Nothing
## ---- echo = TRUE, results = "hide", eval = FALSE-----------------------------
# data.loader <- DatasetLoader$new()
# data <- data.loader$load(filepath, header = TRUE, sep = ",",
# skip.lines = 0, normalize.names = FALSE,
# ignore.columns = NULL)
## ---- echo = TRUE, results = "hide", eval = FALSE-----------------------------
# ## DATASET INFORMATION OBTAINER
# data$getNcol()
# data$getNrow()
# data$getColumnNames()
# data$getDataset()
#
# ## DATASET COLUMN REMOVAL
# data$cleanData(columns = NULL,
# remove.funcs = NULL,
# remove.na = FALSE,
# remove.const = FALSE)
#
# ## DATASET HANDLING AND SPLITTING
# data$createPartitions(num.folds = NULL,
# percent.folds = NULL,
# class.balance = NULL)
# subset <- data$createSubset(num.folds = NULL,
# column.id = NULL,
# opts = list(remove.na = TRUE,
# remove.const = FALSE),
# class.index = NULL,
# positive.class = NULL)
# train <- data$createTrain(num.folds = NULL,
# class.index,
# positive.class,
# opts = list(remove.na = TRUE,
# remove.const = FALSE))
#
## ---- echo = TRUE, results = "hide", eval = FALSE-----------------------------
# ## FEATURE-CLUSTERING ALGORITHM CREATION
# conf <- DependencyBasedStrategyConfiguration$new()
# dbs <- DependencyBasedStrategy$new(subset,
# heuristic,
# configuration = conf)
#
# ## FEATURE-CLUSTERING ALGORITHM EXECUTION
# dbs$execute(verbose = FALSE)
#
# ## FEATURE-CLUSTERING ALGORITHM FUNCTIONALITIES
# dbs$getBestClusterDistribution()
# dbs.train <- dbs$createTrain(subset,
# num.clusters = NULL,
# num.groups = NULL,
# include.unclustered = FALSE)
#
## ---- echo = TRUE, results = "hide", eval = FALSE-----------------------------
# ## D2MCS FRAMEWORK INITIALIZATION
# d2mcs <- D2MCS$new(dir.path,
# num.cores = 2,
# socket.type = "PSOCK",
# outfile = NULL)
#
# ## MCS BEHAVIOUR CUSTOMIZATION OPTIONS
# trFunction <- TwoClass$new(method,
# number,
# savePredictions,
# classProbs,
# allowParallel,
# verboseIter,
# seed = NULL)
#
# ## EXECUTION OF MCS DISCOVERY OPERATION
# trained <- d2mcs$train(train.set,
# train.function,
# num.clusters = NULL,
# model.recipe = DefaultModelFit$new(),
# ex.classifiers = c(),
# ig.classifiers = c(),
# metrics = NULL,
# saveAllModels = FALSE)
#
## ---- echo = TRUE, results = "hide", eval = FALSE-----------------------------
# ## VOTING SCHEMES AVAILABLE IN THE CLASSIFICATION STAGE
# voting.types <- c(SingleVoting$new(voting.schemes,
# metrics),
# CombinedVoting$new(voting.schemes,
# combined.metrics,
# methodology,
# metrics))
#
# ## EXECUTE THE CLASSIFICATION STAGE
# predictions <- d2mcs$classify(train.output,
# subset,
# voting.types,
# positive.class = NULL)
#
# ## COMPUTE THE PERFORMANCE OF EACH VOTING SCHEME
# predictions$getPerformances(test.set,
# measures,
# voting.names = NULL,
# metric.names = NULL,
# cutoff.values = NULL)
#
# ## OBTAIN THE PREDICTIONS OBTAINED OF EACH VOTING SCHEME USED
# prediction$getPredictions(voting.names = NULL,
# metric.names = NULL,
# cutoff.values = NULL,
# type = NULL,
# target = NULL,
# filter = FALSE)
#
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