Implement DelphiBERT model and data preparation scripts for tabular time series analysis
- Added `model.py` containing the DelphiBERT architecture, including TabularEncoder and AutoDiscretization classes for handling tabular features. - Introduced `prepare_data.R` for merging disease and other data from UK Biobank, ensuring proper column selection and data integrity. - Created `prepare_data.py` to process UK Biobank data, including mapping field IDs, handling date variables, and preparing tabular features and event data for model training.
This commit is contained in:
376
model.py
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376
model.py
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import numpy as np
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from typing import Optional, List
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from backbones import Block
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from age_encoder import AgeSinusoidalEncoder, AgeMLPEncoder
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import torch.nn.functional as F
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import torch.nn as nn
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import torch
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class TabularEncoder(nn.Module):
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"""
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Encoder for tabular features (continuous and categorical).
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Args:
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n_embd (int): Embedding dimension.
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n_cont (int): Number of continuous features.
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n_cate (int): Number of categorical features.
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cate_dims (List[int]): List of dimensions for each categorical feature.
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n_bins (int): Number of soft bins for continuous AutoDiscretization.
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"""
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def __init__(
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self,
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n_embd: int,
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n_cont: int,
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n_cate: int,
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cate_dims: List[int],
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n_bins: int = 16,
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):
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super().__init__()
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self.n_embd = n_embd
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self.n_cont = n_cont
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self.n_cate = n_cate
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# Continuous feature path
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# - BatchNorm on raw (NaN-filled) continuous values
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# - AutoDiscretization (soft binning) per feature
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if n_cont > 0:
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self.cont_bn = nn.BatchNorm1d(n_cont)
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self.cont_discretizer = AutoDiscretization(
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n_features=n_cont,
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n_bins=n_bins,
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n_embd=n_embd,
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)
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else:
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self.cont_bn = None
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self.cont_discretizer = None
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if n_cate > 0:
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assert len(cate_dims) == n_cate, \
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"Length of cate_dims must match n_cate"
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self.cate_embds = nn.ModuleList([
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nn.Embedding(dim, n_embd) for dim in cate_dims
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])
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self.cate_mask_embds = nn.ModuleList([
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nn.Embedding(2, n_embd) for _ in range(n_cate)
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])
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else:
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self.cate_embds = None
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self.cate_mask_embds = None
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self.cont_mask_proj = (
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nn.Linear(n_cont, n_embd) if n_cont > 0 else None
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)
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# Fuse aggregated value + aggregated mask via MLP
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self.fuse_mlp = nn.Sequential(
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nn.Linear(2 * n_embd, 2 * n_embd),
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nn.GELU(),
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nn.Linear(2 * n_embd, n_embd),
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)
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self.apply(self._init_weights)
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self.out_ln = nn.LayerNorm(n_embd)
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def _init_weights(self, module):
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if isinstance(module, nn.Linear):
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torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
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if module.bias is not None:
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torch.nn.init.zeros_(module.bias)
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elif isinstance(module, nn.Embedding):
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torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
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def forward(
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self,
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cont_features: Optional[torch.Tensor],
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cate_features: Optional[torch.Tensor],
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) -> torch.Tensor:
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"""Encode tabular features into a per-timestep embedding.
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Inputs:
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cont_features: (B, L, n_cont) float tensor; NaN indicates missing.
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cate_features: (B, L, n_cate) long/int tensor; 0 indicates missing/pad.
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Returns:
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(B, L, n_embd) encoded embedding.
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"""
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if self.n_cont == 0 and self.n_cate == 0:
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# infer (B, L) from whichever input is not None
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if cont_features is not None:
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B, L = cont_features.shape[:2]
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device = cont_features.device
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elif cate_features is not None:
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B, L = cate_features.shape[:2]
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device = cate_features.device
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else:
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raise ValueError(
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"TabularEncoder received no features but cannot infer (B, L)."
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)
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return torch.zeros(B, L, self.n_embd, device=device)
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value_parts: List[torch.Tensor] = []
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mask_parts: List[torch.Tensor] = []
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if self.n_cont > 0 and cont_features is not None:
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if cont_features.dim() != 3:
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raise ValueError(
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"cont_features must be 3D tensor (B, L, n_cont)")
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B, L, D_cont = cont_features.shape
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if D_cont != self.n_cont:
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raise ValueError(
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f"Expected cont_features last dim to be {self.n_cont}, got {D_cont}")
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# Missingness mask: 1 for valid, 0 for missing
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cont_mask = (~torch.isnan(cont_features)).float() # (B, L, n_cont)
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# BatchNorm cannot handle NaNs; fill missing with 0 before BN.
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cont_filled = torch.nan_to_num(
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cont_features, nan=0.0) # (B, L, n_cont)
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# Apply BN over the feature dimension: (B, L, C) -> (B*L, C) -> (B, L, C)
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cont_flat = cont_filled.reshape(-1, self.n_cont)
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cont_norm_flat = self.cont_bn(cont_flat) # (B*L, n_cont)
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# Soft-binning per feature: (B*L, n_cont) -> (B*L, n_cont, n_embd)
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cont_emb_flat = self.cont_discretizer(cont_norm_flat)
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cont_emb = cont_emb_flat.view(B, L, self.n_cont, self.n_embd)
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# Mask-out missing continuous features before aggregating across features
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# (B, L, n_cont, n_embd)
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cont_emb = cont_emb * cont_mask.unsqueeze(-1)
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denom = cont_mask.sum(
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dim=-1, keepdim=True).clamp(min=1.0) # (B, L, 1)
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h_cont_value = cont_emb.sum(dim=2) / denom # (B, L, n_embd)
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value_parts.append(h_cont_value)
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# Explicit continuous mask embedding (fused later)
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if self.cont_mask_proj is not None:
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h_cont_mask = self.cont_mask_proj(cont_mask) # (B, L, n_embd)
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mask_parts.append(h_cont_mask)
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if self.n_cate > 0 and cate_features is not None:
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if cate_features.dim() != 3:
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raise ValueError(
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"cate_features must be 3D tensor (B, L, n_cate)")
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B, L, D_cate = cate_features.shape
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if D_cate != self.n_cate:
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raise ValueError(
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f"Expected cate_features last dim to be {self.n_cate}, got {D_cate}")
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for i in range(self.n_cate):
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cate_feat = cate_features[:, :, i]
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cate_embd = self.cate_embds[i]
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cate_mask_embd = self.cate_mask_embds[i]
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cate_value = cate_embd(
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torch.clamp(cate_feat, min=0))
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cate_mask = (cate_feat > 0).long()
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cate_mask_value = cate_mask_embd(cate_mask)
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value_parts.append(cate_value)
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mask_parts.append(cate_mask_value)
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if not value_parts:
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if cont_features is not None:
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B, L = cont_features.shape[:2]
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device = cont_features.device
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elif cate_features is not None:
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B, L = cate_features.shape[:2]
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device = cate_features.device
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else:
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raise ValueError("No features provided to TabularEncoder.")
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return torch.zeros(B, L, self.n_embd, device=device)
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# Aggregate across feature groups (continuous block counts as one part;
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# each categorical feature counts as one part).
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h_value = torch.stack(value_parts, dim=0).mean(dim=0) # (B, L, n_embd)
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if mask_parts:
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h_mask = torch.stack(mask_parts, dim=0).mean(
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dim=0) # (B, L, n_embd)
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else:
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h_mask = torch.zeros_like(h_value)
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# Fuse by concatenation + MLP projection
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h_fused = torch.cat([h_value, h_mask], dim=-1) # (B, L, 2*n_embd)
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h_out = self.fuse_mlp(h_fused) # (B, L, n_embd)
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h_out = self.out_ln(h_out)
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return h_out
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class AutoDiscretization(nn.Module):
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"""AutoDiscretization / soft-binning for continuous tabular scalars.
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For each feature scalar $x$, compute a soft assignment over `n_bins`:
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p = softmax(x * w + b)
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Then compute the embedding as a weighted sum of learnable bin embeddings:
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emb = sum_k p_k * E_k
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Shapes:
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Input: (N, n_features)
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Output: (N, n_features, n_embd)
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"""
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def __init__(self, n_features: int, n_bins: int, n_embd: int):
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super().__init__()
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if n_features <= 0:
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raise ValueError("n_features must be > 0")
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if n_bins <= 1:
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raise ValueError("n_bins must be > 1")
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if n_embd <= 0:
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raise ValueError("n_embd must be > 0")
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self.n_features = n_features
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self.n_bins = n_bins
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self.n_embd = n_embd
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# Per-feature, per-bin affine transform to produce logits
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self.weight = nn.Parameter(torch.empty(n_features, n_bins))
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self.bias = nn.Parameter(torch.empty(n_features, n_bins))
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# Learnable embeddings for each (feature, bin)
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self.bin_emb = nn.Parameter(torch.empty(n_features, n_bins, n_embd))
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self.reset_parameters()
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def reset_parameters(self) -> None:
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nn.init.normal_(self.weight, mean=0.0, std=0.02)
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nn.init.zeros_(self.bias)
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nn.init.normal_(self.bin_emb, mean=0.0, std=0.02)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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if x.dim() != 2:
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raise ValueError(
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"AutoDiscretization expects input of shape (N, n_features)")
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if x.size(1) != self.n_features:
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raise ValueError(
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f"Expected x.size(1) == {self.n_features}, got {x.size(1)}"
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)
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# logits: (N, n_features, n_bins)
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logits = x.unsqueeze(-1) * self.weight.unsqueeze(0) + \
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self.bias.unsqueeze(0)
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probs = torch.softmax(logits, dim=-1)
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# Weighted sum over bins -> (N, n_features, n_embd)
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emb = (probs.unsqueeze(-1) * self.bin_emb.unsqueeze(0)).sum(dim=-2)
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return emb
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class DelphiBERT(nn.Module):
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"""
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DelphiBERT model for tabular time series data.
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Args:
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n_embd (int): Embedding dimension.
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n_head (int): Number of attention heads.
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n_layer (int): Number of transformer blocks.
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pdrop (float): Dropout probability.
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"""
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def __init__(
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self,
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n_disease: int,
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n_embd: int,
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n_head: int,
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n_layer: int,
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n_cont: int = 0,
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n_cate: int = 0,
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cate_dims: Optional[List[int]] = None,
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age_encoder_type: str = 'sinusoidal',
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pdrop: float = 0.0,
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):
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super().__init__()
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if n_cont > 0 or n_cate > 0:
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if cate_dims is None:
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raise ValueError(
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"cate_dims must be provided if n_cate > 0"
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)
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self.tabular_encoder = TabularEncoder(
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n_embd=n_embd,
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n_cont=n_cont,
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n_cate=n_cate,
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cate_dims=cate_dims,
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)
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else:
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self.tabular_encoder = None
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self.vocab_size = n_disease + 4
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self.n_disease = n_disease
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self.n_embd = n_embd
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self.n_head = n_head
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self.token_embedding = nn.Embedding(
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self.vocab_size, n_embd, padding_idx=0)
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if age_encoder_type == 'sinusoidal':
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self.age_encoder = AgeSinusoidalEncoder(n_embd)
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elif age_encoder_type == 'mlp':
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self.age_encoder = AgeMLPEncoder(n_embd)
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else:
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raise ValueError(
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f"Unsupported age_encoder_type: {age_encoder_type}"
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)
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self.sex_embedding = nn.Embedding(2, n_embd)
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self.blocks = nn.ModuleList([
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Block(
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n_embd=n_embd,
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n_head=n_head,
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pdrop=pdrop,
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) for _ in range(n_layer)
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])
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self.ln_f = nn.LayerNorm(n_embd)
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def forward(
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self,
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event_seq: torch.Tensor,
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time_seq: torch.Tensor,
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sex: torch.Tensor,
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cont_seq: Optional[torch.Tensor] = None,
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cate_seq: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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"""Forward pass of DelphiBERT.
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Inputs:
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event_seq: (B, L) long tensor of token IDs.
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time_seq: (B, L) float tensor of ages/times.
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sex: (B,) long tensor of sex
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cont_seq: (B, Lc, n_cont) float tensor of continuous features.
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cate_seq: (B, Lc, n_cate) long tensor of categorical features.
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Returns:
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(B, L, n_embd) output embeddings.
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"""
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B, L = event_seq.shape
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token_emb = self.token_embedding(event_seq) # (B, L, n_embd)
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age_emb = self.age_encoder(time_seq) # (B, L, n_embd)
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sex_emb = self.sex_embedding(sex.unsqueeze(-1)) # (B, n_embd)
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if self.tabular_encoder is not None and cont_seq is not None and cate_seq is not None:
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tabular_emb = self.tabular_encoder(
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cont_seq, cate_seq) # (B, L, n_embd)
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mask = (event_seq == 2)
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Lc = tabular_emb.size(1)
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D = tabular_emb.size(2)
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occ = torch.cumsum(mask.to(torch.long), dim=1) - 1
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tab_idx = occ.clamp(min=0, max=max(Lc - 1, 0))
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tab_idx = tab_idx.masked_fill(~mask, 0) # (B, L)
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tab_inject = tabular_emb.gather(
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dim=1,
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index=tab_idx.unsqueeze(-1).expand(-1, -1, D)
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) # (B, L, n_embd)
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final_embds = torch.where(
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mask.unsqueeze(-1), tab_inject, token_emb)
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h = final_embds + age_emb + sex_emb
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else:
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h = token_emb + age_emb + sex_emb
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is_padding = (event_seq == 0)
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attn_mask = is_padding.view(B, 1, 1, L) # (B, 1, 1, L)
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for block in self.blocks:
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h = block(h, attn_mask=attn_mask)
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h = self.ln_f(h)
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cls_output = h[:, 0, :]
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return cls_output
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