[Misc] Black auto fix. (dmlc#4642)

* [Misc] Black auto fix. * sort Co-authored-by: Steve <[email protected]>
wwddd66 · Sep 26, 2022 · 23d0905 · 23d0905
1 parent a9f2acf
commit 23d0905
Show file tree

Hide file tree

Showing 99 changed files with 6,243 additions and 3,552 deletions.
diff --git a/examples/pytorch/GATNE-T/src/main.py b/examples/pytorch/GATNE-T/src/main.py
@@ -1,24 +1,24 @@
-from collections import defaultdict
 import math
 import os
 import sys
 import time
+from collections import defaultdict
 
 import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from tqdm.auto import tqdm
 from numpy import random
 from torch.nn.parameter import Parameter
+from tqdm.auto import tqdm
+from utils import *
+
 import dgl
 import dgl.function as fn
 
-from utils import *
-
 
 def get_graph(network_data, vocab):
-    """ Build graph, treat all nodes as the same type
+    """Build graph, treat all nodes as the same type
 
     Parameters
     ----------
@@ -57,7 +57,9 @@ def __init__(self, g, num_fanouts):
 
     def sample(self, pairs):
         heads, tails, types = zip(*pairs)
-        seeds, head_invmap = torch.unique(torch.LongTensor(heads), return_inverse=True)
+        seeds, head_invmap = torch.unique(
+            torch.LongTensor(heads), return_inverse=True
+        )
         blocks = []
         for fanout in reversed(self.num_fanouts):
             sampled_graph = dgl.sampling.sample_neighbors(self.g, seeds, fanout)
@@ -90,7 +92,9 @@ def __init__(
         self.edge_type_count = edge_type_count
         self.dim_a = dim_a
 
-        self.node_embeddings = Parameter(torch.FloatTensor(num_nodes, embedding_size))
+        self.node_embeddings = Parameter(
+            torch.FloatTensor(num_nodes, embedding_size)
+        )
         self.node_type_embeddings = Parameter(
             torch.FloatTensor(num_nodes, edge_type_count, embedding_u_size)
         )
@@ -100,16 +104,24 @@ def __init__(
         self.trans_weights_s1 = Parameter(
             torch.FloatTensor(edge_type_count, embedding_u_size, dim_a)
         )
-        self.trans_weights_s2 = Parameter(torch.FloatTensor(edge_type_count, dim_a, 1))
+        self.trans_weights_s2 = Parameter(
+            torch.FloatTensor(edge_type_count, dim_a, 1)
+        )
 
         self.reset_parameters()
 
     def reset_parameters(self):
         self.node_embeddings.data.uniform_(-1.0, 1.0)
         self.node_type_embeddings.data.uniform_(-1.0, 1.0)
-        self.trans_weights.data.normal_(std=1.0 / math.sqrt(self.embedding_size))
-        self.trans_weights_s1.data.normal_(std=1.0 / math.sqrt(self.embedding_size))
-        self.trans_weights_s2.data.normal_(std=1.0 / math.sqrt(self.embedding_size))
+        self.trans_weights.data.normal_(
+            std=1.0 / math.sqrt(self.embedding_size)
+        )
+        self.trans_weights_s1.data.normal_(
+            std=1.0 / math.sqrt(self.embedding_size)
+        )
+        self.trans_weights_s2.data.normal_(
+            std=1.0 / math.sqrt(self.embedding_size)
+        )
 
     # embs: [batch_size, embedding_size]
     def forward(self, block):
@@ -122,10 +134,16 @@ def forward(self, block):
         with block.local_scope():
             for i in range(self.edge_type_count):
                 edge_type = self.edge_types[i]
-                block.srcdata[edge_type] = self.node_type_embeddings[input_nodes, i]
-                block.dstdata[edge_type] = self.node_type_embeddings[output_nodes, i]
+                block.srcdata[edge_type] = self.node_type_embeddings[
+                    input_nodes, i
+                ]
+                block.dstdata[edge_type] = self.node_type_embeddings[
+                    output_nodes, i
+                ]
                 block.update_all(
-                    fn.copy_u(edge_type, "m"), fn.sum("m", edge_type), etype=edge_type
+                    fn.copy_u(edge_type, "m"),
+                    fn.sum("m", edge_type),
+                    etype=edge_type,
                 )
                 node_type_embed.append(block.dstdata[edge_type])
 
@@ -152,7 +170,9 @@ def forward(self, block):
             attention = (
                 F.softmax(
                     torch.matmul(
-                        torch.tanh(torch.matmul(tmp_node_type_embed, trans_w_s1)),
+                        torch.tanh(
+                            torch.matmul(tmp_node_type_embed, trans_w_s1)
+                        ),
                         trans_w_s2,
                     )
                     .squeeze(2)
@@ -173,7 +193,9 @@ def forward(self, block):
             )
             last_node_embed = F.normalize(node_embed, dim=2)
 
-            return last_node_embed  # [batch_size, edge_type_count, embedding_size]
+            return (
+                last_node_embed  # [batch_size, edge_type_count, embedding_size]
+            )
 
 
 class NSLoss(nn.Module):
@@ -187,7 +209,8 @@ def __init__(self, num_nodes, num_sampled, embedding_size):
         self.sample_weights = F.normalize(
             torch.Tensor(
                 [
-                    (math.log(k + 2) - math.log(k + 1)) / math.log(num_nodes + 1)
+                    (math.log(k + 2) - math.log(k + 1))
+                    / math.log(num_nodes + 1)
                     for k in range(num_nodes)
                 ]
             ),
@@ -257,14 +280,20 @@ def train_model(network_data):
         pin_memory=True,
     )
     model = DGLGATNE(
-        num_nodes, embedding_size, embedding_u_size, edge_types, edge_type_count, dim_a
+        num_nodes,
+        embedding_size,
+        embedding_u_size,
+        edge_types,
+        edge_type_count,
+        dim_a,
     )
     nsloss = NSLoss(num_nodes, num_sampled, embedding_size)
     model.to(device)
     nsloss.to(device)
 
     optimizer = torch.optim.Adam(
-        [{"params": model.parameters()}, {"params": nsloss.parameters()}], lr=1e-3
+        [{"params": model.parameters()}, {"params": nsloss.parameters()}],
+        lr=1e-3,
     )
 
     best_score = 0
@@ -286,7 +315,10 @@ def train_model(network_data):
             block_types = block_types.to(device)
             embs = model(block[0].to(device))[head_invmap]
             embs = embs.gather(
-                1, block_types.view(-1, 1, 1).expand(embs.shape[0], 1, embs.shape[2])
+                1,
+                block_types.view(-1, 1, 1).expand(
+                    embs.shape[0], 1, embs.shape[2]
+                ),
             )[:, 0]
             loss = nsloss(
                 block[0].dstdata[dgl.NID][head_invmap].to(device),
@@ -307,15 +339,19 @@ def train_model(network_data):
 
         model.eval()
         # {'1': {}, '2': {}}
-        final_model = dict(zip(edge_types, [dict() for _ in range(edge_type_count)]))
+        final_model = dict(
+            zip(edge_types, [dict() for _ in range(edge_type_count)])
+        )
         for i in range(num_nodes):
             train_inputs = (
                 torch.tensor([i for _ in range(edge_type_count)])
                 .unsqueeze(1)
                 .to(device)
             )  # [i, i]
             train_types = (
-                torch.tensor(list(range(edge_type_count))).unsqueeze(1).to(device)
+                torch.tensor(list(range(edge_type_count)))
+                .unsqueeze(1)
+                .to(device)
             )  # [0, 1]
             pairs = torch.cat(
                 (train_inputs, train_inputs, train_types), dim=1
@@ -343,7 +379,9 @@ def train_model(network_data):
         valid_aucs, valid_f1s, valid_prs = [], [], []
         test_aucs, test_f1s, test_prs = [], [], []
         for i in range(edge_type_count):
-            if args.eval_type == "all" or edge_types[i] in args.eval_type.split(","):
+            if args.eval_type == "all" or edge_types[i] in args.eval_type.split(
+                ","
+            ):
                 tmp_auc, tmp_f1, tmp_pr = evaluate(
                     final_model[edge_types[i]],
                     valid_true_data_by_edge[edge_types[i]],