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1607c63 912886b 1607c63 912886b 1607c63 912886b 1607c63 912886b 1607c63 912886b 1607c63 | 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 | """
Plot generators for the README results section.
Produces two PNG files in ``results/``:
reward_curve.png — per-step training reward (from training_state.json)
before_after.png — per-task baseline vs trained score bar chart
Run after both ``baseline_scores.json`` and ``trained_scores.json``
exist::
python training/plot.py
"""
from __future__ import annotations
import json
import sys
from pathlib import Path
from typing import Dict, List
import matplotlib.pyplot as plt
import numpy as np
ROOT = Path(__file__).resolve().parent.parent
REPO_ROOT = ROOT.parent
RESULTS = ROOT / "results"
BASELINE = REPO_ROOT / "baseline_scores.json"
BASELINE_7B = REPO_ROOT / "baseline_7b_scores.json"
TRAINED = REPO_ROOT / "trained_scores.json"
TRAIN_STATE = RESULTS / "training_state.json"
def _load_scores(path: Path) -> Dict[str, float]:
if not path.exists():
sys.exit(f"missing {path}")
return json.loads(path.read_text())["scores"]
def plot_reward_curve() -> None:
if not TRAIN_STATE.exists():
print(f"[WARN] {TRAIN_STATE} not found — skipping reward_curve.png")
return
history = json.loads(TRAIN_STATE.read_text())
rows = [h for h in history if "reward" in h and "step" in h]
if not rows:
print("[WARN] no reward entries in training_state.json")
return
steps = [h["step"] for h in rows]
rewards = [h["reward"] for h in rows]
plt.figure(figsize=(8, 5))
plt.plot(steps, rewards, linewidth=2, color="#2563eb",
label="GRPO training reward")
if "loss" in rows[0]:
plt.twinx().plot(
steps, [h.get("loss", 0) for h in rows],
linewidth=1, color="#9ca3af", linestyle="--", label="loss",
)
plt.xlabel("Training step")
plt.ylabel("Mean episode reward")
plt.title("Enterprise Contract Guardian — GRPO Training")
plt.grid(alpha=0.3)
plt.legend(loc="lower right")
plt.tight_layout()
out = RESULTS / "reward_curve.png"
plt.savefig(out, dpi=150)
plt.close()
print(f"[INFO] wrote {out}")
def plot_before_after() -> None:
"""Three-bar comparison: 72B baseline, 7B baseline, 7B + LoRA trained."""
baseline72 = _load_scores(BASELINE)
baseline7 = _load_scores(BASELINE_7B) if BASELINE_7B.exists() else None
trained = _load_scores(TRAINED)
tasks = list(trained.keys()) # preserve task order from trained_scores.json
b72 = [baseline72.get(t, 0.0) for t in tasks]
b7 = [baseline7.get(t, 0.0) for t in tasks] if baseline7 else None
tr = [trained.get(t, 0.0) for t in tasks]
x = np.arange(len(tasks))
n_bars = 3 if baseline7 else 2
width = 0.8 / n_bars
plt.figure(figsize=(12, 6))
if baseline7:
plt.bar(x - width, b72, width, label="Qwen2.5-72B (untrained)",
color="#6b7280")
plt.bar(x, b7, width, label="Qwen2.5-7B (untrained, same base)",
color="#9ca3af")
plt.bar(x + width, tr, width, label="Qwen2.5-7B + LoRA (GRPO-trained)",
color="#16a34a")
else:
plt.bar(x - width / 2, b72, width, label="Baseline (Qwen2.5-72B)",
color="#9ca3af")
plt.bar(x + width / 2, tr, width, label="Trained 7B + LoRA",
color="#16a34a")
plt.xticks(x, tasks, rotation=30, ha="right", fontsize=9)
plt.ylabel("Episode score (0–1)")
plt.ylim(0, 1.0)
plt.title(
"Per-task score: untrained baselines vs GRPO-trained adapter\n"
"GRPO unlocks `detect_breaking_changes` (0.01 → 0.67) — neither baseline can do it"
)
plt.legend(loc="upper right", fontsize=9)
plt.grid(alpha=0.3, axis="y")
plt.tight_layout()
out = RESULTS / "before_after.png"
plt.savefig(out, dpi=150)
plt.close()
print(f"[INFO] wrote {out}")
def main() -> None:
RESULTS.mkdir(parents=True, exist_ok=True)
plot_reward_curve()
plot_before_after()
if __name__ == "__main__":
main()
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