pool matrix

tests/test_pool_matrix.py

@@ -0,0 +1,133 @@
+"""Tests for Pool Matrix questions generation"""
+
+import numpy as np
+import pytest
+
+from reasoning_gym.algorithmic.pool_matrix import PoolMatrixConfig, PoolMatrixDataset
+
+
+def test_pool_matrix_config_validation():
+    """Test that invalid configs raise appropriate errors"""
+
+    for field in ["max_rows", "max_cols", "max_pool_size"]:
+        with pytest.raises(AssertionError):
+            config = PoolMatrixConfig(**{field: -1})  # Negative not allowed
+            config.validate()
+
+        with pytest.raises(AssertionError):
+            config = PoolMatrixConfig(**{field: 0})  # Zero not allowed
+            config.validate()
+
+
+def test_pool_matrix_dataset_deterministic():
+    """Test that dataset generates same items with same seed"""
+    config = PoolMatrixConfig(seed=42, size=10)
+    dataset1 = PoolMatrixDataset(config)
+    dataset2 = PoolMatrixDataset(config)
+
+    for i in range(len(dataset1)):
+        assert dataset1[i] == dataset2[i]
+
+
+def test_pool_matrix_dataset_items():
+    """Test basic properties of generated items"""
+    config = PoolMatrixConfig(max_rows=10, max_cols=10, max_pool_size=3, size=10, seed=42)
+    dataset = PoolMatrixDataset(config)
+
+    for i in range(len(dataset)):
+        item = dataset[i]
+        # Check item structure
+        assert isinstance(item, dict)
+        assert "question" in item
+        assert "answer" in item
+        assert "metadata" in item
+
+        # Check metadata
+        assert "matrix" in item["metadata"]
+        assert "pool_type" in item["metadata"]
+        assert "pool_size" in item["metadata"]
+        assert "solution" in item["metadata"]
+
+        matrix = item["metadata"]["matrix"]
+        pool_type = item["metadata"]["pool_type"]
+        pool_size = item["metadata"]["pool_size"]
+        solution = item["metadata"]["solution"]
+
+        # Verify dimensions
+        assert len(matrix) <= config.max_rows
+        assert all(len(row) <= config.max_cols for row in matrix)
+        assert len(solution) <= len(matrix)
+        assert len(solution[0]) <= len(matrix[0])
+        assert pool_size <= config.max_pool_size
+        assert pool_type in ["average", "max"]
+
+
+def test_pool_matrix_dataset_iteration():
+    """Test that iteration respects dataset size"""
+    config = PoolMatrixConfig(size=5, seed=42)
+    dataset = PoolMatrixDataset(config)
+
+    items = list(dataset)
+    assert len(items) == config.size
+
+    # Test multiple iterations yield same items
+    assert items == list(dataset)
+
+
+def test_pool_matrix_answer():
+    """Test the pooling methods"""
+    config = PoolMatrixConfig(seed=42)
+    dataset = PoolMatrixDataset(config)
+
+    # 1. Max pooling
+    matrix = np.array([[1]])
+    assert np.allclose(dataset._max_pool(matrix, 2), np.array([[1]]))
+
+    matrix = np.array([[1, 2], [3, 4]])
+    assert np.allclose(dataset._max_pool(matrix, 2), np.array([[4]]))
+
+    matrix = np.array(
+        [
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+        ]
+    )
+    assert np.allclose(dataset._max_pool(matrix, 2), np.array([[6, 8], [10, 12]]))
+
+    matrix = np.array(
+        [
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]
+    )
+    assert np.allclose(dataset._max_pool(matrix, 2), np.array([[6, 8], [14, 16]]))
+
+    # 2. Average pooling
+
+    matrix = np.array([[1]])
+    assert np.allclose(dataset._average_pool(matrix, 2), np.array([[1]]))
+
+    matrix = np.array([[1, 2], [3, 4]])
+    assert np.allclose(dataset._average_pool(matrix, 2), np.array([[2.5]]))
+
+    matrix = np.array(
+        [
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+        ]
+    )
+    assert np.allclose(dataset._average_pool(matrix, 2), np.array([[3.5, 5.5], [9.5, 11.5]]))
+
+    matrix = np.array(
+        [
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [9, 10, 11, 12],
+            [13, 14, 15, 16],
+        ]
+    )
+    assert np.allclose(dataset._average_pool(matrix, 2), np.array([[3.5, 5.5], [11.5, 13.5]]))