forest.cpp
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/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
* Copyright 2012 The MITRE Corporation *
* *
* Licensed under the Apache License, Version 2.0 (the "License"); *
* you may not use this file except in compliance with the License. *
* You may obtain a copy of the License at *
* *
* http://www.apache.org/licenses/LICENSE-2.0 *
* *
* Unless required by applicable law or agreed to in writing, software *
* distributed under the License is distributed on an "AS IS" BASIS, *
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. *
* See the License for the specific language governing permissions and *
* limitations under the License. *
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
#include <openbr/plugins/openbr_internal.h>
#include <openbr/core/opencvutils.h>
using namespace cv;
namespace br
{
/*!
* \ingroup transforms
* \brief Wraps OpenCV's random trees framework
* \author Scott Klum \cite sklum
* \brief http://docs.opencv.org/modules/ml/doc/random_trees.html
*/
class ForestTransform : public Transform
{
Q_OBJECT
void train(const TemplateList &data)
{
trainForest(data);
}
void project(const Template &src, Template &dst) const
{
dst = src;
float response;
if (classification && returnConfidence) {
// Fuzzy class label
response = forest.predict_prob(src.m().reshape(1,1));
} else {
response = forest.predict(src.m().reshape(1,1));
}
if (overwriteMat) {
dst.m() = Mat(1, 1, CV_32F);
dst.m().at<float>(0, 0) = response;
} else {
dst.file.set(outputVariable, response);
}
}
void load(QDataStream &stream)
{
OpenCVUtils::loadModel(forest,stream);
}
void store(QDataStream &stream) const
{
OpenCVUtils::storeModel(forest,stream);
}
void init()
{
if (outputVariable.isEmpty())
outputVariable = inputVariable;
}
protected:
Q_ENUMS(TerminationCriteria)
Q_PROPERTY(bool classification READ get_classification WRITE set_classification RESET reset_classification STORED false)
Q_PROPERTY(float splitPercentage READ get_splitPercentage WRITE set_splitPercentage RESET reset_splitPercentage STORED false)
Q_PROPERTY(int maxDepth READ get_maxDepth WRITE set_maxDepth RESET reset_maxDepth STORED false)
Q_PROPERTY(int maxTrees READ get_maxTrees WRITE set_maxTrees RESET reset_maxTrees STORED false)
Q_PROPERTY(float forestAccuracy READ get_forestAccuracy WRITE set_forestAccuracy RESET reset_forestAccuracy STORED false)
Q_PROPERTY(bool returnConfidence READ get_returnConfidence WRITE set_returnConfidence RESET reset_returnConfidence STORED false)
Q_PROPERTY(bool overwriteMat READ get_overwriteMat WRITE set_overwriteMat RESET reset_overwriteMat STORED false)
Q_PROPERTY(QString inputVariable READ get_inputVariable WRITE set_inputVariable RESET reset_inputVariable STORED false)
Q_PROPERTY(QString outputVariable READ get_outputVariable WRITE set_outputVariable RESET reset_outputVariable STORED false)
Q_PROPERTY(bool weight READ get_weight WRITE set_weight RESET reset_weight STORED false)
Q_PROPERTY(TerminationCriteria termCrit READ get_termCrit WRITE set_termCrit RESET reset_termCrit STORED false)
public:
enum TerminationCriteria { Iter = CV_TERMCRIT_ITER,
EPS = CV_TERMCRIT_EPS,
Both = CV_TERMCRIT_EPS | CV_TERMCRIT_ITER};
protected:
BR_PROPERTY(bool, classification, true)
BR_PROPERTY(float, splitPercentage, .01)
BR_PROPERTY(int, maxDepth, std::numeric_limits<int>::max())
BR_PROPERTY(int, maxTrees, 10)
BR_PROPERTY(float, forestAccuracy, .1)
BR_PROPERTY(bool, returnConfidence, true)
BR_PROPERTY(bool, overwriteMat, true)
BR_PROPERTY(QString, inputVariable, "Label")
BR_PROPERTY(QString, outputVariable, "")
BR_PROPERTY(bool, weight, false)
BR_PROPERTY(TerminationCriteria, termCrit, Iter)
CvRTrees forest;
void trainForest(const TemplateList &data)
{
Mat samples = OpenCVUtils::toMat(data.data());
Mat labels = OpenCVUtils::toMat(File::get<float>(data, inputVariable));
Mat types = Mat(samples.cols + 1, 1, CV_8U);
types.setTo(Scalar(CV_VAR_NUMERICAL));
if (classification) {
types.at<char>(samples.cols, 0) = CV_VAR_CATEGORICAL;
} else {
types.at<char>(samples.cols, 0) = CV_VAR_NUMERICAL;
}
bool usePrior = classification && weight;
float priors[2];
if (usePrior) {
int nonZero = countNonZero(labels);
priors[0] = 1;
priors[1] = (float)(samples.rows-nonZero)/nonZero;
}
int minSamplesForSplit = data.size()*splitPercentage;
forest.train( samples, CV_ROW_SAMPLE, labels, Mat(), Mat(), types, Mat(),
CvRTParams(maxDepth,
minSamplesForSplit,
0,
false,
2,
usePrior ? priors : 0,
false,
0,
maxTrees,
forestAccuracy,
termCrit));
if (Globals->verbose) {
qDebug() << "Number of trees:" << forest.get_tree_count();
if (classification) {
QTime timer;
timer.start();
int correctClassification = 0;
float regressionError = 0;
for (int i=0; i<samples.rows; i++) {
float prediction = forest.predict_prob(samples.row(i));
int label = forest.predict(samples.row(i));
if (label == labels.at<float>(i,0)) {
correctClassification++;
}
regressionError += fabs(prediction-labels.at<float>(i,0));
}
qDebug("Time to classify %d samples: %d ms\n \
Classification Accuracy: %f\n \
MAE: %f\n \
Sample dimensionality: %d",
samples.rows,timer.elapsed(),(float)correctClassification/samples.rows,regressionError/samples.rows,samples.cols);
}
}
}
};
BR_REGISTER(Transform, ForestTransform)
/*!
* \ingroup transforms
* \brief Wraps OpenCV's random trees framework to induce features
* \author Scott Klum \cite sklum
* \brief https://lirias.kuleuven.be/bitstream/123456789/316661/1/icdm11-camready.pdf
*/
class ForestInductionTransform : public ForestTransform
{
Q_OBJECT
Q_PROPERTY(bool useRegressionValue READ get_useRegressionValue WRITE set_useRegressionValue RESET reset_useRegressionValue STORED false)
BR_PROPERTY(bool, useRegressionValue, false)
int totalSize;
QList< QList<const CvDTreeNode*> > nodes;
void fillNodes()
{
for (int i=0; i<forest.get_tree_count(); i++) {
nodes.append(QList<const CvDTreeNode*>());
const CvDTreeNode* node = forest.get_tree(i)->get_root();
// traverse the tree and save all the nodes in depth-first order
for(;;)
{
CvDTreeNode* parent;
for(;;)
{
if( !node->left )
break;
node = node->left;
}
nodes.last().append(node);
for( parent = node->parent; parent && parent->right == node;
node = parent, parent = parent->parent )
;
if( !parent )
break;
node = parent->right;
}
totalSize += nodes.last().size();
}
}
void train(const TemplateList &data)
{
trainForest(data);
if (!useRegressionValue) fillNodes();
}
void project(const Template &src, Template &dst) const
{
dst = src;
Mat responses;
if (useRegressionValue) {
responses = Mat::zeros(forest.get_tree_count(),1,CV_32F);
for (int i=0; i<forest.get_tree_count(); i++) {
responses.at<float>(i,0) = forest.get_tree(i)->predict(src.m().reshape(1,1))->value;
}
} else {
responses = Mat::zeros(totalSize,1,CV_32F);
int offset = 0;
for (int i=0; i<nodes.size(); i++) {
int index = nodes[i].indexOf(forest.get_tree(i)->predict(src.m().reshape(1,1)));
responses.at<float>(offset+index,0) = 1;
offset += nodes[i].size();
}
}
dst.m() = responses;
}
void load(QDataStream &stream)
{
OpenCVUtils::loadModel(forest,stream);
if (!useRegressionValue) fillNodes();
}
void store(QDataStream &stream) const
{
OpenCVUtils::storeModel(forest,stream);
}
};
BR_REGISTER(Transform, ForestInductionTransform)
} // namespace br
#include "classification/forest.moc"