Datasets:

legexbenchmark
/

code

	import csv

	from legex.analysis.report import build_report


	def _write(path, header, rows):
	with path.open("w", encoding="utf-8", newline="") as f:
	w = csv.writer(f)
	w.writerow(header)
	w.writerows(rows)


	def test_build_report_renders_sections_and_live_numbers(tmp_path) -> None:
	iaa = tmp_path / "iaa"
	iaa.mkdir()
	analysis = tmp_path

	_write(
	iaa / "pairwise_agreement.csv",
	["annotator_a", "annotator_b", "country", "field", "n",
	"pct_exact", "pct_tolerant", "cohen_kappa"],
	[
	# categorical (fixed-vocab) -> kappa shown
	["primary", "a1b2c3d4e5", "de", "plaintiff_no1_ISIC1_industry_category", "20", "0.5000", "0.5000", "0.4000"],
	# monetary -> kappa computed in CSV but suppressed in the report
	["primary", "a1b2c3d4e5", "de", "court_cost_awarded_nominal", "20", "0.6000", "0.6500", "0.3000"],
	# free text -> percent only, kappa undefined
	["primary", "a1b2c3d4e5", "de", "legal_subject_judgement", "20", "0.1000", "0.1000", ""],
	],
	)
	# Reference-implementation output (scripts/alt_test_reference.py); an
	# empty string marks an untestable (country, field, variant) cell.
	_write(
	iaa / "alt_test_reference_gpt-5.4-mini.csv",
	["candidate", "country", "field",
	"winning_rate", "advantage_probability", "passes",
	"winning_rate_nontrivial", "advantage_probability_nontrivial",
	"passes_nontrivial"],
	[
	["gpt-5.4-mini", "de", "plaintiffs_all_count",
	"1.0", "0.95", "1", "1.0", "0.93", "1"],
	["gpt-5.4-mini", "de", "dispute_value_nominal",
	"0.3333", "0.7", "0", "", "", ""],
	],
	)
	_write(
	analysis / "per_column.csv",
	["model", "column", "tp", "mismatch", "missed", "hallucinated", "tn",
	"accuracy", "recall_when_filled", "precision_when_emitted",
	"hallucination_rate", "miss_rate", "wrong_when_both_filled", "f1"],
	[["gpt-5.4-mini", "plaintiffs_all_count", "8", "1", "1", "0", "10",
	"0.9000", "0.8000", "0.8889", "0.0000", "0.1000", "0.1000", "0.8400"]],
	)

	md = build_report(iaa, analysis)

	for header in (
	"# Inter-Annotator Agreement",
	"## 1. Scope",
	"## 2. Human–human agreement",
	"## 3. Alternative-annotator test",
	"## 4. Headline extraction metrics",
	"Landis–Koch",
	):
	assert header in md, header

	assert "### 2.1 By variable" in md
	assert "0.400" in md # ISIC (categorical) kappa is shown
	assert "0.300" not in md # monetary kappa is suppressed, not displayed
	assert "65.0%" in md # monetary tolerant percent agreement
	assert "±" in md # uncertainty (±1 SE) is shown
	assert "nominal" in md and "monetary" in md # measurement-level labels
	assert "a1b2c3d4e5" in md # by-pair table
	assert "80.0%" in md # recall_when_filled rendered as percent (section 4)
	assert "1/2" in md # alt-test pass count (1 of 2 testable cells)
	assert "1/1" in md # non-trivial pass count (untestable cell excluded)
	assert "0.82" in md # mean advantage probability rho
	assert "0.93" in md # non-trivial mean rho (only the testable cell)
	# provenance: numbers come from the authors' reference implementation
	assert "nitaytech/AltTest" in md
	assert "alt_test_reference.py" in md
	# per-field grids for every headline metric
	assert "### 4.3 Precision by field" in md
	assert "### 4.4 Hallucination rate by field" in md
	assert "### 4.5 F1 by field" in md
	assert "0.840" in md # F1 rendered as 0–1, not percent
	# the alt-test formula block is always rendered, CSVs or not
	assert "### 3.1 How ω and ρ are computed" in md
	assert "rho_j = (1/n_j)" in md # advantage probability, with its >=
	assert "(1 - epsilon) / 2 = 0.4" in md # the threshold epsilon actually buys
	# without the decomposition CSV, §3.4 degrades to a pointer
	assert "### 3.4 Substitutable vs. better" in md
	assert "alt_test_decomposition.py" in md


	def test_alt_test_decomposition_separates_substitutable_from_better(tmp_path) -> None:
	"""§3.4 reports the win/tie/loss counts rho hides, and rho with ties split."""
	iaa = tmp_path / "iaa"
	iaa.mkdir()
	_write(
	iaa / "alt_test_decomposition.csv",
	["candidate", "country", "variant", "n_comparisons",
	"refs_disagree", "llm_better", "human_better",
	"tie", "tie_same", "tie_diff", "tie_at_1", "tie_at_half", "tie_at_0",
	"rho_alttest", "rho_tiebroken"],
	[
	# ties dominate: rho looks strong, but the human wins the decisive ones
	["gpt-5.4-mini", "de", "all", "100",
	"36", "10", "14", "76", "57", "19", "38", "26", "12", "0.86", "0.48"],
	["gpt-5.4-mini", "de", "nontrivial", "80",
	"30", "8", "8", "64", "48", "16", "32", "22", "10", "0.90", "0.50"],
	],
	)

	md = build_report(iaa, tmp_path)

	assert "### 3.4 Substitutable vs. better" in md
	assert "76 (76%)" in md # tie count and share
	assert "0.86" in md # rho as the alt-test computes it
	assert "0.48" in md # rho once ties are split -> chance
	# the tie bucket is itemised, both by score level and by same/different answer
	assert "57 (75%)" in md # tie_same / tie
	assert "19 (25%)" in md # tie_diff / tie -- ties that are real disagreements
	assert "38 (50%)" in md # tie_at_1 / tie
	assert "26 (34%)" in md # tie_at_half / tie -- only possible if refs conflict
	assert "12 (16%)" in md # tie_at_0 / tie
	assert "631" not in md # refs_disagree comes from the CSV, not hard-coded
	assert "36 of 100 comparisons (36%;" in md # reference conflict rate
	# the reading: mostly real agreement, but the human takes the decisive calls
	assert "75% of those ties are real agreement" in md
	assert "the human wins 58% (14 vs 10)" in md # 14 / (14 + 10) decisive
	assert "0.50." in md # non-trivial rho (ties split) survives the filter