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pythondatasetsactive-learningtext-annotationdatasetnatural-language-processingdata-labelingmachine-learningannotation-tool
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98 lines
3.1 KiB
98 lines
3.1 KiB
import abc
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from itertools import groupby
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from typing import Dict, List
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from pydantic import UUID4
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from .label import Label
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from .label_types import LabelTypes
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from examples.models import Example
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from labels.models import Category as CategoryModel
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from labels.models import Label as LabelModel
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from labels.models import Relation as RelationModel
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from labels.models import Span as SpanModel
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from labels.models import TextLabel as TextLabelModel
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from projects.models import Project
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class Labels(abc.ABC):
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label_model = LabelModel
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def __init__(self, labels: List[Label], types: LabelTypes):
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self.labels = labels
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self.types = types
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def __len__(self) -> int:
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return len(self.labels)
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def clean(self, project: Project):
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pass
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def save_types(self, project: Project):
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types = [label.create_type(project) for label in self.labels]
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filtered_types = list(filter(None, types))
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self.types.save(filtered_types)
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self.types.update(project)
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@property
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def uuid_to_example(self) -> Dict[UUID4, Example]:
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example_uuids = {str(label.example_uuid) for label in self.labels}
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examples = Example.objects.filter(uuid__in=example_uuids)
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return {example.uuid: example for example in examples}
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def save(self, user, **kwargs):
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uuid_to_example = self.uuid_to_example
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labels = [
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label.create(user, uuid_to_example[label.example_uuid], self.types, **kwargs)
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for label in self.labels
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if label.example_uuid in uuid_to_example
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]
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self.label_model.objects.bulk_create(labels)
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class Categories(Labels):
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label_model = CategoryModel
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def clean(self, project: Project):
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exclusive = getattr(project, "single_class_classification", False)
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if exclusive:
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groups = groupby(self.labels, lambda label: label.example_uuid)
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self.labels = [next(group) for _, group in groups]
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class Spans(Labels):
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label_model = SpanModel
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def clean(self, project: Project):
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allow_overlapping = getattr(project, "allow_overlapping", False)
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if allow_overlapping:
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return
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spans = []
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groups = groupby(self.labels, lambda label: label.example_uuid)
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for _, group in groups:
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labels = sorted(group)
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last_offset = -1
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for label in labels:
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if getattr(label, "start_offset") >= last_offset:
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last_offset = getattr(label, "end_offset")
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spans.append(label)
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self.labels = spans
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@property
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def id_to_span(self) -> Dict[int, SpanModel]:
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span_uuids = [str(label.uuid) for label in self.labels]
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spans = SpanModel.objects.filter(uuid__in=span_uuids)
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uuid_to_span = {span.uuid: span for span in spans}
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return {span.id: uuid_to_span[span.uuid] for span in self.labels}
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class Texts(Labels):
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label_model = TextLabelModel
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class Relations(Labels):
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label_model = RelationModel
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def save(self, user, **kwargs):
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id_to_span = kwargs["spans"].id_to_span
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super().save(user, id_to_span=id_to_span)
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