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Cleanup regression objectives. (#8539)

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Jiaming Yuan 2022-12-07 01:05:42 +08:00 committed by GitHub
parent 7774bf628e
commit e38fe21e0d
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1 changed files with 8 additions and 51 deletions
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59
src/objective/regression_loss.h
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@ -6,33 +6,23 @@
#include <dmlc/omp.h>
#include <xgboost/logging.h>
#include <algorithm>
#include "xgboost/task.h"
#include <cmath>
#include "../common/math.h"
#include "xgboost/task.h" // ObjInfo
namespace xgboost {
namespace obj {
// common regressions
// linear regression
struct LinearSquareLoss {
// duplication is necessary, as __device__ specifier
// cannot be made conditional on template parameter
XGBOOST_DEVICE static bst_float PredTransform(bst_float x) { return x; }
XGBOOST_DEVICE static bool CheckLabel(bst_float) { return true; }
XGBOOST_DEVICE static bst_float FirstOrderGradient(bst_float predt, bst_float label) {
return predt - label;
}
XGBOOST_DEVICE static bst_float SecondOrderGradient(bst_float, bst_float) {
return 1.0f;
}
template <typename T>
static T PredTransform(T x) { return x; }
template <typename T>
static T FirstOrderGradient(T predt, T label) { return predt - label; }
template <typename T>
static T SecondOrderGradient(T predt, T label) { return T(1.0f); }
XGBOOST_DEVICE static bst_float SecondOrderGradient(bst_float, bst_float) { return 1.0f; }
static bst_float ProbToMargin(bst_float base_score) { return base_score; }
static const char* LabelErrorMsg() { return ""; }
static const char* DefaultEvalMetric() { return "rmse"; }
@ -43,17 +33,14 @@ struct LinearSquareLoss {
struct SquaredLogError {
XGBOOST_DEVICE static bst_float PredTransform(bst_float x) { return x; }
XGBOOST_DEVICE static bool CheckLabel(bst_float label) {
return label > -1;
}
XGBOOST_DEVICE static bool CheckLabel(bst_float label) { return label > -1; }
XGBOOST_DEVICE static bst_float FirstOrderGradient(bst_float predt, bst_float label) {
predt = fmaxf(predt, -1 + 1e-6); // ensure correct value for log1p
return (std::log1p(predt) - std::log1p(label)) / (predt + 1);
}
XGBOOST_DEVICE static bst_float SecondOrderGradient(bst_float predt, bst_float label) {
predt = fmaxf(predt, -1 + 1e-6);
float res = (-std::log1p(predt) + std::log1p(label) + 1) /
std::pow(predt + 1, 2);
float res = (-std::log1p(predt) + std::log1p(label) + 1) / std::pow(predt + 1, 2);
res = fmaxf(res, 1e-6f);
return res;
}
@ -70,8 +57,6 @@ struct SquaredLogError {
// logistic loss for probability regression task
struct LogisticRegression {
// duplication is necessary, as __device__ specifier
// cannot be made conditional on template parameter
XGBOOST_DEVICE static bst_float PredTransform(bst_float x) { return common::Sigmoid(x); }
XGBOOST_DEVICE static bool CheckLabel(bst_float x) { return x >= 0.0f && x <= 1.0f; }
XGBOOST_DEVICE static bst_float FirstOrderGradient(bst_float predt, bst_float label) {
@ -81,23 +66,12 @@ struct LogisticRegression {
const float eps = 1e-16f;
return fmaxf(predt * (1.0f - predt), eps);
}
template <typename T>
static T PredTransform(T x) { return common::Sigmoid(x); }
template <typename T>
static T FirstOrderGradient(T predt, T label) { return predt - label; }
template <typename T>
static T SecondOrderGradient(T predt, T label) {
const T eps = T(1e-16f);
return std::max(predt * (T(1.0f) - predt), eps);
}
static bst_float ProbToMargin(bst_float base_score) {
CHECK(base_score > 0.0f && base_score < 1.0f)
<< "base_score must be in (0,1) for logistic loss, got: " << base_score;
return -logf(1.0f / base_score - 1.0f);
}
static const char* LabelErrorMsg() {
return "label must be in [0,1] for logistic regression";
}
static const char* LabelErrorMsg() { return "label must be in [0,1] for logistic regression"; }
static const char* DefaultEvalMetric() { return "rmse"; }
static const char* Name() { return "reg:logistic"; }
@ -114,8 +88,6 @@ struct LogisticClassification : public LogisticRegression {
// logistic loss, but predict un-transformed margin
struct LogisticRaw : public LogisticRegression {
// duplication is necessary, as __device__ specifier
// cannot be made conditional on template parameter
XGBOOST_DEVICE static bst_float PredTransform(bst_float x) { return x; }
XGBOOST_DEVICE static bst_float FirstOrderGradient(bst_float predt, bst_float label) {
predt = common::Sigmoid(predt);
@ -126,22 +98,7 @@ struct LogisticRaw : public LogisticRegression {
predt = common::Sigmoid(predt);
return fmaxf(predt * (1.0f - predt), eps);
}
template <typename T>
static T PredTransform(T x) { return x; }
template <typename T>
static T FirstOrderGradient(T predt, T label) {
predt = common::Sigmoid(predt);
return predt - label;
}
template <typename T>
static T SecondOrderGradient(T predt, T label) {
const T eps = T(1e-16f);
predt = common::Sigmoid(predt);
return std::max(predt * (T(1.0f) - predt), eps);
}
static bst_float ProbToMargin(bst_float base_score) {
return base_score;
}
static bst_float ProbToMargin(bst_float base_score) { return base_score; }
static const char* DefaultEvalMetric() { return "logloss"; }
static const char* Name() { return "binary:logitraw"; }