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68 lines
2.6 KiB
Python
68 lines
2.6 KiB
Python
import unittest
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import numpy as np
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from server.common.utils.matrix_utils import is_matrix_sparse, get_column_shift_encode_for_matrix
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class TestMatrixUtils(unittest.TestCase):
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def test__is_matrix_sparse__zero_and_one_hundred_percent_threshold(self):
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matrix = np.array([1, 2, 3])
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self.assertFalse(is_matrix_sparse(matrix, 0))
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self.assertTrue(is_matrix_sparse(matrix, 100))
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def test__is_matrix_sparse__partially_populated_sparse_matrix_returns_true(self):
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matrix = np.zeros([3, 4])
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matrix[2][3] = 1.0
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matrix[1][1] = 2.2
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self.assertTrue(is_matrix_sparse(matrix, 50))
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def test__is_matrix_sparse__partially_populated_dense_matrix_returns_false(self):
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matrix = np.zeros([2, 2])
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matrix[0][0] = 1.0
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matrix[0][1] = 2.2
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matrix[1][1] = 3.7
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self.assertFalse(is_matrix_sparse(matrix, 50))
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def test__is_matrix_sparse__giant_matrix_returns_false_early(self):
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matrix = np.ones([20000, 20])
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with self.assertLogs(level="INFO") as logger:
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self.assertFalse(is_matrix_sparse(matrix, 1))
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# Because the function returns early a log will output the _estimate_ instead of the _exact_ percentage of
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# non-zero elements in the matrix.
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self.assertIn("Percentage of non-zero elements (estimate)", logger.output[0])
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def test__is_matrix_sparse_with_column_shift_encoding__regular_sparse_returns_true(self):
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matrix = np.zeros([2, 2])
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matrix[0][0] = 1.0
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self.assertIsNotNone(get_column_shift_encode_for_matrix(matrix, 50))
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def test__is_matrix_sparse_with_column_shift_encoding__column_shift_returns_same_value(self):
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matrix = np.ones([2, 2])
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expected_column_shift = [1, 1]
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actual_column_shift = get_column_shift_encode_for_matrix(matrix, 50)
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self.assertTrue((expected_column_shift == actual_column_shift).all())
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def test__is_matrix_sparse_with_column_shift_encoding__impossible_column_shift_returns_none(self):
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matrix = np.array([[1, 2], [3, 4]])
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self.assertIsNone(get_column_shift_encode_for_matrix(matrix, 50))
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def test__is_matrix_sparse_with_column_shift_encoding__giant_matrix_returns_false_early(self):
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matrix = np.random.rand(20000, 20)
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with self.assertLogs(level="INFO") as logger:
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self.assertFalse(is_matrix_sparse(matrix, 1))
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# Because the function returns early a log will output the _estimate_ instead of the _exact_ percentage of
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# non-zero elements in the matrix.
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self.assertIn("Percentage of non-zero elements (estimate)", logger.output[0])
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