Jiarui Li
cb7adb70d9
- Removed `prepare_data.py` as it is no longer needed. - Introduced `losses.py` containing ExponentialNLLLoss and WeibullLosses classes for calculating negative log-likelihood losses with regularization. - Added `model.py` which defines the DelphiFork model architecture, including a tabular encoder for handling continuous and categorical features, and merging sequences based on time order.
40 lines
1.5 KiB
Python
40 lines
1.5 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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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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def __init__(self, n_embd: int):
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super().__init__()
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self.mlp = nn.Sequential(
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nn.Linear(2, 4 * n_embd),
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nn.ReLU(),
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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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ages = ages.unsqueeze(-1).float() # (B, L, 1)
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ages_normalized = ages / 365.25 # normalize to years
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log1page = torch.log1p(ages_normalized) # (B, L, 1)
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ages = torch.cat([ages_normalized, log1page], dim=-1) # (B, L, 2)
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output = self.mlp(ages) # (B, L, n_embd)
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return output |