saveRF: Saves/loads random forests model to/from file

Description

saveRF: the internal structure of given random forests model is saved to file. loadRF: the internal structure of random forests model is loaded from given file and a model is created and returned.

Usage

saveRF(model, fileName) 
loadRF(fileName)

Arguments

model

The model structure as returned by CoreModel.

fileName

Name of the file to save/load the model to/from.

Value

saveRF invisibly returns some debugging information, while loadRF returns a loaded model as a list, similarly to CoreModel.

Details

The function saveRF saves the internal structure of given random forests model to file. The structures from C++ code are stored to the file with specified file, while internal structures from R are stored to file named fileName.Rda. The model must be a valid structure returned by CoreModel.

The function loadRF loads the internal structure of random forests saved in a specified files and returns access to it.

Examples

Run this code

# NOT RUN {
# use iris data set


# build random forests model with certain parameters
modelRF <- CoreModel(Species ~ ., iris, model="rf", 
              selectionEstimator="MDL",minNodeWeightRF=5,
              rfNoTrees=100, maxThreads=1)
print(modelRF)

# prediction with node distribution
pred <- predict(modelRF, iris, rfPredictClass=FALSE, type="both")
# print(pred)


# saves the random forests model to file
saveRF(modelRF, "tempRF.txt")

# restore the model to another model
loadedRF = loadRF("tempRF.txt")

# prediction should be the same
predLoaded <- predict(loadedRF, iris, rfPredictClass=FALSE, type="both")
# print(predLoaded)
# sum of differences should be zero subject to numeric imprecision 
sum(pred$probabilities - predLoaded$probabilities) 

cat("Are predicted classes of original and retrieved models equal? ", 
                           all(pred$class == predLoaded$class), "\n" ) 
# cat("Are predicted probabilities of original and retrieved model equal? ", 
#                 all(pred$probabilities == predLoaded$probabilities), "\n" ) 

# clean up the models when no longer needed
destroyModels(modelRF) 
destroyModels(loadedRF) 

# }