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"""Public Financial Dataset Pipeline & Non-IID Benchmark Dataset Generator (Section 8.1).
Prepares Non-IID financial transaction dataset splits simulating three distinct bank nodes:
- Bank A: IEEE-CIS e-commerce (~3.5% fraud rate, online merchants, European/US subnets)
- Bank B: PaySim mobile transfer (~0.13% fraud rate, peer-to-peer mobile payments)
- Bank C: Credit Card Fraud (~0.17% extreme imbalance, card-not-present/POS)
"""
from __future__ import annotations
import argparse
import json
import logging
import random
from datetime import UTC, datetime, timedelta
from pathlib import Path
from typing import Any
import numpy as np
import pandas as pd
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger("benchmark_prepare_datasets")
def generate_bank_a_ieee_cis(samples: int, seed: int = 42) -> pd.DataFrame:
"""Generate Bank A dataset (IEEE-CIS e-commerce, moderate fraud ~3.5%)."""
rng = np.random.default_rng(seed)
random.seed(seed)
n_fraud = max(1, int(samples * 0.035))
n_legit = samples - n_fraud
is_fraud = np.array([1] * n_fraud + [0] * n_legit)
rng.shuffle(is_fraud)
now = datetime.now(UTC)
timestamps = [now - timedelta(minutes=int(i * 3.5)) for i in range(samples)]
amounts = np.where(
is_fraud == 1,
rng.lognormal(mean=5.5, sigma=1.0, size=samples),
rng.lognormal(mean=4.2, sigma=0.8, size=samples),
)
mccs = np.where(
is_fraud == 1,
rng.choice(["5999", "5732", "4814"], size=samples),
rng.choice(["5411", "5812", "5912"], size=samples),
)
channels = rng.choice(["ONLINE", "MOBILE_WEB"], size=samples, p=[0.7, 0.3])
countries = rng.choice(["US", "GB", "DE", "CA"], size=samples, p=[0.5, 0.2, 0.2, 0.1])
df = pd.DataFrame(
{
"transaction_id": [f"tx_a_{i:06d}" for i in range(samples)],
"account_id": [f"acc_a_{rng.integers(1000, 5000)}" for _ in range(samples)],
"counterparty_account_id": [
f"merchant_a_{rng.integers(100, 500)}" for _ in range(samples)
],
"amount": np.round(amounts, 2),
"currency": "USD",
"timestamp": [ts.isoformat() for ts in timestamps],
"merchant_category_code": mccs,
"origin_country": countries,
"destination_country": countries,
"device_fingerprint": [f"fp_a_{rng.integers(10000, 99999)}" for _ in range(samples)],
"ip_subnet": [f"192.168.{rng.integers(1, 50)}.0/24" for _ in range(samples)],
"channel_type": channels,
"is_fraud": is_fraud,
"dataset_source": "IEEE-CIS-Simulated",
}
)
return df
def generate_bank_b_paysim(samples: int, seed: int = 43) -> pd.DataFrame:
"""Generate Bank B dataset (PaySim mobile money transfers, low fraud ~0.13%)."""
rng = np.random.default_rng(seed)
random.seed(seed)
n_fraud = max(1, int(samples * 0.0013))
n_legit = samples - n_fraud
is_fraud = np.array([1] * n_fraud + [0] * n_legit)
rng.shuffle(is_fraud)
now = datetime.now(UTC)
timestamps = [now - timedelta(minutes=int(i * 1.5)) for i in range(samples)]
amounts = np.where(
is_fraud == 1,
rng.lognormal(mean=7.0, sigma=1.5, size=samples),
rng.lognormal(mean=3.5, sigma=1.0, size=samples),
)
mccs = np.where(
is_fraud == 1,
rng.choice(["6012", "4829"], size=samples),
rng.choice(["6011", "4814"], size=samples),
)
channels = rng.choice(["MOBILE_APP", "P2P_TRANSFER"], size=samples, p=[0.8, 0.2])
countries = rng.choice(["US", "SG", "IN", "BR"], size=samples, p=[0.4, 0.3, 0.2, 0.1])
df = pd.DataFrame(
{
"transaction_id": [f"tx_b_{i:06d}" for i in range(samples)],
"account_id": [f"acc_b_{rng.integers(5000, 9000)}" for _ in range(samples)],
"counterparty_account_id": [
f"acc_b_{rng.integers(1000, 5000)}" for _ in range(samples)
],
"amount": np.round(amounts, 2),
"currency": "USD",
"timestamp": [ts.isoformat() for ts in timestamps],
"merchant_category_code": mccs,
"origin_country": countries,
"destination_country": countries,
"device_fingerprint": [f"fp_b_{rng.integers(10000, 99999)}" for _ in range(samples)],
"ip_subnet": [f"10.0.{rng.integers(1, 50)}.0/24" for _ in range(samples)],
"channel_type": channels,
"is_fraud": is_fraud,
"dataset_source": "PaySim-Simulated",
}
)
return df
def generate_bank_c_credit_card(samples: int, seed: int = 44) -> pd.DataFrame:
"""Generate Bank C dataset (Credit Card Fraud, extreme imbalance ~0.17%)."""
rng = np.random.default_rng(seed)
random.seed(seed)
n_fraud = max(1, int(samples * 0.0017))
n_legit = samples - n_fraud
is_fraud = np.array([1] * n_fraud + [0] * n_legit)
rng.shuffle(is_fraud)
now = datetime.now(UTC)
timestamps = [now - timedelta(minutes=int(i * 2.0)) for i in range(samples)]
amounts = np.where(
is_fraud == 1,
rng.lognormal(mean=6.0, sigma=1.2, size=samples),
rng.lognormal(mean=3.0, sigma=0.9, size=samples),
)
mccs = np.where(
is_fraud == 1,
rng.choice(["5541", "5812", "5999"], size=samples),
rng.choice(["5411", "5541", "5912"], size=samples),
)
channels = rng.choice(["CARD_NOT_PRESENT", "POS"], size=samples, p=[0.6, 0.4])
countries = rng.choice(["FR", "DE", "ES", "IT"], size=samples, p=[0.4, 0.3, 0.2, 0.1])
df = pd.DataFrame(
{
"transaction_id": [f"tx_c_{i:06d}" for i in range(samples)],
"account_id": [f"acc_c_{rng.integers(9000, 12000)}" for _ in range(samples)],
"counterparty_account_id": [
f"merchant_c_{rng.integers(500, 900)}" for _ in range(samples)
],
"amount": np.round(amounts, 2),
"currency": "EUR",
"timestamp": [ts.isoformat() for ts in timestamps],
"merchant_category_code": mccs,
"origin_country": countries,
"destination_country": countries,
"device_fingerprint": [f"fp_c_{rng.integers(10000, 99999)}" for _ in range(samples)],
"ip_subnet": [f"172.16.{rng.integers(1, 50)}.0/24" for _ in range(samples)],
"channel_type": channels,
"is_fraud": is_fraud,
"dataset_source": "CreditCard-Simulated",
}
)
return df
def prepare_benchmark_datasets(
samples: int = 2000, out_dir: str | Path = "storage/benchmark_datasets"
) -> dict[str, Any]:
"""Generates Non-IID benchmark transaction dataset splits and saves Parquet/CSV files."""
target_dir = Path(out_dir)
target_dir.mkdir(parents=True, exist_ok=True)
logger.info("Preparing Non-IID benchmark datasets (samples=%d)...", samples)
df_a = generate_bank_a_ieee_cis(samples)
df_b = generate_bank_b_paysim(samples)
df_c = generate_bank_c_credit_card(samples)
manifest = {
"generated_at": datetime.now(UTC).isoformat(),
"sample_count_per_bank": samples,
"banks": {
"bank_a": {
"dataset_name": "IEEE-CIS Fraud Detection (Simulated)",
"fraud_count": int(df_a["is_fraud"].sum()),
"fraud_rate_pct": float(np.round(df_a["is_fraud"].mean() * 100, 3)),
"file": "bank_a.parquet",
},
"bank_b": {
"dataset_name": "PaySim Mobile Money (Simulated)",
"fraud_count": int(df_b["is_fraud"].sum()),
"fraud_rate_pct": float(np.round(df_b["is_fraud"].mean() * 100, 3)),
"file": "bank_b.parquet",
},
"bank_c": {
"dataset_name": "Credit Card Fraud (Simulated Extreme Imbalance)",
"fraud_count": int(df_c["is_fraud"].sum()),
"fraud_rate_pct": float(np.round(df_c["is_fraud"].mean() * 100, 3)),
"file": "bank_c.parquet",
},
},
}
# Write Parquet and CSV fallbacks
for bank_key, df in [("bank_a", df_a), ("bank_b", df_b), ("bank_c", df_c)]:
parquet_path = target_dir / f"{bank_key}.parquet"
csv_path = target_dir / f"{bank_key}.csv"
try:
df.to_parquet(parquet_path, index=False)
logger.info("Saved Parquet dataset: %s (%d rows)", parquet_path, len(df))
except Exception as err:
logger.warning("Parquet write unavailable (%s) -> writing CSV fallback", err)
df.to_csv(csv_path, index=False)
logger.info("Saved CSV dataset fallback: %s (%d rows)", csv_path, len(df))
manifest_path = target_dir / "dataset_manifest.json"
manifest_path.write_text(json.dumps(manifest, indent=2), encoding="utf-8")
logger.info("Saved benchmark manifest: %s", manifest_path)
return manifest
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Prepare Non-IID Benchmark Datasets")
parser.add_argument("--samples", type=int, default=2000, help="Number of samples per bank")
parser.add_argument(
"--out-dir", type=str, default="storage/benchmark_datasets", help="Output directory"
)
args = parser.parse_args()
manifest = prepare_benchmark_datasets(samples=args.samples, out_dir=args.out_dir)
print(json.dumps(manifest, indent=2))
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