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fcdb8dd | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 | """Generate training evidence plots from baseline and training artifacts."""
import json
import sys
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
ARTIFACTS_DIR = Path("artifacts")
PLOTS_DIR = ARTIFACTS_DIR / "plots"
PLOTS_DIR.mkdir(parents=True, exist_ok=True)
def load_baseline(task: str) -> dict | None:
path = ARTIFACTS_DIR / f"baseline_{task}.json"
if not path.exists():
return None
with open(path) as f:
return json.load(f)
def load_training_results() -> list[dict]:
results = []
grpo_dir = ARTIFACTS_DIR / "grpo"
if not grpo_dir.exists():
return results
for subdir in grpo_dir.iterdir():
if subdir.is_dir():
result_file = subdir / "results.json"
if result_file.exists():
with open(result_file) as f:
data = json.load(f)
if data.get("status") != "not_run":
results.append(data)
return results
def plot_reward_curve(baselines: dict[str, dict], training: list[dict]) -> None:
fig, ax = plt.subplots(figsize=(10, 6))
if training:
episodes = list(range(1, len(training) + 1))
rewards = [r.get("mean_reward", 0) for r in training]
ax.plot(episodes, rewards, "o-", color="green", linewidth=2, label="Training Reward")
ax.fill_between(
episodes,
[r - 0.1 for r in rewards],
[r + 0.1 for r in rewards],
alpha=0.2,
color="green",
)
else:
ax.text(
0.5,
0.5,
"No training data available\nRun training to generate reward curves",
transform=ax.transAxes,
ha="center",
va="center",
fontsize=14,
color="gray",
)
ax.set_xlabel("Training Episode / Checkpoint")
ax.set_ylabel("Mean Eval Reward")
ax.set_title("Training Reward Curve")
ax.grid(True, alpha=0.3)
if training:
ax.legend()
fig.savefig(PLOTS_DIR / "reward_curve.png", dpi=300, bbox_inches="tight")
plt.close(fig)
def plot_grader_score_curve(baselines: dict[str, dict], training: list[dict]) -> None:
fig, ax = plt.subplots(figsize=(10, 6))
if training:
episodes = list(range(1, len(training) + 1))
scores = [r.get("mean_grader_score", 0) for r in training]
ax.plot(episodes, scores, "o-", color="blue", linewidth=2, label="Grader Score")
ax.fill_between(
episodes,
[max(0, s - 0.05) for s in scores],
[min(1, s + 0.05) for s in scores],
alpha=0.2,
color="blue",
)
else:
ax.text(
0.5,
0.5,
"No training data available\nRun training to generate grader score curves",
transform=ax.transAxes,
ha="center",
va="center",
fontsize=14,
color="gray",
)
ax.set_xlabel("Training Episode / Checkpoint")
ax.set_ylabel("Mean Grader Score")
ax.set_title("Training Grader Score Curve")
ax.set_ylim(0, 1)
ax.grid(True, alpha=0.3)
if training:
ax.legend()
fig.savefig(PLOTS_DIR / "grader_curve.png", dpi=300, bbox_inches="tight")
plt.close(fig)
def plot_before_after(baselines: dict[str, dict]) -> None:
tasks = ["easy", "medium", "hard"]
baseline_scores = []
trained_scores = []
labels = []
for task in tasks:
if task in baselines:
baseline_scores.append(baselines[task]["summary"]["mean_grader_score"])
trained_scores.append(0.0) # placeholder
labels.append(task.capitalize())
if not labels:
fig, ax = plt.subplots(figsize=(8, 6))
ax.text(
0.5,
0.5,
"No baseline data available",
transform=ax.transAxes,
ha="center",
va="center",
fontsize=14,
color="gray",
)
fig.savefig(PLOTS_DIR / "before_after.png", dpi=300, bbox_inches="tight")
plt.close(fig)
return
x = np.arange(len(labels))
width = 0.35
fig, ax = plt.subplots(figsize=(8, 6))
bars1 = ax.bar(x - width / 2, baseline_scores, width, label="Baseline", color="#e74c3c")
bars2 = ax.bar(x + width / 2, trained_scores, width, label="Trained (TBD)", color="#2ecc71")
ax.set_ylabel("Mean Grader Score")
ax.set_title("Before vs After: Baseline vs Trained Agent")
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.set_ylim(0, 1)
ax.legend()
ax.grid(True, alpha=0.3, axis="y")
# Add value labels on bars
for bar in bars1:
height = bar.get_height()
ax.annotate(
f"{height:.3f}",
xy=(bar.get_x() + bar.get_width() / 2, height),
xytext=(0, 3),
textcoords="offset points",
ha="center",
va="bottom",
fontsize=9,
)
fig.savefig(PLOTS_DIR / "before_after.png", dpi=300, bbox_inches="tight")
plt.close(fig)
def plot_per_task_comparison(baselines: dict[str, dict]) -> None:
tasks = ["easy", "medium", "hard"]
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
for idx, task in enumerate(tasks):
ax = axes[idx]
if task in baselines:
episodes = baselines[task]["episodes"]
scores = [e["grader_score"] for e in episodes]
rewards = [e["total_reward"] for e in episodes]
ax.scatter(scores, rewards, alpha=0.6, s=50)
ax.set_xlabel("Grader Score")
ax.set_ylabel("Total Reward")
ax.set_title(f"{task.capitalize()} Task")
ax.grid(True, alpha=0.3)
else:
ax.text(
0.5,
0.5,
"No data",
transform=ax.transAxes,
ha="center",
va="center",
fontsize=12,
color="gray",
)
ax.set_title(f"{task.capitalize()} Task")
fig.suptitle("Per-Task Baseline Distribution: Grader Score vs Total Reward")
fig.tight_layout()
fig.savefig(PLOTS_DIR / "per_task_comparison.png", dpi=300, bbox_inches="tight")
plt.close(fig)
def main():
print("Loading baseline data...", file=sys.stderr)
baselines = {}
for task in ["easy", "medium", "hard"]:
data = load_baseline(task)
if data:
baselines[task] = data
print(f" {task}: {len(data['episodes'])} episodes", file=sys.stderr)
print("Loading training data...", file=sys.stderr)
training = load_training_results()
print(f" Found {len(training)} training result files", file=sys.stderr)
print("Generating plots...", file=sys.stderr)
plot_reward_curve(baselines, training)
plot_grader_score_curve(baselines, training)
plot_before_after(baselines)
plot_per_task_comparison(baselines)
for png in sorted(PLOTS_DIR.glob("*.png")):
print(f" {png.name}: {png.stat().st_size / 1024:.1f} KB", file=sys.stderr)
print("Done.", file=sys.stderr)
if __name__ == "__main__":
main()
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