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Implement feature score for linear model. (#7048)

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* Add feature score support for linear model.
* Port R interface to the new implementation.
* Add linear model support in Python.

Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
This commit is contained in:
Jiaming Yuan
2021-06-25 14:34:02 +08:00
committed by GitHub
parent b2d300e727
commit 663136aa08
18 changed files with 367 additions and 232 deletions
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62
src/c_api/c_api.cc
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@@ -927,14 +927,17 @@ XGB_DLL int XGBoosterSlice(BoosterHandle handle, int begin_layer,
API_END();
}
inline void XGBoostDumpModelImpl(BoosterHandle handle, const FeatureMap &fmap,
inline void XGBoostDumpModelImpl(BoosterHandle handle, FeatureMap* fmap,
int with_stats, const char *format,
xgboost::bst_ulong *len,
const char ***out_models) {
auto *bst = static_cast<Learner*>(handle);
bst->Configure();
GenerateFeatureMap(bst, {}, bst->GetNumFeature(), fmap);
std::vector<std::string>& str_vecs = bst->GetThreadLocal().ret_vec_str;
std::vector<const char*>& charp_vecs = bst->GetThreadLocal().ret_vec_charp;
str_vecs = bst->DumpModel(fmap, with_stats != 0, format);
str_vecs = bst->DumpModel(*fmap, with_stats != 0, format);
charp_vecs.resize(str_vecs.size());
for (size_t i = 0; i < str_vecs.size(); ++i) {
charp_vecs[i] = str_vecs[i].c_str();
@@ -962,14 +965,9 @@ XGB_DLL int XGBoosterDumpModelEx(BoosterHandle handle,
const char*** out_models) {
API_BEGIN();
CHECK_HANDLE();
FeatureMap featmap;
if (strlen(fmap) != 0) {
std::unique_ptr<dmlc::Stream> fs(
dmlc::Stream::Create(fmap, "r"));
dmlc::istream is(fs.get());
featmap.LoadText(is);
}
XGBoostDumpModelImpl(handle, featmap, with_stats, format, len, out_models);
std::string uri{fmap};
FeatureMap featmap = LoadFeatureMap(uri);
XGBoostDumpModelImpl(handle, &featmap, with_stats, format, len, out_models);
API_END();
}
@@ -980,8 +978,8 @@ XGB_DLL int XGBoosterDumpModelWithFeatures(BoosterHandle handle,
int with_stats,
xgboost::bst_ulong* len,
const char*** out_models) {
return XGBoosterDumpModelExWithFeatures(handle, fnum, fname, ftype, with_stats,
"text", len, out_models);
return XGBoosterDumpModelExWithFeatures(handle, fnum, fname, ftype,
with_stats, "text", len, out_models);
}
XGB_DLL int XGBoosterDumpModelExWithFeatures(BoosterHandle handle,
@@ -998,7 +996,7 @@ XGB_DLL int XGBoosterDumpModelExWithFeatures(BoosterHandle handle,
for (int i = 0; i < fnum; ++i) {
featmap.PushBack(i, fname[i], ftype[i]);
}
XGBoostDumpModelImpl(handle, featmap, with_stats, format, len, out_models);
XGBoostDumpModelImpl(handle, &featmap, with_stats, format, len, out_models);
API_END();
}
@@ -1098,11 +1096,12 @@ 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) {
XGB_DLL int XGBoosterFeatureScore(BoosterHandle handle, char const *json_config,
xgboost::bst_ulong *out_n_features,
char const ***out_features,
bst_ulong *out_dim,
bst_ulong const **out_shape,
float const **out_scores) {
API_BEGIN();
CHECK_HANDLE();
auto *learner = static_cast<Learner *>(handle);
@@ -1113,14 +1112,17 @@ XGB_DLL int XGBoosterFeatureScore(BoosterHandle handle,
feature_map_uri = get<String const>(config["feature_map"]);
}
FeatureMap feature_map = LoadFeatureMap(feature_map_uri);
std::vector<Json> custom_feature_names;
if (!IsA<Null>(config["feature_names"])) {
custom_feature_names = get<Array const>(config["feature_names"]);
}
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);
GenerateFeatureMap(learner, custom_feature_names, n_features, &feature_map);
auto& feature_names = learner->GetThreadLocal().ret_vec_str;
feature_names.resize(features.size());
@@ -1131,10 +1133,24 @@ XGB_DLL int XGBoosterFeatureScore(BoosterHandle handle,
feature_names[i] = feature_map.Name(features[i]);
feature_names_c[i] = feature_names[i].data();
}
*out_n_features = feature_names.size();
CHECK_EQ(scores.size(), features.size());
CHECK_EQ(scores.size(), feature_names.size());
*out_length = scores.size();
CHECK_LE(features.size(), scores.size());
auto &shape = learner->GetThreadLocal().prediction_shape;
if (scores.size() > features.size()) {
// Linear model multi-class model
CHECK_EQ(scores.size() % features.size(), 0ul);
auto n_classes = scores.size() / features.size();
*out_dim = 2;
shape = {n_features, n_classes};
} else {
CHECK_EQ(features.size(), scores.size());
*out_dim = 1;
shape.resize(1);
shape.front() = scores.size();
}
*out_shape = dmlc::BeginPtr(shape);
*out_scores = scores.data();
*out_features = dmlc::BeginPtr(feature_names_c);
API_END();