Update jolma_subset.py

remove deepbindweight
remove protein and aptamer prefix

jolma_subset.py

@@ -1,9 +1,7 @@
-import os
 import re
 import pandas as pd
-import numpy as np
 import datasets
-
+from functools import cached_property, cache
 
 logger = datasets.logging.get_logger(__name__)
 
@@ -54,32 +52,19 @@ _URL = "ftp://ftp.sra.ebi.ac.uk/vol1/run/"
 # _REVERSE_PRIMER = "CCTATGCGTGCTAGTGTGA"
 # _DESIGN_LENGTH = 30
 
-import datasets
 
-config = datasets.load_dataset(path="thewall/deepbindweight", split="all")
-info = pd.read_excel(config['selex'][0])
-protein_info = pd.read_excel(config['tf'][0], index_col=0)
-_URLS = {"min_count_10": "",
-         "min_count_3": ""}
-_DESIGN_LENGTH = {"min_count_10": None,
-                  "min_count_3": None}
+_DOWNLODE_MANAGER = datasets.DownloadManager()
+_RESOURCE_URL = "https://huggingface.co/datasets/thewall/DeepBindWeight/resolve/main"
+SELEX_INFO_FILE = _DOWNLODE_MANAGER.download(f"{_RESOURCE_URL}/ERP001824-deepbind.xlsx")
+PROTEIN_INFO_FILE = _DOWNLODE_MANAGER.download(f"{_RESOURCE_URL}/ERP001824-UniprotKB.xlsx")
 pattern = re.compile("(\d+)")
-for idx, row in info.iterrows():
-    sra_id = row["SRA ID"]
-    file = row["file"]
-    _URLS[sra_id] = "/".join([_URL, sra_id[:6], sra_id, file])
-    _DESIGN_LENGTH[sra_id] = int(pattern.search(row["Ligand"]).group(0))
-
 URL = "https://huggingface.co/datasets/thewall/jolma_subset/resolve/main"
 
+
 class JolmaSubsetConfig(datasets.BuilderConfig):
-    def __init__(self, length_match=True, design_length=None, filter_N=True,
-                 protein_prefix="1", protein_suffix="2", max_length=1000, max_gene_num=1,
-                 aptamer_prefix="[BOS]", aptamer_suffix="[EOS]", **kwargs):
+    def __init__(self, protein_prefix="", protein_suffix="", max_length=1000, max_gene_num=1,
+                 aptamer_prefix="", aptamer_suffix="", **kwargs):
         super(JolmaSubsetConfig, self).__init__(**kwargs)
-        self.length_match = length_match
-        self.design_length = design_length
-        self.filter_N = filter_N
         self.data_dir = kwargs.get("data_dir")
         self.protein_prefix = protein_prefix
         self.protein_suffix = protein_suffix
@@ -90,8 +75,12 @@ class JolmaSubsetConfig(datasets.BuilderConfig):
 
 
 class JolmaSubset(datasets.GeneratorBasedBuilder):
+
+    SELEX_INFO = pd.read_excel(SELEX_INFO_FILE, index_col=0)
+    PROTEIN_INFO = pd.read_excel(PROTEIN_INFO_FILE, index_col=0)
+
     BUILDER_CONFIGS = [
-        JolmaSubsetConfig(name=key, design_length=_DESIGN_LENGTH[key]) for key in ["min_count_3", "min_count_10"]
+        JolmaSubsetConfig(name=key) for key in ["min_count_3", "min_count_10"]
     ]
 
     DEFAULT_CONFIG_NAME = "min_count_3"
@@ -114,11 +103,29 @@ class JolmaSubset(datasets.GeneratorBasedBuilder):
             citation=_CITATION,
         )
 
+    @cached_property
+    def selex_info(self):
+        return self.SELEX_INFO.loc[self.config.name]
+
+    @cached_property
+    def protein_info(self):
+        return self.PROTEIN_INFO.loc[self.config.name]
+
+    def design_length(self):
+        return int(pattern.search(self.protein_info["Ligand"]).group(0))
+
+    def get_selex_info(self, sra_id):
+        return self.SELEX_INFO.loc[sra_id]
+
+    def get_protein_info(self, sra_id):
+        return self.PROTEIN_INFO.loc[sra_id]
+
+    @cache
+    def get_design_length(self, sra_id):
+        return int(pattern.search(self.get_protein_info(sra_id)["Ligand"]).group(0))
+
     def _split_generators(self, dl_manager):
-        # downloaded_files = dl_manager.download_and_extract(self.config.url)
-        # logger.info(f"Download from {self.config.url}")
         file = dl_manager.download(f"{URL}/{self.config.name}.gz.csv")
-        # file = os.path.join(filepath, os.listdir(filepath)[0])
         return [
             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": file}),
         ]
@@ -126,23 +133,24 @@ class JolmaSubset(datasets.GeneratorBasedBuilder):
     def _generate_examples(self, filepath):
         """This function returns the examples in the raw (text) form."""
         logger.info("generating examples from = %s", filepath)
-        proteins = protein_info["Sequence"]
-        protein_id = protein_info["Entry"]
-        gene_num = protein_info["Unique Gene"]
         data = pd.read_csv(filepath)
         for key, row in data.iterrows():
             sra_id = row["identifier"].split(":")[0]
-            protein_seq = f"{self.config.protein_prefix}{proteins.loc[sra_id]}{self.config.protein_suffix}"
+            protein_info = self.get_protein_info(sra_id)
+            proteins = protein_info["Sequence"]
+            gene_num = protein_info["Unique Gene"]
+            protein_id = protein_info["Entry"]
+            protein_seq = f"{self.config.protein_prefix}{proteins}{self.config.protein_suffix}"
             aptamer_seq = f'{self.config.aptamer_prefix}{row["seq"]}{self.config.aptamer_suffix}'
             if len(protein_seq)>self.config.max_length:
                 continue
-            if gene_num.loc[sra_id]>self.config.max_gene_num:
+            if gene_num>self.config.max_gene_num:
                 continue
-            if str(proteins.loc[sra_id])=="nan":
+            if str(proteins)=="nan" or len(str(proteins))==0:
                 continue
             ans = {"id": key,
                    "protein": protein_seq,
-                   "protein_id": protein_id.loc[sra_id],
+                   "protein_id": protein_id,
                    "seq": aptamer_seq,
                    "identifier": row["identifier"],
                    "count": int(row["count"]),
@@ -150,15 +158,6 @@ class JolmaSubset(datasets.GeneratorBasedBuilder):
             yield key, ans
 
 
-    def filter_fn(self, example):
-        seq = example["seq"]
-        if self.config.length_match and len(seq)!=self.config.design_length:
-            return False
-        if self.config.filter_N and "N" in seq:
-            return False
-        return True
-
-
 if __name__=="__main__":
     from datasets import load_dataset
     dataset = load_dataset("jolma_subset.py", split="all")