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//
// randomforest.cpp
// Mothur
//
// Created by Sarah Westcott on 10/2/12.
// Copyright (c) 2012 Schloss Lab. All rights reserved.
//
#include "randomforest.hpp"
/***********************************************************************/
RandomForest::RandomForest(const vector <vector<int> > dataSet,
const int numDecisionTrees,
const string treeSplitCriterion = "gainratio",
const bool doPruning = false,
const float pruneAggressiveness = 0.9,
const bool discardHighErrorTrees = true,
const float highErrorTreeDiscardThreshold = 0.4,
const string optimumFeatureSubsetSelectionCriteria = "log2",
const float featureStandardDeviationThreshold = 0.0)
: Forest(dataSet, numDecisionTrees, treeSplitCriterion, doPruning, pruneAggressiveness, discardHighErrorTrees, highErrorTreeDiscardThreshold, optimumFeatureSubsetSelectionCriteria, featureStandardDeviationThreshold) {
m = MothurOut::getInstance();
}
/***********************************************************************/
// DONE
int RandomForest::calcForrestErrorRate() {
try {
int numCorrect = 0;
for (map<int, vector<int> >::iterator it = globalOutOfBagEstimates.begin(); it != globalOutOfBagEstimates.end(); it++) {
if (m->control_pressed) { return 0; }
int indexOfSample = it->first;
vector<int> predictedOutComes = it->second;
vector<int>::iterator maxPredictedOutComeIterator = max_element(predictedOutComes.begin(), predictedOutComes.end());
int majorityVotedOutcome = (int)(maxPredictedOutComeIterator - predictedOutComes.begin());
int realOutcome = dataSet[indexOfSample][numFeatures];
if (majorityVotedOutcome == realOutcome) { numCorrect++; }
}
// TODO: save or return forrestErrorRate for future use;
double forrestErrorRate = 1 - ((double)numCorrect / (double)globalOutOfBagEstimates.size());
m->mothurOut("numCorrect = " + toString(numCorrect)+ "\n");
m->mothurOut("forrestErrorRate = " + toString(forrestErrorRate)+ "\n");
return 0;
}
catch(exception& e) {
m->errorOut(e, "RandomForest", "calcForrestErrorRate");
exit(1);
}
}
/***********************************************************************/
int RandomForest::printConfusionMatrix(map<int, string> intToTreatmentMap) {
try {
int numGroups = intToTreatmentMap.size();
vector<vector<int> > cm(numGroups, vector<int>(numGroups, 0));
for (map<int, vector<int> >::iterator it = globalOutOfBagEstimates.begin(); it != globalOutOfBagEstimates.end(); it++) {
if (m->control_pressed) { return 0; }
int indexOfSample = it->first; //key
vector<int> predictedOutComes = it->second; //value, vector of all predicted classes
vector<int>::iterator maxPredictedOutComeIterator = max_element(predictedOutComes.begin(), predictedOutComes.end());
int majorityVotedOutcome = (int)(maxPredictedOutComeIterator - predictedOutComes.begin());
int realOutcome = dataSet[indexOfSample][numFeatures];
cm[realOutcome][majorityVotedOutcome] = cm[realOutcome][majorityVotedOutcome] + 1;
}
vector<int> fw;
for (int w = 0; w <numGroups; w++) {
fw.push_back(intToTreatmentMap[w].length());
}
m->mothurOut("confusion matrix:\n\t\t");
for (int k = 0; k < numGroups; k++) {
//m->mothurOut(intToTreatmentMap[k] + "\t");
cout << setw(fw[k]) << intToTreatmentMap[k] << "\t";
}
for (int i = 0; i < numGroups; i++) {
cout << "\n" << setw(fw[i]) << intToTreatmentMap[i] << "\t";
//m->mothurOut("\n" + intToTreatmentMap[i] + "\t");
if (m->control_pressed) { return 0; }
for (int j = 0; j < numGroups; j++) {
//m->mothurOut(toString(cm[i][j]) + "\t");
cout << setw(fw[i]) << cm[i][j] << "\t";
}
}
//m->mothurOut("\n");
cout << "\n";
return 0;
}
catch(exception& e) {
m->errorOut(e, "RandomForest", "printConfusionMatrix");
exit(1);
}
}
/***********************************************************************/
int RandomForest::getMissclassifications(string filename, map<int, string> intToTreatmentMap, vector<string> names) {
try {
ofstream out;
m->openOutputFile(filename, out);
out <<"Sample\tRF classification\tActual classification\n";
for (map<int, vector<int> >::iterator it = globalOutOfBagEstimates.begin(); it != globalOutOfBagEstimates.end(); it++) {
if (m->control_pressed) { return 0; }
int indexOfSample = it->first;
vector<int> predictedOutComes = it->second;
vector<int>::iterator maxPredictedOutComeIterator = max_element(predictedOutComes.begin(), predictedOutComes.end());
int majorityVotedOutcome = (int)(maxPredictedOutComeIterator - predictedOutComes.begin());
int realOutcome = dataSet[indexOfSample][numFeatures];
if (majorityVotedOutcome != realOutcome) {
out << names[indexOfSample] << "\t" << intToTreatmentMap[majorityVotedOutcome] << "\t" << intToTreatmentMap[realOutcome] << endl;
}
}
out.close();
return 0;
}
catch(exception& e) {
m->errorOut(e, "RandomForest", "getMissclassifications");
exit(1);
}
}
/***********************************************************************/
int RandomForest::calcForrestVariableImportance(string filename) {
try {
// follow the link: http://en.wikipedia.org/wiki/Dynamic_cast
//if you are going to dynamically cast, aren't you undoing the advantage of abstraction. Why abstract at all?
//could cause maintenance issues later if other types of Abstract decison trees are created that cannot be cast as a decision tree.
for (int i = 0; i < decisionTrees.size(); i++) {
if (m->control_pressed) { return 0; }
DecisionTree* decisionTree = dynamic_cast<DecisionTree*>(decisionTrees[i]);
for (int j = 0; j < numFeatures; j++) {
globalVariableImportanceList[j] += (double)decisionTree->variableImportanceList[j];
}
}
for (int i = 0; i < numFeatures; i++) {
globalVariableImportanceList[i] /= (double)numDecisionTrees;
}
vector< pair<int, double> > globalVariableRanks;
for (int i = 0; i < globalVariableImportanceList.size(); i++) {
//cout << "[" << i << ',' << globalVariableImportanceList[i] << "], ";
if (globalVariableImportanceList[i] > 0) {
pair<int, double> globalVariableRank(0, 0.0);
globalVariableRank.first = i;
globalVariableRank.second = globalVariableImportanceList[i];
globalVariableRanks.push_back(globalVariableRank);
}
}
// for (int i = 0; i < globalVariableRanks.size(); i++) {
// cout << m->currentBinLabels[(int)globalVariableRanks[i][0]] << '\t' << globalVariableImportanceList[globalVariableRanks[i][0]] << endl;
// }
VariableRankDescendingSorterDouble variableRankDescendingSorter;
sort(globalVariableRanks.begin(), globalVariableRanks.end(), variableRankDescendingSorter);
ofstream out;
m->openOutputFile(filename, out);
out <<"OTU\tMean decrease accuracy\n";
for (int i = 0; i < globalVariableRanks.size(); i++) {
out << m->currentSharedBinLabels[(int)globalVariableRanks[i].first] << '\t' << globalVariableImportanceList[globalVariableRanks[i].first] << endl;
}
out.close();
return 0;
}
catch(exception& e) {
m->errorOut(e, "RandomForest", "calcForrestVariableImportance");
exit(1);
}
}
/***********************************************************************/
int RandomForest::populateDecisionTrees() {
try {
vector<double> errorRateImprovements;
for (int i = 0; i < numDecisionTrees; i++) {
if (m->control_pressed) { return 0; }
if (((i+1) % 100) == 0) { m->mothurOut("Creating " + toString(i+1) + " (th) Decision tree\n"); }
// TODO: need to first fix if we are going to use pointer based system or anything else
DecisionTree* decisionTree = new DecisionTree(dataSet, globalDiscardedFeatureIndices, OptimumFeatureSubsetSelector(optimumFeatureSubsetSelectionCriteria), treeSplitCriterion, featureStandardDeviationThreshold);
if (m->debug && doPruning) {
m->mothurOut("Before pruning\n");
decisionTree->printTree(decisionTree->rootNode, "ROOT");
}
int numCorrect;
double treeErrorRate;
decisionTree->calcTreeErrorRate(numCorrect, treeErrorRate);
double prePrunedErrorRate = treeErrorRate;
if (m->debug) {
m->mothurOut("treeErrorRate: " + toString(treeErrorRate) + " numCorrect: " + toString(numCorrect) + "\n");
}
if (doPruning) {
decisionTree->pruneTree(pruneAggressiveness);
if (m->debug) {
m->mothurOut("After pruning\n");
decisionTree->printTree(decisionTree->rootNode, "ROOT");
}
decisionTree->calcTreeErrorRate(numCorrect, treeErrorRate);
}
double postPrunedErrorRate = treeErrorRate;
decisionTree->calcTreeVariableImportanceAndError(numCorrect, treeErrorRate);
double errorRateImprovement = (prePrunedErrorRate - postPrunedErrorRate) / prePrunedErrorRate;
if (m->debug) {
m->mothurOut("treeErrorRate: " + toString(treeErrorRate) + " numCorrect: " + toString(numCorrect) + "\n");
if (doPruning) {
m->mothurOut("errorRateImprovement: " + toString(errorRateImprovement) + "\n");
}
}
if (discardHighErrorTrees) {
if (treeErrorRate < highErrorTreeDiscardThreshold) {
updateGlobalOutOfBagEstimates(decisionTree);
decisionTree->purgeDataSetsFromTree();
decisionTrees.push_back(decisionTree);
if (doPruning) {
errorRateImprovements.push_back(errorRateImprovement);
}
} else {
delete decisionTree;
}
} else {
updateGlobalOutOfBagEstimates(decisionTree);
decisionTree->purgeDataSetsFromTree();
decisionTrees.push_back(decisionTree);
if (doPruning) {
errorRateImprovements.push_back(errorRateImprovement);
}
}
}
double avgErrorRateImprovement = -1.0;
if (errorRateImprovements.size() > 0) {
avgErrorRateImprovement = accumulate(errorRateImprovements.begin(), errorRateImprovements.end(), 0.0);
// cout << "Total " << avgErrorRateImprovement << " size " << errorRateImprovements.size() << endl;
avgErrorRateImprovement /= errorRateImprovements.size();
}
if (m->debug && doPruning) {
m->mothurOut("avgErrorRateImprovement:" + toString(avgErrorRateImprovement) + "\n");
}
// m->mothurOut("globalOutOfBagEstimates = " + toStringVectorMap(globalOutOfBagEstimates)+ "\n");
return 0;
}
catch(exception& e) {
m->errorOut(e, "RandomForest", "populateDecisionTrees");
exit(1);
}
}
/***********************************************************************/
// TODO: need to finalize bettween reference and pointer for DecisionTree [partially solved]
// DONE: make this pure virtual in superclass
// DONE
int RandomForest::updateGlobalOutOfBagEstimates(DecisionTree* decisionTree) {
try {
for (map<int, int>::iterator it = decisionTree->outOfBagEstimates.begin(); it != decisionTree->outOfBagEstimates.end(); it++) {
if (m->control_pressed) { return 0; }
int indexOfSample = it->first;
int predictedOutcomeOfSample = it->second;
if (globalOutOfBagEstimates.count(indexOfSample) == 0) {
globalOutOfBagEstimates[indexOfSample] = vector<int>(decisionTree->numOutputClasses, 0);
};
globalOutOfBagEstimates[indexOfSample][predictedOutcomeOfSample] += 1;
}
return 0;
}
catch(exception& e) {
m->errorOut(e, "RandomForest", "updateGlobalOutOfBagEstimates");
exit(1);
}
}
/***********************************************************************/
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