`cv_revision_forecast` fold selection can produce uneven amounts of `test_lag` data, including 0

[brookslogan]

When rows are in a certain order, fold assignment can place uneven amounts of test_lag data into the different folds, even placing 0 rows in a fold. This seems to be in part due to assigning folds before filtering to test_lag. (There's also some row reordering and/or exclusion that seems to be happening somewhere that's making it hard to understand why exactly this is happening when the number of lags is not a multiple of the number of folds.)

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(vctrs)
#> 
#> Attaching package: 'vctrs'
#> The following object is masked from 'package:dplyr':
#> 
#>     data_frame
library(DelphiRF)
withr::with_tempdir({
  dir.create("receiving")
  tibble(report_date = as.Date("2020-01-01") + 7L * (0:21)) %>%
    reframe(
      .by = report_date,
      reference_date = seq(as.Date("2020-01-01"), report_date, by = "week")
    ) %>%
    transmute(
      reference_date,
      lag = as.numeric(report_date - reference_date)
    ) %>%
    filter(lag <= 7 * 10) %>%
    # filter(! (reference_date == unique(reference_date)[[6]] & lag == 0L)) %>%
    # ^ excluding this row makes the problem crop up first in fold 3
    # rather than fold 1
    mutate(value = 100/(1+exp(-lag)) + rnorm(n())) %>%
    DelphiRF::data_preprocessing(
      value_col = "value",
      refd_col = "reference_date",
      lag_col = "lag",
      ref_lag = 7L * 6L,
      temporal_resol = "weekly",
      smoothed = TRUE
    ) %>%
    DelphiRF::add_weights_related() %>%
    DelphiRF::cv_revision_forecast(
      test_lag = 7L,
      # ^ basing parameter selections solely on this lag.
      # TODO: revisit
      taus = (1:10 - 0.5)/10,
      smoothed_target = TRUE,
      # default lagged_term_list
      temporal_resol = "weekly",
      # default lambda, gamma candidates
      lag_pad_candidates = 0L,
      lp_solver = "glpk", # XXX
      geo = "network-available",
      indicator = "covid-net",
      signal = "n",
      geo_level = "national",
      # no training_end_date
      training_days = 90
      # default n_folds
    ) %>%
    {}
})
#> Error in `z[i, ]`:
#> ! subscript out of bounds

^{Created on 2026-07-20 with reprex v2.1.1}

Potential workaround: add this before data_preprocessing:

    complete(lag, reference_date) %>%
    arrange(lag, reference_date) %>%

This ensures that each lag appears consecutively N times, once for each of the N reference dates, at least in the input to data_preprocessing, so unless whatever reordering/exclusion is happening in the above also messes this up and/or NA exclusion removes the completed row here and causes a problem, fold selection should still look something like 3:5, 1:5, 1:5, ..., 1:5, 1:5, 1:2 when filtering to the test_lag later on.

Potential fix: filter to test_lag before assigning folds.