96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
|
"""Tests for fusionagi.gpu.tensor_similarity."""
|
||
|
|
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
from fusionagi.gpu.backend import reset_backend, get_backend
|
||
|
|
from fusionagi.gpu.tensor_similarity import (
|
||
|
|
pairwise_text_similarity,
|
||
|
|
deduplicate_claims,
|
||
|
|
nearest_neighbors,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.fixture(autouse=True)
|
||
|
|
def _use_numpy():
|
||
|
|
reset_backend()
|
||
|
|
get_backend(force="numpy")
|
||
|
|
yield
|
||
|
|
reset_backend()
|
||
|
|
|
||
|
|
|
||
|
|
class TestPairwiseTextSimilarity:
|
||
|
|
def test_basic(self):
|
||
|
|
sim = pairwise_text_similarity(["hello world"], ["hello world"])
|
||
|
|
assert sim.shape == (1, 1)
|
||
|
|
assert sim[0, 0] > 0.9
|
||
|
|
|
||
|
|
def test_different_texts(self):
|
||
|
|
sim = pairwise_text_similarity(["hello world"], ["completely different text"])
|
||
|
|
assert sim.shape == (1, 1)
|
||
|
|
assert sim[0, 0] < 1.0
|
||
|
|
|
||
|
|
def test_multi(self):
|
||
|
|
sim = pairwise_text_similarity(
|
||
|
|
["cat", "dog"],
|
||
|
|
["car", "bike", "train"],
|
||
|
|
)
|
||
|
|
assert sim.shape == (2, 3)
|
||
|
|
|
||
|
|
|
||
|
|
class TestDeduplicateClaims:
|
||
|
|
def test_empty(self):
|
||
|
|
assert deduplicate_claims([]) == []
|
||
|
|
|
||
|
|
def test_single(self):
|
||
|
|
groups = deduplicate_claims(["one claim"])
|
||
|
|
assert groups == [[0]]
|
||
|
|
|
||
|
|
def test_identical(self):
|
||
|
|
groups = deduplicate_claims(
|
||
|
|
["the sky is blue", "the sky is blue"],
|
||
|
|
threshold=0.9,
|
||
|
|
)
|
||
|
|
assert len(groups) == 1
|
||
|
|
assert sorted(groups[0]) == [0, 1]
|
||
|
|
|
||
|
|
def test_different(self):
|
||
|
|
groups = deduplicate_claims(
|
||
|
|
["the sky is blue", "python is a programming language"],
|
||
|
|
threshold=0.99,
|
||
|
|
)
|
||
|
|
assert len(groups) == 2
|
||
|
|
|
||
|
|
def test_all_indices_covered(self):
|
||
|
|
claims = ["a", "b", "c", "d"]
|
||
|
|
groups = deduplicate_claims(claims, threshold=0.99)
|
||
|
|
all_indices = sorted(idx for group in groups for idx in group)
|
||
|
|
assert all_indices == [0, 1, 2, 3]
|
||
|
|
|
||
|
|
|
||
|
|
class TestNearestNeighbors:
|
||
|
|
def test_empty_query(self):
|
||
|
|
result = nearest_neighbors([], ["corpus text"])
|
||
|
|
assert result == []
|
||
|
|
|
||
|
|
def test_empty_corpus(self):
|
||
|
|
result = nearest_neighbors(["query"], [])
|
||
|
|
assert result == [[]]
|
||
|
|
|
||
|
|
def test_basic(self):
|
||
|
|
result = nearest_neighbors(
|
||
|
|
["hello world"],
|
||
|
|
["hello world", "goodbye moon", "hello planet"],
|
||
|
|
top_k=2,
|
||
|
|
)
|
||
|
|
assert len(result) == 1
|
||
|
|
assert len(result[0]) == 2
|
||
|
|
# Each result is (index, score)
|
||
|
|
assert isinstance(result[0][0], tuple)
|
||
|
|
assert isinstance(result[0][0][0], int)
|
||
|
|
assert isinstance(result[0][0][1], float)
|
||
|
|
|
||
|
|
def test_top_k_limit(self):
|
||
|
|
corpus = [f"text {i}" for i in range(20)]
|
||
|
|
result = nearest_neighbors(["text 5"], corpus, top_k=3)
|
||
|
|
assert len(result[0]) == 3
|