File size: 4,342 Bytes
1521ce5 | 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 | """Convert a5_user_data.json -> per-user txt, then plot token length distribution.
Usage:
python /mnt/train-gui-agent/zhangxinyuan/data_mem/output/export_and_plot_lengths.py
"""
import json
import os
import glob
import statistics
# ====== Config ======
A5_PATH = "/mnt/train-gui-agent/zhangxinyuan/data_mem/output/a5_user_data.json"
USER_TEXT_DIR = "/mnt/train-gui-agent/zhangxinyuan/data_mem/output/user_text"
MODEL_PATH = "/mnt/train-gui-agent/zhangzeyu/models/Qwen3-8B"
OUT_DIR = "/mnt/train-gui-agent/zhangxinyuan/data_mem/output/eval"
def format_session(session):
"""Format a single session as plain text."""
lines = [f"[Session: {session.get('timestamp', 'unknown')}]"]
for turn in session.get("turns", []):
content = turn.get("content")
if not content:
continue
role = turn.get("role", "unknown")
# Fix corrupted roles (e.g. "assistantWinvalid" -> "Assistant")
if role.startswith("assistant"):
role = "Assistant"
elif role.startswith("user"):
role = "User"
else:
role = role.capitalize()
lines.append(f"{role}: {content}")
return "\n".join(lines)
def step1_export_txt():
"""a5_user_data.json -> user_text/{user_id}.txt"""
print(f"[Step 1] Loading {A5_PATH}")
with open(A5_PATH, "r", encoding="utf-8") as f:
data = json.load(f)
print(f" {len(data)} users")
os.makedirs(USER_TEXT_DIR, exist_ok=True)
count = 0
for user in data:
user_id = user["user_id"]
sessions = user.get("all_sessions", [])
if not sessions:
continue
sessions_sorted = sorted(sessions, key=lambda s: s.get("timestamp", ""))
full_text = "\n\n".join(format_session(s) for s in sessions_sorted)
out_path = os.path.join(USER_TEXT_DIR, f"{user_id}.txt")
with open(out_path, "w", encoding="utf-8") as f:
f.write(full_text)
count += 1
print(f" Exported {count} user txt files -> {USER_TEXT_DIR}/")
return count
def step2_token_stats():
"""Tokenize all txt files and plot distribution."""
from transformers import AutoTokenizer
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
print(f"\n[Step 2] Loading tokenizer from {MODEL_PATH}")
tok = AutoTokenizer.from_pretrained(MODEL_PATH)
files = sorted(glob.glob(os.path.join(USER_TEXT_DIR, "*.txt")))
print(f" Tokenizing {len(files)} files...")
counts = []
for f in files:
txt = open(f, encoding="utf-8").read()
counts.append(len(tok.encode(txt, add_special_tokens=False)))
# Stats
s = sorted(counts)
n = len(s)
def pct(p):
return s[min(n - 1, int(p / 100 * n))]
stats = {
"n": n,
"min": s[0],
"max": s[-1],
"mean": round(statistics.mean(s), 1),
"median": s[n // 2],
"p10": pct(10),
"p25": pct(25),
"p75": pct(75),
"p90": pct(90),
"p95": pct(95),
"std": round(statistics.pstdev(s), 1),
}
print(f"\n === Token Length Stats ===")
for k, v in stats.items():
print(f" {k}: {v:,}" if isinstance(v, int) else f" {k}: {v}")
# Plot
os.makedirs(OUT_DIR, exist_ok=True)
fig, ax = plt.subplots(figsize=(10, 5))
ax.hist(counts, bins=40, color="#4C72B0", edgecolor="white", alpha=0.85)
ax.axvline(stats["median"], color="black", ls="--", lw=1.5,
label=f"median={stats['median']:,}")
ax.axvline(stats["mean"], color="dimgray", ls=":", lw=1.5,
label=f"mean={stats['mean']:,.0f}")
ax.set_title(f"User history token length (n={n}, Qwen3-8B tokens)\n"
f"zhangxinyuan a5_user_data")
ax.set_xlabel("tokens per user")
ax.set_ylabel("number of users")
ax.legend()
fig.tight_layout()
out_png = os.path.join(OUT_DIR, "user_history_tokens.png")
fig.savefig(out_png, dpi=130)
plt.close(fig)
print(f"\n Plot saved -> {out_png}")
# Save JSON
out_json = os.path.join(OUT_DIR, "user_history_tokens.json")
with open(out_json, "w") as f:
json.dump({"stats": stats, "raw_counts": counts}, f, indent=2)
print(f" Stats saved -> {out_json}")
if __name__ == "__main__":
step1_export_txt()
step2_token_stats()
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