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"""Post-hoc subgroup audit for the frozen DRiLL graph OOF predictions.
This script does not fit or select a model. It asks whether the observed graph
gain is confined to the easy document-clustering proxy, and whether queries
whose candidate pools expose VBPL-derived causal-labelled links behave
differently from queries for which those links are unavailable.
"""
from __future__ import annotations
import argparse
import json
import platform
import sys
from datetime import datetime, timezone
from pathlib import Path
from typing import Mapping, Sequence
import numpy as np
REPO_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO_ROOT / "src"))
from scripts.run_drill_graph_evaluation import ( # noqa: E402
_ranking_observations,
_stable_candidate_rankings,
)
from telen.drill_graph import ( # noqa: E402
DrillLegalGraph,
build_drill_legal_graph,
load_drill_graph_dataset,
)
from telen.static_benchmark import ( # noqa: E402
paired_bootstrap_difference,
sha256_file,
)
CAUSAL_RELATIONS = ("AMENDS", "REPEALS", "REPLACES", "TERMINATES_VERSION")
def causal_candidate_masks(
graph: DrillLegalGraph,
candidates: np.ndarray,
relevant_indices: Sequence[frozenset[int]],
) -> tuple[np.ndarray, np.ndarray]:
"""Return query masks for exposed causal links and gold-incident links."""
if candidates.ndim != 2 or len(candidates) != len(relevant_indices):
raise ValueError("candidate matrix and qrels must align")
candidate_exposed = np.zeros(len(candidates), dtype=bool)
gold_incident = np.zeros(len(candidates), dtype=bool)
reverse: dict[str, dict[str, set[str]]] = {
relation: {} for relation in CAUSAL_RELATIONS
}
for relation in CAUSAL_RELATIONS:
for source, targets in graph.document_neighbors[relation].items():
for target in targets:
reverse[relation].setdefault(target, set()).add(source)
for query_index, (row, relevant) in enumerate(
zip(candidates, relevant_indices, strict=True)
):
candidate_documents = {
graph.article_document_ids[int(aid)] for aid in row
}
gold_documents = {
graph.article_document_ids[int(aid)] for aid in relevant
}
for relation in CAUSAL_RELATIONS:
outgoing = graph.document_neighbors[relation]
incoming = reverse[relation]
for document in candidate_documents:
linked_candidates = (
set(outgoing.get(document, ()))
| incoming.get(document, set())
) & candidate_documents
if linked_candidates:
candidate_exposed[query_index] = True
if document in gold_documents or linked_candidates & gold_documents:
gold_incident[query_index] = True
if candidate_exposed[query_index] and gold_incident[query_index]:
break
return candidate_exposed, gold_incident
def build_subgroup_masks(
article_document_ids: Sequence[str],
relevant_indices: Sequence[frozenset[int]],
causal_exposed: np.ndarray,
causal_gold_incident: np.ndarray,
) -> Mapping[str, np.ndarray]:
"""Create mutually interpretable, label-audited query strata."""
if not (
len(relevant_indices) == len(causal_exposed) == len(causal_gold_incident)
):
raise ValueError("subgroup inputs must align")
qrel_counts = np.asarray(list(map(len, relevant_indices)), dtype=np.int64)
gold_document_counts = np.asarray(
[
len({article_document_ids[int(aid)] for aid in relevant})
for relevant in relevant_indices
],
dtype=np.int64,
)
all_queries = np.ones(len(relevant_indices), dtype=bool)
return {
"all_queries": all_queries,
"single_qrel": qrel_counts == 1,
"multiple_qrels": qrel_counts > 1,
"multiple_qrels_single_gold_document":
(qrel_counts > 1) & (gold_document_counts == 1),
"multiple_gold_documents": gold_document_counts > 1,
"causal_link_exposed_in_candidate_pool": causal_exposed,
"no_causal_link_exposed_in_candidate_pool": ~causal_exposed,
"gold_document_incident_to_exposed_causal_link": causal_gold_incident,
"gold_document_not_incident_to_exposed_causal_link": ~causal_gold_incident,
}
def summarize_subgroups(
observations: Mapping[str, np.ndarray],
masks: Mapping[str, np.ndarray],
*,
bootstrap_samples: int,
bootstrap_seed: int,
) -> dict[str, object]:
"""Summarize fixed graph/text/sham nDCG observations by subgroup."""
required = {"graph", "text", "sham", "causal_ablation"}
if set(observations) != required:
raise ValueError(f"observations must contain {sorted(required)}")
size = len(observations["graph"])
if any(len(values) != size for values in observations.values()):
raise ValueError("observation vectors must align")
output: dict[str, object] = {}
for name, mask in masks.items():
indices = np.flatnonzero(mask)
if not len(indices):
output[name] = {"query_count": 0}
continue
graph = observations["graph"][indices]
text = observations["text"][indices]
sham = observations["sham"][indices]
causal_ablation = observations["causal_ablation"][indices]
output[name] = {
"query_count": int(len(indices)),
"mean_ndcg_at_10": {
"learned_text_residual": float(text.mean()),
"learned_graph_residual": float(graph.mean()),
"learned_sham_graph_residual": float(sham.mean()),
"graph_vbpl_causal_labeled_edges_removed": float(
causal_ablation.mean()
),
},
"graph_minus_text": paired_bootstrap_difference(
text,
graph,
samples=bootstrap_samples,
seed=bootstrap_seed,
),
"graph_minus_sham": paired_bootstrap_difference(
sham,
graph,
samples=bootstrap_samples,
seed=bootstrap_seed,
),
"graph_minus_causal_edge_ablation": paired_bootstrap_difference(
causal_ablation,
graph,
samples=bootstrap_samples,
seed=bootstrap_seed,
),
}
return output
def run(args: argparse.Namespace) -> dict[str, object]:
dataset = load_drill_graph_dataset(args.corpus, args.questions)
graph = build_drill_legal_graph(
dataset,
metadata_path=args.metadata,
canonical_relationships_path=args.relationships,
)
predictions = np.load(args.predictions)
candidates = np.asarray(predictions["candidates"], dtype=np.int32)
causal_exposed, causal_gold_incident = causal_candidate_masks(
graph, candidates, dataset.retrieval.relevant_indices
)
masks = build_subgroup_masks(
dataset.article_document_ids,
dataset.retrieval.relevant_indices,
causal_exposed,
causal_gold_incident,
)
system_keys = {
"graph": "scores_learned_graph_residual",
"text": "scores_learned_text_residual",
"sham": "scores_learned_sham_graph_residual",
"causal_ablation": "scores_graph_vbpl_causal_labeled_edges_removed",
}
ndcg_observations: dict[str, np.ndarray] = {}
for role, key in system_keys.items():
rankings = _stable_candidate_rankings(
candidates,
np.asarray(predictions[key], dtype=np.float32),
dataset.retrieval.corpus_ids,
)
_summary, per_query = _ranking_observations(
rankings, dataset.retrieval.relevant_indices
)
ndcg_observations[role] = per_query["ndcg_at_10"]
subgroups = summarize_subgroups(
ndcg_observations,
masks,
bootstrap_samples=args.bootstrap_samples,
bootstrap_seed=args.bootstrap_seed,
)
receipt: dict[str, object] = {
"schema_version": 1,
"artifact_type": "drill_graph_fixed_prediction_posthoc_subgroup_audit",
"generated_at": datetime.now(timezone.utc).isoformat(),
"claim_status": "exploratory_subgroup_analysis_not_model_selection",
"protocol": {
"models_refit": False,
"hyperparameters_selected": False,
"predictions_frozen_before_subgroup_definition": True,
"multiplicity_adjustment": None,
"interpretation": (
"descriptive heterogeneity audit; subgroup intervals do not replace "
"the predeclared all-query graph credit gate"
),
},
"dataset_audit": dataset.retrieval.audit,
"graph_audit": graph.audit,
"subgroups": subgroups,
"artifacts": {
"corpus": {"path": str(args.corpus), "sha256": sha256_file(args.corpus)},
"questions": {
"path": str(args.questions),
"sha256": sha256_file(args.questions),
},
"metadata": {
"path": str(args.metadata),
"sha256": sha256_file(args.metadata),
},
"relationships": {
"path": str(args.relationships),
"sha256": sha256_file(args.relationships),
},
"predictions": {
"path": str(args.predictions),
"sha256": sha256_file(args.predictions),
},
},
"limitations": [
"Subgroups were defined after observing the primary aggregate result and are exploratory.",
"VBPL-derived causal labels and canonical directions come from a pinned community mirror and are not expert-verified event truth.",
"Candidate exposure depends on the shared RRF top-100 pool; absence of an exposed edge is not absence from the full legal graph.",
],
"environment": {"python": platform.python_version(), "numpy": np.__version__},
}
args.output.parent.mkdir(parents=True, exist_ok=True)
args.output.write_text(
json.dumps(receipt, ensure_ascii=False, indent=2) + "\n", encoding="utf-8"
)
return receipt
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--corpus",
type=Path,
default=REPO_ROOT
/ "tmp/telen/drill-public/legal-corpus/data/train-00000-of-00001.parquet",
)
parser.add_argument(
"--questions",
type=Path,
default=REPO_ROOT
/ "tmp/telen/drill-public/train/data/train-00000-of-00001.parquet",
)
parser.add_argument(
"--metadata",
type=Path,
default=REPO_ROOT
/ "tmp/telen/vietnamese-legal-documents-source/data/metadata.parquet",
)
parser.add_argument(
"--relationships",
type=Path,
default=REPO_ROOT
/ "tmp/telen/bootstrap-audit/canonical_relationships.parquet",
)
parser.add_argument(
"--predictions",
type=Path,
default=REPO_ROOT
/ "tmp/telen/static-benchmarks/cache/drill/drill-rrf-graph-semantic-rank64-oof-predictions.npz",
)
parser.add_argument("--bootstrap-samples", type=int, default=10_000)
parser.add_argument("--bootstrap-seed", type=int, default=20260829)
parser.add_argument(
"--output",
type=Path,
default=REPO_ROOT
/ "research/results/telen_drill_graph_subgroup_audit_2026-08-29.json",
)
return parser.parse_args()
if __name__ == "__main__":
result = run(parse_args())
print(json.dumps(result, ensure_ascii=False, indent=2))
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