rotten oranges

tests/test_rotting_oranges.py

@@ -0,0 +1,119 @@
+"""Tests for Binary Matrix questions generation"""
+
+import pytest
+
+from reasoning_gym.algorithmic.rotten_oranges import RottenOrangesConfig, RottenOrangesDataset
+
+
+def test_rotten_oranges_config_validation():
+    """Test that invalid configs raise appropriate errors"""
+    with pytest.raises(AssertionError):
+        config = RottenOrangesConfig(max_n=-1)  # Negative not allowed
+        config.validate()
+
+    with pytest.raises(AssertionError):
+        config = RottenOrangesConfig(max_n=0)  # Zero not allowed
+        config.validate()
+
+    with pytest.raises(AssertionError):
+        config = RottenOrangesConfig(min_n=-1)  # Negative not allowed
+        config.validate()
+
+    with pytest.raises(AssertionError):
+        config = RottenOrangesConfig(min_n=0)  # Zero not allowed
+        config.validate()
+
+    with pytest.raises(AssertionError):
+        config = RottenOrangesConfig(p_oranges=0)  # <= 0 not allowed
+        config.validate()
+
+    with pytest.raises(AssertionError):
+        config = RottenOrangesConfig(p_oranges=1.01)  # > 1 not allowed
+        config.validate()
+
+    with pytest.raises(AssertionError):
+        config = RottenOrangesConfig(p_rotten=0)  # <= 0 not allowed
+        config.validate()
+
+    with pytest.raises(AssertionError):
+        config = RottenOrangesConfig(p_rotten=1.01)  # > 1 not allowed
+        config.validate()
+
+
+def test_rotten_oranges_dataset_deterministic():
+    """Test that dataset generates same items with same seed"""
+    config = RottenOrangesConfig(seed=42, size=10)
+    dataset1 = RottenOrangesDataset(config)
+    dataset2 = RottenOrangesDataset(config)
+
+    for i in range(len(dataset1)):
+        assert dataset1[i] == dataset2[i]
+
+
+def test_rotten_oranges_dataset_items():
+    """Test basic properties of generated items"""
+    config = RottenOrangesConfig(min_n=10, max_n=15, size=10, seed=42)
+    dataset = RottenOrangesDataset(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 "solution" in item["metadata"]
+
+        matrix = item["metadata"]["matrix"]
+
+        # Verify dimensions
+        assert config.min_n <= len(matrix) <= config.max_n
+        assert all(config.min_n <= len(row) <= config.max_n for row in matrix)
+        for r in range(len(matrix)):
+            for c in range(len(matrix[0])):
+                assert matrix[r][c] in [0, 1, 2]
+
+
+def test_rotten_oranges_dataset_iteration():
+    """Test that iteration respects dataset size"""
+    config = RottenOrangesConfig(size=5, seed=42)
+    dataset = RottenOrangesDataset(config)
+
+    items = list(dataset)
+    assert len(items) == config.size
+
+    # Test multiple iterations yield same items
+    assert items == list(dataset)
+
+
+def test_rotten_oranges_answer():
+    """Test the _get_distances method"""
+    config = RottenOrangesConfig(seed=42)
+    dataset = RottenOrangesDataset(config)
+
+    # All oranges are rotten
+    matrix = [
+        [2, 2, 2],
+        [2, 2, 2],
+        [2, 2, 2],
+    ]
+    assert dataset._get_answer(matrix) == 0
+
+    # All oranges are healthy
+    matrix = [
+        [1, 1, 1],
+        [1, 1, 1],
+        [1, 1, 1],
+    ]
+    assert dataset._get_answer(matrix) == -1
+
+    # 1 shot example
+    matrix = [
+        [2, 1, 1],
+        [1, 1, 0],
+        [0, 1, 1],
+    ]
+    assert dataset._get_answer(matrix) == 4