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run_cache.py
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run_cache.py
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import importlib.util
import os
import time
from datetime import datetime
from functools import partial
global_st = time.time()
if importlib.util.find_spec("src") is None:
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import dask.dataframe as dd
import pandas as pd
from dotenv import load_dotenv
from sqlalchemy import create_engine
if load_dotenv():
print(".env loaded")
from src.campaign import CAMPAIGNS, get_checkpoint_dates
from src.constant import Chain
from src.erc20 import Token
from src.process_queue import TABLE_NAME_MAPPER
from src.utils import print_log
ROOT_DIR = os.getenv("ROOT_DIR", "/home/ubuntu")
MIN_VALUE = 1e-7
LATEST_DATE = None
CHAINS = [
Chain.ARBITRUM_ONE,
Chain.POLYGON,
Chain.OPTIMISM,
Chain.BNB_CHAIN,
Chain.GNOSIS
]
def resolve_action(action: int) -> str:
action = int(action)
if action == 1:
return "mint"
elif action == 2:
return "burn"
elif action == 3:
return "transfer_in"
elif action == 4:
return "transfer_out"
def load_dataset(chain: Chain):
cnx = create_engine(
f"mysql+pymysql://{os.getenv('AWS_RDS_USERNAME')}:{os.getenv('AWS_RDS_PASSWORD')}@{os.getenv('AWS_RDS_HOSTNAME')}/connext")
dataset = []
table_name = TABLE_NAME_MAPPER.get(chain)
df = pd.read_sql(
f"SELECT * FROM {table_name}",
cnx
)
df["datetime"] = df["timestamp"].copy().map(datetime.fromtimestamp)
df["action"] = df["action"].copy().map(resolve_action)
df["chain"] = df["chain"].copy().map(Chain.resolve_connext_domain)
df["token"] = df["token_address"].map(
lambda x: Token.address_lookup(x, chain=chain).symbol
)
df = df.drop([
"id",
"timestamp",
], axis=1).drop_duplicates()
df = df[
["datetime", "chain", "hash", "user_address", "token", "token_amount", "action"]
]
dataset.append(df)
dataset = pd.concat(dataset, axis=0)
dataset = dataset.set_index("datetime").sort_index()
dataset["balance_change"] = dataset["token_amount"].copy() \
* dataset["action"].copy().map(
lambda x: 1 if x in ["mint", "transfer_in"] else -1
)
return dataset
def save_cache(df: pd.DataFrame, filename: str):
os.makedirs(os.path.dirname(filename), exist_ok=True)
df.to_csv(filename)
def calculate_average_balance_by_minute(
user_balance: pd.Series,
start_time: datetime,
end_time: datetime,
) -> float:
"""
Calculate average user balance by minute
"""
temp = pd.Series(data=[0., 0.], index=[start_time, end_time], name="balance_change")
resampled_score = pd.concat(
[user_balance, temp]
).sort_index().cumsum().resample("T").last().ffill()[start_time:end_time]
score = resampled_score.mean()
minutes_qualified = int((resampled_score.apply(
lambda x: 0 if x < MIN_VALUE else x
) > 0).sum())
del resampled_score
return score, minutes_qualified
def main() -> None:
print_log(f"process started in {time.time() - global_st:.2f} seconds")
all_chains_data = []
for chain in CHAINS:
# Load dataset from RDS
print_log(f"loading dataset for {Chain.resolve_connext_domain(chain)}...")
st = time.time()
dataset = load_dataset(chain)
all_chains_data.append(dataset)
# get latest date from each chaind database
global LATEST_DATE
LATEST_DATE = dataset.index.max()
print_log(f"{len(dataset)} data loaded")
print_log(f"dataset loaded in {time.time() - st:.2f} seconds")
for campaign_name, campaign in CAMPAIGNS.items():
# if campaign already ended, skip
if campaign["end"] < datetime.now():
print_log(f"{campaign_name} already ended, skip caching...")
continue
print_log(f"processing {campaign_name}...")
checkpoint_dates = get_checkpoint_dates(
campaign_name=campaign_name,
latest_date=LATEST_DATE)
if checkpoint_dates is None:
print_log(f"no checkpoint dates for {campaign_name}, skipping...")
continue
print_log(f"Campaign checkpoint dates: {checkpoint_dates['start']} - {checkpoint_dates['end']}")
# compute user scores of each campaign
st = time.time()
dask_dataset = dd.from_pandas(dataset[dataset["token"].isin(campaign["tokens"])], npartitions=5)
groupby_list = ["user_address", "token"]
user_scores = dask_dataset.groupby(groupby_list)["balance_change"].apply(
partial(
calculate_average_balance_by_minute,
start_time=checkpoint_dates["start"],
end_time=checkpoint_dates["end"],
),
meta=("balance_change", float)
).compute()
print_log(f"compute user scores: {time.time() - st:.2f} seconds")
user_scores = pd.DataFrame(user_scores.tolist(), index=user_scores.index, columns=["score", "minutes_qualified"])
st = time.time()
save_cache(
user_scores,
f"{ROOT_DIR}/cache/{campaign_name}/{Chain.resolve_connext_domain(chain)}_user_scores.csv")
print_log(f"cache user scores: {time.time() - st:.2f} seconds")
print_log(f"cache for {Chain.resolve_connext_domain(chain)} completed")
all_chains_data = pd.concat(all_chains_data, axis=0).to_csv(f"{ROOT_DIR}/cache/full_dataset.csv")
print_log(f"total time: {time.time() - global_st:.2f} seconds")
if __name__ == "__main__":
main()