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from __future__ import annotations
import random
from collections.abc import Iterator, Sequence
from graph.graph import EdgeRecord, GraphStore
from graph.real.corpus import (
RealGraph,
_competing_paths,
_extract_neighborhood,
_matching_paths,
_query_text,
_sample_path,
_without_support,
relation_phrase,
)
from graph.synthetic.generator import ReasoningExample
from graph.types import GraphPath, GraphQuery, PathStep, ReasoningStatus
ROBUSTNESS_MODES = (
"standard",
"hard_negative",
"missing_edge",
"adversarial_contradiction",
"long_hop",
)
def _step(edge: EdgeRecord) -> PathStep:
return PathStep(
edge.source_id,
edge.relation_id,
edge.target_id,
edge.source_type,
edge.target_type,
edge.attributes,
)
def _single_step_paths(
graph: GraphStore, start: str, relation: str
) -> tuple[GraphPath, ...]:
return tuple(
GraphPath((_step(edge),))
for edge in graph.outgoing(start)
if edge.relation_id == relation
)
def _inject_contradiction(
neighbourhood: GraphStore,
supporting: Sequence[GraphPath],
*,
seed: int,
) -> tuple[GraphStore, tuple[GraphPath, ...]] | None:
if not supporting or len(supporting[0].steps) != 1:
return None
gold_step = supporting[0].steps[0]
candidates = [
node
for node, node_type in neighbourhood.nodes.items()
if node != gold_step.target_id
and node != gold_step.source_id
and node_type == gold_step.target_type
]
if not candidates:
candidates = [f"adv:{seed}:alternative"]
target = random.Random(seed).choice(candidates)
nodes = dict(neighbourhood.nodes)
nodes.setdefault(target, gold_step.target_type or "entity")
node_attributes = dict(neighbourhood.node_attributes)
node_attributes.setdefault(target, {"label": "adversarial alternative"})
relation_attributes = {
key: dict(value) for key, value in neighbourhood.relation_attributes.items()
}
relation_attributes.setdefault(gold_step.relation_id, {})["functional"] = "true"
injected = EdgeRecord(
gold_step.source_id,
gold_step.relation_id,
target,
gold_step.source_type or nodes[gold_step.source_id],
gold_step.target_type or nodes[target],
gold_step.relation_id,
{
"evidence_kind": "adversarial_perturbation",
"perturbation": "functional_relation_alternative_value",
},
)
edges = (*neighbourhood.edges, injected)
graph = GraphStore(
nodes,
edges,
graph_version=neighbourhood.graph_version,
node_attributes=node_attributes,
relation_attributes=relation_attributes,
)
conflicts = _single_step_paths(graph, gold_step.source_id, gold_step.relation_id)
if len({path.endpoint_id for path in conflicts}) < 2:
return None
return graph, conflicts
def generate_robust_examples(
real_graph: RealGraph,
*,
count: int,
seed: int = 0,
mode: str,
min_hops: int = 1,
max_hops: int = 4,
max_branch: int = 12,
max_nodes: int = 80,
) -> Iterator[ReasoningExample]:
"""Yield deterministic examples for one explicit robustness mode.
Contradictions are derived perturbations: they are not claims that the raw
source contains two incompatible facts. The generated graph records that
distinction on the injected edge and the example's ``robustness_mode``.
"""
if mode not in ROBUSTNESS_MODES:
raise ValueError(f"unknown robustness mode {mode!r}")
if count < 0:
raise ValueError("count must be non-negative")
if min_hops < 1 or min_hops > max_hops:
raise ValueError("hop bounds must satisfy 1 <= min_hops <= max_hops")
if mode == "long_hop" and min_hops < 5:
min_hops = 5
max_hops = max(max_hops, 8)
if mode == "adversarial_contradiction":
min_hops = max_hops = 1
graph = real_graph.graph
starts = [node for node in graph.nodes if graph.outgoing(node)]
if not starts and count:
raise ValueError("real graph contains no nodes with outgoing edges")
rng = random.Random(seed)
produced = attempts = 0
max_attempts = max(100, count * 150)
while produced < count and attempts < max_attempts:
attempts += 1
start = rng.choice(starts)
sampled = _sample_path(graph, start, rng, min_hops, max_hops)
if sampled is None:
continue
composition = sampled.relation_sequence
neighbourhood = _extract_neighborhood(
graph,
start,
sampled,
rng,
max_hops=max_hops,
max_branch=max_branch,
max_nodes=max_nodes,
)
supporting = _matching_paths(neighbourhood, start, composition)
if not supporting:
continue
active_graph = neighbourhood
active_supporting = supporting
answers = tuple(
dict.fromkeys(path.endpoint_id for path in supporting if path.endpoint_id)
)
conflicting: tuple[GraphPath, ...] = ()
status = ReasoningStatus.SUPPORTED
if mode == "missing_edge":
active_graph = _without_support(
neighbourhood,
supporting,
blocked_prefix=(start, composition[0]),
)
active_supporting = ()
answers = ()
status = ReasoningStatus.INSUFFICIENT_EVIDENCE
elif mode == "adversarial_contradiction":
injected = _inject_contradiction(
neighbourhood, supporting, seed=seed + produced
)
if injected is None:
continue
active_graph, conflicting = injected
active_supporting = ()
answers = ()
status = ReasoningStatus.CONTRADICTED
competing = _competing_paths(
active_graph,
start,
len(composition),
composition,
limit=64,
)
negative_answers = tuple(
endpoint
for endpoint in dict.fromkeys(path.endpoint_id for path in competing)
if endpoint is not None and endpoint not in answers
)
if mode == "hard_negative" and not negative_answers:
continue
phrases = [
real_graph.relation_phrases.get(relation, relation_phrase(relation))
for relation in composition
]
label = real_graph.node_labels.get(start, start)
target_type = sampled.steps[-1].target_type or "entity"
query = GraphQuery(
text=_query_text(label, target_type, phrases, len(composition), rng),
start_entities=(start,),
start_entity_ids=(start,),
max_hops=max_hops,
min_hops=1,
return_paths=True,
verify=True,
operation="find_entities",
answer_mode="entity",
)
yield ReasoningExample(
example_id=f"{real_graph.source.source_id}-{mode}-{seed}-{produced}",
graph=active_graph.to_example(),
query=query,
answers=answers,
negative_answers=negative_answers,
supporting_paths=active_supporting,
conflicting_paths=conflicting,
status=status,
difficulty=len(composition),
seed=seed + produced,
generator_version="real-corpus-robustness-v1",
task_type=query.operation,
intent_id=f"{real_graph.source.domain}:{mode}",
composition=composition,
competing_paths=competing,
stage_name=f"real_{mode}",
robustness_mode=mode,
)
produced += 1
if produced < count:
raise RuntimeError(
f"generated only {produced}/{count} {mode} examples from "
f"{real_graph.source.source_id} after {attempts} attempts"
)
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