upload hubscripts/cantemist_hub.py to hub from bigbio repo

cantemist.py

@@ -0,0 +1,370 @@
+# 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 loading script for the CANTEMIST corpus.
+
+The CANTEMIST datset is collection of 1301 oncological clinical case reports 
+written in Spanish, with tumor morphology mentions manually annotated and 
+mapped by clinical experts to a controlled terminology. Every tumor morphology 
+mention is linked to an eCIE-O code (the Spanish equivalent of ICD-O).
+"""
+
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['Spanish']
+_PUBMED = False
+_LOCAL = False
+_CITATION = """\
+@article{miranda2020named,
+  title={Named Entity Recognition, Concept Normalization and Clinical Coding: Overview of the Cantemist Track for Cancer Text Mining in Spanish, Corpus, Guidelines, Methods and Results.},
+  author={Miranda-Escalada, Antonio and Farr{\'e}, Eul{\`a}lia and Krallinger, Martin},
+  journal={IberLEF@ SEPLN},
+  pages={303--323},
+  year={2020}
+}
+"""
+
+_DATASETNAME = "cantemist"
+_DISPLAYNAME = "CANTEMIST"
+
+_DESCRIPTION = """\
+Collection of 1301 oncological clinical case reports written in Spanish, with tumor morphology mentions \
+manually annotated and mapped by clinical experts to a controlled terminology. Every tumor morphology \
+mention is linked to an eCIE-O code (the Spanish equivalent of ICD-O).
+
+The original dataset is distributed in Brat format, and was randomly sampled into 3 subsets. \
+The training, development and test sets contain 501, 500 and 300 documents each, respectively.
+
+This dataset was designed for the CANcer TExt Mining Shared Task, sponsored by Plan-TL. \
+The task is divided in 3 subtasks: CANTEMIST-NER, CANTEMIST_NORM and CANTEMIST-CODING.
+
+CANTEMIST-NER track: requires finding automatically tumor morphology mentions. All tumor morphology \
+mentions are defined by their corresponding character offsets in UTF-8 plain text medical documents. 
+
+CANTEMIST-NORM track: clinical concept normalization or named entity normalization task that requires \
+to return all tumor morphology entity mentions together with their corresponding eCIE-O-3.1 codes \
+i.e. finding and normalizing tumor morphology mentions.
+
+CANTEMIST-CODING track: requires returning for each of document a ranked list of its corresponding ICD-O-3 \
+codes. This it is essentially a sort of indexing or multi-label classification task or oncology clinical coding. 
+
+For further information, please visit https://temu.bsc.es/cantemist or send an email to [email protected]
+"""
+
+_HOMEPAGE = "https://temu.bsc.es/cantemist/?p=4338"
+
+_LICENSE = 'Creative Commons Attribution 4.0 International'
+
+_URLS = {
+    "cantemist": "https://zenodo.org/record/3978041/files/cantemist.zip?download=1",
+}
+
+_SUPPORTED_TASKS = [
+    Tasks.NAMED_ENTITY_RECOGNITION,
+    Tasks.NAMED_ENTITY_DISAMBIGUATION,
+    Tasks.TEXT_CLASSIFICATION,
+]
+
+_SOURCE_VERSION = "1.6.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class CantemistDataset(datasets.GeneratorBasedBuilder):
+    """Manually annotated collection of oncological clinical case reports written in Spanish."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="cantemist_source",
+            version=SOURCE_VERSION,
+            description="CANTEMIST source schema",
+            schema="source",
+            subset_id="cantemist",
+        ),
+        BigBioConfig(
+            name="cantemist_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="CANTEMIST BigBio schema for the NER and NED tasks",
+            schema="bigbio_kb",
+            subset_id="subtracks_1_2",
+        ),
+        BigBioConfig(
+            name="cantemist_bigbio_text",
+            version=BIGBIO_VERSION,
+            description="CANTEMIST BigBio schema for the CODING task",
+            schema="bigbio_text",
+            subset_id="subtrack_3",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "cantemist_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"),
+                    "labels": [datasets.Value("string")],  # subtrack 3 codes
+                    "text_bound_annotations": [  # T line in brat
+                        {
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                            "text": datasets.Sequence(datasets.Value("string")),
+                            "type": datasets.Value("string"),
+                            "id": datasets.Value("string"),
+                        }
+                    ],
+                    "events": [  # E line in brat
+                        {
+                            "trigger": datasets.Value("string"),
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "arguments": datasets.Sequence(
+                                {
+                                    "role": datasets.Value("string"),
+                                    "ref_id": datasets.Value("string"),
+                                }
+                            ),
+                        }
+                    ],
+                    "relations": [  # R line in brat
+                        {
+                            "id": datasets.Value("string"),
+                            "head": {
+                                "ref_id": datasets.Value("string"),
+                                "role": datasets.Value("string"),
+                            },
+                            "tail": {
+                                "ref_id": datasets.Value("string"),
+                                "role": datasets.Value("string"),
+                            },
+                            "type": datasets.Value("string"),
+                        }
+                    ],
+                    "equivalences": [  # Equiv line in brat
+                        {
+                            "id": datasets.Value("string"),
+                            "ref_ids": datasets.Sequence(datasets.Value("string")),
+                        }
+                    ],
+                    "attributes": [  # M or A lines in brat
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "ref_id": datasets.Value("string"),
+                            "value": datasets.Value("string"),
+                        }
+                    ],
+                    "normalizations": [  # N lines in brat
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "ref_id": datasets.Value("string"),
+                            "resource_name": datasets.Value("string"),
+                            "cuid": datasets.Value("string"),
+                            "text": datasets.Value("string"),
+                        }
+                    ],
+                    "notes": [  # # lines in brat
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "ref_id": datasets.Value("string"),
+                            "text": datasets.Value("string"),
+                        }
+                    ],
+                },
+            )
+
+        elif self.config.schema == "bigbio_kb":
+            features = kb_features
+
+        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) -> List[datasets.SplitGenerator]:
+        """
+        Downloads/extracts the data to generate the train, validation and test splits.
+
+        Each split is created by instantiating a `datasets.SplitGenerator`, which will
+        call `this._generate_examples` with the keyword arguments in `gen_kwargs`.
+        """
+
+        data_dir = dl_manager.download_and_extract(_URLS["cantemist"])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepaths": {
+                        "task1": Path(
+                            os.path.join(data_dir, "train-set/cantemist-ner")
+                        ),
+                        "task2": Path(
+                            os.path.join(data_dir, "train-set/cantemist-norm")
+                        ),
+                        "task3": Path(
+                            os.path.join(data_dir, "train-set/cantemist-coding")
+                        ),
+                    },
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepaths": {
+                        "task1": Path(os.path.join(data_dir, "test-set/cantemist-ner")),
+                        "task2": Path(
+                            os.path.join(data_dir, "test-set/cantemist-norm")
+                        ),
+                        "task3": Path(
+                            os.path.join(data_dir, "test-set/cantemist-coding")
+                        ),
+                    },
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepaths": {
+                        "task1_set1": Path(
+                            os.path.join(data_dir, "dev-set1/cantemist-ner")
+                        ),
+                        "task1_set2": Path(
+                            os.path.join(data_dir, "dev-set2/cantemist-ner")
+                        ),
+                        "task2_set1": Path(
+                            os.path.join(data_dir, "dev-set1/cantemist-norm")
+                        ),
+                        "task2_set2": Path(
+                            os.path.join(data_dir, "dev-set2/cantemist-norm")
+                        ),
+                        "task3_set1": Path(
+                            os.path.join(data_dir, "dev-set1/cantemist-coding")
+                        ),
+                        "task3_set2": Path(
+                            os.path.join(data_dir, "dev-set2/cantemist-coding")
+                        ),
+                    },
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepaths, split: str) -> Tuple[int, Dict]:
+        """
+        This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        Method parameters are unpacked from `gen_kwargs` as given in `_split_generators`.
+        """
+
+        if split != "dev":
+            txt_files_task1 = list(filepaths["task1"].glob("*txt"))
+            txt_files_task2 = list(filepaths["task2"].glob("*txt"))
+            tsv_file_task3 = Path(
+                os.path.join(filepaths["task3"], f"{split}-coding.tsv")
+            )
+            task3_df = pd.read_csv(tsv_file_task3, sep="\t", header=None)
+        else:
+            txt_files_task1, txt_files_task2, dfs = [], [], []
+            for i in range(1, 3):
+                txt_files_task1 += list(filepaths[f"task1_set{i}"].glob("*txt"))
+                txt_files_task2 += list(filepaths[f"task2_set{i}"].glob("*txt"))
+                tsv_file_task3 = Path(
+                    os.path.join(filepaths[f"task3_set{i}"], f"{split}{i}-coding.tsv")
+                )
+                df = pd.read_csv(tsv_file_task3, sep="\t", header=0)
+                dfs.append(df)
+            task3_df = pd.concat(dfs)
+
+        if self.config.schema == "source" or self.config.schema == "bigbio_text":
+            task3_dict = {}
+            for idx, row in task3_df.iterrows():
+                file, code = row[0], row[1]
+                if file not in task3_dict:
+                    task3_dict[file] = [code]
+                else:
+                    task3_dict[file] += [code]
+
+        if self.config.schema == "source":
+            for guid, txt_file in enumerate(txt_files_task2):
+                example = parsing.parse_brat_file(txt_file, parse_notes=True)
+                if example["document_id"] in task3_dict:
+                    example["labels"] = task3_dict[example["document_id"]]
+                else:
+                    example[
+                        "labels"
+                    ] = (
+                        []
+                    )  # few cases where subtrack 3 has no codes for the current document
+                example["id"] = str(guid)
+                yield guid, example
+
+        elif self.config.schema == "bigbio_kb":
+            for guid, txt_file in enumerate(txt_files_task2):
+                parsed_brat = parsing.parse_brat_file(txt_file, parse_notes=True)
+                example = parsing.brat_parse_to_bigbio_kb(parsed_brat)
+                example["id"] = str(guid)
+                for i in range(0, len(example["entities"])):
+                    normalized_dict = {
+                        "db_id": parsed_brat["notes"][i]["text"],
+                        "db_name": "eCIE-O-3.1",
+                    }
+                    example["entities"][i]["normalized"].append(normalized_dict)
+                yield guid, example
+
+        elif self.config.schema == "bigbio_text":
+            for guid, txt_file in enumerate(txt_files_task1):
+                parsed_brat = parsing.parse_brat_file(txt_file, parse_notes=False)
+                if parsed_brat["document_id"] in task3_dict:
+                    labels = task3_dict[parsed_brat["document_id"]]
+                else:
+                    labels = (
+                        []
+                    )  # few cases where subtrack 3 has no codes for the current document
+                example = {
+                    "id": str(guid),
+                    "document_id": parsed_brat["document_id"],
+                    "text": parsed_brat["text"],
+                    "labels": labels,
+                }
+                yield guid, example
+
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")