upload hubscripts/n2c2_2008_hub.py to hub from bigbio repo

n2c2_2008.py

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+# coding=utf-8
+# Copyright 2022 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 loader for the n2c2 2008 obesity and comorbidities dataset.
+
+https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/
+
+The dataset consists of eight xml files,
+
+* obesity_patient_records_training.xml
+* obesity_patient_records_training2.xml
+* obesity_standoff_annotations_training.xml
+* obesity_standoff_annotations_training_addendum.xml
+* obesity_standoff_annotations_training_addendum2.xml
+* obesity_standoff_annotations_training_addendum3.xml
+* obesity_patient_records_test.xml
+* obesity_standoff_annotations_test.xml
+
+containing patient records as well as textual and intuitive annotations.
+
+
+The files comprising this dataset must be on the users local machine
+in a single directory that is passed to `datasets.load_datset` via
+the `data_dir` kwarg. This loader script will read the xml files
+directly. For example, if the following directory structure exists
+on the users local machine,
+
+
+n2c2_2008
+├── obesity_patient_records_training.xml
+├── obesity_patient_records_training2.xml
+├── obesity_standoff_annotations_training.xml
+├── obesity_standoff_annotations_training_addendum.xml
+├── obesity_standoff_annotations_training_addendum2.xml
+├── obesity_standoff_annotations_training_addendum3.xml
+├── obesity_patient_records_test.xml
+├── obesity_standoff_annotations_test.xml
+
+
+Data Access
+
+from https://www.i2b2.org/NLP/DataSets/Main.php
+
+"As always, you must register AND submit a DUA for access. If you previously
+accessed the data sets here on i2b2.org, you will need to set a new password
+for your account on the Data Portal, but your original DUA will be retained."
+
+
+"""
+
+import os
+import xml.etree.ElementTree as et
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from .bigbiohub import text_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_DATASETNAME = "n2c2_2008"
+_DISPLAYNAME = "n2c2 2008 Obesity"
+
+# https://academic.oup.com/jamia/article/16/4/561/766997
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = True
+_CITATION = """\
+@article{uzuner2009recognizing,
+    author = {
+        Uzuner, Ozlem
+    },
+    title     = {Recognizing Obesity and Comorbidities in Sparse Data},
+    journal   = {Journal of the American Medical Informatics Association},
+    volume    = {16},
+    number    = {4},
+    pages     = {561-570},
+    year      = {2009},
+    month     = {07},
+    url       = {https://doi.org/10.1197/jamia.M3115},
+    doi       = {10.1197/jamia.M3115},
+    eprint    = {https://academic.oup.com/jamia/article-pdf/16/4/561/2302602/16-4-561.pdf}
+}
+"""
+
+_DESCRIPTION = """\
+The data for the n2c2 2008 obesity challenge consisted of discharge summaries from
+the Partners HealthCare Research Patient Data Repository. These data were chosen 
+from the discharge summaries of patients who were overweight or diabetic and had 
+been hospitalized for obesity or diabetes sometime since 12/1/04. De-identification
+was performed semi-automatically. All private health information was replaced with
+synthetic identifiers.
+
+The data for the challenge were annotated by two obesity experts from the 
+Massachusetts General Hospital Weight Center. The experts were given a textual task, 
+which asked them to classify each disease (see list of diseases above) as Present, 
+Absent, Questionable, or Unmentioned based on explicitly documented information in 
+the discharge summaries, e.g., the statement “the patient is obese”. The experts were 
+also given an intuitive task, which asked them to classify each disease as Present, 
+Absent, or Questionable by applying their intuition and judgment to information in 
+the discharge summaries.
+"""
+
+_HOMEPAGE = "https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/"
+
+_LICENSE = 'Data User Agreement'
+
+_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]
+
+_CLASS_NAMES = ["present", "absent", "unmentioned", "questionable"]
+_disease_names = [
+    "Obesity",
+    "Asthma",
+    "CAD",
+    "CHF",
+    "Depression",
+    "Diabetes",
+    "Gallstones",
+    "GERD",
+    "Gout",
+    "Hypercholesterolemia",
+    "Hypertension",
+    "Hypertriglyceridemia",
+    "OA",
+    "OSA",
+    "PVD",
+    "Venous Insufficiency",
+]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+def _map_labels(doc, task):
+    """
+    Map obesity and comorbidity labels.
+    :param doc: a document indexde by id
+    :param task: textual or intuitive annotation task
+    """
+    lmap = {"Y": "present", "N": "absent", "U": "unmentioned", "Q": "questionable"}
+
+    def _map_label(doc, task, label_name):
+        if label_name in doc[task].keys():
+            return lmap[doc[task][label_name]]
+        else:
+            return None
+
+    if task in doc.keys():
+        return {
+            "Obesity": _map_label(doc, task, "Obesity"),
+            "Asthma": _map_label(doc, task, "Asthma"),
+            "CAD": _map_label(doc, task, "CAD"),
+            "CHF": _map_label(doc, task, "CHF"),
+            "Depression": _map_label(doc, task, "Depression"),
+            "Diabetes": _map_label(doc, task, "Diabetes"),
+            "Gallstones": _map_label(doc, task, "Gallstones"),
+            "GERD": _map_label(doc, task, "GERD"),
+            "Gout": _map_label(doc, task, "Gout"),
+            "Hypercholesterolemia": _map_label(doc, task, "Hypercholesterolemia"),
+            "Hypertension": _map_label(doc, task, "Hypertension"),
+            "Hypertriglyceridemia": _map_label(doc, task, "Hypertriglyceridemia"),
+            "OA": _map_label(doc, task, "OA"),
+            "OSA": _map_label(doc, task, "OSA"),
+            "PVD": _map_label(doc, task, "PVD"),
+            "Venous Insufficiency": _map_label(doc, task, "Venous Insufficiency"),
+        }
+    else:
+        return {task: None}
+
+
+def _read_xml(partition, data_dir):
+    """
+    Load the data split.
+    :param partition: train/test
+    :param data_dir: train and test data directory
+    """
+    documents = {}
+    all_diseases = set()
+    notes = tuple()
+    if partition == "train":
+        with open(data_dir / "obesity_patient_records_training.xml") as t1, open(
+            data_dir / "obesity_patient_records_training2.xml"
+        ) as t2:
+            notes1 = t1.read().strip()
+            notes2 = t2.read().strip()
+        notes = (notes1, notes2)
+    elif partition == "test":
+        with open(data_dir / "obesity_patient_records_test.xml") as t1:
+            notes1 = t1.read().strip()
+        notes = (notes1,)
+
+    for file in notes:
+        root = et.fromstring(file)
+        root = root.findall("./docs")[0]
+        for document in root.findall("./doc"):
+            assert document.attrib["id"] not in documents
+            documents[document.attrib["id"]] = {}
+            documents[document.attrib["id"]]["text"] = document.findall("./text")[
+                0
+            ].text
+
+    annotation_files = tuple()
+    if partition == "train":
+        with open(data_dir / "obesity_standoff_annotations_training.xml") as t1, open(
+            data_dir / "obesity_standoff_annotations_training_addendum.xml"
+        ) as t2, open(
+            data_dir / "obesity_standoff_annotations_training_addendum2.xml"
+        ) as t3, open(
+            data_dir / "obesity_standoff_annotations_training_addendum3.xml"
+        ) as t4:
+            train1 = t1.read().strip()
+            train2 = t2.read().strip()
+            train3 = t3.read().strip()
+            train4 = t4.read().strip()
+        annotation_files = (train1, train2, train3, train4)
+    elif partition == "test":
+        with open(data_dir / "obesity_standoff_annotations_test.xml") as t1:
+            test1 = t1.read().strip()
+        annotation_files = (test1,)
+
+    for file in annotation_files:
+        root = et.fromstring(file)
+        for diseases_annotation in root.findall("./diseases"):
+
+            annotation_source = diseases_annotation.attrib["source"]
+            assert isinstance(annotation_source, str)
+            for disease in diseases_annotation.findall("./disease"):
+                disease_name = disease.attrib["name"]
+                all_diseases.add(disease_name)
+                for annotation in disease.findall("./doc"):
+                    doc_id = annotation.attrib["id"]
+                    if not annotation_source in documents[doc_id]:
+                        documents[doc_id][annotation_source] = {}
+                    assert doc_id in documents
+                    judgment = annotation.attrib["judgment"]
+                    documents[doc_id][annotation_source][disease_name] = judgment
+    return [
+        {
+            "document_id": str(id),
+            "text": documents[id]["text"],
+            "textual": _map_labels(documents[id], "textual"),
+            "intuitive": _map_labels(documents[id], "intuitive"),
+        }
+        for id in documents
+    ]
+
+
+class N2C22008ObesityDataset(datasets.GeneratorBasedBuilder):
+    """n2c2 2008 obesity and comorbidities recognition task"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="n2c2_2008_source",
+            version=SOURCE_VERSION,
+            description="n2c2_2008 source schema",
+            schema="source",
+            subset_id="n2c2_2008",
+        ),
+        BigBioConfig(
+            name="n2c2_2008_bigbio_text",
+            version=BIGBIO_VERSION,
+            description="n2c2_2008 BigBio schema",
+            schema="bigbio_text",
+            subset_id="n2c2_2008",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "n2c2_2008_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "labels": [
+                        {
+                            "annotation": datasets.ClassLabel(
+                                names=["textual", "intuitive"]
+                            ),
+                            "disease_name": datasets.ClassLabel(names=_disease_names),
+                            "label": datasets.ClassLabel(names=_CLASS_NAMES),
+                        }
+                    ],
+                }
+            )
+
+        elif self.config.schema == "bigbio_text":
+            features = text_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(
+        self, dl_manager: datasets.DownloadManager
+    ) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        if self.config.data_dir is None:
+            raise ValueError(
+                "This is a local dataset. Please pass the data_dir kwarg to load_dataset."
+            )
+        else:
+            data_dir = self.config.data_dir
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    @staticmethod
+    def _get_source_sample(sample):
+        textual_labels = [
+            ("textual", disease_name, sample["textual"][disease_name])
+            for disease_name in sample["textual"].keys()
+            if sample["textual"][disease_name]
+        ]
+        intuitive_labels = [
+            ("intuitive", disease_name, sample["intuitive"][disease_name])
+            for disease_name in sample["intuitive"].keys()
+            if sample["intuitive"][disease_name]
+        ]
+
+        return {
+            "document_id": sample["document_id"],
+            "text": sample["text"],
+            "labels": [
+                {
+                    "annotation": label[0],
+                    "disease_name": label[1],
+                    "label": label[2],
+                }
+                for label in textual_labels + intuitive_labels
+            ],
+        }
+
+    @staticmethod
+    def _get_bigbio_sample(sample_id, sample):
+        textual_labels = [
+            ("textual", disease_name, sample["textual"][disease_name])
+            for disease_name in sample["textual"].keys()
+            if sample["textual"][disease_name]
+        ]
+        intuitive_labels = [
+            ("intuitive", disease_name, sample["intuitive"][disease_name])
+            for disease_name in sample["intuitive"].keys()
+            if sample["intuitive"][disease_name]
+        ]
+
+        return {
+            "id": str(sample_id),
+            "document_id": sample["document_id"],
+            "text": sample["text"],
+            "labels": [
+                {
+                    "annotation": label[0],
+                    "disease_name": label[1],
+                    "label": label[2],
+                }
+                for label in textual_labels + intuitive_labels
+            ],
+        }
+
+    def _generate_examples(self, data_dir, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        data_dir = Path(data_dir).resolve()
+        if split == "train":
+            _id = 0
+            samples = _read_xml(split, data_dir)
+            for sample in samples:
+                if self.config.schema == "source":
+                    yield _id, self._get_source_sample(sample)
+
+                elif self.config.schema == "bigbio_text":
+                    yield _id, self._get_bigbio_sample(_id, sample)
+                _id += 1
+
+        elif split == "test":
+            _id = 0
+            samples = _read_xml(split, data_dir)
+            for sample in samples:
+                if self.config.schema == "source":
+                    yield _id, self._get_source_sample(sample)
+
+                elif self.config.schema == "bigbio_text":
+                    yield _id, self._get_bigbio_sample(_id, sample)
+                _id += 1