feat: proof of concept dataset_script

pubchem_selfies.py

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+# 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
+"""TODO: File Description"""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+# Using https://www.bibtex.com/c/doi-to-bibtex-converter/ (doi: 10.1093/nar/gkv951)
+_CITATION = """\
+@ARTICLE{Kim2016-sz,
+  title     = "{PubChem} Substance and Compound databases",
+  author    = "Kim, Sunghwan and Thiessen, Paul A and Bolton, Evan E and Chen,
+               Jie and Fu, Gang and Gindulyte, Asta and Han, Lianyi and He,
+               Jane and He, Siqian and Shoemaker, Benjamin A and Wang, Jiyao
+               and Yu, Bo and Zhang, Jian and Bryant, Stephen H",
+  abstract  = "PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public
+               repository for information on chemical substances and their
+               biological activities, launched in 2004 as a component of the
+               Molecular Libraries Roadmap Initiatives of the US National
+               Institutes of Health (NIH). For the past 11 years, PubChem has
+               grown to a sizable system, serving as a chemical information
+               resource for the scientific research community. PubChem consists
+               of three inter-linked databases, Substance, Compound and
+               BioAssay. The Substance database contains chemical information
+               deposited by individual data contributors to PubChem, and the
+               Compound database stores unique chemical structures extracted
+               from the Substance database. Biological activity data of
+               chemical substances tested in assay experiments are contained in
+               the BioAssay database. This paper provides an overview of the
+               PubChem Substance and Compound databases, including data sources
+               and contents, data organization, data submission using PubChem
+               Upload, chemical structure standardization, web-based interfaces
+               for textual and non-textual searches, and programmatic access.
+               It also gives a brief description of PubChem3D, a resource
+               derived from theoretical three-dimensional structures of
+               compounds in PubChem, as well as PubChemRDF, Resource
+               Description Framework (RDF)-formatted PubChem data for data
+               sharing, analysis and integration with information contained in
+               other databases.",
+  journal   = "Nucleic Acids Res.",
+  publisher = "Oxford University Press (OUP)",
+  volume    =  44,
+  number    = "D1",
+  pages     = "D1202--13",
+  month     =  jan,
+  year      =  2016,
+  language  = "en"
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+_HOMEPAGE = "https://pubchem.ncbi.nlm.nih.gov/"
+
+# TODO: Add the licence for the dataset here if you can find it
+# _LICENSE = ""
+
+# _BASE_URL = "https://huggingface.co/datasets/zpn/pubchem-selfies/resolve/refs%2Fpr%2F2"
+_URLS = []  # TODO: define urls
+
+
+class PubchemSelfies(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # You will be able to load one or the other configurations in the following list with
+    BUILDER_CONFIG = datasets.BuilderConfig(
+        version=VERSION, description=""
+    )  # TODO: Short description of my dataset.
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=datasets.Features(
+                {
+                    "PUBCHEM_COMPOUND_CID": datasets.Value("string"),
+                    "CAN_SELFIES": datasets.Value("string"),
+                }
+            ),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            # license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        # 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
+        data_dir = dl_manager.download_and_extract(_URLS)
+        subdirs = os.listdir(data_dir)
+
+        import random
+
+        random.shuffle(subdirs)
+
+        train_len = int(len(subdirs) * 0.8)
+        valid_len = int(len(subdirs) * 0.1)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "subdirs": subdirs[:train_len],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "subdirs": subdirs[train_len : train_len + valid_len],
+                    "split": "valid",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "subdirs": subdirs[-valid_len:],
+                    "split": "test",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, subdirs, split):
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        for subdir in subdirs:
+            filepath = subdir + f"{subdir.split('/')[-1]}_SELFIES.jsonl"
+            with open(filepath, encoding="utf-8") as f:
+                for key, row in enumerate(f):
+                    data = json.loads(row)
+                    properties = data["molecules"][0]["properties"]
+                    yield key, {
+                        "PUBCHEM_COMPOUND_CID": properties["PUBCHEM_COMPOUND_CID"],
+                        "CAN_SELFIES": properties["CAN_SELFIES"],
+                    }