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import argparse
import code
import datetime
import json
import os
from pytz import timezone
import time
import pandas as pd # pandas>=2.0.3
import plotly.express as px
import plotly.graph_objects as go
from tqdm import tqdm
NUM_SERVERS = 14
LOG_ROOT_DIR = "~/fastchat_logs"
def get_log_files(max_num_files=None):
log_root = os.path.expanduser(LOG_ROOT_DIR)
filenames = []
for i in range(NUM_SERVERS):
for filename in os.listdir(f"{log_root}/server{i}"):
if filename.endswith("-conv.json"):
filepath = f"{log_root}/server{i}/{filename}"
name_tstamp_tuple = (filepath, os.path.getmtime(filepath))
filenames.append(name_tstamp_tuple)
# sort by tstamp
filenames = sorted(filenames, key=lambda x: x[1])
filenames = [x[0] for x in filenames]
max_num_files = max_num_files or len(filenames)
filenames = filenames[-max_num_files:]
return filenames
def load_log_files(filename):
data = []
for retry in range(5):
try:
lines = open(filename).readlines()
break
except FileNotFoundError:
time.sleep(2)
for l in lines:
row = json.loads(l)
data.append(
dict(
type=row["type"],
tstamp=row["tstamp"],
model=row.get("model", ""),
models=row.get("models", ["", ""]),
)
)
return data
def load_log_files_parallel(log_files, num_threads=16):
data_all = []
from multiprocessing import Pool
with Pool(num_threads) as p:
ret_all = list(tqdm(p.imap(load_log_files, log_files), total=len(log_files)))
for ret in ret_all:
data_all.extend(ret)
return data_all
def get_anony_vote_df(df):
anony_vote_df = df[
df["type"].isin(["leftvote", "rightvote", "tievote", "bothbad_vote"])
]
anony_vote_df = anony_vote_df[anony_vote_df["models"].apply(lambda x: x[0] == "")]
return anony_vote_df
def merge_counts(series, on, names):
ret = pd.merge(series[0], series[1], on=on)
for i in range(2, len(series)):
ret = pd.merge(ret, series[i], on=on)
ret = ret.reset_index()
old_names = list(ret.columns)[-len(series) :]
rename = {old_name: new_name for old_name, new_name in zip(old_names, names)}
ret = ret.rename(columns=rename)
return ret
def report_basic_stats(log_files):
df_all = load_log_files_parallel(log_files)
df_all = pd.DataFrame(df_all)
now_t = df_all["tstamp"].max()
df_1_hour = df_all[df_all["tstamp"] > (now_t - 3600)]
df_1_day = df_all[df_all["tstamp"] > (now_t - 3600 * 24)]
anony_vote_df_all = get_anony_vote_df(df_all)
# Chat trends
chat_dates = [
datetime.datetime.fromtimestamp(x, tz=timezone("US/Pacific")).strftime(
"%Y-%m-%d"
)
for x in df_all[df_all["type"] == "chat"]["tstamp"]
]
chat_dates_counts = pd.value_counts(chat_dates)
vote_dates = [
datetime.datetime.fromtimestamp(x, tz=timezone("US/Pacific")).strftime(
"%Y-%m-%d"
)
for x in anony_vote_df_all["tstamp"]
]
vote_dates_counts = pd.value_counts(vote_dates)
chat_dates_bar = go.Figure(
data=[
go.Bar(
name="Anony. Vote",
x=vote_dates_counts.index,
y=vote_dates_counts,
text=[f"{val:.0f}" for val in vote_dates_counts],
textposition="auto",
),
go.Bar(
name="Chat",
x=chat_dates_counts.index,
y=chat_dates_counts,
text=[f"{val:.0f}" for val in chat_dates_counts],
textposition="auto",
),
]
)
chat_dates_bar.update_layout(
barmode="stack",
xaxis_title="Dates",
yaxis_title="Count",
height=300,
width=1200,
)
# Model call counts
model_hist_all = df_all[df_all["type"] == "chat"]["model"].value_counts()
model_hist_1_day = df_1_day[df_1_day["type"] == "chat"]["model"].value_counts()
model_hist_1_hour = df_1_hour[df_1_hour["type"] == "chat"]["model"].value_counts()
model_hist = merge_counts(
[model_hist_all, model_hist_1_day, model_hist_1_hour],
on="model",
names=["All", "Last Day", "Last Hour"],
)
model_hist_md = model_hist.to_markdown(index=False, tablefmt="github")
# Action counts
action_hist_all = df_all["type"].value_counts()
action_hist_1_day = df_1_day["type"].value_counts()
action_hist_1_hour = df_1_hour["type"].value_counts()
action_hist = merge_counts(
[action_hist_all, action_hist_1_day, action_hist_1_hour],
on="type",
names=["All", "Last Day", "Last Hour"],
)
action_hist_md = action_hist.to_markdown(index=False, tablefmt="github")
# Anony vote counts
anony_vote_hist_all = anony_vote_df_all["type"].value_counts()
anony_vote_df_1_day = get_anony_vote_df(df_1_day)
anony_vote_hist_1_day = anony_vote_df_1_day["type"].value_counts()
# anony_vote_df_1_hour = get_anony_vote_df(df_1_hour)
# anony_vote_hist_1_hour = anony_vote_df_1_hour["type"].value_counts()
anony_vote_hist = merge_counts(
[anony_vote_hist_all, anony_vote_hist_1_day],
on="type",
names=["All", "Last Day"],
)
anony_vote_hist_md = anony_vote_hist.to_markdown(index=False, tablefmt="github")
# Last 24 hours
chat_1_day = df_1_day[df_1_day["type"] == "chat"]
num_chats_last_24_hours = []
base = df_1_day["tstamp"].min()
for i in range(24, 0, -1):
left = base + (i - 1) * 3600
right = base + i * 3600
num = ((chat_1_day["tstamp"] >= left) & (chat_1_day["tstamp"] < right)).sum()
num_chats_last_24_hours.append(num)
times = [
datetime.datetime.fromtimestamp(
base + i * 3600, tz=timezone("US/Pacific")
).strftime("%Y-%m-%d %H:%M:%S %Z")
for i in range(24, 0, -1)
]
last_24_hours_df = pd.DataFrame({"time": times, "value": num_chats_last_24_hours})
last_24_hours_md = last_24_hours_df.to_markdown(index=False, tablefmt="github")
# Last update datetime
last_updated_tstamp = now_t
last_updated_datetime = datetime.datetime.fromtimestamp(
last_updated_tstamp, tz=timezone("US/Pacific")
).strftime("%Y-%m-%d %H:%M:%S %Z")
# code.interact(local=locals())
return {
"chat_dates_bar": chat_dates_bar,
"model_hist_md": model_hist_md,
"action_hist_md": action_hist_md,
"anony_vote_hist_md": anony_vote_hist_md,
"num_chats_last_24_hours": last_24_hours_md,
"last_updated_datetime": last_updated_datetime,
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--max-num-files", type=int)
args = parser.parse_args()
log_files = get_log_files(args.max_num_files)
basic_stats = report_basic_stats(log_files)
print(basic_stats["action_hist_md"] + "\n")
print(basic_stats["model_hist_md"] + "\n")
print(basic_stats["anony_vote_hist_md"] + "\n")
print(basic_stats["num_chats_last_24_hours"] + "\n")
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