Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Reduce compile warnings (#6198)

Browse Source 
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
This commit is contained in:
vcarpani
2020-10-08 17:14:59 +02:00
committed by GitHub
parent a4ce0eae43
commit 6bc9747df5
6 changed files with 40 additions and 21 deletions
Download Patch File Download Diff File Expand all files Collapse all files

38
src/linear/coordinate_common.h
View File

@@ -220,10 +220,10 @@ class FeatureSelector {
* \param lambda Regularisation lambda.
* \param param A parameter with algorithm-dependent use.
*/
virtual void Setup(const gbm::GBLinearModel &model,
const std::vector<GradientPair> &gpair,
DMatrix *p_fmat,
float alpha, float lambda, int param) {}
virtual void Setup(const gbm::GBLinearModel &,
const std::vector<GradientPair> &,
DMatrix *,
float , float , int ) {}
/**
* \brief Select next coordinate to update.
*
@@ -250,8 +250,8 @@ class FeatureSelector {
class CyclicFeatureSelector : public FeatureSelector {
public:
int NextFeature(int iteration, const gbm::GBLinearModel &model,
int group_idx, const std::vector<GradientPair> &gpair,
DMatrix *p_fmat, float alpha, float lambda) override {
int , const std::vector<GradientPair> &,
DMatrix *, float, float) override {
return iteration % model.learner_model_param->num_feature;
}
};
@@ -263,8 +263,8 @@ class CyclicFeatureSelector : public FeatureSelector {
class ShuffleFeatureSelector : public FeatureSelector {
public:
void Setup(const gbm::GBLinearModel &model,
const std::vector<GradientPair> &gpair,
DMatrix *p_fmat, float alpha, float lambda, int param) override {
const std::vector<GradientPair> &g,
DMatrix *, float, float, int) override {
if (feat_index_.size() == 0) {
feat_index_.resize(model.learner_model_param->num_feature);
std::iota(feat_index_.begin(), feat_index_.end(), 0);
@@ -273,8 +273,8 @@ class ShuffleFeatureSelector : public FeatureSelector {
}
int NextFeature(int iteration, const gbm::GBLinearModel &model,
int group_idx, const std::vector<GradientPair> &gpair,
DMatrix *p_fmat, float alpha, float lambda) override {
int, const std::vector<GradientPair> &,
DMatrix *, float, float) override {
return feat_index_[iteration % model.learner_model_param->num_feature];
}
@@ -288,9 +288,9 @@ class ShuffleFeatureSelector : public FeatureSelector {
*/
class RandomFeatureSelector : public FeatureSelector {
public:
int NextFeature(int iteration, const gbm::GBLinearModel &model,
int group_idx, const std::vector<GradientPair> &gpair,
DMatrix *p_fmat, float alpha, float lambda) override {
int NextFeature(int, const gbm::GBLinearModel &model,
int, const std::vector<GradientPair> &,
DMatrix *, float, float) override {
return common::GlobalRandom()() % model.learner_model_param->num_feature;
}
};
@@ -307,8 +307,8 @@ class RandomFeatureSelector : public FeatureSelector {
class GreedyFeatureSelector : public FeatureSelector {
public:
void Setup(const gbm::GBLinearModel &model,
const std::vector<GradientPair> &gpair,
DMatrix *p_fmat, float alpha, float lambda, int param) override {
const std::vector<GradientPair> &,
DMatrix *, float, float, int param) override {
top_k_ = static_cast<bst_uint>(param);
const bst_uint ngroup = model.learner_model_param->num_output_group;
if (param <= 0) top_k_ = std::numeric_limits<bst_uint>::max();
@@ -321,7 +321,7 @@ class GreedyFeatureSelector : public FeatureSelector {
}
}
int NextFeature(int iteration, const gbm::GBLinearModel &model,
int NextFeature(int, const gbm::GBLinearModel &model,
int group_idx, const std::vector<GradientPair> &gpair,
DMatrix *p_fmat, float alpha, float lambda) override {
// k-th selected feature for a group
@@ -438,9 +438,9 @@ class ThriftyFeatureSelector : public FeatureSelector {
}
}
int NextFeature(int iteration, const gbm::GBLinearModel &model,
int group_idx, const std::vector<GradientPair> &gpair,
DMatrix *p_fmat, float alpha, float lambda) override {
int NextFeature(int, const gbm::GBLinearModel &model,
int group_idx, const std::vector<GradientPair> &,
DMatrix *, float, float) override {
// k-th selected feature for a group
auto k = counter_[group_idx]++;
// stop after either reaching top-N or going through all the features in a group