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reduce duplicate computation in poisson, gamma, and tweedie objectives #4950

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merged 2 commits into from
Jan 20, 2022
Merged

reduce duplicate computation in poisson, gamma, and tweedie objectives #4950

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39f56c5
ENH save computations of exp in objectives
lorentzenchr Jan 15, 2022
d09e6fe
CLN missing declaration
lorentzenchr Jan 16, 2022
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37 changes: 23 additions & 14 deletions src/objective/regression_objective.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -439,17 +439,20 @@ class RegressionPoissonLoss: public RegressionL2loss {
*/
void GetGradients(const double* score, score_t* gradients,
score_t* hessians) const override {
double exp_max_delta_step_ = std::exp(max_delta_step_);
if (weights_ == nullptr) {
#pragma omp parallel for schedule(static)
for (data_size_t i = 0; i < num_data_; ++i) {
gradients[i] = static_cast<score_t>(std::exp(score[i]) - label_[i]);
hessians[i] = static_cast<score_t>(std::exp(score[i] + max_delta_step_));
double exp_score = std::exp(score[i]);
gradients[i] = static_cast<score_t>(exp_score - label_[i]);
hessians[i] = static_cast<score_t>(exp_score * exp_max_delta_step_);
}
} else {
#pragma omp parallel for schedule(static)
for (data_size_t i = 0; i < num_data_; ++i) {
gradients[i] = static_cast<score_t>((std::exp(score[i]) - label_[i]) * weights_[i]);
hessians[i] = static_cast<score_t>(std::exp(score[i] + max_delta_step_) * weights_[i]);
double exp_score = std::exp(score[i]);
gradients[i] = static_cast<score_t>((exp_score - label_[i]) * weights_[i]);
hessians[i] = static_cast<score_t>(exp_score * exp_max_delta_step_ * weights_[i]);
}
}
}
Expand Down Expand Up @@ -689,14 +692,16 @@ class RegressionGammaLoss : public RegressionPoissonLoss {
if (weights_ == nullptr) {
#pragma omp parallel for schedule(static)
for (data_size_t i = 0; i < num_data_; ++i) {
gradients[i] = static_cast<score_t>(1.0 - label_[i] * std::exp(-score[i]));
hessians[i] = static_cast<score_t>(label_[i] * std::exp(-score[i]));
double exp_score = std::exp(-score[i]);
gradients[i] = static_cast<score_t>(1.0 - label_[i] * exp_score);
hessians[i] = static_cast<score_t>(label_[i] * exp_score);
}
} else {
#pragma omp parallel for schedule(static)
for (data_size_t i = 0; i < num_data_; ++i) {
gradients[i] = static_cast<score_t>((1.0 - label_[i] * std::exp(-score[i])) * weights_[i]);
hessians[i] = static_cast<score_t>(label_[i] * std::exp(-score[i]) * weights_[i]);
double exp_score = std::exp(-score[i]);
gradients[i] = static_cast<score_t>((1.0 - label_[i] * exp_score) * weights_[i]);
hessians[i] = static_cast<score_t>(label_[i] * exp_score * weights_[i]);
}
}
}
Expand Down Expand Up @@ -725,16 +730,20 @@ class RegressionTweedieLoss: public RegressionPoissonLoss {
if (weights_ == nullptr) {
#pragma omp parallel for schedule(static)
for (data_size_t i = 0; i < num_data_; ++i) {
gradients[i] = static_cast<score_t>(-label_[i] * std::exp((1 - rho_) * score[i]) + std::exp((2 - rho_) * score[i]));
hessians[i] = static_cast<score_t>(-label_[i] * (1 - rho_) * std::exp((1 - rho_) * score[i]) +
(2 - rho_) * std::exp((2 - rho_) * score[i]));
double exp_1_score = std::exp((1 - rho_) * score[i]);
double exp_2_score = std::exp((2 - rho_) * score[i]);
gradients[i] = static_cast<score_t>(-label_[i] * exp_1_score + exp_2_score);
hessians[i] = static_cast<score_t>(-label_[i] * (1 - rho_) * exp_1_score +
(2 - rho_) * exp_2_score);
}
} else {
#pragma omp parallel for schedule(static)
for (data_size_t i = 0; i < num_data_; ++i) {
gradients[i] = static_cast<score_t>((-label_[i] * std::exp((1 - rho_) * score[i]) + std::exp((2 - rho_) * score[i])) * weights_[i]);
hessians[i] = static_cast<score_t>((-label_[i] * (1 - rho_) * std::exp((1 - rho_) * score[i]) +
(2 - rho_) * std::exp((2 - rho_) * score[i])) * weights_[i]);
double exp_1_score = std::exp((1 - rho_) * score[i]);
double exp_2_score = std::exp((2 - rho_) * score[i]);
gradients[i] = static_cast<score_t>((-label_[i] * exp_1_score + exp_2_score) * weights_[i]);
hessians[i] = static_cast<score_t>((-label_[i] * (1 - rho_) * exp_1_score +
(2 - rho_) * exp_2_score) * weights_[i]);
}
}
}
Expand Down