examples (#5323)

Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>

examples/pytorch/arma/citation.py

@@ -7,10 +7,10 @@ import numpy as np
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from model import ARMA4NC
-from tqdm import trange
 
 from dgl.data import CiteseerGraphDataset, CoraGraphDataset, PubmedGraphDataset
+from model import ARMA4NC
+from tqdm import trange
 
 
 def main(args):

examples/pytorch/arma/model.py

 import math
 
+import dgl.function as fn
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-import dgl.function as fn
-
 
 def glorot(tensor):
     if tensor is not None:

examples/pytorch/bgnn/BGNN.py

@@ -188,7 +188,6 @@ class BGNNPredictor:
     def init_optimizer(
         self, node_features, optimize_node_features, learning_rate
     ):
-
         params = [self.model.parameters()]
         if optimize_node_features:
             params.append([node_features])

examples/pytorch/bgnn/run.py

@@ -7,15 +7,17 @@ import torch
 import torch.nn.functional as F
 from BGNN import BGNNPredictor
 from category_encoders import CatBoostEncoder
-from sklearn import preprocessing
-from torch.nn import ELU, Dropout, Linear, ReLU, Sequential
 
 from dgl.data.utils import load_graphs
-from dgl.nn.pytorch import AGNNConv as AGNNConvDGL
-from dgl.nn.pytorch import APPNPConv
-from dgl.nn.pytorch import ChebConv as ChebConvDGL
-from dgl.nn.pytorch import GATConv as GATConvDGL
-from dgl.nn.pytorch import GraphConv
+from dgl.nn.pytorch import (
+    AGNNConv as AGNNConvDGL,
+    APPNPConv,
+    ChebConv as ChebConvDGL,
+    GATConv as GATConvDGL,
+    GraphConv,
+)
+from sklearn import preprocessing
+from torch.nn import Dropout, ELU, Linear, ReLU, Sequential
 
 
 class GNNModelDGL(torch.nn.Module):

examples/pytorch/bgrl/eval_function.py

@@ -2,10 +2,9 @@ import numpy as np
 import torch
 from sklearn import metrics
 from sklearn.linear_model import LogisticRegression
-from sklearn.model_selection import (GridSearchCV, ShuffleSplit,
-                                     train_test_split)
+from sklearn.model_selection import GridSearchCV, ShuffleSplit, train_test_split
 from sklearn.multiclass import OneVsRestClassifier
-from sklearn.preprocessing import OneHotEncoder, normalize
+from sklearn.preprocessing import normalize, OneHotEncoder
 
 
 def fit_logistic_regression(X, y, data_random_seed=1, repeat=1):

examples/pytorch/bgrl/main.py

@@ -2,20 +2,27 @@ import copy
 import os
 import warnings
 
+import dgl
+
 import numpy as np
 import torch
-from eval_function import (fit_logistic_regression,
+from eval_function import (
+    fit_logistic_regression,
     fit_logistic_regression_preset_splits,
-                           fit_ppi_linear)
-from model import (BGRL, GCN, GraphSAGE_GCN, MLP_Predictor,
-                   compute_representations)
+    fit_ppi_linear,
+)
+from model import (
+    BGRL,
+    compute_representations,
+    GCN,
+    GraphSAGE_GCN,
+    MLP_Predictor,
+)
 from torch.nn.functional import cosine_similarity
 from torch.optim import AdamW
 from tqdm import tqdm
 from utils import CosineDecayScheduler, get_dataset, get_graph_drop_transform
 
-import dgl
-
 warnings.filterwarnings("ignore")
 
 

examples/pytorch/bgrl/model.py

 import copy
 
+import dgl
+
 import torch
+from dgl.nn.pytorch.conv import GraphConv, SAGEConv
 from torch import nn
 from torch.nn import BatchNorm1d, Parameter
 from torch.nn.init import ones_, zeros_
 
-import dgl
-from dgl.nn.pytorch.conv import GraphConv, SAGEConv
-
 
 class LayerNorm(nn.Module):
     def __init__(self, in_channels, eps=1e-5, affine=True):

examples/pytorch/bgrl/utils.py

@@ -3,9 +3,14 @@ import copy
 import numpy as np
 import torch
 
-from dgl.data import (AmazonCoBuyComputerDataset, AmazonCoBuyPhotoDataset,
-                      CoauthorCSDataset, CoauthorPhysicsDataset, PPIDataset,
-                      WikiCSDataset)
+from dgl.data import (
+    AmazonCoBuyComputerDataset,
+    AmazonCoBuyPhotoDataset,
+    CoauthorCSDataset,
+    CoauthorPhysicsDataset,
+    PPIDataset,
+    WikiCSDataset,
+)
 from dgl.dataloading import GraphDataLoader
 from dgl.transforms import Compose, DropEdge, FeatMask, RowFeatNormalizer
 

examples/pytorch/capsule/DGLDigitCapsule.py

+import dgl
+import dgl.function as fn
 import torch
 from DGLRoutingLayer import DGLRoutingLayer
 from torch import nn
 from torch.nn import functional as F
 
-import dgl
-import dgl.function as fn
-
 
 class DGLDigitCapsuleLayer(nn.Module):
     def __init__(

examples/pytorch/capsule/DGLRoutingLayer.py

+import dgl
 import torch as th
 import torch.nn as nn
 import torch.nn.functional as F
 
-import dgl
-
 
 class DGLRoutingLayer(nn.Module):
     def __init__(self, in_nodes, out_nodes, f_size, batch_size=0, device="cpu"):

examples/pytorch/capsule/simple_routing.py

+import dgl
 import torch as th
 import torch.nn as nn
 from DGLRoutingLayer import DGLRoutingLayer
 from torch.nn import functional as F
 
-import dgl
-
 g = dgl.DGLGraph()
 g.graph_data = {}
 

examples/pytorch/caregnn/main.py

 import argparse
 
+import dgl
+
 import torch as th
 import torch.optim as optim
 from model import CAREGNN
@@ -7,8 +9,6 @@ from sklearn.metrics import recall_score, roc_auc_score
 from torch.nn.functional import softmax
 from utils import EarlyStopping
 
-import dgl
-
 
 def main(args):
     # Step 1: Prepare graph data and retrieve train/validation/test index ============================= #

examples/pytorch/caregnn/main_sampling.py

 import argparse
 
+import dgl
+
 import torch as th
 import torch.optim as optim
-from model_sampling import CAREGNN, CARESampler, _l1_dist
+from model_sampling import _l1_dist, CAREGNN, CARESampler
 from sklearn.metrics import recall_score, roc_auc_score
 from torch.nn.functional import softmax
 from utils import EarlyStopping
 
-import dgl
-
 
 def evaluate(model, loss_fn, dataloader, device="cpu"):
     loss = 0

examples/pytorch/caregnn/model.py

+import dgl.function as fn
 import numpy as np
 import torch as th
 import torch.nn as nn
 
-import dgl.function as fn
-
 
 class CAREConv(nn.Module):
     """One layer of CARE-GNN."""

examples/pytorch/caregnn/model_sampling.py

+import dgl
+import dgl.function as fn
 import numpy as np
 import torch as th
 import torch.nn as nn
 
-import dgl
-import dgl.function as fn
-
 
 def _l1_dist(edges):
     # formula 2

examples/pytorch/cluster_gcn/cluster_gcn.py

 import time
 
+import dgl
+import dgl.nn as dglnn
+
 import numpy as np
 import torch
 import torch.nn as nn
@@ -7,9 +10,6 @@ import torch.nn.functional as F
 import torchmetrics.functional as MF
 from ogb.nodeproppred import DglNodePropPredDataset
 
-import dgl
-import dgl.nn as dglnn
-
 
 class SAGE(nn.Module):
     def __init__(self, in_feats, n_hidden, n_classes):

examples/pytorch/compGCN/data_loader.py

 from collections import defaultdict as ddict
 
+import dgl
+
 import numpy as np
 import torch
 from ordered_set import OrderedSet
 from torch.utils.data import DataLoader, Dataset
 
-import dgl
-
 
 class TrainDataset(Dataset):
     """

examples/pytorch/compGCN/main.py

 import argparse
 from time import time
 
+import dgl.function as fn
+
 import numpy as np
 import torch as th
 import torch.nn as nn
@@ -10,8 +12,6 @@ from data_loader import Data
 from models import CompGCN_ConvE
 from utils import in_out_norm
 
-import dgl.function as fn
-
 
 # predict the tail for (head, rel, -1) or head for (-1, rel, tail)
 def predict(model, graph, device, data_iter, split="valid", mode="tail"):
@@ -96,7 +96,6 @@ def evaluate(model, graph, device, data_iter, split="valid"):
 
 
 def main(args):
-
     # Step 1: Prepare graph data and retrieve train/validation/test index ============================= #
     # check cuda
     if args.gpu >= 0 and th.cuda.is_available():

examples/pytorch/compGCN/models.py

+import dgl
+import dgl.function as fn
 import torch as th
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 from utils import ccorr
 
-import dgl
-import dgl.function as fn
-
 
 class CompGraphConv(nn.Module):
     """One layer of CompGCN."""
@@ -41,7 +40,6 @@ class CompGraphConv(nn.Module):
         nn.init.xavier_normal_(self.loop_rel)
 
     def forward(self, g, n_in_feats, r_feats):
-
         with g.local_scope():
             # Assign values to source nodes. In a homogeneous graph, this is equal to
             # assigning them to all nodes.

examples/pytorch/compGCN/utils.py

@@ -2,9 +2,8 @@
 # <https://github.com/malllabiisc/CompGCN/blob/master/helper.py>.
 # It implements the operation of circular convolution in the ccorr function and an additional in_out_norm function for norm computation.
 
-import torch as th
-
 import dgl
+import torch as th
 
 
 def com_mult(a, b):