Add beam search peptide decoding

CHANGELOG.md

@@ -9,6 +9,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 ### Changed
 
 - Update PyTorch Lightning global seed setting.
+- Use beam search decoding rather than greedy decoding to predict the peptides.
 
 ### Fixed
 

casanovo/casanovo.py

@@ -156,6 +156,7 @@ def main(
         weight_decay=float,
         train_batch_size=int,
         predict_batch_size=int,
+        n_beams=int,
         max_epochs=int,
         num_sanity_val_steps=int,
         train_from_scratch=bool,

casanovo/config.yaml

@@ -65,6 +65,7 @@ weight_decay: 1e-5
 # Training/inference options.
 train_batch_size: 32
 predict_batch_size: 1024
+n_beams: 5
 
 logger:
 max_epochs: 30

casanovo/denovo/evaluate.py

@@ -1,6 +1,6 @@
 """Methods to evaluate peptide-spectrum predictions."""
 import re
-from typing import Dict, List, Tuple
+from typing import Dict, Iterable, List, Tuple
 
 import numpy as np
 from spectrum_utils.utils import mass_diff
@@ -182,8 +182,8 @@ def aa_match(
 
 
 def aa_match_batch(
-    peptides1: List[str],
-    peptides2: List[str],
+    peptides1: Iterable,
+    peptides2: Iterable,
     aa_dict: Dict[str, float],
     cum_mass_threshold: float = 0.5,
     ind_mass_threshold: float = 0.1,
@@ -194,10 +194,10 @@ def aa_match_batch(
 
     Parameters
     ----------
-    peptides1 : List[str]
-        The first list of (untokenized) peptide sequences to be compared.
-    peptides2 : List[str]
-        The second list of (untokenized) peptide sequences to be compared.
+    peptides1 : Iterable
+        The first list of peptide sequences to be compared.
+    peptides2 : Iterable
+        The second list of peptide sequences to be compared.
     aa_dict : Dict[str, float]
         Mapping of amino acid tokens to their mass values.
     cum_mass_threshold : float
@@ -221,13 +221,16 @@ def aa_match_batch(
     """
     aa_matches_batch, n_aa1, n_aa2 = [], 0, 0
     for peptide1, peptide2 in zip(peptides1, peptides2):
-        tokens1 = re.split(r"(?<=.)(?=[A-Z])", peptide1)
-        tokens2 = re.split(r"(?<=.)(?=[A-Z])", peptide2)
-        n_aa1, n_aa2 = n_aa1 + len(tokens1), n_aa2 + len(tokens2)
+        # Split peptides into individual AAs if necessary.
+        if isinstance(peptide1, str):
+            peptide1 = re.split(r"(?<=.)(?=[A-Z])", peptide1)
+        if isinstance(peptide2, str):
+            peptide2 = re.split(r"(?<=.)(?=[A-Z])", peptide2)
+        n_aa1, n_aa2 = n_aa1 + len(peptide1), n_aa2 + len(peptide2)
         aa_matches_batch.append(
             aa_match(
-                tokens1,
-                tokens2,
+                peptide1,
+                peptide2,
                 aa_dict,
                 cum_mass_threshold,
                 ind_mass_threshold,