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semantica/benchmarks/ontology/conftest.py
T

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2.3 KiB
Python

import random
import string
from unittest.mock import MagicMock, patch
import pytest
# Data generators
def _random_str(length=8):
return "".join(random.choices(string.ascii_letters, k=length))
@pytest.fixture
def generate_ontology_data():
"""
Generates a synthetic dataset of entities and relationships
designed to triger class and property inference class.
"""
def _generate(entity_count: int, relationship_density: float = 1.5):
num_classes = max(5, entity_count // 50)
class_names = [f"Class_{_random_str(4)}" for _ in range(num_classes)]
entities = []
for i in range(entity_count):
cls = random.choice(class_names)
props = {
f"prop_{_random_str(3)}": random.choice([10, "text", 1.5, True])
for _ in range(random.randint(1, 5))
}
entity = {
"id": f"e_{i}",
"type": cls,
"name": f"Entity_{i}",
"confidence": 0.95,
**props,
}
entities.append(entity)
relationships = []
rel_count = int(entity_count * relationship_density)
rel_types = ["relatedTo", "hasPart", "worksFor", "contains", "memberOf"]
for _ in range(rel_count):
src = random.choice(entities)
tgt = random.choice(entities)
rel = {
"source": src["name"],
"target": tgt["name"],
"type": random.choice(rel_types),
"source_type": src["type"],
"target_type": tgt["type"],
"confidence": 0.8,
}
relationships.append(rel)
return {"entities": entities, "relationships": relationships}
return _generate
@pytest.fixture
def large_ontology_definition(generate_ontology_data):
"""Pre-calculates a structured ontology
definition dictionary.
"""
from semantica.ontology.ontology_generator import OntologyGenerator
data = generate_ontology_data(entity_count=1000)
# Mocking validation in 6-step pipeline to speed up setup
with patch(
"semantica.ontology.ontology_validator.OntologyValidator.validate"
) as mock_val:
mock_val.return_value.valid = True
gen = OntologyGenerator()
return gen.generate_ontology(data, validate=False)