Add dataset loading script

pmc_open_access.py

@@ -0,0 +1,210 @@
+# coding=utf-8
+# 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.
+"""PMC Open Access Subset."""
+
+import datetime
+
+import pandas as pd
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+_DESCRIPTION = """\
+The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under
+license terms that allow reuse. 
+
+Not all articles in PMC are available for text mining and other reuse, many have copyright protection, however articles
+in the PMC Open Access Subset are made available under Creative Commons or similar licenses that generally allow more
+liberal redistribution and reuse than a traditional copyrighted work. 
+
+The PMC Open Access Subset is one part of the PMC Article Datasets
+"""
+
+_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_URL = "https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_bulk/{subset}/txt/"
+_SUBSETS = {
+    "commercial": "oa_comm",
+    "non_commercial": "oa_noncomm",
+    "other": "oa_other",
+}
+_BASELINE_DATE = "2021-12-17"
+
+
+class PmcOpenAccessConfig(datasets.BuilderConfig):
+    """BuilderConfig for the PMC Open Access Subset."""
+
+    def __init__(self, subsets=None, **kwargs):
+        """BuilderConfig for the PMC Open Access Subset.
+
+        Args:
+            subsets (:obj:`List[str]`): List of subsets/groups to load.
+            **kwargs: Keyword arguments forwarded to super.
+        """
+        subsets = [subsets] if isinstance(subsets, str) else subsets
+        super().__init__(
+            name="+".join(subsets), **kwargs,
+        )
+        self.subsets = subsets if subsets != ["all"] else list(_SUBSETS.keys())
+
+
+class PmcOpenAccess(datasets.GeneratorBasedBuilder):
+    """PMC Open Access Subset."""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIG_CLASS = PmcOpenAccessConfig
+    BUILDER_CONFIGS = [PmcOpenAccessConfig(subsets="all")] + [
+        PmcOpenAccessConfig(subsets=subset) for subset in _SUBSETS
+    ]
+    DEFAULT_CONFIG_NAME = "all"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "pmid": datasets.Value("string"),
+                    "accession_id": datasets.Value("string"),
+                    "license": datasets.Value("string"),
+                    "last_updated": datasets.Value("string"),
+                    "retracted": datasets.Value("string"),
+                    "citation": datasets.Value("string"),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        for subset in self.config.subsets:
+            url = _URL.format(subset=_SUBSETS[subset])
+            basename = f"{_SUBSETS[subset]}_txt."
+            # Baselines
+            baselines = [f"PMC00{i}xxxxxx.baseline.{_BASELINE_DATE}" for i in range(9)]
+            # baseline_urls = {
+            #     "baseline_file_lists": [f"{url}{basename}{baseline}.filelist.csv" for baseline in baselines],
+            #     "baseline_archives": [f"{url}{basename}{baseline}.tar.gz" for baseline in baselines],
+            # }
+            # baseline_paths = dl_manager.download(baseline_urls)
+            baseline_file_lists = []
+            baseline_archives = []
+            for baseline in baselines:
+                baseline_file_list_url = f"{url}{basename}{baseline}.filelist.csv"
+                try:
+                    baseline_file_list = dl_manager.download(baseline_file_list_url)
+                except FileNotFoundError:  # non-commercial PMC000xxxxxx baseline does not exist
+                    continue
+                baseline_archive_url = f"{url}{basename}{baseline}.tar.gz"
+                try:
+                    baseline_archive = dl_manager.download(baseline_archive_url)
+                except FileNotFoundError:
+                    continue
+                baseline_file_lists.append(baseline_file_list)
+                baseline_archives.append(baseline_archive)
+            # Incremental
+            date_delta = datetime.date.today() - datetime.date.fromisoformat(_BASELINE_DATE)
+            incremental_dates = [
+                (datetime.date.fromisoformat(_BASELINE_DATE) + datetime.timedelta(days=i + 1)).isoformat()
+                for i in range(date_delta.days)
+            ]
+            incrementals = [f"incr.{date}" for date in incremental_dates]
+            incremental_urls = {
+                "incremental_file_lists": [
+                    f"{url}{basename}{incremental}.filelist.csv" for incremental in incrementals
+                ],
+                "incremental_archives": [f"{url}{basename}{incremental}.tar.gz" for incremental in incrementals],
+            }
+            incremental_paths = dl_manager.download(incremental_urls)
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "baseline_file_lists": baseline_file_lists,
+                        "baseline_archives": [dl_manager.iter_archive(archive) for archive in baseline_archives],
+                        "incremental_file_lists": incremental_paths["incremental_file_lists"],
+                        "incremental_archives": [
+                            dl_manager.iter_archive(archive) for archive in incremental_paths["incremental_archives"]
+                        ],
+                    },
+                ),
+            ]
+
+    def _generate_examples(self, baseline_file_lists, baseline_archives, incremental_file_lists, incremental_archives):
+        key = 0
+        # Baselines
+        for baseline_file_list, baseline_archive in zip(baseline_file_lists, baseline_archives):
+            try:
+                baselines = pd.read_csv(baseline_file_list, index_col="Article File").to_dict(orient="index")
+                for path, file in baseline_archive:
+                    data = baselines.pop(path)
+                    content = file.read()
+                    try:
+                        text = content.decode("utf-8")
+                    except UnicodeDecodeError as e:
+                        text = content.decode("latin-1")
+                    data = {
+                        "text": text,
+                        "pmid": data["PMID"],
+                        "accession_id": data["AccessionID"],
+                        "license": data["License"],
+                        "last_updated": data["LastUpdated (YYYY-MM-DD HH:MM:SS)"],
+                        "retracted": data["Retracted"],
+                        "citation": data["Article Citation"],
+                    }
+                    yield key, data
+                    key += 1
+            except FileNotFoundError:  # non-commercial PMC000xxxxxx baseline does not exist
+                continue
+        # Incrementals
+        if incremental_file_lists:
+            for incremental_file_list, incremental_archive in zip(incremental_file_lists, incremental_archives):
+                import pdb
+
+                pdb.set_trace()
+                incrementals = pd.read_csv(incremental_file_list, index_col="Article File").to_dict(orient="index")
+                for path, file in incremental_archive:
+                    data = incrementals.pop(path)
+                    content = file.read()
+                    try:
+                        text = content.decode("utf-8")
+                    except UnicodeDecodeError as e:
+                        text = content.decode("latin-1")
+                    data = {
+                        "text": text,
+                        "pmid": data["PMID"],
+                        "accession_id": data["AccessionID"],
+                        "license": data["License"],
+                        "last_updated": data["LastUpdated (YYYY-MM-DD HH:MM:SS)"],
+                        "retracted": data["Retracted"],
+                        "citation": data["Article Citation"],
+                    }
+                    yield key, data
+                    key += 1