#!/usr/bin/env python3 """Connect archived chart witnesses to the frozen evaluation, without inference.""" import argparse from collections import defaultdict import gzip import json from pathlib import Path import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt import numpy as np from analyze_visual_judges import number, valid_parsed from analyze_crossdomain_paper import MODELS, STATES from build_crossdomain_paper_assets import NAMES def build(inputs, analysis_path, output, proofs=None): output.mkdir(parents=True, exist_ok=True) saved = json.loads(analysis_path.read_text())["domains"]["plotqa"]["models"] labels = json.loads((inputs / "plotqa/labels.json").read_text()) manifest = {r["item_id"]: r for r in map(json.loads, (inputs / "plotqa/manifest.jsonl").read_text().splitlines())} groups = defaultdict(dict) for row in labels: assert row["state"] not in groups[row["group_id"]] groups[row["group_id"]][row["state"]] = row assert len(labels) == len(manifest) == 5000 and len(groups) == 1000 assert all(set(g) == set(STATES["plotqa"]) for g in groups.values()) join = {"status": "not repeated; archived witness join required separately"} if proofs is not None: seen = set() with gzip.open(proofs, "rt") as handle: for proof in map(json.loads, handle): gid = proof["group_id"] assert gid not in seen and gid in groups seen.add(gid) for row in groups[gid].values(): assert proof["question"] == manifest[row["item_id"]]["question"] assert proof["source_native_id"] == row["source_native_id"] assert seen == set(groups) join = {"status": "passed", "unique_witness_groups": len(seen), "evaluated_groups": len(groups), "question_and_source_joins": len(labels), "checks": "exact group IDs, source native IDs, and question strings; no digest validation", "scope": "identity join only; semantic and pixel replay reported separately"} (output / "witness-cohort-join.json").write_text(json.dumps(join, indent=2) + "\n") result = {"status": "passed", "inference_calls": 0, "analysis": "post-hoc disjoint group decomposition on existing chart responses", "groups": 1000, "views_per_configuration": 5000, "models": {}} table = [r"\begin{tabular}{@{}lrrrrr@{}}", r"\toprule", r"Configuration & $J_5\uparrow$ & $B_5\uparrow$ & $E_A\downarrow$ & $E_M\downarrow$ & $E_I\downarrow$ \\", r"\midrule"] partitions = [] for model in MODELS: with gzip.open(inputs / "plotqa" / (model + ".jsonl.gz"), "rt") as handle: rows = list(map(json.loads, handle)) records = {r["item_id"]: r for r in rows} assert len(rows) == len(records) == 5000 and set(records) == set(manifest) counts = np.zeros(4, dtype=int) ids = [[], [], [], []] for gid, group in sorted(groups.items()): failed, answers = {}, [] for state, label in group.items(): rec = records[label["item_id"]] assert rec["question"] == manifest[label["item_id"]]["question"] parsed = valid_parsed(rec) failed[state] = parsed is None or parsed["answerable"] != label["answerable"] if label["answerable"]: value = number(parsed["answer"]) if parsed and parsed["answerable"] else None target = number(label["target"]) answers.append(value is not None and target is not None and value == target) if failed["U_MISSING"]: category = 2 elif any(failed.values()): category = 3 elif all(answers): category = 0 else: category = 1 counts[category] += 1 ids[category].append(gid) old = saved[model] assert counts.sum() == 1000 assert counts[0] == old["counts"]["joint_groups_correct"] assert counts[0] + counts[1] == old["counts"]["groups_correct"] assert counts[2] == old["states"]["U_MISSING"]["failures"] assert counts[2] + counts[3] == 1000 - old["counts"]["groups_correct"] partitions.append(counts) names = ["all_decisions_and_supported_answers_correct", "all_decisions_correct_answer_mismatch", "missing_information_decision_failed", "other_decision_failed_missing_correct"] result["models"][model] = {"counts": dict(zip(names, counts.tolist())), "group_ids": dict(zip(names, ids)), "missing_state_invalid": old["states"]["U_MISSING"]["invalid"]} values = [old["counts"]["joint_groups_correct"] / 10, old["counts"]["groups_correct"] / 10, 100 * old["metrics"]["answerable_failure"], old["states"]["U_MISSING"]["failures"] / 10, old["states"]["U_INVALID"]["failures"] / 10] table.append(NAMES[model] + " & " + " & ".join(f"{v:.2f}" for v in values) + r" \\") table.extend([r"\bottomrule", r"\end{tabular}"]) (output / "chart_table.tex").write_text("\n".join(table) + "\n") (output / "chart-diagnostics.json").write_text(json.dumps(result, indent=2) + "\n") plt.rcParams.update({"font.family": "DejaVu Sans", "font.size": 11.5, "pdf.fonttype": 42}) fig, ax = plt.subplots(figsize=(7.3, 3.5)) colors = ["#216b87", "#83b9c5", "#b85030", "#ddd4c8"] legend = ["Decisions + answers correct", "Decisions correct; answer mismatch", "Missing-information decision fails", "Other decision fails; M correct"] values = np.array(partitions) left = np.zeros(6) for j, color in enumerate(colors): bars = ax.barh(np.arange(6), values[:, j], left=left, height=.64, color=color, edgecolor="white", linewidth=.65, label=legend[j]) for bar, count in zip(bars, values[:, j]): if count >= 30: ax.text(bar.get_x()+bar.get_width()/2, bar.get_y()+bar.get_height()/2, str(count), ha="center", va="center", fontsize=9.5 if count < 45 else 11.5, color="white" if j in (0, 2) else "#172b35") left += values[:, j] ax.set_yticks(np.arange(6), [NAMES[m] for m in MODELS]) ax.invert_yaxis() ax.set_xlim(0, 1000) ax.set_xticks(np.arange(0, 1001, 200)) ax.set_xlabel("Chart question groups (1,000 per configuration)", fontsize=11.5) ax.spines[["top", "right", "left"]].set_visible(False) ax.tick_params(axis="y", length=0) ax.grid(axis="x", color="#e4e7eb", linewidth=.6) ax.set_axisbelow(True) ax.legend(loc="lower left", bbox_to_anchor=(-.30, 1.015), ncol=2, frameon=False, fontsize=11.0, columnspacing=1.0, handlelength=1.3) fig.subplots_adjust(left=.25, right=.99, top=.80, bottom=.16) for ext in ("pdf", "png"): fig.savefig(output / ("chart_diagnosis." + ext), dpi=220, bbox_inches="tight", pad_inches=.06) plt.close(fig) return {"status": "passed", "models": len(MODELS), "responses_checked": 30000, "disjoint_partitions": 6, "witness_join": join, "inference_calls": 0} if __name__ == "__main__": p = argparse.ArgumentParser(description=__doc__) p.add_argument("--inputs", type=Path, required=True) p.add_argument("--analysis", type=Path, required=True) p.add_argument("--output", type=Path, required=True) p.add_argument("--proofs", type=Path) a = p.parse_args() print(json.dumps(build(a.inputs, a.analysis, a.output, a.proofs)))