Merge pull request #110 from BlankSpacePlus/main

Reformat Python code based on PEP specification

.gitignore

@@ -15,4 +15,5 @@ doc_html_auto_generator
 .DS_Store
 src/.DS_Store
 result
-data
+data
+.idea

README.md

@@ -1,24 +1,23 @@
 <p align="center">
-    <a href="https://zhoushengisnoob.github.io/projects/DGLD_Fronted/index.html"> <img src="DGLD_logo.jpg" width="200"/></a>
+    <a href="https://zhoushengisnoob.github.io/projects/DGLD_Fronted/index.html"> <img src="images/DGLD_logo.jpg" width="200" alt="DGLD LOGO"/></a>
 <p>
 
-<h1 align="center">
-    <p>A Deep Graph Anomaly Detection Library <br> based on DGL</p>
-</h1>
+<h1 align="center">A Deep Graph Anomaly Detection Library <br> based on DGL</h1>
 
 <p align="center">
     <b> <a href="https://zhoushengisnoob.github.io/projects/DGLD_Fronted/index.html">Website</a> | <a href="https://zhoushengisnoob.github.io/DGLD/doc/docstring_html/html/dgld.html">Doc</a> </b>
 </p>
 
 DGLD is an open-source library for Deep Graph Anomaly Detection based on pytorch and DGL. It provides unified interface of popular graph anomaly detection methods, including the data loader, data augmentation, model training and evaluation. Also, the widely used modules are well organized so that developers and researchers can quickly implement their own designed models. 
 
-
 ## News
+
 * [Aug 2022] We have released an [easy-to-use graphical command line tool](https://zhoushengisnoob.github.io/DGLD/web/index.html) for users to run experiments with different models, datasets and customized parameters. Users can select all the settings in the page, click 'Submit' and copy the shell scripts to the terminal. 
 * [July 2022] For PyG users, we recommend the [PyGOD](https://github.com/pygod-team/pygod/), which is another comprehensive package that also supports many graph anomaly detection methods.
 * [June 2022] Recently we receive feedback that the reported results are slightly different from the original paper. This is due to the anomaly injection setting, the graph augmentation and sampling. We will provide more details on the settings. 
 
 ## Installation
+
 Basic environment installation:
 ```shell
 conda create -n dgld python=3.8.0
@@ -27,17 +26,22 @@ conda install cudatoolkit==11.3.1
 pip install dgl-cu113==0.8.1 dglgo==0.0.1 -f https://data.dgl.ai/wheels/repo.html
 pip install torch==1.11.0+cu113  -f https://download.pytorch.org/whl/torch_stable.html
 ```
-- ###  install from source code
+
+### Install from source code
+
 Then clone the DGLD project, enter the directory and run:
 ```shell
 git clone git@github.com:EagleLab-ZJU/DGLD.git
 pip install -r requirements.txt
 ```
 To check whether you have successfully installed the package and environment, you can simply run
+
 ```shell
 python example.py
 ```
-- ### install from pypi
+
+### Install from pypi
+
 ```shell
 pip install DGLD
 ```
@@ -80,13 +84,13 @@ Function fit need parameters to specify number of epoch and device. For gpu, dev
 ```python
 from DGLD.utils.evaluation import split_auc
 
-model.fit(g, num_epoch = 5, device = 0)
-result = model.predict(g, auc_test_rounds = 2)
+model.fit(g, num_epoch=5, device=0)
+result = model.predict(g, auc_test_rounds=2)
 print(split_auc(label, result))
 ```
 
-
 ## Native Datasets
+
 The DGLD provides native graph anomaly detection datasets that widely used by existing methods.
 
 |   Dataset   | nodes  |  edges  | attributes | anomalies |
@@ -99,9 +103,8 @@ The DGLD provides native graph anomaly detection datasets that widely used by ex
 |   Pubmed    | 19717  |  44338  |    500     |    600    |
 | ogbn-arxiv  | 169343 | 1166243 |    128     |   6000    |
 
-
-
 ## Implemented Results ([Sorted Results](https://zhoushengisnoob.github.io/projects/DGLD_Fronted/leaderboard.html))
+
 |                                  Method                                   |  Cora  | Citeseer | Pubmed | BlogCatalog | Flickr |  ACM   | Arxiv  |
 |:-------------------------------------------------------------------------:|:------:|:--------:|:------:|:-----------:|:------:|:------:|:------:|
 |                 [CoLA](https://arxiv.org/abs/2103.00113)                  | 0.8823 |  0.8765  | 0.9632 |   0.6488    | 0.5790 | 0.8194 | 0.8833 |
@@ -120,9 +123,10 @@ The DGLD provides native graph anomaly detection datasets that widely used by ex
 |        [AdONE](https://dl.acm.org/doi/abs/10.1145/3336191.3371788)        | 0.9629 |  0.8935  | 0.9030 |   0.7438    | 0.7595 |   -    | 0.7651 |
 |                 [GCNAE](https://arxiv.org/abs/1611.07308)                 | 0.7707 |  0.7696  | 0.7941 |   0.7363    | 0.7529 |   -    | 0.7530 |
 |          [MLPAE](https://dl.acm.org/doi/10.1145/2689746.2689747)          | 0.7617 |  0.7538  | 0.7211 |   0.7399    | 0.7514 |   -    | 0.7382 |
-|                                 [SCAN](https://dl.acm.org/doi/10.1145/1281192.1281280)                                  | 0.6508 |  0.6671  | 0.7361 |   0.4926    | 0.6498 |   -    | 0.6905 |
+|          [SCAN](https://dl.acm.org/doi/10.1145/1281192.1281280)           | 0.6508 |  0.6671  | 0.7361 |   0.4926    | 0.6498 |   -    | 0.6905 |
 
 ## Upcoming Features
+
 * More Graph Anomaly Detection Methods
 * Edge/Community/Graph Level Anomaly Detection Tasks
 * Graphical Operation Interface

doc/docstring_html/README.md

@@ -1,2 +1 @@
 # Docstrings for DGLD models
-test

example.py

@@ -1,5 +1,6 @@
 import os
 import sys
+
 sys.path.append('./src')
 
 from dgld.utils.dataset import GraphNodeAnomalyDectionDataset
@@ -11,14 +12,14 @@
 import torch
 import numpy as np
 
-gnd_dataset = GraphNodeAnomalyDectionDataset("Cora", p = 15, k = 50)
+gnd_dataset = GraphNodeAnomalyDectionDataset("Cora", p=15, k=50)
 g = gnd_dataset[0]
 label = gnd_dataset.anomaly_label
 model = CoLA(in_feats=g.ndata['feat'].shape[1])
 print('-' * 10)
 print('Fit : ')
-model.fit(g, num_epoch=5, device = 0)
+model.fit(g, num_epoch=5, device=0)
 print('-' * 10)
 print('Predict : ')
-result = model.predict(g, auc_test_rounds=2, device = 0)
-print(split_auc(label, result))
+result = model.predict(g, auc_test_rounds=2, device=0)
+print(split_auc(label, result))

main.py

@@ -1,48 +1,51 @@
 import sys
+
 sys.path.append('./src')
+
 from dgld.utils.evaluation import split_auc
 from dgld.utils.common import seed_everything
 from dgld.utils.argparser import parse_all_args
-from dgld.utils.load_data import load_data,load_custom_data, load_truth_data
-from dgld.utils.inject_anomalies import inject_contextual_anomalies,inject_structural_anomalies
-from dgld.utils.common_params import Q_MAP,K,P
+from dgld.utils.load_data import load_data, load_custom_data, load_truth_data
+from dgld.utils.inject_anomalies import inject_contextual_anomalies, inject_structural_anomalies
+from dgld.utils.common_params import Q_MAP, K, P
 from dgld.utils.log import Dgldlog
 from dgld.models import *
-import random 
-import os 
-truth_list = ['weibo','tfinance','tsocial','reddit','Amazon','Class','Disney','elliptic','Enron']
+import random
+import os
+
+truth_list = ['weibo', 'tfinance', 'tsocial', 'reddit', 'Amazon', 'Class', 'Disney', 'elliptic', 'Enron']
 if __name__ == "__main__":
-    args_dict,args = parse_all_args()
+    args_dict, args = parse_all_args()
     data_name = args_dict['dataset']
     save_path = args.save_path
     exp_name = args.exp_name
-    log = Dgldlog(save_path,exp_name,args)
+    log = Dgldlog(save_path, exp_name, args)
 
     res_list_final = []
     res_list_attrb = []
     res_list_struct = []
 
-    seed_list = [random.randint(0,99999) for i in range(args.runs)]
+    seed_list = [random.randint(0, 99999) for i in range(args.runs)]
     seed_list[0] = args_dict['seed']
     for runs in range(args.runs):
         log.update_runs()
         seed = seed_list[runs]
         seed_everything(seed)
         args_dict['seed'] = seed
-        
+
         if data_name in truth_list:
-            graph = load_truth_data(data_path=args.data_path,dataset_name=data_name)
+            graph = load_truth_data(data_path=args.data_path, dataset_name=data_name)
         elif data_name == 'custom':
             graph = load_custom_data(data_path=args.data_path)
         else:
             graph = load_data(data_name)
-            graph = inject_contextual_anomalies(graph=graph,k=K,p=P,q=Q_MAP[data_name],seed=seed)
-            graph = inject_structural_anomalies(graph=graph,p=P,q=Q_MAP[data_name],seed=seed)
+            graph = inject_contextual_anomalies(graph=graph, k=K, p=P, q=Q_MAP[data_name], seed=seed)
+            graph = inject_structural_anomalies(graph=graph, p=P, q=Q_MAP[data_name], seed=seed)
 
         label = graph.ndata['label']
 
-        if args.model in ['DOMINANT','AnomalyDAE','ComGA','DONE','AdONE','CONAD','ALARM','ONE','GAAN','GUIDE','CoLA',
-                        'AAGNN', 'SLGAD','ANEMONE','GCNAE','MLPAE','SCAN']:
+        if args.model in ['DOMINANT', 'AnomalyDAE', 'ComGA', 'DONE', 'AdONE', 'CONAD', 'ALARM', 'ONE', 'GAAN', 'GUIDE',
+                          'CoLA', 'AAGNN', 'SLGAD', 'ANEMONE', 'GCNAE', 'MLPAE', 'SCAN']:
             model = eval(f'{args.model}(**args_dict["model"])')
         else:
             raise ValueError(f"{args.model} is not implemented!")
@@ -55,5 +58,5 @@
         res_list_struct.append(s_score)
         print(args_dict)
 
-    log.save_result(res_list_final,res_list_attrb,res_list_struct,seed_list,args)
+    log.save_result(res_list_final, res_list_attrb, res_list_struct, seed_list, args)
     os._exit(0)

requirements.txt

@@ -12,4 +12,5 @@ tqdm==4.63.0
 wget==3.2
 tensorboard==2.8.0
 protobuf==3.19.4
-tabulate==0.8.10
+tabulate==0.8.10
+dgl==1.1.2

scripts/autoread_args.py

@@ -1,11 +1,14 @@
 import os
 import sys
+
 sys.path.append('./src')
-from dgld.utils.argparser import models_set_args_map 
+
+from dgld.utils.argparser import models_set_args_map
 from dgld.utils.common import saveargs2json, loadargs_from_json
 from copy import deepcopy
 
-class FakeParser():
+
+class FakeParser:
     def __init__(self):
         self.options = []
 
@@ -61,6 +64,3 @@ def get_choices(kwargs):
 
             hyper = {"hyper": options}
             saveargs2json(hyper, save_path+f"{_model}_{dataset}.json")
-
-
-

scripts/benchmark_aagnn.sh

@@ -9,6 +9,3 @@ do
   python main_aagnn.py --dataset $dataset --device 0 --seed $seed --logdir logs/$expname > logs/$expname.log 2>&1
   cat logs/$expname.log
 done
-
-
-

scripts/benchmark_alarm.sh

@@ -9,6 +9,3 @@ do
   python main_alarm.py --dataset $dataset --device 0 --seed $seed --logdir logs/$expname > logs/$expname.log 2>&1
   cat logs/$expname.log
 done
-
-
-

scripts/benchmark_anomalydae.sh

@@ -9,6 +9,3 @@ do
   python main_anomalydae.py --dataset $dataset --device 0 --seed $seed --logdir logs/$expname > logs/$expname.log 2>&1
   cat logs/$expname.log
 done
-
-
-

scripts/benchmark_comga.sh

@@ -9,6 +9,3 @@ do
   python main_comga.py --dataset $dataset --device 0 --seed $seed --logdir logs/$expname > logs/$expname.log 2>&1
   cat logs/$expname.log
 done
-
-
-

scripts/benchmark_dominant.sh

@@ -9,6 +9,3 @@ do
   python main_dominant.py --dataset $dataset --device 0 --seed $seed --logdir logs/$expname > logs/$expname.log 2>&1
   cat logs/$expname.log
 done
-
-
-

scripts/logs/README.md

@@ -1 +1 @@
-# Save experiment logs
+# Experiment Logs

scripts/main_aagnn.py

@@ -1,17 +1,17 @@
 import os
 import sys
+
 current_file_name = __file__
 current_dir = os.path.dirname(os.path.dirname(os.path.abspath(current_file_name))) + '/src'
 sys.path.append(current_dir)
 print(current_dir)
 
 import dgld
-
 from dgld.utils.dataset import GraphNodeAnomalyDectionDataset
 from dgld.utils.evaluation import split_auc
 from dgld.models.AAGNN import AAGNN_batch
-
 from dgld.models.AAGNN import get_parse
+
 import dgl
 import torch
 import numpy as np
@@ -29,8 +29,3 @@
     model.fit(g, **args["fit"])
     result = model.predict(g, **args["predict"])
     split_auc(label, result)
-
-
-
-
-

scripts/main_alarm.py

@@ -1,5 +1,6 @@
 import os
 import sys
+
 current_file_name = __file__
 current_dir = os.path.dirname(os.path.dirname(os.path.abspath(current_file_name))) + '/src'
 sys.path.append(current_dir)
@@ -11,6 +12,7 @@
 from dgld.utils.evaluation import split_auc
 from dgld.utils.common import load_ACM
 from dgld.utils.common import seed_everything
+
 import dgl
 import torch
 import numpy as np
@@ -53,4 +55,3 @@
     result = model.predict(g, **args["predict"])
     split_auc(label, result)
     print(args)
-

scripts/main_anomalydae.py

@@ -1,5 +1,6 @@
 import os
 import sys
+
 current_file_name = __file__
 current_dir = os.path.dirname(os.path.dirname(os.path.abspath(current_file_name))) + '/src'
 sys.path.append(current_dir)
@@ -11,6 +12,7 @@
 from dgld.utils.evaluation import split_auc
 from dgld.utils.common import load_ACM
 from dgld.utils.common import seed_everything
+
 import dgl
 import torch
 import numpy as np

scripts/main_cola.py

@@ -1,7 +1,9 @@
 # Author: Peng Zhang <zzhangpeng@zju.edu.cn>
 # License: BSD 2 clause
+
 import os
 import sys
+
 current_file_name = __file__
 current_dir = os.path.dirname(os.path.dirname(os.path.abspath(current_file_name))) + '/src'
 sys.path.append(current_dir)
@@ -54,4 +56,3 @@
     model.fit(g, **args["fit"])
     result = model.predict(g, **args["predict"])
     split_auc(label, result)
-

scripts/main_comga.py

@@ -1,5 +1,6 @@
 import os
 import sys
+
 current_file_name = __file__
 current_dir = os.path.dirname(os.path.dirname(os.path.abspath(current_file_name))) + '/src'
 sys.path.append(current_dir)

scripts/main_conad.py

@@ -1,5 +1,6 @@
 import os
 import sys
+
 current_file_name = __file__
 current_dir = os.path.dirname(os.path.dirname(os.path.abspath(current_file_name))) + '/src'
 sys.path.append(current_dir)
@@ -55,4 +56,4 @@
     model = CONAD(**args["model"])
     model.fit(g, **args["fit"])
     result = model.predict(g, **args["predict"])
-    print(split_auc(label, result))
+    print(split_auc(label, result))