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);
name = randomforest;
sourceTree = "<group>";
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A721AB77161C573B009860A1 /* taxonomynode.cpp in Sources */,
+ 83F25B0C163B031200ABE73D /* forest.cpp in Sources */,
);
runOnlyForDeploymentPostprocessing = 0;
};
--- /dev/null
+//
+// forest.cpp
+// Mothur
+//
+// Created by Kathryn Iverson on 10/26/12.
+// Copyright (c) 2012 Schloss Lab. All rights reserved.
+//
+
+#include "forest.h"
+
+/***********************************************************************/
+Forest::Forest(const std::vector < std::vector<int> > dataSet,
+ const int numDecisionTrees,
+ const string treeSplitCriterion = "informationGain")
+: dataSet(dataSet),
+numDecisionTrees(numDecisionTrees),
+numSamples((int)dataSet.size()),
+numFeatures((int)(dataSet[0].size() - 1)),
+globalDiscardedFeatureIndices(getGlobalDiscardedFeatureIndices()),
+globalVariableImportanceList(numFeatures, 0),
+treeSplitCriterion(treeSplitCriterion) {
+ m = MothurOut::getInstance();
+ // TODO: double check if the implemenatation of 'globalOutOfBagEstimates' is correct
+}
+
+/***********************************************************************/
+
+vector<int> Forest::getGlobalDiscardedFeatureIndices() {
+ try {
+ //vector<int> globalDiscardedFeatureIndices;
+ //globalDiscardedFeatureIndices.push_back(1);
+
+ // calculate feature vectors
+ vector< vector<int> > featureVectors(numFeatures, vector<int>(numSamples, 0) );
+ for (int i = 0; i < numSamples; i++) {
+ if (m->control_pressed) { return globalDiscardedFeatureIndices; }
+ for (int j = 0; j < numFeatures; j++) { featureVectors[j][i] = dataSet[i][j]; }
+ }
+
+ for (int i = 0; i < featureVectors.size(); i++) {
+ if (m->control_pressed) { return globalDiscardedFeatureIndices; }
+ double standardDeviation = m->getStandardDeviation(featureVectors[i]);
+ if (standardDeviation <= 0){ globalDiscardedFeatureIndices.push_back(i); }
+ }
+
+ if (m->debug) {
+ m->mothurOut("number of global discarded features: " + toString(globalDiscardedFeatureIndices.size())+ "\n");
+ m->mothurOut("total features: " + toString(featureVectors.size())+ "\n");
+ }
+
+ return globalDiscardedFeatureIndices;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "Forest", "getGlobalDiscardedFeatureIndices");
+ exit(1);
+ }
+}
+
+/***********************************************************************/
+
--- /dev/null
+//
+// forest.h
+// Mothur
+//
+// Created by Kathryn Iverson on 10/26/12. Modified abstractrandomforest
+// Copyright (c) 2012 Schloss Lab. All rights reserved.
+//
+
+#ifndef __Mothur__forest__
+#define __Mothur__forest__
+
+#include <iostream>
+#include "mothurout.h"
+#include "macros.h"
+#include "decisiontree.hpp"
+#include "abstractdecisiontree.hpp"
+/***********************************************************************/
+//this is a re-implementation of the abstractrandomforest class
+
+class Forest{
+public:
+ // intialization with vectors
+ Forest(const std::vector < std::vector<int> > dataSet,
+ const int numDecisionTrees,
+ const string);
+ virtual ~Forest(){ }
+ virtual int populateDecisionTrees() = 0;
+ virtual int calcForrestErrorRate() = 0;
+ virtual int calcForrestVariableImportance(string) = 0;
+ virtual int updateGlobalOutOfBagEstimates(DecisionTree* decisionTree) = 0;
+
+ /***********************************************************************/
+
+protected:
+
+ // TODO: create a better way of discarding feature
+ // currently we just set FEATURE_DISCARD_SD_THRESHOLD to 0 to solved this
+ // it can be tuned for better selection
+ // also, there might be other factors like Mean or other stuffs
+ // same would apply for createLocalDiscardedFeatureList in the TreeNode class
+
+ // TODO: Another idea is getting an aggregated discarded feature indices after the run, from combining
+ // the local discarded feature indices
+ // this would penalize a feature, even if in global space the feature looks quite good
+ // the penalization would be averaged, so this woould unlikely to create a local optmina
+
+ vector<int> getGlobalDiscardedFeatureIndices();
+
+ int numDecisionTrees;
+ int numSamples;
+ int numFeatures;
+ vector< vector<int> > dataSet;
+ vector<int> globalDiscardedFeatureIndices;
+ vector<double> globalVariableImportanceList;
+ string treeSplitCriterion;
+ // This is a map of each feature to outcome count of each classes
+ // e.g. 1 => [2 7] means feature 1 has 2 outcome of 0 and 7 outcome of 1
+ map<int, vector<int> > globalOutOfBagEstimates;
+
+ // TODO: fix this, do we use pointers?
+ vector<AbstractDecisionTree*> decisionTrees;
+
+ MothurOut* m;
+
+private:
+
+};
+
+#endif /* defined(__Mothur__forest__) */
/***********************************************************************/
RandomForest::RandomForest(const vector <vector<int> > dataSet,const int numDecisionTrees,
- const string treeSplitCriterion = "informationGain") : AbstractRandomForest(dataSet, numDecisionTrees, treeSplitCriterion) {
+ const string treeSplitCriterion = "informationGain") : Forest(dataSet, numDecisionTrees, treeSplitCriterion) {
m = MothurOut::getInstance();
}
//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++) {
}
/***********************************************************************/
// TODO: need to finalize bettween reference and pointer for DecisionTree [partially solved]
-// TODO: make this pure virtual in superclass
+// DONE: make this pure virtual in superclass
// DONE
int RandomForest::updateGlobalOutOfBagEstimates(DecisionTree* decisionTree) {
try {
#define rrf_fs_prototype_randomforest_hpp
#include "macros.h"
-#include "abstractrandomforest.hpp"
+#include "forest.h"
#include "decisiontree.hpp"
-class RandomForest: public AbstractRandomForest {
+class RandomForest: public Forest {
public:
//NOTE:: 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.
- virtual ~RandomForest() {
- for (vector<AbstractDecisionTree*>::iterator it = decisionTrees.begin(); it != decisionTrees.end(); it++) {
- // we know that this is decision tree, so we can do a dynamic_case<DecisionTree*> here
- DecisionTree* decisionTree = dynamic_cast<DecisionTree*>(*it);
- // calling the destructor by deleting
- delete decisionTree;
- }
- }
+// virtual ~RandomForest() {
+
// for (vector<AbstractDecisionTree*>::iterator it = decisionTrees.begin(); it != decisionTrees.end(); it++) {
+// // we know that this is decision tree, so we can do a dynamic_case<DecisionTree*> here
+// DecisionTree* decisionTree = dynamic_cast<DecisionTree*>(*it);
+// // calling the destructor by deleting
+// delete decisionTree;
+// }
+// }
int calcForrestErrorRate();
int calcForrestVariableImportance(string);
projects / mothur.git / commitdiff