Try LFS storage

.gitattributes

@@ -54,3 +54,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
 */*.csv filter=lfs diff=lfs merge=lfs -text
+mouse_BALB_c.tar.gz filter=lfs diff=lfs merge=lfs -text
+mouse_C57BL_6.tar.gz filter=lfs diff=lfs merge=lfs -text
+rat_SD.tar.gz filter=lfs diff=lfs merge=lfs -text

README.md

@@ -4,6 +4,13 @@ language: en
 task_categories:
 - fill-mask
 license: cc-by-4.0
+configs:
+- config_name: rat_SD
+  data_files: "rat_SD.tar.gz"
+- config_name: mouse_BALB_c
+  data_files: "mouse_BALB_c.tar.gz"
+- config_name: mouse_C57BL_6
+  data_files: "mouse_C57BL_6.tar.gz"
 ---
 
 # Dataset Card for OAS Paired Sequence Data

mouse_BALB_c.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:876fdae8478963175654c9d9ed7a8d97847e002b92c3112ee730eddff24118cf
+size 114634

mouse_C57BL_6.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d87e773f30d1ee949d46b2042603b2a060223db87be6309ee22aedbbab227290
+size 130209

oas-data-cleaning.py

@@ -0,0 +1,80 @@
+# import boto3
+# import io
+import os
+import pandas as pd
+import re
+import shutil
+import tarfile
+
+data_dir = os.path.join(os.getcwd(), "data_units")
+# output_path = os.getcwd()
+
+# species_list = ["rat_SD", "mouse_BALB_c", "mouse_C57BL_6", "human"]
+species_list = ["rat_SD", "mouse_BALB_c", "mouse_C57BL_6"]
+
+# S3_BUCKET = "aws-hcls-ml"
+# S3_SRC_PREFIX = "oas-paired-sequence-data/raw"
+# S3_DEST_PREFIX = "oas-paired-sequence-data/parquet"
+# s3 = boto3.client("s3")
+# BASE_URL = "https://aws-hcls-ml.s3.amazonaws.com/oas-paired-sequence-data/raw/rat_SD/SRR9179275_paired.csv.gz"
+BASE_URL = "https://aws-hcls-ml.s3.amazonaws.com/oas-paired-sequence-data/raw/"
+
+for species in species_list:
+    print(f"Downloading {species} files")
+    # list_of_df = []
+    species_url_file = os.path.join(data_dir, species + ".txt")
+    with open(species_url_file, "r") as f:
+        i = 0
+        os.makedirs(species, exist_ok=True)
+        for csv_file in f.readlines():
+            print(csv_file)
+            filename = os.path.basename(csv_file)
+            run_id = str(re.search(r"^(.*)_[Pp]aired", filename)[1])
+            url = os.path.join(BASE_URL, species, csv_file)
+            # s3_key = os.path.join(S3_SRC_PREFIX, species, csv_file.strip())
+            # obj = s3.get_object(Bucket=S3_BUCKET, Key=s3_key)
+            run_data = pd.read_csv(
+                # io.BytesIO(obj["Body"].read()),
+                url,
+                header=1,
+                compression="gzip",
+                on_bad_lines="warn",
+                low_memory=False,
+            )
+            run_data = run_data[
+                [
+                    "sequence_alignment_aa_heavy",
+                    "cdr1_aa_heavy",
+                    "cdr2_aa_heavy",
+                    "cdr3_aa_heavy",
+                    "sequence_alignment_aa_light",
+                    "cdr1_aa_light",
+                    "cdr2_aa_light",
+                    "cdr3_aa_light",
+                ]
+            ]
+            run_data = run_data.dropna()
+            run_data.insert(0, "data_unit", run_id)
+            print(run_data.shape)
+            output_path = os.path.join(species, "train_" + str(i) + ".parquet")
+            run_data.to_parquet(output_path)
+            # list_of_df.append(run_data)
+        # species_df = pd.concat(list_of_df, ignore_index=True)
+        # print(f"{species} output summary:")
+        # print(species_df.head())
+        # print(species_df.shape)
+        # os.makedirs(species, exist_ok=True)
+        # species_df.to_parquet(species, partition_cols=["data_unit"])
+        zip_name = species + ".tar.gz"
+        print(f"Creating {zip_name}")
+        with tarfile.open(zip_name, "w:gz") as tf:
+            tf.add(species, arcname="")
+        # print(
+        #     f"Uploading {zip_name} to {os.path.join('s3://', S3_BUCKET, S3_DEST_PREFIX)}"
+        # )
+        # s3.upload_file(zip_name, S3_BUCKET, os.path.join(S3_DEST_PREFIX, zip_name))
+        print(f"Removing {species}")
+        shutil.rmtree(species)
+        # print(f"Removing {zip_name}")
+        # os.remove(zip_name)
+

oas-paired-sequence-data.py

@@ -1,138 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
-# SPDX-License-Identifier: MIT-0
-"""Paired sequences from the Observed Antibody Space database"""
-import datasets
-import os
-import csv
-
-_CITATION = """\
-@article{Olsen_Boyles_Deane_2022, 
-title={Observed Antibody Space: A diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences}, 
-volume={31}, rights={© 2021 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.}, 
-ISSN={1469-896X}, DOI={10.1002/pro.4205}, 
-number={1}, journal={Protein Science}, author={Olsen, Tobias H. and Boyles, Fergus and Deane, Charlotte M.}, 
-year={2022}, pages={141–146}, language={en} }
-
-"""
-_DESCRIPTION = """\
-Paired heavy and light chain antibody sequences for multiple species.
-"""
-
-_HOMEPAGE = "https://opig.stats.ox.ac.uk/webapps/oas/"
-
-_LICENSE = "cc-by-4.0"
-_BASE_URL = "https://aws-hcls-ml.s3.amazonaws.com/oas-paired-sequence-data/raw/"
-
-# _URLS = {
-#     "human": _BASE_URL + "human.tar.gz",
-#     "rat_SD": _BASE_URL + "rat_SD.tar.gz",
-#     "mouse_BALB_c": _BASE_URL + "mouse_BALB_c.tar.gz",
-#     "mouse_C57BL_6": _BASE_URL + "mouse_C57BL_6.tar.gz",
-# }
-_FEATURES = datasets.Features(
-    {
-        "sequence_alignment_aa_heavy": datasets.Value("string"),
-        "cdr1_aa_heavy": datasets.Value("string"),
-        "cdr2_aa_heavy": datasets.Value("string"),
-        "cdr3_aa_heavy": datasets.Value("string"),
-        "sequence_alignment_aa_light": datasets.Value("string"),
-        "cdr1_aa_light": datasets.Value("string"),
-        "cdr2_aa_light": datasets.Value("string"),
-        "cdr3_aa_light": datasets.Value("string"),
-    }
-)
-
-
-class OasPairedSequenceData(datasets.GeneratorBasedBuilder):
-    """OAS paired sequence data."""
-
-    VERSION = datasets.Version("1.2.0")
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="human", version=VERSION, description="human"),
-        datasets.BuilderConfig(name="rat_SD", version=VERSION, description="rat_SD"),
-        datasets.BuilderConfig(
-            name="mouse_BALB_c", version=VERSION, description="mouse_BALB_c"
-        ),
-        datasets.BuilderConfig(
-            name="mouse_C57BL_6", version=VERSION, description="mouse_C57BL_6"
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=_FEATURES,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    # def _split_generators(self, dl_manager):
-    #     urls = _URLS[self.config.name]
-    #     data_dir = dl_manager.download_and_extract(urls)
-    #     return [
-    #         datasets.SplitGenerator(
-    #             name=datasets.Split.TRAIN,
-    #             gen_kwargs={
-    #                 "filepath": os.path.join(data_dir),
-    #                 "split": "train",
-    #             },
-    #         ),
-    #     ]
-
-    # def _generate_examples(self, filepath, split):
-    #     table = pd.read_parquet(filepath)
-    #     for key, row in enumerate(table.itertuples()):
-    #         if key == 0:
-    #             continue
-    #         yield key, {
-    #             "sequence_alignment_aa_heavy": row[1],
-    #             "cdr1_aa_heavy": row[2],
-    #             "cdr2_aa_heavy": row[3],
-    #             "cdr3_aa_heavy": row[4],
-    #             "sequence_alignment_aa_light": row[5],
-    #             "cdr1_aa_light": row[6],
-    #             "cdr2_aa_light": row[7],
-    #             "cdr3_aa_light": row[8],
-    #         }
-
-    def _split_generators(self, dl_manager):
-        data_unit_file = os.path.join(
-            os.getcwd(), "data_units", self.config.name + ".txt"
-        )
-        with open(data_unit_file, "r") as f:
-            urls = [
-                os.path.join(_BASE_URL, self.config.name, line.strip()) for line in f
-            ]
-        data_files = dl_manager.download_and_extract(urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": data_files,
-                    "split": "train",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepaths):
-        for filepath in filepaths:
-            with open(filepath, "r") as f:
-                reader = csv.reader(f, delimiter=",")
-                for key, row in enumerate(reader):
-                    if key < 2:
-                        continue
-                    else:
-                        yield key - 2, {
-                            "sequence_alignment_aa_heavy": row[14],
-                            "cdr1_aa_heavy": row[37],
-                            "cdr2_aa_heavy": row[41],
-                            "cdr3_aa_heavy": row[47],
-                            "sequence_alignment_aa_light": row[113],
-                            "cdr1_aa_light": row[136],
-                            "cdr2_aa_light": row[140],
-                            "cdr3_aa_light": row[146],
-                        }
-

rat_SD.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1c9af9dc3c63df41e091a9d847dd8aa95fdb0d73d81024516916b78c25ee1722
+size 176753