Refactor data preparation and add loss functions for model training

- 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.

age_encoder.py

@@ -0,0 +1,40 @@
+import torch
+import torch.nn as nn
+
+class AgeSinusoidalEncoder(nn.Module):
+    def __init__(self, n_embd: int):
+        super().__init__()
+        if n_embd % 2 != 0:
+            raise ValueError("n_embd must be even for sinusoidal encoding.")
+        self.n_embd = n_embd
+        i = torch.arange(0, self.n_embd, 2, dtype=torch.float32)
+        divisor = torch.pow(10000, i / self.n_embd)
+        self.register_buffer('divisor', divisor)
+
+    def forward(self, ages: torch.Tensor) -> torch.Tensor:
+        t_years = ages / 365.25
+        # Broadcast (B, L, 1) against (1, 1, D/2) to get (B, L, D/2)
+        args = t_years.unsqueeze(-1) / self.divisor.view(1, 1, -1)
+        # Interleave cos and sin along the last dimension
+        output = torch.zeros(
+            ages.shape[0], ages.shape[1], self.n_embd, device=ages.device)
+        output[:, :, 0::2] = torch.cos(args)
+        output[:, :, 1::2] = torch.sin(args)
+        return output
+
+class AgeMLPEncoder(nn.Module):
+    def __init__(self, n_embd: int):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(2, 4 * n_embd),
+            nn.ReLU(),
+            nn.Linear(4 * n_embd, n_embd),
+        )
+
+    def forward(self, ages: torch.Tensor) -> torch.Tensor:
+        ages = ages.unsqueeze(-1).float()  # (B, L, 1)
+        ages_normalized = ages / 365.25  # normalize to years
+        log1page = torch.log1p(ages_normalized)  # (B, L, 1)
+        ages = torch.cat([ages_normalized, log1page], dim=-1)  # (B, L, 2)
+        output = self.mlp(ages)  # (B, L, n_embd)
+        return output