d8425d8881
Finished with several samples support, need regression testing Gave a more relevant name to function (getVotes) Finished implicit implementation Removed printf, finished regresion testing Fixed conversion warning Finished test for Rtrees Fixed documentation Initialized variable Added doxygen documentation Added parameter name
500 lines
17 KiB
C++
500 lines
17 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2000, Intel Corporation, all rights reserved.
|
|
// Copyright (C) 2014, Itseez Inc, all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * The name of the copyright holders may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#include "precomp.hpp"
|
|
namespace cv {
|
|
namespace ml {
|
|
|
|
//////////////////////////////////////////////////////////////////////////////////////////
|
|
// Random trees //
|
|
//////////////////////////////////////////////////////////////////////////////////////////
|
|
RTreeParams::RTreeParams()
|
|
{
|
|
calcVarImportance = false;
|
|
nactiveVars = 0;
|
|
termCrit = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 50, 0.1);
|
|
}
|
|
|
|
RTreeParams::RTreeParams(bool _calcVarImportance,
|
|
int _nactiveVars,
|
|
TermCriteria _termCrit )
|
|
{
|
|
calcVarImportance = _calcVarImportance;
|
|
nactiveVars = _nactiveVars;
|
|
termCrit = _termCrit;
|
|
}
|
|
|
|
|
|
class DTreesImplForRTrees : public DTreesImpl
|
|
{
|
|
public:
|
|
DTreesImplForRTrees()
|
|
{
|
|
params.setMaxDepth(5);
|
|
params.setMinSampleCount(10);
|
|
params.setRegressionAccuracy(0.f);
|
|
params.useSurrogates = false;
|
|
params.setMaxCategories(10);
|
|
params.setCVFolds(0);
|
|
params.use1SERule = false;
|
|
params.truncatePrunedTree = false;
|
|
params.priors = Mat();
|
|
}
|
|
virtual ~DTreesImplForRTrees() {}
|
|
|
|
void clear()
|
|
{
|
|
DTreesImpl::clear();
|
|
oobError = 0.;
|
|
rng = RNG((uint64)-1);
|
|
}
|
|
|
|
const vector<int>& getActiveVars()
|
|
{
|
|
int i, nvars = (int)allVars.size(), m = (int)activeVars.size();
|
|
for( i = 0; i < nvars; i++ )
|
|
{
|
|
int i1 = rng.uniform(0, nvars);
|
|
int i2 = rng.uniform(0, nvars);
|
|
std::swap(allVars[i1], allVars[i2]);
|
|
}
|
|
for( i = 0; i < m; i++ )
|
|
activeVars[i] = allVars[i];
|
|
return activeVars;
|
|
}
|
|
|
|
void startTraining( const Ptr<TrainData>& trainData, int flags )
|
|
{
|
|
DTreesImpl::startTraining(trainData, flags);
|
|
int nvars = w->data->getNVars();
|
|
int i, m = rparams.nactiveVars > 0 ? rparams.nactiveVars : cvRound(std::sqrt((double)nvars));
|
|
m = std::min(std::max(m, 1), nvars);
|
|
allVars.resize(nvars);
|
|
activeVars.resize(m);
|
|
for( i = 0; i < nvars; i++ )
|
|
allVars[i] = varIdx[i];
|
|
}
|
|
|
|
void endTraining()
|
|
{
|
|
DTreesImpl::endTraining();
|
|
vector<int> a, b;
|
|
std::swap(allVars, a);
|
|
std::swap(activeVars, b);
|
|
}
|
|
|
|
bool train( const Ptr<TrainData>& trainData, int flags )
|
|
{
|
|
startTraining(trainData, flags);
|
|
int treeidx, ntrees = (rparams.termCrit.type & TermCriteria::COUNT) != 0 ?
|
|
rparams.termCrit.maxCount : 10000;
|
|
int i, j, k, vi, vi_, n = (int)w->sidx.size();
|
|
int nclasses = (int)classLabels.size();
|
|
double eps = (rparams.termCrit.type & TermCriteria::EPS) != 0 &&
|
|
rparams.termCrit.epsilon > 0 ? rparams.termCrit.epsilon : 0.;
|
|
vector<int> sidx(n);
|
|
vector<uchar> oobmask(n);
|
|
vector<int> oobidx;
|
|
vector<int> oobperm;
|
|
vector<double> oobres(n, 0.);
|
|
vector<int> oobcount(n, 0);
|
|
vector<int> oobvotes(n*nclasses, 0);
|
|
int nvars = w->data->getNVars();
|
|
int nallvars = w->data->getNAllVars();
|
|
const int* vidx = !varIdx.empty() ? &varIdx[0] : 0;
|
|
vector<float> samplebuf(nallvars);
|
|
Mat samples = w->data->getSamples();
|
|
float* psamples = samples.ptr<float>();
|
|
size_t sstep0 = samples.step1(), sstep1 = 1;
|
|
Mat sample0, sample(nallvars, 1, CV_32F, &samplebuf[0]);
|
|
int predictFlags = _isClassifier ? (PREDICT_MAX_VOTE + RAW_OUTPUT) : PREDICT_SUM;
|
|
|
|
bool calcOOBError = eps > 0 || rparams.calcVarImportance;
|
|
double max_response = 0.;
|
|
|
|
if( w->data->getLayout() == COL_SAMPLE )
|
|
std::swap(sstep0, sstep1);
|
|
|
|
if( !_isClassifier )
|
|
{
|
|
for( i = 0; i < n; i++ )
|
|
{
|
|
double val = std::abs(w->ord_responses[w->sidx[i]]);
|
|
max_response = std::max(max_response, val);
|
|
}
|
|
}
|
|
|
|
if( rparams.calcVarImportance )
|
|
varImportance.resize(nallvars, 0.f);
|
|
|
|
for( treeidx = 0; treeidx < ntrees; treeidx++ )
|
|
{
|
|
for( i = 0; i < n; i++ )
|
|
oobmask[i] = (uchar)1;
|
|
|
|
for( i = 0; i < n; i++ )
|
|
{
|
|
j = rng.uniform(0, n);
|
|
sidx[i] = w->sidx[j];
|
|
oobmask[j] = (uchar)0;
|
|
}
|
|
int root = addTree( sidx );
|
|
if( root < 0 )
|
|
return false;
|
|
|
|
if( calcOOBError )
|
|
{
|
|
oobidx.clear();
|
|
for( i = 0; i < n; i++ )
|
|
{
|
|
if( oobmask[i] )
|
|
oobidx.push_back(i);
|
|
}
|
|
int n_oob = (int)oobidx.size();
|
|
// if there is no out-of-bag samples, we can not compute OOB error
|
|
// nor update the variable importance vector; so we proceed to the next tree
|
|
if( n_oob == 0 )
|
|
continue;
|
|
double ncorrect_responses = 0.;
|
|
|
|
oobError = 0.;
|
|
for( i = 0; i < n_oob; i++ )
|
|
{
|
|
j = oobidx[i];
|
|
sample = Mat( nallvars, 1, CV_32F, psamples + sstep0*w->sidx[j], sstep1*sizeof(psamples[0]) );
|
|
|
|
double val = predictTrees(Range(treeidx, treeidx+1), sample, predictFlags);
|
|
if( !_isClassifier )
|
|
{
|
|
oobres[j] += val;
|
|
oobcount[j]++;
|
|
double true_val = w->ord_responses[w->sidx[j]];
|
|
double a = oobres[j]/oobcount[j] - true_val;
|
|
oobError += a*a;
|
|
val = (val - true_val)/max_response;
|
|
ncorrect_responses += std::exp( -val*val );
|
|
}
|
|
else
|
|
{
|
|
int ival = cvRound(val);
|
|
//Voting scheme to combine OOB errors of each tree
|
|
int* votes = &oobvotes[j*nclasses];
|
|
votes[ival]++;
|
|
int best_class = 0;
|
|
for( k = 1; k < nclasses; k++ )
|
|
if( votes[best_class] < votes[k] )
|
|
best_class = k;
|
|
int diff = best_class != w->cat_responses[w->sidx[j]];
|
|
oobError += diff;
|
|
ncorrect_responses += diff == 0;
|
|
}
|
|
}
|
|
|
|
oobError /= n_oob;
|
|
if( rparams.calcVarImportance && n_oob > 1 )
|
|
{
|
|
Mat sample_clone;
|
|
oobperm.resize(n_oob);
|
|
for( i = 0; i < n_oob; i++ )
|
|
oobperm[i] = oobidx[i];
|
|
for (i = n_oob - 1; i > 0; --i) //Randomly shuffle indices so we can permute features
|
|
{
|
|
int r_i = rng.uniform(0, n_oob);
|
|
std::swap(oobperm[i], oobperm[r_i]);
|
|
}
|
|
|
|
for( vi_ = 0; vi_ < nvars; vi_++ )
|
|
{
|
|
vi = vidx ? vidx[vi_] : vi_; //Ensure that only the user specified predictors are used for training
|
|
double ncorrect_responses_permuted = 0;
|
|
|
|
for( i = 0; i < n_oob; i++ )
|
|
{
|
|
j = oobidx[i];
|
|
int vj = oobperm[i];
|
|
sample0 = Mat( nallvars, 1, CV_32F, psamples + sstep0*w->sidx[j], sstep1*sizeof(psamples[0]) );
|
|
sample0.copyTo(sample_clone); //create a copy so we don't mess up the original data
|
|
sample_clone.at<float>(vi) = psamples[sstep0*w->sidx[vj] + sstep1*vi];
|
|
|
|
double val = predictTrees(Range(treeidx, treeidx+1), sample_clone, predictFlags);
|
|
if( !_isClassifier )
|
|
{
|
|
val = (val - w->ord_responses[w->sidx[j]])/max_response;
|
|
ncorrect_responses_permuted += exp( -val*val );
|
|
}
|
|
else
|
|
{
|
|
ncorrect_responses_permuted += cvRound(val) == w->cat_responses[w->sidx[j]];
|
|
}
|
|
}
|
|
varImportance[vi] += (float)(ncorrect_responses - ncorrect_responses_permuted);
|
|
}
|
|
}
|
|
}
|
|
if( calcOOBError && oobError < eps )
|
|
break;
|
|
}
|
|
|
|
if( rparams.calcVarImportance )
|
|
{
|
|
for( vi_ = 0; vi_ < nallvars; vi_++ )
|
|
varImportance[vi_] = std::max(varImportance[vi_], 0.f);
|
|
normalize(varImportance, varImportance, 1., 0, NORM_L1);
|
|
}
|
|
endTraining();
|
|
return true;
|
|
}
|
|
|
|
void writeTrainingParams( FileStorage& fs ) const
|
|
{
|
|
DTreesImpl::writeTrainingParams(fs);
|
|
fs << "nactive_vars" << rparams.nactiveVars;
|
|
}
|
|
|
|
void write( FileStorage& fs ) const
|
|
{
|
|
if( roots.empty() )
|
|
CV_Error( CV_StsBadArg, "RTrees have not been trained" );
|
|
|
|
writeFormat(fs);
|
|
writeParams(fs);
|
|
|
|
fs << "oob_error" << oobError;
|
|
if( !varImportance.empty() )
|
|
fs << "var_importance" << varImportance;
|
|
|
|
int k, ntrees = (int)roots.size();
|
|
|
|
fs << "ntrees" << ntrees
|
|
<< "trees" << "[";
|
|
|
|
for( k = 0; k < ntrees; k++ )
|
|
{
|
|
fs << "{";
|
|
writeTree(fs, roots[k]);
|
|
fs << "}";
|
|
}
|
|
|
|
fs << "]";
|
|
}
|
|
|
|
void readParams( const FileNode& fn )
|
|
{
|
|
DTreesImpl::readParams(fn);
|
|
|
|
FileNode tparams_node = fn["training_params"];
|
|
rparams.nactiveVars = (int)tparams_node["nactive_vars"];
|
|
}
|
|
|
|
void read( const FileNode& fn )
|
|
{
|
|
clear();
|
|
|
|
//int nclasses = (int)fn["nclasses"];
|
|
//int nsamples = (int)fn["nsamples"];
|
|
oobError = (double)fn["oob_error"];
|
|
int ntrees = (int)fn["ntrees"];
|
|
|
|
readVectorOrMat(fn["var_importance"], varImportance);
|
|
|
|
readParams(fn);
|
|
|
|
FileNode trees_node = fn["trees"];
|
|
FileNodeIterator it = trees_node.begin();
|
|
CV_Assert( ntrees == (int)trees_node.size() );
|
|
|
|
for( int treeidx = 0; treeidx < ntrees; treeidx++, ++it )
|
|
{
|
|
FileNode nfn = (*it)["nodes"];
|
|
readTree(nfn);
|
|
}
|
|
}
|
|
|
|
void getVotes( InputArray input, OutputArray output, int flags ) const
|
|
{
|
|
CV_Assert( !roots.empty() );
|
|
int nclasses = (int)classLabels.size(), ntrees = (int)roots.size();
|
|
Mat samples = input.getMat(), results;
|
|
int i, j, nsamples = samples.rows;
|
|
|
|
int predictType = flags & PREDICT_MASK;
|
|
if( predictType == PREDICT_AUTO )
|
|
{
|
|
predictType = !_isClassifier || (classLabels.size() == 2 && (flags & RAW_OUTPUT) != 0) ?
|
|
PREDICT_SUM : PREDICT_MAX_VOTE;
|
|
}
|
|
|
|
if( predictType == PREDICT_SUM )
|
|
{
|
|
output.create(nsamples, ntrees, CV_32F);
|
|
results = output.getMat();
|
|
for( i = 0; i < nsamples; i++ )
|
|
{
|
|
for( j = 0; j < ntrees; j++ )
|
|
{
|
|
float val = predictTrees( Range(j, j+1), samples.row(i), flags);
|
|
results.at<float> (i, j) = val;
|
|
}
|
|
}
|
|
} else
|
|
{
|
|
vector<int> votes;
|
|
output.create(nsamples+1, nclasses, CV_32S);
|
|
results = output.getMat();
|
|
|
|
for ( j = 0; j < nclasses; j++)
|
|
{
|
|
results.at<int> (0, j) = classLabels[j];
|
|
}
|
|
|
|
for( i = 0; i < nsamples; i++ )
|
|
{
|
|
votes.clear();
|
|
for( j = 0; j < ntrees; j++ )
|
|
{
|
|
int val = (int)predictTrees( Range(j, j+1), samples.row(i), flags);
|
|
votes.push_back(val);
|
|
}
|
|
|
|
for ( j = 0; j < nclasses; j++)
|
|
{
|
|
results.at<int> (i+1, j) = (int)std::count(votes.begin(), votes.end(), classLabels[j]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
RTreeParams rparams;
|
|
double oobError;
|
|
vector<float> varImportance;
|
|
vector<int> allVars, activeVars;
|
|
RNG rng;
|
|
};
|
|
|
|
|
|
class RTreesImpl : public RTrees
|
|
{
|
|
public:
|
|
CV_IMPL_PROPERTY(bool, CalculateVarImportance, impl.rparams.calcVarImportance)
|
|
CV_IMPL_PROPERTY(int, ActiveVarCount, impl.rparams.nactiveVars)
|
|
CV_IMPL_PROPERTY_S(TermCriteria, TermCriteria, impl.rparams.termCrit)
|
|
|
|
CV_WRAP_SAME_PROPERTY(int, MaxCategories, impl.params)
|
|
CV_WRAP_SAME_PROPERTY(int, MaxDepth, impl.params)
|
|
CV_WRAP_SAME_PROPERTY(int, MinSampleCount, impl.params)
|
|
CV_WRAP_SAME_PROPERTY(int, CVFolds, impl.params)
|
|
CV_WRAP_SAME_PROPERTY(bool, UseSurrogates, impl.params)
|
|
CV_WRAP_SAME_PROPERTY(bool, Use1SERule, impl.params)
|
|
CV_WRAP_SAME_PROPERTY(bool, TruncatePrunedTree, impl.params)
|
|
CV_WRAP_SAME_PROPERTY(float, RegressionAccuracy, impl.params)
|
|
CV_WRAP_SAME_PROPERTY_S(cv::Mat, Priors, impl.params)
|
|
|
|
RTreesImpl() {}
|
|
virtual ~RTreesImpl() {}
|
|
|
|
String getDefaultName() const { return "opencv_ml_rtrees"; }
|
|
|
|
bool train( const Ptr<TrainData>& trainData, int flags )
|
|
{
|
|
if (impl.getCVFolds() != 0)
|
|
CV_Error(Error::StsBadArg, "Cross validation for RTrees is not implemented");
|
|
return impl.train(trainData, flags);
|
|
}
|
|
|
|
float predict( InputArray samples, OutputArray results, int flags ) const
|
|
{
|
|
return impl.predict(samples, results, flags);
|
|
}
|
|
|
|
void write( FileStorage& fs ) const
|
|
{
|
|
impl.write(fs);
|
|
}
|
|
|
|
void read( const FileNode& fn )
|
|
{
|
|
impl.read(fn);
|
|
}
|
|
|
|
void getVotes_( InputArray samples, OutputArray results, int flags ) const
|
|
{
|
|
impl.getVotes(samples, results, flags);
|
|
}
|
|
|
|
Mat getVarImportance() const { return Mat_<float>(impl.varImportance, true); }
|
|
int getVarCount() const { return impl.getVarCount(); }
|
|
|
|
bool isTrained() const { return impl.isTrained(); }
|
|
bool isClassifier() const { return impl.isClassifier(); }
|
|
|
|
const vector<int>& getRoots() const { return impl.getRoots(); }
|
|
const vector<Node>& getNodes() const { return impl.getNodes(); }
|
|
const vector<Split>& getSplits() const { return impl.getSplits(); }
|
|
const vector<int>& getSubsets() const { return impl.getSubsets(); }
|
|
|
|
DTreesImplForRTrees impl;
|
|
};
|
|
|
|
|
|
Ptr<RTrees> RTrees::create()
|
|
{
|
|
return makePtr<RTreesImpl>();
|
|
}
|
|
|
|
//Function needed for Python and Java wrappers
|
|
Ptr<RTrees> RTrees::load(const String& filepath, const String& nodeName)
|
|
{
|
|
return Algorithm::load<RTrees>(filepath, nodeName);
|
|
}
|
|
|
|
void RTrees::getVotes(InputArray input, OutputArray output, int flags) const
|
|
{
|
|
const RTreesImpl* this_ = dynamic_cast<const RTreesImpl*>(this);
|
|
if(!this_)
|
|
CV_Error(Error::StsNotImplemented, "the class is not RTreesImpl");
|
|
return this_->getVotes_(input, output, flags);
|
|
}
|
|
|
|
}}
|
|
|
|
// End of file.
|