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Updated ml module interfaces and documentation

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This commit is contained in:
Maksim Shabunin
2015-02-11 13:24:14 +03:00
parent da383e65e2
commit 79e8f0680c
32 changed files with 1403 additions and 1528 deletions
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modules/ml/src/tree.cpp
+40 -78
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@@ -48,18 +48,7 @@ namespace ml {
using std::vector;
void DTrees::setDParams(const DTrees::Params&)
{
CV_Error(CV_StsNotImplemented, "");
}
DTrees::Params DTrees::getDParams() const
{
CV_Error(CV_StsNotImplemented, "");
return DTrees::Params();
}
DTrees::Params::Params()
TreeParams::TreeParams()
{
maxDepth = INT_MAX;
minSampleCount = 10;
@@ -72,11 +61,11 @@ DTrees::Params::Params()
priors = Mat();
}
DTrees::Params::Params( int _maxDepth, int _minSampleCount,
double _regressionAccuracy, bool _useSurrogates,
int _maxCategories, int _CVFolds,
bool _use1SERule, bool _truncatePrunedTree,
const Mat& _priors )
TreeParams::TreeParams(int _maxDepth, int _minSampleCount,
double _regressionAccuracy, bool _useSurrogates,
int _maxCategories, int _CVFolds,
bool _use1SERule, bool _truncatePrunedTree,
const Mat& _priors)
{
maxDepth = _maxDepth;
minSampleCount = _minSampleCount;
@@ -248,7 +237,7 @@ const vector<int>& DTreesImpl::getActiveVars()
int DTreesImpl::addTree(const vector<int>& sidx )
{
size_t n = (params.maxDepth > 0 ? (1 << params.maxDepth) : 1024) + w->wnodes.size();
size_t n = (params.getMaxDepth() > 0 ? (1 << params.getMaxDepth()) : 1024) + w->wnodes.size();
w->wnodes.reserve(n);
w->wsplits.reserve(n);
@@ -257,7 +246,7 @@ int DTreesImpl::addTree(const vector<int>& sidx )
w->wsplits.clear();
w->wsubsets.clear();
int cv_n = params.CVFolds;
int cv_n = params.getCVFolds();
if( cv_n > 0 )
{
@@ -347,34 +336,9 @@ int DTreesImpl::addTree(const vector<int>& sidx )
return root;
}
DTrees::Params DTreesImpl::getDParams() const
void DTreesImpl::setDParams(const TreeParams& _params)
{
return params0;
}
void DTreesImpl::setDParams(const Params& _params)
{
params0 = params = _params;
if( params.maxCategories < 2 )
CV_Error( CV_StsOutOfRange, "params.max_categories should be >= 2" );
params.maxCategories = std::min( params.maxCategories, 15 );
if( params.maxDepth < 0 )
CV_Error( CV_StsOutOfRange, "params.max_depth should be >= 0" );
params.maxDepth = std::min( params.maxDepth, 25 );
params.minSampleCount = std::max(params.minSampleCount, 1);
if( params.CVFolds < 0 )
CV_Error( CV_StsOutOfRange,
"params.CVFolds should be =0 (the tree is not pruned) "
"or n>0 (tree is pruned using n-fold cross-validation)" );
if( params.CVFolds == 1 )
params.CVFolds = 0;
if( params.regressionAccuracy < 0 )
CV_Error( CV_StsOutOfRange, "params.regression_accuracy should be >= 0" );
params = _params;
}
int DTreesImpl::addNodeAndTrySplit( int parent, const vector<int>& sidx )
@@ -385,7 +349,7 @@ int DTreesImpl::addNodeAndTrySplit( int parent, const vector<int>& sidx )
node.parent = parent;
node.depth = parent >= 0 ? w->wnodes[parent].depth + 1 : 0;
int nfolds = params.CVFolds;
int nfolds = params.getCVFolds();
if( nfolds > 0 )
{
@@ -400,7 +364,7 @@ int DTreesImpl::addNodeAndTrySplit( int parent, const vector<int>& sidx )
calcValue( nidx, sidx );
if( n <= params.minSampleCount || node.depth >= params.maxDepth )
if( n <= params.getMinSampleCount() || node.depth >= params.getMaxDepth() )
can_split = false;
else if( _isClassifier )
{
@@ -415,7 +379,7 @@ int DTreesImpl::addNodeAndTrySplit( int parent, const vector<int>& sidx )
}
else
{
if( sqrt(node.node_risk) < params.regressionAccuracy )
if( sqrt(node.node_risk) < params.getRegressionAccuracy() )
can_split = false;
}
@@ -493,7 +457,7 @@ int DTreesImpl::findBestSplit( const vector<int>& _sidx )
void DTreesImpl::calcValue( int nidx, const vector<int>& _sidx )
{
WNode* node = &w->wnodes[nidx];
int i, j, k, n = (int)_sidx.size(), cv_n = params.CVFolds;
int i, j, k, n = (int)_sidx.size(), cv_n = params.getCVFolds();
int m = (int)classLabels.size();
cv::AutoBuffer<double> buf(std::max(m, 3)*(cv_n+1));
@@ -841,8 +805,8 @@ DTreesImpl::WSplit DTreesImpl::findSplitCatClass( int vi, const vector<int>& _si
int m = (int)classLabels.size();
int base_size = m*(3 + mi) + mi + 1;
if( m > 2 && mi > params.maxCategories )
base_size += m*std::min(params.maxCategories, n) + mi;
if( m > 2 && mi > params.getMaxCategories() )
base_size += m*std::min(params.getMaxCategories(), n) + mi;
else
base_size += mi;
AutoBuffer<double> buf(base_size + n);
@@ -880,9 +844,9 @@ DTreesImpl::WSplit DTreesImpl::findSplitCatClass( int vi, const vector<int>& _si
if( m > 2 )
{
if( mi > params.maxCategories )
if( mi > params.getMaxCategories() )
{
mi = std::min(params.maxCategories, n);
mi = std::min(params.getMaxCategories(), n);
cjk = c_weights + _mi;
cluster_labels = (int*)(cjk + m*mi);
clusterCategories( _cjk, _mi, m, cjk, mi, cluster_labels );
@@ -1228,7 +1192,7 @@ int DTreesImpl::pruneCV( int root )
// 2. choose the best tree index (if need, apply 1SE rule).
// 3. store the best index and cut the branches.
int ti, tree_count = 0, j, cv_n = params.CVFolds, n = w->wnodes[root].sample_count;
int ti, tree_count = 0, j, cv_n = params.getCVFolds(), n = w->wnodes[root].sample_count;
// currently, 1SE for regression is not implemented
bool use_1se = params.use1SERule != 0 && _isClassifier;
double min_err = 0, min_err_se = 0;
@@ -1294,7 +1258,7 @@ int DTreesImpl::pruneCV( int root )
double DTreesImpl::updateTreeRNC( int root, double T, int fold )
{
int nidx = root, pidx = -1, cv_n = params.CVFolds;
int nidx = root, pidx = -1, cv_n = params.getCVFolds();
double min_alpha = DBL_MAX;
for(;;)
@@ -1350,7 +1314,7 @@ double DTreesImpl::updateTreeRNC( int root, double T, int fold )
bool DTreesImpl::cutTree( int root, double T, int fold, double min_alpha )
{
int cv_n = params.CVFolds, nidx = root, pidx = -1;
int cv_n = params.getCVFolds(), nidx = root, pidx = -1;
WNode* node = &w->wnodes[root];
if( node->left < 0 )
return true;
@@ -1560,19 +1524,19 @@ float DTreesImpl::predict( InputArray _samples, OutputArray _results, int flags
void DTreesImpl::writeTrainingParams(FileStorage& fs) const
{
fs << "use_surrogates" << (params0.useSurrogates ? 1 : 0);
fs << "max_categories" << params0.maxCategories;
fs << "regression_accuracy" << params0.regressionAccuracy;
fs << "use_surrogates" << (params.useSurrogates ? 1 : 0);
fs << "max_categories" << params.getMaxCategories();
fs << "regression_accuracy" << params.getRegressionAccuracy();
fs << "max_depth" << params0.maxDepth;
fs << "min_sample_count" << params0.minSampleCount;
fs << "cross_validation_folds" << params0.CVFolds;
fs << "max_depth" << params.getMaxDepth();
fs << "min_sample_count" << params.getMinSampleCount();
fs << "cross_validation_folds" << params.getCVFolds();
if( params0.CVFolds > 1 )
fs << "use_1se_rule" << (params0.use1SERule ? 1 : 0);
if( params.getCVFolds() > 1 )
fs << "use_1se_rule" << (params.use1SERule ? 1 : 0);
if( !params0.priors.empty() )
fs << "priors" << params0.priors;
if( !params.priors.empty() )
fs << "priors" << params.priors;
}
void DTreesImpl::writeParams(FileStorage& fs) const
@@ -1724,18 +1688,18 @@ void DTreesImpl::readParams( const FileNode& fn )
FileNode tparams_node = fn["training_params"];
params0 = Params();
TreeParams params0 = TreeParams();
if( !tparams_node.empty() ) // training parameters are not necessary
{
params0.useSurrogates = (int)tparams_node["use_surrogates"] != 0;
params0.maxCategories = (int)(tparams_node["max_categories"].empty() ? 16 : tparams_node["max_categories"]);
params0.regressionAccuracy = (float)tparams_node["regression_accuracy"];
params0.maxDepth = (int)tparams_node["max_depth"];
params0.minSampleCount = (int)tparams_node["min_sample_count"];
params0.CVFolds = (int)tparams_node["cross_validation_folds"];
params0.setMaxCategories((int)(tparams_node["max_categories"].empty() ? 16 : tparams_node["max_categories"]));
params0.setRegressionAccuracy((float)tparams_node["regression_accuracy"]);
params0.setMaxDepth((int)tparams_node["max_depth"]);
params0.setMinSampleCount((int)tparams_node["min_sample_count"]);
params0.setCVFolds((int)tparams_node["cross_validation_folds"]);
if( params0.CVFolds > 1 )
if( params0.getCVFolds() > 1 )
{
params.use1SERule = (int)tparams_node["use_1se_rule"] != 0;
}
@@ -1964,11 +1928,9 @@ void DTreesImpl::read( const FileNode& fn )
readTree(fnodes);
}
Ptr<DTrees> DTrees::create(const DTrees::Params& params)
Ptr<DTrees> DTrees::create()
{
Ptr<DTreesImpl> p = makePtr<DTreesImpl>();
p->setDParams(params);
return p;
return makePtr<DTreesImpl>();
}
}