Implement Data Classes to Model API Responses #203
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This was referenced Dec 6, 2024
This was referenced Dec 9, 2024
Adds: - A Node dataclass which models an individual node (with dcid, name, etc) - A NodeGroup dataclass which represents a list of Node objects - An Arcs dataclass, which represents arcs (with a label and containing NodeGroups) - A Properties dataclass which represents a list of Properties
Adds: - An Observation dataclass which models an individual observation (date, value) - An OrderedFacets dataclass which models the 'ordered facets' of observations - A Variable dataclass which represents a variable data (grouped by entity) - A Facet class which represents the metadata for a facet
Adds: - A candidate dataclass to model candidates in the response (with dcid and dominanType) - An Entity dataclass to model entities with their resolution candidates
Adds: - A Cell dataclass to model a single cell in a row - A Row modelling a row with cells.
Adds tests for node, observation, resolve and sparql models.
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Previous PR in the stack: #202
Next PR in the stack #204
This PR implements dataclasses to model API responses from all endpoints. These data classes represent various entities in the Data Commons knowledge graph. Dan's comments on the design document provide more details about the rationale.
Data Classes
datacommons_client/models/node.py: AddedNode,NodeGroup,Arcs, andPropertiesdata classes to represent nodes, groups of nodes, arcs, and properties in the Data Commons knowledge graph.datacommons_client/models/observation.py: AddedObservation,OrderedFacets,Variable, andFacetdata classes to represent observations, ordered facets, variables, and facets in the Data Commons knowledge graph.datacommons_client/models/resolve.py: AddedCandidateandEntitydata classes to represent candidates and entities in the resolution response.datacommons_client/models/sparql.py: AddedCellandRowdata classes to represent cells and rows in a SPARQL query response. Note that the sparql endpoint does not (currently) work for custom instances.Unit Tests
In general, the tests for these classes are just making sure that the objects (especially objects that incorporate other objects) are built consistently and correctly. We avoid testing any basic python functionality.
datacommons_client/tests/models/test_node_models.py: Added unit tests for theNode,NodeGroup,Arcs, andPropertiesclasses, testing theirfrom_jsonmethods with both complete and partial data.datacommons_client/tests/models/test_observation_models.py: Added unit tests for theObservation,OrderedFacets,Variable, andFacetclasses, testing theirfrom_jsonmethods with various data scenarios.datacommons_client/tests/models/test_resolve_models.py: Added unit tests for theCandidateandEntityclasses, testing theirfrom_jsonmethods with complete, partial, and empty data.datacommons_client/tests/models/test_sparql_models.py: Added unit tests for theCellandRowclasses, testing theirfrom_jsonmethods with different types of cell data, including nested empty cells.