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902cd29 | 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 | from __future__ import annotations
import json
import re
from collections import Counter, defaultdict
from dataclasses import dataclass
from pathlib import Path
from typing import Any
from db.store import Store
from visualizer.pyvis_renderer import _build_network
OUTCOME_COLORS = {
"well_learned": "#22c55e",
"partially_learned": "#f59e0b",
"failed": "#ef4444",
"not_visited": "#6b7280",
}
@dataclass(frozen=True)
class TrainingSummary:
run_id: str
episodes: int
steps: int
avg_reward: float
avg_judge: float
dpo_pairs: int
top_failures: list[str]
top_successes: list[str]
def _outcome(avg_reward: float, judge_score: float, wrong_attr_count: int, touched: bool) -> str:
if not touched:
return "not_visited"
if avg_reward > 0.7 and judge_score > 0.7 and wrong_attr_count == 0:
return "well_learned"
if avg_reward < 0.4 or wrong_attr_count > 0:
return "failed"
return "partially_learned"
def build_training_graph(*, source_root: str, run_id: str, db_path: str | None = None, output_path: str = "outputs/NodeAudit_graph.html") -> Path:
store = Store(source_root=source_root, db_path=db_path)
snapshot = store.get_full_graph()
annotations = store.get_training_annotations(run_id)
by_module: dict[str, list[Any]] = defaultdict(list)
for item in annotations:
by_module[item.module_id].append(item)
net = _build_network(height="920px", width="100%")
failed_edges: set[tuple[str, str]] = set()
all_rewards: list[float] = []
all_judges: list[float] = []
for node in snapshot.nodes:
rows = by_module.get(node.module_id, [])
touched = bool(rows)
rewards = [float(row.avg_reward) for row in rows]
judges = [float(row.thinking_quality) for row in rows]
avg_reward = (sum(rewards) / len(rewards)) if rewards else 0.0
avg_judge = (sum(judges) / len(judges)) if judges else 0.0
all_rewards.extend(rewards)
all_judges.extend(judges)
action_counts: Counter[str] = Counter()
correct: list[str] = []
wrong: list[str] = []
judge_text: list[str] = []
for row in rows:
try:
action_counts.update(json.loads(row.action_counts_json))
except Exception:
if row.action_type:
action_counts[row.action_type] += 1
try:
correct.extend(json.loads(row.correct_attributions_json))
except Exception:
pass
try:
wrong.extend(json.loads(row.wrong_attributions_json))
except Exception:
pass
if row.judge_verdict:
judge_text.append(row.judge_verdict)
if row.action_type == "FLAG_DEPENDENCY_ISSUE":
try:
payload = json.loads(row.action_payload)
except Exception:
payload = {}
target = str(payload.get("attributed_to") or "")
if target and wrong:
failed_edges.add((node.module_id, target))
outcome = _outcome(avg_reward, avg_judge, len(wrong), touched)
actions_pretty = ", ".join(f"{k}x{v}" for k, v in sorted(action_counts.items())) or "none"
judge_verdict = judge_text[-1] if judge_text else "not judged"
tooltip = (
f"Module: {node.module_id}\n"
f"Avg Reward: {avg_reward:.2f}\n"
f"Judge Score: {avg_judge:.2f}\n"
f"Correct Attributions: {', '.join(correct) if correct else 'none'}\n"
f"Wrong: {', '.join(wrong) if wrong else 'none'}\n"
f"Actions: {actions_pretty}\n"
f"Judge Verdict: {judge_verdict}"
)
net.add_node(
n_id=node.module_id,
label=node.module_id,
title=tooltip,
color=OUTCOME_COLORS[outcome],
value=1.0 + max(0.0, avg_reward),
shape="dot",
)
for edge in snapshot.edges:
is_failed = (edge.source_module_id, edge.target_module_id) in failed_edges
net.add_edge(
source=edge.source_module_id,
to=edge.target_module_id,
title=edge.connection_summary or edge.import_line,
color="#ef4444" if is_failed else "#2563eb",
width=2.2 if is_failed else 1.4,
arrows="to",
)
summary = _summarize(run_id=run_id, annotations=annotations, rewards=all_rewards, judges=all_judges)
output = Path(output_path).resolve()
output.parent.mkdir(parents=True, exist_ok=True)
net.write_html(str(output), open_browser=False, notebook=False)
html = output.read_text(encoding="utf-8")
html = re.sub(r'<link[^>]*cdn\.jsdelivr\.net[^>]*>\s*', "", html, flags=re.IGNORECASE)
html = re.sub(r'<script[^>]*cdn\.jsdelivr\.net[^>]*>\s*</script>\s*', "", html, flags=re.IGNORECASE)
panel = (
"<aside style='position:fixed;right:0;top:0;width:340px;height:100%;"
"background:#0f172a;color:#e2e8f0;padding:18px;overflow:auto;z-index:1000;'>"
f"<h3 style='margin:0 0 12px 0;'>Training Run: {summary.run_id}</h3>"
f"<p>Episodes: {summary.episodes} | Steps: {summary.steps}</p>"
f"<p>Avg Reward: {summary.avg_reward:.2f}</p>"
f"<p>Judge Scores: {summary.avg_judge:.2f}</p>"
f"<p>DPO pairs built: {summary.dpo_pairs}</p>"
"<h4>Top 3 Failures</h4>"
f"<ul>{''.join(f'<li>{item}</li>' for item in summary.top_failures)}</ul>"
"<h4>Top 3 Successes</h4>"
f"<ul>{''.join(f'<li>{item}</li>' for item in summary.top_successes)}</ul>"
"</aside>"
)
html = html.replace("</body>", f"{panel}</body>")
output.write_text(html, encoding="utf-8")
return output
def _summarize(*, run_id: str, annotations: list[Any], rewards: list[float], judges: list[float]) -> TrainingSummary:
episodes = len({(item.task_id, item.module_id) for item in annotations})
steps = len(annotations)
avg_reward = (sum(rewards) / len(rewards)) if rewards else 0.0
avg_judge = (sum(judges) / len(judges)) if judges else 0.0
module_scores: dict[str, float] = defaultdict(float)
module_counts: dict[str, int] = defaultdict(int)
for row in annotations:
module_scores[row.module_id] += float(row.avg_reward)
module_counts[row.module_id] += 1
sorted_modules = sorted(
module_scores,
key=lambda module_id: module_scores[module_id] / max(1, module_counts[module_id]),
)
top_failures = sorted_modules[:3]
top_successes = list(reversed(sorted_modules[-3:])) if sorted_modules else []
return TrainingSummary(
run_id=run_id,
episodes=episodes,
steps=steps,
avg_reward=avg_reward,
avg_judge=avg_judge,
dpo_pairs=max(0, steps // 4),
top_failures=top_failures,
top_successes=top_successes,
)
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