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import dataclasses |
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from typing import Any, Callable, Dict, List, Optional |
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import datasets |
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import pytorch_ie.data.builder |
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from pytorch_ie.annotations import BinaryRelation, LabeledSpan |
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from pytorch_ie.core import Annotation, AnnotationList, Document, annotation_field |
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from src import utils |
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log = utils.get_pylogger(__name__) |
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def dl2ld(dict_of_lists): |
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return [dict(zip(dict_of_lists, t)) for t in zip(*dict_of_lists.values())] |
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def ld2dl(list_of_dicts, keys: Optional[List[str]] = None, as_list: bool = False): |
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if keys is None: |
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keys = list_of_dicts[0].keys() |
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if as_list: |
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return [[d[k] for d in list_of_dicts] for k in keys] |
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else: |
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return {k: [d[k] for d in list_of_dicts] for k in keys} |
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@dataclasses.dataclass(frozen=True) |
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class Attribute(Annotation): |
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value: str |
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annotation: Annotation |
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@dataclasses.dataclass |
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class CDCPDocument(Document): |
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text: str |
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id: Optional[str] = None |
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metadata: Dict[str, Any] = dataclasses.field(default_factory=dict) |
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propositions: AnnotationList[LabeledSpan] = annotation_field(target="text") |
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relations: AnnotationList[BinaryRelation] = annotation_field(target="propositions") |
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urls: AnnotationList[Attribute] = annotation_field(target="propositions") |
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def example_to_document( |
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example: Dict[str, Any], |
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relation_int2str: Callable[[int], str], |
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proposition_int2str: Callable[[int], str], |
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): |
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document = CDCPDocument(id=example["id"], text=example["text"]) |
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for proposition_dict in dl2ld(example["propositions"]): |
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proposition = LabeledSpan( |
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start=proposition_dict["start"], |
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end=proposition_dict["end"], |
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label=proposition_int2str(proposition_dict["label"]), |
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) |
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document.propositions.append(proposition) |
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if proposition_dict.get("url", "") != "": |
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url = Attribute(annotation=proposition, value=proposition_dict["url"]) |
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document.urls.append(url) |
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for relation_dict in dl2ld(example["relations"]): |
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relation = BinaryRelation( |
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head=document.propositions[relation_dict["head"]], |
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tail=document.propositions[relation_dict["tail"]], |
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label=relation_int2str(relation_dict["label"]), |
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) |
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document.relations.append(relation) |
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return document |
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def document_to_example( |
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document: CDCPDocument, |
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relation_str2int: Callable[[str], int], |
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proposition_str2int: Callable[[str], int], |
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) -> Dict[str, Any]: |
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result = {"id": document.id, "text": document.text} |
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proposition2dict = {} |
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proposition2idx = {} |
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for idx, proposition in enumerate(document.propositions): |
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proposition2dict[proposition] = { |
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"start": proposition.start, |
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"end": proposition.end, |
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"label": proposition_str2int(proposition.label), |
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"url": "", |
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} |
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proposition2idx[proposition] = idx |
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for url in document.urls: |
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proposition2dict[url.annotation]["url"] = url.value |
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result["propositions"] = ld2dl( |
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proposition2dict.values(), keys=["start", "end", "label", "url"] |
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) |
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relations = [ |
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{ |
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"head": proposition2idx[relation.head], |
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"tail": proposition2idx[relation.tail], |
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"label": relation_str2int(relation.label), |
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} |
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for relation in document.relations |
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] |
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result["relations"] = ld2dl(relations, keys=["head", "tail", "label"]) |
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return result |
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class CDCPConfig(datasets.BuilderConfig): |
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"""BuilderConfig for CDCP.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for CDCP. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__(**kwargs) |
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class CDCP(pytorch_ie.data.builder.GeneratorBasedBuilder): |
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DOCUMENT_TYPE = CDCPDocument |
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BASE_DATASET_PATH = "DFKI-SLT/cdcp" |
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BUILDER_CONFIGS = [datasets.BuilderConfig(name="default")] |
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DEFAULT_CONFIG_NAME = "default" |
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def _generate_document_kwargs(self, dataset): |
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return { |
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"relation_int2str": dataset.features["relations"].feature["label"].int2str, |
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"proposition_int2str": dataset.features["propositions"].feature["label"].int2str, |
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} |
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def _generate_document(self, example, relation_int2str, proposition_int2str): |
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return example_to_document( |
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example, relation_int2str=relation_int2str, proposition_int2str=proposition_int2str |
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) |
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