[Dataset] Add peptides functional dataset in LRGB

docs/source/api/python/dgl.data.rst

@@ -68,6 +68,8 @@ Datasets for node classification/regression tasks
     TolokersDataset
     QuestionsDataset
     MovieLensDataset
+    PeptidesStructuralDataset
+    PeptidesFunctionalDataset
 
 Edge Prediction Datasets
 ---------------------------------------

python/dgl/data/__init__.py

@@ -71,11 +71,11 @@
     TolokersDataset,
 )
 
-# RDKit is required for Peptides-Structural dataset.
+# RDKit is required for Peptides-Structural, Peptides-Functional dataset.
 # Exception handling was added to prevent crashes for users who are using other
 # datasets.
 try:
-    from .lrgb import PeptidesStructuralDataset
+    from .lrgb import PeptidesFunctionalDataset, PeptidesStructuralDataset
 except ImportError:
     pass
 from .pattern import PATTERNDataset

python/dgl/data/lrgb.py

@@ -242,3 +242,211 @@ def __getitem__(self, idx):
             return self.graphs[idx], self.labels[idx]
         else:
             return self._transform(self.graphs[idx]), self.labels[idx]
+
+
+class PeptidesFunctionalDataset(DGLDataset):
+    r"""Peptides functional dataset for the graph classification task.
+
+    DGL dataset of 15,535 peptides represented as their molecular graph
+    (SMILES) with 10-way multi-task binary classification of their
+    functional classes.
+
+    The 10 classes represent the following functional classes (in order):
+        ['antifungal', 'cell_cell_communication', 'anticancer',
+        'drug_delivery_vehicle', 'antimicrobial', 'antiviral',
+        'antihypertensive', 'antibacterial', 'antiparasitic', 'toxic']
+
+    Reference `<https://arxiv.org/abs/2206.08164.pdf>`_
+
+    Statistics:
+
+    - Train examples: 10,873
+    - Valid examples: 2,331
+    - Test examples: 2,331
+    - Average number of nodes: 150.94
+    - Average number of edges: 307.30
+    - Number of atom types: 9
+    - Number of bond types: 3
+
+    Parameters
+    ----------
+    raw_dir : str
+        Raw file directory to download/contains the input data directory.
+        Default: "~/.dgl/".
+    force_reload : bool
+        Whether to reload the dataset.
+        Default: False.
+    verbose : bool
+        Whether to print out progress information.
+        Default: False.
+    transform : callable, optional
+        A transform that takes in a :class:`~dgl.DGLGraph` object and returns
+        a transformed version. The :class:`~dgl.DGLGraph` object will be
+        transformed before every access.
+    smiles2graph (callable):
+        A callable function that converts a SMILES string into a graph object.
+        * The default smiles2graph requires rdkit to be installed *
+
+    Examples
+    ---------
+    >>> from dgl.data import PeptidesFunctionalDataset
+
+    >>> dataset = PeptidesFunctionalDataset()
+    >>> len(dataset)
+    15535
+    >>> dataset.num_classes
+    10
+    >>> graph, label = dataset[0]
+    >>> graph
+    Graph(num_nodes=119, num_edges=244,
+        ndata_schemes={'feat': Scheme(shape=(9,), dtype=torch.int64)}
+        edata_schemes={'feat': Scheme(shape=(3,), dtype=torch.int64)})
+
+    >>> split_dict = dataset.get_idx_split()
+    >>> trainset = dataset[split_dict["train"]]
+    >>> graph, label = trainset[0]
+    >>> graph
+    Graph(num_nodes=338, num_edges=682,
+        ndata_schemes={'feat': Scheme(shape=(9,), dtype=torch.int64)}
+        edata_schemes={'feat': Scheme(shape=(3,), dtype=torch.int64)})
+
+    """
+
+    def __init__(
+        self,
+        raw_dir=None,
+        force_reload=None,
+        verbose=None,
+        transform=None,
+        smiles2graph=smiles2graph,
+    ):
+        self.smiles2graph = smiles2graph
+        # MD5 hash of the dataset file.
+        self.version = "701eb743e899f4d793f0e13c8fa5a1b4"
+        self.url_stratified_split = """
+        https://www.dropbox.com/s/j4zcnx2eipuo0xz/splits_random_stratified_peptide.pickle?dl=1
+        """
+        self.md5sum_stratified_split = "5a0114bdadc80b94fc7ae974f13ef061"
+
+        super(PeptidesFunctionalDataset, self).__init__(
+            name="Peptides-func",
+            raw_dir=raw_dir,
+            url="""
+            https://www.dropbox.com/s/ol2v01usvaxbsr8/peptide_multi_class_dataset.csv.gz?dl=1
+            """,
+            force_reload=force_reload,
+            verbose=verbose,
+            transform=transform,
+        )
+
+    @property
+    def raw_data_path(self):
+        return os.path.join(self.raw_path, "peptide_multi_class_dataset.csv.gz")
+
+    @property
+    def split_data_path(self):
+        return os.path.join(
+            self.raw_path, "splits_random_stratified_peptide.pickle"
+        )
+
+    @property
+    def graph_path(self):
+        return os.path.join(self.save_path, "Peptides-func.bin")
+
+    @property
+    def num_atom_types(self):
+        return 9
+
+    @property
+    def num_bond_types(self):
+        return 3
+
+    @property
+    def num_classes(self):
+        return 10
+
+    def _md5sum(self, path):
+        hash_md5 = hashlib.md5()
+        with open(path, "rb") as f:
+            buffer = f.read()
+            hash_md5.update(buffer)
+        return hash_md5.hexdigest()
+
+    def download(self):
+        path = download(self.url, path=self.raw_data_path)
+        # Save to disk the MD5 hash of the downloaded file.
+        hash = self._md5sum(path)
+        if hash != self.version:
+            raise ValueError("Unexpected MD5 hash of the downloaded file")
+        open(os.path.join(self.raw_path, hash), "w").close()
+        # Download train/val/test splits.
+        path_split = download(
+            self.url_stratified_split, path=self.split_data_path
+        )
+        hash_split = self._md5sum(path_split)
+        if hash_split != self.md5sum_stratified_split:
+            raise ValueError("Unexpected MD5 hash of the split file")
+
+    def process(self):
+        data_df = pd.read_csv(self.raw_data_path)
+        smiles_list = data_df["smiles"]
+        if self.verbose:
+            print("Converting SMILES strings into graphs...")
+        self.graphs = []
+        self.labels = []
+        for i in tqdm(range(len(smiles_list))):
+            smiles = smiles_list[i]
+            graph = self.smiles2graph(smiles)
+
+            assert len(graph["edge_feat"]) == graph["edge_index"].shape[1]
+            assert len(graph["node_feat"]) == graph["num_nodes"]
+            DGLgraph = dgl_graph(
+                (graph["edge_index"][0], graph["edge_index"][1]),
+                num_nodes=graph["num_nodes"],
+            )
+            DGLgraph.edata["feat"] = F.zerocopy_from_numpy(
+                graph["edge_feat"]
+            ).to(F.int64)
+            DGLgraph.ndata["feat"] = F.zerocopy_from_numpy(
+                graph["node_feat"]
+            ).to(F.int64)
+            self.graphs.append(DGLgraph)
+            self.labels.append(eval(data_df["labels"].iloc[i]))
+        self.labels = F.tensor(self.labels, dtype=F.float32)
+
+    def load(self):
+        self.graphs, label_dict = load_graphs(self.graph_path)
+        self.labels = label_dict["labels"]
+
+    def save(self):
+        save_graphs(
+            self.graph_path, self.graphs, labels={"labels": self.labels}
+        )
+
+    def has_cache(self):
+        return os.path.exists(self.graph_path)
+
+    def get_idx_split(self):
+        """Get dataset splits.
+
+        Returns:
+            Dict with 'train', 'val', 'test', splits indices.
+        """
+        with open(self.split_data_path, "rb") as f:
+            split_dict = pickle.load(f)
+        for key in split_dict.keys():
+            split_dict[key] = F.zerocopy_from_numpy(split_dict[key])
+        return split_dict
+
+    def __len__(self):
+        return len(self.graphs)
+
+    def __getitem__(self, idx):
+        """Get datapoint with index"""
+        if F.is_tensor(idx) and idx.dim() == 1:
+            return Subset(self, idx.cpu())
+
+        if self._transform is None:
+            return self.graphs[idx], self.labels[idx]
+        else:
+            return self._transform(self.graphs[idx]), self.labels[idx]

tests/integration/test_data.py

@@ -65,13 +65,29 @@ def test_fakenews():
 def test_peptides_structural():
     transform = dgl.AddSelfLoop(allow_duplicate=True)
     dataset1 = data.PeptidesStructuralDataset()
-    g1, label = dataset1[0]
+    g1 = dataset1[0][0]
     dataset2 = data.PeptidesStructuralDataset(transform=transform)
+    g2 = dataset2[0][0]
+
+    assert g2.num_edges() - g1.num_edges() == g1.num_nodes()
+
+
+@unittest.skipIf(
+    F._default_context_str == "gpu",
+    reason="Datasets don't need to be tested on GPU.",
+)
+@unittest.skipIf(
+    dgl.backend.backend_name != "pytorch", reason="only supports pytorch"
+)
+def test_peptides_functional():
+    transform = dgl.AddSelfLoop(allow_duplicate=True)
+    dataset1 = data.PeptidesFunctionalDataset()
+    g1, label = dataset1[0]
+    dataset2 = data.PeptidesFunctionalDataset(transform=transform)
     g2, _ = dataset2[0]
 
     assert g2.num_edges() - g1.num_edges() == g1.num_nodes()
-    # return a scalar tensor
-    assert not label.shape
+    assert dataset1.num_classes == label.shape[0]
 
 
 @unittest.skipIf(