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pythondatasetsactive-learningtext-annotationdatasetnatural-language-processingdata-labelingmachine-learningannotation-tool
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56 lines
1.8 KiB
56 lines
1.8 KiB
from django.db.models import Count, Q, QuerySet
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from django_filters.rest_framework import BooleanFilter, CharFilter, FilterSet
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from .models import Example
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class ExampleFilter(FilterSet):
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confirmed = BooleanFilter(field_name="states", method="filter_by_state")
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label = CharFilter(method="filter_by_label")
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def filter_by_state(self, queryset, field_name, is_confirmed: bool):
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queryset = queryset.annotate(
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num_confirm=Count(
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expression=field_name,
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filter=Q(**{f"{field_name}__confirmed_by": self.request.user})
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| Q(project__collaborative_annotation=True),
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)
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)
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if is_confirmed:
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queryset = queryset.filter(num_confirm__gte=1)
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else:
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queryset = queryset.filter(num_confirm__lte=0)
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return queryset
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def filter_by_label(self, queryset: QuerySet, field_name: str, label: str) -> QuerySet:
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"""Filter examples by a given label name.
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This performs filtering on all of the following labels at once:
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- categories
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- spans
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- relations
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- bboxes
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- segmentations
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Todo: Consider project type to make filtering more efficient.
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Args:
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queryset (QuerySet): QuerySet to filter.
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field_name (str): This equals to `label`.
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label (str): The label name to filter.
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Returns:
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QuerySet: Filtered examples.
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"""
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queryset = queryset.filter(
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Q(categories__label__text=label)
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| Q(spans__label__text=label)
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| Q(relations__type__text=label)
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| Q(bboxes__label__text=label)
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| Q(segmentations__label__text=label)
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)
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return queryset
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class Meta:
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model = Example
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fields = ("project", "text", "created_at", "updated_at", "label")
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