PR #4 update to v0.1.1

Fix bugs and update to v0.1.1
* fix bugs in ogb dataset support
* change data to dataset in solver and trainer
* fix AutoNE for node clf
* fix some typos

autogl/__init__.py

@@ -1 +1 @@
-__version__ = "0.1.0"
+__version__ = "0.1.1"

autogl/datasets/README.md

@@ -1,7 +1,7 @@
 
-Datasets are derived from CogDL
+Datasets are derived from PyG, OGB and CogDL.
 =================
-Autograph now supports the following benchmarks for different tasks:
+AutoGL now supports the following benchmarks for different tasks:
 - semi-supervised node classification: Cora, Citeseer, Pubmed, Amazon Computers\*, Amazon Photo\*, Coauthor CS\*, Coauthor Physics\*, Reddit （\*: using `utils.random_splits_mask_class` for splitting dataset is recommended.)
 
 
@@ -16,19 +16,32 @@ Autograph now supports the following benchmarks for different tasks:
 | Coauthor Physics | ✓ |    |  ✓  | ✓ |  ✓  |  ✓  |  |  |
 | Reddit | ✓ |    |  ✓  | ✓ |  ✓  |  ✓  |  | ✓ |
 
+
+- supervised graph classification: MUTAG, IMDB-B, IMDB-M, PROTEINS, COLLAB
+
+|  Dataset  |  PyG  |  CogDL  | x | y | edge_index | edge_attr | train/val/test node | train/val/test mask | adj|
+|  ----  |  ----  |  ----  |  ----  |  ----  |  ----  |  ----  |  ----  |  ----  |  ----  |
+| Mutag | ✓ |    |  ✓  | ✓ |  ✓  |  ✓  |  |    |    |
+| IMDB-B | ✓ |    |    | ✓ | ✓   |    |  |    |    |
+| IMDB-M | ✓ |    |    | ✓ | ✓   |    |  |    |    |
+| PROTEINS | ✓ |    |  ✓  | ✓ | ✓   |    |  |    |    |
+| COLLAB | ✓ |    |    | ✓ | ✓   |    |  |    |    |
+
+- node classification datasets from OGB: ogbn-products, ogbn-proteins, ogbn-arxiv, ogbn-papers100M and ogbn-mag.
+
+- graph classification datasets from OGB: ogbg-molhiv, ogbg-molpcba, ogbg-ppa and ogbg-code.
+
 ---
 
 TODO:
-Autograph now supports the following benchmarks for different tasks:
+In future version, AutoGL will support the following benchmarks for different tasks:
 - unsupervised node classification: PPI, Blogcatalog, Wikipedia
-- semi-supervised node classification: Cora, Citeseer, Pubmed
 - heterogeneous node classification: DBLP, ACM, IMDB
 - link prediction: PPI, Wikipedia, Blogcatalog
 - multiplex link prediction: Amazon, YouTube, Twitter
-- unsupervised graph classification: MUTAG, IMDB-B, IMDB-M, PROTEINS, COLLAB
-- supervised graph classification: MUTAG, IMDB-B, IMDB-M, PROTEINS, COLLAB
-
+- link prediction datasets from OGB: ogbl-ppa, ogbl-collab, ogbl-ddi, ogbl-citation, ogbl-wikikg and ogbl-biokg.
 
+<!--
 |  Dataset  |  PyG  |  CogDL  | x | y | edge_index | edge_attr | train/val/test node | train/val/test mask | adj|
 |  ----  |  ----  |  ----  |  ----  |  ----  |  ----  |  ----  |  ----  |  ----  |  ----  |
 |  ACM  |  |  ✓  |  ✓  | ✓ |  ✓  |    | ✓ |    | ✓ list |
@@ -41,27 +54,12 @@ Autograph now supports the following benchmarks for different tasks:
 | Amazon |  |  ✓  |    |  |    |    | ✓ data |    |    |
 | Twitter |  |  ✓  |    |  |    |    | ✓ data |    |    |
 | Youtube |  |  ✓  |    |  |    |    | ✓ data |    |    |
-| Cora | ✓ |    |  ✓  | ✓ |  ✓  |  ✓  |  | ✓ |    |
-| Citeseer | ✓ |    |  ✓  | ✓ |  ✓  |  ✓  |  | ✓ |    |
-| Pubmed | ✓ |    |  ✓  | ✓ |  ✓  |  ✓  |  | ✓ |    |
-| Reddit | ✓ |    |  ✓  | ✓ |  ✓  |  ✓  |  | ✓ |    |
-| Mutag | ✓ |    |  ✓  | ✓ |  ✓  |  ✓  |  |    |    |
-| IMDB-B | ✓ |    |    | ✓ | ✓   |    |  |    |    |
-| IMDB-M | ✓ |    |    | ✓ | ✓   |    |  |    |    |
-| PROTEINS | ✓ |    |  ✓  | ✓ | ✓   |    |  |    |    |
-| COLLAB | ✓ |    |    | ✓ | ✓   |    |  |    |    |
+| NCI1 | ✓ |    |  ✓  | ✓ |  ✓  |    |  |    |    |
+| NCI109 | ✓ |    |  ✓  | ✓ |  ✓  |    |  |    |    |
+| Enzyme | ✓ |    |  ✓  | ✓ |  ✓  |    |  |    |    |
 | Reddit-B | ✓ |    |    | ✓ | ✓   |    |  |    |    |
 | Reddit-Multi-5k | ✓ |    |    | ✓ | ✓   |    |  |    |    |
 | Reddit-Multi-12k | ✓ |    |    | ✓ | ✓   |    |  |    |    |
 | PTC-MR | ✓ |    |  ✓  | ✓ |  ✓  |  ✓  |  |    |    |
-| NCI1 | ✓ |    |  ✓  | ✓ |  ✓  |    |  |    |    |
-| NCI109 | ✓ |    |  ✓  | ✓ |  ✓  |    |  |    |    |
-| Enzyme | ✓ |    |  ✓  | ✓ |  ✓  |    |  |    |    |
-
-
-
-
-
-
-
+-->
 

autogl/datasets/__init__.py

@@ -1,6 +1,7 @@
 import os.path as osp
 import importlib
 import os
+import torch
 from ..data.dataset import Dataset
 
 
@@ -16,7 +17,7 @@
 
 def register_dataset(name):
     """
-    New dataset types can be added to autograph with the :func:`register_dataset`
+    New dataset types can be added to autogl with the :func:`register_dataset`
     function decorator.
 
     For example::
@@ -36,7 +37,7 @@ def register_dataset_cls(cls):
             pyg and not issubclass(cls, torch_geometric.data.Dataset)
         ):
             raise ValueError(
-                "Dataset ({}: {}) must extend autograph.data.Dataset".format(
+                "Dataset ({}: {}) must extend autogl.data.Dataset".format(
                     name, cls.__name__
                 )
             )
@@ -105,27 +106,22 @@ def register_dataset_cls(cls):
     graph_get_split,
 )
 
-"""
-# automatically import any Python files in the datasets/ directory
-for file in os.listdir(os.path.dirname(__file__)):
-    if file.endswith(".py") and not file.startswith("_"):
-        dataset_name = file[: file.find(".py")]
-        if not pyg and dataset_name.startswith("pyg"):
-            continue
-        module = importlib.import_module("autograph.datasets." + dataset_name)
-"""
-
-
 def build_dataset(args, path="~/.cache-autogl/"):
     path = osp.join(path, "data", args.dataset)
     path = os.path.expanduser(path)
     return DATASET_DICT[args.dataset](path)
 
 
-def build_dataset_from_name(dataset, path="~/.cache-autogl/"):
-    path = osp.join(path, "data", dataset)
+def build_dataset_from_name(dataset_name, path="~/.cache-autogl/"):
+    path = osp.join(path, "data", dataset_name)
     path = os.path.expanduser(path)
-    return DATASET_DICT[dataset](path)
+    dataset = DATASET_DICT[dataset_name](path)
+    if 'ogbn' in dataset_name:
+        #dataset.data, dataset.slices = dataset.collate([dataset.data])
+        #dataset.data.num_nodes = dataset.data.num_nodes[0]
+        if dataset.data.y.shape[-1] == 1:
+            dataset.data.y = torch.squeeze(dataset.data.y)
+    return dataset
 
 
 __all__ = [

autogl/datasets/ogb.py

@@ -3,7 +3,8 @@
 from ogb.graphproppred import PygGraphPropPredDataset
 from ogb.linkproppred import PygLinkPropPredDataset
 from . import register_dataset
-
+from .utils import index_to_mask
+from torch_geometric.data import Data
 # OGBN
 
 
@@ -12,64 +13,133 @@ class OGBNproductsDataset(PygNodePropPredDataset):
     def __init__(self, path):
         dataset = "ogbn-products"
         # path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
-        PygNodePropPredDataset(name=dataset, root=path, transform=T.ToSparseTensor())
+        PygNodePropPredDataset(name=dataset, root=path)
         super(OGBNproductsDataset, self).__init__(
-            dataset, path, transform=T.ToSparseTensor()
+            dataset, path
         )
+        # Pre-compute GCN normalization.
+        #adj_t = self.data.adj_t.set_diag()
+        #deg = adj_t.sum(dim=1).to(torch.float)
+        #deg_inv_sqrt = deg.pow(-0.5)
+        #deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
+        #adj_t = deg_inv_sqrt.view(-1, 1) * adj_t * deg_inv_sqrt.view(1, -1)
+        #self.data.adj_t = adj_t
+
         setattr(OGBNproductsDataset, "metric", "Accuracy")
         setattr(OGBNproductsDataset, "loss", "nll_loss")
-
+        split_idx = self.get_idx_split()
+        datalist = []
+        for d in self:
+            setattr(d, "train_mask", index_to_mask(split_idx['train'], d.y.shape[0]))
+            setattr(d, "val_mask", index_to_mask(split_idx['valid'], d.y.shape[0]))
+            setattr(d, "test_mask", index_to_mask(split_idx['test'], d.y.shape[0]))
+            datalist.append(d)
+        self.data, self.slices = self.collate(datalist)
 
 @register_dataset("ogbn-proteins")
 class OGBNproteinsDataset(PygNodePropPredDataset):
     def __init__(self, path):
         dataset = "ogbn-proteins"
         # path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
-        PygNodePropPredDataset(name=dataset, root=path, transform=T.ToSparseTensor())
+        PygNodePropPredDataset(name=dataset, root=path)
         super(OGBNproteinsDataset, self).__init__(
-            dataset, path, transform=T.ToSparseTensor()
+            dataset, path
         )
+        dataset_t = PygNodePropPredDataset(name=dataset, root = path, transform=T.ToSparseTensor())
+
+        # Move edge features to node features.
+        self.data.x = dataset_t[0].adj_t.mean(dim=1)
+        #dataset_t[0].adj_t.set_value_(None)
+        del dataset_t
+
         setattr(OGBNproteinsDataset, "metric", "ROC-AUC")
-        setattr(OGBNproteinsDataset, "loss", "BCEWithLogitsLoss")
+        setattr(OGBNproteinsDataset, "loss", "binary_cross_entropy_with_logits")
+        split_idx = self.get_idx_split()
+        datalist = []
+        for d in self:
+            setattr(d, "train_mask", index_to_mask(split_idx['train'], d.y.shape[0]))
+            setattr(d, "val_mask", index_to_mask(split_idx['valid'], d.y.shape[0]))
+            setattr(d, "test_mask", index_to_mask(split_idx['test'], d.y.shape[0]))
+            datalist.append(d)
+        self.data, self.slices = self.collate(datalist)
 
 
 @register_dataset("ogbn-arxiv")
 class OGBNarxivDataset(PygNodePropPredDataset):
     def __init__(self, path):
         dataset = "ogbn-arxiv"
         # path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
-        PygNodePropPredDataset(name=dataset, root=path, transform=T.ToSparseTensor())
+        PygNodePropPredDataset(name=dataset, root=path)
         super(OGBNarxivDataset, self).__init__(
-            dataset, path, transform=T.ToSparseTensor()
+            dataset, path
         )
+
+        #self[0].adj_t = self[0].adj_t.to_symmetric()
+
         setattr(OGBNarxivDataset, "metric", "Accuracy")
         setattr(OGBNarxivDataset, "loss", "nll_loss")
+        split_idx = self.get_idx_split()
 
+        datalist = []
+        for d in self:
+            setattr(d, "train_mask", index_to_mask(split_idx['train'], d.y.shape[0]))
+            setattr(d, "val_mask", index_to_mask(split_idx['valid'], d.y.shape[0]))
+            setattr(d, "test_mask", index_to_mask(split_idx['test'], d.y.shape[0]))
+            datalist.append(d)
+        self.data, self.slices = self.collate(datalist)
 
 @register_dataset("ogbn-papers100M")
 class OGBNpapers100MDataset(PygNodePropPredDataset):
     def __init__(self, path):
         dataset = "ogbn-papers100M"
         # path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
-        PygNodePropPredDataset(name=dataset, root=path, transform=T.ToSparseTensor())
+        PygNodePropPredDataset(name=dataset, root=path)
         super(OGBNpapers100MDataset, self).__init__(
-            dataset, path, transform=T.ToSparseTensor()
+            dataset, path
         )
         setattr(OGBNpapers100MDataset, "metric", "Accuracy")
         setattr(OGBNpapers100MDataset, "loss", "nll_loss")
-
+        split_idx = self.get_idx_split()
+        datalist = []
+        for d in self:
+            setattr(d, "train_mask", index_to_mask(split_idx['train'], d.y.shape[0]))
+            setattr(d, "val_mask", index_to_mask(split_idx['valid'], d.y.shape[0]))
+            setattr(d, "test_mask", index_to_mask(split_idx['test'], d.y.shape[0]))
+            datalist.append(d)
+        self.data, self.slices = self.collate(datalist)
 
 @register_dataset("ogbn-mag")
 class OGBNmagDataset(PygNodePropPredDataset):
     def __init__(self, path):
         dataset = "ogbn-mag"
         # path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
-        PygNodePropPredDataset(name=dataset, root=path, transform=T.ToSparseTensor())
+        PygNodePropPredDataset(name=dataset, root=path)
         super(OGBNmagDataset, self).__init__(
-            dataset, path, transform=T.ToSparseTensor()
+            dataset, path
         )
+
+        # Preprocessing
+        rel_data = self[0]
+        # We are only interested in paper <-> paper relations.
+        self.data = Data(
+            x=rel_data.x_dict['paper'],
+            edge_index=rel_data.edge_index_dict[('paper', 'cites', 'paper')],
+            y=rel_data.y_dict['paper'])
+
+        #self.data = T.ToSparseTensor()(data)
+        #self[0].adj_t = self[0].adj_t.to_symmetric()
+
         setattr(OGBNmagDataset, "metric", "Accuracy")
         setattr(OGBNmagDataset, "loss", "nll_loss")
+        split_idx = self.get_idx_split()
+
+        datalist = []
+        for d in self:
+            setattr(d, "train_mask", index_to_mask(split_idx['train'], d.y.shape[0]))
+            setattr(d, "val_mask", index_to_mask(split_idx['valid'], d.y.shape[0]))
+            setattr(d, "test_mask", index_to_mask(split_idx['test'], d.y.shape[0]))
+            datalist.append(d)
+        self.data, self.slices = self.collate(datalist)
 
 
 # OGBG
@@ -83,7 +153,7 @@ def __init__(self, path):
         PygGraphPropPredDataset(name=dataset, root=path)
         super(OGBGmolhivDataset, self).__init__(dataset, path)
         setattr(OGBGmolhivDataset, "metric", "ROC-AUC")
-        setattr(OGBGmolhivDataset, "loss", "BCEWithLogitsLoss")
+        setattr(OGBGmolhivDataset, "loss", "binary_cross_entropy_with_logits")
 
 
 @register_dataset("ogbg-molpcba")
@@ -94,7 +164,7 @@ def __init__(self, path):
         PygGraphPropPredDataset(name=dataset, root=path)
         super(OGBGmolpcbaDataset, self).__init__(dataset, path)
         setattr(OGBGmolpcbaDataset, "metric", "AP")
-        setattr(OGBGmolpcbaDataset, "loss", "BCEWithLogitsLoss")
+        setattr(OGBGmolpcbaDataset, "loss", "binary_cross_entropy_with_logits")
 
 
 @register_dataset("ogbg-ppa")
@@ -105,7 +175,7 @@ def __init__(self, path):
         PygGraphPropPredDataset(name=dataset, root=path)
         super(OGBGppaDataset, self).__init__(dataset, path)
         setattr(OGBGppaDataset, "metric", "Accuracy")
-        setattr(OGBGppaDataset, "loss", "CrossEntropyLoss")
+        setattr(OGBGppaDataset, "loss", "cross_entropy")
 
 
 @register_dataset("ogbg-code")
@@ -116,7 +186,7 @@ def __init__(self, path):
         PygGraphPropPredDataset(name=dataset, root=path)
         super(OGBGcodeDataset, self).__init__(dataset, path)
         setattr(OGBGcodeDataset, "metric", "F1 score")
-        setattr(OGBGcodeDataset, "loss", "CrossEntropyLoss")
+        setattr(OGBGcodeDataset, "loss", "cross_entropy")
 
 
 # OGBL
@@ -127,7 +197,7 @@ class OGBLppaDataset(PygLinkPropPredDataset):
     def __init__(self, path):
         dataset = "ogbl-ppa"
         # path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
-        PygLinkPropPredDataset(name=dataset, root=path, transform=T.ToSparseTensor())
+        PygLinkPropPredDataset(name=dataset, root=path)
         super(OGBLppaDataset, self).__init__(dataset, path)
         setattr(OGBLppaDataset, "metric", "Hits@100")
         setattr(OGBLppaDataset, "loss", "pos_neg_loss")
@@ -138,7 +208,7 @@ class OGBLcollabDataset(PygLinkPropPredDataset):
     def __init__(self, path):
         dataset = "ogbl-collab"
         # path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
-        PygLinkPropPredDataset(name=dataset, root=path, transform=T.ToSparseTensor())
+        PygLinkPropPredDataset(name=dataset, root=path)
         super(OGBLcollabDataset, self).__init__(dataset, path)
         setattr(OGBLcollabDataset, "metric", "Hits@50")
         setattr(OGBLcollabDataset, "loss", "pos_neg_loss")
@@ -149,7 +219,7 @@ class OGBLddiDataset(PygLinkPropPredDataset):
     def __init__(self, path):
         dataset = "ogbl-ddi"
         # path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
-        PygLinkPropPredDataset(name=dataset, root=path, transform=T.ToSparseTensor())
+        PygLinkPropPredDataset(name=dataset, root=path)
         super(OGBLddiDataset, self).__init__(dataset, path)
         setattr(OGBLddiDataset, "metric", "Hits@20")
         setattr(OGBLddiDataset, "loss", "pos_neg_loss")
@@ -160,7 +230,7 @@ class OGBLcitationDataset(PygLinkPropPredDataset):
     def __init__(self, path):
         dataset = "ogbl-citation"
         # path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
-        PygLinkPropPredDataset(name=dataset, root=path, transform=T.ToSparseTensor())
+        PygLinkPropPredDataset(name=dataset, root=path)
         super(OGBLcitationDataset, self).__init__(dataset, path)
         setattr(OGBLcitationDataset, "metric", "MRR")
         setattr(OGBLcitationDataset, "loss", "pos_neg_loss")
@@ -171,7 +241,7 @@ class OGBLwikikgDataset(PygLinkPropPredDataset):
     def __init__(self, path):
         dataset = "ogbl-wikikg"
         # path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
-        PygLinkPropPredDataset(name=dataset, root=path, transform=T.ToSparseTensor())
+        PygLinkPropPredDataset(name=dataset, root=path)
         super(OGBLwikikgDataset, self).__init__(dataset, path)
         setattr(OGBLwikikgDataset, "metric", "MRR")
         setattr(OGBLwikikgDataset, "loss", "pos_neg_loss")
@@ -182,7 +252,7 @@ class OGBLbiokgDataset(PygLinkPropPredDataset):
     def __init__(self, path):
         dataset = "ogbl-biokg"
         # path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
-        PygLinkPropPredDataset(name=dataset, root=path, transform=T.ToSparseTensor())
+        PygLinkPropPredDataset(name=dataset, root=path)
         super(OGBLbiokgDataset, self).__init__(dataset, path)
         setattr(OGBLbiokgDataset, "metric", "MRR")
         setattr(OGBLbiokgDataset, "loss", "pos_neg_loss")