upload hubscripts/pubhealth_hub.py to hub from bigbio repo

pubhealth.py

@@ -0,0 +1,209 @@
+# 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.
+"""
+A dataset of 11,832 claims for fact- checking, which are related a range of health topics
+including biomedical subjects (e.g., infectious diseases, stem cell research), government healthcare policy
+(e.g., abortion, mental health, women’s health), and other public health-related stories
+"""
+
+import csv
+import os
+from pathlib import Path
+
+import datasets
+
+from .bigbiohub import pairs_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+logger = datasets.utils.logging.get_logger(__name__)
+
+_LANGUAGES = ['English']
+_PUBMED = False
+_LOCAL = False
+_CITATION = """\
+@article{kotonya2020explainable,
+  title={Explainable automated fact-checking for public health claims},
+  author={Kotonya, Neema and Toni, Francesca},
+  journal={arXiv preprint arXiv:2010.09926},
+  year={2020}
+}
+"""
+
+_DATASETNAME = "pubhealth"
+_DISPLAYNAME = "PUBHEALTH"
+
+_DESCRIPTION = """\
+A dataset of 11,832 claims for fact- checking, which are related a range of health topics
+including biomedical subjects (e.g., infectious diseases, stem cell research), government healthcare policy
+(e.g., abortion, mental health, women’s health), and other public health-related stories
+"""
+
+_HOMEPAGE = "https://github.com/neemakot/Health-Fact-Checking/tree/master/data"
+
+_LICENSE = 'MIT License'
+
+_URLs = {
+    _DATASETNAME: "https://drive.google.com/uc?export=download&id=1eTtRs5cUlBP5dXsx-FTAlmXuB6JQi2qj"
+}
+
+_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+_CLASSES = ["true", "false", "unproven", "mixture"]
+
+
+class PUBHEALTHDataset(datasets.GeneratorBasedBuilder):
+    """Pubhealth text classification dataset"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="pubhealth_source",
+            version=SOURCE_VERSION,
+            description="PUBHEALTH source schema",
+            schema="source",
+            subset_id="pubhealth",
+        ),
+        BigBioConfig(
+            name="pubhealth_bigbio_pairs",
+            version=BIGBIO_VERSION,
+            description="PUBHEALTH BigBio schema",
+            schema="bigbio_pairs",
+            subset_id="pubhealth",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "pubhealth_source"
+
+    def _info(self):
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "claim_id": datasets.Value("string"),
+                    "claim": datasets.Value("string"),
+                    "date_published": datasets.Value("string"),
+                    "explanation": datasets.Value("string"),
+                    "fact_checkers": datasets.Value("string"),
+                    "main_text": datasets.Value("string"),
+                    "sources": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=_CLASSES),
+                    "subjects": datasets.Value("string"),
+                }
+            )
+
+        # Using in entailment schema
+        elif self.config.schema == "bigbio_pairs":
+            features = pairs_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls = _URLs[_DATASETNAME]
+        data_dir = Path(dl_manager.download_and_extract(urls))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "PUBHEALTH/train.tsv"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "PUBHEALTH/test.tsv"),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "PUBHEALTH/dev.tsv"),
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples as (key, example) tuples."""
+
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(
+                csv_file,
+                quotechar='"',
+                delimiter="\t",
+                quoting=csv.QUOTE_NONE,
+                skipinitialspace=True,
+            )
+            next(csv_reader, None)  # remove column headers
+            for id_, row in enumerate(csv_reader):
+                # train.tsv/dev.tsv only has 9 columns
+                # test.tsv has an additional column at the beginning
+                #  Some entries are malformed, will log skipped lines
+                if len(row) < 9:
+                    logger.info("Line %s is malformed", id_)
+                    continue
+                (
+                    claim_id,
+                    claim,
+                    date_published,
+                    explanation,
+                    fact_checkers,
+                    main_text,
+                    sources,
+                    label,
+                    subjects,
+                ) = row[
+                    -9:
+                ]  # only take last 9 columns to fix test.tsv disparity
+
+                if label not in _CLASSES:
+                    logger.info("Line %s is missing label", id_)
+                    continue
+
+                if self.config.schema == "source":
+                    yield id_, {
+                        "claim_id": claim_id,
+                        "claim": claim,
+                        "date_published": date_published,
+                        "explanation": explanation,
+                        "fact_checkers": fact_checkers,
+                        "main_text": main_text,
+                        "sources": sources,
+                        "label": label,
+                        "subjects": subjects,
+                    }
+
+                elif self.config.schema == "bigbio_pairs":
+                    yield id_, {
+                        "id": id_,  # uid is an unique identifier for every record that starts from 0
+                        "document_id": claim_id,
+                        "text_1": claim,
+                        "text_2": explanation,
+                        "label": label,
+                    }