6f16f0ef58
* Fix various typos * Add override to functions that are overridden gcc gives warnings about functions that are being overridden by not being marked as oveirridden. This fixes it. * Use bst_float consistently Use bst_float for all the variables that involve weight, leaf value, gradient, hessian, gain, loss_chg, predictions, base_margin, feature values. In some cases, when due to additions and so on the value can take a larger value, double is used. This ensures that type conversions are minimal and reduces loss of precision.
81 lines
2.7 KiB
C++
81 lines
2.7 KiB
C++
/*!
|
|
* Copyright 2015 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 <dmlc/parameter.h>
|
|
#include <xgboost/objective.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 dmlc::Parameter<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_.InitAllowUnknown(args);
|
|
}
|
|
void GetGradient(const std::vector<bst_float> &preds,
|
|
const MetaInfo &info,
|
|
int iter,
|
|
std::vector<bst_gpair> *out_gpair) override {
|
|
out_gpair->resize(preds.size());
|
|
for (size_t i = 0; i < preds.size(); ++i) {
|
|
bst_float w = info.GetWeight(i);
|
|
// scale the negative examples!
|
|
if (info.labels[i] == 0.0f) w *= param_.scale_neg_weight;
|
|
// logistic transformation
|
|
bst_float p = 1.0f / (1.0f + std::exp(-preds[i]));
|
|
// this is the gradient
|
|
bst_float grad = (p - info.labels[i]) * w;
|
|
// this is the second order gradient
|
|
bst_float hess = p * (1.0f - p) * w;
|
|
out_gpair->at(i) = bst_gpair(grad, hess);
|
|
}
|
|
}
|
|
const char* DefaultEvalMetric() const override {
|
|
return "error";
|
|
}
|
|
void PredTransform(std::vector<bst_float> *io_preds) override {
|
|
// transform margin value to probability.
|
|
std::vector<bst_float> &preds = *io_preds;
|
|
for (size_t i = 0; i < preds.size(); ++i) {
|
|
preds[i] = 1.0f / (1.0f + std::exp(-preds[i]));
|
|
}
|
|
}
|
|
bst_float ProbToMargin(bst_float base_score) const override {
|
|
// transform probability to margin value
|
|
return -std::log(1.0f / base_score - 1.0f);
|
|
}
|
|
|
|
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
|