| """ |
| The GRU, copied verbatim from the training repository so the Space is |
| self-contained. Architecture must match the checkpoint exactly. |
| |
| Source: https://github.com/adwitiyashukla/DL-based-sequential-fraud-detection |
| """ |
|
|
| from __future__ import annotations |
|
|
| import torch |
| from torch import nn |
|
|
|
|
| class GRUFraudModel(nn.Module): |
| """ |
| Per timestep the model sees a learned embedding of the merchant category |
| and a linear projection of the standardised numeric features. Those are |
| concatenated and fed to a single layer GRU. The hidden state at the final |
| timestep, which is the transaction being scored, goes through dropout and a |
| linear head to one logit. |
| """ |
|
|
| def __init__( |
| self, |
| n_numeric: int, |
| n_categories: int, |
| emb_dim: int = 16, |
| proj_dim: int = 32, |
| hidden: int = 64, |
| dropout: float = 0.2, |
| ) -> None: |
| super().__init__() |
| self.cat_emb = nn.Embedding(n_categories + 1, emb_dim, padding_idx=0) |
| self.num_proj = nn.Linear(n_numeric, proj_dim) |
| self.gru = nn.GRU( |
| input_size=proj_dim + emb_dim, |
| hidden_size=hidden, |
| num_layers=1, |
| batch_first=True, |
| ) |
| self.dropout = nn.Dropout(dropout) |
| self.head = nn.Linear(hidden, 1) |
|
|
| def forward(self, x_num: torch.Tensor, x_cat: torch.Tensor) -> torch.Tensor: |
| emb = self.cat_emb(x_cat) |
| proj = torch.relu(self.num_proj(x_num)) |
| out, _ = self.gru(torch.cat([proj, emb], dim=-1)) |
| return self.head(self.dropout(out[:, -1, :])).squeeze(-1) |
|
|
