add test for tox21molnet

Author: aditya0by0

tests/unit/dataset_classes/testTox21MolNet.py

@@ -0,0 +1,181 @@
+import unittest
+from typing import List
+from unittest.mock import MagicMock, mock_open, patch
+
+import torch
+
+from chebai.preprocessing.datasets.tox21 import Tox21MolNet
+from chebai.preprocessing.reader import ChemDataReader
+from tests.unit.mock_data.tox_mock_data import Tox21MolNetMockData
+
+
+class TestTox21MolNet(unittest.TestCase):
+    @classmethod
+    @patch("os.makedirs", return_value=None)
+    def setUpClass(cls, mock_makedirs: MagicMock) -> None:
+        """
+        Initialize a Tox21MolNet instance for testing.
+
+        Args:
+            mock_makedirs (MagicMock): Mocked `os.makedirs` function.
+        """
+        Tox21MolNet.READER = ChemDataReader
+        cls.data_module = Tox21MolNet()
+
+    @patch(
+        "builtins.open",
+        new_callable=mock_open,
+        read_data=Tox21MolNetMockData.get_raw_data(),
+    )
+    def test_load_data_from_file(self, mock_open_file: mock_open) -> None:
+        """
+        Test the `_load_data_from_file` method for correct output.
+
+        Args:
+            mock_open_file (mock_open): Mocked open function to simulate file reading.
+        """
+        actual_data = self.data_module._load_data_from_file("fake/file/path.csv")
+
+        first_instance = next(actual_data)
+
+        # Check for required keys
+        required_keys = ["features", "labels", "ident"]
+        for key in required_keys:
+            self.assertIn(
+                key, first_instance, f"'{key}' key is missing in the output data."
+            )
+
+        self.assertTrue(
+            all(isinstance(feature, int) for feature in first_instance["features"]),
+            "Not all elements in 'features' are integers.",
+        )
+
+        # Check that 'features' can be converted to a tensor
+        features = first_instance["features"]
+        try:
+            tensor_features = torch.tensor(features)
+            self.assertTrue(
+                tensor_features.ndim > 0,
+                "'features' should be convertible to a non-empty tensor.",
+            )
+        except Exception as e:
+            self.fail(f"'features' cannot be converted to a tensor: {str(e)}")
+
+    @patch(
+        "builtins.open",
+        new_callable=mock_open,
+        read_data=Tox21MolNetMockData.get_raw_data(),
+    )
+    @patch("torch.save")
+    def test_setup_processed_simple_split(
+        self,
+        mock_torch_save: MagicMock,
+        mock_open_file: mock_open,
+    ) -> None:
+        """
+        Test the `setup_processed` method for basic data splitting and saving.
+
+        Args:
+            mock_torch_save (MagicMock): Mocked `torch.save` function to avoid actual file writes.
+            mock_open_file (mock_open): Mocked `open` function to simulate file reading.
+        """
+        self.data_module.setup_processed()
+
+        # Verify if torch.save was called for each split (train, test, validation)
+        self.assertEqual(
+            mock_torch_save.call_count, 3, "Expected torch.save to be called 3 times."
+        )
+        call_args_list = mock_torch_save.call_args_list
+        self.assertIn("test", call_args_list[0][0][1], "Missing 'test' split.")
+        self.assertIn("train", call_args_list[1][0][1], "Missing 'train' split.")
+        self.assertIn(
+            "validation", call_args_list[2][0][1], "Missing 'validation' split."
+        )
+
+        # Check for non-overlap between train, test, and validation splits
+        test_split: List[str] = [d["ident"] for d in call_args_list[0][0][0]]
+        train_split: List[str] = [d["ident"] for d in call_args_list[1][0][0]]
+        validation_split: List[str] = [d["ident"] for d in call_args_list[2][0][0]]
+
+        self.assertTrue(
+            set(train_split).isdisjoint(test_split),
+            "Overlap detected between the train and test splits.",
+        )
+        self.assertTrue(
+            set(train_split).isdisjoint(validation_split),
+            "Overlap detected between the train and validation splits.",
+        )
+        self.assertTrue(
+            set(test_split).isdisjoint(validation_split),
+            "Overlap detected between the test and validation splits.",
+        )
+
+    @patch.object(
+        Tox21MolNet,
+        "_load_data_from_file",
+        return_value=Tox21MolNetMockData.get_processed_grouped_data(),
+    )
+    @patch("torch.save")
+    def test_setup_processed_with_group_split(
+        self, mock_torch_save: MagicMock, mock_load_file: MagicMock
+    ) -> None:
+        """
+        Test the `setup_processed` method for group-based splitting and saving.
+
+        Args:
+            mock_torch_save (MagicMock): Mocked `torch.save` function to avoid actual file writes.
+            mock_load_file (MagicMock): Mocked `_load_data_from_file` to provide custom data.
+        """
+        self.data_module.train_split = 0.5
+        self.data_module.setup_processed()
+
+        # Verify if torch.save was called for each split
+        self.assertEqual(
+            mock_torch_save.call_count, 3, "Expected torch.save to be called 3 times."
+        )
+        call_args_list = mock_torch_save.call_args_list
+        self.assertIn("test", call_args_list[0][0][1], "Missing 'test' split.")
+        self.assertIn("train", call_args_list[1][0][1], "Missing 'train' split.")
+        self.assertIn(
+            "validation", call_args_list[2][0][1], "Missing 'validation' split."
+        )
+
+        # Check for non-overlap between train, test, and validation splits (based on 'ident')
+        test_split: List[str] = [d["ident"] for d in call_args_list[0][0][0]]
+        train_split: List[str] = [d["ident"] for d in call_args_list[1][0][0]]
+        validation_split: List[str] = [d["ident"] for d in call_args_list[2][0][0]]
+
+        self.assertTrue(
+            set(train_split).isdisjoint(test_split),
+            "Overlap detected between the train and test splits (based on 'ident').",
+        )
+        self.assertTrue(
+            set(train_split).isdisjoint(validation_split),
+            "Overlap detected between the train and validation splits (based on 'ident').",
+        )
+        self.assertTrue(
+            set(test_split).isdisjoint(validation_split),
+            "Overlap detected between the test and validation splits (based on 'ident').",
+        )
+
+        # Check for non-overlap between train, test, and validation splits (based on 'group')
+        test_split_grp: List[str] = [d["group"] for d in call_args_list[0][0][0]]
+        train_split_grp: List[str] = [d["group"] for d in call_args_list[1][0][0]]
+        validation_split_grp: List[str] = [d["group"] for d in call_args_list[2][0][0]]
+
+        self.assertTrue(
+            set(train_split_grp).isdisjoint(test_split_grp),
+            "Overlap detected between the train and test splits (based on 'group').",
+        )
+        self.assertTrue(
+            set(train_split_grp).isdisjoint(validation_split_grp),
+            "Overlap detected between the train and validation splits (based on 'group').",
+        )
+        self.assertTrue(
+            set(test_split_grp).isdisjoint(validation_split_grp),
+            "Overlap detected between the test and validation splits (based on 'group').",
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()