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https://github.com/open-thought/reasoning-gym.git
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25b8e35589
* binary matrix curriculum * register BinaryMatrixCurriculum --------- Co-authored-by: Andreas Koepf <andreas.koepf@provisio.com>
148 lines
4.8 KiB
Python
148 lines
4.8 KiB
Python
"""Tests for Binary Matrix questions generation"""
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import pytest
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from reasoning_gym.algorithmic.binary_matrix import BinaryMatrixConfig, BinaryMatrixCurriculum, BinaryMatrixDataset
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def test_binary_matrix_config_validation():
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"""Test that invalid configs raise appropriate errors"""
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with pytest.raises(AssertionError):
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config = BinaryMatrixConfig(max_n=-1) # Negative not allowed
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config.validate()
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with pytest.raises(AssertionError):
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config = BinaryMatrixConfig(max_n=0) # Zero not allowed
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config.validate()
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with pytest.raises(AssertionError):
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config = BinaryMatrixConfig(min_n=-1) # Negative not allowed
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config.validate()
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with pytest.raises(AssertionError):
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config = BinaryMatrixConfig(min_n=0) # Zero not allowed
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config.validate()
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with pytest.raises(AssertionError):
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config = BinaryMatrixConfig(p_zero=0) # <= 0 not allowed
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config.validate()
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with pytest.raises(AssertionError):
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config = BinaryMatrixConfig(p_zero=1.01) # > 1 not allowed
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config.validate()
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def test_binary_matrix_dataset_deterministic():
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"""Test that dataset generates same items with same seed"""
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config = BinaryMatrixConfig(seed=42, size=10)
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dataset1 = BinaryMatrixDataset(config)
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dataset2 = BinaryMatrixDataset(config)
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for i in range(len(dataset1)):
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assert dataset1[i] == dataset2[i]
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def test_binary_matrix_dataset_items():
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"""Test basic properties of generated items"""
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config = BinaryMatrixConfig(max_n=5, size=10, seed=42)
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dataset = BinaryMatrixDataset(config)
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for i in range(len(dataset)):
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item = dataset[i]
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# Check item structure
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assert isinstance(item, dict)
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assert "question" in item
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assert "answer" in item
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assert "metadata" in item
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# Check metadata
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assert "matrix" in item["metadata"]
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assert "solution" in item["metadata"]
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matrix = item["metadata"]["matrix"]
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solution = item["metadata"]["solution"]
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# Verify list dimensions
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assert len(matrix) <= config.max_n
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assert all(len(row) <= config.max_n for row in matrix)
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assert all(len(row) <= config.max_n for row in solution)
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# Verify matrix values
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for r in range(len(matrix)):
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for c in range(len(matrix[r])):
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assert matrix[r][c] in {0, 1}
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assert solution[r][c] >= matrix[r][c]
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def test_binary_matrix_dataset_iteration():
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"""Test that iteration respects dataset size"""
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config = BinaryMatrixConfig(size=5, seed=42)
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dataset = BinaryMatrixDataset(config)
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items = list(dataset)
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assert len(items) == config.size
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# Test multiple iterations yield same items
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assert items == list(dataset)
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def test_binary_matrix_answer():
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"""Test the _get_distances method"""
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config = BinaryMatrixConfig(seed=42)
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dataset = BinaryMatrixDataset(config)
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# 1x1 matrix
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matrix = [[0]]
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assert dataset._get_distances(matrix) == [[0]]
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# 2x2 matrix
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matrix = [[0, 1], [1, 1]]
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assert dataset._get_distances(matrix) == [[0, 1], [1, 2]]
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# 3x3 matrix
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matrix = [[0, 0, 0], [0, 1, 0], [1, 1, 1]]
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assert dataset._get_distances(matrix) == [[0, 0, 0], [0, 1, 0], [1, 2, 1]]
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# Empty matrix
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matrix = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
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assert dataset._get_distances(matrix) == [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
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# String representation of answer
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answer = "0 0 0\n0 1 0\n1 2 1"
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entry = {"answer": "0 0 0\n0 1 0\n1 2 1"}
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assert dataset.score_answer(answer, entry) == 1.0
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# Answer is a python list (partially correct answer)
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answer = "[[0, 0, 0], [0, 1, 0], [1, 2, 1]]"
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entry = {"answer": "0 0 0\n0 1 0\n1 2 1"}
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assert dataset.score_answer(answer, entry) == 0.1
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# Answer is null
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answer = None
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entry = {"answer": "0 0 0\n0 1 0\n1 2 1"}
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assert dataset.score_answer(answer, entry) == 0.0
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def test_n_queens_curriculum():
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curriculum = BinaryMatrixCurriculum()
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base_value = {"size": 150, "seed": 1}
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base_cfg: BinaryMatrixConfig = curriculum.generate_configuration(base_value)
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assert base_cfg.seed == 1
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assert base_cfg.size == 150
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assert base_cfg.p_zero == 0.5
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assert base_cfg.min_n == 10 and base_cfg.max_n == 10
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# test incrementing attribute levels for n and p_zero
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curriculum.increment_attr_level("n")
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curriculum.increment_attr_level("p_zero")
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increased_cfg = curriculum.generate_configuration(base_value)
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assert increased_cfg.p_zero == 0.25
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assert increased_cfg.min_n == 10 and increased_cfg.max_n == 50
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# test decrementing attribute level for n again
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curriculum.decrement_attr_level("n")
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partially_decreased_cfg = curriculum.generate_configuration(base_value)
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assert partially_decreased_cfg.p_zero == 0.25
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assert partially_decreased_cfg.min_n == 10 and partially_decreased_cfg.max_n == 10
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