Create cdcp.py

cdcp.py

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