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Adaptive step log accessor.

Source: R/adaptive_print.R
adaptive_step_log.Rd

Adaptive step log accessor.

Usage

adaptive_step_log(state)

Arguments

state

Adaptive state.

Value

A tibble with one row per attempted step, in execution order.

Details

step_log is the canonical per-step audit log for the adaptive workflow. It records candidate pipeline outcomes, selected pair/order, and commit status. A step with invalid judge response keeps committed fields as NA and must not update model state. The selected endpoints i/j are the pre-orientation item indices, while A/B are the displayed / judged item indices after order assignment. Y is defined relative to displayed order: Y = 1 means A wins and Y = 0 means B wins. For cross-run audit and reuse, prefer the stable *_id columns plus unordered_key/ordered_key rather than transient integer item positions from the live state. Judge provenance, token counts, and raw_response_json are canonical step-log outputs, with raw_response_json stored as serialized character data rather than a list-column.

Core columns:

  • Identity/outcome: step_id, timestamp, pair_id, i, j, i_id, j_id, A, B, A_id, B_id, unordered_key, ordered_key, Y, status.

  • Judge audit: judge_backend, judge_model, judge_endpoint, judge_valid, judge_invalid_reason, llm_status_code, llm_error_message, llm_custom_id, prompt_tokens, completion_tokens, total_tokens, raw_response_json.

  • Routing/scheduling: round_id, round_stage, pair_type, pairing_strategy, stage_committed_so_far, stage_quota. Direct strategies use direct_pairing rather than hybrid stages; hybrid-only diagnostics are missing where inapplicable.

  • Exposure/strata: used_in_round_i, used_in_round_j, is_anchor_i, is_anchor_j, stratum_i, stratum_j, dist_stratum.

  • Candidate health: is_explore_step, explore_mode, explore_reason, explore_rate_used, local_priority_mode, long_gate_pass, long_gate_reason, star_override_used, star_override_reason, candidate_starved, fallback_used, fallback_path, starvation_reason.

  • Candidate counts: n_candidates_generated, n_candidates_after_hard_filters, n_candidates_after_duplicates, n_candidates_after_star_caps, n_candidates_scored.

  • Endpoint diagnostics: deg_i, deg_j, recent_deg_i, recent_deg_j, mu_i, mu_j, sigma_i, sigma_j, p_ij, U0_ij, target_distance. For direct strategies, i_id is the focal item and p_ij is the pre-judgment TrueSkill probability for presented A over B. target_distance is missing for random selection.

  • Star-cap diagnostics: star_cap_rejects, star_cap_reject_items.

See also

adaptive_get_logs(), adaptive_round_log(), adaptive_rank_run_live()

Other adaptive logs: adaptive_get_logs(), adaptive_item_log(), adaptive_results_history(), adaptive_round_log(), summarize_items(), summarize_refits()

Examples

state <- adaptive_rank_start(c("a", "b", "c"), seed = 1)
adaptive_step_log(state)
#> # A tibble: 0 × 99
#> # ℹ 99 variables: step_id <int>, timestamp <dttm>, pair_id <int>, i <int>,
#> #   j <int>, i_id <chr>, j_id <chr>, A <int>, B <int>, A_id <chr>, B_id <chr>,
#> #   unordered_key <chr>, ordered_key <chr>, Y <int>, status <chr>,
#> #   judge_backend <chr>, judge_model <chr>, judge_endpoint <chr>,
#> #   judge_valid <lgl>, judge_invalid_reason <chr>, llm_status_code <int>,
#> #   llm_error_message <chr>, llm_custom_id <chr>, prompt_tokens <dbl>,
#> #   completion_tokens <dbl>, total_tokens <dbl>, raw_response_json <chr>, …