add generic brat dataset

dataset_builders/pie/brat/README.md

@@ -0,0 +1,80 @@
+# PIE Dataset Card for "conll2003"
+
+This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for the
+[BRAT Huggingface dataset loading script](https://huggingface.co/datasets/DFKI-SLT/brat).
+
+## Data Schema
+
+The document type for this dataset is `BratDocument` or `BratDocumentWithMergedSpans`, depending on if the
+data was loaded with `merge_fragmented_spans=True` (default: `False`). They define the following data fields:
+
+- `text` (str)
+- `id` (str, optional)
+- `metadata` (dictionary, optional)
+
+and the following annotation layers:
+
+- `spans` (annotation type: `LabeledMultiSpan` in the case of `BratDocument` and `LabeledSpan` and in the case of `BratDocumentWithMergedSpans`, target: `text`)
+- `relations` (annotation type: `BinaryRelation`, target: `spans`)
+- `span_attributes` (annotation type: `Attribute`, target: `spans`)
+- `relation_attributes` (annotation type: `Attribute`, target: `relations`)
+
+The `Attribute` annotation type is defined as follows:
+
+- `annotation` (type: `Annotation`): the annotation to which the attribute is attached
+- `label` (type: `str`)
+- `value` (type: `str`, optional)
+- `score` (type: `float`, optional, not included in comparison)
+
+See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/annotations.py) for the remaining annotation type definitions.
+
+## Document Converters
+
+The dataset provides no predefined document converters because the BRAT format is very flexible and can be used
+for many different tasks. You can add your own document converter by doing the following:
+
+```python
+import dataclasses
+from typing import Optional
+
+from pytorch_ie.core import AnnotationList, annotation_field
+from pytorch_ie.documents import TextBasedDocument
+from pytorch_ie.annotations import LabeledSpan
+
+from pie_datasets import DatasetDict
+
+# define your document class
+@dataclasses.dataclass
+class MyDocument(TextBasedDocument):
+    my_field: Optional[str] = None
+    my_span_annotations: AnnotationList[LabeledSpan] = annotation_field(target="text")
+
+# define your document converter
+def my_converter(document: BratDocumentWithMergedSpans) -> MyDocument:
+    # create your document with the data from the original document.
+    # The fields "text", "id" and "metadata" are derived from the TextBasedDocument.
+    my_document = MyDocument(id=document.id, text=document.text, metadata=document.metadata, my_field="my_value")
+
+    # create a new span annotation
+    new_span = LabeledSpan(label="my_label", start=2, end=10)
+    # add the new span annotation to your document
+    my_document.my_span_annotations.append(new_span)
+
+    # add annotations from the document to your document
+    for span in document.spans:
+        # we need to copy the span because an annotation can only be attached to one document
+        my_document.my_span_annotations.append(span.copy())
+
+    return my_document
+
+
+# load the dataset. We use the "merge_fragmented_spans" dataset variant here
+# because it provides documents of type BratDocumentWithMergedSpans.
+dataset = DatasetDict.load_dataset("pie/brat", name="merge_fragmented_spans", data_dir="path/to/brat/data")
+
+# attach your document converter to the dataset
+dataset.register_document_converter(my_converter)
+
+# convert the dataset
+converted_dataset = dataset.to_document_type(MyDocument)
+```

dataset_builders/pie/brat/brat.py

@@ -0,0 +1,305 @@
+import dataclasses
+import logging
+from collections import defaultdict
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import datasets
+from pytorch_ie.annotations import BinaryRelation, LabeledMultiSpan, LabeledSpan
+from pytorch_ie.core import Annotation, AnnotationList, annotation_field
+from pytorch_ie.documents import TextBasedDocument
+
+from pie_datasets import GeneratorBasedBuilder
+
+logger = logging.getLogger(__name__)
+
+
+def dl2ld(dict_of_lists: Dict[str, List[Any]]) -> List[Dict[str, Any]]:
+    return [dict(zip(dict_of_lists, t)) for t in zip(*dict_of_lists.values())]
+
+
+def ld2dl(
+    list_fo_dicts: List[Dict[str, Any]], keys: Optional[List[str]] = None
+) -> Dict[str, List[Any]]:
+    keys = keys or list(list_fo_dicts[0])
+    return {k: [dic[k] for dic in list_fo_dicts] for k in keys}
+
+
+@dataclasses.dataclass(eq=True, frozen=True)
+class Attribute(Annotation):
+    annotation: Annotation
+    label: str
+    value: Optional[str] = None
+    score: Optional[float] = dataclasses.field(default=None, compare=False)
+
+
+@dataclasses.dataclass
+class BratDocument(TextBasedDocument):
+    spans: AnnotationList[LabeledMultiSpan] = annotation_field(target="text")
+    relations: AnnotationList[BinaryRelation] = annotation_field(target="spans")
+    span_attributes: AnnotationList[Attribute] = annotation_field(target="spans")
+    relation_attributes: AnnotationList[Attribute] = annotation_field(target="relations")
+
+
+@dataclasses.dataclass
+class BratDocumentWithMergedSpans(TextBasedDocument):
+    spans: AnnotationList[LabeledSpan] = annotation_field(target="text")
+    relations: AnnotationList[BinaryRelation] = annotation_field(target="spans")
+    span_attributes: AnnotationList[Attribute] = annotation_field(target="spans")
+    relation_attributes: AnnotationList[Attribute] = annotation_field(target="relations")
+
+
+def example_to_document(
+    example: Dict[str, Any], merge_fragmented_spans: bool = False
+) -> BratDocument:
+    if merge_fragmented_spans:
+        doc = BratDocumentWithMergedSpans(text=example["context"], id=example["file_name"])
+    else:
+        doc = BratDocument(text=example["context"], id=example["file_name"])
+
+    spans: Dict[str, LabeledSpan] = dict()
+    span_locations: List[Tuple[Tuple[int, int]]] = []
+    span_texts: List[str] = []
+    for span_dict in dl2ld(example["spans"]):
+        starts: List[int] = span_dict["locations"]["start"]
+        ends: List[int] = span_dict["locations"]["end"]
+        slices = tuple(zip(starts, ends))
+        span_locations.append(slices)
+        span_texts.append(span_dict["text"])
+        # sanity check
+        span_text_parts = [doc.text[start:end] for start, end in slices]
+        joined_span_texts_stripped = " ".join(span_text_parts).strip()
+        span_text_stripped = span_dict["text"].strip()
+        if joined_span_texts_stripped != span_text_stripped:
+            logger.warning(
+                f"joined span parts do not match stripped span text field content. "
+                f'joined_span_texts_stripped: "{joined_span_texts_stripped}" != stripped "text": "{span_text_stripped}"'
+            )
+        if merge_fragmented_spans:
+            if len(starts) > 1:
+                # check if the text in between the fragments holds only space
+                merged_content_texts = [
+                    doc.text[start:end] for start, end in zip(ends[:-1], starts[1:])
+                ]
+                merged_content_texts_not_empty = [
+                    text.strip() for text in merged_content_texts if text.strip() != ""
+                ]
+                if len(merged_content_texts_not_empty) > 0:
+                    logger.warning(
+                        f"document '{doc.id}' contains a non-contiguous span with text content in between "
+                        f"(will be merged into a single span): "
+                        f"newly covered text parts: {merged_content_texts_not_empty}, "
+                        f"merged span text: '{doc.text[starts[0]:ends[-1]]}', "
+                        f"annotation: {span_dict}"
+                    )
+            # just take everything
+            start = min(starts)
+            end = max(ends)
+            span = LabeledSpan(start=start, end=end, label=span_dict["type"])
+        else:
+            span = LabeledMultiSpan(slices=slices, label=span_dict["type"])
+        spans[span_dict["id"]] = span
+
+    doc.spans.extend(spans.values())
+    doc.metadata["span_ids"] = list(spans.keys())
+    doc.metadata["span_locations"] = span_locations
+    doc.metadata["span_texts"] = span_texts
+
+    relations: Dict[str, BinaryRelation] = dict()
+    for rel_dict in dl2ld(example["relations"]):
+        arguments = dict(zip(rel_dict["arguments"]["type"], rel_dict["arguments"]["target"]))
+        assert set(arguments) == {"Arg1", "Arg2"}
+        head = spans[arguments["Arg1"]]
+        tail = spans[arguments["Arg2"]]
+        rel = BinaryRelation(head=head, tail=tail, label=rel_dict["type"])
+        relations[rel_dict["id"]] = rel
+
+    doc.relations.extend(relations.values())
+    doc.metadata["relation_ids"] = list(relations.keys())
+
+    equivalence_relations = dl2ld(example["equivalence_relations"])
+    if len(equivalence_relations) > 0:
+        raise NotImplementedError("converting equivalence_relations is not yet implemented")
+
+    events = dl2ld(example["events"])
+    if len(events) > 0:
+        raise NotImplementedError("converting events is not yet implemented")
+
+    attribute_annotations: Dict[str, Dict[str, Attribute]] = defaultdict(dict)
+    attribute_ids = []
+    for attribute_dict in dl2ld(example["attributions"]):
+        target_id = attribute_dict["target"]
+        if target_id in spans:
+            target_layer_name = "spans"
+            annotation = spans[target_id]
+        elif target_id in relations:
+            target_layer_name = "relations"
+            annotation = relations[target_id]
+        else:
+            raise Exception("only span and relation attributes are supported yet")
+        attribute = Attribute(
+            annotation=annotation,
+            label=attribute_dict["type"],
+            value=attribute_dict["value"],
+        )
+        attribute_annotations[target_layer_name][attribute_dict["id"]] = attribute
+        attribute_ids.append((target_layer_name, attribute_dict["id"]))
+
+    doc.span_attributes.extend(attribute_annotations["spans"].values())
+    doc.relation_attributes.extend(attribute_annotations["relations"].values())
+    doc.metadata["attribute_ids"] = attribute_ids
+
+    normalizations = dl2ld(example["normalizations"])
+    if len(normalizations) > 0:
+        raise NotImplementedError("converting normalizations is not yet implemented")
+
+    notes = dl2ld(example["notes"])
+    if len(notes) > 0:
+        raise NotImplementedError("converting notes is not yet implemented")
+
+    return doc
+
+
+def document_to_example(
+    document: Union[BratDocument, BratDocumentWithMergedSpans]
+) -> Dict[str, Any]:
+    example = {
+        "context": document.text,
+        "file_name": document.id,
+    }
+    span_dicts: Dict[Union[LabeledSpan, LabeledMultiSpan], Dict[str, Any]] = dict()
+    assert len(document.metadata["span_locations"]) == len(document.spans)
+    assert len(document.metadata["span_texts"]) == len(document.spans)
+    assert len(document.metadata["span_ids"]) == len(document.spans)
+    for i, span in enumerate(document.spans):
+        locations = tuple((start, end) for start, end in document.metadata["span_locations"][i])
+        if isinstance(span, LabeledSpan):
+            assert locations[0][0] == span.start
+            assert locations[-1][1] == span.end
+        elif isinstance(span, LabeledMultiSpan):
+            assert span.slices == locations
+        else:
+            raise TypeError(f"span has unknown type [{type(span)}]: {span}")
+
+        starts, ends = zip(*locations)
+        span_dict = {
+            "id": document.metadata["span_ids"][i],
+            "locations": {
+                "start": list(starts),
+                "end": list(ends),
+            },
+            "text": document.metadata["span_texts"][i],
+            "type": span.label,
+        }
+        if span in span_dicts:
+            prev_ann_dict = span_dicts[span]
+            ann_dict = span_dict
+            logger.warning(
+                f"document {document.id}: annotation exists twice: {prev_ann_dict['id']} and {ann_dict['id']} "
+                f"are identical"
+            )
+        span_dicts[span] = span_dict
+    example["spans"] = ld2dl(list(span_dicts.values()), keys=["id", "type", "locations", "text"])
+
+    relation_dicts: Dict[BinaryRelation, Dict[str, Any]] = dict()
+    assert len(document.metadata["relation_ids"]) == len(document.relations)
+    for i, rel in enumerate(document.relations):
+        arg1_id = span_dicts[rel.head]["id"]
+        arg2_id = span_dicts[rel.tail]["id"]
+        relation_dict = {
+            "id": document.metadata["relation_ids"][i],
+            "type": rel.label,
+            "arguments": {
+                "type": ["Arg1", "Arg2"],
+                "target": [arg1_id, arg2_id],
+            },
+        }
+        if rel in relation_dicts:
+            prev_ann_dict = relation_dicts[rel]
+            ann_dict = relation_dict
+            logger.warning(
+                f"document {document.id}: annotation exists twice: {prev_ann_dict['id']} and {ann_dict['id']} "
+                f"are identical"
+            )
+        relation_dicts[rel] = relation_dict
+
+    example["relations"] = ld2dl(list(relation_dicts.values()), keys=["id", "type", "arguments"])
+
+    example["equivalence_relations"] = ld2dl([], keys=["type", "targets"])
+    example["events"] = ld2dl([], keys=["id", "type", "trigger", "arguments"])
+
+    annotation_dicts = {
+        "spans": span_dicts,
+        "relations": relation_dicts,
+    }
+    all_attribute_annotations = {
+        "spans": document.span_attributes,
+        "relations": document.relation_attributes,
+    }
+    attribute_dicts: Dict[Annotation, Dict[str, Any]] = dict()
+    attribute_ids_per_target = defaultdict(list)
+    for target_layer, attribute_id in document.metadata["attribute_ids"]:
+        attribute_ids_per_target[target_layer].append(attribute_id)
+
+    for target_layer, attribute_ids in attribute_ids_per_target.items():
+        attribute_annotations = all_attribute_annotations[target_layer]
+        assert len(attribute_ids) == len(attribute_annotations)
+        for i, attribute_annotation in enumerate(attribute_annotations):
+            target_id = annotation_dicts[target_layer][attribute_annotation.annotation]["id"]
+            attribute_dict = {
+                "id": attribute_ids_per_target[target_layer][i],
+                "type": attribute_annotation.label,
+                "target": target_id,
+                "value": attribute_annotation.value,
+            }
+            if attribute_annotation in attribute_dicts:
+                prev_ann_dict = attribute_dicts[attribute_annotation]
+                ann_dict = attribute_annotation
+                logger.warning(
+                    f"document {document.id}: annotation exists twice: {prev_ann_dict['id']} and {ann_dict['id']} "
+                    f"are identical"
+                )
+            attribute_dicts[attribute_annotation] = attribute_dict
+
+    example["attributions"] = ld2dl(
+        list(attribute_dicts.values()), keys=["id", "type", "target", "value"]
+    )
+    example["normalizations"] = ld2dl(
+        [], keys=["id", "type", "target", "resource_id", "entity_id"]
+    )
+    example["notes"] = ld2dl([], keys=["id", "type", "target", "note"])
+
+    return example
+
+
+class BratConfig(datasets.BuilderConfig):
+    """BuilderConfig for BratDatasetLoader."""
+
+    def __init__(self, merge_fragmented_spans: bool = False, **kwargs):
+        """BuilderConfig for DocRED.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(**kwargs)
+        self.merge_fragmented_spans = merge_fragmented_spans
+
+
+class BratDatasetLoader(GeneratorBasedBuilder):
+    # this requires https://github.com/ChristophAlt/pytorch-ie/pull/288
+    DOCUMENT_TYPES = {
+        "default": BratDocument,
+        "merge_fragmented_spans": BratDocumentWithMergedSpans,
+    }
+
+    DEFAULT_CONFIG_NAME = "default"
+    BUILDER_CONFIGS = [
+        BratConfig(name="default"),
+        BratConfig(name="merge_fragmented_spans", merge_fragmented_spans=True),
+    ]
+
+    BASE_DATASET_PATH = "DFKI-SLT/brat"
+
+    def _generate_document(self, example, **kwargs):
+        return example_to_document(
+            example, merge_fragmented_spans=self.config.merge_fragmented_spans
+        )

tests/dataset_builders/common.py

@@ -11,6 +11,7 @@
 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"
+PIE_DS_FIXTURE_DATA_PATH = FIXTURES_ROOT / "dataset_builders" / "pie"
 
 logger = logging.getLogger(__name__)