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A pytest test function that verifies the SimilarityCleaner's ability to identify and remove nearly similar text entries while preserving distinct ones.

Purpose

This test validates that the SimilarityCleaner correctly handles a dataset containing two nearly identical sentences and one distinct sentence. It ensures the cleaner removes one of the similar texts (keeping only one representative) while preserving the completely different sentence, resulting in exactly 2 unique texts from the original 3.

Source Code

def test_nearly_similar_text_handling(setup_similarity_cleaner):
    texts = [
        "The quick brown fox jumps over the lazy dog.",
        "The quick brown fox jumped over the lazy dog.",
        "A completely different sentence."
    ]
    cleaned_texts = setup_similarity_cleaner.clean(texts)
    assert len(cleaned_texts) == 2
    assert "The quick brown fox jumps over the lazy dog." in cleaned_texts
    assert "A completely different sentence." in cleaned_texts

Parameters

Parameter Details

setup_similarity_cleaner: A pytest fixture that provides an initialized instance of SimilarityCleaner. This fixture is expected to be defined elsewhere in the test suite and provides the cleaner object with appropriate configuration for similarity detection.

Return Value

This function does not return any value (implicitly returns None). It performs assertions to validate the behavior of the SimilarityCleaner. The test passes if all assertions succeed, otherwise it raises an AssertionError.

Dependencies

• pytest
• src.cleaners.similarity_cleaner

Required Imports

import pytest
from src.cleaners.similarity_cleaner import SimilarityCleaner

Usage Example

# In conftest.py or test file:
import pytest
from src.cleaners.similarity_cleaner import SimilarityCleaner

@pytest.fixture
def setup_similarity_cleaner():
    return SimilarityCleaner(threshold=0.9)

# Run the test:
# pytest test_file.py::test_nearly_similar_text_handling

# Or use the fixture directly in another test:
def test_example(setup_similarity_cleaner):
    texts = ["Hello world", "Hello world!", "Goodbye"]
    result = setup_similarity_cleaner.clean(texts)
    assert len(result) == 2

Best Practices

• This test assumes the SimilarityCleaner keeps the first occurrence of similar texts and removes subsequent similar ones
• The test uses a fixture for setup, following pytest best practices for test isolation and reusability
• The test validates both the count of results and the specific content, ensuring comprehensive verification
• When implementing similar tests, ensure the similarity threshold in the fixture is appropriate for the test cases
• The test assumes deterministic behavior - if the cleaner's behavior is non-deterministic, the assertions may need adjustment

Tags

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