upload hubscripts/ask_a_patient_hub.py to hub from bigbio repo

ask_a_patient.py

@@ -0,0 +1,170 @@
+# 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.
+
+import glob
+import os
+import re
+
+import datasets
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_DATASETNAME = "ask_a_patient"
+_DISPLAYNAME = "AskAPatient"
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """
+@inproceedings{limsopatham-collier-2016-normalising,
+    title = "Normalising Medical Concepts in Social Media Texts by Learning Semantic Representation",
+    author = "Limsopatham, Nut  and
+      Collier, Nigel",
+    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = aug,
+    year = "2016",
+    address = "Berlin, Germany",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/P16-1096",
+    doi = "10.18653/v1/P16-1096",
+    pages = "1014--1023",
+}
+"""
+
+_DESCRIPTION = """
+The AskAPatient dataset contains medical concepts written on social media \
+mapped to how they are formally written in medical ontologies (SNOMED-CT and AMT).
+"""
+
+_HOMEPAGE = "https://zenodo.org/record/55013"
+
+_LICENSE = 'Creative Commons Attribution 4.0 International'
+
+_URLs = "https://zenodo.org/record/55013/files/datasets.zip"
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION]
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class AskAPatient(datasets.GeneratorBasedBuilder):
+    """AskAPatient: Dataset for Normalising Medical Concepts in Social Media Text."""
+
+    DEFAULT_CONFIG_NAME = "ask_a_patient_source"
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="ask_a_patient_source",
+            version=SOURCE_VERSION,
+            description="AskAPatient source schema",
+            schema="source",
+            subset_id="ask_a_patient",
+        ),
+        BigBioConfig(
+            name="ask_a_patient_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="AskAPatient simplified BigBio schema",
+            schema="bigbio_kb",
+            subset_id="ask_a_patient",
+        ),
+    ]
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "cui": datasets.Value("string"),
+                    "medical_concept": datasets.Value("string"),
+                    "social_media_text": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "bigbio_kb":
+            features = kb_features
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        dl_dir = dl_manager.download_and_extract(_URLs)
+        dataset_dir = os.path.join(dl_dir, "datasets", "AskAPatient")
+        # dataset supports k-folds
+        splits = []
+        for split_name in [
+            datasets.Split.TRAIN,
+            datasets.Split.VALIDATION,
+            datasets.Split.TEST,
+        ]:
+            for fold_filepath in glob.glob(
+                os.path.join(dataset_dir, f"AskAPatient.fold-*.{split_name}.txt")
+            ):
+                fold_id = re.search("AskAPatient\.fold-(\d)\.", fold_filepath).group(1)
+                split_id = f"{split_name}_{fold_id}"
+                splits.append(
+                    datasets.SplitGenerator(
+                        name=split_id,
+                        gen_kwargs={"filepath": fold_filepath, "split_id": split_id},
+                    )
+                )
+        return splits
+
+    def _generate_examples(self, filepath, split_id):
+        with open(filepath, "r", encoding="latin-1") as f:
+            for i, line in enumerate(f):
+                id = f"{split_id}_{i}"
+                cui, medical_concept, social_media_text = line.strip().split("\t")
+                if self.config.schema == "source":
+                    yield id, {
+                        "cui": cui,
+                        "medical_concept": medical_concept,
+                        "social_media_text": social_media_text,
+                    }
+                elif self.config.schema == "bigbio_kb":
+                    text_type = "social_media_text"
+                    offset = (0, len(social_media_text))
+                    yield id, {
+                        "id": id,
+                        "document_id": id,
+                        "passages": [
+                            {
+                                "id": f"{id}_passage",
+                                "type": text_type,
+                                "text": [social_media_text],
+                                "offsets": [offset],
+                            }
+                        ],
+                        "entities": [
+                            {
+                                "id": f"{id}_entity",
+                                "type": text_type,
+                                "text": [social_media_text],
+                                "offsets": [offset],
+                                "normalized": [
+                                    {"db_name": "SNOMED-CT|AMT", "db_id": cui}
+                                ],
+                            }
+                        ],
+                        "events": [],
+                        "coreferences": [],
+                        "relations": [],
+                    }