File size: 3,704 Bytes
94da461 | 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 | """Render v6 confuser trie images for the n50 and n20 counterpart datasets.
Reuses the canonical renderer (distractor_generation_2/visualize.py) exactly like
visualize_v5.py, but over the v6 level subsets. Calls are byte-identical to v5, so the
n100 trie is unchanged (see trie_v5.*); these are the matching sub-tries for n50 / n20.
Outputs (into this folder): trie_v6_n50.{txt,svg,png}, trie_v6_n20.{txt,svg,png}
Run: python -u temp/story_remediation/unbundle/visualize_v6_levels.py
"""
from __future__ import annotations
import json, sys
from pathlib import Path
HERE = Path(__file__).resolve().parent
ROOT = HERE.parents[2]
sys.path.insert(0, str(ROOT))
from models.trie import Trie # noqa: E402
from distractor_generation_2.visualize import ( # noqa: E402
_load_names, _text_tree, _render_image)
N100 = ROOT / "distractor_generation_2" / "datasets" / "n100"
REAL_APIS = ROOT / "data" / "tau-2" / "processed" / "apis.jsonl"
OUT = HERE / "out"
LEVELS = {"n50": OUT / "trajectories_all_v6_n50.jsonl",
"n20": OUT / "trajectories_all_v6_n20.jsonl"}
def seqs_from(path: Path):
out = []
for line in path.read_text(encoding="utf-8").splitlines():
if not line.strip():
continue
s = [c["name"] for c in json.loads(line).get("calls", []) if c.get("name")]
if s:
out.append(s)
return out
def build(seqs):
t = Trie()
for s in seqs:
t.insert(list(s))
return t
def stats(trie, classify):
lines, st, _ = _text_tree(trie.root, classify, 1)
depths = []
def walk(node, d):
if not node.children:
depths.append(d); return
for c in node.children.values():
walk(c, d + 1)
walk(trie.root, 0)
return dict(nodes=st["nodes"], max_depth=st["max_depth"],
root_branch=len(trie.root.children), leaves=len(depths),
mean_leaf_depth=sum(depths) / max(len(depths), 1)), lines
def main():
try:
sys.stdout.reconfigure(encoding="utf-8", errors="replace")
except Exception:
pass
real_names = _load_names(REAL_APIS)
conf_names = _load_names(N100 / "apis.jsonl")
def classify(name: str) -> str:
if name in conf_names:
return "confuser"
if name in real_names:
return "real"
return "?"
for level, path in LEVELS.items():
seqs = seqs_from(path)
trie = build(seqs)
st, lines = stats(trie, classify)
stem = HERE / f"trie_v6_{level}"
header = [
f"v6 UNBUNDLED CONFUSER TRIE ({level}) — subset of the finalized v6 dataset",
"=" * 72,
f"rows: {len(seqs)} nodes: {st['nodes']} max depth: {st['max_depth']} "
f"root branches: {st['root_branch']} mean leaf depth: {st['mean_leaf_depth']:.2f}",
"Legend: [real] BLUE = real prefix the confuser anchors to; "
"[confuser] ORANGE = retail look-alike (the divergence node F).",
"=" * 72, "",
]
stem.with_suffix(".txt").write_text("\n".join(header + lines), encoding="utf-8")
png = _render_image(trie.root, classify, stem, 1, "png")
svg = _render_image(trie.root, classify, stem, 1, "svg")
print(f"{level}: rows={len(seqs)} nodes={st['nodes']} max_depth={st['max_depth']} "
f"root_branch={st['root_branch']} mean_leaf_depth={st['mean_leaf_depth']:.2f}")
print(f" txt -> {stem.with_suffix('.txt').name} png -> {png} svg -> {svg}")
if __name__ == "__main__":
main()
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