File size: 7,100 Bytes
d606d10 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 | #!/usr/bin/env python3
"""Render current failure-taxonomy summary tables and SVG figures.
The legacy renderer uses the 35-row preliminary evidence table. This renderer
uses the post-PR193 paper-grade taxonomy surface so paper/deck figures can cite
the same CSV as the current failure-analysis text.
"""
from __future__ import annotations
import csv
import html
from collections import Counter
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
METRICS_DIR = ROOT / "results" / "metrics"
FIGURES_DIR = ROOT / "results" / "figures"
SOURCE_CSV = METRICS_DIR / "failure_taxonomy_current.csv"
LABELS = {
"low_task_completion": "Task completion",
"low_data_retrieval_accuracy": "Data retrieval accuracy",
"low_agent_sequence_correct": "Agent sequence correctness",
"low_generalized_result_verification": "Result verification",
}
PALETTE = ["#1f6f78", "#c85a3a", "#6f7d2a", "#5b6aa8"]
def read_rows() -> list[dict[str, str]]:
with SOURCE_CSV.open(newline="") as f:
return list(csv.DictReader(f))
def write_csv(path: Path, fieldnames: list[str], rows: list[dict[str, object]]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", newline="") as f:
writer = csv.DictWriter(f, fieldnames=fieldnames, lineterminator="\n")
writer.writeheader()
writer.writerows(rows)
def pct(numerator: int, denominator: int) -> str:
return f"{(100.0 * numerator / denominator):.1f}" if denominator else "0.0"
def xml(value: object) -> str:
return html.escape(str(value), quote=True)
def write_auto_label_counts(rows: list[dict[str, str]]) -> list[dict[str, object]]:
paper_failed = [r for r in rows if r["paper_eligible"] == "true"]
counts = Counter(r["auto_taxonomy_label"] for r in paper_failed)
total = len(paper_failed)
out = []
for label, count in counts.most_common():
out.append(
{
"auto_taxonomy_label": label,
"display_label": LABELS.get(label, label.replace("_", " ")),
"rows": count,
"percent_of_paper_failures": pct(count, total),
"source_rows": total,
"source_csv": "results/metrics/failure_taxonomy_current.csv",
}
)
write_csv(
METRICS_DIR / "failure_taxonomy_current_auto_label_counts.csv",
[
"auto_taxonomy_label",
"display_label",
"rows",
"percent_of_paper_failures",
"source_rows",
"source_csv",
],
out,
)
return out
def write_failed_dim_counts(rows: list[dict[str, str]]) -> list[dict[str, object]]:
paper_failed = [r for r in rows if r["paper_eligible"] == "true"]
counts = Counter(r["failed_dim_count"] for r in paper_failed)
total = len(paper_failed)
out = []
for failed_dim_count in sorted(counts, key=lambda v: int(v)):
count = counts[failed_dim_count]
out.append(
{
"failed_dim_count": failed_dim_count,
"rows": count,
"percent_of_paper_failures": pct(count, total),
"source_rows": total,
"source_csv": "results/metrics/failure_taxonomy_current.csv",
}
)
write_csv(
METRICS_DIR / "failure_taxonomy_current_failed_dim_counts.csv",
[
"failed_dim_count",
"rows",
"percent_of_paper_failures",
"source_rows",
"source_csv",
],
out,
)
return out
def write_manual_audit_counts(rows: list[dict[str, str]]) -> list[dict[str, object]]:
audited = [r for r in rows if r["audit_status"] == "manual_confirmed"]
categories = [
("audit_decision", "audit_decision"),
("berkeley_label", "berkeley_label"),
("failure_stage", "failure_stage"),
]
out = []
for section, column in categories:
counts = Counter(r[column] or "blank" for r in audited)
for value, count in counts.most_common():
out.append(
{
"section": section,
"value": value,
"rows": count,
"percent_of_manual_sample": pct(count, len(audited)),
"source_rows": len(audited),
"source_csv": "results/metrics/failure_taxonomy_current.csv",
}
)
write_csv(
METRICS_DIR / "failure_taxonomy_current_manual_audit_counts.csv",
[
"section",
"value",
"rows",
"percent_of_manual_sample",
"source_rows",
"source_csv",
],
out,
)
return out
def svg_auto_label_bar_chart(rows: list[dict[str, object]], path: Path) -> None:
width, height = 1080, 520
left, top = 390, 118
bar_h, gap = 56, 24
max_count = max(int(r["rows"]) for r in rows) or 1
parts = [
f'<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 {width} {height}">',
'<rect width="100%" height="100%" fill="#fbf7ef"/>',
'<text x="40" y="46" font-family="Georgia, serif" font-size="30" fill="#1b1b1b">Current paper-grade failure taxonomy</text>',
'<text x="40" y="76" font-family="Verdana, sans-serif" font-size="14" fill="#5b5147">1,276 paper-eligible failed judge rows from failure_taxonomy_current.csv</text>',
'<text x="40" y="98" font-family="Verdana, sans-serif" font-size="13" fill="#5b5147">Auto label is the highest-priority failed judge dimension; manual audit sample is tracked separately.</text>',
]
for i, row in enumerate(rows):
y = top + i * (bar_h + gap)
count = int(row["rows"])
bar_w = int((width - left - 150) * count / max_count)
color = PALETTE[i % len(PALETTE)]
percent = row["percent_of_paper_failures"]
parts.extend(
[
f'<text x="40" y="{y + 35}" font-family="Verdana, sans-serif" font-size="16" fill="#26231f">{xml(row["display_label"])}</text>',
f'<rect x="{left}" y="{y}" width="{bar_w}" height="{bar_h}" rx="6" fill="{color}"/>',
f'<text x="{left + bar_w + 14}" y="{y + 34}" font-family="Verdana, sans-serif" font-size="18" font-weight="700" fill="#26231f">{count} ({percent}%)</text>',
]
)
parts.append("</svg>")
path.write_text("\n".join(parts) + "\n")
def main() -> None:
rows = read_rows()
FIGURES_DIR.mkdir(parents=True, exist_ok=True)
auto_label_counts = write_auto_label_counts(rows)
failed_dim_counts = write_failed_dim_counts(rows)
manual_counts = write_manual_audit_counts(rows)
svg_auto_label_bar_chart(
auto_label_counts,
FIGURES_DIR / "failure_taxonomy_current_auto_label_counts.svg",
)
print(
"Rendered "
f"{len(auto_label_counts)} auto-label rows, "
f"{len(failed_dim_counts)} failed-dim rows, "
f"{len(manual_counts)} manual-audit rows."
)
if __name__ == "__main__":
main()
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