[breaking] Save booster feature info in JSON, remove feature name generation. (#6605)

* Save feature info in booster in JSON model.
* [breaking] Remove automatic feature name generation in `DMatrix`.

This PR is to enable reliable feature validation in Python package.

src/c_api/c_api.cc

@@ -1022,5 +1022,50 @@ XGB_DLL int XGBoosterGetAttrNames(BoosterHandle handle,
   API_END();
 }
 
+XGB_DLL int XGBoosterSetStrFeatureInfo(BoosterHandle handle, const char *field,
+                                       const char **features,
+                                       const xgboost::bst_ulong size) {
+  API_BEGIN();
+  CHECK_HANDLE();
+  auto *learner = static_cast<Learner *>(handle);
+  std::vector<std::string> feature_info;
+  for (size_t i = 0; i < size; ++i) {
+    feature_info.emplace_back(features[i]);
+  }
+  if (!std::strcmp(field, "feature_name")) {
+    learner->SetFeatureNames(feature_info);
+  } else if (!std::strcmp(field, "feature_type")) {
+    learner->SetFeatureTypes(feature_info);
+  } else {
+    LOG(FATAL) << "Unknown field for Booster feature info:" << field;
+  }
+  API_END();
+}
+
+XGB_DLL int XGBoosterGetStrFeatureInfo(BoosterHandle handle, const char *field,
+                                       xgboost::bst_ulong *len,
+                                       const char ***out_features) {
+  API_BEGIN();
+  CHECK_HANDLE();
+  auto const *learner = static_cast<Learner const *>(handle);
+  std::vector<const char *> &charp_vecs =
+      learner->GetThreadLocal().ret_vec_charp;
+  std::vector<std::string> &str_vecs = learner->GetThreadLocal().ret_vec_str;
+  if (!std::strcmp(field, "feature_name")) {
+    learner->GetFeatureNames(&str_vecs);
+  } else if (!std::strcmp(field, "feature_type")) {
+    learner->GetFeatureTypes(&str_vecs);
+  } else {
+    LOG(FATAL) << "Unknown field for Booster feature info:" << field;
+  }
+  charp_vecs.resize(str_vecs.size());
+  for (size_t i = 0; i < str_vecs.size(); ++i) {
+    charp_vecs[i] = str_vecs[i].c_str();
+  }
+  *out_features = dmlc::BeginPtr(charp_vecs);
+  *len = static_cast<xgboost::bst_ulong>(charp_vecs.size());
+  API_END();
+}
+
 // force link rabit
 static DMLC_ATTRIBUTE_UNUSED int XGBOOST_LINK_RABIT_C_API_ = RabitLinkTag();

src/learner.cc

@@ -256,6 +256,11 @@ class LearnerConfiguration : public Learner {
   std::map<std::string, std::string> cfg_;
   // Stores information like best-iteration for early stopping.
   std::map<std::string, std::string> attributes_;
+  // Name of each feature, usually set from DMatrix.
+  std::vector<std::string> feature_names_;
+  // Type of each feature, usually set from DMatrix.
+  std::vector<std::string> feature_types_;
+
   common::Monitor monitor_;
   LearnerModelParamLegacy mparam_;
   LearnerModelParam learner_model_param_;
@@ -460,6 +465,23 @@ class LearnerConfiguration : public Learner {
     return true;
   }
 
+  void SetFeatureNames(std::vector<std::string> const& fn) override {
+    feature_names_ = fn;
+  }
+
+  void GetFeatureNames(std::vector<std::string>* fn) const override {
+    *fn = feature_names_;
+  }
+
+  void SetFeatureTypes(std::vector<std::string> const& ft) override {
+    this->feature_types_ = ft;
+  }
+
+  void GetFeatureTypes(std::vector<std::string>* p_ft) const override {
+    auto& ft = *p_ft;
+    ft = this->feature_types_;
+  }
+
   std::vector<std::string> GetAttrNames() const override {
     std::vector<std::string> out;
     for (auto const& kv : attributes_) {
@@ -666,6 +688,25 @@ class LearnerIO : public LearnerConfiguration {
       attributes_[kv.first] = get<String const>(kv.second);
     }
 
+    // feature names and types are saved in xgboost 1.4
+    auto it = learner.find("feature_names");
+    if (it != learner.cend()) {
+      auto const &feature_names = get<Array const>(it->second);
+      feature_names_.clear();
+      for (auto const &name : feature_names) {
+        feature_names_.emplace_back(get<String const>(name));
+      }
+    }
+    it = learner.find("feature_types");
+    if (it != learner.cend()) {
+      auto const &feature_types = get<Array const>(it->second);
+      feature_types_.clear();
+      for (auto const &name : feature_types) {
+        auto type = get<String const>(name);
+        feature_types_.emplace_back(type);
+      }
+    }
+
     this->need_configuration_ = true;
   }
 
@@ -691,6 +732,17 @@ class LearnerIO : public LearnerConfiguration {
     for (auto const& kv : attributes_) {
       learner["attributes"][kv.first] = String(kv.second);
     }
+
+    learner["feature_names"] = Array();
+    auto& feature_names = get<Array>(learner["feature_names"]);
+    for (auto const& name : feature_names_) {
+      feature_names.emplace_back(name);
+    }
+    learner["feature_types"] = Array();
+    auto& feature_types = get<Array>(learner["feature_types"]);
+    for (auto const& type : feature_types_) {
+      feature_types.emplace_back(type);
+    }
   }
   // About to be deprecated by JSON format
   void LoadModel(dmlc::Stream* fi) override {

src/tree/tree_model.cc

@@ -385,7 +385,7 @@ class JsonGenerator : public TreeGenerator {
   std::string PlainNode(RegTree const& tree, int32_t nid, uint32_t depth) const override {
     auto cond = tree[nid].SplitCond();
     static std::string const kNodeTemplate =
-        R"I( "nodeid": {nid}, "depth": {depth}, "split": {fname}, )I"
+        R"I( "nodeid": {nid}, "depth": {depth}, "split": "{fname}", )I"
         R"I("split_condition": {cond}, "yes": {left}, "no": {right}, )I"
         R"I("missing": {missing})I";
     return SplitNodeImpl(tree, nid, kNodeTemplate, SuperT::ToStr(cond), depth);