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code_structure.txt
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code_structure.txt
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1. libraries.R
2. utils.R
1. setFolder()
2. directories()
3. creatingFoldersPrincipals(dataset_name)
4. converteArff(arg1, arg2, arg3)
5. criaPastasCSC(dataset_name)
6. infoDataSet(dataset)
7. AddCSV(filenames)
8. RemoveCSV(filenames)
9. RemoveARFF(filenames)
3. preprocessing.R
1. crossValidation(ds, dataset_name, number_folds)
2. verifyDataset(ds, dataset_name, number_folds, Folder)
3. labelSpace(ds, dataset_name, number_folds, Folder)
4. miscellaneous.R
1. evaluateGeneral(ds, dataset_name, number_folds, Folder)
2. resumeHP(dataset_name, FolderHClust, FolderHybPart, FolderReports)
3. compareMethods(dataset_name, FolderRandom1, FolderRandom2, FolderRandom3, FolderLocal, FolderGlobal, FolderHybrid, FolderDataset, FolderReports)
4. deleteAll(number_folds, FolderHybrid, FolderHybPart, FolderHClust, FolderLocal, FolderGlobal, FolderRandom1, FolderRandom2, FolderRandom3)
5. resumePartitions(ds, dataset_name, FolderRandom1, FolderRandom2, FolderRandom3, FolderHybPart, FolderDataset, FolderReports)
5. correlations.R
1. computeJaccard(ds, dataset_name, number_folds, namesLabels, resLS, FolderHC)
2. CutreeHClust(ds, dataset_name, number_folds, namesLabels, resLS, FolderHClust)
3. bestCoefficient(number_folds, FolderHClust)
6. partitions.R
1.hybridPartitions(ds, dataset_name, number_folds, DsFolders, FolderHClust, FolderHybPart)
7. clusValidation.R
# Gather the Predicts Hybrid Partition
1. gatherPredsHybPartVAL(ds, dataset_name, number_folds, FolderHybPart)
# Evaluate Hybrid Partitions Validated
2. evalHybPartVAL(ds, dataset_name, number_folds, FolderHybPart)
# Gather Evaluations Hybrid Partitions
3. gatherEvaluationVAL(ds, dataset_name, number_folds, FolderHybPart)
# Gather F1 Macro Hybrid Partitions
4. gF1macroVAL(ds, dataset_name, number_folds, FolderHybPart, FolderReports)
# Delete Files Hybrid Partitions Validated
5. delHybPartVAL(ds, number_folds, FolderHybPart)
# Validate and evaluate Hybrid Partitions Generated
6. clusValidation(ds, dataset_name, number_folds, FolderHybPart, FolderReports)
8. clusHybrid.R
# Set up the Split groups and execute CLUS
1. mountHybPartTEST(ds, dataset_name, number_folds, DsFolds, FolderHClust, FolderHybPart, FolderHybrid)
# Split predictions
2. splitsPredsHybTEST(ds, dataset_name, number_folds, DsFolds, FolderHybrid, FolderHybPart)
# Joins the real outputs and the predicted outputs in a single file
3. gatherPredsHybTEST(ds, dataset_name, number_folds, FolderHybrid, FolderHybPart)
# Evaluation Choosen Hybrid Partition
4. evalHybTEST(ds, dataset_name, number_folds, FolderHybrid, FolderReports)
# Delete Files Hybrid Partitions Tested
5. delHybTEST(ds, dataset_name, number_folds, FolderHybrid, FolderHybPart)
# Test and evaluate Choosen Hybrid Partitions
6. clusHybrid(ds, dataset_name, number_folds, DsFolds, FolderHClust, FolderHybPart, FolderHybrid, FolderReports)
9. clusLocal.R
# Joins training and test files in a single folder for running the clus
1. gatherFilesFoldsLocal(ds, dataset_name, number_folds, FolderCV, FolderLocal)
# Split the labels
2. splitLabels(ds, dataset_name, namesLabels, number_folds, FolderCV, FolderLocal)
# Splits the real outputs and the predicted outputs
3. gatherLocalPredicts(ds, dataset_name, number_folds, namesLabels, FolderLocal)
# Evaluates Local Partition
4. evaluateLocal(ds, dataset_name, number_folds, Folder)
# Gather Evaluated Measures
5. gatherEvalLocal(ds, dataset_name, number_folds, Folder, FolderReports)
# Test and evaluate the local partitions
6. clusLocal(ds, dataset_name, number_folds, namesLabels, FolderCV, FolderLocal, FolderReports)
10. clusGlobal.R
# Generating the CLUS configuration files for the splits of each dataset
1. configFilesClus(ds, dataset_name, number_folds, FolderConfigFiles)
# Joins the configuration, training and test files for running the clus
2. gatherFilesFoldsGlobal(ds, dataset_name, number_folds, FolderConfigFiles, FolderGlobal)
# Execute CLUS GLOBAL
3. executeClusGlobal(ds, dataset_name, number_folds, Folder)
# Splits the real outputs and the predicted outputs
4. gatherPredictsGlobal(ds, dataset_name, number_folds, Folder)
# Evaluates Global Partitions
5. evaluateGlobal(ds, dataset_name, number_folds, FolderGlobal)
# Gather Evaluated Measures
6. gatherEvalGlobal(ds, dataset_name, number_folds, Folder, FolderReports)
# Delete files
7. deleteGlobal(ds, dataset_name, number_folds, FolderGlobal)
# Test and evaluate the global partitions
8. clusGlobal(ds, dataset_name, number_folds, FolderRD, FolderConfigFiles,FolderGlobal, FolderReports)
11. clusRandom1.R
# VALIDATING RANDOM PARTITIONS V1
# generated random partitions v1
1. generatedRandomPartitionsVAL(ds, namesLabels, dataset_name, number_folds, FolderRandom)
# build and validate random partitions v1
2. RandomPartitionsVAL(ds, namesLabels, dataset_name, number_folds, FolderRandom, FolderDSF)
# gather predictions random partitions v1
3. gatherPredsRandomVAL(ds, dataset_name, number_folds, FolderRandom)
# eval random partitions v1
4. evalRandomVAL(ds, dataset_name, number_folds, FolderRandom)
# gather eval random partitions v1
5. gatherEvaluationsVALfunction(ds, dataset_name, number_folds, FolderRandom)
# gather f1 random partitions v1
6. gatherF1macroVAL(ds, dataset_name, number_folds, FolderRandom, FolderReports)
# TEST BEST RANDOM PARTITIONS V1
# test clus random partitions v1
7. mountRandomParTEST(ds, dataset_name, number_folds, FolderDSF, FolderRandom)
# predictions random partitions v1
8. splitsPredictionsRandomTEST(ds, dataset_name, number_folds, FolderDSF, FolderRandom)
# gather random predictions v1
9. gatherPredsRandomTEST(ds, dataset_name, number_folds, FolderRandom)
# evaluation random partitions v1
10. evalRandomTEST(ds, dataset_name, number_folds, FolderRandom, FolderReports)
# delete files
11. deleteRandomFiles(ds, namesLabels, number_folds, FolderRandom)
# validate and test the random partitions v1
12. clusRandom_1(ds, dataset_name, number_folds, namesLabels, FolderDSF, FolderResDataset, FolderRandom, FolderReports)
12. clusRandom2.R
# generated random partitions v2
1. generatedRandomPartitions2(ds, dataset_name, number_folds, namesLabels, FolderRandom)
# build and validate partitions v2
2. RandomPartitions2(ds, namesLabels, dataset_name, number_folds, FolderRandom, FolderDSF)
# gather predictions
3. gatherPredsRandom2(ds, dataset_name, number_folds, FolderRandom)
# eval random partitions 2
4. evalRandom2 <- function(ds, dataset_name, number_folds, FolderRandom)
# Validate and test the random partitions v2
5. clusRandom_2(ds, namesLabels, dataset_name, number_folds, FolderDSF, FolderResDataset, FolderRandom, FolderReports)
13. clusRandom3.R
# VALIDATE RANDOM PARTTIONS V3
# generated random partitions v3
1. generatedRandomPartitionsV3(ds, namesLabels, dataset_name, number_folds, FolderRandom3)
# build and validate partitions
2. RandomPartitionsV3(ds, namesLabels, dataset_name, number_folds, FolderRandom3, FolderDSF)
# gather predictions partitions
3. gatherPredsRandomV3(ds, dataset_name, number_folds, FolderRandom3)
# eval partitions
4. evalRandomV3(ds, dataset_name, number_folds, FolderRandom3)
# gather eval partitions
5. gatherEvaluationsV3(ds, dataset_name, number_folds, FolderRandom3)
# gather f1 partitions
6. gatherF1macroV3(ds, dataset_name, number_folds, FolderRandom3, FolderReports)
# TEST BEST RANDOM PARTITION 3
# test clus
7. mountRandomParT3(ds, dataset_name, number_folds, FolderDSF, FolderRandom3)
# predictions
8. splitsPredictionsRandomT3(ds, dataset_name, number_folds, FolderDSF, FolderRandom3)
# gather predictions
9. gatherPredsRandomT3(ds, dataset_name, number_folds, FolderRandom3)
# evaluation
10. evalRandomT3(ds, dataset_name, number_folds, FolderRandom3)
# gather partitions
11. gatherPartitionsT3(ds, namesLabels, number_folds, FolderRandom3)
# delete unecessary files
12. deleteRandomFiles3(ds, namesLabels, number_folds, FolderRandom3)
# validate and test the random partitions v3
13. clusRandom_3(ds, dataset_name, number_folds, namesLabels, FolderDSF, FolderResDataset, FolderRandom3, FolderReports)
14. run.R
hpmlj(number_dataset, number_cores, number_folds)
15. hpmlj.R
# Rscript