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created attribute subclasses for used across BIA packages (#294)
* created attribute subclasses for used across BIA packages * removed to_pascal
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109 changes: 109 additions & 0 deletions
109
bia-shared-datamodels/src/bia_shared_datamodels/attribute_models.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,109 @@ | ||
| from __future__ import annotations | ||
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| from typing import List, Optional, Any | ||
| from typing_extensions import Self | ||
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| from pydantic import BaseModel, Field, model_validator, field_validator, ValidationError | ||
| from .semantic_models import Attribute, AttributeProvenance | ||
| from uuid import UUID | ||
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| # For shared models that are placed inside Attribute objects | ||
| # The API does not enforce what is created inside an Attribute object's value (beyond it being a dicitionary). | ||
| # This allows us to have additional freeform information from submitters. | ||
| # Model here are for use by BIA internal code packages that require a shared source of truth. | ||
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| class SubAttributeMixin(BaseModel): | ||
| def __eq__(self, other): | ||
| if other.__class__ == Attribute: | ||
| attribute_self = Attribute.model_validate(self.model_dump()) | ||
| return attribute_self.__eq__(other) | ||
| else: | ||
| return BaseModel.__eq__(self, other) | ||
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| class DatasetAssociationValue(BaseModel): | ||
| # Allows None, but requires fields to be present | ||
| image_analysis: Optional[str] = Field() | ||
| image_correlation: Optional[str] = Field() | ||
| biosample: Optional[str] = Field() | ||
| image_acquisition: Optional[str] = Field() | ||
| specimen: Optional[str] = Field() | ||
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| class DatasetAssociationAttribute(Attribute, SubAttributeMixin): | ||
| """ | ||
| Model for storing user provided Associations from biostudies in an Attribute on a dataset. | ||
| """ | ||
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| @field_validator("provenance", mode="after") | ||
| @classmethod | ||
| def attribute_provenance(cls, value: AttributeProvenance) -> AttributeProvenance: | ||
| if value != AttributeProvenance.bia_ingest: | ||
| raise ValueError( | ||
| f"Provenance for this type of attribute must be {AttributeProvenance.bia_ingest}" | ||
| ) | ||
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| return value | ||
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| @field_validator("name", mode="after") | ||
| @classmethod | ||
| def attribute_name(cls, value: str) -> str: | ||
| if value != "associations": | ||
| raise ValueError( | ||
| f"name field for this type of attribute must be 'associations'" | ||
| ) | ||
| return value | ||
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| @field_validator("value", mode="after") | ||
| @classmethod | ||
| def attribute_value(cls, value: dict) -> dict: | ||
| if len(value.keys()) != 1: | ||
| raise ValueError("Value dictionary should have exactly one 1 key") | ||
| elif "associations" not in value.keys(): | ||
| raise ValueError(f'The value dictionary key must be "associations"') | ||
| return value | ||
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| value: dict[str, list[DatasetAssociationValue]] = Field() | ||
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| class DatasetAssociatedUUIDAttribute(Attribute, SubAttributeMixin): | ||
| """ | ||
| Model for storing uuid of objects linked to a dataset, for use in code downstream of ingest. | ||
| """ | ||
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| @field_validator("provenance", mode="after") | ||
| @classmethod | ||
| def attribute_provenance(cls, value: AttributeProvenance) -> AttributeProvenance: | ||
| if value != AttributeProvenance.bia_ingest: | ||
| raise ValueError( | ||
| f"Provenance for this type of attribute must be {AttributeProvenance.bia_ingest}" | ||
| ) | ||
| return value | ||
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| @field_validator("name", mode="after") | ||
| @classmethod | ||
| def validate_attribute_name(cls, value) -> Self: | ||
| valid_associations = [ | ||
| "image_acquisition_protocol_uuid", | ||
| "specimen_imaging_preparation_protocol_uuid", | ||
| "bio_sample_uuid", | ||
| "annotation_method_uuid", | ||
| "protocol_uuid", | ||
| ] | ||
| if value not in valid_associations: | ||
| raise ValueError( | ||
| f"Name for this type of attribute must be one of: {valid_associations}" | ||
| ) | ||
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| return value | ||
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| @model_validator(mode="after") | ||
| def validate_attribute_value_key(self) -> Self: | ||
| if len(self.value.keys()) != 1: | ||
| raise ValueError("Value dictionary should have exactly one 1 key") | ||
| elif self.name not in self.value.keys(): | ||
| raise ValueError(f"The key for this type of attribute must be {self.name}") | ||
| return self | ||
|
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| value: dict[str, list[str]] = Field() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,59 @@ | ||
| import pytest | ||
| from pydantic import ValidationError | ||
| from bia_shared_datamodels import ( | ||
| semantic_models, | ||
| mock_objects, | ||
| attribute_models, | ||
| ) | ||
| from typing import Callable | ||
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| @pytest.mark.parametrize( | ||
| ("expected_model_type", "dict_creation_func"), | ||
| ( | ||
| ( | ||
| attribute_models.DatasetAssociatedUUIDAttribute, | ||
| mock_objects.get_dataset_associated_uuid_attribute, | ||
| ), | ||
| ( | ||
| attribute_models.DatasetAssociationAttribute, | ||
| mock_objects.get_dataset_associatation_attribute, | ||
| ), | ||
| ), | ||
| ) | ||
| def test_sub_attribute_models( | ||
| expected_model_type: semantic_models.Attribute, | ||
| dict_creation_func: Callable[[mock_objects.Completeness], dict], | ||
| ): | ||
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| model_completeness_list = [ | ||
| mock_objects.Completeness.COMPLETE, | ||
| mock_objects.Completeness.MINIMAL, | ||
| ] | ||
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| for model_completion in model_completeness_list: | ||
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| model_dict = dict_creation_func(model_completion) | ||
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| attribute_model = semantic_models.Attribute.model_validate(model_dict) | ||
| sub_attribute_model = expected_model_type.model_validate(model_dict) | ||
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| # We have modified the __eq__ function, so it is good to check: | ||
| assert sub_attribute_model == sub_attribute_model | ||
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| assert attribute_model.model_dump() == sub_attribute_model.model_dump() | ||
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| assert attribute_model == sub_attribute_model | ||
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| assert attribute_model == semantic_models.Attribute.model_validate( | ||
| sub_attribute_model | ||
| ) | ||
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| # Check basic attribute doesn't pass validation | ||
| try: | ||
| expected_model_type.model_validate( | ||
| mock_objects.get_attribute_dict(model_completion) | ||
| ) | ||
| assert False | ||
| except ValidationError: | ||
| assert True |