bioLeak (version 0.1.0)

plot_time_acf: Plot ACF of test predictions for time-series leakage checks

Description

Uses the autocorrelation function of out-of-fold predictions to detect temporal dependence that may indicate leakage. Predictions are ordered by the split time column before computing the ACF. Requires numeric predictions (regression or survival). Requires ggplot2.

Usage

plot_time_acf(fit, lag.max = 20)

Value

A list with the autocorrelation results, lag.max, and a ggplot object.

Arguments

fit

LeakFit.

lag.max

maximum lag to show.

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  set.seed(42)
  df <- data.frame(
    id = 1:30,
    time = seq.Date(as.Date("2020-01-01"), by = "day", length.out = 30),
    y = rnorm(30),
    x1 = rnorm(30),
    x2 = rnorm(30)
  )
  splits <- make_split_plan(df, outcome = "y", mode = "time_series",
                            time = "time", v = 3, progress = FALSE)
  custom <- list(
    lm = list(
      fit = function(x, y, task, weights, ...) {
        stats::lm(y ~ ., data = data.frame(y = y, x))
      },
      predict = function(object, newdata, task, ...) {
        as.numeric(stats::predict(object, newdata = as.data.frame(newdata)))
      }
    )
  )
  fit <- fit_resample(df, outcome = "y", splits = splits,
                      learner = "lm", custom_learners = custom,
                      metrics = "rmse", refit = FALSE, seed = 1)
  plot_time_acf(fit, lag.max = 10)
}