From 86ca8f5a9c756267b746ee05d26242e1c6e2d5c5 Mon Sep 17 00:00:00 2001
From: Kiarash Jamali <kjamali@mrc-lmb.cam.ac.uk>
Date: Thu, 11 Jan 2024 07:33:58 +0000
Subject: [PATCH] Create cluster_dataset.py (#63)

---
 protein_lm/dataset/cluster_dataset.py | 50 +++++++++++++++++++++++++++
 1 file changed, 50 insertions(+)
 create mode 100644 protein_lm/dataset/cluster_dataset.py

diff --git a/protein_lm/dataset/cluster_dataset.py b/protein_lm/dataset/cluster_dataset.py
new file mode 100644
index 0000000..373ebb2
--- /dev/null
+++ b/protein_lm/dataset/cluster_dataset.py
@@ -0,0 +1,50 @@
+from typing import Callable
+from torch.utils.data import Dataset
+import pandas as pd
+import os
+import numpy as np
+from Bio import SeqIO
+
+
+class ClusterDataset(Dataset):
+    def __init__(
+            self, 
+            dataset_path: str, 
+            cluster_table_path: str,
+            size_to_sample_prob: Callable = lambda x: x,
+            seed: int = 42,    
+        ) -> None:
+        super().__init__()
+        self.dataset_path = dataset_path
+        self.cluster_table_path = cluster_table_path
+        self.cluster_to_seqs = {}
+        self.cluster_table = pd.read_csv(
+            cluster_table_path, dtype={'cluster_name': str, 'cluster_size': int}
+        )
+        self.cluster_table['sample_prob'] = self.cluster_table['cluster_size'].apply(size_to_sample_prob)
+        self.cluster_table['sample_prob'] /= self.cluster_table['sample_prob'].sum()
+        self.generator = np.random.default_rng(seed)
+
+    def __len__(self) -> int:
+        return len(self.cluster_table)
+    
+    def get_cluster_seqs(self, cluster_path: str) -> list:
+        if cluster_path not in self.cluster_to_seqs:
+            self.cluster_to_seqs[cluster_path] = [
+                str(x.seq) for x in SeqIO.parse(cluster_path, 'fasta')
+            ]
+        return self.cluster_to_seqs[cluster_path]
+
+    def __iter__(self):
+        for _ in range(len(self)):
+            cluster_name = self.cluster_table.sample(
+                n=1, weights='sample_prob', random_state=self.generator
+            )[['cluster_name']].values[0][0]
+            # Now we map cluster_name to the folder it is in
+            if cluster_name == "unk":
+                cluster_path = os.path.join(self.dataset_path, "unk", "unk.fasta")
+            else:
+                cluster_dir = f"{int(cluster_name) // 1000}000"
+                cluster_path = os.path.join(self.dataset_path, cluster_dir, f"{cluster_name}.fasta")
+            seqs = self.get_cluster_seqs(cluster_path)
+            yield seqs[self.generator.integers(len(seqs))]