originalThresholdout: Original Thresholdout algorithm

In insilico/privateEC: Privacy Preserving Evaporative Cooling Feature Selection and Classification with Relief-F and Random Forests

Description

Original Thresholdout with Dwork’s linear classifier (Dwork, et al., 2015)

Usage

originalThresholdout(
  train.ds = NULL,
  holdout.ds = NULL,
  validation.ds = NULL,
  label = "class",
  is.simulated = TRUE,
  update.freq = 50,
  pec.file = NULL,
  threshold = 4/sqrt(nrow(holdout.ds)),
  tolerance = 1/sqrt(nrow(holdout.ds)),
  signal.names = NULL,
  save.file = NULL,
  verbose = FALSE
)

Arguments

train.ds	A data frame with training data and outcome labels
holdout.ds	A data frame with holdout data and outcome labels
validation.ds	A data frame with validation data and outcome labels
label	A character vector of the outcome variable column name
is.simulated	Is the data simulated (or real?)
update.freq	A integer for the number of steps before update
pec.file	A character vector filename of privateEC results
threshold	A numeric, default 4 / sqrt(n) suggested in the thresholdout’s supplementary material (Dwork, et al.,2015)
tolerance	A numeric, default 1 / sqrt(n) suggested in the thresholdout’s supplementary material (Dwork, et al.,2015)
signal.names	A character vector of signal names in simulated data
save.file	A character vector for results filename or NULL to skip
verbose	A flag indicating whether verbose output be sent to stdout

Value

A list containing:

algo.acc

data frame of results, a row for each update

ggplot.data

melted results data frame for plotting

correct

number of variables detected correctly in each data set

elapsed

total elapsed time

See Also

Other classification: epistasisRank(), getImportanceScores(), privateEC(), privateRF(), standardRF(), xgboostRF()

Examples

num.samples <- 100
num.variables <- 100
pct.signals <- 0.1
label <- "class"
temp.pec.file <- tempfile(pattern = "pEc_temp", tmpdir = tempdir())
sim.data <- createSimulation(num.variables = num.variables,
                             num.samples = num.samples,
                             pct.signals = pct.signals,
                             label = label,
                             sim.type = "mainEffect",
                             pct.train = 1 / 3,
                             pct.holdout = 1 / 3,
                             pct.validation = 1 / 3,
                             verbose = FALSE)
pec.results <- privateEC(train.ds = sim.data$train,
                         holdout.ds = sim.data$holdout,
                         validation.ds = sim.data$validation,
                         label = sim.data$label,
                         is.simulated = TRUE,
                         signal.names = sim.data$signal.names,
                         save.file = temp.pec.file,
                         verbose = FALSE)
por.results <- originalThresholdout(train.ds = sim.data$train,
                                    holdout.ds = sim.data$holdout,
                                    validation.ds = sim.data$validation,
                                    label = sim.data$label,
                                    is.simulated = TRUE,
                                    signal.names = sim.data$signal.names,
                                    pec.file = temp.pec.file,
                                    verbose = FALSE)
file.remove(temp.pec.file)

insilico/privateEC documentation built on May 22, 2020, 5:12 p.m.