c70ff9aa1e
Add quality.section_label(title, content): for section-aware .tex ingest, keep the controlled bucket (intro/theorem/proof/abstract/bibliography) when the real \section heading maps to one, else store the cleaned title verbatim (e.g. 'preliminaries', 'rigidity') instead of collapsing to 'body'. Math papers title most sections descriptively, so R-C's vocab-only mapping left ~90% as 'body'; this lifts the section signal toward near-full. .pdf/.txt/run_quality_pass keep content-based classify_section unchanged. Safe because the section column is write-only (no consumer filters on the vocab). New _clean_title (strip LaTeX/numbering, lower-case, truncate). Tests: section_label bucket/verbatim/fallback paths + _clean_title; .tex ingest stores a descriptive heading as its title. Full suite 377 passed; ruff + mypy clean. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
281 lines
10 KiB
Python
281 lines
10 KiB
Python
"""Tests for codex.quality — chunk quality gate and section classification (F-16)."""
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from __future__ import annotations
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from unittest.mock import MagicMock
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from codex.quality import (
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_bib_score,
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_clean_title,
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classify_section,
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filter_chunks,
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is_quality_chunk,
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run_quality_pass,
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section_label,
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)
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _settings(min_chars: int = 60, min_alpha: float = 0.40, max_bib: float = 0.70):
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s = MagicMock()
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s.chunk_min_chars = min_chars
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s.chunk_min_alpha_ratio = min_alpha
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s.chunk_max_bib_score = max_bib
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return s
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GOOD_TEXT = (
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"In differential geometry, the discrete Laplace–Beltrami operator "
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"generalises the smooth operator to polyhedral surfaces by using cotangent "
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"weights. This discretisation preserves key spectral properties."
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)
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BIB_TEXT = (
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"Bobenko, A. and Pinkall, U. (2015) Discrete differential geometry. "
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"doi:10.1145/2010324.1964978 Smith et al. (2018) Surface remeshing. "
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"doi:10.1007/s00211-019-01082-w Jones et al. (2020) doi:10.1145/1234567.890123"
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)
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# ---------------------------------------------------------------------------
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# _bib_score
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# ---------------------------------------------------------------------------
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class TestBibScore:
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def test_empty_string(self):
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assert _bib_score("") == 0.0
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def test_clean_prose_is_low(self):
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assert _bib_score(GOOD_TEXT) < 0.10
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def test_bib_like_text_is_high(self):
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assert _bib_score(BIB_TEXT) > 0.70
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def test_single_doi_short_text_spikes(self):
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text = "See doi:10.1145/2010324.1964978 for details."
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# 8 words, 1 DOI (weight 3) → raw = 3 / max(0.8, 1) = 3.0 → capped 1.0
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assert _bib_score(text) == 1.0
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def test_score_capped_at_one(self):
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# _DOI_RE requires \d{4,} — use a valid registrant length
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heavy = "10.1145/abcdef " * 20
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assert _bib_score(heavy) == 1.0
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# ---------------------------------------------------------------------------
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# is_quality_chunk
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# ---------------------------------------------------------------------------
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class TestIsQualityChunk:
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def test_passes_good_text(self):
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assert is_quality_chunk(GOOD_TEXT, settings=_settings()) is True
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def test_fails_too_short(self):
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assert is_quality_chunk("Short.", settings=_settings(min_chars=60)) is False
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def test_fails_low_alpha_ratio(self):
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# Mostly digits and symbols
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junk = "1234 5678 9012 3456 7890 |||| ==== ---- ____ .... ;;;; 0000 9999 8888 7777"
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assert is_quality_chunk(junk, settings=_settings(min_alpha=0.40)) is False
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def test_fails_high_bib_score(self):
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assert is_quality_chunk(BIB_TEXT, settings=_settings(max_bib=0.70)) is False
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def test_bib_threshold_is_strict_gt(self):
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# A text that scores exactly at the threshold should still pass (> not >=)
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s = _settings(max_bib=1.0)
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assert is_quality_chunk(GOOD_TEXT, settings=s) is True
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def test_exactly_min_chars_passes(self):
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# check is `len < min_chars`, so exactly at the threshold passes
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text = "a" * 60
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assert is_quality_chunk(text, settings=_settings(min_chars=60)) is True
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def test_one_below_min_chars_fails(self):
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text = "a" * 59
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assert is_quality_chunk(text, settings=_settings(min_chars=60)) is False
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# ---------------------------------------------------------------------------
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# classify_section
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# ---------------------------------------------------------------------------
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class TestClassifySection:
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def test_abstract_header(self):
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assert classify_section("Abstract\n\nThis paper presents...") == "abstract"
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def test_abstract_german(self):
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assert classify_section("Zusammenfassung\nDiese Arbeit...") == "abstract"
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def test_intro_header(self):
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assert classify_section("Introduction\nOver the past decade...") == "intro"
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def test_intro_numbered(self):
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assert classify_section("1. Introduction\nWe study...") == "intro"
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def test_theorem_header(self):
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assert classify_section("Theorem 3.1 (Existence). Let M be...") == "theorem"
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def test_lemma_header(self):
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assert classify_section("Lemma 2. If f is smooth then...") == "theorem"
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def test_proof_header(self):
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assert classify_section("Proof. We proceed by induction...") == "proof"
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def test_bibliography_header(self):
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assert classify_section("References\n[1] Bobenko, A....") == "bibliography"
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def test_bibliography_german(self):
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assert classify_section("Literatur\n[1] Müller, H....") == "bibliography"
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def test_body_fallback(self):
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assert classify_section("The cotangent weight for edge (i,j) is...") == "body"
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def test_doi_density_fallback_to_bibliography(self):
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# No "References" header but heavy DOI density → bibliography
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dense = BIB_TEXT
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assert classify_section(dense) == "bibliography"
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def test_only_first_200_chars_checked(self):
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# Pattern in position >200 should NOT be matched as section header
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prefix = "x" * 201
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text = prefix + "\nAbstract"
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assert classify_section(text) == "body"
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# ---------------------------------------------------------------------------
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# filter_chunks
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# ---------------------------------------------------------------------------
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class TestFilterChunks:
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def test_empty_list(self):
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assert filter_chunks([], settings=_settings()) == []
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def test_all_good(self):
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chunks = [GOOD_TEXT, GOOD_TEXT + " More content here."]
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assert filter_chunks(chunks, settings=_settings()) == chunks
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def test_all_bad(self):
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chunks = ["x", "y"]
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assert filter_chunks(chunks, settings=_settings()) == []
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def test_mixed(self):
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chunks = [GOOD_TEXT, "Short.", GOOD_TEXT]
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result = filter_chunks(chunks, settings=_settings())
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assert len(result) == 2
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assert all(c == GOOD_TEXT for c in result)
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# ---------------------------------------------------------------------------
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# run_quality_pass
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# ---------------------------------------------------------------------------
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def _make_mock_conn(rows: list[dict]) -> MagicMock:
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"""Return a mock psycopg connection whose execute().fetchall() returns rows."""
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conn = MagicMock()
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conn.execute.return_value.fetchall.return_value = rows
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return conn
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class TestRunQualityPass:
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def test_removes_bad_chunk(self):
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conn = _make_mock_conn([{"id": 1, "content": "x"}])
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stats = run_quality_pass(conn=conn, settings=_settings())
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assert stats["removed"] == 1
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assert stats["kept"] == 0
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# The DELETE must have been called with chunk id=1
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delete_calls = [c for c in conn.execute.call_args_list if "DELETE" in str(c)]
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assert len(delete_calls) == 1
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def test_tags_good_chunk(self):
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conn = _make_mock_conn([{"id": 2, "content": GOOD_TEXT}])
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stats = run_quality_pass(conn=conn, settings=_settings())
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assert stats["kept"] == 1
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assert stats["tagged"] == 1
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assert stats["removed"] == 0
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# UPDATE must have been called
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update_calls = [c for c in conn.execute.call_args_list if "UPDATE" in str(c)]
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assert len(update_calls) == 1
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def test_mixed_batch(self):
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rows = [
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{"id": 1, "content": "x"},
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{"id": 2, "content": GOOD_TEXT},
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{"id": 3, "content": "y"},
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{"id": 4, "content": GOOD_TEXT + " extra words here."},
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]
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conn = _make_mock_conn(rows)
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stats = run_quality_pass(conn=conn, settings=_settings())
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assert stats["removed"] == 2
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assert stats["kept"] == 2
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assert stats["tagged"] == 2
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def test_paper_id_scopes_query(self):
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conn = _make_mock_conn([])
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run_quality_pass(paper_id="2301.07041", conn=conn, settings=_settings())
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first_call = conn.execute.call_args_list[0]
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sql = first_call[0][0]
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assert "paper_id" in sql
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def test_all_papers_query_has_no_filter(self):
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conn = _make_mock_conn([])
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run_quality_pass(paper_id=None, conn=conn, settings=_settings())
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first_call = conn.execute.call_args_list[0]
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sql = first_call[0][0]
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assert "paper_id" not in sql
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def test_commit_is_called(self):
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conn = _make_mock_conn([])
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run_quality_pass(conn=conn, settings=_settings())
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conn.commit.assert_called_once()
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def test_section_label_stored_correctly(self):
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conn = _make_mock_conn([{"id": 5, "content": "Abstract\n\n" + GOOD_TEXT}])
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run_quality_pass(conn=conn, settings=_settings())
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update_calls = [c for c in conn.execute.call_args_list if "UPDATE" in str(c)]
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assert len(update_calls) == 1
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kwargs = update_calls[0][0][1]
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assert kwargs["section"] == "abstract"
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assert kwargs["id"] == 5
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class TestCleanTitle:
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def test_strips_leading_numbering(self):
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assert _clean_title("3.2 Variational Principles") == "variational principles"
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def test_strips_latex_and_braces(self):
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assert _clean_title(r"The \emph{Discrete} Laplacian") == "the discrete laplacian"
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def test_collapses_and_lowercases(self):
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assert _clean_title(" Rigidity Results ") == "rigidity results"
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class TestSectionLabel:
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def test_title_maps_to_controlled_bucket(self):
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# Headings matching the controlled vocab keep the canonical bucket.
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assert section_label("Introduction", "body text") == "intro"
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assert section_label("Proof of Theorem 3", "body text") == "proof"
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assert section_label("References", "body text") == "bibliography"
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def test_descriptive_title_stored_verbatim(self):
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# R-F win: a non-vocab heading is stored as its cleaned title, not "body".
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assert section_label("Preliminaries", "body text") == "preliminaries"
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assert section_label("3.1 Discrete Conformal Maps", "x") == "discrete conformal maps"
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def test_none_or_blank_title_falls_back_to_content(self):
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# .pdf/.txt path: unchanged content-based classification.
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assert section_label(None, "Theorem 1. Let X be ...") == "theorem"
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assert section_label("", "ordinary body prose here") == "body"
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def test_title_cleaning_to_empty_falls_back_to_content(self):
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# A pure-numbering heading cleans to "" → classify by content instead.
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assert section_label("3.2", "Lemma 2. Suppose ...") == "theorem"
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