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https://github.com/opencv/opencv.git
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430 lines
15 KiB
C++
430 lines
15 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Copyright (C) 2014, Itseez Inc, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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namespace cv {
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namespace ml {
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//////////////////////////////////////////////////////////////////////////////////////////
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// Random trees //
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//////////////////////////////////////////////////////////////////////////////////////////
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RTrees::Params::Params()
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: DTrees::Params(5, 10, 0.f, false, 10, 0, false, false, Mat())
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{
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calcVarImportance = false;
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nactiveVars = 0;
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termCrit = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 50, 0.1);
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}
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RTrees::Params::Params( int _maxDepth, int _minSampleCount,
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double _regressionAccuracy, bool _useSurrogates,
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int _maxCategories, const Mat& _priors,
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bool _calcVarImportance, int _nactiveVars,
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TermCriteria _termCrit )
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: DTrees::Params(_maxDepth, _minSampleCount, _regressionAccuracy, _useSurrogates,
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_maxCategories, 0, false, false, _priors)
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{
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calcVarImportance = _calcVarImportance;
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nactiveVars = _nactiveVars;
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termCrit = _termCrit;
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}
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class DTreesImplForRTrees : public DTreesImpl
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{
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public:
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DTreesImplForRTrees() {}
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virtual ~DTreesImplForRTrees() {}
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void setRParams(const RTrees::Params& p)
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{
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rparams = p;
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}
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RTrees::Params getRParams() const
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{
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return rparams;
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}
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void clear()
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{
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DTreesImpl::clear();
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oobError = 0.;
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rng = RNG(-1);
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}
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const vector<int>& getActiveVars()
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{
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int i, nvars = (int)allVars.size(), m = (int)activeVars.size();
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for( i = 0; i < nvars; i++ )
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{
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int i1 = rng.uniform(0, nvars);
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int i2 = rng.uniform(0, nvars);
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std::swap(allVars[i1], allVars[i2]);
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}
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for( i = 0; i < m; i++ )
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activeVars[i] = allVars[i];
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return activeVars;
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}
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void startTraining( const Ptr<TrainData>& trainData, int flags )
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{
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DTreesImpl::startTraining(trainData, flags);
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int nvars = w->data->getNVars();
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int i, m = rparams.nactiveVars > 0 ? rparams.nactiveVars : cvRound(std::sqrt((double)nvars));
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m = std::min(std::max(m, 1), nvars);
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allVars.resize(nvars);
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activeVars.resize(m);
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for( i = 0; i < nvars; i++ )
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allVars[i] = varIdx[i];
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}
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void endTraining()
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{
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DTreesImpl::endTraining();
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vector<int> a, b;
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std::swap(allVars, a);
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std::swap(activeVars, b);
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}
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bool train( const Ptr<TrainData>& trainData, int flags )
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{
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Params dp(rparams.maxDepth, rparams.minSampleCount, rparams.regressionAccuracy,
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rparams.useSurrogates, rparams.maxCategories, rparams.CVFolds,
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rparams.use1SERule, rparams.truncatePrunedTree, rparams.priors);
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setDParams(dp);
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startTraining(trainData, flags);
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int treeidx, ntrees = (rparams.termCrit.type & TermCriteria::COUNT) != 0 ?
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rparams.termCrit.maxCount : 10000;
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int i, j, k, vi, vi_, n = (int)w->sidx.size();
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int nclasses = (int)classLabels.size();
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double eps = (rparams.termCrit.type & TermCriteria::EPS) != 0 &&
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rparams.termCrit.epsilon > 0 ? rparams.termCrit.epsilon : 0.;
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vector<int> sidx(n);
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vector<uchar> oobmask(n);
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vector<int> oobidx;
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vector<int> oobperm;
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vector<double> oobres(n, 0.);
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vector<int> oobcount(n, 0);
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vector<int> oobvotes(n*nclasses, 0);
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int nvars = w->data->getNVars();
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int nallvars = w->data->getNAllVars();
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const int* vidx = !varIdx.empty() ? &varIdx[0] : 0;
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vector<float> samplebuf(nallvars);
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Mat samples = w->data->getSamples();
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float* psamples = samples.ptr<float>();
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size_t sstep0 = samples.step1(), sstep1 = 1;
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Mat sample0, sample(nallvars, 1, CV_32F, &samplebuf[0]);
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int predictFlags = _isClassifier ? (PREDICT_MAX_VOTE + RAW_OUTPUT) : PREDICT_SUM;
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bool calcOOBError = eps > 0 || rparams.calcVarImportance;
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double max_response = 0.;
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if( w->data->getLayout() == COL_SAMPLE )
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std::swap(sstep0, sstep1);
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if( !_isClassifier )
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{
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for( i = 0; i < n; i++ )
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{
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double val = std::abs(w->ord_responses[w->sidx[i]]);
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max_response = std::max(max_response, val);
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}
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}
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if( rparams.calcVarImportance )
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varImportance.resize(nallvars, 0.f);
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for( treeidx = 0; treeidx < ntrees; treeidx++ )
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{
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putchar('.'); fflush(stdout);
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for( i = 0; i < n; i++ )
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oobmask[i] = (uchar)1;
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for( i = 0; i < n; i++ )
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{
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j = rng.uniform(0, n);
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sidx[i] = w->sidx[j];
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oobmask[j] = (uchar)0;
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}
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int root = addTree( sidx );
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if( root < 0 )
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return false;
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if( calcOOBError )
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{
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oobidx.clear();
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for( i = 0; i < n; i++ )
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{
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if( !oobmask[i] )
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oobidx.push_back(i);
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}
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int n_oob = (int)oobidx.size();
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// if there is no out-of-bag samples, we can not compute OOB error
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// nor update the variable importance vector; so we proceed to the next tree
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if( n_oob == 0 )
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continue;
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double ncorrect_responses = 0.;
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oobError = 0.;
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for( i = 0; i < n_oob; i++ )
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{
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j = oobidx[i];
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sample = Mat( nallvars, 1, CV_32F, psamples + sstep0*w->sidx[j], sstep1*sizeof(psamples[0]) );
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double val = predictTrees(Range(treeidx, treeidx+1), sample, predictFlags);
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if( !_isClassifier )
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{
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oobres[j] += val;
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oobcount[j]++;
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double true_val = w->ord_responses[w->sidx[j]];
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double a = oobres[j]/oobcount[j] - true_val;
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oobError += a*a;
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val = (val - true_val)/max_response;
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ncorrect_responses += std::exp( -val*val );
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}
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else
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{
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int ival = cvRound(val);
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int* votes = &oobvotes[j*nclasses];
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votes[ival]++;
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int best_class = 0;
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for( k = 1; k < nclasses; k++ )
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if( votes[best_class] < votes[k] )
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best_class = k;
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int diff = best_class != w->cat_responses[w->sidx[j]];
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oobError += diff;
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ncorrect_responses += diff == 0;
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}
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}
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oobError /= n_oob;
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if( rparams.calcVarImportance && n_oob > 1 )
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{
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oobperm.resize(n_oob);
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for( i = 0; i < n_oob; i++ )
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oobperm[i] = oobidx[i];
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for( vi_ = 0; vi_ < nvars; vi_++ )
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{
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vi = vidx ? vidx[vi_] : vi_;
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double ncorrect_responses_permuted = 0;
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for( i = 0; i < n_oob; i++ )
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{
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int i1 = rng.uniform(0, n_oob);
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int i2 = rng.uniform(0, n_oob);
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std::swap(i1, i2);
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}
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for( i = 0; i < n_oob; i++ )
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{
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j = oobidx[i];
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int vj = oobperm[i];
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sample0 = Mat( nallvars, 1, CV_32F, psamples + sstep0*w->sidx[j], sstep1*sizeof(psamples[0]) );
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for( k = 0; k < nallvars; k++ )
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sample.at<float>(k) = sample0.at<float>(k);
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sample.at<float>(vi) = psamples[sstep0*w->sidx[vj] + sstep1*vi];
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double val = predictTrees(Range(treeidx, treeidx+1), sample, predictFlags);
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if( !_isClassifier )
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{
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val = (val - w->ord_responses[w->sidx[j]])/max_response;
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ncorrect_responses_permuted += exp( -val*val );
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}
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else
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ncorrect_responses_permuted += cvRound(val) == w->cat_responses[w->sidx[j]];
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}
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varImportance[vi] += (float)(ncorrect_responses - ncorrect_responses_permuted);
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}
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}
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}
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if( calcOOBError && oobError < eps )
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break;
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}
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printf("done!\n");
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if( rparams.calcVarImportance )
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{
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for( vi_ = 0; vi_ < nallvars; vi_++ )
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varImportance[vi_] = std::max(varImportance[vi_], 0.f);
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normalize(varImportance, varImportance, 1., 0, NORM_L1);
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}
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endTraining();
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return true;
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}
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void writeTrainingParams( FileStorage& fs ) const
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{
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DTreesImpl::writeTrainingParams(fs);
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fs << "nactive_vars" << rparams.nactiveVars;
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}
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void write( FileStorage& fs ) const
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{
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if( roots.empty() )
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CV_Error( CV_StsBadArg, "RTrees have not been trained" );
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writeParams(fs);
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fs << "oob_error" << oobError;
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if( !varImportance.empty() )
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fs << "var_importance" << varImportance;
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int k, ntrees = (int)roots.size();
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fs << "ntrees" << ntrees
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<< "trees" << "[";
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for( k = 0; k < ntrees; k++ )
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{
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fs << "{";
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writeTree(fs, roots[k]);
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fs << "}";
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}
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fs << "]";
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}
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void readParams( const FileNode& fn )
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{
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DTreesImpl::readParams(fn);
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rparams.maxDepth = params0.maxDepth;
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rparams.minSampleCount = params0.minSampleCount;
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rparams.regressionAccuracy = params0.regressionAccuracy;
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rparams.useSurrogates = params0.useSurrogates;
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rparams.maxCategories = params0.maxCategories;
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rparams.priors = params0.priors;
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FileNode tparams_node = fn["training_params"];
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rparams.nactiveVars = (int)tparams_node["nactive_vars"];
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}
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void read( const FileNode& fn )
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{
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clear();
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//int nclasses = (int)fn["nclasses"];
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//int nsamples = (int)fn["nsamples"];
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oobError = (double)fn["oob_error"];
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int ntrees = (int)fn["ntrees"];
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fn["var_importance"] >> varImportance;
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readParams(fn);
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FileNode trees_node = fn["trees"];
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FileNodeIterator it = trees_node.begin();
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CV_Assert( ntrees == (int)trees_node.size() );
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for( int treeidx = 0; treeidx < ntrees; treeidx++, ++it )
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{
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FileNode nfn = (*it)["nodes"];
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readTree(nfn);
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}
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}
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RTrees::Params rparams;
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double oobError;
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vector<float> varImportance;
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vector<int> allVars, activeVars;
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RNG rng;
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};
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class RTreesImpl : public RTrees
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{
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public:
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RTreesImpl() {}
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virtual ~RTreesImpl() {}
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String getDefaultModelName() const { return "opencv_ml_rtrees"; }
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bool train( const Ptr<TrainData>& trainData, int flags )
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{
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return impl.train(trainData, flags);
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}
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float predict( InputArray samples, OutputArray results, int flags ) const
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{
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return impl.predict(samples, results, flags);
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}
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void write( FileStorage& fs ) const
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{
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impl.write(fs);
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}
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void read( const FileNode& fn )
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{
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impl.read(fn);
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}
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void setRParams(const Params& p) { impl.setRParams(p); }
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Params getRParams() const { return impl.getRParams(); }
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Mat getVarImportance() const { return Mat_<float>(impl.varImportance, true); }
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int getVarCount() const { return impl.getVarCount(); }
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bool isTrained() const { return impl.isTrained(); }
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bool isClassifier() const { return impl.isClassifier(); }
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const vector<int>& getRoots() const { return impl.getRoots(); }
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const vector<Node>& getNodes() const { return impl.getNodes(); }
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const vector<Split>& getSplits() const { return impl.getSplits(); }
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const vector<int>& getSubsets() const { return impl.getSubsets(); }
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DTreesImplForRTrees impl;
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};
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Ptr<RTrees> RTrees::create(const Params& params)
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{
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Ptr<RTreesImpl> p = makePtr<RTreesImpl>();
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p->setRParams(params);
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return p;
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}
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}}
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// End of file.
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