add load file

oas-paired-sequence-data.py

@@ -0,0 +1,141 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# TODO: Address all TODOs and remove all explanatory comments
+"""Paired sequences from the Observed Antibody Space database"""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+_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"
+
+_URLS = {
+    "human": "human.parquet",
+    "rat_SD": "rat_SD.parquet",
+    "mouse_BALB_c": "mouse_BALB_c.parquet",
+    "mouse_C57BL_6": "mouse_C57BL_6.parquet",
+}
+
+_FEATURES = datasets.Features(
+    {
+        "pair_id": datasets.Value("string"),
+        "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("1.1.0")
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    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):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        urls = _URLS[self.config.name]
+        data = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    # "filepath": os.path.join(data_dir, "train.jsonl"),
+                    "filepath": data,
+                    "split": "train",
+                },
+            ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.VALIDATION,
+            #     # These kwargs will be passed to _generate_examples
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "dev.jsonl"),
+            #         "split": "dev",
+            #     },
+            # ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.TEST,
+            #     # These kwargs will be passed to _generate_examples
+            #     gen_kwargs={
+            #         "filepath": os.path.join(data_dir, "test.jsonl"),
+            #         "split": "test",
+            #     },
+            # ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                yield key, data
+                # yield key, {
+                #     "sentence": data["sentence"],
+                #     "option2": data["option2"],
+                #     "second_domain_answer": ""
+                # }
+