langchain/tests/unit_tests/test_math_utils.py
hwaking e57ebf3922
add get_top_k_cosine_similarity method to get max top k score and index (#5059)
# Row-wise cosine similarity between two equal-width matrices and return
the max top_k score and index, the score all greater than
threshold_score.

Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
2023-05-22 11:55:48 -07:00

79 lines
2.5 KiB
Python

"""Test math utility functions."""
from typing import List
import numpy as np
import pytest
from langchain.math_utils import cosine_similarity, cosine_similarity_top_k
@pytest.fixture
def X() -> List[List[float]]:
return [[1.0, 2.0, 3.0], [0.0, 1.0, 0.0], [1.0, 2.0, 0.0]]
@pytest.fixture
def Y() -> List[List[float]]:
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]]
def test_cosine_similarity_zero() -> None:
X = np.zeros((3, 3))
Y = np.random.random((3, 3))
expected = np.zeros((3, 3))
actual = cosine_similarity(X, Y)
assert np.allclose(expected, actual)
def test_cosine_similarity_identity() -> None:
X = np.random.random((4, 4))
expected = np.ones(4)
actual = np.diag(cosine_similarity(X, X))
assert np.allclose(expected, actual)
def test_cosine_similarity_empty() -> None:
empty_list: List[List[float]] = []
assert len(cosine_similarity(empty_list, empty_list)) == 0
assert len(cosine_similarity(empty_list, np.random.random((3, 3)))) == 0
def test_cosine_similarity(X: List[List[float]], Y: List[List[float]]) -> None:
expected = [
[1.0, 0.26726124, 0.83743579, 0.0],
[0.53452248, 0.0, 0.87038828, 0.0],
[0.5976143, 0.4472136, 0.93419873, 0.0],
]
actual = cosine_similarity(X, Y)
assert np.allclose(expected, actual)
def test_cosine_similarity_top_k(X: List[List[float]], Y: List[List[float]]) -> None:
expected_idxs = [(0, 0), (2, 2), (1, 2), (0, 2), (2, 0)]
expected_scores = [1.0, 0.93419873, 0.87038828, 0.83743579, 0.5976143]
actual_idxs, actual_scores = cosine_similarity_top_k(X, Y)
assert actual_idxs == expected_idxs
assert np.allclose(expected_scores, actual_scores)
def test_cosine_similarity_score_threshold(
X: List[List[float]], Y: List[List[float]]
) -> None:
expected_idxs = [(0, 0), (2, 2)]
expected_scores = [1.0, 0.93419873]
actual_idxs, actual_scores = cosine_similarity_top_k(
X, Y, top_k=None, score_threshold=0.9
)
assert actual_idxs == expected_idxs
assert np.allclose(expected_scores, actual_scores)
def test_cosine_similarity_top_k_and_score_threshold(
X: List[List[float]], Y: List[List[float]]
) -> None:
expected_idxs = [(0, 0), (2, 2), (1, 2), (0, 2)]
expected_scores = [1.0, 0.93419873, 0.87038828, 0.83743579]
actual_idxs, actual_scores = cosine_similarity_top_k(X, Y, score_threshold=0.8)
assert actual_idxs == expected_idxs
assert np.allclose(expected_scores, actual_scores)