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/*
* Copyright (C) 2005-2022 Centre National d'Etudes Spatiales (CNES)
*
* This file is part of Orfeo Toolbox
*
* https://www.orfeo-toolbox.org/
*
* 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.
*/
#ifndef otbTrainRandomForests_hxx
#define otbTrainRandomForests_hxx
#include "otbLearningApplicationBase.h"
#include "otbRandomForestsMachineLearningModel.h"
namespace otb
{
namespace Wrapper
{
template <class TInputValue, class TOutputValue>
void LearningApplicationBase<TInputValue, TOutputValue>::InitRandomForestsParams()
{
AddChoice("classifier.rf", "Random forests classifier");
SetParameterDescription("classifier.rf", "http://docs.opencv.org/modules/ml/doc/random_trees.html");
// MaxDepth
AddParameter(ParameterType_Int, "classifier.rf.max", "Maximum depth of the tree");
SetParameterInt("classifier.rf.max", 5);
SetParameterDescription("classifier.rf.max",
"The depth of the tree. A low value will likely underfit and conversely a high value will likely overfit. "
"The optimal value can be obtained using cross validation or other suitable methods.");
// MinSampleCount
AddParameter(ParameterType_Int, "classifier.rf.min", "Minimum number of samples in each node");
SetParameterInt("classifier.rf.min", 10);
SetParameterDescription("classifier.rf.min",
"If the number of samples in a node is smaller than this parameter, "
"then the node will not be split. A reasonable value is a small percentage of the total data e.g. 1 percent.");
// RegressionAccuracy
AddParameter(ParameterType_Float, "classifier.rf.ra", "Termination Criteria for regression tree");
SetParameterFloat("classifier.rf.ra", 0.);
SetParameterDescription("classifier.rf.ra",
"If all absolute differences between an estimated value in a node "
"and the values of the train samples in this node are smaller than this regression accuracy parameter, "
"then the node will not be split.");
// MaxNumberOfCategories
AddParameter(ParameterType_Int, "classifier.rf.cat", "Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split");
SetParameterInt("classifier.rf.cat", 10);
SetParameterDescription("classifier.rf.cat", "Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split.");
// Priors are not exposed.
// CalculateVariableImportance not exposed
// MaxNumberOfVariables
AddParameter(ParameterType_Int, "classifier.rf.var", "Size of the randomly selected subset of features at each tree node");
SetParameterInt("classifier.rf.var", 0);
SetParameterDescription("classifier.rf.var",
"The size of the subset of features, randomly selected at each tree node, that are used to find the best split(s). "
"If you set it to 0, then the size will be set to the square root of the total number of features.");
// MaxNumberOfTrees
AddParameter(ParameterType_Int, "classifier.rf.nbtrees", "Maximum number of trees in the forest");
SetParameterInt("classifier.rf.nbtrees", 100);
SetParameterDescription("classifier.rf.nbtrees",
"The maximum number of trees in the forest. Typically, the more trees you have, the better the accuracy. "
"However, the improvement in accuracy generally diminishes and reaches an asymptote for a certain number of trees. "
"Also to keep in mind, increasing the number of trees increases the prediction time linearly.");
// ForestAccuracy
AddParameter(ParameterType_Float, "classifier.rf.acc", "Sufficient accuracy (OOB error)");
SetParameterFloat("classifier.rf.acc", 0.01);
SetParameterDescription("classifier.rf.acc", "Sufficient accuracy (OOB error).");
// TerminationCriteria not exposed
}
template <class TInputValue, class TOutputValue>
void LearningApplicationBase<TInputValue, TOutputValue>::TrainRandomForests(typename ListSampleType::Pointer trainingListSample,
typename TargetListSampleType::Pointer trainingLabeledListSample,
std::string modelPath)
{
typedef otb::RandomForestsMachineLearningModel<InputValueType, OutputValueType> RandomForestType;
typename RandomForestType::Pointer classifier = RandomForestType::New();
classifier->SetRegressionMode(this->m_RegressionFlag);
classifier->SetInputListSample(trainingListSample);
classifier->SetTargetListSample(trainingLabeledListSample);
classifier->SetMaxDepth(GetParameterInt("classifier.rf.max"));
classifier->SetMinSampleCount(GetParameterInt("classifier.rf.min"));
classifier->SetRegressionAccuracy(GetParameterFloat("classifier.rf.ra"));
classifier->SetMaxNumberOfCategories(GetParameterInt("classifier.rf.cat"));
classifier->SetMaxNumberOfVariables(GetParameterInt("classifier.rf.var"));
classifier->SetMaxNumberOfTrees(GetParameterInt("classifier.rf.nbtrees"));
classifier->SetForestAccuracy(GetParameterFloat("classifier.rf.acc"));
classifier->Train();
classifier->Save(modelPath);
}
} // end namespace wrapper
} // end namespace otb
#endif
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