src/generators/user_generator.py

from __future__ import annotations

import numpy as np
import pandas as pd
from typing import Dict
from src.core.config_loader import Config


USER_TYPE_PROBS: Dict[str, float] = {
    "customer": 0.6,
    "merchant": 0.15,
    "supplier": 0.05,
    "employer": 0.1,
    "fraudster": 0.05,
    "mule": 0.05,
}

KYC_LEVELS = ["low", "medium", "full"]
KYC_PROBS = [0.2, 0.3, 0.5]

RISK_LEVELS = ["low", "medium", "high"]
RISK_PROBS = [0.6, 0.3, 0.1]


def _sample_user_types(n: int) -> np.ndarray:
    types = list(USER_TYPE_PROBS.keys())
    probs = list(USER_TYPE_PROBS.values())
    return np.random.choice(types, size=n, p=probs)


def _sample_kyc(n: int) -> np.ndarray:
    return np.random.choice(KYC_LEVELS, size=n, p=KYC_PROBS)


def _sample_risk(n: int) -> np.ndarray:
    return np.random.choice(RISK_LEVELS, size=n, p=RISK_PROBS)


def generate_users(config: Config) -> pd.DataFrame:
    n = config.num_users
    p = config.user_params

    user_ids = np.arange(n)

    # Transaction frequency (λ_u) ~ LogNormal
    lambda_u = np.random.lognormal(
        mean=np.log(p.lambda_mean),
        sigma=p.lambda_std,
        size=n
    )

    # Amount distribution parameters
    mu_u = np.random.normal(
        loc=p.mu_mean,
        scale=p.mu_std,
        size=n
    )

    sigma_u = np.random.uniform(
        low=max(1e-6, p.sigma_mean - p.sigma_std),
        high=p.sigma_mean + p.sigma_std,
        size=n
    )

    # Ensure strictly positive
    lambda_u = np.clip(lambda_u, 1e-6, None)
    sigma_u = np.clip(sigma_u, 1e-6, None)

    # Balance ~ LogNormal
    balance = np.random.lognormal(mean=10.0, sigma=1.0, size=n)

    user_type = _sample_user_types(n)
    kyc_level = _sample_kyc(n)
    risk_profile = _sample_risk(n)

    df = pd.DataFrame({
        "user_id": user_ids,
        "user_type": user_type,
        "lambda_u": lambda_u,
        "mu_u": mu_u,
        "sigma_u": sigma_u,
        "balance": balance,
        "kyc_level": kyc_level,
        "risk_profile": risk_profile,
    })

    # Basic validation checks
    if df.isnull().any().any():
        raise ValueError("NaNs detected in generated users")

    if (df["lambda_u"] <= 0).any():
        raise ValueError("Invalid lambda_u values")

    if (df["sigma_u"] <= 0).any():
        raise ValueError("Invalid sigma_u values")

    return df