Merge pull request #574 from DeepRank/570_new_patch_release_gcroci2

release: patch 3.0.1

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 3.0.0
+current_version = 3.0.1
 
 [comment]
 comment = The contents of this file cannot be merged with that of setup.cfg until https://github.com/c4urself/bump2version/issues/185 is resolved

CITATION.cff

@@ -68,5 +68,5 @@ keywords:
   - DeepRank
 license: Apache-2.0
 commit: 4e8823758ba03f824b4281f5689cb6a335ab2f6c
-version: "3.0.0"
-date-released: "2024-01-25"
+version: "3.0.1"
+date-released: "2024-02-22"

CONTRIBUTING.rst

@@ -37,8 +37,8 @@ You want to make some kind of change to the code base
 #. if needed, fork the repository to your own Github profile and create your own feature branch off of the latest main commit. While working on your feature branch, make sure to stay up to date with the main branch by pulling in changes, possibly from the 'upstream' repository (follow the instructions `here <https://help.github.com/articles/configuring-a-remote-for-a-fork/>`__ and `here <https://help.github.com/articles/syncing-a-fork/>`__);
 #. make sure the existing tests still work by running ``python setup.py test``;
 #. add your own tests (if necessary);
-#. ensure the code is correctly linted (`ruff .`) and formatted (`ruff format .`);
-#. see our `developer's readme <README.dev.md>`` for detailed information on our style conventions, etc.;
+#. ensure the code is correctly linted (``ruff .``) and formatted (``ruff format .``);
+#. see our `developer's readme <README.dev.md>`_ for detailed information on our style conventions, etc.;
 #. update or expand the documentation;
 #. `push <http://rogerdudler.github.io/git-guide/>`_ your feature branch to (your fork of) the DeepRank2 repository on GitHub;
 #. create the pull request, e.g. following the instructions `here <https://help.github.com/articles/creating-a-pull-request/>`__.

README.dev.md

@@ -79,7 +79,7 @@ During the development cycle, three main supporting branches are used:
 
 ## Making a release
 
-1. Branch from `dev` and prepare the branch for the release (e.g., removing the unnecessary dev files such as the current one, fix minor bugs if necessary).
+1. Branch from `dev` and prepare the branch for the release (e.g., removing the unnecessary dev files, fix minor bugs if necessary).
 2. [Bump the version](https://github.com/DeepRank/deeprank2/blob/dev/README.dev.md#versioning).
 3. Verify that the information in `CITATION.cff` is correct (update the release date), and that `.zenodo.json` contains equivalent data.
 4. Merge the release branch into `main` (and `dev`), and [run the tests](https://github.com/DeepRank/deeprank2/blob/dev/README.dev.md#running-the-tests).

deeprank2/__init__.py

@@ -1 +1 @@
-__version__ = "3.0.0"
+__version__ = "3.0.1"

deeprank2/trainer.py

@@ -947,7 +947,7 @@ def _save_model(self) -> dict[str, Any]:
                 if key["transform"] is None:
                     continue
                 str_expr = inspect.getsource(key["transform"])
-                match = re.search(r"\'transform\':.*(lambda.*).*,.*\'standardize\'.*", str_expr).group(1)
+                match = re.search(r"[\"|\']transform[\"|\']:.*(lambda.*).*,.*[\"|\']standardize[\"|\'].*", str_expr).group(1)
                 key["transform"] = match
 
         state = {

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "deeprank2"
-version = "3.0.0"
+version = "3.0.1"
 description = "DeepRank2 is an open-source deep learning framework for data mining of protein-protein interfaces or single-residue missense variants."
 readme = "README.md"
 requires-python = ">=3.10"

tests/features/test_irc.py

@@ -32,7 +32,7 @@ def test_irc_atom() -> None:
     pdb_path = "tests/data/pdb/1A0Z/1A0Z.pdb"
     graph, _ = build_testgraph(
         pdb_path=pdb_path,
-        detail="residue",
+        detail="atom",
         influence_radius=4.5,
         max_edge_length=4.5,
     )

tests/test_trainer.py

@@ -86,6 +86,7 @@ def _model_base_test(
         dataset_train,
         dataset_val,
         dataset_test,
+        cuda=use_cuda,
         output_exporters=output_exporters,
     )
 
@@ -94,20 +95,6 @@ def _model_base_test(
         for parameter in trainer.model.parameters():
             assert parameter.is_cuda, f"{parameter} is not cuda"
 
-        data = dataset_train.get(0)
-
-        for name, data_tensor in (
-            ("x", data.x),
-            ("y", data.y),
-            (Efeat.INDEX, data.edge_index),
-            ("edge_attr", data.edge_attr),
-            (Nfeat.POSITION, data.pos),
-            ("cluster0", data.cluster0),
-            ("cluster1", data.cluster1),
-        ):
-            if data_tensor is not None:
-                assert data_tensor.is_cuda, f"data.{name} is not cuda"
-
     with warnings.catch_warnings(record=UserWarning):
         trainer.train(
             nepoch=3,
@@ -774,6 +761,37 @@ def test_test_method_pretrained_model_on_dataset_without_target(self) -> None:
         assert output.target.unique().tolist()[0] is None
         assert output.loss.unique().tolist()[0] is None
 
+    def test_graph_save_and_load_model(self) -> None:
+        test_data_graph = "tests/data/hdf5/test.hdf5"
+        n = 10
+        features_transform = {
+            Nfeat.RESTYPE: {"transform": lambda x: x / 2, "standardize": True},
+            Nfeat.BSA: {"transform": None, "standardize": False},
+        }
+
+        dataset = GraphDataset(
+            hdf5_path=test_data_graph,
+            node_features=[Nfeat.RESTYPE, Nfeat.POLARITY, Nfeat.BSA],
+            target=targets.BINARY,
+            task=targets.CLASSIF,
+            features_transform=features_transform,
+        )
+        trainer = Trainer(NaiveNetwork, dataset)
+        # during the training the model is saved
+        trainer.train(nepoch=2, batch_size=2, filename=self.save_path)
+        assert trainer.features_transform == features_transform
+
+        # load the model into a new GraphDataset instance
+        dataset_test = GraphDataset(
+            hdf5_path="tests/data/hdf5/test.hdf5",
+            train_source=self.save_path,
+        )
+
+        # Check if the features_transform is correctly loaded from the saved model
+        assert dataset_test.features_transform[Nfeat.RESTYPE]["transform"](n) == n / 2  # the only way to test the transform in this case is to apply it
+        assert dataset_test.features_transform[Nfeat.RESTYPE]["standardize"] == features_transform[Nfeat.RESTYPE]["standardize"]
+        assert dataset_test.features_transform[Nfeat.BSA] == features_transform[Nfeat.BSA]
+
 
 if __name__ == "__main__":
     unittest.main()