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Guillaume Perréal authored01c23089
/* * Copyright (C) 2005-2017 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_txx#define otbTrainRandomForests_txx#include "otbLearningApplicationBase.h"#include "otbRandomForestsMachineLearningModel.h"namespace otb{namespace Wrapper{template <class TInputValue, class TOutputValue>voidLearningApplicationBase<TInputValue,TOutputValue>::InitRandomForestsParams(){ AddChoice("classifier.rf", "Random forests classifier"); SetParameterDescription("classifier.rf", "This group of parameters allows setting Random Forests classifier parameters. " "See complete documentation here \\url{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."); //UseSurrogates : don't need to be exposed ! //AddParameter(ParameterType_Empty, "classifier.rf.sur", "Surrogate splits will be built"); //SetParameterDescription("classifier.rf.sur","These splits allow working with missing data and compute variable importance correctly."); //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(7172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135
"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