5b1161bb64
* Add a new ctor to tensor for `initilizer_list`. * Change labels from host device vector to tensor. * Rename the field from `labels_` to `labels` since it's a public member.
100 lines
3.2 KiB
C++
100 lines
3.2 KiB
C++
/*!
|
|
* Copyright 2015-2019 by Contributors
|
|
* \file custom_metric.cc
|
|
* \brief This is an example to define plugin of xgboost.
|
|
* This plugin defines the additional metric function.
|
|
*/
|
|
#include <xgboost/base.h>
|
|
#include <xgboost/parameter.h>
|
|
#include <xgboost/objective.h>
|
|
#include <xgboost/json.h>
|
|
|
|
namespace xgboost {
|
|
namespace 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
|
|
// for introduction of this module.
|
|
struct MyLogisticParam : public XGBoostParameter<MyLogisticParam> {
|
|
float scale_neg_weight;
|
|
// declare parameters
|
|
DMLC_DECLARE_PARAMETER(MyLogisticParam) {
|
|
DMLC_DECLARE_FIELD(scale_neg_weight).set_default(1.0f).set_lower_bound(0.0f)
|
|
.describe("Scale the weight of negative examples by this factor");
|
|
}
|
|
};
|
|
|
|
DMLC_REGISTER_PARAMETER(MyLogisticParam);
|
|
|
|
// Define a customized logistic regression objective in C++.
|
|
// Implement the interface.
|
|
class MyLogistic : public ObjFunction {
|
|
public:
|
|
void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
|
|
param_.UpdateAllowUnknown(args);
|
|
}
|
|
|
|
struct ObjInfo Task() const override {
|
|
return {ObjInfo::kRegression, false};
|
|
}
|
|
|
|
void GetGradient(const HostDeviceVector<bst_float> &preds,
|
|
const MetaInfo &info,
|
|
int 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();
|
|
auto const labels_h = info.labels.HostView();
|
|
for (size_t i = 0; i < preds_h.size(); ++i) {
|
|
bst_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]));
|
|
// this is the gradient
|
|
bst_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);
|
|
}
|
|
}
|
|
const char* DefaultEvalMetric() const override {
|
|
return "logloss";
|
|
}
|
|
void PredTransform(HostDeviceVector<bst_float> *io_preds) const override {
|
|
// transform margin value to probability.
|
|
std::vector<bst_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 {
|
|
// transform probability to margin value
|
|
return -std::log(1.0f / base_score - 1.0f);
|
|
}
|
|
|
|
void SaveConfig(Json* p_out) const override {
|
|
auto& out = *p_out;
|
|
out["name"] = String("my_logistic");
|
|
out["my_logistic_param"] = ToJson(param_);
|
|
}
|
|
|
|
void LoadConfig(Json const& in) override {
|
|
FromJson(in["my_logistic_param"], ¶m_);
|
|
}
|
|
|
|
private:
|
|
MyLogisticParam param_;
|
|
};
|
|
|
|
// Finally register the objective function.
|
|
// After it succeeds you can try use xgboost with objective=mylogistic
|
|
XGBOOST_REGISTER_OBJECTIVE(MyLogistic, "mylogistic")
|
|
.describe("User defined logistic regression plugin")
|
|
.set_body([]() { return new MyLogistic(); });
|
|
|
|
} // namespace obj
|
|
} // namespace xgboost
|