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//
// 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 treeSplitCriterion,
const bool doPruning,
const float pruneAggressiveness,
const bool discardHighErrorTrees,
const float highErrorTreeDiscardThreshold,
const string optimumFeatureSubsetSelectionCriteria,
const float featureStandardDeviationThreshold);
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;
bool doPruning;
float pruneAggressiveness;
bool discardHighErrorTrees;
float highErrorTreeDiscardThreshold;
string optimumFeatureSubsetSelectionCriteria;
float featureStandardDeviationThreshold;
// 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__) */
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