Overview

matchbox.common.factories

Factory functions for the testkit system.

Modules:

• dags –

  Simplified TestkitDAG that’s just a registry of test data.

• entities –

  Classes and functions for generating and comparing entities.

• models –

  Factory functions for generating model testkits and data for testing.

• resolvers –

  Factory helpers for resolver testkits.

• scenarios –

  Scenario factories for creating TestkitDAG scenarios.

• sources –

  Factories for generating sources and linked source testkits for testing.

Using the system

The factory system aims to provide *Testkit objects that facilitate three groups of testing scenarios:

• Realistic mock Source, Model, and Resolver objects to test client-side connectivity functions
• Realistic mock data to test server-side adapter functions
• Realistic mock pipelines with controlled completeness to test client-side methodologies

Four broad factory functions are provided:

• source_factory() generates SourceTestkit objects, which contain dummy Sources and associated data
• linked_sources_factory() generates LinkedSourcesTestkit objects, which contain a collection of interconnected SourceTestkit objects, and the true entities this data describes
• model_factory() generates ModelTestkit objects, which mock score tables that can connect both SourceTestkit and ResolverTestkit objects in ways that fail and succeed predictably
• resolver_factory() generates ResolverTestkit objects, which turn one or more model outputs into cluster assignments

Underneath, these factories and objects use a system of SourceEntity and ClusterEntity objects to share data. The source entities are the ground truth, and the cluster entities are the intermediate or final groupings as data moves through a pipeline.

All factory functions are configured to provide a sensible, useful default.

The system is designed to be as hashable as possible to enable caching. Often you’ll need to provide tuples where you might normally provide lists.

There are some common patterns you might consider using when editing or extending tests.

Client-side connectivity

We can use the factories to test inserting or retrieving isolated Source, Model, or Resolver objects.

Perhaps you’re testing DAG reconstruction and want to add a realistic Source DTO to a DAG:

from matchbox.client.dags import DAG

source_testkit = source_factory()

dag = DAG("companies")
dag.source(**source_testkit.into_dag())

source_factory() can be configured with a powerful range of FeatureConfig objects, including a variety of rules which distort and duplicate the data in predictable ways. These use Faker to generate data.

source_factory(
    n_true_entities=1_000,
    features=(
        FeatureConfig(
            name="name",
            base_generator="first_name_female",
            drop_base=False,
            variations=(PrefixRule(prefix="Ms "),),
        ),
        FeatureConfig(
            name="title",
            base_generator="job",
            drop_base=True,
            variations=(
                SuffixRule(suffix=" MBE"),
                ReplaceRule(old="Manager", new="Leader"),
            ),
        ),
    ),
    repetition=3,
)

By default, each SourceTestkit, ModelTestkit, or ResolverTestkit creates a new DAG. This DAG is only created to satisfy the step’s requirements so it can be used standalone, and as such doesn’t do things like adding the step to that DAG.

If DAG membership is important to your tests, you should either set it manually:

dag = DAG("companies")
source_testkit = source_factory(dag=dag)

Or you can unpack your objects into DAG methods:

source_testkit = source_factory()
dag = DAG("companies")
dag.source(**source_testkit.into_dag())

LinkedSourcesTestkit objects attach all linked sources to the same new DAG.

Server-side adapters

The factories can generate data suitable for MatchboxDBAdapter.create_step(), MatchboxDBAdapter.insert_source_data(), MatchboxDBAdapter.insert_model_data(), and MatchboxDBAdapter.insert_resolver_data(). Between these functions, we can set up any backend in any configuration we need to test the other adapter methods.

Adding a source step and its indexed data:

source_testkit = source_factory().fake_run()
backend.create_step(step=source_testkit.source.to_dto(), path=source_testkit.source.path)
backend.insert_source_data(
    path=source_testkit.source.path,
    data_hashes=source_testkit.data_hashes,
)

Adding a model step and its score data:

model_testkit = model_factory().fake_run()
backend.create_step(step=model_testkit.model.to_dto(), path=model_testkit.model.path)
backend.insert_model_data(
    path=model_testkit.model.path,
    results=model_testkit.scores.to_arrow(),
)

Adding a resolver step and its cluster assignments:

model_testkit = model_factory().fake_run()
resolver_testkit = resolver_factory(inputs=(model_testkit,)).fake_run()

backend.create_step(
    step=resolver_testkit.resolver.to_dto(),
    path=resolver_testkit.resolver.path,
)
backend.insert_resolver_data(
    path=resolver_testkit.resolver.path,
    data=resolver_testkit.assignments.to_arrow(),
)

linked_sources_factory() and the step factories work together to create broader systems of data that connect, or do not connect, in controlled ways.

Methodologies

Configure the true state of your data with linked_sources_factory(). Its default is a set of three tables of ten unique company entities.

• CRN (company name, CRN ID) contains all entities with three unique variations of the company’s name
• CDMS (CRN ID, DH ID) contains all entities repeated twice
• DH (company name, DH ID) contains half the entities

linked_sources_factory() can be configured using tuples of SourceTestkitParameters objects. Using these you can create complex sets of interweaving sources for methodologies to be tested against.

The model_factory() is designed so you can chain together known processes and then plug in your real scoring methodology. LinkedSourcesTestkit.diff_model_edges() makes a score table comparable with the true source entities and gives a detailed diff to help you debug. Pass the .entities from the upstream SourceTestkit or ResolverTestkit objects that fed the methodology you are checking.

linked_testkit = linked_sources_factory()
left = linked_testkit.sources["crn"]
right = linked_testkit.sources["cdms"]

link_model = model_factory(
    left_testkit=left,
    right_testkit=right,
    true_entities=tuple(linked_testkit.true_entities),
).fake_run()

identical, report = linked_testkit.diff_model_edges(
    scores=link_model.scores,
    left_clusters=left.entities,
    right_clusters=right.entities,
    sources=["crn", "cdms"],
    threshold=0,
)

assert identical, report

The resolver_factory() serves the same role for clustering methodologies. It lets you compare resolver assignments against the true entity grouping while keeping the upstream model scores fixed.

Testing with scenarios

For more complex integration tests, the factory provides a scenario system. This allows you to stand up a fully-populated backend with a single context manager, setup_scenario(). This is particularly useful for testing database adapters and end-to-end methodologies.

The main usage pattern is to call setup_scenario() with a backend adapter, a warehouse engine, and a named scenario. The context manager yields a TestkitDAG containing all the sources, models, and resolvers created for the scenario, giving you access to the ground truth.

from matchbox.common.factories.scenarios import setup_scenario

def test_my_adapter_function(my_backend_adapter, engine):
    with setup_scenario(my_backend_adapter, "link", warehouse=engine) as dag:
        assert dag.sources
        assert dag.models
        assert dag.resolvers

The scenario system is cached, so subsequent runs of the same scenario are significantly faster.

See the scenarios API for a full list of available scenarios.

Creating new scenarios

You can create your own scenarios by writing a builder function and registering it with the @register_scenario decorator. This allows you to build reusable, complex data setups for your tests.