17 size_t readVariables(tinyxml2::XMLElement* root,
const char*
key, std::vector<std::string>&
names) {
21 if (root !=
nullptr) {
22 for (tinyxml2::XMLElement*
e = root->FirstChildElement(key);
e !=
nullptr;
e =
e->NextSiblingElement(key)) {
23 names.push_back(
e->Attribute(
"Expression"));
31 bool isTerminal(tinyxml2::XMLElement* node) {
33 for (tinyxml2::XMLElement*
e = node->FirstChildElement(
"Node");
e !=
nullptr;
e =
e->NextSiblingElement(
"Node")) {
39 unsigned int countIntermediateNodes(tinyxml2::XMLElement* node) {
40 unsigned int count = 0;
41 for (tinyxml2::XMLElement*
e = node->FirstChildElement(
"Node");
e !=
nullptr;
e =
e->NextSiblingElement(
"Node")) {
42 count += countIntermediateNodes(
e);
44 return count > 0 ? count + 1 : 0;
47 unsigned int countTerminalNodes(tinyxml2::XMLElement* node) {
48 unsigned int count = 0;
49 for (tinyxml2::XMLElement*
e = node->FirstChildElement(
"Node");
e !=
nullptr;
e =
e->NextSiblingElement(
"Node")) {
50 count += countTerminalNodes(
e);
52 return count > 0 ? count : 1;
56 tinyxml2::XMLElement* node,
61 bool isAdaClassifier) {
62 bool nodeIsTerminal = isTerminal(node);
66 node->QueryDoubleAttribute(
"res", &response);
69 node->QueryDoubleAttribute(
"nType", &response);
71 if (isAdaClassifier) {
72 node->QueryDoubleAttribute(
"purity", &response);
74 node->QueryDoubleAttribute(
"res", &response);
87 node->QueryIntAttribute(
"IVar", &selector);
88 node->QueryFloatAttribute(
"Cut", &cutval);
89 node->QueryBoolAttribute(
"cType", &ctype);
91 tree.
CutIndices().push_back(static_cast<unsigned char>(selector));
96 cutval = std::nextafter(cutval, std::numeric_limits<float>::lowest());
98 tree.
CutVals().push_back(cutval);
102 tinyxml2::XMLElement* left =
nullptr;
103 tinyxml2::XMLElement* right =
nullptr;
104 for (tinyxml2::XMLElement*
e = node->FirstChildElement(
"Node");
e !=
nullptr;
e =
e->NextSiblingElement(
"Node")) {
105 if (*(
e->Attribute(
"pos")) ==
'l')
107 else if (*(
e->Attribute(
"pos")) ==
'r')
115 addNode(tree, left, scale, isRegression, useYesNoLeaf, adjustboundary, isAdaClassifier);
118 addNode(tree, right, scale, isRegression, useYesNoLeaf, adjustboundary, isAdaClassifier);
127 TFile gbrForestFile(weightsFileFullPath.c_str());
128 std::unique_ptr<GBRForest>
up(gbrForestFile.Get<
GBRForest>(
"gbrForest"));
129 gbrForestFile.Close(
"nodelete");
136 tinyxml2::XMLDocument xmlDoc;
138 using namespace reco::details;
140 if (
hasEnding(weightsFileFullPath,
".xml")) {
141 xmlDoc.LoadFile(weightsFileFullPath.c_str());
142 }
else if (
hasEnding(weightsFileFullPath,
".gz") ||
hasEnding(weightsFileFullPath,
".gzip")) {
144 xmlDoc.Parse(buffer);
148 tinyxml2::XMLElement* root = xmlDoc.FirstChildElement(
"MethodSetup");
149 readVariables(root->FirstChildElement(
"Variables"),
"Variable",
varNames);
152 std::map<std::string, std::string>
info;
153 tinyxml2::XMLElement* infoElem = xmlDoc.FirstChildElement(
"MethodSetup")->FirstChildElement(
"GeneralInfo");
154 if (infoElem ==
nullptr) {
155 throw cms::Exception(
"XMLError") <<
"No GeneralInfo found in " << weightsFileFullPath <<
" !!\n";
157 for (tinyxml2::XMLElement*
e = infoElem->FirstChildElement(
"Info");
e !=
nullptr;
158 e =
e->NextSiblingElement(
"Info")) {
161 if (tinyxml2::XML_SUCCESS !=
e->QueryStringAttribute(
"name", &name)) {
162 throw cms::Exception(
"XMLERROR") <<
"no 'name' attribute found in 'Info' element in " << weightsFileFullPath;
164 if (tinyxml2::XML_SUCCESS !=
e->QueryStringAttribute(
"value", &value)) {
165 throw cms::Exception(
"XMLERROR") <<
"no 'value' attribute found in 'Info' element in " << weightsFileFullPath;
171 std::map<std::string, std::string>
options;
172 tinyxml2::XMLElement* optionsElem = xmlDoc.FirstChildElement(
"MethodSetup")->FirstChildElement(
"Options");
173 if (optionsElem ==
nullptr) {
174 throw cms::Exception(
"XMLError") <<
"No Options found in " << weightsFileFullPath <<
" !!\n";
176 for (tinyxml2::XMLElement*
e = optionsElem->FirstChildElement(
"Option");
e !=
nullptr;
177 e =
e->NextSiblingElement(
"Option")) {
179 if (tinyxml2::XML_SUCCESS !=
e->QueryStringAttribute(
"name", &name)) {
180 throw cms::Exception(
"XMLERROR") <<
"no 'name' attribute found in 'Option' element in " << weightsFileFullPath;
186 int rootTrainingVersion(0);
187 if (
info.find(
"ROOT Release") !=
info.end()) {
189 rootTrainingVersion = std::stoi(s.substr(s.find(
'[') + 1, s.find(
']') - s.find(
'[') - 1));
193 std::vector<double> boostWeights;
194 tinyxml2::XMLElement* weightsElem = xmlDoc.FirstChildElement(
"MethodSetup")->FirstChildElement(
"Weights");
195 if (weightsElem ==
nullptr) {
196 throw cms::Exception(
"XMLError") <<
"No Weights found in " << weightsFileFullPath <<
" !!\n";
198 bool hasTrees =
false;
199 for (tinyxml2::XMLElement*
e = weightsElem->FirstChildElement(
"BinaryTree");
e !=
nullptr;
200 e =
e->NextSiblingElement(
"BinaryTree")) {
203 if (tinyxml2::XML_SUCCESS !=
e->QueryDoubleAttribute(
"boostWeight", &w)) {
204 throw cms::Exception(
"XMLERROR") <<
"problem with 'boostWeight' attribute found in 'BinaryTree' element in "
205 << weightsFileFullPath;
207 boostWeights.push_back(w);
210 throw cms::Exception(
"XMLError") <<
"No BinaryTrees found in " << weightsFileFullPath <<
" !!\n";
213 bool isRegression =
info[
"AnalysisType"] ==
"Regression";
217 bool isAdaClassifier = !isRegression &&
options[
"BoostType"] !=
"Grad";
218 bool useYesNoLeaf = isAdaClassifier &&
options[
"UseYesNoLeaf"] ==
"True";
222 bool adjustBoundaries =
223 (rootTrainingVersion >= ROOT_VERSION(5, 34, 20) && rootTrainingVersion < ROOT_VERSION(6, 0, 0)) ||
224 rootTrainingVersion >= ROOT_VERSION(6, 2, 0);
226 auto forest = std::make_unique<GBRForest>();
227 forest->SetInitialResponse(isRegression ? boostWeights[0] : 0.);
230 if (isAdaClassifier) {
231 for (
double w : boostWeights) {
236 forest->Trees().reserve(boostWeights.size());
239 for (tinyxml2::XMLElement*
e = weightsElem->FirstChildElement(
"BinaryTree");
e !=
nullptr;
240 e =
e->NextSiblingElement(
"BinaryTree")) {
241 double scale = isAdaClassifier ? boostWeights[itree] / norm : 1.0;
243 tinyxml2::XMLElement* root =
e->FirstChildElement(
"Node");
244 forest->Trees().push_back(
GBRTree(countIntermediateNodes(root), countTerminalNodes(root)));
245 auto& tree = forest->Trees().back();
247 addNode(tree, root, scale, isRegression, useYesNoLeaf, adjustBoundaries, isAdaClassifier);
278 std::unique_ptr<GBRForest> gbrForest;
280 if (weightsFile[0] ==
'/') {
281 gbrForest =
init(weightsFile, varNames);
290 std::vector<std::string>& varNames) {
std::vector< float > & Responses()
bool hasEnding(std::string const &fullString, std::string const &ending)
constexpr char const * varNames[]
const std::string names[nVars_]
std::vector< float > & CutVals()
void swap(edm::DataFrameContainer &lhs, edm::DataFrameContainer &rhs)
tuple key
prepare the HTCondor submission files and eventually submit them
std::vector< int > & LeftIndices()
char * readGzipFile(const std::string &weightFile)
std::string fullPath() const
std::vector< int > & RightIndices()
std::vector< unsigned char > & CutIndices()