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9e6be29 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 | from __future__ import annotations
import argparse
import json
from collections import Counter
from dataclasses import asdict
from pathlib import Path
from typing import Any
from osint_env.config import clone_environment_config, load_seeding_config, load_shared_config
from osint_env.data.generator import DatasetGenerator
from osint_env.domain.models import Edge, TaskInstance
from osint_env.llm import build_llm_client
def edge_to_dict(edge: Edge) -> dict[str, Any]:
return {
"src": edge.src,
"rel": edge.rel,
"dst": edge.dst,
"confidence": float(edge.confidence),
}
def task_to_dict(task: TaskInstance) -> dict[str, Any]:
return {
"task_id": task.task_id,
"task_type": task.task_type,
"question": task.question,
"answer": task.answer,
"supporting_edges": [edge_to_dict(e) for e in task.supporting_edges],
"metadata": dict(task.metadata),
}
def build_fixed_snapshot(seed_path: Path) -> dict[str, Any]:
seeding = load_seeding_config(seed_path)
fixed_nodes = []
for node in seeding.seeded_nodes:
fixed_nodes.append(
{
"node_id": node.node_id,
"node_type": str(getattr(node.node_type, "value", node.node_type)),
"attrs": dict(node.attrs),
}
)
fixed_edges = [
{
"src": edge.src,
"rel": edge.rel,
"dst": edge.dst,
"confidence": float(edge.confidence),
}
for edge in seeding.seeded_edges
]
fixed_questions = []
for idx, q in enumerate(seeding.seeded_questions):
fixed_questions.append(
{
"task_id": f"fixed_task_{idx:02d}",
"task_type": q.task_type,
"question": q.question,
"answer": q.answer,
"supporting_edges": [
{
"src": edge.src,
"rel": edge.rel,
"dst": edge.dst,
"confidence": float(edge.confidence),
}
for edge in q.supporting_edges
],
"metadata": dict(q.metadata),
}
)
difficulty_counts = Counter(str(q.get("metadata", {}).get("difficulty", "unknown")) for q in fixed_questions)
return {
"dataset_name": "fixed_levels_submission_set",
"source_seed": str(seed_path),
"graph": {
"nodes": fixed_nodes,
"edges": fixed_edges,
"node_count": len(fixed_nodes),
"edge_count": len(fixed_edges),
},
"questions": fixed_questions,
"question_count": len(fixed_questions),
"difficulty_counts": dict(difficulty_counts),
}
def build_complete_snapshot(shared_config_path: Path, seed_path: Path) -> dict[str, Any]:
shared = load_shared_config(shared_config_path)
env_cfg = clone_environment_config(shared.environment)
env_cfg.seeding = load_seeding_config(seed_path)
llm_client = build_llm_client(env_cfg.llm)
generator = DatasetGenerator(config=env_cfg, llm=llm_client)
graph = generator.build_canonical_graph()
views = generator.build_platform_views(graph)
tasks = generator.generate_tasks(graph, views, count=max(15, len(env_cfg.seeding.seeded_questions)))
difficulty_counts = Counter(str(task.metadata.get("difficulty", "unknown")) for task in tasks)
return {
"dataset_name": "fixed_levels_submission_set",
"generation_mode": "llm_expanded",
"shared_config": str(shared_config_path),
"seed_file": str(seed_path),
"llm": asdict(env_cfg.llm),
"environment": {
"n_users": env_cfg.n_users,
"alias_density": env_cfg.alias_density,
"noise_level": env_cfg.noise_level,
"red_herring_rate": env_cfg.red_herring_rate,
"seed": env_cfg.seed,
},
"canonical_graph": {
"node_count": len(graph.nodes),
"edge_count": len(graph.edges),
"nodes": [
{
"node_id": node.node_id,
"node_type": node.node_type.value,
"attrs": dict(node.attrs),
}
for node in sorted(graph.nodes.values(), key=lambda n: n.node_id)
],
"edges": [edge_to_dict(edge) for edge in graph.edges],
},
"platform_views": {
"microblog_posts": views.microblog_posts,
"forum_threads": views.forum_threads,
"profiles": views.profiles,
"counts": {
"microblog_posts": len(views.microblog_posts),
"forum_threads": len(views.forum_threads),
"profiles": len(views.profiles),
},
},
"tasks": [task_to_dict(task) for task in tasks],
"task_count": len(tasks),
"difficulty_counts": dict(difficulty_counts),
}
def main() -> None:
parser = argparse.ArgumentParser(description="Build fixed difficulty dataset artifacts.")
parser.add_argument(
"--seed-file",
default="datasets/fixed_levels/seed_fixed_levels.json",
help="Path to seeding JSON with fixed graph/questions.",
)
parser.add_argument(
"--shared-config",
default="datasets/fixed_levels/shared_config_fixed_levels.json",
help="Path to shared config used for LLM-expanded generation.",
)
parser.add_argument(
"--output-dir",
default="datasets/fixed_levels",
help="Directory where dataset artifacts are written.",
)
args = parser.parse_args()
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
seed_path = Path(args.seed_file)
shared_path = Path(args.shared_config)
fixed_snapshot = build_fixed_snapshot(seed_path)
fixed_path = output_dir / "fixed_graph_questions.json"
fixed_path.write_text(json.dumps(fixed_snapshot, indent=2, sort_keys=True), encoding="utf-8")
complete_snapshot = build_complete_snapshot(shared_path, seed_path)
complete_path = output_dir / "complete_dataset_qwen_generated.json"
complete_path.write_text(json.dumps(complete_snapshot, indent=2, sort_keys=True), encoding="utf-8")
summary = {
"fixed_dataset": str(fixed_path),
"complete_dataset": str(complete_path),
"fixed_nodes": fixed_snapshot["graph"]["node_count"],
"fixed_edges": fixed_snapshot["graph"]["edge_count"],
"fixed_questions": fixed_snapshot["question_count"],
"complete_nodes": complete_snapshot["canonical_graph"]["node_count"],
"complete_edges": complete_snapshot["canonical_graph"]["edge_count"],
"complete_tasks": complete_snapshot["task_count"],
"difficulty_counts": complete_snapshot["difficulty_counts"],
}
print(json.dumps(summary, indent=2, sort_keys=True))
if __name__ == "__main__":
main()
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