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Upgrade clang-tidy on CI. (#5469)

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* Correct all clang-tidy errors.
* Upgrade clang-tidy to 10 on CI.

Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
This commit is contained in:
Jiaming Yuan
2020-04-05 04:42:29 +08:00
committed by GitHub
parent 30e94ddd04
commit 0012f2ef93
107 changed files with 932 additions and 903 deletions
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26
src/linear/coordinate_common.h
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@@ -252,7 +252,7 @@ class CyclicFeatureSelector : 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 {
return iteration % model.learner_model_param_->num_feature;
return iteration % model.learner_model_param->num_feature;
}
};
@@ -266,7 +266,7 @@ class ShuffleFeatureSelector : public FeatureSelector {
const std::vector<GradientPair> &gpair,
DMatrix *p_fmat, float alpha, float lambda, int param) override {
if (feat_index_.size() == 0) {
feat_index_.resize(model.learner_model_param_->num_feature);
feat_index_.resize(model.learner_model_param->num_feature);
std::iota(feat_index_.begin(), feat_index_.end(), 0);
}
std::shuffle(feat_index_.begin(), feat_index_.end(), common::GlobalRandom());
@@ -275,7 +275,7 @@ 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 {
return feat_index_[iteration % model.learner_model_param_->num_feature];
return feat_index_[iteration % model.learner_model_param->num_feature];
}
protected:
@@ -291,7 +291,7 @@ class RandomFeatureSelector : 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 {
return common::GlobalRandom()() % model.learner_model_param_->num_feature;
return common::GlobalRandom()() % model.learner_model_param->num_feature;
}
};
@@ -310,11 +310,11 @@ class GreedyFeatureSelector : public FeatureSelector {
const std::vector<GradientPair> &gpair,
DMatrix *p_fmat, float alpha, float lambda, int param) override {
top_k_ = static_cast<bst_uint>(param);
const bst_uint ngroup = model.learner_model_param_->num_output_group;
const bst_uint ngroup = model.learner_model_param->num_output_group;
if (param <= 0) top_k_ = std::numeric_limits<bst_uint>::max();
if (counter_.size() == 0) {
counter_.resize(ngroup);
gpair_sums_.resize(model.learner_model_param_->num_feature * ngroup);
gpair_sums_.resize(model.learner_model_param->num_feature * ngroup);
}
for (bst_uint gid = 0u; gid < ngroup; ++gid) {
counter_[gid] = 0u;
@@ -327,10 +327,10 @@ class GreedyFeatureSelector : public FeatureSelector {
// k-th selected feature for a group
auto k = counter_[group_idx]++;
// stop after either reaching top-K or going through all the features in a group
if (k >= top_k_ || counter_[group_idx] == model.learner_model_param_->num_feature) return -1;
if (k >= top_k_ || counter_[group_idx] == model.learner_model_param->num_feature) return -1;
const int ngroup = model.learner_model_param_->num_output_group;
const bst_omp_uint nfeat = model.learner_model_param_->num_feature;
const int ngroup = model.learner_model_param->num_output_group;
const bst_omp_uint nfeat = model.learner_model_param->num_feature;
// Calculate univariate gradient sums
std::fill(gpair_sums_.begin(), gpair_sums_.end(), std::make_pair(0., 0.));
for (const auto &batch : p_fmat->GetBatches<CSCPage>()) {
@@ -387,8 +387,8 @@ class ThriftyFeatureSelector : public FeatureSelector {
DMatrix *p_fmat, float alpha, float lambda, int param) override {
top_k_ = static_cast<bst_uint>(param);
if (param <= 0) top_k_ = std::numeric_limits<bst_uint>::max();
const bst_uint ngroup = model.learner_model_param_->num_output_group;
const bst_omp_uint nfeat = model.learner_model_param_->num_feature;
const bst_uint ngroup = model.learner_model_param->num_output_group;
const bst_omp_uint nfeat = model.learner_model_param->num_feature;
if (deltaw_.size() == 0) {
deltaw_.resize(nfeat * ngroup);
@@ -444,9 +444,9 @@ class ThriftyFeatureSelector : public FeatureSelector {
// 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
if (k >= top_k_ || counter_[group_idx] == model.learner_model_param_->num_feature) return -1;
if (k >= top_k_ || counter_[group_idx] == model.learner_model_param->num_feature) return -1;
// note that sorted_idx stores the "long" indices
const size_t grp_offset = group_idx * model.learner_model_param_->num_feature;
const size_t grp_offset = group_idx * model.learner_model_param->num_feature;
return static_cast<int>(sorted_idx_[grp_offset + k] - grp_offset);
}