72cd1517d6
* Replaced std::vector with HostDeviceVector in MetaInfo and SparsePage. - added distributions to HostDeviceVector - using HostDeviceVector for labels, weights and base margings in MetaInfo - using HostDeviceVector for offset and data in SparsePage - other necessary refactoring * Added const version of HostDeviceVector API calls. - const versions added to calls that can trigger data transfers, e.g. DevicePointer() - updated the code that uses HostDeviceVector - objective functions now accept const HostDeviceVector<bst_float>& for predictions * Updated src/linear/updater_gpu_coordinate.cu. * Added read-only state for HostDeviceVector sync. - this means no copies are performed if both host and devices access the HostDeviceVector read-only * Fixed linter and test errors. - updated the lz4 plugin - added ConstDeviceSpan to HostDeviceVector - using device % dh::NVisibleDevices() for the physical device number, e.g. in calls to cudaSetDevice() * Fixed explicit template instantiation errors for HostDeviceVector. - replaced HostDeviceVector<unsigned int> with HostDeviceVector<int> * Fixed HostDeviceVector tests that require multiple GPUs. - added a mock set device handler; when set, it is called instead of cudaSetDevice()
84 lines
2.9 KiB
C++
84 lines
2.9 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 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();
|
|
const std::vector<bst_float>& labels_h = info.labels_.HostVector();
|
|
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 "error";
|
|
}
|
|
void PredTransform(HostDeviceVector<bst_float> *io_preds) override {
|
|
// transform margin value to probability.
|
|
std::vector<bst_float> &preds = io_preds->HostVector();
|
|
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
|