Forest.cc

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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); }
 
// _______________________Destructor____________________________________//
 
Forest::~Forest() {
  // When the forest is destroyed it will delete the trees as well as the
  // events from the training and testing sets.
  // The user may want the events to remain after they destroy the forest
  // this should be changed in future upgrades.
 
  for (unsigned int i = 0; i < trees.size(); i++) {
    if (trees[i])
      delete trees[i];
  }
}
 
Forest::Forest(const Forest& forest) {
  transform(forest.trees.cbegin(), forest.trees.cend(), back_inserter(trees), [](const Tree* tree) {
    return new Tree(*tree);
  });
}
 
Forest& Forest::operator=(const Forest& forest) {
  for (unsigned int i = 0; i < trees.size(); i++) {
    if (trees[i])
      delete trees[i];
  }
  trees.resize(0);
 
  transform(forest.trees.cbegin(), forest.trees.cend(), back_inserter(trees), [](const Tree* tree) {
    return new 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];
  else {
    //std::cout << i << "is an invalid input for getTree. Out of range." << std::endl;
    return nullptr;
  }
}
 
// ______________________Various_Helpful_Functions______________________//
 
unsigned int Forest::size() {
  // 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) {
  // 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) {
  // 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) {
  // 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) {
  // 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;
    Tree* tree = new Tree(events);
    trees.push_back(tree);
    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];
  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];
  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 = std::vector<Tree*>(numTrees);
 
  // Load the Forest.
  // std::cout << std::endl << "Loading Forest from XML ... " << std::endl;
  for (unsigned int i = 0; i < numTrees; i++) {
    trees[i] = new 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();
 
  // clean-up leftovers from previous initialization (if any)
  for (unsigned int i = 0; i < trees.size(); i++) {
    if (trees[i])
      delete trees[i];
  }
 
  trees = std::vector<Tree*>(numTrees);
 
  // Load the Forest.
  for (unsigned int i = 0; i < numTrees; i++) {
    trees[i] = new 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 = std::vector<Tree*>(treeLimit);
 
  // 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] = new 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], 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;
}