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scripts/convert_grpo_csv.py
Converts GRPO training CSV logs to JSON format
for the FastAPI /training/* story endpoints.
CSV format expected:
step, reward, fn1_valid, fn2_no_halluc, fn3_env_score
Usage:
python scripts/convert_grpo_csv.py \
--csv grpo_training_log.csv \
--task mixed_urgency_medium
Output:
data/training_logs/{task_id}_training_log.json
"""
from __future__ import annotations
import csv
import json
import argparse
from pathlib import Path
def load_csv(csv_path: str) -> list[dict]:
rows = []
with open(csv_path, newline="", encoding="utf-8") as f:
reader = csv.DictReader(f)
fieldnames = set(reader.fieldnames or [])
reward_values: list[float] = []
raw_rows: list[dict] = []
def _pick(row: dict, names: list[str], default: float) -> float:
for name in names:
if name in row and str(row.get(name, "")).strip() != "":
try:
return float(row[name])
except (TypeError, ValueError):
continue
return float(default)
for row in reader:
raw_rows.append(row)
reward_values.append(_pick(row, ["reward", "total_reward"], 0.0))
r_min = min(reward_values) if reward_values else 0.0
r_rng = (max(reward_values) - r_min) if reward_values else 1.0
if r_rng == 0:
r_rng = 1.0
for i, row in enumerate(raw_rows):
reward_val = _pick(row, ["reward", "total_reward"], 0.0)
fallback_norm = (reward_val - r_min) / r_rng
step_default = i + 1
if "step" in row and str(row.get("step", "")).strip() != "":
try:
step_default = int(float(row["step"]))
except (TypeError, ValueError):
step_default = i + 1
rows.append({
"step": step_default,
"reward": reward_val,
"fn1_valid": _pick(row, ["fn1_valid", "valid_action_rate"], 1.0),
"fn2_no_halluc": _pick(row, ["fn2_no_halluc", "hallucination_free"], 1.0),
"fn3_env_score": _pick(row, ["fn3_env_score", "env_score"], fallback_norm),
})
return rows
def build_log(rows: list[dict], task_id: str) -> dict:
n = len(rows)
rewards = [r["reward"] for r in rows]
fn1_vals = [r["fn1_valid"] for r in rows]
fn2_vals = [r["fn2_no_halluc"] for r in rows]
fn3_vals = [r["fn3_env_score"] for r in rows]
fn3_min = min(fn3_vals)
fn3_rng = (max(fn3_vals) - fn3_min) or 1.0
episodes = []
for i, r in enumerate(rows):
norm_env = (r["fn3_env_score"] - fn3_min) / fn3_rng
combined = round(
r["fn1_valid"] * 0.3 + r["fn2_no_halluc"] * 0.2 + norm_env * 0.5,
4
)
phase = (
"random" if i < n * 0.25 else
"exploring" if i < n * 0.50 else
"learning" if i < n * 0.75 else
"converged"
)
episodes.append({
"episode": r["step"],
"total_reward": round(r["reward"], 4),
"score": combined,
"fn1_valid": round(r["fn1_valid"], 4),
"fn2_no_halluc": round(r["fn2_no_halluc"], 4),
"fn3_env_score": round(r["fn3_env_score"], 4),
"phase": phase,
"actions": {
"valid_action_rate": round(r["fn1_valid"], 4),
"hallucination_free": round(r["fn2_no_halluc"], 4),
"env_score": round(norm_env, 4),
},
})
scores = [e["score"] for e in episodes]
return {
"task_id": task_id,
"total_episodes": n,
"base_model": "Qwen/Qwen2-1.5B-Instruct",
"adapter_path": f"artifacts/llm/{task_id.split('_')[1]}/",
"training_method": "GRPO",
"lora_rank": 16,
"reward_functions": {
"fn1_valid": "Action validity - legal JSON action output (0->1)",
"fn2_no_halluc": "No hallucination - stayed on gov workflow topic (0->1)",
"fn3_env_score": "Environment score - improved gov workflow quality",
},
"summary": {
"first_episode_reward": round(rewards[0], 4),
"last_episode_reward": round(rewards[-1], 4),
"best_episode_reward": round(max(rewards), 4),
"first_episode_score": round(scores[0], 4),
"last_episode_score": round(scores[-1], 4),
"best_episode_score": round(max(scores), 4),
"reward_improvement_pct": round(
((rewards[-1] - rewards[0]) / abs(rewards[0])) * 100, 2
) if rewards[0] != 0 else 0.0,
"invalid_action_steps": sum(1 for r in rows if r["fn1_valid"] < 1.0),
"hallucination_steps": sum(1 for r in rows if r["fn2_no_halluc"] < 1.0),
"avg_fn1_valid": round(sum(fn1_vals) / n, 4),
"avg_fn2_no_halluc": round(sum(fn2_vals) / n, 4),
"avg_fn3_env_score": round(sum(fn3_vals) / n, 4),
},
"episodes": episodes,
}
def save_log(log: dict, out_dir: str) -> str:
Path(out_dir).mkdir(parents=True, exist_ok=True)
out_path = f"{out_dir}/{log['task_id']}_training_log.json"
with open(out_path, "w", encoding="utf-8") as f:
json.dump(log, f, indent=2)
return out_path
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--csv", required=True, help="Path to GRPO CSV file")
parser.add_argument("--task", required=True, help="Task ID e.g. mixed_urgency_medium")
parser.add_argument("--out", default="data/training_logs", help="Output directory")
args = parser.parse_args()
print(f"Reading CSV : {args.csv}")
rows = load_csv(args.csv)
print(f"Steps found : {len(rows)}")
log = build_log(rows, args.task)
out = save_log(log, args.out)
print(f"Saved JSON : {out}")
print(f"Steps : {log['total_episodes']}")
print(f"Reward range : {log['summary']['first_episode_reward']} -> {log['summary']['last_episode_reward']}")
print(f"Score range : {log['summary']['first_episode_score']} -> {log['summary']['last_episode_score']}")
print(f"Invalid steps: {log['summary']['invalid_action_steps']}")
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