autojudge-evaluate 0.3.10

Evaluation tools for TREC AutoJudge: meta-evaluate, qrel-evaluate, leaderboard statistics

autojudge-evaluate

Evaluation tools for the TREC AutoJudge framework. Computes leaderboard correlations, inter-annotator agreement on qrels, leaderboard statistics, and format conversion for evaluation result files.

Installation

uv pip install autojudge-evaluate

CLI Commands

All commands are available via auto-judge-evaluate <command>.

---

meta-evaluate — Leaderboard correlation

Correlate predicted leaderboards against a ground-truth leaderboard.

auto-judge-evaluate meta-evaluate \
    --truth-leaderboard truth.eval.jsonl --truth-format jsonl \
    --eval-format tot -i results/*eval.txt \
    --correlation kendall --correlation spearman --correlation tauap_b \
    --truth-measure nugget_coverage --truth-measure f1 \
    --on-missing default \
    --output correlations.jsonl

Key options:

Option	Description
--truth-leaderboard FILE	Ground-truth leaderboard file (required)
--truth-format FMT	Format: trec_eval, tot, ir_measures, ranking, jsonl
--eval-format FMT	Format of input leaderboard files
-i FILE / positional	Input leaderboard file(s), supports globs. Repeatable
--correlation METHOD	Correlation method. Repeatable. Supports kendall, pearson, spearman, tauap_b, and top-k variants like kendall@15
--truth-measure NAME	Truth measure(s) to correlate against. Repeatable. Omit for all
--eval-measure NAME	Eval measure(s) to include. Repeatable. Omit for all
--on-missing MODE	Handle run mismatches: error, warn, skip, default (fill 0.0)
--only-shared-topics	Intersect topics across truth and eval (default: --all-topics)
--only-shared-runs	Intersect runs across truth and eval (default: --all-runs)
--truth-drop-aggregate	Recompute aggregates from per-topic data
--output FILE	Output .jsonl or .txt
--out-format FMT	jsonl (default) or table
--aggregate	Report only mean across all judges

Output: One row per (Judge, TruthMeasure, EvalMeasure) with correlation values as columns.

---

qrel-evaluate — Inter-annotator agreement on qrels

Compare predicted relevance judgments (qrels) against truth qrels. Computes set overlap (precision, recall, F1) and agreement metrics (Cohen's Kappa, Krippendorff's Alpha, Jaccard, ARI).

auto-judge-evaluate qrel-evaluate \
    --truth-qrels official.qrels \
    --predict-qrels predicted.qrels

Key options:

Option	Description
--truth-qrels FILE	Truth qrels in TREC format
--truth-nugget-docs DIR	Alternative: truth as nugget-docs directory
--predict-qrels FILE	Predicted qrels in TREC format
--predict-nugget-docs DIR	Alternative: predicted as nugget-docs directory
--truth-max-grade N	Grade scale upper bound for truth (default: 1 = binary)
--predict-max-grade N	Grade scale upper bound for predicted (default: 1)
--truth-relevance-threshold N	Binary threshold for truth side (default: 1)
--predict-relevance-threshold N	Binary threshold for predicted side (default: 1)
--on-missing MODE	Handle topics in only one side: error, warn, default, skip
--output FILE	Output .jsonl or .txt

Output: Per-topic table with Precision, Recall, F1, Jaccard, Kappa, Krippendorff's Alpha, ARI, plus a MEAN row.

---

leaderboard — Leaderboard statistics

Compute per-run statistics (mean, stderr, stdev, min, max) from leaderboard files.

auto-judge-evaluate leaderboard \
    --eval-format tot -i results/*eval.txt --sort

Key options:

Option	Description
--eval-format FMT	Input format (required)
-i FILE / positional	Input file(s), supports globs. Repeatable
--eval-measure NAME	Filter to specific measures. Repeatable
--sort	Sort runs by mean score (descending)
--output FILE	Output .jsonl or .csv

Output: One row per (Judge, RunID, Measure) with Topics, Mean, Stderr, Stdev, Min, Max.

---

Analysis Module

Post-hoc analysis, tables, plots of meta-evaluate output. Produces correlation tables and bar plots with judge categorization.

python -m autojudge_evaluate.analysis.correlation_table \
    -d ragtime:ragtime-correlations.jsonl \
    -d rag:rag-correlations.jsonl \
    -d dragun:dragun-correlations.jsonl \
    --judges judges.yml \
    --correlation kendall \
    --truth-measure nugget_coverage \
    --format latex \
    --plot-dir plots/

Judge configuration (judges.yml) maps cryptic filenames to display names and categories, with optional plot styling:

styles:
  colors:
    pointwise: "#4A90D9"
    pairwise:  "#D94A4A"
  hatches:
    gpt-4o:    ""
    llama-3:   "//"

judges:
  my-judge-A.eval:
    name: System A
    method: pointwise     # category column
    model: gpt-4o         # category column
  my-judge-B.eval:
    name: System B
    method: pairwise
    model: llama-3

• styles.colors: maps category values to fill colors (any matplotlib color string)
• styles.hatches: maps category values to hatch patterns (//, .., xx, \\, etc). Values combine across categories.
• Color is picked from the first matching category value; hatches are combined from all matches.
• Without a styles: section, bars use a sequential grayscale fallback.
• Judges not in the YAML are excluded unless --all-judges is passed.

Key options: --format (github, latex, tsv, plain, html, pipe), --columns (correlations or measures), --summary (add mean/max rows), --aggregate (aggregate across datasets), --same THRESHOLD (highlight near-equal values).

---

eval-result — Format conversion and verification

Clean and convert evaluation result files.

# Convert tot to jsonl
auto-judge-evaluate eval-result data.txt -if tot -of jsonl -o data.jsonl

# Filter to specific runs and topics
auto-judge-evaluate eval-result data.txt -if tot -of jsonl -o filtered.jsonl \
    --filter-runs system_A --filter-runs system_B \
    --filter-topics topic_1

Key options:

Option	Description
-if FMT	Input format: trec_eval, tot, ir_measures, ranking, jsonl
-of FMT	Output format (defaults to input format)
-o FILE	Output file. Omit for roundtrip test to temp file
--filter-runs ID	Keep only these runs. Repeatable
--filter-topics ID	Keep only these topics. Repeatable
--filter-measures NAME	Keep only these measures. Repeatable
--compare-aggregates	Compare file aggregates vs recomputed from per-topic data
--drop-aggregates	Drop existing aggregate rows
--recompute-aggregates	Recompute from per-topic data (implies --drop-aggregates)
--roundtrip / --no-roundtrip	Enable/disable roundtrip verification (default: on)

Supported formats:

Format	Columns
trec_eval	measure topic value (3 cols, run_id from filename)
tot	run measure topic value (4 cols)
ir_measures	run topic measure value (4 cols)
ranking	topic Q0 doc_id rank score run (6 cols)
jsonl	JSON lines with run_id, topic_id, measure, value