Merge pull request #1 from ArneBinder/tacred_dataset

add TACRED dataset

dataset_builders/pie/tacred/tacred.py

@@ -0,0 +1,207 @@
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, List, Optional, Tuple
+
+import datasets
+import pytorch_ie.data.builder
+from pytorch_ie import token_based_document_to_text_based
+from pytorch_ie.annotations import BinaryRelation, LabeledSpan, _post_init_single_label
+from pytorch_ie.core import Annotation, AnnotationList, Document, annotation_field
+from pytorch_ie.documents import (
+    TextDocumentWithLabeledSpansAndBinaryRelations,
+    TokenBasedDocument,
+)
+
+
+@dataclass(eq=True, frozen=True)
+class TokenRelation(Annotation):
+    head_idx: int
+    tail_idx: int
+    label: str
+    score: float = 1.0
+
+    def __post_init__(self) -> None:
+        _post_init_single_label(self)
+
+
+@dataclass(eq=True, frozen=True)
+class TokenAttribute(Annotation):
+    idx: int
+    label: str
+
+
+@dataclass
+class TacredDocument(Document):
+    tokens: Tuple[str, ...]
+    id: Optional[str] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    stanford_ner: AnnotationList[TokenAttribute] = annotation_field(target="tokens")
+    stanford_pos: AnnotationList[TokenAttribute] = annotation_field(target="tokens")
+    entities: AnnotationList[LabeledSpan] = annotation_field(target="tokens")
+    relations: AnnotationList[BinaryRelation] = annotation_field(target="entities")
+    dependency_relations: AnnotationList[TokenRelation] = annotation_field(target="tokens")
+
+
+@dataclass
+class SimpleTacredDocument(TokenBasedDocument):
+    labeled_spans: AnnotationList[LabeledSpan] = annotation_field(target="tokens")
+    binary_relations: AnnotationList[BinaryRelation] = annotation_field(target="labeled_spans")
+
+
+def example_to_document(
+    example: Dict[str, Any],
+    relation_int2str: Callable[[int], str],
+    ner_int2str: Callable[[int], str],
+) -> TacredDocument:
+    document = TacredDocument(
+        tokens=tuple(example["token"]), id=example["id"], metadata=dict(doc_id=example["docid"])
+    )
+
+    for idx, (ner, pos) in enumerate(zip(example["stanford_ner"], example["stanford_pos"])):
+        document.stanford_ner.append(TokenAttribute(idx=idx, label=ner))
+        document.stanford_pos.append(TokenAttribute(idx=idx, label=pos))
+
+    for tail_idx, (deprel_label, head_idx) in enumerate(
+        zip(example["stanford_deprel"], example["stanford_head"])
+    ):
+        if head_idx >= 0:
+            document.dependency_relations.append(
+                TokenRelation(
+                    head_idx=head_idx,
+                    tail_idx=tail_idx,
+                    label=deprel_label,
+                )
+            )
+
+    head = LabeledSpan(
+        start=example["subj_start"],
+        end=example["subj_end"],
+        label=ner_int2str(example["subj_type"]),
+    )
+    tail = LabeledSpan(
+        start=example["obj_start"],
+        end=example["obj_end"],
+        label=ner_int2str(example["obj_type"]),
+    )
+    document.entities.append(head)
+    document.entities.append(tail)
+
+    relation_str = relation_int2str(example["relation"])
+    relation = BinaryRelation(head=head, tail=tail, label=relation_str)
+    document.relations.append(relation)
+
+    return document
+
+
+def _entity_to_dict(
+    entity: LabeledSpan, key_prefix: str = "", label_mapping: Optional[Dict[str, Any]] = None
+) -> Dict[str, Any]:
+    return {
+        f"{key_prefix}start": entity.start,
+        f"{key_prefix}end": entity.end,
+        f"{key_prefix}type": label_mapping[entity.label]
+        if label_mapping is not None
+        else entity.label,
+    }
+
+
+def document_to_example(
+    document: TacredDocument,
+    ner_names: Optional[List[str]] = None,
+    relation_names: Optional[List[str]] = None,
+) -> Dict[str, Any]:
+    ner2idx = {name: idx for idx, name in enumerate(ner_names)} if ner_names is not None else None
+    rel2idx = (
+        {name: idx for idx, name in enumerate(relation_names)}
+        if relation_names is not None
+        else None
+    )
+
+    token = list(document.tokens)
+    stanford_ner_dict = {ner.idx: ner.label for ner in document.stanford_ner}
+    stanford_pos_dict = {pos.idx: pos.label for pos in document.stanford_pos}
+    stanford_ner = [stanford_ner_dict[idx] for idx in range(len(token))]
+    stanford_pos = [stanford_pos_dict[idx] for idx in range(len(token))]
+
+    stanford_deprel = ["ROOT"] * len(document.tokens)
+    stanford_head = [-1] * len(document.tokens)
+    for dep_rel in document.dependency_relations:
+        stanford_deprel[dep_rel.tail_idx] = dep_rel.label
+        stanford_head[dep_rel.tail_idx] = dep_rel.head_idx
+
+    rel = document.relations[0]
+    obj: LabeledSpan = rel.tail
+    subj: LabeledSpan = rel.head
+    return {
+        "id": document.id,
+        "docid": document.metadata["doc_id"],
+        "relation": rel.label if rel2idx is None else rel2idx[rel.label],
+        "token": token,
+        "stanford_ner": stanford_ner,
+        "stanford_pos": stanford_pos,
+        "stanford_deprel": stanford_deprel,
+        "stanford_head": stanford_head,
+        **_entity_to_dict(obj, key_prefix="obj_", label_mapping=ner2idx),
+        **_entity_to_dict(subj, key_prefix="subj_", label_mapping=ner2idx),
+    }
+
+
+def convert_to_text_document_with_labeled_spans_and_binary_relations(
+    document: TacredDocument,
+) -> TextDocumentWithLabeledSpansAndBinaryRelations:
+    doc_simplified = document.as_type(
+        SimpleTacredDocument,
+        field_mapping={"entities": "labeled_spans", "relations": "binary_relations"},
+    )
+    result = token_based_document_to_text_based(
+        doc_simplified,
+        result_document_type=TextDocumentWithLabeledSpansAndBinaryRelations,
+        join_tokens_with=" ",
+    )
+    return result
+
+
+class TacredConfig(datasets.BuilderConfig):
+    """BuilderConfig for Tacred."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Tacred.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(**kwargs)
+
+
+class Tacred(pytorch_ie.data.builder.GeneratorBasedBuilder):
+    DOCUMENT_TYPE = TacredDocument
+
+    DOCUMENT_CONVERTERS = {
+        TextDocumentWithLabeledSpansAndBinaryRelations: convert_to_text_document_with_labeled_spans_and_binary_relations,
+    }
+
+    BASE_DATASET_PATH = "DFKI-SLT/tacred"
+
+    BUILDER_CONFIGS = [
+        TacredConfig(
+            name="original", version=datasets.Version("1.1.0"), description="The original TACRED."
+        ),
+        TacredConfig(
+            name="revisited",
+            version=datasets.Version("1.1.0"),
+            description="The revised TACRED (corrected labels in dev and test split).",
+        ),
+        TacredConfig(
+            name="re-tacred",
+            version=datasets.Version("1.1.0"),
+            description="Relabeled TACRED (corrected labels for all splits and pruned)",
+        ),
+    ]
+
+    def _generate_document_kwargs(self, dataset):
+        return {
+            "ner_int2str": dataset.features["subj_type"].int2str,
+            "relation_int2str": dataset.features["relation"].int2str,
+        }
+
+    def _generate_document(self, example, **kwargs):
+        return example_to_document(example, **kwargs)

tests/dataset_builders/common.py

@@ -0,0 +1,69 @@
+import json
+import logging
+import os
+import re
+from pathlib import Path
+from typing import List, Optional
+
+from tests import FIXTURES_ROOT
+
+DATASET_BUILDER_BASE_PATH = Path("dataset_builders")
+HF_BASE_PATH = DATASET_BUILDER_BASE_PATH / "hf"
+PIE_BASE_PATH = DATASET_BUILDER_BASE_PATH / "pie"
+HF_DS_FIXTURE_DATA_PATH = FIXTURES_ROOT / "dataset_builders" / "hf"
+
+logger = logging.getLogger(__name__)
+
+
+def _deep_compare(
+    obj,
+    obj_expected,
+    path: Optional[str] = None,
+    excluded_paths: Optional[List[str]] = None,
+    enforce_equal_dict_keys: bool = True,
+):
+    if path is not None and excluded_paths is not None:
+        for excluded_path in excluded_paths:
+            if re.match(excluded_path, path):
+                return
+
+    if type(obj) != type(obj_expected):
+        raise AssertionError(f"{path}: {obj} != {obj_expected}")
+    if isinstance(obj, (list, tuple)):
+        if len(obj) != len(obj_expected):
+            raise AssertionError(f"{path}: {obj} != {obj_expected}")
+        for i in range(len(obj)):
+            _deep_compare(
+                obj[i],
+                obj_expected[i],
+                path=f"{path}.{i}" if path is not None else str(i),
+                excluded_paths=excluded_paths,
+                enforce_equal_dict_keys=enforce_equal_dict_keys,
+            )
+    elif isinstance(obj, dict):
+        if enforce_equal_dict_keys and obj.keys() != obj_expected.keys():
+            raise AssertionError(f"{path}: {obj} != {obj_expected}")
+        for k in set(obj) | set(obj_expected):
+            _deep_compare(
+                obj.get(k, None),
+                obj_expected.get(k, None),
+                path=f"{path}.{k}" if path is not None else str(k),
+                excluded_paths=excluded_paths,
+                enforce_equal_dict_keys=enforce_equal_dict_keys,
+            )
+    else:
+        if obj != obj_expected:
+            raise AssertionError(f"{path}: {obj} != {obj_expected}")
+
+
+def _dump_json(obj, fn):
+    logger.warning(f"dump fixture data: {fn}")
+    os.makedirs(os.path.dirname(fn), exist_ok=True)
+    with open(fn, "w") as f:
+        json.dump(obj, f, indent=2, sort_keys=True)
+
+
+def _load_json(fn: str):
+    with open(fn) as f:
+        ex = json.load(f)
+    return ex