diff --git a/bigbio/biodatasets/medical_data/__init__.py b/bigbio/biodatasets/medical_data/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/bigbio/hub/hub_repos/samd/README.md b/bigbio/hub/hub_repos/samd/README.md new file mode 100644 index 000000000..949ac7ade --- /dev/null +++ b/bigbio/hub/hub_repos/samd/README.md @@ -0,0 +1,37 @@ + +--- +language: +- en +bigbio_language: +- English +license: unknown +multilinguality: monolingual +bigbio_license_shortname: UNKNOWN +pretty_name: Sentiment Analysis for Medical Drugs (SAMD) +homepage: https://www.kaggle.com/datasets/arbazkhan971/analyticvidhyadatasetsentiment +bigbio_pubmed: False +bigbio_public: False +bigbio_tasks: +- TEXT_PAIRS_CLASSIFICATION +--- + + +# Dataset Card for Sentiment Analysis for Medical Drugs (SAMD) + +## Dataset Description + +- **Homepage:** https://www.kaggle.com/datasets/arbazkhan971/analyticvidhyadatasetsentiment +- **Pubmed:** False +- **Public:** False +- **Tasks:** TXT2CLASS + +## Citation Information + +``` +@misc{ask9medicaldata, + author = {Khan, Arbaaz}, + title = {Sentiment Analysis for Medical Drugs}, + year = {2019}, + url = {https://www.kaggle.com/datasets/arbazkhan971/analyticvidhyadatasetsentiment}, +} +``` diff --git a/bigbio/hub/hub_repos/samd/bigbiohub.py b/bigbio/hub/hub_repos/samd/bigbiohub.py new file mode 100644 index 000000000..a4792b4b5 --- /dev/null +++ b/bigbio/hub/hub_repos/samd/bigbiohub.py @@ -0,0 +1,592 @@ +from collections import defaultdict +from dataclasses import dataclass +from enum import Enum +import logging +from pathlib import Path +from types import SimpleNamespace +from typing import TYPE_CHECKING, Dict, Iterable, List, Tuple + +import datasets + +if TYPE_CHECKING: + import bioc + +logger = logging.getLogger(__name__) + + +BigBioValues = SimpleNamespace(NULL="") + + +@dataclass +class BigBioConfig(datasets.BuilderConfig): + """BuilderConfig for BigBio.""" + + name: str = None + version: datasets.Version = None + description: str = None + schema: str = None + subset_id: str = None + + +class Tasks(Enum): + NAMED_ENTITY_RECOGNITION = "NER" + NAMED_ENTITY_DISAMBIGUATION = "NED" + EVENT_EXTRACTION = "EE" + RELATION_EXTRACTION = "RE" + COREFERENCE_RESOLUTION = "COREF" + QUESTION_ANSWERING = "QA" + TEXTUAL_ENTAILMENT = "TE" + SEMANTIC_SIMILARITY = "STS" + TEXT_PAIRS_CLASSIFICATION = "TXT2CLASS" + PARAPHRASING = "PARA" + TRANSLATION = "TRANSL" + SUMMARIZATION = "SUM" + TEXT_CLASSIFICATION = "TXTCLASS" + + +entailment_features = datasets.Features( + { + "id": datasets.Value("string"), + "premise": datasets.Value("string"), + "hypothesis": datasets.Value("string"), + "label": datasets.Value("string"), + } +) + +pairs_features = datasets.Features( + { + "id": datasets.Value("string"), + "document_id": datasets.Value("string"), + "text_1": datasets.Value("string"), + "text_2": datasets.Value("string"), + "label": datasets.Value("string"), + } +) + +qa_features = datasets.Features( + { + "id": datasets.Value("string"), + "question_id": datasets.Value("string"), + "document_id": datasets.Value("string"), + "question": datasets.Value("string"), + "type": datasets.Value("string"), + "choices": [datasets.Value("string")], + "context": datasets.Value("string"), + "answer": datasets.Sequence(datasets.Value("string")), + } +) + +text_features = datasets.Features( + { + "id": datasets.Value("string"), + "document_id": datasets.Value("string"), + "text": datasets.Value("string"), + "labels": [datasets.Value("string")], + } +) + +text2text_features = datasets.Features( + { + "id": datasets.Value("string"), + "document_id": datasets.Value("string"), + "text_1": datasets.Value("string"), + "text_2": datasets.Value("string"), + "text_1_name": datasets.Value("string"), + "text_2_name": datasets.Value("string"), + } +) + +kb_features = datasets.Features( + { + "id": datasets.Value("string"), + "document_id": datasets.Value("string"), + "passages": [ + { + "id": datasets.Value("string"), + "type": datasets.Value("string"), + "text": datasets.Sequence(datasets.Value("string")), + "offsets": datasets.Sequence([datasets.Value("int32")]), + } + ], + "entities": [ + { + "id": datasets.Value("string"), + "type": datasets.Value("string"), + "text": datasets.Sequence(datasets.Value("string")), + "offsets": datasets.Sequence([datasets.Value("int32")]), + "normalized": [ + { + "db_name": datasets.Value("string"), + "db_id": datasets.Value("string"), + } + ], + } + ], + "events": [ + { + "id": datasets.Value("string"), + "type": datasets.Value("string"), + # refers to the text_bound_annotation of the trigger + "trigger": { + "text": datasets.Sequence(datasets.Value("string")), + "offsets": datasets.Sequence([datasets.Value("int32")]), + }, + "arguments": [ + { + "role": datasets.Value("string"), + "ref_id": datasets.Value("string"), + } + ], + } + ], + "coreferences": [ + { + "id": datasets.Value("string"), + "entity_ids": datasets.Sequence(datasets.Value("string")), + } + ], + "relations": [ + { + "id": datasets.Value("string"), + "type": datasets.Value("string"), + "arg1_id": datasets.Value("string"), + "arg2_id": datasets.Value("string"), + "normalized": [ + { + "db_name": datasets.Value("string"), + "db_id": datasets.Value("string"), + } + ], + } + ], + } +) + + +TASK_TO_SCHEMA = { + Tasks.NAMED_ENTITY_RECOGNITION.name: "KB", + Tasks.NAMED_ENTITY_DISAMBIGUATION.name: "KB", + Tasks.EVENT_EXTRACTION.name: "KB", + Tasks.RELATION_EXTRACTION.name: "KB", + Tasks.COREFERENCE_RESOLUTION.name: "KB", + Tasks.QUESTION_ANSWERING.name: "QA", + Tasks.TEXTUAL_ENTAILMENT.name: "TE", + Tasks.SEMANTIC_SIMILARITY.name: "PAIRS", + Tasks.TEXT_PAIRS_CLASSIFICATION.name: "PAIRS", + Tasks.PARAPHRASING.name: "T2T", + Tasks.TRANSLATION.name: "T2T", + Tasks.SUMMARIZATION.name: "T2T", + Tasks.TEXT_CLASSIFICATION.name: "TEXT", +} + +SCHEMA_TO_TASKS = defaultdict(set) +for task, schema in TASK_TO_SCHEMA.items(): + SCHEMA_TO_TASKS[schema].add(task) +SCHEMA_TO_TASKS = dict(SCHEMA_TO_TASKS) + +VALID_TASKS = set(TASK_TO_SCHEMA.keys()) +VALID_SCHEMAS = set(TASK_TO_SCHEMA.values()) + +SCHEMA_TO_FEATURES = { + "KB": kb_features, + "QA": qa_features, + "TE": entailment_features, + "T2T": text2text_features, + "TEXT": text_features, + "PAIRS": pairs_features, +} + + +def get_texts_and_offsets_from_bioc_ann(ann: "bioc.BioCAnnotation") -> Tuple: + + offsets = [(loc.offset, loc.offset + loc.length) for loc in ann.locations] + + text = ann.text + + if len(offsets) > 1: + i = 0 + texts = [] + for start, end in offsets: + chunk_len = end - start + texts.append(text[i : chunk_len + i]) + i += chunk_len + while i < len(text) and text[i] == " ": + i += 1 + else: + texts = [text] + + return offsets, texts + + +def remove_prefix(a: str, prefix: str) -> str: + if a.startswith(prefix): + a = a[len(prefix) :] + return a + + +def parse_brat_file( + txt_file: Path, + annotation_file_suffixes: List[str] = None, + parse_notes: bool = False, +) -> Dict: + """ + Parse a brat file into the schema defined below. + `txt_file` should be the path to the brat '.txt' file you want to parse, e.g. 'data/1234.txt' + Assumes that the annotations are contained in one or more of the corresponding '.a1', '.a2' or '.ann' files, + e.g. 'data/1234.ann' or 'data/1234.a1' and 'data/1234.a2'. + Will include annotator notes, when `parse_notes == True`. + brat_features = datasets.Features( + { + "id": datasets.Value("string"), + "document_id": datasets.Value("string"), + "text": datasets.Value("string"), + "text_bound_annotations": [ # T line in brat, e.g. type or event trigger + { + "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" + ), # refers to the text_bound_annotation of the trigger, + "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" + ), # Name of the resource, e.g. "Wikipedia" + "cuid": datasets.Value( + "string" + ), # ID in the resource, e.g. 534366 + "text": datasets.Value( + "string" + ), # Human readable description/name of the entity, e.g. "Barack Obama" + } + ], + ### OPTIONAL: Only included when `parse_notes == True` + "notes": [ # # lines in brat + { + "id": datasets.Value("string"), + "type": datasets.Value("string"), + "ref_id": datasets.Value("string"), + "text": datasets.Value("string"), + } + ], + }, + ) + """ + + example = {} + example["document_id"] = txt_file.with_suffix("").name + with txt_file.open() as f: + example["text"] = f.read() + + # If no specific suffixes of the to-be-read annotation files are given - take standard suffixes + # for event extraction + if annotation_file_suffixes is None: + annotation_file_suffixes = [".a1", ".a2", ".ann"] + + if len(annotation_file_suffixes) == 0: + raise AssertionError( + "At least one suffix for the to-be-read annotation files should be given!" + ) + + ann_lines = [] + for suffix in annotation_file_suffixes: + annotation_file = txt_file.with_suffix(suffix) + try: + with annotation_file.open() as f: + ann_lines.extend(f.readlines()) + except Exception: + continue + + example["text_bound_annotations"] = [] + example["events"] = [] + example["relations"] = [] + example["equivalences"] = [] + example["attributes"] = [] + example["normalizations"] = [] + + if parse_notes: + example["notes"] = [] + + for line in ann_lines: + line = line.strip() + if not line: + continue + + if line.startswith("T"): # Text bound + ann = {} + fields = line.split("\t") + + ann["id"] = fields[0] + ann["type"] = fields[1].split()[0] + ann["offsets"] = [] + span_str = remove_prefix(fields[1], (ann["type"] + " ")) + text = fields[2] + for span in span_str.split(";"): + start, end = span.split() + ann["offsets"].append([int(start), int(end)]) + + # Heuristically split text of discontiguous entities into chunks + ann["text"] = [] + if len(ann["offsets"]) > 1: + i = 0 + for start, end in ann["offsets"]: + chunk_len = end - start + ann["text"].append(text[i : chunk_len + i]) + i += chunk_len + while i < len(text) and text[i] == " ": + i += 1 + else: + ann["text"] = [text] + + example["text_bound_annotations"].append(ann) + + elif line.startswith("E"): + ann = {} + fields = line.split("\t") + + ann["id"] = fields[0] + + ann["type"], ann["trigger"] = fields[1].split()[0].split(":") + + ann["arguments"] = [] + for role_ref_id in fields[1].split()[1:]: + argument = { + "role": (role_ref_id.split(":"))[0], + "ref_id": (role_ref_id.split(":"))[1], + } + ann["arguments"].append(argument) + + example["events"].append(ann) + + elif line.startswith("R"): + ann = {} + fields = line.split("\t") + + ann["id"] = fields[0] + ann["type"] = fields[1].split()[0] + + ann["head"] = { + "role": fields[1].split()[1].split(":")[0], + "ref_id": fields[1].split()[1].split(":")[1], + } + ann["tail"] = { + "role": fields[1].split()[2].split(":")[0], + "ref_id": fields[1].split()[2].split(":")[1], + } + + example["relations"].append(ann) + + # '*' seems to be the legacy way to mark equivalences, + # but I couldn't find any info on the current way + # this might have to be adapted dependent on the brat version + # of the annotation + elif line.startswith("*"): + ann = {} + fields = line.split("\t") + + ann["id"] = fields[0] + ann["ref_ids"] = fields[1].split()[1:] + + example["equivalences"].append(ann) + + elif line.startswith("A") or line.startswith("M"): + ann = {} + fields = line.split("\t") + + ann["id"] = fields[0] + + info = fields[1].split() + ann["type"] = info[0] + ann["ref_id"] = info[1] + + if len(info) > 2: + ann["value"] = info[2] + else: + ann["value"] = "" + + example["attributes"].append(ann) + + elif line.startswith("N"): + ann = {} + fields = line.split("\t") + + ann["id"] = fields[0] + ann["text"] = fields[2] + + info = fields[1].split() + + ann["type"] = info[0] + ann["ref_id"] = info[1] + ann["resource_name"] = info[2].split(":")[0] + ann["cuid"] = info[2].split(":")[1] + example["normalizations"].append(ann) + + elif parse_notes and line.startswith("#"): + ann = {} + fields = line.split("\t") + + ann["id"] = fields[0] + ann["text"] = fields[2] if len(fields) == 3 else BigBioValues.NULL + + info = fields[1].split() + + ann["type"] = info[0] + ann["ref_id"] = info[1] + example["notes"].append(ann) + + return example + + +def brat_parse_to_bigbio_kb(brat_parse: Dict) -> Dict: + """ + Transform a brat parse (conforming to the standard brat schema) obtained with + `parse_brat_file` into a dictionary conforming to the `bigbio-kb` schema (as defined in ../schemas/kb.py) + :param brat_parse: + """ + + unified_example = {} + + # Prefix all ids with document id to ensure global uniqueness, + # because brat ids are only unique within their document + id_prefix = brat_parse["document_id"] + "_" + + # identical + unified_example["document_id"] = brat_parse["document_id"] + unified_example["passages"] = [ + { + "id": id_prefix + "_text", + "type": "abstract", + "text": [brat_parse["text"]], + "offsets": [[0, len(brat_parse["text"])]], + } + ] + + # get normalizations + ref_id_to_normalizations = defaultdict(list) + for normalization in brat_parse["normalizations"]: + ref_id_to_normalizations[normalization["ref_id"]].append( + { + "db_name": normalization["resource_name"], + "db_id": normalization["cuid"], + } + ) + + # separate entities and event triggers + unified_example["events"] = [] + non_event_ann = brat_parse["text_bound_annotations"].copy() + for event in brat_parse["events"]: + event = event.copy() + event["id"] = id_prefix + event["id"] + trigger = next( + tr + for tr in brat_parse["text_bound_annotations"] + if tr["id"] == event["trigger"] + ) + if trigger in non_event_ann: + non_event_ann.remove(trigger) + event["trigger"] = { + "text": trigger["text"].copy(), + "offsets": trigger["offsets"].copy(), + } + for argument in event["arguments"]: + argument["ref_id"] = id_prefix + argument["ref_id"] + + unified_example["events"].append(event) + + unified_example["entities"] = [] + anno_ids = [ref_id["id"] for ref_id in non_event_ann] + for ann in non_event_ann: + entity_ann = ann.copy() + entity_ann["id"] = id_prefix + entity_ann["id"] + entity_ann["normalized"] = ref_id_to_normalizations[ann["id"]] + unified_example["entities"].append(entity_ann) + + # massage relations + unified_example["relations"] = [] + skipped_relations = set() + for ann in brat_parse["relations"]: + if ( + ann["head"]["ref_id"] not in anno_ids + or ann["tail"]["ref_id"] not in anno_ids + ): + skipped_relations.add(ann["id"]) + continue + unified_example["relations"].append( + { + "arg1_id": id_prefix + ann["head"]["ref_id"], + "arg2_id": id_prefix + ann["tail"]["ref_id"], + "id": id_prefix + ann["id"], + "type": ann["type"], + "normalized": [], + } + ) + if len(skipped_relations) > 0: + example_id = brat_parse["document_id"] + logger.info( + f"Example:{example_id}: The `bigbio_kb` schema allows `relations` only between entities." + f" Skip (for now): " + f"{list(skipped_relations)}" + ) + + # get coreferences + unified_example["coreferences"] = [] + for i, ann in enumerate(brat_parse["equivalences"], start=1): + is_entity_cluster = True + for ref_id in ann["ref_ids"]: + if not ref_id.startswith("T"): # not textbound -> no entity + is_entity_cluster = False + elif ref_id not in anno_ids: # event trigger -> no entity + is_entity_cluster = False + if is_entity_cluster: + entity_ids = [id_prefix + i for i in ann["ref_ids"]] + unified_example["coreferences"].append( + {"id": id_prefix + str(i), "entity_ids": entity_ids} + ) + return unified_example diff --git a/bigbio/biodatasets/medical_data/medical_data.py b/bigbio/hub/hub_repos/samd/samd.py similarity index 53% rename from bigbio/biodatasets/medical_data/medical_data.py rename to bigbio/hub/hub_repos/samd/samd.py index 2b60a5c18..893363a25 100644 --- a/bigbio/biodatasets/medical_data/medical_data.py +++ b/bigbio/hub/hub_repos/samd/samd.py @@ -19,14 +19,12 @@ import datasets import pandas as pd -from bigbio.utils import schemas -from bigbio.utils.configs import BigBioConfig -from bigbio.utils.constants import Lang, Tasks -from bigbio.utils.license import Licenses +from .bigbiohub import BigBioConfig, Tasks, pairs_features -_LANGUAGES = [Lang.EN] +_LANGUAGES = ["English"] _PUBMED = False _LOCAL = True + _CITATION = """\ @misc{ask9medicaldata, author = {Khan, Arbaaz}, @@ -36,32 +34,36 @@ } """ -_DATASETNAME = "medical_data" -_DISPLAYNAME = "Medical Data" +_DATASETNAME = "samd" +_DISPLAYNAME = "Sentiment Analysis for Medical Drugs" _DESCRIPTION = """\ - This dataset is designed to do multiclass classification on medical drugs -""" +This dataset contains comments about patients and the sentiment in those comments about a specific drug that's \ +mentioned. -_HOMEPAGE = "" +The dataset has to be download from the Kaggle challenge: + https://www.kaggle.com/datasets/arbazkhan971/analyticvidhyadatasetsentiment/data +""" -_LICENSE = Licenses.UNKNOWN +_HOMEPAGE = "https://www.kaggle.com/datasets/arbazkhan971/analyticvidhyadatasetsentiment" +_LICENSE = "UNKNOWN" _URLS = {} -_SUPPORTED_TASKS = [Tasks.TEXTUAL_ENTAILMENT] - +_SUPPORTED_TASKS = [Tasks.TEXT_PAIRS_CLASSIFICATION] _SOURCE_VERSION = "1.0.0" - _BIGBIO_VERSION = "1.0.0" -class MedicaldataDatatset(datasets.GeneratorBasedBuilder): - """This dataset contains comments about patients and the sentiment in those comments about a specific drug that's mentioned. +class SentimentAnalysisMedicalDrugsDatatset(datasets.GeneratorBasedBuilder): + """This dataset contains comments about patients and the sentiment in those comments about + a specific drug that's mentioned. + 1 - Negative sentiment 2 - Positive sentiment - 0 - Neutral""" + 0 - Neutral + """ SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) @@ -75,10 +77,10 @@ class MedicaldataDatatset(datasets.GeneratorBasedBuilder): subset_id=f"{_DATASETNAME}", ), BigBioConfig( - name=f"{_DATASETNAME}_bigbio_te", + name=f"{_DATASETNAME}_bigbio_pairs", version=BIGBIO_VERSION, description=f"{_DATASETNAME} BigBio schema", - schema="bigbio_te", + schema="bigbio_pairs", subset_id=f"{_DATASETNAME}", ), ] @@ -94,12 +96,14 @@ def _info(self) -> datasets.DatasetInfo: "hash": datasets.Value("string"), "text": datasets.Value("string"), "drug_name": datasets.Value("string"), - "sentiment": datasets.Value("string"), + "label": datasets.Value("string"), } ) - elif self.config.schema == "bigbio_te": - features = schemas.entailment_features + elif self.config.schema == "bigbio_pairs": + features = pairs_features + else: + raise NotImplementedError(f"Schema {self.config.schema} is not supported") return datasets.DatasetInfo( description=_DESCRIPTION, @@ -110,11 +114,11 @@ def _info(self) -> datasets.DatasetInfo: ) def _split_generators(self, dl_manager) -> 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." + "This is a local dataset. Please download the data from Kaggle abd pass the directory containing " + "both data files via data_dir kwarg to load_dataset." ) else: data_dir = self.config.data_dir @@ -139,42 +143,28 @@ def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" - if self.config.schema == "source": - csv_reader = pd.read_csv(filepath, dtype="object") - # Train and test splits handled differently as test split is missing values - if split == "train": - for _cols, line in csv_reader.iterrows(): - document = {} - document["hash"] = line["unique_hash"] - document["text"] = line["text"] - document["drug_name"] = line["drug"] - document["sentiment"] = line["sentiment"] - yield document["hash"], document - else: - for _cols, line in csv_reader.iterrows(): - document = {} - document["hash"] = line["unique_hash"] - document["text"] = line["text"] - document["drug_name"] = line["drug"] - document["sentiment"] = None - yield document["hash"], document - - elif self.config.schema == "bigbio_te": - csv_reader = pd.read_csv(filepath, dtype="object") - # Train and test splits handled differently as test split is missing values - if split == "train": - for _cols, line in csv_reader.iterrows(): - document = {} - document["id"] = line["unique_hash"] - document["premise"] = line["text"] - document["hypothesis"] = line["drug"] - document["label"] = line["sentiment"] - yield document["id"], document + csv_reader = pd.read_csv(filepath, dtype="object") + for _cols, line in csv_reader.iterrows(): + if self.config.schema == "source": + document = { + "hash": line["unique_hash"], + "text": line["text"], + "drug_name": line["drug"], + "label": line["sentiment"] if split == "train" else None, + } + + yield document["hash"], document + + elif self.config.schema == "bigbio_pairs": + document = { + "id": line["unique_hash"], + "document_id": line["unique_hash"], + "text_1": line["text"], + "text_2": line["drug"], + "label": line["sentiment"] if split == "train" else None, # test split labels are not given + } + + yield document["id"], document + else: - for _cols, line in csv_reader.iterrows(): - document = {} - document["id"] = line["unique_hash"] - document["premise"] = line["text"] - document["hypothesis"] = line["drug"] - document["label"] = None - yield document["id"], document + raise NotImplementedError(f"Schema {self.config.schema} is not supported")