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Clean up warnings (#6325)

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Igor Moura 2020-10-30 12:50:29 -03:00 committed by GitHub
parent f0fe18fc28
commit 5e1e972aea
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16 changed files with 53 additions and 61 deletions
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2
include/xgboost/data.h
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@ -421,7 +421,7 @@ class BatchIterator {
return *(*impl_);
}
bool operator!=(const BatchIterator& rhs) const {
bool operator!=(const BatchIterator&) const {
CHECK(impl_ != nullptr);
return !impl_->AtEnd();
}

2
include/xgboost/predictor.h
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@ -117,7 +117,7 @@ class Predictor {
*
* \param cfg The configuration.
*/
virtual void Configure(const std::vector<std::pair<std::string, std::string>>& cfg);
virtual void Configure(const std::vector<std::pair<std::string, std::string>>&);
/**
* \brief Generate batch predictions for a given feature matrix. May use

2
src/c_api/c_api.cc
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@ -163,7 +163,7 @@ XGB_DLL int XGDMatrixCreateFromCSCEx(const size_t* col_ptr,
const unsigned* indices,
const bst_float* data,
size_t nindptr,
size_t nelem,
size_t,
size_t num_row,
DMatrixHandle* out) {
API_BEGIN();

2
src/common/hist_util.cc
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@ -163,7 +163,7 @@ void GHistIndexMatrix::Init(DMatrix* p_fmat, int max_bins) {
} else {
common::Span<uint32_t> index_data_span = {index.data<uint32_t>(), n_index};
SetIndexData(index_data_span, batch_threads, batch, rbegin, nbins,
[](auto idx, auto i) { return idx; });
[](auto idx, auto) { return idx; });
}
#pragma omp parallel for num_threads(nthread) schedule(static)

2
src/common/version.cc
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@ -16,7 +16,7 @@ namespace xgboost {
const Version::TripletT Version::kInvalid {-1, -1, -1};
Version::TripletT Version::Load(Json const& in, bool check) {
Version::TripletT Version::Load(Json const& in) {
if (get<Object const>(in).find("version") == get<Object const>(in).cend()) {
return kInvalid;
}

2
src/common/version.h
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@ -18,7 +18,7 @@ struct Version {
static const TripletT kInvalid;
// Save/Load version info to Json document
static TripletT Load(Json const& in, bool check = false);
static TripletT Load(Json const& in);
static void Save(Json* out);
// Save/Load version info to dmlc::Stream

2
src/data/sparse_page_dmatrix.h
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@ -37,7 +37,7 @@ class SparsePageDMatrix : public DMatrix {
const MetaInfo& Info() const override;
bool SingleColBlock() const override { return false; }
DMatrix *Slice(common::Span<int32_t const> ridxs) override {
DMatrix *Slice(common::Span<int32_t const>) override {
LOG(FATAL) << "Slicing DMatrix is not supported for external memory.";
return nullptr;
}

2
src/gbm/gblinear_model.h
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@ -49,7 +49,7 @@ class GBLinearModel : public Model {
public:
explicit GBLinearModel(LearnerModelParam const* learner_model_param) :
learner_model_param {learner_model_param} {}
void Configure(Args const &cfg) { }
void Configure(Args const &) { }
// weight for each of feature, bias is the last one
std::vector<bst_float> weight;

4
src/learner.cc
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@ -341,7 +341,7 @@ class LearnerConfiguration : public Learner {
void LoadConfig(Json const& in) override {
CHECK(IsA<Object>(in));
Version::Load(in, true);
Version::Load(in);
auto const& learner_parameters = get<Object>(in["learner"]);
FromJson(learner_parameters.at("learner_train_param"), &tparam_);
@ -623,7 +623,7 @@ class LearnerIO : public LearnerConfiguration {
void LoadModel(Json const& in) override {
CHECK(IsA<Object>(in));
Version::Load(in, false);
Version::Load(in);
auto const& learner = get<Object>(in["learner"]);
mparam_.FromJson(learner.at("learner_model_param"));

6
src/metric/metric.cc
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@ -11,7 +11,7 @@
namespace xgboost {
template <typename MetricRegistry>
Metric* CreateMetricImpl(const std::string& name, GenericParameter const* tparam) {
Metric* CreateMetricImpl(const std::string& name) {
std::string buf = name;
std::string prefix = name;
const char* param;
@ -44,7 +44,7 @@ Metric* CreateMetricImpl(const std::string& name, GenericParameter const* tparam
Metric *
Metric::Create(const std::string& name, GenericParameter const* tparam) {
auto metric = CreateMetricImpl<MetricReg>(name, tparam);
auto metric = CreateMetricImpl<MetricReg>(name);
if (metric == nullptr) {
LOG(FATAL) << "Unknown metric function " << name;
}
@ -55,7 +55,7 @@ Metric::Create(const std::string& name, GenericParameter const* tparam) {
Metric *
GPUMetric::CreateGPUMetric(const std::string& name, GenericParameter const* tparam) {
auto metric = CreateMetricImpl<MetricGPUReg>(name, tparam);
auto metric = CreateMetricImpl<MetricGPUReg>(name);
if (metric == nullptr) {
LOG(WARNING) << "Cannot find a GPU metric builder for metric " << name
<< ". Resorting to the CPU builder";

16
src/metric/rank_metric.cc
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@ -55,13 +55,13 @@ class PerInstanceWeightPolicy {
public:
inline static xgboost::bst_float
GetWeightOfInstance(const xgboost::MetaInfo& info,
unsigned instance_id, unsigned group_id) {
unsigned instance_id, unsigned) {
return info.GetWeight(instance_id);
}
inline static xgboost::bst_float
GetWeightOfSortedRecord(const xgboost::MetaInfo& info,
const PredIndPairContainer& rec,
unsigned record_id, unsigned group_id) {
unsigned record_id, unsigned) {
return info.GetWeight(rec[record_id].second);
}
};
@ -70,14 +70,14 @@ class PerGroupWeightPolicy {
public:
inline static xgboost::bst_float
GetWeightOfInstance(const xgboost::MetaInfo& info,
unsigned instance_id, unsigned group_id) {
unsigned, unsigned group_id) {
return info.GetWeight(group_id);
}
inline static xgboost::bst_float
GetWeightOfSortedRecord(const xgboost::MetaInfo& info,
const PredIndPairContainer& rec,
unsigned record_id, unsigned group_id) {
const PredIndPairContainer&,
unsigned, unsigned group_id) {
return info.GetWeight(group_id);
}
};
@ -651,11 +651,11 @@ XGBOOST_REGISTER_METRIC(AMS, "ams")
XGBOOST_REGISTER_METRIC(Auc, "auc")
.describe("Area under curve for both classification and rank.")
.set_body([](const char* param) { return new EvalAuc(); });
.set_body([](const char*) { return new EvalAuc(); });
XGBOOST_REGISTER_METRIC(AucPR, "aucpr")
.describe("Area under PR curve for both classification and rank.")
.set_body([](const char* param) { return new EvalAucPR(); });
.set_body([](const char*) { return new EvalAucPR(); });
XGBOOST_REGISTER_METRIC(Precision, "pre")
.describe("precision@k for rank.")
@ -671,6 +671,6 @@ XGBOOST_REGISTER_METRIC(MAP, "map")
XGBOOST_REGISTER_METRIC(Cox, "cox-nloglik")
.describe("Negative log partial likelihood of Cox proportioanl hazards model.")
.set_body([](const char* param) { return new EvalCox(); });
.set_body([](const char*) { return new EvalCox(); });
} // namespace metric
} // namespace xgboost

2
src/predictor/predictor.cc
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@ -47,7 +47,7 @@ decltype(PredictionContainer::container_) const& PredictionContainer::Container(
}
void Predictor::Configure(
const std::vector<std::pair<std::string, std::string>>& cfg) {
const std::vector<std::pair<std::string, std::string>>&) {
}
Predictor* Predictor::Create(
std::string const& name, GenericParameter const* generic_param) {

4
src/tree/split_evaluator.h
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@ -107,7 +107,7 @@ class TreeEvaluator {
return w;
}
}
XGBOOST_DEVICE float CalcGainGivenWeight(bst_node_t nid, ParamT const &p,
XGBOOST_DEVICE float CalcGainGivenWeight(bst_node_t, ParamT const &p,
tree::GradStats stats, float w) const {
if (stats.GetHess() <= 0) {
return .0f;
@ -149,7 +149,7 @@ class TreeEvaluator {
return;
}
common::Transform<>::Init(
[=] XGBOOST_DEVICE(size_t idx, common::Span<float> lower,
[=] XGBOOST_DEVICE(size_t, common::Span<float> lower,
common::Span<float> upper,
common::Span<int> monotone) {
lower[leftid] = lower[nodeid];

30
src/tree/tree_model.cc
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@ -69,20 +69,24 @@ class TreeGenerator {
return result;
}
virtual std::string Indicator(RegTree const& tree, int32_t nid, uint32_t depth) const {
virtual std::string Indicator(RegTree const& /*tree*/,
int32_t /*nid*/, uint32_t /*depth*/) const {
return "";
}
virtual std::string Integer(RegTree const& tree, int32_t nid, uint32_t depth) const {
virtual std::string Integer(RegTree const& /*tree*/,
int32_t /*nid*/, uint32_t /*depth*/) const {
return "";
}
virtual std::string Quantitive(RegTree const& tree, int32_t nid, uint32_t depth) const {
virtual std::string Quantitive(RegTree const& /*tree*/,
int32_t /*nid*/, uint32_t /*depth*/) const {
return "";
}
virtual std::string NodeStat(RegTree const& tree, int32_t nid) const {
virtual std::string NodeStat(RegTree const& /*tree*/, int32_t /*nid*/) const {
return "";
}
virtual std::string PlainNode(RegTree const& tree, int32_t nid, uint32_t depth) const = 0;
virtual std::string PlainNode(RegTree const& /*tree*/,
int32_t /*nid*/, uint32_t /*depth*/) const = 0;
virtual std::string SplitNode(RegTree const& tree, int32_t nid, uint32_t depth) {
auto const split_index = tree[nid].SplitIndex();
@ -179,7 +183,7 @@ class TextGenerator : public TreeGenerator {
using SuperT = TreeGenerator;
public:
TextGenerator(FeatureMap const& fmap, std::string const& attrs, bool with_stats) :
TextGenerator(FeatureMap const& fmap, bool with_stats) :
TreeGenerator(fmap, with_stats) {}
std::string LeafNode(RegTree const& tree, int32_t nid, uint32_t depth) const override {
@ -196,7 +200,7 @@ class TextGenerator : public TreeGenerator {
return result;
}
std::string Indicator(RegTree const& tree, int32_t nid, uint32_t depth) const override {
std::string Indicator(RegTree const& tree, int32_t nid, uint32_t) const override {
static std::string const kIndicatorTemplate = "{nid}:[{fname}] yes={yes},no={no}";
int32_t nyes = tree[nid].DefaultLeft() ?
tree[nid].RightChild() : tree[nid].LeftChild();
@ -288,14 +292,14 @@ class TextGenerator : public TreeGenerator {
XGBOOST_REGISTER_TREE_IO(TextGenerator, "text")
.describe("Dump text representation of tree")
.set_body([](FeatureMap const& fmap, std::string const& attrs, bool with_stats) {
return new TextGenerator(fmap, attrs, with_stats);
return new TextGenerator(fmap, with_stats);
});
class JsonGenerator : public TreeGenerator {
using SuperT = TreeGenerator;
public:
JsonGenerator(FeatureMap const& fmap, std::string attrs, bool with_stats) :
JsonGenerator(FeatureMap const& fmap, bool with_stats) :
TreeGenerator(fmap, with_stats) {}
std::string Indent(uint32_t depth) const {
@ -306,7 +310,7 @@ class JsonGenerator : public TreeGenerator {
return result;
}
std::string LeafNode(RegTree const& tree, int32_t nid, uint32_t depth) const override {
std::string LeafNode(RegTree const& tree, int32_t nid, uint32_t) const override {
static std::string const kLeafTemplate =
R"L({ "nodeid": {nid}, "leaf": {leaf} {stat}})L";
static std::string const kStatTemplate =
@ -426,7 +430,7 @@ class JsonGenerator : public TreeGenerator {
XGBOOST_REGISTER_TREE_IO(JsonGenerator, "json")
.describe("Dump json representation of tree")
.set_body([](FeatureMap const& fmap, std::string const& attrs, bool with_stats) {
return new JsonGenerator(fmap, attrs, with_stats);
return new JsonGenerator(fmap, with_stats);
});
struct GraphvizParam : public XGBoostParameter<GraphvizParam> {
@ -531,7 +535,7 @@ class GraphvizGenerator : public TreeGenerator {
protected:
// Only indicator is different, so we combine all different node types into this
// function.
std::string PlainNode(RegTree const& tree, int32_t nid, uint32_t depth) const override {
std::string PlainNode(RegTree const& tree, int32_t nid, uint32_t) const override {
auto split = tree[nid].SplitIndex();
auto cond = tree[nid].SplitCond();
static std::string const kNodeTemplate =
@ -565,7 +569,7 @@ class GraphvizGenerator : public TreeGenerator {
return result;
};
std::string LeafNode(RegTree const& tree, int32_t nid, uint32_t depth) const override {
std::string LeafNode(RegTree const& tree, int32_t nid, uint32_t) const override {
static std::string const kLeafTemplate =
" {nid} [ label=\"leaf={leaf-value}\" {params}]\n";
auto result = SuperT::Match(kLeafTemplate, {

7
src/tree/updater_colmaker.cc
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@ -163,7 +163,7 @@ class ColMaker: public TreeUpdater {
DMatrix* p_fmat,
RegTree* p_tree) {
std::vector<int> newnodes;
this->InitData(gpair, *p_fmat, *p_tree);
this->InitData(gpair, *p_fmat);
this->InitNewNode(qexpand_, gpair, *p_fmat, *p_tree);
for (int depth = 0; depth < param_.max_depth; ++depth) {
this->FindSplit(depth, qexpand_, gpair, p_fmat, p_tree);
@ -200,8 +200,7 @@ class ColMaker: public TreeUpdater {
protected:
// initialize temp data structure
inline void InitData(const std::vector<GradientPair>& gpair,
const DMatrix& fmat,
const RegTree& tree) {
const DMatrix& fmat) {
{
// setup position
position_.resize(gpair.size());
@ -439,7 +438,7 @@ class ColMaker: public TreeUpdater {
virtual void UpdateSolution(const SparsePage &batch,
const std::vector<bst_feature_t> &feat_set,
const std::vector<GradientPair> &gpair,
DMatrix*p_fmat) {
DMatrix*) {
// start enumeration
const auto num_features = static_cast<bst_omp_uint>(feat_set.size());
#if defined(_OPENMP)

29
src/tree/updater_histmaker.cc
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@ -56,13 +56,6 @@ class HistMaker: public BaseMaker {
HistUnit(const float *cut, GradStats *data, uint32_t size)
: cut{cut}, data{data}, size{size} {}
/*! \brief add a histogram to data */
void Add(float fv, const std::vector<GradientPair> &gpair,
const MetaInfo &info, const size_t ridx) {
unsigned bin = std::upper_bound(cut, cut + size, fv) - cut;
CHECK_NE(size, 0U) << "try insert into size=0";
CHECK_LT(bin, size);
data[bin].Add(gpair[ridx]);
}
};
/*! \brief a set of histograms from different index */
struct HistSet {
@ -86,7 +79,7 @@ class HistMaker: public BaseMaker {
// per thread histset
std::vector<HistSet> hset;
// initialize the hist set
inline void Configure(const TrainParam &param, int nthread) {
inline void Configure(int nthread) {
hset.resize(nthread);
// cleanup statistics
for (int tid = 0; tid < nthread; ++tid) {
@ -127,7 +120,7 @@ class HistMaker: public BaseMaker {
// create histogram
this->CreateHist(gpair, p_fmat, selected_features_, *p_tree);
// find split based on histogram statistics
this->FindSplit(depth, gpair, p_fmat, selected_features_, p_tree);
this->FindSplit(selected_features_, p_tree);
// reset position after split
this->ResetPositionAfterSplit(p_fmat, *p_tree);
this->UpdateQueueExpand(*p_tree);
@ -159,9 +152,9 @@ class HistMaker: public BaseMaker {
const RegTree &tree) {
}
virtual void CreateHist(const std::vector<GradientPair> &gpair,
DMatrix *p_fmat,
DMatrix *,
const std::vector <bst_feature_t> &fset,
const RegTree &tree) = 0;
const RegTree &) = 0;
private:
void EnumerateSplit(const HistUnit &hist,
@ -202,10 +195,7 @@ class HistMaker: public BaseMaker {
}
}
void FindSplit(int depth,
const std::vector<GradientPair> &gpair,
DMatrix *p_fmat,
const std::vector <bst_feature_t> &feature_set,
void FindSplit(const std::vector <bst_feature_t> &feature_set,
RegTree *p_tree) {
const size_t num_feature = feature_set.size();
// get the best split condition for each node
@ -288,7 +278,6 @@ class CQHistMaker: public HistMaker {
*/
inline void Add(bst_float fv,
const std::vector<GradientPair> &gpair,
const MetaInfo &info,
const bst_uint ridx) {
while (istart < hist.size && !(fv < hist.cut[istart])) ++istart;
CHECK_NE(istart, hist.size);
@ -342,7 +331,7 @@ class CQHistMaker: public HistMaker {
feat2workindex_[fset[i]] = static_cast<int>(i);
}
// start to work
this->wspace_.Configure(this->param_, 1);
this->wspace_.Configure(1);
// if it is C++11, use lazy evaluation for Allreduce,
// to gain speedup in recovery
auto lazy_get_hist = [&]() {
@ -376,7 +365,7 @@ class CQHistMaker: public HistMaker {
this->wspace_.hset[0].data.size(), lazy_get_hist);
}
void ResetPositionAfterSplit(DMatrix *p_fmat,
void ResetPositionAfterSplit(DMatrix *,
const RegTree &tree) override {
this->GetSplitSet(this->qexpand_, tree, &fsplit_set_);
}
@ -533,7 +522,7 @@ class CQHistMaker: public HistMaker {
const bst_uint ridx = c.index;
const int nid = this->position_[ridx];
if (nid >= 0) {
hbuilder[nid].Add(c.fvalue, gpair, info, ridx);
hbuilder[nid].Add(c.fvalue, gpair, ridx);
}
}
}
@ -689,7 +678,7 @@ class GlobalProposalHistMaker: public CQHistMaker {
this->feat2workindex_[fset[i]] = static_cast<int>(i);
}
// start to work
this->wspace_.Configure(this->param_, 1);
this->wspace_.Configure(1);
// to gain speedup in recovery
{
this->thread_hist_.resize(omp_get_max_threads());