checkpoint_earlystop: Early stopping check
In egenn/rtemis: Machine Learning and Visualization

checkpoint_earlystop

R Documentation

Early stopping check

Description

Returns list with relative variance over n.steps, absolute.threshold, last value, and logical "stop", if conditions are met and training should stop. The final stop decision is: check.thresh | (check.rthresh & check.rvar) if combine.relative.thresholds = "AND" or check.thresh | (check.rthresh | check.rvar) if combine.relative.thresholds = "OR"

Usage

checkpoint_earlystop(
  x,
  absolute.threshold = NA,
  relative.threshold = NA,
  minimize = TRUE,
  relativeVariance.threshold = NA,
  n.steps = 10,
  combine.relative.thresholds = "AND",
  min.steps = 50,
  na.response = c("stop", "continue"),
  verbose = TRUE
)

Arguments

`x`	Float, vector: Input - this would normally be the loss at each iteration
`absolute.threshold`	Float: If set and the last value of `x` is less than or equal to this (if `minimize = TRUE`) or greater than or equal to this (if `minimize = FALSE`), then return `stop = TRUE`. See output under Value. Default = NA
`relative.threshold`	Float: If set, checks if the relative change from the first to last value of `x` exceeds this number. i.e. if set to .9 and `minimize = TRUE`, if there is a 90 percent drop from `x[1]` to `x[length(x)]`, then the function returns `stop = TRUE`. If `minimize = FALSE`, then checks if there is a 90 percent increase, accordingly.
`minimize`	Logical: See `absolute.threshold`. Default = TRUE
`relativeVariance.threshold`	Float: If relative variance over last `n.steps` is less than or equal to this, return `stop = TRUE`. See output under Value
`n.steps`	Integer; > 1: Calculate relative variance over this many last values of `x`
`combine.relative.thresholds`	Character: "AND" or "OR": How to combine the criteria `relative.threshold` and `relativeVariance.threshold`. Default = "AND", which means both must be TRUE to stop. The scenario is you might want to check relastiveVariance threshold only after a certain amount of learning has taken place, which you can't predict with `min.steps` but would rather quantify with `relative.threshold`.
`min.steps`	Integer: Do not calculate relativeVariance unless `x` is at least this length
`na.response`	Character: "stop" or "continue": what should happen if the last value of `x` is `NA`
`verbose`	Logical: If TRUE, print messages to console

Value

List with the following items:

last.value Float: Last value of x
relativeVariance Float: relative variance of last n.steps
check.thresh Logical: TRUE, if absolute threshold was reached
check.rvar Logical: TRUE, if relative variance threshold was reached
stop Logical: TRUE, if either criterion was met - absolute threshold or relativeVariance.threshold