[Dataset] Add ZINC Dataset (#5428)

* Update dgl.data.rst

* Add files via upload

* Add files via upload

* Add files via upload

* Update zinc.py

* Update dgl.data.rst

* Update test_data.py

* Add files via upload

* Update cluster.py

* Update pattern.py

* Update zinc.py

* Update zinc.py

* Update test_data.py

* lint

* fix

* fix path

* update test on label shape

---------
Co-authored-by: rudongyu <ru_dongyu@outlook.com>

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

@@ -93,6 +93,7 @@ Datasets for graph classification/regression tasks
     GINDataset
     FakeNewsDataset
     BA2MotifDataset
+    ZINCDataset
 
 Dataset adapters
 -------------------

python/dgl/data/__init__.py

@@ -56,6 +56,7 @@ from .cluster import CLUSTERDataset
 from .pattern import PATTERNDataset
 from .wikics import WikiCSDataset
 from .yelp import YelpDataset
+from .zinc import ZINCDataset
 
 
 def register_data_args(parser):

python/dgl/data/cluster.py

@@ -49,7 +49,7 @@ class CLUSTERDataset(DGLBuiltinDataset):
         Number of classes for each node.
 
     Examples
-    —-------
+    --------
     >>> from dgl.data import CLUSTERDataset
     >>>
     >>> trainset = CLUSTERDataset(mode='train')

python/dgl/data/pattern.py

@@ -50,7 +50,7 @@ class PATTERNDataset(DGLBuiltinDataset):
         Number of classes for each node.
 
     Examples
-    —-------
+    --------
     >>> from dgl.data import PATTERNDataset
     >>> data = PATTERNDataset(mode='train')
     >>> data.num_classes

python/dgl/data/zinc.py

+import os
+
+from .dgl_dataset import DGLBuiltinDataset
+from .utils import _get_dgl_url, load_graphs
+
+
+class ZINCDataset(DGLBuiltinDataset):
+    r"""ZINC dataset for the graph regression task.
+
+    A subset (12K) of ZINC molecular graphs (250K) dataset is used to
+    regress a molecular property known as the constrained solubility.
+    For each molecular graph, the node features are the types of heavy
+    atoms, between which the edge features are the types of bonds.
+    Each graph contains 9-37 nodes and 16-84 edges.
+
+    Reference `<https://arxiv.org/pdf/2003.00982.pdf>`_
+
+    Statistics:
+
+    Train examples: 10,000
+    Valid examples: 1,000
+    Test examples: 1,000
+    Average number of nodes: 23.16
+    Average number of edges: 39.83
+    Number of atom types: 28
+    Number of bond types: 4
+
+    Parameters
+    ----------
+    mode : str, optional
+        Should be chosen from ["train", "valid", "test"]
+        Default: "train".
+    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.
+
+    Attributes
+    ----------
+    num_atom_types : int
+        Number of atom types.
+    num_bond_types : int
+        Number of bond types.
+
+    Examples
+    ---------
+    >>> from dgl.data import ZINCDataset
+
+    >>> training_set = ZINCDataset(mode="train")
+    >>> training_set.num_atom_types
+    28
+    >>> len(training_set)
+    10000
+    >>> graph, label = training_set[0]
+    >>> graph
+    Graph(num_nodes=29, num_edges=64,
+        ndata_schemes={'feat': Scheme(shape=(), dtype=torch.int64)}
+        edata_schemes={'feat': Scheme(shape=(), dtype=torch.int64)})
+    """
+
+    def __init__(
+        self,
+        mode="train",
+        raw_dir=None,
+        force_reload=False,
+        verbose=False,
+        transform=None,
+    ):
+        self._url = _get_dgl_url("dataset/ZINC12k.zip")
+        self.mode = mode
+
+        super(ZINCDataset, self).__init__(
+            name="zinc",
+            url=self._url,
+            raw_dir=raw_dir,
+            force_reload=force_reload,
+            verbose=verbose,
+            transform=transform,
+        )
+
+    def process(self):
+        self.load()
+
+    def has_cache(self):
+        graph_path = os.path.join(
+            self.save_path, "ZincDGL_{}.bin".format(self.mode)
+        )
+        return os.path.exists(graph_path)
+
+    def load(self):
+        graph_path = os.path.join(
+            self.save_path, "ZincDGL_{}.bin".format(self.mode)
+        )
+        self._graphs, self._labels = load_graphs(graph_path)
+
+    @property
+    def num_atom_types(self):
+        return 28
+
+    @property
+    def num_bond_types(self):
+        return 4
+
+    def __len__(self):
+        return len(self._graphs)
+
+    def __getitem__(self, idx):
+        r"""Get one example by index.
+
+        Parameters
+        ----------
+        idx : int
+            The sample index.
+
+        Returns
+        -------
+        dgl.DGLGraph
+            Each graph contains:
+
+            - ``ndata['feat']``: Types of heavy atoms as node features
+            - ``edata['feat']``: Types of bonds as edge features
+
+        Tensor
+            Constrained solubility as graph label
+        """
+        labels = self._labels["g_label"]
+        if self._transform is None:
+            return self._graphs[idx], labels[idx]
+        else:
+            return self._transform(self._graphs[idx]), labels[idx]

tests/python/common/data/test_data.py

@@ -408,6 +408,63 @@ def test_cluster():
         assert ds.num_classes == 6
 
 
+@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_zinc():
+    mode_n_graphs = {
+        "train": 10000,
+        "valid": 1000,
+        "test": 1000,
+    }
+    transform = dgl.AddSelfLoop(allow_duplicate=True)
+    for mode, n_graphs in mode_n_graphs.items():
+        dataset1 = data.ZINCDataset(mode=mode)
+        g1, label = dataset1[0]
+        dataset2 = data.ZINCDataset(mode=mode, transform=transform)
+        g2, _ = dataset2[0]
+
+        assert g2.num_edges() - g1.num_edges() == g1.num_nodes()
+        # return a scalar tensor
+        assert not label.shape
+
+
+@unittest.skipIf(
+    F._default_context_str == "gpu",
+    reason="Datasets don't need to be tested on GPU.",
+)
+@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="Skip MXNet")
+def test_extract_archive():
+    # gzip
+    with tempfile.TemporaryDirectory() as src_dir:
+        gz_file = "gz_archive"
+        gz_path = os.path.join(src_dir, gz_file + ".gz")
+        content = b"test extract archive gzip"
+        with gzip.open(gz_path, "wb") as f:
+            f.write(content)
+        with tempfile.TemporaryDirectory() as dst_dir:
+            data.utils.extract_archive(gz_path, dst_dir, overwrite=True)
+            assert os.path.exists(os.path.join(dst_dir, gz_file))
+
+    # tar
+    with tempfile.TemporaryDirectory() as src_dir:
+        tar_file = "tar_archive"
+        tar_path = os.path.join(src_dir, tar_file + ".tar")
+        # default encode to utf8
+        content = "test extract archive tar\n".encode()
+        info = tarfile.TarInfo(name="tar_archive")
+        info.size = len(content)
+        with tarfile.open(tar_path, "w") as f:
+            f.addfile(info, io.BytesIO(content))
+        with tempfile.TemporaryDirectory() as dst_dir:
+            data.utils.extract_archive(tar_path, dst_dir, overwrite=True)
+            assert os.path.exists(os.path.join(dst_dir, tar_file))
+
+
 def _test_construct_graphs_node_ids():
     from dgl.data.csv_dataset_base import (
         DGLGraphConstructor,