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Build canonical results_tbl data for Bayesian BTL MCMC

Source: R/bayes_btl_mcmc.R
build_btl_results_data.Rd

Converts non-adaptive pairwise outcomes (for example, rows like example_writing_pairs with ID1, ID2, better_id) into the canonical results_tbl schema required by fit_bayes_btl_mcmc().

Usage

build_btl_results_data(
  results,
  phase = "phase2",
  backend = "non_adaptive_import",
  model = "unknown",
  iter_start = 1L,
  received_at_start = as.POSIXct("1970-01-01 00:00:00", tz = "UTC")
)

Arguments

results

A data frame or tibble containing columns ID1, ID2, and better_id.

phase

Length-1 phase label for all rows. Must be one of "phase1", "phase2", or "phase3". Defaults to "phase2".

backend

Length-1 backend label to record in output metadata.

model

Length-1 model label to record in output metadata.

iter_start

Integer starting value for iter. Defaults to 1L.

received_at_start

Length-1 POSIXct timestamp for the first row. Subsequent rows increment by one second.

Value

A tibble in canonical results_tbl format with columns: pair_uid, unordered_key, ordered_key, A_id, B_id, better_id, winner_pos, phase, iter, received_at, backend, model.

Details

The output is deterministic and schema-valid:

  • stable unordered_key / ordered_key values,

  • deterministic pair_uid as "<unordered_key>#<occurrence>",

  • deterministic iter and received_at sequences.

Examples

data("example_writing_pairs", package = "pairwiseLLM")

results_tbl <- build_btl_results_data(example_writing_pairs)
head(results_tbl)
#> # A tibble: 6 × 12
#>   pair_uid  unordered_key ordered_key A_id  B_id  better_id winner_pos phase 
#>   <chr>     <chr>         <chr>       <chr> <chr> <chr>          <int> <chr> 
#> 1 S01:S02#1 S01:S02       S01:S02     S01   S02   S02                2 phase2
#> 2 S01:S03#1 S01:S03       S01:S03     S01   S03   S03                2 phase2
#> 3 S01:S04#1 S01:S04       S01:S04     S01   S04   S04                2 phase2
#> 4 S01:S05#1 S01:S05       S01:S05     S01   S05   S01                1 phase2
#> 5 S01:S06#1 S01:S06       S01:S06     S01   S06   S06                2 phase2
#> 6 S01:S07#1 S01:S07       S01:S07     S01   S07   S07                2 phase2
#> # ℹ 4 more variables: iter <int>, received_at <dttm>, backend <chr>,
#> #   model <chr>

ids <- sort(unique(c(results_tbl$A_id, results_tbl$B_id)))
ids
#>  [1] "S01" "S02" "S03" "S04" "S05" "S06" "S07" "S08" "S09" "S10" "S11" "S12"
#> [13] "S13" "S14" "S15" "S16" "S17" "S18" "S19" "S20"