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

In bioLeak: Leakage-Safe Modeling and Auditing for Genomic and Clinical Data

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)

Arguments

fit	LeakFit.
lag.max	maximum lag to show.

Value

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

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)
}

bioLeak documentation built on March 6, 2026, 1:06 a.m.