upload hubscripts/n2c2_2014_deid_hub.py to hub from bigbio repo

n2c2_2014_deid.py

@@ -0,0 +1,295 @@
+# 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 2014  Deidentification & Heart Disease.
+https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/
+The dataset consists of 3  archive files,
+* 2014_training-PHI-Gold-Set1.tar.gz
+* training-PHI-Gold-Set2.tar.gz
+* testing-PHI-Gold-fixed.tar.gz
+Each tar.gz contain a set of .xml files. One .xml per clinical report.
+The file names follow a consistent pattern with the first set of digits identifying the
+patient and the last set of digits identifying the sequential record number
+ie: XXX-YY.xml
+where XXX is the patient number,  and YY is the record number.
+Example: 320-03.xml
+This is the third (03) record for patient 320
+Each file has a root level xml node which will contain a
+<TEXT> node that holds the medical annotation text and a <TAGS> node containing
+annotations for the document text.
+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 archive files
+directly (i.e. the user should not uncompress, untar or unzip any of
+the files). For example, if the following directory structure exists
+on the users local machine,
+n2c2_2014
+├── 2014_training-PHI-Gold-Set1.tar.gz
+├── training-PHI-Gold-Set2.tar.gz
+├── testing-PHI-Gold-fixed.tar.gz
+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."
+Made in collaboration with @JoaoRacedo
+"""
+
+import itertools as it
+import os
+import re
+import tarfile
+import xml.etree.ElementTree as et
+from typing import Dict, List, Tuple
+
+import datasets
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = False
+_LOCAL = True
+_CITATION = """\
+@article{stubbs2015automated,
+title = {Automated systems for the de-identification of longitudinal
+clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1},
+journal = {Journal of Biomedical Informatics},
+volume = {58},
+pages = {S11-S19},
+year = {2015},
+issn = {1532-0464},
+doi = {https://doi.org/10.1016/j.jbi.2015.06.007},
+url = {https://www.sciencedirect.com/science/article/pii/S1532046415001173},
+author = {Amber Stubbs and Christopher Kotfila and Özlem Uzuner}
+}
+"""
+
+_DATASETNAME = "n2c2_2014_deid"
+_DISPLAYNAME = "n2c2 2014 De-identification"
+
+_DESCRIPTION = """\
+The 2014 i2b2/UTHealth Natural Language Processing (NLP) shared task featured two tracks.
+The first of these was the de-identification track focused on identifying protected health
+information (PHI) in longitudinal clinical narratives.
+
+TRACK 1: NER PHI\n
+HIPAA requires that patient medical records have all identifying information removed in order to
+protect patient privacy. There are 18 categories of Protected Health Information (PHI) identifiers of the
+patient or of relatives, employers, or household members of the patient that must be removed in order
+for a file to be considered de-identified.
+In order to de-identify the records, each file has PHI marked up. All PHI has an
+XML tag indicating its category and type, where applicable. For the purposes of this task,
+the 18 HIPAA categories have been grouped into 6 main categories and 25 sub categories
+"""
+
+_HOMEPAGE = "https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/"
+
+_LICENSE = 'Data User Agreement'
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class N2C22014DeidDataset(datasets.GeneratorBasedBuilder):
+    """n2c2 2014 Deidentification Challenge"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="n2c2_2014_source",
+            version=SOURCE_VERSION,
+            description="n2c2_2014 source schema",
+            schema="source",
+            subset_id="n2c2_2014_deid",
+        ),
+        BigBioConfig(
+            name="n2c2_2014_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="n2c2_2014 BigBio schema",
+            schema="bigbio_kb",
+            subset_id="n2c2_2014_deid",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "n2c2_2014_deid_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "phi": [
+                        {
+                            "id": datasets.Value("string"),
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                            "type": datasets.Value("string"),
+                            "text": datasets.Value("string"),
+                            "comment": datasets.Value("string"),
+                        }
+                    ],
+                },
+            )
+
+        elif self.config.schema == "bigbio_kb":
+            features = kb_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,
+                    "file_names": [
+                        ("2014_training-PHI-Gold-Set1.tar.gz", "track1"),
+                        ("training-PHI-Gold-Set2.tar.gz", "track1"),
+                    ],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "file_names": [
+                        ("testing-PHI-Gold-fixed.tar.gz", "track1"),
+                    ],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_dir, file_names: List[Tuple]) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        if self.config.schema == "source":
+            uid = it.count(0)
+            for fname, task in file_names:
+                full_path = os.path.join(data_dir, fname)
+                for x in self._read_tar_gz(full_path):
+                    xml_flag = x["xml_flag"]
+                    if xml_flag:
+                        document = self._read_task1_file(
+                            file_object=x["file_object"], file_name=x["file_name"]
+                        )
+                        document["id"] = next(uid)
+
+        elif self.config.schema == "bigbio_kb":
+            uid = it.count(0)
+            for fname, task in file_names:
+                full_path = os.path.join(data_dir, fname)
+                for x in self._read_tar_gz(full_path):
+                    xml_flag = x["xml_flag"]
+                    if xml_flag:
+                        document = self._read_task1_file(
+                            file_object=x["file_object"], file_name=x["file_name"]
+                        )
+                        document["id"] = next(uid)
+                        entity_list = document.pop("phi")
+                        full_text = document.pop("text")
+                        entities_ = []
+                        for entity in entity_list:
+                            entities_.append(
+                                {
+                                    "id": next(uid),
+                                    "type": entity["type"],
+                                    "text": entity["text"],
+                                    "offsets": entity["offsets"],
+                                    "normalized": entity["normalized"],
+                                }
+                            )
+                        document["entities"] = entities_
+
+                        document["passages"] = [
+                            {
+                                "id": next(uid),
+                                "type": "full_text",
+                                "text": [full_text],
+                                "offsets": [[0, len(full_text)]],
+                            },
+                        ]
+
+                        # additional fields required that can be empty
+                        document["relations"] = []
+                        document["events"] = []
+                        document["coreferences"] = []
+                        yield document["document_id"], document
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")
+
+    def _read_tar_gz(self, fpath: str) -> Dict:
+        """
+        Read .tar.gz file
+        """
+        # Open tar file
+        tf = tarfile.open(fpath, "r:gz")
+
+        for tf_member in tf.getmembers():
+            file_object = tf.extractfile(tf_member)
+            name = tf_member.name
+            file_name = os.path.basename(name).split(".")[0]
+            if re.search(r"\.xml", name) is not None:
+                xml_flag = True
+            else:
+                xml_flag = False
+            yield {
+                "file_object": file_object,
+                "file_name": file_name,
+                "xml_flag": xml_flag,
+            }
+
+    def _read_task1_file(self, file_object, file_name):
+        xmldoc = et.parse(file_object).getroot()
+        entities = xmldoc.findall("TAGS")[0]
+        text = xmldoc.findall("TEXT")[0].text
+        phi = []
+        for entity in entities:
+            phi.append(
+                {
+                    "id": entity.attrib["id"],
+                    "offsets": [[entity.attrib["start"], entity.attrib["end"]]],
+                    "type": entity.attrib["TYPE"],
+                    "text": [entity.attrib["text"]],
+                    "comment": entity.attrib["comment"],
+                    "normalized": [],
+                }
+            )
+
+        document = {"document_id": file_name, "text": text, "phi": phi}
+        return document