upload hubscripts/scai_chemical_hub.py to hub from bigbio repo

scai_chemical.py

@@ -0,0 +1,257 @@
+# 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 SCAI Chemical dataset.
+
+SCAI Chemical is a corpus of MEDLINE abstracts that has been annotated
+to give an overview of the different chemical name classes
+found in MEDLINE text.
+"""
+
+import gzip
+from typing import Dict, List, Tuple
+
+import datasets
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@inproceedings{kolarik:lrec-ws08,
+  author    = {Kol{\'a}{\vr}ik, Corinna and Klinger, Roman and Friedrich, Christoph M and Hofmann-Apitius, Martin and Fluck, Juliane},
+  title     = {Chemical Names: {T}erminological Resources and Corpora Annotation},
+  booktitle = {LREC Workshop on Building and Evaluating Resources for Biomedical Text Mining},
+  year      = {2008},
+}
+"""
+
+_DATASETNAME = "scai_chemical"
+_DISPLAYNAME = "SCAI Chemical"
+
+_DESCRIPTION = """\
+SCAI Chemical is a corpus of MEDLINE abstracts that has been annotated
+to give an overview of the different chemical name classes
+found in MEDLINE text.
+"""
+
+_HOMEPAGE = "https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/corpora-for-chemical-entity-recognition.html"
+
+_LICENSE = 'License information unavailable'
+
+_URLS = {
+    _DATASETNAME: "https://www.scai.fraunhofer.de/content/dam/scai/de/downloads/bioinformatik/Corpora-for-Chemical-Entity-Recognition/chemicals-test-corpus-27-04-2009-v3_iob.gz",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "3.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class ScaiChemicalDataset(datasets.GeneratorBasedBuilder):
+    """SCAI Chemical is a dataset annotated in 2008 with mentions of chemicals."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="scai_chemical_source",
+            version=SOURCE_VERSION,
+            description="SCAI Chemical source schema",
+            schema="source",
+            subset_id="scai_chemical",
+        ),
+        BigBioConfig(
+            name="scai_chemical_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="SCAI Chemical BigBio schema",
+            schema="bigbio_kb",
+            subset_id="scai_chemical",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "scai_chemical_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "tokens": [
+                        {
+                            "offsets": [datasets.Value("int64")],
+                            "text": datasets.Value("string"),
+                            "tag": datasets.Value("string"),
+                        }
+                    ],
+                    "entities": [
+                        {
+                            "offsets": [datasets.Value("int64")],
+                            "text": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                        }
+                    ],
+                }
+            )
+
+        elif self.config.schema == "bigbio_kb":
+            features = kb_features
+        else:
+            raise ValueError("Unrecognized schema: %s" % self.config.schema)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        url = _URLS[_DATASETNAME]
+        filepath = dl_manager.download(url)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": filepath,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        # Iterates through lines in file, collecting all lines belonging
+        # to an example and converting into a single dict
+        examples = []
+        tokens = None
+        with gzip.open(filepath, "rt", encoding="mac_roman") as data_file:
+            print(filepath)
+            for line in data_file:
+                line = line.strip()
+                if line.startswith("###"):
+                    tokens = [line]
+                elif line == "":
+                    examples.append(self._make_example(tokens))
+                else:
+                    tokens.append(line)
+
+        # Returns the examples using the desired schema
+        if self.config.schema == "source":
+            for i, example in enumerate(examples):
+                yield i, example
+
+        elif self.config.schema == "bigbio_kb":
+            for i, example in enumerate(examples):
+                bigbio_example = {
+                    "id": "example-" + str(i),
+                    "document_id": example["document_id"],
+                    "passages": [
+                        {
+                            "id": "passage-" + str(i),
+                            "type": "abstract",
+                            "text": [example["text"]],
+                            "offsets": [[0, len(example["text"])]],
+                        }
+                    ],
+                    "entities": [],
+                    "events": [],
+                    "coreferences": [],
+                    "relations": [],
+                }
+
+                # Converts entities to BigBio format
+                for j, entity in enumerate(example["entities"]):
+                    bigbio_example["entities"].append(
+                        {
+                            "id": "entity-" + str(i) + "-" + str(j),
+                            "offsets": [entity["offsets"]],
+                            "text": [entity["text"]],
+                            "type": entity["type"],
+                            "normalized": [],
+                        }
+                    )
+
+                yield i, bigbio_example
+
+    @staticmethod
+    def _make_example(tokens):
+        """
+        Converts a list of lines representing tokens into an example dictionary
+        formatted according to the source schema
+
+        :param tokens: list of strings
+        :return: dictionary in the source schema
+        """
+        document_id = tokens[0][4:]
+
+        text = ""
+        processed_tokens = []
+        entities = []
+        last_offset = 0
+
+        for token in tokens[1:]:
+            token_pieces = token.split("\t")
+            if len(token_pieces) != 5:
+                raise ValueError("Failed to parse line: %s" % token)
+
+            token_text = str(token_pieces[0])
+            token_start = int(token_pieces[1])
+            token_end = int(token_pieces[2])
+            entity_text = str(token_pieces[3])
+            token_tag = str(token_pieces[4])[1:]
+
+            if token_start > last_offset:
+                for _ in range(token_start - last_offset):
+                    text += " "
+            elif token_start < last_offset:
+                raise ValueError("Invalid start index: %s" % token)
+            last_offset = token_end
+
+            text += token_text
+            processed_tokens.append(
+                {
+                    "offsets": [token_start, token_end],
+                    "text": token_text,
+                    "tag": token_tag,
+                }
+            )
+            if entity_text != "":
+                entities.append(
+                    {
+                        "offsets": [token_start, token_start + len(entity_text)],
+                        "text": entity_text,
+                        "type": token_tag[2:],
+                    }
+                )
+
+        return {
+            "document_id": document_id,
+            "text": text,
+            "entities": entities,
+            "tokens": processed_tokens,
+        }