Jiarui Li
6984b254b3
- Implemented ExponentialNLLLoss and WeibullNLLLoss in losses.py for negative log-likelihood calculations. - Developed TabularEncoder class in model.py for encoding tabular features. - Created DelphiFork and SapDelphi classes in model.py for time-to-event prediction using transformer architecture. - Added data preparation scripts in prepare_data.R and prepare_data.py for processing UK Biobank data, including handling field mappings and event data extraction.
56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
import torch
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import torch.nn as nn
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class AgeSinusoidalEncoder(nn.Module):
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"""
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Sinusoidal encoder for age.
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Args:
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n_embd (int): Embedding dimension. Must be even.
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"""
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def __init__(self, n_embd: int):
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super().__init__()
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if n_embd % 2 != 0:
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raise ValueError("n_embd must be even for sinusoidal encoding.")
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self.n_embd = n_embd
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i = torch.arange(0, self.n_embd, 2, dtype=torch.float32)
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divisor = torch.pow(10000, i / self.n_embd)
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self.register_buffer('divisor', divisor)
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def forward(self, ages: torch.Tensor) -> torch.Tensor:
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t_years = ages / 365.25
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# Broadcast (B, L, 1) against (1, 1, D/2) to get (B, L, D/2)
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args = t_years.unsqueeze(-1) / self.divisor.view(1, 1, -1)
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# Interleave cos and sin along the last dimension
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output = torch.zeros(
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ages.shape[0], ages.shape[1], self.n_embd, device=ages.device)
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output[:, :, 0::2] = torch.cos(args)
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output[:, :, 1::2] = torch.sin(args)
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return output
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class AgeMLPEncoder(nn.Module):
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"""
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MLP encoder for age, using sinusoidal encoding as input.
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Args:
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n_embd (int): Embedding dimension.
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"""
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def __init__(self, n_embd: int):
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super().__init__()
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self.sin_encoder = AgeSinusoidalEncoder(n_embd=n_embd)
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self.mlp = nn.Sequential(
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nn.Linear(n_embd, 4 * n_embd),
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nn.GELU(),
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nn.Linear(4 * n_embd, n_embd),
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)
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def forward(self, ages: torch.Tensor) -> torch.Tensor:
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x = self.sin_encoder(ages)
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output = self.mlp(x)
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return output
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