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+ yield temp_path + shutil.rmtree(temp_path) + + +@pytest.fixture +def mock_config(): + """Provide a mock configuration object for testing.""" + class MockConfig: + def __init__(self): + self.dataset = "test_dataset" + self.model = "test_model" + self.batch_size = 32 + self.epochs = 10 + self.lr = 0.001 + self.hidden_dim = 64 + self.output_dim = 10 + self.dropout = 0.5 + self.weight_decay = 0.0001 + self.seed = 42 + self.device = "cpu" + self.log_dir = None + self.save_dir = None + + return MockConfig() + + +@pytest.fixture +def sample_graph(): + """Create a sample NetworkX graph for testing.""" + if not NETWORKX_AVAILABLE: + pytest.skip("NetworkX not available") + + G = nx.Graph() + G.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 0), (0, 2)]) + + for node in G.nodes(): + if TORCH_AVAILABLE: + G.nodes[node]['feat'] = torch.randn(10) + else: + import random + G.nodes[node]['feat'] = [random.random() for _ in range(10)] + G.nodes[node]['label'] = node % 2 + + return G + + +@pytest.fixture +def sample_tensor_data(): + """Provide sample tensor data for testing.""" + if not TORCH_AVAILABLE: + pytest.skip("Torch not available") + + return { + 'features': torch.randn(100, 10), + 'labels': torch.randint(0, 2, (100,)), + 'edge_index': torch.randint(0, 100, (2, 200)), + 'batch': torch.zeros(100, dtype=torch.long) + } + + +@pytest.fixture +def mock_model(): + """Create a mock model for testing.""" + if not TORCH_AVAILABLE: + pytest.skip("Torch not available") + + class MockModel(torch.nn.Module): + def __init__(self, input_dim=10, hidden_dim=64, output_dim=2): + super().__init__() + self.fc1 = torch.nn.Linear(input_dim, hidden_dim) + self.fc2 = torch.nn.Linear(hidden_dim, output_dim) + + def forward(self, x): + x = torch.relu(self.fc1(x)) + return self.fc2(x) + + return MockModel() + + +@pytest.fixture +def capture_stdout(): + """Capture stdout for testing print statements.""" + import io + import sys + + captured_output = io.StringIO() + old_stdout = sys.stdout + + class OutputCapture: + def __enter__(self): + sys.stdout = captured_output + return self + + def __exit__(self, *args): + sys.stdout = old_stdout + + def get_output(self): + return captured_output.getvalue() + + return OutputCapture() + + +@pytest.fixture(autouse=True) +def reset_random_seeds(): + """Reset random seeds before each test for reproducibility.""" + import random + + random.seed(42) + + try: + import numpy as np + np.random.seed(42) + except ImportError: + pass + + if TORCH_AVAILABLE: + torch.manual_seed(42) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(42) + + +@pytest.fixture +def mock_data_loader(): + """Create a mock data loader for testing.""" + if not TORCH_AVAILABLE: + pytest.skip("Torch not available") + + dataset = torch.utils.data.TensorDataset( + torch.randn(100, 10), + torch.randint(0, 2, (100,)) + ) + return torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=False) + + +@pytest.fixture +def cleanup_files(): + """Cleanup helper to remove files created during tests.""" + files_to_cleanup = [] + + def add_file(filepath): + files_to_cleanup.append(filepath) + + yield add_file + + for filepath in files_to_cleanup: + if os.path.exists(filepath): + if os.path.isdir(filepath): + shutil.rmtree(filepath) + else: + os.remove(filepath) + + +@pytest.fixture +def mock_logger(mocker): + """Create a mock logger for testing logging functionality.""" + logger = mocker.MagicMock() + logger.info = mocker.MagicMock() + logger.debug = mocker.MagicMock() + logger.warning = mocker.MagicMock() + logger.error = mocker.MagicMock() + return logger \ No newline at end of file diff --git a/tests/integration/__init__.py b/tests/integration/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_setup_validation.py b/tests/test_setup_validation.py new file mode 100644 index 0000000..2f8ea4d --- /dev/null +++ b/tests/test_setup_validation.py @@ -0,0 +1,152 @@ +"""Validation tests to verify the testing infrastructure is set up correctly.""" + +import pytest +from pathlib import Path + +try: + import torch + TORCH_AVAILABLE = True +except ImportError: + TORCH_AVAILABLE = False + +try: + import networkx as nx + NETWORKX_AVAILABLE = True +except ImportError: + NETWORKX_AVAILABLE = False + + +class TestInfrastructureSetup: + """Validate that the testing infrastructure is properly configured.""" + + def test_pytest_is_importable(self): + """Test that pytest can be imported.""" + import pytest + assert pytest is not None + + def test_fixtures_are_available(self, temp_dir, mock_config): + """Test that conftest fixtures are available and working.""" + assert isinstance(temp_dir, Path) + assert temp_dir.exists() + + assert hasattr(mock_config, 'dataset') + assert mock_config.batch_size == 32 + + @pytest.mark.skipif(not NETWORKX_AVAILABLE, reason="NetworkX not available") + def test_sample_graph_fixture(self, sample_graph): + """Test sample graph fixture when NetworkX is available.""" + assert isinstance(sample_graph, nx.Graph) + assert sample_graph.number_of_nodes() > 0 + + def test_temp_dir_cleanup(self, temp_dir): + """Test that temporary directory is created and will be cleaned up.""" + test_file = temp_dir / "test.txt" + test_file.write_text("test content") + assert test_file.exists() + + @pytest.mark.unit + def test_unit_marker(self): + """Test that unit test marker works.""" + assert True + + @pytest.mark.integration + def test_integration_marker(self): + """Test that integration test marker works.""" + assert True + + @pytest.mark.slow + def test_slow_marker(self): + """Test that slow test marker works.""" + assert True + + @pytest.mark.skipif(not TORCH_AVAILABLE, reason="Torch not available") + def test_mock_model_fixture(self, mock_model): + """Test that mock model fixture works correctly.""" + input_tensor = torch.randn(10, 10) + output = mock_model(input_tensor) + assert output.shape == (10, 2) + + @pytest.mark.skipif(not TORCH_AVAILABLE, reason="Torch not available") + def test_sample_tensor_data_fixture(self, sample_tensor_data): + """Test that sample tensor data fixture provides correct data.""" + assert 'features' in sample_tensor_data + assert 'labels' in sample_tensor_data + assert 'edge_index' in sample_tensor_data + assert 'batch' in sample_tensor_data + + assert sample_tensor_data['features'].shape == (100, 10) + assert sample_tensor_data['labels'].shape == (100,) + + def test_capture_stdout_fixture(self, capture_stdout): + """Test that stdout capture fixture works.""" + with capture_stdout as capture: + print("Test output") + print("Another line") + + output = capture.get_output() + + assert "Test output" in output + assert "Another line" in output + + @pytest.mark.skipif(not TORCH_AVAILABLE, reason="Torch not available") + def test_mock_data_loader_fixture(self, mock_data_loader): + """Test that mock data loader fixture works.""" + batch_count = 0 + for batch in mock_data_loader: + batch_count += 1 + assert len(batch) == 2 # features and labels + assert batch[0].shape[1] == 10 # feature dimension + + assert batch_count > 0 + + def test_cleanup_files_fixture(self, cleanup_files, temp_dir): + """Test that cleanup files fixture works.""" + test_file = temp_dir / "cleanup_test.txt" + test_file.write_text("test") + + cleanup_files(str(test_file)) + assert test_file.exists() # Should still exist during test + + @pytest.mark.skipif(not TORCH_AVAILABLE, reason="Torch not available") + def test_random_seed_reset(self): + """Test that random seeds are reset for reproducibility.""" + torch.manual_seed(42) + first_random = torch.rand(5) + + torch.manual_seed(42) + second_random = torch.rand(5) + + assert torch.allclose(first_random, second_random) + + +class TestCoverageConfiguration: + """Validate coverage configuration.""" + + def test_coverage_runs_with_tests(self): + """Test that coverage is configured to run with tests.""" + # This test will pass if coverage is properly configured + # The actual coverage check happens via pytest-cov + assert True + + +def test_project_structure(): + """Test that the expected project structure exists.""" + expected_dirs = [ + Path("tests"), + Path("tests/unit"), + Path("tests/integration"), + ] + + expected_files = [ + Path("tests/__init__.py"), + Path("tests/unit/__init__.py"), + Path("tests/integration/__init__.py"), + Path("tests/conftest.py"), + Path("pyproject.toml"), + ] + + for dir_path in expected_dirs: + assert dir_path.exists(), f"Directory {dir_path} should exist" + + for file_path in expected_files: + assert file_path.exists(), f"File {file_path} should exist" \ No newline at end of file diff --git a/tests/unit/__init__.py b/tests/unit/__init__.py new file mode 100644 index 0000000..e69de29