New Dataset superclass

netam/common.py

@@ -368,3 +368,15 @@ def linear_bump_lr(epoch, warmup_epochs, total_epochs, max_lr, min_lr):
             epoch - warmup_epochs
         )
     return lr
+
+
+def encode_sequences(sequences, encoder):
+    encoded_parents, wt_base_modifiers = zip(
+        *[encoder.encode_sequence(sequence) for sequence in sequences]
+    )
+    masks = [nt_mask_tensor_of(sequence, encoder.site_count) for sequence in sequences]
+    return (
+        torch.stack(encoded_parents),
+        torch.stack(masks),
+        torch.stack(wt_base_modifiers),
+    )

netam/dasm.py

@@ -11,6 +11,7 @@
 import netam.framework as framework
 import netam.molevol as molevol
 import netam.sequences as sequences
+import copy
 
 
 class DASMDataset(DXSMDataset):

netam/dxsm.py

@@ -4,7 +4,6 @@
 from functools import partial
 
 import torch
-from torch.utils.data import Dataset
 
 # Amazingly, using one thread makes things 50x faster for branch length
 # optimization on our server.
@@ -32,7 +31,7 @@
 )
 
 
-class DXSMDataset(Dataset, ABC):
+class DXSMDataset(framework.BranchLengthDataset, ABC):
     prefix = "dxsm"
 
     def __init__(
@@ -222,19 +221,6 @@ def branch_lengths(self, new_branch_lengths):
         self._branch_lengths = new_branch_lengths
         self.update_neutral_probs()
 
-    def __len__(self):
-        return len(self.aa_parents_idxss)
-
-    def export_branch_lengths(self, out_csv_path):
-        pd.DataFrame({"branch_length": self.branch_lengths}).to_csv(
-            out_csv_path, index=False
-        )
-
-    def load_branch_lengths(self, in_csv_path):
-        self.branch_lengths = torch.Tensor(
-            pd.read_csv(in_csv_path)["branch_length"].values
-        )
-
     @abstractmethod
     def update_neutral_probs(self):
         pass

netam/framework.py

@@ -25,6 +25,7 @@
     BASES_AND_N_TO_INDEX,
     BIG,
     VRC01_NT_SEQ,
+    encode_sequences,
 )
 from netam import models
 import netam.molevol as molevol
@@ -132,7 +133,23 @@ def parameters(self):
         return {}
 
 
-class SHMoofDataset(Dataset):
+class BranchLengthDataset(Dataset):
+    def __len__(self):
+        return len(self.branch_lengths)
+
+    def export_branch_lengths(self, out_csv_path):
+        pd.DataFrame({"branch_length": self.branch_lengths}).to_csv(
+            out_csv_path, index=False
+        )
+
+    def load_branch_lengths(self, in_csv_path):
+        self.branch_lengths = pd.read_csv(in_csv_path)["branch_length"].values
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}(Size: {len(self)}) on {self.branch_lengths.device}"
+
+
+class SHMoofDataset(BranchLengthDataset):
     def __init__(self, dataframe, kmer_length, site_count):
         super().__init__()
         self.encoder = KmerSequenceEncoder(kmer_length, site_count)
@@ -146,9 +163,6 @@ def __init__(self, dataframe, kmer_length, site_count):
         ) = self.encode_pcps(dataframe)
         assert self.encoded_parents.shape[0] == self.branch_lengths.shape[0]
 
-    def __len__(self):
-        return len(self.encoded_parents)
-
     def __getitem__(self, idx):
         return (
             self.encoded_parents[idx],
@@ -159,9 +173,6 @@ def __getitem__(self, idx):
             self.branch_lengths[idx],
         )
 
-    def __repr__(self):
-        return f"{self.__class__.__name__}(Size: {len(self)}) on {self.encoded_parents.device}"
-
     def to(self, device):
         self.encoded_parents = self.encoded_parents.to(device)
         self.masks = self.masks.to(device)
@@ -224,9 +235,6 @@ def export_branch_lengths(self, out_csv_path):
             }
         ).to_csv(out_csv_path, index=False)
 
-    def load_branch_lengths(self, in_csv_path):
-        self.branch_lengths = pd.read_csv(in_csv_path)["branch_length"].values
-
 
 class Crepe:
     """A lightweight wrapper around a model that can be used for prediction but not
@@ -243,6 +251,11 @@ def __init__(self, encoder, model, training_hyperparameters={}):
         self.model = model
         self.training_hyperparameters = training_hyperparameters
 
+    def __call__(self, sequences):
+        return self.model.selection_factors_of_sequences(
+            sequences, encoder=self.encoder
+        )
+
     @property
     def device(self):
         return next(self.model.parameters()).device
@@ -251,27 +264,7 @@ def to(self, device):
         self.model.to(device)
 
     def encode_sequences(self, sequences):
-        encoded_parents, wt_base_modifiers = zip(
-            *[self.encoder.encode_sequence(sequence) for sequence in sequences]
-        )
-        masks = [
-            nt_mask_tensor_of(sequence, self.encoder.site_count)
-            for sequence in sequences
-        ]
-        return (
-            torch.stack(encoded_parents),
-            torch.stack(masks),
-            torch.stack(wt_base_modifiers),
-        )
-
-    def __call__(self, sequences):
-        encoded_parents, masks, wt_base_modifiers = self.encode_sequences(sequences)
-        encoded_parents = encoded_parents.to(self.device)
-        masks = masks.to(self.device)
-        wt_base_modifiers = wt_base_modifiers.to(self.device)
-        with torch.no_grad():
-            outputs = self.model(encoded_parents, masks, wt_base_modifiers)
-            return tuple(t.detach().cpu() for t in outputs)
+        return encode_sequences(sequences, self.encoder)
 
     def save(self, prefix):
         torch.save(self.model.state_dict(), f"{prefix}.pth")

netam/models.py

@@ -16,6 +16,7 @@
     PositionalEncoding,
     generate_kmers,
     aa_mask_tensor_of,
+    encode_sequences,
 )
 
 warnings.filterwarnings(
@@ -59,6 +60,18 @@ def unfreeze(self):
         for param in self.parameters():
             param.requires_grad = True
 
+    def selection_factors_of_sequences(self, sequences, encoder=None):
+        if encoder is None:
+            raise ValueError("An encoder must be provided.")
+        device = next(self.parameters()).device
+        encoded_parents, masks, wt_base_modifiers = encode_sequences(sequences, encoder)
+        encoded_parents = encoded_parents.to(device)
+        masks = masks.to(device)
+        wt_base_modifiers = wt_base_modifiers.to(device)
+        with torch.no_grad():
+            outputs = self(encoded_parents, masks, wt_base_modifiers)
+            return tuple(t.detach().cpu() for t in outputs)
+
 
 class KmerModel(ModelBase):
     def __init__(self, kmer_length):
@@ -536,6 +549,9 @@ class AbstractBinarySelectionModel(ABC, nn.Module):
     def __init__(self):
         super().__init__()
 
+    def selection_factors_of_sequences(self, sequences: list[str], **kwargs) -> Tensor:
+        return tuple(self.selection_factors_of_aa_str(seq) for seq in sequences)
+
     def selection_factors_of_aa_str(self, aa_str: str) -> Tensor:
         """Do the forward method then exponentiation without gradients from an amino
         acid string.