50e7b0a8a4
* feat(kb): automatic KB builder, ingestion & validation tooling (#35) Closes #118, #119, #120, #121, #122. - Ingest committed Danbooru alias/implication snapshots during generation: synonyms reach canonicals (long black stockings -> thighhighs), implications become expand relations. Curated-wins preserved. - Automatic candidate validator (structural rules reused from kb_validation + confidence heuristic, no approval gate; SC0022). - Duplicate/conflict detection tool (scripts/detect_kb_conflicts.py) + reusable detect_conflicts; clean reference KB reports zero. - KB coverage benchmark harness (scripts/coverage_benchmark.py) + JSON summary. - Single fully-automated, CI-runnable build pipeline (scripts/build_knowledge_base.py) that fails fast on rejections/conflicts and never prompts. Docs added. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * test(kb): use pytest.approx for confidence float assertions (SonarCloud reliability) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> --------- Co-authored-by: kunail0804 <kunail0804@gmail.com> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
91 lines
2.9 KiB
Python
91 lines
2.9 KiB
Python
"""Tests for the automatic candidate validator (issue #119, epic #35)."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import pytest
|
|
|
|
from compiler.common.kb_autovalidate import (
|
|
DEFAULT_CONFIDENCE_THRESHOLD,
|
|
compute_confidence,
|
|
validate_candidate,
|
|
)
|
|
|
|
VOCAB = {"pantyhose", "elbow_gloves", "gloves", "katana"}
|
|
|
|
|
|
def decide(entry, **overrides):
|
|
kwargs = dict(
|
|
curated_ids={"glasses", "thighhighs"},
|
|
curated_aliases={"spectacles", "thigh highs"},
|
|
known_ids={"pantyhose", "elbow_gloves", "gloves", "katana", "weapon"},
|
|
source_vocab=VOCAB,
|
|
)
|
|
kwargs.update(overrides)
|
|
return validate_candidate(entry, **kwargs)
|
|
|
|
|
|
def codes(decision):
|
|
return [m.code for m in decision.reasons]
|
|
|
|
|
|
def test_well_formed_candidate_is_accepted() -> None:
|
|
decision = decide({"id": "pantyhose", "tags": ["pantyhose"], "category": "clothing"})
|
|
assert decision.accepted
|
|
assert decision.confidence == pytest.approx(1.0)
|
|
assert decision.reasons == []
|
|
|
|
|
|
def test_id_collision_with_curated_is_rejected() -> None:
|
|
decision = decide({"id": "thighhighs", "tags": ["thighhighs"], "category": "clothing"})
|
|
assert not decision.accepted
|
|
assert "SC0005" in codes(decision)
|
|
|
|
|
|
def test_alias_collision_with_curated_is_rejected() -> None:
|
|
decision = decide(
|
|
{
|
|
"id": "pantyhose",
|
|
"tags": ["pantyhose"],
|
|
"category": "clothing",
|
|
"aliases": ["spectacles"],
|
|
}
|
|
)
|
|
assert not decision.accepted
|
|
assert "SC0004" in codes(decision)
|
|
|
|
|
|
def test_unknown_category_is_rejected() -> None:
|
|
decision = decide({"id": "pantyhose", "tags": ["pantyhose"], "category": "not_a_category"})
|
|
assert not decision.accepted
|
|
assert "SC0008" in codes(decision)
|
|
|
|
|
|
def test_self_cycle_expansion_is_rejected() -> None:
|
|
decision = decide(
|
|
{"id": "katana", "tags": ["katana"], "category": "objects", "expand": ["katana"]}
|
|
)
|
|
assert not decision.accepted
|
|
assert "SC0003" in codes(decision)
|
|
|
|
|
|
def test_unknown_expand_target_is_rejected() -> None:
|
|
decision = decide(
|
|
{"id": "katana", "tags": ["katana"], "category": "objects", "expand": ["nonexistent"]}
|
|
)
|
|
assert not decision.accepted
|
|
assert "SC0004" in codes(decision)
|
|
|
|
|
|
def test_below_confidence_threshold_is_rejected() -> None:
|
|
# A tag not in the source vocab scores below threshold (no invention).
|
|
decision = decide({"id": "invented_tag", "tags": ["invented tag"], "category": "clothing"})
|
|
assert not decision.accepted
|
|
assert "SC0022" in codes(decision)
|
|
assert decision.confidence < DEFAULT_CONFIDENCE_THRESHOLD
|
|
|
|
|
|
def test_confidence_scoring() -> None:
|
|
assert compute_confidence({"id": "katana", "category": "objects"}, VOCAB) == pytest.approx(1.0)
|
|
assert compute_confidence({"id": "katana", "category": "bogus"}, VOCAB) == pytest.approx(0.7)
|
|
assert compute_confidence({"id": "unknown", "category": "objects"}, VOCAB) == pytest.approx(0.3)
|