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 //////////////////////////////////////////////////////////////////////////
 //                            Forest.cxx                                //
 // =====================================================================//
 // This is the object implementation of a forest of decision trees.     //
 // We need this to implement gradient boosting.                         //
 // References include                                                   //
 //    *Elements of Statistical Learning by Hastie,                      //
 //     Tibshirani, and Friedman.                                        //
 //    *Greedy Function Approximation: A Gradient Boosting Machine.      //
 //     Friedman. The Annals of Statistics, Vol. 29, No. 5. Oct 2001.    //
 //    *Inductive Learning of Tree-based Regression Models. Luis Torgo.  //
 //                                                                      //
 //////////////////////////////////////////////////////////////////////////
 
 ///////////////////////////////////////////////////////////////////////////
 // _______________________Includes_______________________________________//
 ///////////////////////////////////////////////////////////////////////////
 
 #include "L1Trigger/L1TMuonEndCap/interface/bdt/Forest.h"
 #include "L1Trigger/L1TMuonEndCap/interface/bdt/Utilities.h"
 
 #include "FWCore/ParameterSet/interface/FileInPath.h"
 
 #include "TStopwatch.h"
 #include "TString.h"
 
 #include <iostream>
 #include <sstream>
 #include <algorithm>
 #include <iterator>
 #include <fstream>
 #include <utility>
 
 using namespace emtf;
 
 //////////////////////////////////////////////////////////////////////////
 // _______________________Constructor(s)________________________________//
 //////////////////////////////////////////////////////////////////////////
 
 Forest::Forest() { events = std::vector<std::vector<Event*>>(1); }
 
 //////////////////////////////////////////////////////////////////////////
 // ----------------------------------------------------------------------
 //////////////////////////////////////////////////////////////////////////
 
 Forest::Forest(std::vector<Event*>& trainingEvents) { setTrainingEvents(trainingEvents); }
 
 Forest::Forest(const Forest& forest) {
   transform(forest.trees.cbegin(), forest.trees.cend(), back_inserter(trees), [](const std::unique_ptr<Tree>& tree) {
     return std::make_unique<Tree>(*tree);
   });
 }
 
 Forest& Forest::operator=(const Forest& forest) {
   trees.resize(0);
   trees.reserve(forest.trees.size());
   transform(forest.trees.cbegin(), forest.trees.cend(), back_inserter(trees), [](const std::unique_ptr<Tree>& tree) {
     return std::make_unique<Tree>(*tree);
   });
   return *this;
 }
 
 //////////////////////////////////////////////////////////////////////////
 // ______________________Get/Set_Functions______________________________//
 //////////////////////////////////////////////////////////////////////////
 
 void Forest::setTrainingEvents(std::vector<Event*>& trainingEvents) {
   // tell the forest which events to use for training
 
   Event* e = trainingEvents[0];
   // Unused variable
   // unsigned int numrows = e->data.size();
 
   // Reset the events matrix.
   events = std::vector<std::vector<Event*>>();
 
   for (unsigned int i = 0; i < e->data.size(); i++) {
     events.push_back(trainingEvents);
   }
 }
 
 //////////////////////////////////////////////////////////////////////////
 // ----------------------------------------------------------------------
 //////////////////////////////////////////////////////////////////////////
 
 // return a copy of the training events
 std::vector<Event*> Forest::getTrainingEvents() { return events[0]; }
 
 //////////////////////////////////////////////////////////////////////////
 // ----------------------------------------------------------------------
 //////////////////////////////////////////////////////////////////////////
 
 // return the ith tree
 Tree* Forest::getTree(unsigned int i) {
   if (/*i>=0 && */ i < trees.size())
     return trees[i].get();
   else {
     //std::cout << i << "is an invalid input for getTree. Out of range." << std::endl;
     return nullptr;
   }
 }
 
 //////////////////////////////////////////////////////////////////////////
 // ______________________Various_Helpful_Functions______________________//
 //////////////////////////////////////////////////////////////////////////
 
 unsigned int Forest::size() const {
   // Return the number of trees in the forest.
   return trees.size();
 }
 
 //!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
 //*** Need to make a data structure that includes the next few functions ***
 //*** pertaining to events. These don't really have much to do with the  ***
 //*** forest class.                                                      ***
 //!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
 
 //////////////////////////////////////////////////////////////////////////
 // ----------------------------------------------------------------------
 //////////////////////////////////////////////////////////////////////////
 
 void Forest::listEvents(std::vector<std::vector<Event*>>& e) const {
   // Simply list the events in each event vector. We have multiple copies
   // of the events vector. Each copy is sorted according to a different
   // determining variable.
   std::cout << std::endl << "Listing Events... " << std::endl;
 
   for (unsigned int i = 0; i < e.size(); i++) {
     std::cout << std::endl << "Variable " << i << " vector contents: " << std::endl;
     for (unsigned int j = 0; j < e[i].size(); j++) {
       e[i][j]->outputEvent();
     }
     std::cout << std::endl;
   }
 }
 
 //////////////////////////////////////////////////////////////////////////
 // ----------------------------------------------------------------------
 //////////////////////////////////////////////////////////////////////////
 
 // We have to initialize Event::sortingIndex outside of a function since
 // it is a static member.
 int Event::sortingIndex = 1;
 
 bool compareEvents(Event* e1, Event* e2) {
   // Sort the events according to the variable given by the sortingIndex.
   return e1->data[Event::sortingIndex] < e2->data[Event::sortingIndex];
 }
 //////////////////////////////////////////////////////////////////////////
 // ----------------------------------------------------------------------
 //////////////////////////////////////////////////////////////////////////
 
 bool compareEventsById(Event* e1, Event* e2) {
   // Sort the events by ID. We need this to produce rate plots.
   return e1->id < e2->id;
 }
 //////////////////////////////////////////////////////////////////////////
 // ----------------------------------------------------------------------
 //////////////////////////////////////////////////////////////////////////
 
 void Forest::sortEventVectors(std::vector<std::vector<Event*>>& e) const {
   // When a node chooses the optimum split point and split variable it needs
   // the events to be sorted according to the variable it is considering.
 
   for (unsigned int i = 0; i < e.size(); i++) {
     Event::sortingIndex = i;
     std::sort(e[i].begin(), e[i].end(), compareEvents);
   }
 }
 
 //////////////////////////////////////////////////////////////////////////
 // ----------------------------------------------------------------------
 //////////////////////////////////////////////////////////////////////////
 
 void Forest::rankVariables(std::vector<int>& rank) const {
   // This function ranks the determining variables according to their importance
   // in determining the fit. Use a low learning rate for better results.
   // Separates completely useless variables from useful ones well,
   // but isn't the best at separating variables of similar importance.
   // This is calculated using the error reduction on the training set. The function
   // should be changed to use the testing set, but this works fine for now.
   // I will try to change this in the future.
 
   // Initialize the vector v, which will store the total error reduction
   // for each variable i in v[i].
   std::vector<double> v(events.size(), 0);
 
   //std::cout << std::endl << "Ranking Variables by Net Error Reduction... " << std::endl;
 
   for (unsigned int j = 0; j < trees.size(); j++) {
     trees[j]->rankVariables(v);
   }
 
   double max = *std::max_element(v.begin(), v.end());
 
   // Scale the importance. Maximum importance = 100.
   for (unsigned int i = 0; i < v.size(); i++) {
     v[i] = 100 * v[i] / max;
   }
 
   // Change the storage format so that we can keep the index
   // and the value associated after sorting.
   std::vector<std::pair<double, int>> w(events.size());
 
   for (unsigned int i = 0; i < v.size(); i++) {
     w[i] = std::pair<double, int>(v[i], i);
   }
 
   // Sort so that we can output in order of importance.
   std::sort(w.begin(), w.end());
 
   // Output the results.
   for (int i = (v.size() - 1); i >= 0; i--) {
     rank.push_back(w[i].second);
     // std::cout << "x" << w[i].second  << ": " << w[i].first  << std::endl;
   }
 
   // std::cout << std::endl << "Done." << std::endl << std::endl;
 }
 
 //////////////////////////////////////////////////////////////////////////
 // ----------------------------------------------------------------------
 //////////////////////////////////////////////////////////////////////////
 
 void Forest::saveSplitValues(const char* savefilename) const {
   // This function gathers all of the split values from the forest and puts them into lists.
 
   std::ofstream splitvaluefile;
   splitvaluefile.open(savefilename);
 
   // Initialize the matrix v, which will store the list of split values
   // for each variable i in v[i].
   std::vector<std::vector<double>> v(events.size(), std::vector<double>());
 
   //std::cout << std::endl << "Gathering split values... " << std::endl;
 
   // Gather the split values from each tree in the forest.
   for (unsigned int j = 0; j < trees.size(); j++) {
     trees[j]->getSplitValues(v);
   }
 
   // Sort the lists of split values and remove the duplicates.
   for (unsigned int i = 0; i < v.size(); i++) {
     std::sort(v[i].begin(), v[i].end());
     v[i].erase(unique(v[i].begin(), v[i].end()), v[i].end());
   }
 
   // Output the results after removing duplicates.
   // The 0th variable is special and is not used for splitting, so we start at 1.
   for (unsigned int i = 1; i < v.size(); i++) {
     TString splitValues;
     for (unsigned int j = 0; j < v[i].size(); j++) {
       std::stringstream ss;
       ss.precision(14);
       ss << std::scientific << v[i][j];
       splitValues += ",";
       splitValues += ss.str().c_str();
     }
 
     splitValues = splitValues(1, splitValues.Length());
     splitvaluefile << splitValues << std::endl << std::endl;
     ;
   }
 }
 //////////////////////////////////////////////////////////////////////////
 // ______________________Update_Events_After_Fitting____________________//
 //////////////////////////////////////////////////////////////////////////
 
 void Forest::updateRegTargets(Tree* tree, double learningRate, LossFunction* l) {
   // Prepare the global vector of events for the next tree.
   // Update the fit for each event and set the new target value
   // for the next tree.
 
   // Get the list of terminal nodes for this tree.
   std::list<Node*>& tn = tree->getTerminalNodes();
 
   // Loop through the terminal nodes.
   for (std::list<Node*>::iterator it = tn.begin(); it != tn.end(); it++) {
     // Get the events in the current terminal region.
     std::vector<Event*>& v = (*it)->getEvents()[0];
 
     // Fit the events depending on the loss function criteria.
     double fit = l->fit(v);
 
     // Scale the rate at which the algorithm converges.
     fit = learningRate * fit;
 
     // Store the official fit value in the terminal node.
     (*it)->setFitValue(fit);
 
     // Loop through each event in the terminal region and update the
     // the target for the next tree.
     for (unsigned int j = 0; j < v.size(); j++) {
       Event* e = v[j];
       e->predictedValue += fit;
       e->data[0] = l->target(e);
     }
 
     // Release memory.
     (*it)->getEvents() = std::vector<std::vector<Event*>>();
   }
 }
 
 /////////////////////////////////////////////////////////////////////////
 // ----------------------------------------------------------------------
 /////////////////////////////////////////////////////////////////////////
 
 void Forest::updateEvents(Tree* tree) {
   // Prepare the test events for the next tree.
 
   // Get the list of terminal nodes for this tree.
   std::list<Node*>& tn = tree->getTerminalNodes();
 
   // Loop through the terminal nodes.
   for (std::list<Node*>::iterator it = tn.begin(); it != tn.end(); it++) {
     std::vector<Event*>& v = (*it)->getEvents()[0];
     double fit = (*it)->getFitValue();
 
     // Loop through each event in the terminal region and update the
     // the global event it maps to.
     for (unsigned int j = 0; j < v.size(); j++) {
       Event* e = v[j];
       e->predictedValue += fit;
     }
 
     // Release memory.
     (*it)->getEvents() = std::vector<std::vector<Event*>>();
   }
 }
 
 //////////////////////////////////////////////////////////////////////////
 // ____________________Do/Test_the Regression___________________________//
 //////////////////////////////////////////////////////////////////////////
 
 void Forest::doRegression(int nodeLimit,
                           int treeLimit,
                           double learningRate,
                           LossFunction* l,
                           const char* savetreesdirectory,
                           bool saveTrees) {
   // Build the forest using the training sample.
 
   //std::cout << std::endl << "--Building Forest..." << std::endl << std::endl;
 
   // The trees work with a matrix of events where the rows have the same set of events. Each row however
   // is sorted according to the feature variable given by event->data[row].
   // If we only had one set of events we would have to sort it according to the
   // feature variable every time we want to calculate the best split point for that feature.
   // By keeping sorted copies we avoid the sorting operation during splint point calculation
   // and save computation time. If we do not sort each of the rows the regression will fail.
   //std::cout << "Sorting event vectors..." << std::endl;
   sortEventVectors(events);
 
   // See how long the regression takes.
   TStopwatch timer;
   timer.Start(kTRUE);
 
   for (unsigned int i = 0; i < (unsigned)treeLimit; i++) {
     // std::cout << "++Building Tree " << i << "... " << std::endl;
     trees.emplace_back(std::make_unique<Tree>(events));
     Tree* tree = trees.back().get();
     tree->buildTree(nodeLimit);
 
     // Update the targets for the next tree to fit.
     updateRegTargets(tree, learningRate, l);
 
     // Save trees to xml in some directory.
     std::ostringstream ss;
     ss << savetreesdirectory << "/" << i << ".xml";
     std::string s = ss.str();
     const char* c = s.c_str();
 
     if (saveTrees)
       tree->saveToXML(c);
   }
   //std::cout << std::endl;
   //std::cout << std::endl << "Done." << std::endl << std::endl;
 
   //    std::cout << std::endl << "Total calculation time: " << timer.RealTime() << std::endl;
 }
 
 //////////////////////////////////////////////////////////////////////////
 // ----------------------------------------------------------------------
 //////////////////////////////////////////////////////////////////////////
 
 void Forest::predictEvents(std::vector<Event*>& eventsp, unsigned int numtrees) {
   // Predict values for eventsp by running them through the forest up to numtrees.
 
   //std::cout << "Using " << numtrees << " trees from the forest to predict events ... " << std::endl;
   if (numtrees > trees.size()) {
     //std::cout << std::endl << "!! Input greater than the forest size. Using forest.size() = " << trees.size() << " to predict instead." << std::endl;
     numtrees = trees.size();
   }
 
   // i iterates through the trees in the forest. Each tree corrects the last prediction.
   for (unsigned int i = 0; i < numtrees; i++) {
     //std::cout << "++Tree " << i << "..." << std::endl;
     appendCorrection(eventsp, i);
   }
 }
 
 //////////////////////////////////////////////////////////////////////////
 // ----------------------------------------------------------------------
 //////////////////////////////////////////////////////////////////////////
 
 void Forest::appendCorrection(std::vector<Event*>& eventsp, int treenum) {
   // Update the prediction by appending the next correction.
 
   Tree* tree = trees[treenum].get();
   tree->filterEvents(eventsp);
 
   // Update the events with their new prediction.
   updateEvents(tree);
 }
 
 //////////////////////////////////////////////////////////////////////////
 // ----------------------------------------------------------------------
 //////////////////////////////////////////////////////////////////////////
 
 void Forest::predictEvent(Event* e, unsigned int numtrees) {
   // Predict values for eventsp by running them through the forest up to numtrees.
 
   //std::cout << "Using " << numtrees << " trees from the forest to predict events ... " << std::endl;
   if (numtrees > trees.size()) {
     //std::cout << std::endl << "!! Input greater than the forest size. Using forest.size() = " << trees.size() << " to predict instead." << std::endl;
     numtrees = trees.size();
   }
 
   // just like in line #2470 of https://root.cern.ch/doc/master/MethodBDT_8cxx_source.html for gradient boosting
   e->predictedValue = trees[0]->getBoostWeight();
 
   // i iterates through the trees in the forest. Each tree corrects the last prediction.
   for (unsigned int i = 0; i < numtrees; i++) {
     //std::cout << "++Tree " << i << "..." << std::endl;
     appendCorrection(e, i);
   }
 }
 
 //////////////////////////////////////////////////////////////////////////
 // ----------------------------------------------------------------------
 //////////////////////////////////////////////////////////////////////////
 
 void Forest::appendCorrection(Event* e, int treenum) {
   // Update the prediction by appending the next correction.
 
   Tree* tree = trees[treenum].get();
   Node* terminalNode = tree->filterEvent(e);
 
   // Update the event with its new prediction.
   double fit = terminalNode->getFitValue();
   e->predictedValue += fit;
 }
 /////////////////////////////////////////////////////////////////////////////////////
 // ----------------------------------------------------------------------------------
 /////////////////////////////////////////////////////////////////////////////////////
 
 void Forest::loadForestFromXML(const char* directory, unsigned int numTrees) {
   // Load a forest that has already been created and stored into XML somewhere.
 
   // Initialize the vector of trees.
   trees.resize(numTrees);
   trees.shrink_to_fit();
 
   // Load the Forest.
   // std::cout << std::endl << "Loading Forest from XML ... " << std::endl;
   for (unsigned int i = 0; i < numTrees; i++) {
     trees[i] = std::make_unique<Tree>();
 
     std::stringstream ss;
     ss << directory << "/" << i << ".xml";
 
     trees[i]->loadFromXML(edm::FileInPath(ss.str().c_str()).fullPath().c_str());
   }
 
   //std::cout << "Done." << std::endl << std::endl;
 }
 
 void Forest::loadFromCondPayload(const L1TMuonEndCapForest::DForest& forest) {
   // Load a forest that has already been created and stored in CondDB.
   // Initialize the vector of trees.
   unsigned int numTrees = forest.size();
 
   trees.resize(numTrees);
   trees.shrink_to_fit();
 
   // Load the Forest.
   for (unsigned int i = 0; i < numTrees; i++) {
     trees[i] = std::make_unique<Tree>();
     trees[i]->loadFromCondPayload(forest[i]);
   }
 }
 
 //////////////////////////////////////////////////////////////////////////
 // ___________________Stochastic_Sampling_&_Regression__________________//
 //////////////////////////////////////////////////////////////////////////
 
 void Forest::prepareRandomSubsample(double fraction) {
   // We use this for Stochastic Gradient Boosting. Basically you
   // take a subsample of the training events and build a tree using
   // those. Then use the tree built from the subsample to update
   // the predictions for all the events.
 
   subSample = std::vector<std::vector<Event*>>(events.size());
   size_t subSampleSize = fraction * events[0].size();
 
   // Randomize the first subSampleSize events in events[0].
   shuffle(events[0].begin(), events[0].end(), subSampleSize);
 
   // Get a copy of the random subset we just made.
   std::vector<Event*> v(events[0].begin(), events[0].begin() + subSampleSize);
 
   // Initialize and sort the subSample collection.
   for (unsigned int i = 0; i < subSample.size(); i++) {
     subSample[i] = v;
   }
 
   sortEventVectors(subSample);
 }
 
 //////////////////////////////////////////////////////////////////////////
 // ----------------------------------------------------------------------
 //////////////////////////////////////////////////////////////////////////
 
 void Forest::doStochasticRegression(
     int nodeLimit, int treeLimit, double learningRate, double fraction, LossFunction* l) {
   // If the fraction of events to use is one then this algorithm is slower than doRegression due to the fact
   // that we have to sort the events every time we extract a subsample. Without random sampling we simply
   // use all of the events and keep them sorted.
 
   // Anyways, this algorithm uses a portion of the events to train each tree. All of the events are updated
   // afterwards with the results from the subsample built tree.
 
   // Prepare some things.
   sortEventVectors(events);
   trees.resize(treeLimit);
   trees.shrink_to_fit();
 
   // See how long the regression takes.
   TStopwatch timer;
   timer.Start(kTRUE);
 
   // Output the current settings.
   // std::cout << std::endl << "Running stochastic regression ... " << std::endl;
   //std::cout << "# Nodes: " << nodeLimit << std::endl;
   //std::cout << "Learning Rate: " << learningRate << std::endl;
   //std::cout << "Bagging Fraction: " << fraction << std::endl;
   //std::cout << std::endl;
 
   for (unsigned int i = 0; i < (unsigned)treeLimit; i++) {
     // Build the tree using a random subsample.
     prepareRandomSubsample(fraction);
     trees[i] = std::make_unique<Tree>(subSample);
     trees[i]->buildTree(nodeLimit);
 
     // Fit all of the events based upon the tree we built using
     // the subsample of events.
     trees[i]->filterEvents(events[0]);
 
     // Update the targets for the next tree to fit.
     updateRegTargets(trees[i].get(), learningRate, l);
 
     // Save trees to xml in some directory.
     std::ostringstream ss;
     ss << "trees/" << i << ".xml";
     std::string s = ss.str();
     const char* c = s.c_str();
 
     trees[i]->saveToXML(c);
   }
 
   //std::cout << std::endl << "Done." << std::endl << std::endl;
 
   //std::cout << std::endl << "Total calculation time: " << timer.RealTime() << std::endl;
 }

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