src/models/model.py

import torch.nn as nn


class FraudMLP(nn.Module):
    """Small MLP baseline for flat-row fraud classification.

    Outputs a single logit per row — pair with BCEWithLogitsLoss
    (no sigmoid here; the loss applies it internally).
    """
    def __init__(self, input_dim: int, hidden: int = 64, p_drop: float = 0.3):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(input_dim, hidden),
            nn.BatchNorm1d(hidden),
            nn.ReLU(),
            nn.Dropout(p_drop),

            nn.Linear(hidden, hidden // 2),
            nn.BatchNorm1d(hidden // 2),
            nn.ReLU(),
            nn.Dropout(p_drop),

            nn.Linear(hidden // 2, 1),   # single logit
        )

    def forward(self, x):
        return self.net(x)