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https://github.com/hwchase17/langchain.git
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7f1e444efa
The simsimd package [now has types](https://github.com/ashvardanian/SimSIMD/releases/tag/v5.0.0)
97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
"""Test math utility functions."""
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import importlib
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from typing import List
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import numpy as np
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import pytest
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from langchain_community.utils.math import cosine_similarity, cosine_similarity_top_k
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@pytest.fixture
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def X() -> List[List[float]]:
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return [[1.0, 2.0, 3.0], [0.0, 1.0, 0.0], [1.0, 2.0, 0.0]]
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@pytest.fixture
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def Y() -> List[List[float]]:
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return [[0.5, 1.0, 1.5], [1.0, 0.0, 0.0], [2.0, 5.0, 2.0], [0.0, 0.0, 0.0]]
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def test_cosine_similarity_zero() -> None:
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X = np.zeros((3, 3))
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Y = np.random.random((3, 3))
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expected = np.zeros((3, 3))
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actual = cosine_similarity(X, Y)
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assert np.allclose(expected, actual)
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def test_cosine_similarity_identity() -> None:
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X = np.random.random((4, 4))
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expected = np.ones(4)
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actual = np.diag(cosine_similarity(X, X))
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assert np.allclose(expected, actual)
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def test_cosine_similarity_empty() -> None:
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empty_list: List[List[float]] = []
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assert len(cosine_similarity(empty_list, empty_list)) == 0
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assert len(cosine_similarity(empty_list, np.random.random((3, 3)))) == 0
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def test_cosine_similarity(X: List[List[float]], Y: List[List[float]]) -> None:
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expected = [
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[1.0, 0.26726124, 0.83743579, 0.0],
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[0.53452248, 0.0, 0.87038828, 0.0],
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[0.5976143, 0.4472136, 0.93419873, 0.0],
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]
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actual = cosine_similarity(X, Y)
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assert np.allclose(expected, actual)
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def test_cosine_similarity_top_k(X: List[List[float]], Y: List[List[float]]) -> None:
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expected_idxs = [(0, 0), (2, 2), (1, 2), (0, 2), (2, 0)]
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expected_scores = [1.0, 0.93419873, 0.87038828, 0.83743579, 0.5976143]
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actual_idxs, actual_scores = cosine_similarity_top_k(X, Y)
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assert actual_idxs == expected_idxs
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assert np.allclose(expected_scores, actual_scores)
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def test_cosine_similarity_score_threshold(
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X: List[List[float]], Y: List[List[float]]
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) -> None:
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expected_idxs = [(0, 0), (2, 2)]
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expected_scores = [1.0, 0.93419873]
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actual_idxs, actual_scores = cosine_similarity_top_k(
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X, Y, top_k=None, score_threshold=0.9
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)
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assert actual_idxs == expected_idxs
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assert np.allclose(expected_scores, actual_scores)
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def invoke_cosine_similarity_top_k_score_threshold(
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X: List[List[float]], Y: List[List[float]]
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) -> None:
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expected_idxs = [(0, 0), (2, 2), (1, 2), (0, 2)]
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expected_scores = [1.0, 0.93419873, 0.87038828, 0.83743579]
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actual_idxs, actual_scores = cosine_similarity_top_k(X, Y, score_threshold=0.8)
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assert actual_idxs == expected_idxs
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assert np.allclose(expected_scores, actual_scores, rtol=1.0e-3)
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def test_cosine_similarity_top_k_and_score_threshold(
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X: List[List[float]], Y: List[List[float]]
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) -> None:
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if importlib.util.find_spec("simsimd"):
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raise ValueError("test should be run without simsimd installed.")
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invoke_cosine_similarity_top_k_score_threshold(X, Y)
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@pytest.mark.requires("simsimd")
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def test_cosine_similarity_top_k_and_score_threshold_with_simsimd(
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X: List[List[float]], Y: List[List[float]]
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) -> None:
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# Same test, but ensuring simsimd is available in the project through the import.
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invoke_cosine_similarity_top_k_score_threshold(X, Y)
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