Clean up C++ warnings (#6213)

src/gbm/gblinear.cc

@@ -113,7 +113,7 @@ class GBLinear : public GradientBooster {
 
   void DoBoost(DMatrix *p_fmat,
                HostDeviceVector<GradientPair> *in_gpair,
-               PredictionCacheEntry* predt) override {
+               PredictionCacheEntry*) override {
     monitor_.Start("DoBoost");
 
     model_.LazyInitModel();
@@ -128,8 +128,7 @@ class GBLinear : public GradientBooster {
 
   void PredictBatch(DMatrix *p_fmat,
                     PredictionCacheEntry *predts,
-                    bool training,
-                    unsigned ntree_limit) override {
+                    bool, unsigned ntree_limit) override {
     monitor_.Start("PredictBatch");
     auto* out_preds = &predts->predictions;
     CHECK_EQ(ntree_limit, 0U)
@@ -140,7 +139,7 @@ class GBLinear : public GradientBooster {
   // add base margin
   void PredictInstance(const SparsePage::Inst &inst,
                        std::vector<bst_float> *out_preds,
-                       unsigned ntree_limit) override {
+                       unsigned) override {
     const int ngroup = model_.learner_model_param->num_output_group;
     for (int gid = 0; gid < ngroup; ++gid) {
       this->Pred(inst, dmlc::BeginPtr(*out_preds), gid,
@@ -148,16 +147,15 @@ class GBLinear : public GradientBooster {
     }
   }
 
-  void PredictLeaf(DMatrix *p_fmat,
-                   std::vector<bst_float> *out_preds,
-                   unsigned ntree_limit) override {
+  void PredictLeaf(DMatrix*,
+                   std::vector<bst_float>*,
+                   unsigned) override {
     LOG(FATAL) << "gblinear does not support prediction of leaf index";
   }
 
   void PredictContribution(DMatrix* p_fmat,
                            HostDeviceVector<bst_float>* out_contribs,
-                           unsigned ntree_limit, bool approximate, int condition = 0,
-                           unsigned condition_feature = 0) override {
+                           unsigned ntree_limit, bool, int, unsigned) override {
     model_.LazyInitModel();
     CHECK_EQ(ntree_limit, 0U)
         << "GBLinear::PredictContribution: ntrees is only valid for gbtree predictor";
@@ -196,7 +194,7 @@ class GBLinear : public GradientBooster {
 
   void PredictInteractionContributions(DMatrix* p_fmat,
                                        HostDeviceVector<bst_float>* out_contribs,
-                                       unsigned ntree_limit, bool approximate) override {
+                                       unsigned, bool) override {
     std::vector<bst_float>& contribs = out_contribs->HostVector();
 
     // linear models have no interaction effects

src/gbm/gblinear_model.h

@@ -95,7 +95,7 @@ class GBLinearModel : public Model {
     return &weight[i * learner_model_param->num_output_group];
   }
 
-  std::vector<std::string> DumpModel(const FeatureMap &fmap, bool with_stats,
+  std::vector<std::string> DumpModel(const FeatureMap &, bool,
                                      std::string format) const {
     const int ngroup = learner_model_param->num_output_group;
     const unsigned nfeature = learner_model_param->num_feature;

src/gbm/gbtree.cc

@@ -401,7 +401,7 @@ void GBTree::SaveModel(Json* p_out) const {
 
 void GBTree::PredictBatch(DMatrix* p_fmat,
                           PredictionCacheEntry* out_preds,
-                          bool training,
+                          bool,
                           unsigned ntree_limit) {
   CHECK(configured_);
   GetPredictor(&out_preds->predictions, p_fmat)
@@ -601,8 +601,8 @@ class Dart : public GBTree {
 
   void PredictContribution(DMatrix* p_fmat,
                            HostDeviceVector<bst_float>* out_contribs,
-                           unsigned ntree_limit, bool approximate, int condition,
-                           unsigned condition_feature) override {
+                           unsigned ntree_limit, bool approximate, int,
+                           unsigned) override {
     CHECK(configured_);
     cpu_predictor_->PredictContribution(p_fmat, out_contribs, model_,
                                         ntree_limit, &weight_drop_, approximate);
@@ -674,8 +674,7 @@ class Dart : public GBTree {
   // commit new trees all at once
   void
   CommitModel(std::vector<std::vector<std::unique_ptr<RegTree>>>&& new_trees,
-              DMatrix* m,
-              PredictionCacheEntry* predts) override {
+              DMatrix*, PredictionCacheEntry*) override {
     int num_new_trees = 0;
     for (uint32_t gid = 0; gid < model_.learner_model_param->num_output_group; ++gid) {
       num_new_trees += new_trees[gid].size();

src/gbm/gbtree.h

@@ -239,7 +239,7 @@ class GBTree : public GradientBooster {
   void PredictContribution(DMatrix* p_fmat,
                            HostDeviceVector<bst_float>* out_contribs,
                            unsigned ntree_limit, bool approximate,
-                           int condition, unsigned condition_feature) override {
+                           int, unsigned) override {
     CHECK(configured_);
     this->GetPredictor()->PredictContribution(
         p_fmat, out_contribs, model_, ntree_limit, nullptr, approximate);