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Use matrix for gradient. (#9508)

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- Use the `linalg::Matrix` for storing gradients.
- New API for the custom objective.
- Custom objective for multi-class/multi-target is now required to return the correct shape.
- Custom objective for Python can accept arrays with any strides. (row-major, column-major)
This commit is contained in:
Jiaming Yuan
2023-08-24 05:29:52 +08:00
committed by GitHub
parent 6103dca0bb
commit 972730cde0
77 changed files with 1052 additions and 651 deletions
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41
plugin/example/custom_obj.cc
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@@ -1,5 +1,5 @@
/*!
* Copyright 2015-2022 by Contributors
/**
* Copyright 2015-2023, XGBoost Contributors
* \file custom_metric.cc
* \brief This is an example to define plugin of xgboost.
* This plugin defines the additional metric function.
@@ -9,9 +9,7 @@
#include <xgboost/objective.h>
#include <xgboost/json.h>
namespace xgboost {
namespace obj {
namespace xgboost::obj {
// This is a helpful data structure to define parameters
// You do not have to use it.
// see http://dmlc-core.readthedocs.org/en/latest/parameter.html
@@ -33,38 +31,38 @@ class MyLogistic : public ObjFunction {
public:
void Configure(const Args& args) override { param_.UpdateAllowUnknown(args); }
ObjInfo Task() const override { return ObjInfo::kRegression; }
[[nodiscard]] ObjInfo Task() const override { return ObjInfo::kRegression; }
void GetGradient(const HostDeviceVector<bst_float>& preds, const MetaInfo& info, int32_t /*iter*/,
HostDeviceVector<GradientPair>* out_gpair) override {
out_gpair->Resize(preds.Size());
const std::vector<bst_float>& preds_h = preds.HostVector();
std::vector<GradientPair>& out_gpair_h = out_gpair->HostVector();
void GetGradient(const HostDeviceVector<float>& preds, MetaInfo const& info,
std::int32_t /*iter*/, linalg::Matrix<GradientPair>* out_gpair) override {
out_gpair->Reshape(info.num_row_, 1);
const std::vector<float>& preds_h = preds.HostVector();
auto out_gpair_h = out_gpair->HostView();
auto const labels_h = info.labels.HostView();
for (size_t i = 0; i < preds_h.size(); ++i) {
bst_float w = info.GetWeight(i);
float w = info.GetWeight(i);
// scale the negative examples!
if (labels_h(i) == 0.0f) w *= param_.scale_neg_weight;
// logistic transformation
bst_float p = 1.0f / (1.0f + std::exp(-preds_h[i]));
float p = 1.0f / (1.0f + std::exp(-preds_h[i]));
// this is the gradient
bst_float grad = (p - labels_h(i)) * w;
float grad = (p - labels_h(i)) * w;
// this is the second order gradient
bst_float hess = p * (1.0f - p) * w;
out_gpair_h.at(i) = GradientPair(grad, hess);
float hess = p * (1.0f - p) * w;
out_gpair_h(i) = GradientPair(grad, hess);
}
}
const char* DefaultEvalMetric() const override {
[[nodiscard]] const char* DefaultEvalMetric() const override {
return "logloss";
}
void PredTransform(HostDeviceVector<bst_float> *io_preds) const override {
void PredTransform(HostDeviceVector<float> *io_preds) const override {
// transform margin value to probability.
std::vector<bst_float> &preds = io_preds->HostVector();
std::vector<float> &preds = io_preds->HostVector();
for (auto& pred : preds) {
pred = 1.0f / (1.0f + std::exp(-pred));
}
}
bst_float ProbToMargin(bst_float base_score) const override {
[[nodiscard]] float ProbToMargin(float base_score) const override {
// transform probability to margin value
return -std::log(1.0f / base_score - 1.0f);
}
@@ -89,5 +87,4 @@ XGBOOST_REGISTER_OBJECTIVE(MyLogistic, "mylogistic")
.describe("User defined logistic regression plugin")
.set_body([]() { return new MyLogistic(); });
} // namespace obj
} // namespace xgboost
} // namespace xgboost::obj