118 lines
4.9 KiB
Python
118 lines
4.9 KiB
Python
from typing import List, Optional, Tuple
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from code_to_optimize.math_utils import Matrix, cosine_similarity_top_k
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def use_cosine_similarity(
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X: Matrix,
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Y: Matrix,
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top_k: Optional[int] = 5,
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score_threshold: Optional[float] = None,
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) -> Tuple[List[Tuple[int, int]], List[float]]:
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return cosine_similarity_top_k(X, Y, top_k, score_threshold)
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CACHED_TESTS = """import unittest
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import numpy as np
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from sklearn.metrics.pairwise import cosine_similarity
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from typing import List, Optional, Tuple, Union
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Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
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def cosine_similarity_top_k(X: Matrix, Y: Matrix, top_k: Optional[int]=5, score_threshold: Optional[float]=None) -> Tuple[List[Tuple[int, int]], List[float]]:
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\"\"\"Row-wise cosine similarity with optional top-k and score threshold filtering.
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Args:
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X: Matrix.
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Y: Matrix, same width as X.
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top_k: Max number of results to return.
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score_threshold: Minimum cosine similarity of results.
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Returns:
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Tuple of two lists. First contains two-tuples of indices (X_idx, Y_idx),
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second contains corresponding cosine similarities.
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\"\"\"
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if len(X) == 0 or len(Y) == 0:
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return ([], [])
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score_array = cosine_similarity(X, Y)
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sorted_idxs = score_array.flatten().argsort()[::-1]
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top_k = top_k or len(sorted_idxs)
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top_idxs = sorted_idxs[:top_k]
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score_threshold = score_threshold or -1.0
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top_idxs = top_idxs[score_array.flatten()[top_idxs] > score_threshold]
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ret_idxs = [(x // score_array.shape[1], x % score_array.shape[1]) for x in top_idxs]
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scores = score_array.flatten()[top_idxs].tolist()
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return (ret_idxs, scores)
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def use_cosine_similarity(X: Matrix, Y: Matrix, top_k: Optional[int]=5, score_threshold: Optional[float]=None) -> Tuple[List[Tuple[int, int]], List[float]]:
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return cosine_similarity_top_k(X, Y, top_k, score_threshold)
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class TestUseCosineSimilarity(unittest.TestCase):
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def test_normal_scenario(self):
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X = [[1, 2, 3], [4, 5, 6]]
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Y = [[7, 8, 9], [10, 11, 12]]
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result = use_cosine_similarity(X, Y, top_k=1, score_threshold=0.5)
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self.assertEqual(result, ([(0, 1)], [0.9746318461970762]))
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def test_edge_case_empty_matrices(self):
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X = []
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Y = []
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result = use_cosine_similarity(X, Y)
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self.assertEqual(result, ([], []))
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def test_edge_case_different_widths(self):
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X = [[1, 2, 3]]
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Y = [[4, 5]]
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with self.assertRaises(ValueError):
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use_cosine_similarity(X, Y)
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def test_edge_case_negative_top_k(self):
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X = [[1, 2, 3]]
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Y = [[4, 5, 6]]
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with self.assertRaises(IndexError):
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use_cosine_similarity(X, Y, top_k=-1)
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def test_edge_case_zero_top_k(self):
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X = [[1, 2, 3]]
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Y = [[4, 5, 6]]
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result = use_cosine_similarity(X, Y, top_k=0)
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self.assertEqual(result, ([], []))
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def test_edge_case_negative_score_threshold(self):
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X = [[1, 2, 3]]
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Y = [[4, 5, 6]]
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result = use_cosine_similarity(X, Y, score_threshold=-1.0)
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self.assertEqual(result, ([(0, 0)], [0.9746318461970762]))
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def test_edge_case_large_score_threshold(self):
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X = [[1, 2, 3]]
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Y = [[4, 5, 6]]
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result = use_cosine_similarity(X, Y, score_threshold=2.0)
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self.assertEqual(result, ([], []))
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def test_exceptional_case_non_matrix_X(self):
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X = [1, 2, 3]
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Y = [[4, 5, 6]]
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with self.assertRaises(ValueError):
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use_cosine_similarity(X, Y)
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def test_exceptional_case_non_integer_top_k(self):
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X = [[1, 2, 3]]
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Y = [[4, 5, 6]]
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with self.assertRaises(TypeError):
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use_cosine_similarity(X, Y, top_k='5')
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def test_exceptional_case_non_float_score_threshold(self):
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X = [[1, 2, 3]]
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Y = [[4, 5, 6]]
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with self.assertRaises(TypeError):
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use_cosine_similarity(X, Y, score_threshold='0.5')
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def test_special_values_nan_in_matrices(self):
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X = [[1, 2, np.nan]]
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Y = [[4, 5, 6]]
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with self.assertRaises(ValueError):
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use_cosine_similarity(X, Y)
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def test_special_values_none_top_k(self):
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X = [[1, 2, 3]]
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Y = [[4, 5, 6]]
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result = use_cosine_similarity(X, Y, top_k=None)
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self.assertEqual(result, ([(0, 0)], [0.9746318461970762]))
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def test_special_values_none_score_threshold(self):
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X = [[1, 2, 3]]
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Y = [[4, 5, 6]]
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result = use_cosine_similarity(X, Y, score_threshold=None)
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self.assertEqual(result, ([(0, 0)], [0.9746318461970762]))
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def test_large_inputs(self):
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X = np.random.rand(1000, 1000)
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Y = np.random.rand(1000, 1000)
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result = use_cosine_similarity(X, Y, top_k=10, score_threshold=0.5)
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self.assertEqual(len(result[0]), 10)
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self.assertEqual(len(result[1]), 10)
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self.assertTrue(all((score > 0.5 for score in result[1])))
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if __name__ == '__main__':
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unittest.main()"""
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