambdaLoss(label_[i], weights_[i], hhat);
         }
       }
diff --git a/src/objective/xentropy_objective.hpp b/src/objective/xentropy_objective.hpp
index 22f9b4d33cbb..baee5bf991e4 100644
--- a/src/objective/xentropy_objective.hpp
+++ b/src/objective/xentropy_objective.hpp
@@ -203,7 +203,7 @@ class CrossEntropyLambda: public ObjectiveFunction {
         const double w = weights_[i];
         const double y = label_[i];
         const double epf = std::exp(score[i]);
-        const double hhat = std::log(1.0f + epf);
+        const double hhat = std::log1p(epf);
         const double z = 1.0f - std::exp(-w*hhat);
         const double enf = 1.0f / epf;  // = std::exp(-score[i]);
         gradients[i] = static_cast<score_t>((1.0f - y / z) * w / (1.0f + enf));
@@ -231,7 +231,7 @@ class CrossEntropyLambda: public ObjectiveFunction {
   //
 
   void ConvertOutput(const double* input, double* output) const override {
-    output[0] = std::log(1.0f + std::exp(input[0]));
+    output[0] = std::log1p(std::exp(input[0]));
   }
 
   std::string ToString() const override {
@@ -259,7 +259,7 @@ class CrossEntropyLambda: public ObjectiveFunction {
       }
     }
     double havg = suml / sumw;
-    double initscore = std::log(std::exp(havg) - 1.0f);
+    double initscore = std::log(std::expm1(havg));
     Log::Info("[%s:%s]: havg = %f -> initscore = %f",  GetName(), __func__, havg, initscore);
     return initscore;
   }