Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

fix possible precision loss in xentropy and fair loss objectives #4651

Merged
merged 2 commits into from
Oct 8, 2021
Merged

fix possible precision loss in xentropy and fair loss objectives #4651

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
7a447bb
fix possible precision loss in xentropy and fair loss objectives
jameslamb Sep 13, 2021
a6efaf6
Merge branch 'master' into cppcheck/log1p
jameslamb Oct 5, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion src/metric/regression_metric.hpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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() {
Expand Down
4 changes: 2 additions & 2 deletions src/metric/xentropy_metric.hpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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);
}
}
Expand Down
6 changes: 3 additions & 3 deletions src/objective/xentropy_objective.hpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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));
Expand Down Expand Up @@ -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 {
Expand Down Expand Up @@ -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;
}
Expand Down