階層分類
階層分類
TypeSafe Choice の確率に対する並列ビームサーチで、特許・小売商品・生物医学・ソースコードの深い階層へ文書を分類します。
多くのデータは、分類体系、ファイルシステムの階層、ウェブサイトの構造、コードベース、
組織図、生物学的オントロジー、LLM のスキル、モデレーション方針など、構造化された
階層として存在します。階層分類の目的は、階層をたどって正しい葉ノードに到達すること、
つまり最終的な分類を得ることです。これは typesafe の Choice プリミティブに最適です。
ルートから始めて各ノードで文書を分類し、最も確率の高い葉を見つけ、さらに次に確率の
高いノードへと反復的に進み、葉で止まります（貪欲探索）。
API の並列性を活かすと、ビームサーチで並列に質問しながら複数の経路を探索でき、
性能を高められます。この cookbook の TypeSafe API 呼び出しは、階層の K 本の経路を
同時に評価します。ビームサーチは幾何平均の辺確率
product(edge_probabilities) ** (1 / decisions) で上位 K 本の経路を残し、残りは
枝刈りします。確率は長さで正規化されるため、浅い葉と深い葉が公平に比較されます。
このように問題を階層へ分解することには、それ自体の利点があります：
- 可観測性
- 誤分類が最も多く起きるノードを特定できます
- 各ノードと辺がたどられた回数を測定できます
- テスト容易性
- 階層の更新が分類性能に与える影響を単体テストで測定できます
-
この cookbook で使う階層
- CPC 2026.05: 特許の主題で、広い技術区分から狭い発明までを扱います。
- Shopify 2026-02: 小売商品のカテゴリで、店舗の部門から具体的な商品種別までを扱います。
- MeSH 2026: 広い領域から特定の疾患までの生物医学の主題です。MeSH は DAG なので、1 つの 記述子が複数の親の下に現れます。この demo では、公式のツリーナンバーの経路を 展開しています。
- CookSafe files: TypeSafe の cookbook リポジトリの階層で、フォルダから ソースファイルまでを検索します。
手法
- 貪欲探索： 最も確率の高い子を選び、その他の選択肢はすべて捨てます。 早い段階の誤りは取り返せません。
- ビームサーチ： もっともらしい
K本の経路を保持し、各前線を並列に分類します。 より深い証拠が、曖昧な初期の決定を修正できます。幾何平均確率が最も高い経路の 葉が、最終的な分類です。 - TypeSafe Choice： 各ノードは
Choiceの質問で、その完全な確率分布が その辺になります。ビームの各経路は並列の質問として実行されるため、追加の探索でも 実時間のレイテンシはほとんど増えません。 - 数式：
path_score = product(edge_probabilities) ** (1 / decisions)- 枝刈りと経路の比較に使います
separation = top_path_score / second_path_score- 有用な指標ですが、枝刈りには使いません
- この比は、最上位経路の幾何平均を最も近い競合と比較します。
1×に近い場合は曖昧です- 比が大きい場合は明確に分離していることを意味します。
- 指標に関する注記：
min(top_prob/second_top_prob)のような別の指標なら、各ノードで 非常に明確な決定ができる経路を優先できます。- 非常に深い階層（例：10 層超）では、精度誤差を避けるために
product(edge_probabilities) ** (1 / decisions)の代わりにexp(mean(log(probs)))を使います。
例の階層を読み込んで可視化する
これらのヘルパーは、固定した分類データ源をダウンロードし、直接子ノードの木へ解析し、 各探索のたどり方を静的な SVG として描画します。
import html
import os
import shutil
import textwrap
import urllib.request
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from typing import NamedTuple, TypeAlias
from xml.etree import ElementTree
from zipfile import ZipFile
from cooksafe import JsonCache
from IPython.display import Markdown, display
from typesafe_sdk import Choice, RetryPolicy, TypeSafeClient
Tree: TypeAlias = dict[str, "Tree"]
class Hierarchy(NamedTuple):
"""One query and a complete hierarchy.
:param slug: filename-safe taxonomy name.
:param name: display name.
:param version: pinned dataset version.
:param source_url: hierarchy source.
:param node_count: number of loaded hierarchy nodes.
:param document: unstructured text classified by TypeSafe.
:param expected_leaf: expected final classification.
:param tree: nested direct-child menus.
"""
slug: str
name: str
version: str
source_url: str
node_count: int
document: str
expected_leaf: str
tree: Tree
CPC_URL = (
"https://www.cooperativepatentclassification.org/sites/default/files/"
"cpc/bulk/CPCSchemeXML202605.zip"
)
SHOPIFY_URL = (
"https://raw.githubusercontent.com/Shopify/product-taxonomy/"
"v2026-02/dist/en/categories.txt"
)
MESH_URL = "https://nlmpubs.nlm.nih.gov/projects/mesh/MESH_FILES/xmlmesh/desc2026.zip"
MESH_CATEGORIES = {
"A": "Anatomy",
"B": "Organisms",
"C": "Diseases",
"D": "Chemicals and Drugs",
"E": "Analytical, Diagnostic and Therapeutic Techniques, and Equipment",
"F": "Psychiatry and Psychology",
"G": "Phenomena and Processes",
"H": "Disciplines and Occupations",
"I": "Anthropology, Education, Sociology, and Social Phenomena",
"J": "Technology, Industry, and Agriculture",
"K": "Humanities",
"L": "Information Science",
"M": "Named Groups",
"N": "Health Care",
"V": "Publication Characteristics",
"Z": "Geographicals",
}
def _download(url: str, path: Path) -> Path:
"""Download a pinned dataset once.
:param url: official dataset URL.
:param path: local cache path.
:returns: local dataset path.
"""
if path.exists():
return path
path.parent.mkdir(parents=True, exist_ok=True)
temporary_path: Path = path.with_suffix(path.suffix + ".tmp")
request = urllib.request.Request(
url, headers={"User-Agent": "typesafe-taxonomy/1.0"}
)
with urllib.request.urlopen(request, timeout=120) as response:
with temporary_path.open("wb") as file:
shutil.copyfileobj(response, file)
temporary_path.replace(path)
return path
def _insert(tree: Tree, path: tuple[str, ...]) -> None:
subtree_value: Tree = tree
for label in path:
subtree_value = subtree_value.setdefault(label, {})
def _cpc_title(item: ElementTree.Element) -> str:
class_title: ElementTree.Element | None = item.find("class-title")
if class_title is None:
return ""
return " ".join(" ".join(class_title.itertext()).split())
def _load_cpc(path: Path) -> tuple[Tree, int]:
titles: dict[str, str] = {}
levels: dict[str, int] = {}
parent_by_symbol: dict[str, str] = {}
children_by_symbol: defaultdict[str, list[str]] = defaultdict(list)
def visit(item: ElementTree.Element, parent_symbol: str | None) -> None:
symbol: str | None = item.findtext("classification-symbol")
next_parent: str | None = parent_symbol
if symbol:
title: str = _cpc_title(item)
if title:
titles[symbol] = title
levels[symbol] = min(levels.get(symbol, 99), int(item.attrib["level"]))
if (
parent_symbol
and parent_symbol != symbol
and symbol not in parent_by_symbol
):
parent_by_symbol[symbol] = parent_symbol
children_by_symbol[parent_symbol].append(symbol)
next_parent = symbol
for child in item.findall("classification-item"):
visit(child, next_parent)
with ZipFile(path) as zip_file:
names = sorted(
name
for name in zip_file.namelist()
if name.startswith("cpc-scheme-") and name.endswith(".xml")
)
for name in names:
root = ElementTree.fromstring(zip_file.read(name))
for item in root.findall("classification-item"):
visit(item, None)
labels: dict[str, str] = {
symbol: f"{symbol} {titles.get(symbol, '')}".strip() for symbol in levels
}
def build(symbol: str) -> Tree:
return {
labels[child]: build(child) for child in children_by_symbol.get(symbol, [])
}
root_symbols: list[str] = sorted(
symbol for symbol, level in levels.items() if level == 2
)
tree: Tree = {labels[symbol]: build(symbol) for symbol in root_symbols}
return tree, len(labels)
def _load_shopify(path: Path) -> tuple[Tree, int]:
tree: Tree = {}
category_count: int = 0
for line in path.read_text().splitlines():
if not line or line.startswith("#"):
continue
_, path_text = line.split(" : ", maxsplit=1)
category_path: tuple[str, ...] = tuple(path_text.strip().split(" > "))
_insert(tree, category_path)
category_count += 1
return tree, category_count
def _load_mesh(path: Path) -> tuple[Tree, int]:
"""Load every official MeSH tree-number path.
A descriptor may have multiple tree numbers because MeSH is a DAG. Expanding
those positions into paths makes it usable by the tree-oriented beam search.
:param path: MeSH descriptor XML ZIP.
:returns: expanded tree and position count.
"""
with ZipFile(path) as zip_file:
root: ElementTree.Element = ElementTree.fromstring(
zip_file.read("desc2026.xml")
)
names_by_tree_number: dict[str, str] = {
tree_number.text: descriptor_record.findtext("DescriptorName/String", "")
for descriptor_record in root.findall("DescriptorRecord")
for tree_number in descriptor_record.findall("TreeNumberList/TreeNumber")
if tree_number.text
}
tree: Tree = {}
for tree_number in sorted(names_by_tree_number):
parts: list[str] = tree_number.split(".")
prefixes: list[str] = [
".".join(parts[:index]) for index in range(1, len(parts) + 1)
]
category_code: str = tree_number[0]
category_path: tuple[str, ...] = (
f"{category_code} {MESH_CATEGORIES[category_code]}",
*(f"{prefix} {names_by_tree_number[prefix]}" for prefix in prefixes),
)
_insert(tree, category_path)
position_count: int = len(names_by_tree_number) + len(tree)
return tree, position_count
CODEBASE_SNAPSHOT = Path("codebase_files.txt")
def _load_codebase(path: Path) -> tuple[Tree, int]:
"""Load the frozen CookSafe source-file hierarchy.
The listing is a snapshot of the repository's source files in the order a walk found them,
taken when this cookbook was rendered, rather than a walk of whatever tree the cookbook
happens to sit in. A live walk makes the taxonomy -- and every number derived from it --
depend on the reader's checkout, including untracked scratch files, so the shipped cache
stops describing the same tree. Line order is significant: sibling options are asked in the
order they appear here, so it is part of the question, not presentation.
:param path: file holding one repository-relative source path per line.
:returns: nested file tree and node count.
"""
tree: Tree = {}
node_paths: set[tuple[str, ...]] = set()
for line in path.read_text(encoding="utf-8").splitlines():
if not line.strip():
continue
hierarchy_path: tuple[str, ...] = ("CookSafe", *line.split("/"))
_insert(tree, hierarchy_path)
node_paths.update(
hierarchy_path[:index] for index in range(1, len(hierarchy_path) + 1)
)
return tree, len(node_paths)
def load_hierarchies(data_directory: Path = Path("datasets")) -> tuple[Hierarchy, ...]:
"""Load three public taxonomies and one frozen code hierarchy.
:param data_directory: cache directory for official raw files.
:returns: CPC, Shopify, MeSH, and CookSafe examples.
"""
cpc_tree, cpc_nodes = _load_cpc(
_download(CPC_URL, data_directory / "CPCSchemeXML202605.zip")
)
shopify_tree, shopify_nodes = _load_shopify(
_download(SHOPIFY_URL, data_directory / "shopify_categories_2026-02.txt")
)
mesh_tree, mesh_nodes = _load_mesh(
_download(MESH_URL, data_directory / "mesh_descriptors_2026.zip")
)
codebase_tree, codebase_nodes = _load_codebase(CODEBASE_SNAPSHOT)
return (
Hierarchy(
slug="cpc",
name="CPC patents",
version="2026.05",
source_url=CPC_URL,
node_count=cpc_nodes,
document=(
"Patent abstract: a freestanding structural wooden perch for poultry or "
"pet birds. The elevated roost has crossbars sized for bird feet and mounts "
"inside an aviary."
),
expected_leaf="A01K31/12 Perches for poultry or birds, e.g. roosts",
tree=cpc_tree,
),
Hierarchy(
slug="shopify",
name="Shopify products",
version="2026-02",
source_url=SHOPIFY_URL,
node_count=shopify_nodes,
document=(
"Furniture listing: a wall-mounted window shelf bed. This padded floating shelf "
"uses suction cups and a washable cushion as a sunny perch for one cat."
),
expected_leaf="Cat Window Beds & Perches",
tree=shopify_tree,
),
Hierarchy(
slug="mesh",
name="MeSH biomedical subjects",
version="2026",
source_url=MESH_URL,
node_count=mesh_nodes,
document=(
"Clinical abstract: Crohn disease with transmural ileocolonic inflammation, "
"skip lesions, abdominal pain, and chronic diarrhea. Colonoscopy showed "
"cobblestoning and biopsy found noncaseating granulomas; treatment with "
"infliximab produced remission."
),
expected_leaf="C06.405.469.432.500 Crohn Disease",
tree=mesh_tree,
),
Hierarchy(
slug="codebase",
name="CookSafe files",
version="snapshot 2026-08-06",
source_url=str(CODEBASE_SNAPSHOT),
node_count=codebase_nodes,
document=(
"Developer search: find the experimental Python module under x/eugene that "
"implements BM25, dense, and fused retrievers for legal RAG."
),
expected_leaf="retrievers.py",
tree=codebase_tree,
),
)
NODE_W, NODE_H = 300, 38
COL_W, ROW_H = 360, 50
PAD_X = 28
EDGE_TOP_K = 5
def subtree(tree: Tree, path: tuple[str, ...]) -> Tree:
"""Return the direct-child menu below ``path``.
:param tree: taxonomy root.
:param path: path from the taxonomy root.
:returns: child mapping at the path.
"""
subtree_value: Tree = tree
for label in path:
subtree_value = subtree_value[label]
return subtree_value
def _escape(value: object) -> str:
return html.escape(str(value), quote=True)
def _truncate(value: str, length: int = 33) -> str:
return value if len(value) <= length else value[: length - 1] + "…"
def _build_nodes(hierarchy: Hierarchy, result: dict) -> dict:
records: dict[tuple[str, ...], dict] = {
tuple(record["parent"]): record for record in result["records"]
}
best_path: tuple[str, ...] = tuple(result["beam"][0]["path"])
greedy_path: tuple[str, ...] = tuple(result["greedy"]["path"])
retained: set[tuple[str, ...]] = {tuple(path) for path in result["retained_paths"]}
def grow(path: tuple[str, ...]) -> list[dict]:
record: dict | None = records.get(path)
if record is None:
return []
children: list[dict] = []
probabilities: dict[str, float] = record["probabilities"]
ranked: list[tuple[str, float]] = sorted(
probabilities.items(), key=lambda item: item[1], reverse=True
)
shown_labels: set[str] = {label for label, _ in ranked[:EDGE_TOP_K]}
shown_labels.update(
label
for label, _ in ranked
if path + (label,) in retained
or path + (label,) == best_path[: len(path) + 1]
or path + (label,) == greedy_path[: len(path) + 1]
)
for label, probability in ranked:
if label not in shown_labels:
continue
child_path: tuple[str, ...] = path + (label,)
on_best_path: bool = child_path == best_path[: len(child_path)]
on_greedy_path: bool = child_path == greedy_path[: len(child_path)]
kind: str = (
"winner"
if on_best_path
else "greedy"
if on_greedy_path
else "beam"
if child_path in retained
else "alt"
)
children.append(
{
"label": label,
"probability": probability,
"kind": kind,
"children": grow(child_path),
}
)
return children
return {
"label": hierarchy.name,
"probability": None,
"kind": "root",
"children": grow(()),
}
def _layout(root: dict) -> tuple[int, int]:
rows: list[int] = [0]
maximum_depth: list[int] = [0]
def walk(node: dict, depth: int) -> None:
node["depth"] = depth
maximum_depth[0] = max(maximum_depth[0], depth)
if node["children"]:
for child in node["children"]:
walk(child, depth + 1)
node["row"] = (node["children"][0]["row"] + node["children"][-1]["row"]) / 2
else:
node["row"] = rows[0]
rows[0] += 1
walk(root, 0)
return maximum_depth[0], rows[0]
def render_svg(hierarchy: Hierarchy, result: dict, path: Path) -> None:
"""Write a standalone traversal SVG matching the Customer_ProdX visual language.
:param hierarchy: taxonomy demonstration.
:param result: beam-search result from the notebook.
:param path: output SVG path.
"""
root: dict = _build_nodes(hierarchy, result)
maximum_depth, row_count = _layout(root)
document_lines: list[str] = textwrap.wrap(
hierarchy.document,
width=105,
break_long_words=False,
break_on_hyphens=False,
) or [""]
document_y: int = 124
greedy_y: int = document_y + (len(document_lines) - 1) * 21 + 34
beam_y: int = greedy_y + 25
method_y: int = beam_y + 29
legend_y: int = method_y + 23
header_height: int = legend_y + 32
width: int = PAD_X * 2 + maximum_depth * COL_W + NODE_W
height: int = header_height + max(row_count, 1) * ROW_H + 34
edges: list[str] = []
nodes: list[str] = []
def node_x(node: dict) -> float:
return PAD_X + node["depth"] * COL_W
def node_y(node: dict) -> float:
return header_height + node["row"] * ROW_H
def walk(node: dict) -> None:
x_value, y_value = node_x(node), node_y(node)
for child in node["children"]:
child_x, child_y = node_x(child), node_y(child)
x1, y1 = x_value + NODE_W, y_value + NODE_H / 2
x2, y2 = child_x, child_y + NODE_H / 2
bend: float = COL_W * 0.38
edges.append(
f'<path class="edge {child["kind"]}" '
f'd="M{x1:.0f},{y1:.0f} C{x1 + bend:.0f},{y1:.0f} '
f'{x2 - bend:.0f},{y2:.0f} {x2:.0f},{y2:.0f}"/>'
)
edges.append(
f'<text class="prob" x="{x2 - 7:.0f}" y="{y2 - 5:.0f}" '
f'text-anchor="end">{child["probability"]:.2f}</text>'
)
walk(child)
kind: str = node["kind"]
label: str = _truncate(node["label"], 40)
nodes.append(
f'<g class="node {kind}"><title>{_escape(node["label"])}</title>'
f'<rect x="{x_value:.0f}" y="{y_value:.0f}" width="{NODE_W}" '
f'height="{NODE_H}" rx="7"/>'
f'<text x="{x_value + 11:.0f}" y="{y_value + 24:.0f}">'
f"{_escape(label)}</text></g>"
)
walk(root)
best: dict = result["beam"][0]
best_path: tuple[str, ...] = tuple(best["path"])
greedy_path: tuple[str, ...] = tuple(result["greedy"]["path"])
beam_leaf: str = best_path[-1] if best_path else "no leaf"
greedy_leaf: str = greedy_path[-1] if greedy_path else "no leaf"
beam_width: int = result["beam_width"]
separation_ratio: float = result["separation_ratio"]
document_text: str = "".join(
f'<text class="document" x="24" y="{document_y + index * 21}">'
f"{_escape(line)}</text>"
for index, line in enumerate(document_lines)
)
separation_text: str = (
">999×" if separation_ratio > 999 else f"{separation_ratio:.2f}×"
)
svg: str = f'''<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 {width} {height}"
width="{width}" height="{height}" role="img" aria-label="{_escape(hierarchy.name)} taxonomy beam search">
<style>
.bg {{ fill:#f6f7fb }}
text {{ font-family:ui-monospace,"SF Mono",Menlo,Consolas,monospace }}
.eyebrow {{ font-size:14px; font-weight:700; letter-spacing:1.2px; fill:#4f46e5 }}
.title {{ font:700 28px system-ui,-apple-system,"Segoe UI",sans-serif; fill:#181b28 }}
.document-label {{ font:700 12px system-ui,-apple-system,"Segoe UI",sans-serif; letter-spacing:1px; fill:#777c91 }}
.document {{ font:500 17px system-ui,-apple-system,"Segoe UI",sans-serif; fill:#303449 }}
.copy {{ font-size:14px; fill:#5c6178 }}
.result {{ font-size:14px; font-weight:700 }}
.greedy-result {{ fill:#c2410c }}
.beam-result {{ fill:#15803d }}
.edge {{ fill:none; stroke:#c9cee0; stroke-width:2 }}
.edge.winner {{ stroke:#15803d; stroke-width:2.5 }}
.edge.greedy {{ stroke:#ea580c; stroke-width:2.5 }}
.edge.beam {{ stroke:#4f46e5; stroke-width:2.2 }}
.edge.alt {{ opacity:.48 }}
.prob {{ font-size:12px; font-weight:600; fill:#5c6178 }}
.node rect {{ stroke-width:1.7 }}
.node text {{ font-size:13px }}
.node.root rect {{ fill:#f0f2f8; stroke:#e2e5f0 }}
.node.root text {{ fill:#5c6178 }}
.node.winner rect {{ fill:#e4f5ea; stroke:#15803d }}
.node.winner text {{ fill:#15803d; font-weight:700 }}
.node.greedy rect {{ fill:#fff0e8; stroke:#ea580c }}
.node.greedy text {{ fill:#c2410c; font-weight:700 }}
.node.beam rect {{ fill:#ecebfd; stroke:#4f46e5 }}
.node.beam text {{ fill:#181b28 }}
.node.alt rect {{ fill:#fff; stroke:#e2e5f0; stroke-dasharray:3 3 }}
.node.alt text {{ fill:#5c6178 }}
</style>
<rect class="bg" width="{width}" height="{height}" rx="14"/>
<text class="eyebrow" x="24" y="32">TYPESAFE · {hierarchy.name.upper()} · {hierarchy.version.upper()} · {hierarchy.node_count:,} NODES</text>
<text class="title" x="24" y="69">Greedy vs parallel beam search</text>
<text class="document-label" x="24" y="99">DOCUMENT</text>
{document_text}
<text class="result greedy-result" x="24" y="{greedy_y}">GREEDY TOP-1 → {_escape(_truncate(greedy_leaf, 105))}</text>
<text class="result beam-result" x="24" y="{beam_y}">BEAM K={beam_width} → {_escape(_truncate(beam_leaf, 105))}</text>
<text class="copy" x="24" y="{method_y}">parallel sibling Choices → keep {beam_width} by geometric mean p → top/second = {separation_text}</text>
<text class="copy" x="24" y="{legend_y}">orange = greedy green = beam winner purple = retained beam dashed = pruned</text>
{"".join(edges)}{"".join(nodes)}
</svg>'''
path.write_text(svg)貪欲探索とビームサーチを実装する
各兄弟ノードの集合は、次の節で 1 つの Choice の質問になります。その節では
両方のたどり方の戦略も実装し、静的な図に必要な確率を保持します。
HIERARCHIES = load_hierarchies()
MODEL, BEAM_WIDTH, MAX_DEPTH, EPSILON = "jev-1.12", 3, 12, 1e-9
client = TypeSafeClient(
api_key=os.environ["TYPESAFE_API_KEY"],
retry=RetryPolicy(max_retries=5, backoff_initial=1.0, backoff_max=20.0),
)
json_cache = JsonCache(Path("json_cache.json"))
@json_cache
def choose(state: str, labels: tuple[str, ...]) -> dict[str, float]:
"""Ask one atomic direct-child question and return its distribution."""
if len(labels) == 1:
return {labels[0]: 1.0}
question, keys = child_question(labels)
response = client.system_one(
state=state, questions={"child": question}, model=MODEL
)
probabilities = response.answers["child"].probabilities
return {label: probabilities[key] for key, label in keys.items()}
def child_question(labels: tuple[str, ...]) -> tuple[Choice, dict[str, str]]:
"""Build the direct-child Choice and its reversible option mapping."""
keys = {f"c{i}": label for i, label in enumerate(labels)}
question = Choice(
instructions="Which direct child category best matches this document?",
criteria=keys,
)
return question, keys
def extend_candidate(
candidate: dict, label: str, probabilities: dict[str, float]
) -> dict:
"""Append one edge and recompute its geometric-mean path score."""
is_decision: bool = len(probabilities) > 1
# Use log space for very deep trees to avoid floating-point precision loss.
probability_product: float = candidate["probability_product"] * (
max(probabilities[label], EPSILON) if is_decision else 1.0
)
decision_count: int = candidate["decision_count"] + is_decision
return {
"path": candidate["path"] + (label,),
"probability_product": probability_product,
"decision_count": decision_count,
"score": probability_product ** (1 / decision_count) if decision_count else 1.0,
}
def choice_record(path: tuple[str, ...], probabilities: dict[str, float]) -> dict:
"""Package one sibling decision for the traversal diagram."""
return {"parent": path, "probabilities": probabilities}
def beam_search(hierarchy: Hierarchy) -> dict:
"""Parallel width-three beam search using geometric-mean probability."""
beam = [{"path": (), "probability_product": 1.0, "decision_count": 0, "score": 1.0}]
records, retained_paths = [], {()}
for _ in range(MAX_DEPTH):
expandable = [
candidate
for candidate in beam
if subtree(hierarchy.tree, candidate["path"])
]
finished = [
candidate
for candidate in beam
if not subtree(hierarchy.tree, candidate["path"])
]
if not expandable:
break
with ThreadPoolExecutor(max_workers=BEAM_WIDTH) as executor:
distributions = list(
executor.map(
lambda candidate: choose(
hierarchy.document,
tuple(subtree(hierarchy.tree, candidate["path"])),
),
expandable,
)
)
expanded = []
round_records = []
for candidate, probabilities in zip(expandable, distributions, strict=True):
round_records.append(choice_record(candidate["path"], probabilities))
candidate_expanded = []
for label in probabilities:
candidate_expanded.append(
extend_candidate(candidate, label, probabilities)
)
expanded.extend(candidate_expanded)
beam = sorted(
finished + expanded,
key=lambda candidate: candidate["score"],
reverse=True,
)[:BEAM_WIDTH]
retained_paths.update(candidate["path"] for candidate in beam)
records.extend(round_records)
beam = sorted(beam, key=lambda candidate: candidate["score"], reverse=True)
return {
"beam": beam,
"records": records,
"retained_paths": sorted(retained_paths, key=lambda path: (len(path), path)),
}
def greedy_search(hierarchy: Hierarchy) -> dict:
"""Follow only the locally highest-probability child."""
path, probability_product, decision_count, records = (), 1.0, 0, []
for _ in range(MAX_DEPTH):
labels = tuple(subtree(hierarchy.tree, path))
if not labels:
break
probabilities = choose(hierarchy.document, labels)
records.append(choice_record(path, probabilities))
label = max(probabilities, key=probabilities.get)
if len(probabilities) > 1:
probability_product *= max(probabilities[label], EPSILON)
decision_count += 1
path += (label,)
score: float = (
probability_product ** (1 / decision_count) if decision_count else 1.0
)
return {"path": path, "score": score, "records": records}
def compare_searches(hierarchy: Hierarchy) -> dict:
"""Run beam and greedy, then merge their queried nodes for rendering."""
result = beam_search(hierarchy)
greedy = greedy_search(hierarchy)
recorded_paths = {tuple(record["parent"]) for record in result["records"]}
result["records"].extend(
record
for record in greedy["records"]
if tuple(record["parent"]) not in recorded_paths
)
result["greedy"] = greedy
result["beam_width"] = BEAM_WIDTH
top_score: float = result["beam"][0]["score"]
second_score: float = result["beam"][1]["score"]
result["separation_ratio"] = top_score / max(second_score, EPSILON)
return result手法を比較する
ラベル付きの 4 つの例で両方の戦略を実行し、その葉を期待される分類と比較し、 探索した経路を可視化します。
with ThreadPoolExecutor(max_workers=len(HIERARCHIES)) as executor:
results = list(executor.map(compare_searches, HIERARCHIES))
rows: list[dict[str, str | int | bool]] = []
for hierarchy, result in zip(HIERARCHIES, results, strict=True):
svg_path: Path = Path(f"{hierarchy.slug}_tree.svg")
render_svg(hierarchy, result, svg_path)
beam_path: tuple[str, ...] = tuple(result["beam"][0]["path"])
greedy_path: tuple[str, ...] = tuple(result["greedy"]["path"])
beam_leaf: str = beam_path[-1]
greedy_leaf: str = greedy_path[-1]
rows.append(
{
"hierarchy": hierarchy.name,
"nodes": hierarchy.node_count,
"expected leaf": hierarchy.expected_leaf,
"greedy leaf": greedy_leaf,
"beam K=3 leaf": beam_leaf,
"greedy correct": greedy_leaf == hierarchy.expected_leaf,
"beam correct": beam_leaf == hierarchy.expected_leaf,
"mean p": f"{result['beam'][0]['score']:.2f}",
"top/second": f"{result['separation_ratio']:.2f}×",
}
)
greedy_correct_count: int = sum(bool(row["greedy correct"]) for row in rows)
beam_correct_count: int = sum(bool(row["beam correct"]) for row in rows)
recovered_names: str = ", ".join(
str(row["hierarchy"])
for row in rows
if not row["greedy correct"] and row["beam correct"]
)
table_lines: list[str] = [
"| Hierarchy | Expected leaf | Greedy leaf | Beam K=3 leaf | Greedy correct | Beam correct |",
"| --- | --- | --- | --- | --- | --- |",
]
table_lines.extend(
"| "
+ " | ".join(
(
str(row["hierarchy"]),
str(row["expected leaf"]),
str(row["greedy leaf"]),
str(row["beam K=3 leaf"]),
"yes" if row["greedy correct"] else "no",
"yes" if row["beam correct"] else "no",
)
)
+ " |"
for row in rows
)
display(
Markdown(
"## Results\n\n"
"Each example has a known expected leaf. "
f"Beam search matched {beam_correct_count} of {len(rows)} expected leaves; "
f"greedy search matched {greedy_correct_count} of {len(rows)}. "
f"Keeping three paths recovered the expected classification for {recovered_names}.\n\n"
+ "\n".join(table_lines)
+ "\n\nThe diagrams show why the methods differ. Orange marks the greedy route, "
"green marks the winning beam route, purple marks other retained paths, and "
"dashed edges were pruned.\n\n"
+ "\n\n".join(
f"### {hierarchy.name}\n\n"
for hierarchy in HIERARCHIES
)
)
)結果
各例には、あらかじめ分かっている期待される葉があります。ビームサーチは 4 つの期待される葉のうち 4 つに一致し、貪欲探索は 4 つのうち 2 つに一致しました。3 本の経路を保持することで、CPC 特許と Shopify 商品の期待される分類を取り戻せました。
| 階層 | 期待される葉 | 貪欲探索の葉 | ビーム K=3 の葉 | 貪欲探索の正誤 | ビームの正誤 |
|---|---|---|---|---|---|
| CPC 特許 | A01K31/12 家禽や鳥用の止まり木（例：ねぐら） | E99Z99/00 本節に別段の規定のない主題 | A01K31/12 家禽や鳥用の止まり木（例：ねぐら） | いいえ | はい |
| Shopify 商品 | 猫用の窓辺ベッドと止まり台 | ペット用の椅子 | 猫用の窓辺ベッドと止まり台 | いいえ | はい |
| MeSH 生物医学主題 | C06.405.469.432.500 クローン病 | C06.405.469.432.500 クローン病 | C06.405.469.432.500 クローン病 | はい | はい |
| CookSafe ファイル | retrievers.py | retrievers.py | retrievers.py | はい | はい |
図は、両手法がなぜ異なるかを示しています。オレンジは貪欲探索の経路、緑は勝ち残ったビームの経路、紫は保持されたその他の経路、破線の辺は枝刈りされたことを表します。
CPC 特許
Shopify 商品
MeSH 生物医学主題
CookSafe ファイル
