Tests into Develop

[cebarboza]

Working version.

[AniekMarkus]

When outputMethod = "EVERY" the trainExplore function becomes very slow now that I call resultsExplore at the end of trainExplore. An option could be to allow an option in resultsExplore that does a 'fast return of results' e.g. only model, candidateModels, countCombinations?

I now updated the default of OutputMethod -> "BEST" (instead of "EVERY").

[cebarboza]

@AniekMarkus Tests passing for binary_3, binary_10, continous_4_small, mix_4 with the following config:

result <- trainExplore(train_data = train_data,
                       settings_path = NULL,
                       output_path = output_path,
                       file_name = file_name,
                       OutputFile = NULL,
                       StartRulelength = StartRulelength,
                       EndRulelength = EndRulelength,
                       OperatorMethod = "EXHAUSTIVE",
                       CutoffMethod = "ALL", #"RVAC",
                       ClassFeature = config$class_feature,
                       PositiveClass = config$positive_class,
                       FeatureInclude = "",
                       Maximize = "BALANCEDACCURACY",#"ACCURACY",
                       Accuracy = 0,
                       BalancedAccuracy = 0,
                       Specificity = 0,
                       PrintSettings = TRUE,
                       PrintPerformance = TRUE,
                       Subsumption = TRUE,
                       BranchBound = TRUE,
                       Parallel = FALSE,
                       PrintCutoffSets = TRUE,
                       Sorted = "none",
                       OutputMethod = "BEST", #"EVERY",
                       BinaryReduction = BinaryReduction)

[AniekMarkus]

@cebarboza

continuous_4 ->
-> the expected values were incorrect and should be:
Length 1 80
Length 2 5160
Length 3 235608 (?) -> value needs to be checked, let's fix later (remove this test for now or leave commented out)
-> setting subsumption=TRUE -> should be FALSE, this gives the correct number for rule length 1
-> if still crashing maybe because candidateModels is (very) large? So might be better to use countRulesWithoutConstraints instead of length(candidateModels) here!
 
categorical_4 ->
-> these expected values are correct
-> problem is reading in the data file to R when the columns are type 'factor':
train_data <- farff::readARFF("~/Documents/Git_Projects/Explore/inst/examples/complexity/categorical_4.arff")
-> let's fix later (remove these tests for now or leave commented out)
 
categorical_4_large
-> can be removed completely (I don't have the expected values for this yet)