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Implement feature score in GBTree. (#7041)

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* Categorical data support.
* Eliminate text parsing during feature score computation.
This commit is contained in:
Jiaming Yuan
2021-06-18 11:53:16 +08:00
committed by GitHub
parent dcd84b3979
commit 7dd29ffd47
10 changed files with 285 additions and 84 deletions
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42
src/c_api/c_api.cc
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@@ -1098,5 +1098,47 @@ XGB_DLL int XGBoosterGetStrFeatureInfo(BoosterHandle handle, const char *field,
API_END();
}
XGB_DLL int XGBoosterFeatureScore(BoosterHandle handle,
const char *json_config,
xgboost::bst_ulong* out_length,
const char ***out_features,
float **out_scores) {
API_BEGIN();
CHECK_HANDLE();
auto *learner = static_cast<Learner *>(handle);
auto config = Json::Load(StringView{json_config});
auto importance = get<String const>(config["importance_type"]);
std::string feature_map_uri;
if (!IsA<Null>(config["feature_map"])) {
feature_map_uri = get<String const>(config["feature_map"]);
}
FeatureMap feature_map = LoadFeatureMap(feature_map_uri);
auto& scores = learner->GetThreadLocal().ret_vec_float;
std::vector<bst_feature_t> features;
learner->CalcFeatureScore(importance, &features, &scores);
auto n_features = learner->GetNumFeature();
GenerateFeatureMap(learner, n_features, &feature_map);
CHECK_LE(features.size(), n_features);
auto& feature_names = learner->GetThreadLocal().ret_vec_str;
feature_names.resize(features.size());
auto& feature_names_c = learner->GetThreadLocal().ret_vec_charp;
feature_names_c.resize(features.size());
for (bst_feature_t i = 0; i < features.size(); ++i) {
feature_names[i] = feature_map.Name(features[i]);
feature_names_c[i] = feature_names[i].data();
}
CHECK_EQ(scores.size(), features.size());
CHECK_EQ(scores.size(), feature_names.size());
*out_length = scores.size();
*out_scores = scores.data();
*out_features = dmlc::BeginPtr(feature_names_c);
API_END();
}
// force link rabit
static DMLC_ATTRIBUTE_UNUSED int XGBOOST_LINK_RABIT_C_API_ = RabitLinkTag();