thumnlab / autogl Goto Github PK
View Code? Open in Web Editor NEWAn autoML framework & toolkit for machine learning on graphs.
Home Page: http://mn.cs.tsinghua.edu.cn/AutoGL/
License: Apache License 2.0
An autoML framework & toolkit for machine learning on graphs.
Home Page: http://mn.cs.tsinghua.edu.cn/AutoGL/
License: Apache License 2.0
As titled. Hopefully there could be more discussion groups via wechat or qq. Thanks for the great work.
Describe the bug
following the doc example on FE:
# 1. Choose a dataset.
from autogl.datasets import build_dataset_from_name
data = build_dataset_from_name('cora')
# 2. Compose a feature engineering pipeline
from autogl.module.feature import BaseFeature,AutoFeatureEngineer
from autogl.module.feature.generators import GeEigen
from autogl.module.feature.selectors import SeGBDT
from autogl.module.feature.graph import SgNetLSD
# you may compose feature engineering bases through BaseFeature.compose
fe = BaseFeature.compose([
GeEigen(size=32) ,
SeGBDT(fixlen=100),
SgNetLSD()
])
# or just through '&' operator
fe = fe & AutoFeatureEngineer(fixlen=200,max_epoch=3)
# 3. Fit and transform the data
fe.fit(data)
data1=fe.transform(data,inplace=False)
I always get
Traceback (most recent call last):
File "", line 1, in
File "/home/ruhkopf/miniconda3/envs/AutoGL/lib/python3.9/site-packages/autogl-0.2.0rc0-py3.9.egg/autogl/module/feature/base.py", line 113, in fit
dataset = self._rebuild(dataset, _dataset)
File "/home/ruhkopf/miniconda3/envs/AutoGL/lib/python3.9/site-packages/autogl-0.2.0rc0-py3.9.egg/autogl/module/feature/base.py", line 55, in _rebuild
dataset.slices.update(slices)
AttributeError: 'NoneType' object has no attribute 'update'
To Reproduce
Steps to reproduce the behavior:
Environment (please complete the following information):
OS: Linux Ubuntu 20.04 LTS
python version: 3.9.7
autogl version: 0.2.0-pre
pip list: conda list
_libgcc_mutex 0.1 main
_openmp_mutex 4.5 1_gnu
astor 0.8.1 pypi_0 pypi
bayesian-optimization 1.2.0 pypi_0 pypi
blas 1.0 mkl
bottleneck 1.3.2 py39hdd57654_1
brotlipy 0.7.0 py39h27cfd23_1003
ca-certificates 2021.10.26 h06a4308_2
certifi 2021.10.8 py39h06a4308_0
cffi 1.14.5 py39h261ae71_0
charset-normalizer 2.0.4 pyhd3eb1b0_0
chocolate 0.0.2 pypi_0 pypi
click 8.0.3 pypi_0 pypi
cloudpickle 2.0.0 pypi_0 pypi
colorama 0.4.4 pypi_0 pypi
configparser 5.1.0 pypi_0 pypi
contextlib2 21.6.0 pypi_0 pypi
cryptography 35.0.0 py39hd23ed53_0
cudatoolkit 11.3.1 h2bc3f7f_2
cycler 0.11.0 pypi_0 pypi
dill 0.3.4 pypi_0 pypi
docker-pycreds 0.4.0 pypi_0 pypi
filelock 3.3.2 pypi_0 pypi
fonttools 4.28.1 pypi_0 pypi
future 0.18.2 pypi_0 pypi
gitdb 4.0.9 pypi_0 pypi
gitpython 3.1.24 pypi_0 pypi
googledrivedownloader 0.4 pyhd3deb0d_1 conda-forge
hyperopt 0.1.2 pypi_0 pypi
idna 3.2 pyhd3eb1b0_0
intel-openmp 2021.4.0 h06a4308_3561
isodate 0.6.0 pypi_0 pypi
jinja2 3.0.2 pyhd3eb1b0_0
joblib 1.1.0 pyhd3eb1b0_0
json-tricks 3.15.5 pypi_0 pypi
kiwisolver 1.3.2 pypi_0 pypi
ld_impl_linux-64 2.35.1 h7274673_9
libffi 3.3 he6710b0_2
libgcc-ng 9.3.0 h5101ec6_17
libgfortran-ng 7.5.0 ha8ba4b0_17
libgfortran4 7.5.0 ha8ba4b0_17
libgomp 9.3.0 h5101ec6_17
libstdcxx-ng 9.3.0 hd4cf53a_17
libuv 1.40.0 h7b6447c_0
lightgbm 3.3.1 pypi_0 pypi
littleutils 0.2.2 pypi_0 pypi
markupsafe 2.0.1 py39h27cfd23_0
matplotlib 3.5.0rc1 pypi_0 pypi
mkl 2021.4.0 h06a4308_640
mkl-service 2.4.0 py39h7f8727e_0
mkl_fft 1.3.1 py39hd3c417c_0
mkl_random 1.2.2 py39h51133e4_0
ncurses 6.3 h7f8727e_2
netlsd 1.0.2 pypi_0 pypi
networkx 2.6.3 pyhd3eb1b0_0
nni 2.5 pypi_0 pypi
numexpr 2.7.3 py39h22e1b3c_1
numpy 1.21.2 py39h20f2e39_0
numpy-base 1.21.2 py39h79a1101_0
ogb 1.3.2 pypi_0 pypi
openssl 1.1.1l h7f8727e_0
outdated 0.2.1 pypi_0 pypi
packaging 21.2 pypi_0 pypi
pandas 1.3.4 py39h8c16a72_0
pathtools 0.1.2 pypi_0 pypi
pillow 8.4.0 pypi_0 pypi
pip 21.2.4 py39h06a4308_0
prettytable 2.4.0 pypi_0 pypi
promise 2.3 pypi_0 pypi
protobuf 3.19.1 pypi_0 pypi
psutil 5.8.0 pypi_0 pypi
pycparser 2.21 pyhd3eb1b0_0
pyg 2.0.2 py39_torch_1.10.0_cu113 pyg
pymongo 3.12.1 pypi_0 pypi
pyopenssl 21.0.0 pyhd3eb1b0_1
pyparsing 2.4.7 pypi_0 pypi
pysocks 1.7.1 py39h06a4308_0
python 3.9.7 h12debd9_1
python-dateutil 2.8.2 pyhd3eb1b0_0
python-louvain 0.15 pyhd3eb1b0_0
python_abi 3.9 2_cp39 conda-forge
pythonwebhdfs 0.2.3 pypi_0 pypi
pytorch 1.10.0 py3.9_cuda11.3_cudnn8.2.0_0 pytorch
pytorch-cluster 1.5.9 py39_torch_1.10.0_cu113 pyg
pytorch-mutex 1.0 cuda pytorch
pytorch-scatter 2.0.9 py39_torch_1.10.0_cu113 pyg
pytorch-sparse 0.6.12 py39_torch_1.10.0_cu113 pyg
pytorch-spline-conv 1.2.1 py39_torch_1.10.0_cu113 pyg
pytz 2021.3 pyhd3eb1b0_0
pyyaml 6.0 py39h7f8727e_1
rdflib 6.0.2 pypi_0 pypi
readline 8.1 h27cfd23_0
requests 2.26.0 pyhd3eb1b0_0
responses 0.15.0 pypi_0 pypi
schema 0.7.4 pypi_0 pypi
scikit-learn 1.0.1 py39h51133e4_0
scipy 1.7.1 py39h292c36d_2
sentry-sdk 1.4.3 pypi_0 pypi
setuptools 58.0.4 py39h06a4308_0
setuptools-scm 6.3.2 pypi_0 pypi
shortuuid 1.0.8 pypi_0 pypi
simplejson 3.17.5 pypi_0 pypi
six 1.16.0 pyhd3eb1b0_0
smmap 5.0.0 pypi_0 pypi
sqlite 3.36.0 hc218d9a_0
subprocess32 3.5.4 pypi_0 pypi
tabulate 0.8.9 pypi_0 pypi
termcolor 1.1.0 pypi_0 pypi
threadpoolctl 2.2.0 pyh0d69192_0
tk 8.6.11 h1ccaba5_0
tomli 1.2.2 pypi_0 pypi
tqdm 4.62.3 pyhd3eb1b0_1
typing_extensions 3.10.0.2 pyh06a4308_0
tzdata 2021e hda174b7_0
urllib3 1.26.7 pyhd3eb1b0_0
wandb 0.12.6 pypi_0 pypi
wcwidth 0.2.5 pypi_0 pypi
websockets 10.0 pypi_0 pypi
wheel 0.37.0 pyhd3eb1b0_1
xz 5.2.5 h7b6447c_0
yacs 0.1.8 pypi_0 pypi
yaml 0.2.5 h7b6447c_0
yaspin 2.1.0 pypi_0 pypi
zlib 1.2.11 h7b6447c_3
pip list
Package Version
astor 0.8.1
autogl 0.2.0rc0
bayesian-optimization 1.2.0
Bottleneck 1.3.2
brotlipy 0.7.0
certifi 2021.10.8
cffi 1.14.5
charset-normalizer 2.0.4
chocolate 0.0.2
click 8.0.3
cloudpickle 2.0.0
colorama 0.4.4
configparser 5.1.0
contextlib2 21.6.0
cryptography 35.0.0
cycler 0.11.0
dill 0.3.4
docker-pycreds 0.4.0
filelock 3.3.2
fonttools 4.28.1
future 0.18.2
gitdb 4.0.9
GitPython 3.1.24
googledrivedownloader 0.4
hyperopt 0.1.2
idna 3.2
isodate 0.6.0
Jinja2 3.0.2
joblib 1.1.0
json-tricks 3.15.5
kiwisolver 1.3.2
lightgbm 3.3.1
littleutils 0.2.2
MarkupSafe 2.0.1
matplotlib 3.5.0rc1
mkl-fft 1.3.1
mkl-random 1.2.2
mkl-service 2.4.0
NetLSD 1.0.2
networkx 2.6.3
nni 2.5
numexpr 2.7.3
numpy 1.21.2
ogb 1.3.2
outdated 0.2.1
packaging 21.2
pandas 1.3.4
pathtools 0.1.2
Pillow 8.4.0
pip 21.2.4
prettytable 2.4.0
promise 2.3
protobuf 3.19.1
psutil 5.8.0
pycparser 2.21
pymongo 3.12.1
pyOpenSSL 21.0.0
pyparsing 2.4.7
PySocks 1.7.1
python-dateutil 2.8.2
python-louvain 0.15
PythonWebHDFS 0.2.3
pytz 2021.3
PyYAML 6.0
rdflib 6.0.2
requests 2.26.0
responses 0.15.0
schema 0.7.4
scikit-learn 1.0.1
scipy 1.7.1
sentry-sdk 1.4.3
setuptools 58.0.4
setuptools-scm 6.3.2
shortuuid 1.0.8
simplejson 3.17.5
six 1.16.0
smmap 5.0.0
subprocess32 3.5.4
tabulate 0.8.9
termcolor 1.1.0
threadpoolctl 2.2.0
tomli 1.2.2
torch 1.10.0
torch-cluster 1.5.9
torch-geometric 2.0.2
torch-scatter 2.0.9
torch-sparse 0.6.12
torch-spline-conv 1.2.1
tqdm 4.62.3
typing-extensions 3.10.0.2
urllib3 1.26.7
wandb 0.12.6
wcwidth 0.2.5
websockets 10.0
wheel 0.37.0
yacs 0.1.6
yaspin 2.1.0
Describe the bug
When getting the accuracy shared in the documentation using Cora Dataset I get this
AttributeError: 'CoraDataset' object has no attribute 'data'
To Reproduce
Steps to reproduce the behavior:
import torch
from autogl.datasets import build_dataset_from_name
from autogl.solver import AutoNodeClassifier
from autogl.module.train import Acc
cora_dataset = build_dataset_from_name('cora')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
solver = AutoNodeClassifier(
feature_module='deepgl',
graph_models=['gcn', 'gat'],
hpo_module='anneal',
ensemble_module='voting',
device=device
)
solver.fit(cora_dataset, time_limit=3600)
predicted = solver.predict_proba()
print('Test accuracy: ', Acc.evaluate(predicted,
cora_dataset.data.y[cora_dataset.data.test_mask].cpu().numpy()))
Expected behavior
it should print out the accuracy
Test accuracy: 0.824
Environment (please complete the following information):
Additional Info (Optional)
Torch version is ('1.12.1', 'cu113')
I installed this
!pip install torch-scatter -f https://data.pyg.org/whl/torch-{tver}+{cuver}.html
!pip install torch-sparse -f https://data.pyg.org/whl/torch-{tver}+{cuver}.html
!pip install torch-geometric
!pip install dgl-cu113 dglgo -f https://data.dgl.ai/wheels/repo.html
!pip install netlsd
!pip install chocolate
!-m pip install git+https://github.com/fmfn/BayesianOptimization
!pip install autogl
please upgrade this UserWarnning
Describe the bug
FileNotFoundError: [Errno 2] No such file or directory: '../configs/nodeclf_nas_gasso.yml'
To Reproduce
Steps to reproduce the behavior:
Expected behavior
I expect to be able to reproduce results in GASSO paper
Environment (please complete the following information):
the procedure of import frozen when the backend is selected as PyG.
When the backend is selected as DGL, the import of autogl is good.
Environment (please complete the following information):
To Reproduce
Steps to reproduce the behavior:
AUTOGL_BACKEND=pyg python
.import autogl
in Python shell, then the is import is frozen.Ctrl + C
to interrupt the process and you may see the traces as follows.Trace
Traceback (most recent call last):
File "backend.py", line 2, in
import autogl
File "/home/featurize/PycharmProjects/AutoGL-develop/AutoGL-dev/autogl/init.py", line 1, in
from . import (
File "/home/featurize/PycharmProjects/AutoGL-develop/AutoGL-dev/autogl/datasets/init.py", line 14, in
from ._ogb import (
File "/home/featurize/PycharmProjects/AutoGL-develop/AutoGL-dev/autogl/datasets/_ogb.py", line 4, in
from ogb.nodeproppred import NodePropPredDataset
File "/environment/miniconda3/envs/AutoGL-develop/lib/python3.7/site-packages/ogb/nodeproppred/init.py", line 1, in
from .evaluate import Evaluator
File "/environment/miniconda3/envs/AutoGL-develop/lib/python3.7/site-packages/ogb/nodeproppred/evaluate.py", line 1, in
from sklearn.metrics import roc_auc_score
File "/environment/miniconda3/envs/AutoGL-develop/lib/python3.7/site-packages/sklearn/init.py", line 82, in
from .base import clone
File "/environment/miniconda3/envs/AutoGL-develop/lib/python3.7/site-packages/sklearn/base.py", line 17, in
from .utils import _IS_32BIT
File "/environment/miniconda3/envs/AutoGL-develop/lib/python3.7/site-packages/sklearn/utils/init.py", line 25, in
from . import _joblib
File "/environment/miniconda3/envs/AutoGL-develop/lib/python3.7/site-packages/sklearn/utils/_joblib.py", line 7, in
import joblib
File "/environment/miniconda3/envs/AutoGL-develop/lib/python3.7/site-packages/joblib/init.py", line 113, in
from .memory import Memory, MemorizedResult, register_store_backend
File "/environment/miniconda3/envs/AutoGL-develop/lib/python3.7/site-packages/joblib/memory.py", line 32, in
from ._store_backends import StoreBackendBase, FileSystemStoreBackend
File "/environment/miniconda3/envs/AutoGL-develop/lib/python3.7/site-packages/joblib/_store_backends.py", line 15, in
from .backports import concurrency_safe_rename
File "/environment/miniconda3/envs/AutoGL-develop/lib/python3.7/site-packages/joblib/backports.py", line 7, in
from distutils.version import LooseVersion
File "", line 983, in _find_and_load
File "", line 963, in _find_and_load_unlocked
File "", line 906, in _find_spec
File "/environment/miniconda3/envs/AutoGL-develop/lib/python3.7/site-packages/_distutils_hack/init.py", line 97, in find_spec
return method()
File "/environment/miniconda3/envs/AutoGL-develop/lib/python3.7/site-packages/_distutils_hack/init.py", line 108, in spec_for_distutils
mod = importlib.import_module('setuptools._distutils')
File "/environment/miniconda3/envs/AutoGL-develop/lib/python3.7/importlib/init.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/environment/miniconda3/envs/AutoGL-develop/lib/python3.7/site-packages/setuptools/init.py", line 16, in
import setuptools.version
File "/environment/miniconda3/envs/AutoGL-develop/lib/python3.7/site-packages/setuptools/version.py", line 1, in
import pkg_resources
File "", line 202, in _lock_unlock_module
File "", line 98, in acquire
KeyboardInterrupt
Screenshots
(omitted)
It seems that the first line of https://github.com/THUMNLab/AutoGL/tree/main/autogl/module/model/pyg/robust/gnnguard.py at main branch should be deleted:
from this import d
otherwise it keeps printing 'The Zen of Python' when running examples:
Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than right now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!
The Zen of Python, by Tim Peters
@THUMNLab/aglteam
We are now very close to the new version, and thanks all of the developers for your hardworking and contributions!
We still need the following things done before we finally comes to v0.2:
Let's keep working on new versions!
Describe the bug
When I run the graph_classification.py code, I got following error.
How can I fix the error? Any advice will be greatly appreciated.
Thanks
`
~/src/AutoGL/examples$ python graph_classification.py
Traceback (most recent call last):
File "graph_classification.py", line 19, in
cv_split=10,
File "/home/user/src/AutoGL/autogl/solver/classifier/graph_classifier.py", line 294, in fit
self.feature_module.fit(dataset.train_split)
File "/home/user/src/AutoGL/autogl/module/feature/base.py", line 116, in fit
dataset = self._rebuild(dataset, _dataset)
File "/home/user/src/AutoGL/autogl/module/feature/base.py", line 57, in _rebuild
data, slices = dataset.collate(datalist)
File "/home/user/miniconda3/envs/grapl/lib/python3.7/site-packages/torch_geometric/data/in_memory_dataset.py", line 128, in collate
dim=data.cat_dim(key, item))
RuntimeError: Sizes of tensors must match except in dimension 0. Got 1 and 2 in dimension 1 (The offending index is 5)
`
Environment (please complete the following information):
The online docs only have some content for SSL, and the guidance is not enough. I found it difficult to solve it by reading source code myself. Could you please provide a clear sample for using 'GraphCLUnsupervisedTrainer' and make the online docs more complete? Thanks a lot.
@CoreLeader @lihy96
Hi all, I've merged the dev-szx/sampling-reproduction
into branch refactor_test
, a copy of branch dev. And provide the performance check codes under benchmark.
@lihy96 Please help to run the comparison experiments on the new branch.
@CoreLeader I notice some bugs when running pc_link_prediction_trainer.py
, The error message is:
Traceback (most recent call last):
File "pc_link_prediction_trainer.py", line 75, in <module>
trainer.train(dataset, keep_valid_result=True)
File "../autogl/module/train/link_prediction.py", line 270, in train
self.train_only(data)
File "../autogl/module/train/link_prediction.py", line 200, in train_only
z = self.model.model.lp_encode(data)
File "../autogl/module/model/gcn.py", line 228, in lp_encode
data.x = self.__sequential_encoding_layers[i](data)
File "/home/guancy/.pyenv/versions/miniconda3-4.3.30/envs/autograph/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
result = self.forward(*input, **kwargs)
File "../autogl/module/model/gcn.py", line 54, in forward
edge_weight: _typing.Optional[torch.Tensor] = getattr(data, "edge_weight")
AttributeError: 'Data' object has no attribute 'edge_weight'
Can you help fix this bug? You can fix the bug directly on this branch (refactor_test) since all the codes inside benchmark will be kept for future development. When the branch is ready, you can merge this to your dev branch~
Is your feature request related to a problem? Please describe.
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
Describe the solution you'd like
A clear and concise description of what you want to happen.
Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.
Additional context
Add any other context or screenshots about the feature request here.
Hello!
Thanks for your project!
I think that there is a need to add an extended description of creating your own dataset. Without references to other sources, since there, too, the description leaves much to be desired.
Or the simplest complete example from scratch.
The need is, since I see the main users cases just on the use of autogl on their own graphs. And for people who don’t really want to understand the intricacies of creating datasets of other libraries (maybe this wasn’t a big problem if there were normal examples in the documentation of these libraries).
Thanks!
Describe the bug
安装成功dgl和autogl后,import验证时报错,pytorch-sparse安装后python.exe无法找到入口,卸载pytorch-sparse又会提示不存在模块,问题在torch_sparse循环。
To Reproduce
import autogl
Traceback (most recent call last):
File "", line 1, in
File "C:\Users\All Users\anaconda3\envs\autogl\lib\site-packages\autogl_init_.py", line 1, in
from . import (
File "C:\Users\All Users\anaconda3\envs\autogl\lib\site-packages\autogl\datasets_init_.py", line 14, in
from .ogb import (
File "C:\Users\All Users\anaconda3\envs\autogl\lib\site-packages\autogl\datasets_ogb.py", line 8, in
from torch_sparse import SparseTensor
File "C:\Users\llzhang\AppData\Roaming\Python\Python38\site-packages\torch_sparse_init.py", line 18, in
torch.ops.load_library(spec.origin)
File "C:\Users\All Users\anaconda3\envs\autogl\lib\site-packages\torch_ops.py", line 255, in load_library
ctypes.CDLL(path)
File "C:\Users\All Users\anaconda3\envs\autogl\lib\ctypes_init_.py", line 373, in init
self._handle = _dlopen(self._name, mode)
OSError: [WinError 127] 找不到指定的程序。
并且跳出警告,如下图:
无法定位程序输入点于动态链接库\torch_sparse_version_cpu.pyd上
Expected behavior
成功import autogl,如何更好的安装torch_sparse,使之成功安装并且不报错。
Screenshots
If applicable, add screenshots to help explain your problem.
Environment (please complete the following information):
appdirs 1.4.4 pyhd3eb1b0_0 defaults
astor 0.8.1 pypi_0 pypi
autogl 0.4.0 pypi_0 pypi
bayesian-optimization 1.4.3 pypi_0 pypi
blas 1.0 mkl defaults
brotlipy 0.7.0 py38h2bbff1b_1003 defaults
ca-certificates 2023.05.30 haa95532_0 defaults
certifi 2023.7.22 py38haa95532_0 defaults
cffi 1.15.1 py38h2bbff1b_3 defaults
charset-normalizer 2.0.4 pyhd3eb1b0_0 defaults
chocolate 0.0.2 pypi_0 pypi
cloudpickle 2.2.1 pypi_0 pypi
colorama 0.4.6 py38haa95532_0 defaults
contextlib2 21.6.0 pypi_0 pypi
contourpy 1.1.0 pypi_0 pypi
cryptography 41.0.2 py38hac1b9e3_0 defaults
cycler 0.11.0 pypi_0 pypi
cython 0.29.14 pypi_0 pypi
deeprobust 0.2.8 pypi_0 pypi
dgl 1.1.1 py38_0 dglteam
dill 0.3.7 pypi_0 pypi
dnspython 2.4.2 pypi_0 pypi
filelock 3.12.2 pypi_0 pypi
fonttools 4.42.0 pypi_0 pypi
gensim 3.8.3 pypi_0 pypi
hyperopt 0.1.2 pypi_0 pypi
icc_rt 2022.1.0 h6049295_2 defaults
idna 3.4 py38haa95532_0 defaults
imageio 2.31.1 pypi_0 pypi
importlib-metadata 6.8.0 pypi_0 pypi
importlib-resources 6.0.1 pypi_0 pypi
intel-openmp 2023.1.0 h59b6b97_46319 defaults
jinja2 3.1.2 py38haa95532_0 defaults
joblib 1.3.2 pypi_0 pypi
json-tricks 3.17.2 pypi_0 pypi
kiwisolver 1.4.4 pypi_0 pypi
lazy-loader 0.3 pypi_0 pypi
libffi 3.4.4 hd77b12b_0 defaults
libuv 1.44.2 h2bbff1b_0 defaults
lightgbm 4.0.0 pypi_0 pypi
littleutils 0.2.2 pypi_0 pypi
llvmlite 0.40.1 pypi_0 pypi
markupsafe 2.1.3 pypi_0 pypi
matplotlib 3.7.2 pypi_0 pypi
mkl 2023.1.0 h6b88ed4_46357 defaults
mkl-service 2.4.0 py38h2bbff1b_1 defaults
mkl_fft 1.3.6 py38hf11a4ad_1 defaults
mkl_random 1.2.2 py38hf11a4ad_1 defaults
mpmath 1.3.0 py38haa95532_0 defaults
netlsd 1.0.2 pypi_0 pypi
networkx 3.1 py38haa95532_0 defaults
ninja 1.10.2 haa95532_5 defaults
ninja-base 1.10.2 h6d14046_5 defaults
nni 2.8 pypi_0 pypi
numba 0.57.1 pypi_0 pypi
numpy 1.24.3 py38h79a8e48_1 defaults
numpy-base 1.24.3 py38h8a87ada_1 defaults
ogb 1.3.6 pypi_0 pypi
openssl 3.0.10 h2bbff1b_0 defaults
outdated 0.2.2 pypi_0 pypi
packaging 23.0 py38haa95532_0 defaults
pandas 2.0.3 pypi_0 pypi
pillow 10.0.0 pypi_0 pypi
pip 23.2.1 py38haa95532_0 defaults
pooch 1.4.0 pyhd3eb1b0_0 defaults
prettytable 3.8.0 pypi_0 pypi
protobuf 4.24.0 pypi_0 pypi
psutil 5.9.0 py38h2bbff1b_0 defaults
pycparser 2.21 pyhd3eb1b0_0 defaults
pymongo 4.4.1 pypi_0 pypi
pyopenssl 23.2.0 py38haa95532_0 defaults
pyparsing 3.0.9 pypi_0 pypi
pysocks 1.7.1 py38haa95532_0 defaults
python 3.8.17 h1aa4202_0 defaults
python-dateutil 2.8.2 pypi_0 pypi
pythonwebhdfs 0.2.3 pypi_0 pypi
pytorch 2.0.1 cpu_py38hb0bdfb8_0 defaults
pytz 2023.3 pypi_0 pypi
pywavelets 1.4.1 pypi_0 pypi
pyyaml 6.0.1 pypi_0 pypi
requests 2.31.0 py38haa95532_0 defaults
responses 0.23.3 pypi_0 pypi
schema 0.7.5 pypi_0 pypi
scikit-image 0.21.0 pypi_0 pypi
scikit-learn 1.3.0 pypi_0 pypi
scipy 1.10.1 py38hdcfc7df_1 defaults
setuptools 68.0.0 py38haa95532_0 defaults
simplejson 3.19.1 pypi_0 pypi
smart-open 6.3.0 pypi_0 pypi
sqlite 3.41.2 h2bbff1b_0 defaults
sympy 1.12 pypi_0 pypi
tabulate 0.9.0 pypi_0 pypi
tbb 2021.8.0 h59b6b97_0 defaults
tensorboardx 2.6.2 pypi_0 pypi
texttable 1.6.7 pypi_0 pypi
threadpoolctl 3.2.0 pypi_0 pypi
tifffile 2023.7.10 pypi_0 pypi
torch-sparse 0.6.17+pt20cpu pypi_0 pypi
torchvision 0.15.2 pypi_0 pypi
tqdm 4.65.0 py38hd4e2768_0 defaults
typeguard 4.1.0 pypi_0 pypi
types-pyyaml 6.0.12.11 pypi_0 pypi
typing-extensions 4.7.1 py38haa95532_0 defaults
typing_extensions 4.7.1 py38haa95532_0 defaults
tzdata 2023.3 pypi_0 pypi
urllib3 1.26.16 py38haa95532_0 defaults
vc 14.2 h21ff451_1 defaults
vs2015_runtime 14.27.29016 h5e58377_2 defaults
wcwidth 0.2.6 pypi_0 pypi
websockets 11.0.3 pypi_0 pypi
wheel 0.38.4 py38haa95532_0 defaults
win_inet_pton 1.1.0 py38haa95532_0 defaults
zipp 3.16.2 pypi_0 pypi
]
Additional Info (Optional)
提示的定位不到的动态链接库_version_cpu,本地可以找到几个,替换报错位置的文档,也没有解决。
Describe the bug
A clear and concise description of what the bug is.
To Reproduce
Steps to reproduce the behavior:
Expected behavior
A clear and concise description of what you expected to happen.
Screenshots
If applicable, add screenshots to help explain your problem.
Environment (please complete the following information):
python -V
]python -c 'import autogl; print(autogl.__version__)'
]pip list
]Additional Info (Optional)
Add any other context about the problem here. Such as the possible reason you think, or how to solve this bug if you are interested.
GraphClassification not working.
""""""""""""""""""""""""""""""""""""""""
HPO Search Phase:
ModuleNotFoundError Traceback (most recent call last)
in <cell line: 2>()
1 # train
----> 2 autoClassifier.fit(dataset , evaluation_method=[Acc])
3 #autoClassifier.get_leaderboard().show()
4
5 #print("best single model:\n", autoClassifier.get_leaderboard().get_best_model(0))
4 frames
/usr/local/lib/python3.10/dist-packages/autogl/datasets/utils/_general.py in graph_get_split(dataset, mask, is_loader, batch_size, num_workers, shuffle)
369 raise RuntimeError("Unsupported backend")
370 elif _backend.DependentBackend.is_dgl():
--> 371 from dgl.dataloading.pytorch import GraphDataLoader
372 return GraphDataLoader(
373 sub_dataset,
ModuleNotFoundError: No module named 'dgl.dataloading.pytorch'
""""""""""""""""""""""""""""""""""""""""""""""""""""'''
Hey Guys,
I am still intrigued by your package and strongly consider using it.
There is unfortunately a potentially boring hinderance:
You need to specify the copyright in the License:
Copyright [yyyy] [name of copyright owner]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for gensim
Running setup.py clean for gensim
Successfully built autogl
Failed to build gensim
ERROR: Could not build wheels for gensim, which is required to install pyproject.toml-based projects
Is there a possibility of python 3.11 support? or bumping gensim dependency to it's latest version?
Is your feature request related to a problem? Please describe.
When writing the Models into the interface, the hyperparameter space is tedious.
Describe the solution you'd like
To validate that the model works appropriately under the search space, it would be nice to be able to sanity check the h.space.
Describe alternatives you've considered
A concise way for declaring complex h.spaces is the Configspace package
It also allows to sample from the space and declare distributions explicit.
Additional context
The returned value should be an instance of torch_geometric.data.InMemoryDataset
.
Current returned value is of type autogl.data._dataset._dataset.InMemoryDataset
.
Please make it succeed the correct class. @CoreLeader
run file:node_classification.py
*begin: wrong information in python console
WARNING:root:The OGB package is out of date. Your version is 1.2.3, while the latest version is 1.2.4.
Using backend: pytorch
Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.x
Traceback (most recent call last):
File "D:/autocogdl/AutoGL-main/examples/node_classification.py", line 42, in
dataset = build_dataset_from_name(args.dataset)
File "D:\autocogdl\AutoGL-main\autogl\datasets_init_.py", line 118, in build_dataset_from_name
dataset = DATASET_DICTdataset_name
File "D:\autocogdl\AutoGL-main\autogl\datasets\pyg.py", line 60, in init
Planetoid(path, dataset)
File "D:\Anaconda3\envs\pytorch\lib\site-packages\torch_geometric\datasets\planetoid.py", line 55, in init
super(Planetoid, self).init(root, transform, pre_transform)
File "D:\Anaconda3\envs\pytorch\lib\site-packages\torch_geometric\data\in_memory_dataset.py", line 54, in init
pre_filter)
File "D:\Anaconda3\envs\pytorch\lib\site-packages\torch_geometric\data\dataset.py", line 89, in init
self._download()
File "D:\Anaconda3\envs\pytorch\lib\site-packages\torch_geometric\data\dataset.py", line 141, in _download
self.download()
File "D:\Anaconda3\envs\pytorch\lib\site-packages\torch_geometric\datasets\planetoid.py", line 105, in download
download_url('{}/{}'.format(self.url, name), self.raw_dir)
File "D:\Anaconda3\envs\pytorch\lib\site-packages\torch_geometric\data\download.py", line 31, in download_url
data = urllib.request.urlopen(url)
File "D:\Anaconda3\envs\pytorch\lib\urllib\request.py", line 222, in urlopen
return opener.open(url, data, timeout)
File "D:\Anaconda3\envs\pytorch\lib\urllib\request.py", line 531, in open
response = meth(req, response)
File "D:\Anaconda3\envs\pytorch\lib\urllib\request.py", line 641, in http_response
'http', request, response, code, msg, hdrs)
File "D:\Anaconda3\envs\pytorch\lib\urllib\request.py", line 563, in error
result = self._call_chain(*args)
File "D:\Anaconda3\envs\pytorch\lib\urllib\request.py", line 503, in _call_chain
result = func(*args)
File "D:\Anaconda3\envs\pytorch\lib\urllib\request.py", line 755, in http_error_302
return self.parent.open(new, timeout=req.timeout)
File "D:\Anaconda3\envs\pytorch\lib\urllib\request.py", line 531, in open
response = meth(req, response)
File "D:\Anaconda3\envs\pytorch\lib\urllib\request.py", line 641, in http_response
'http', request, response, code, msg, hdrs)
File "D:\Anaconda3\envs\pytorch\lib\urllib\request.py", line 569, in error
return self._call_chain(*args)
File "D:\Anaconda3\envs\pytorch\lib\urllib\request.py", line 503, in _call_chain
result = func(*args)
File "D:\Anaconda3\envs\pytorch\lib\urllib\request.py", line 649, in http_error_default
raise HTTPError(req.full_url, code, msg, hdrs, fp)
urllib.error.HTTPError: HTTP Error 404: Not Found
Process finished with exit code 1
end******
In documentation Hyper Parameter Optimization https://autogl.readthedocs.io/en/latest/docfile/tutorial/t_hpo.html#add-your-hpoptimizer
The custom function provided has a
loss, self.is_higher_better = current_trainer.get_valid_score(dset)
in function fn
First of all, I think the evaluation index obtained here is not loss, but ACC or AUC, according to the code at ./solver/classifier/node_classifier.py:
if evaluation_method == "infer":
if hasattr(dataset, "metric"):
evaluation_method = [dataset.metric]
else:
num_of_label = dataset.num_classes
if num_of_label == 2:
evaluation_method = ["auc"]
else:
evaluation_method = ["acc"]
I think if you don't explicitly specify LogLoss as the evaluation metric in the fit function, it should not be Loss by default.
AND
I'm not sure why there's a parameter dset here? I checked the function ‘get_valid_score’, There is no need to pass in the parameter of dataset. If it's because I didn't look at the code carefully, I got the wrong conclusion. Please point out my mistake, thank you.
How do I run the entire code on my own dataset? I have 44k graphs in networkx format.
Known issues:
Describe the bug
Missing key in FEATURE_DICT line 764, in from_config from file
"/home/scotthoang/anaconda3/envs/autogl/lib/python3.8/site-packages/autogl/solver/classifier/node_classifier.py",
To Reproduce
Steps to reproduce the behavior:
Expected behavior
To run normally without error.
Screenshots
Traceback (most recent call last):
File "gasso_test.py", line 20, in <module>
solver = AutoNodeClassifier.from_config(args.config)
File "/home/scotthoang/anaconda3/envs/autogl/lib/python3.8/site-packages/autogl/solver/classifier/node_classifier.py", line 764, in from_config
fe_list_ele.append(FEATURE_DICT[name](**feature_engineer))
KeyError: 'NormalizeFeatures'
Environment (please complete the following information):
Describe the bug
When running /autogl/test/nas/node_classification.py with AUTOGL_BACKEND=dgl, I got the following error.
Traceback (most recent call last):
File "node_classification.py", line 116, in <module>
model = algo.search(space, dataset, esti)
File "/villa/zhangyp/anaconda3/envs/graproj/lib/python3.7/site-packages/autogl-0.3.0rc0-py3.7.egg/autogl/module/nas/algorithm/random_search.py", line 69, in search
metric, loss, hardware_metric = self._infer(mask="val")
File "/villa/zhangyp/anaconda3/envs/graproj/lib/python3.7/site-packages/autogl-0.3.0rc0-py3.7.egg/autogl/module/nas/algorithm/random_search.py", line 86, in _infer
metric, loss = self.estimator.infer(self.arch._model, self.dataset, mask=mask)
File "/villa/zhangyp/anaconda3/envs/graproj/lib/python3.7/site-packages/autogl-0.3.0rc0-py3.7.egg/autogl/module/nas/estimator/one_shot.py", line 35, in infer
pred = model(dset)[mask]
File "/villa/zhangyp/anaconda3/envs/graproj/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/villa/zhangyp/anaconda3/envs/graproj/lib/python3.7/site-packages/autogl-0.3.0rc0-py3.7.egg/autogl/module/nas/space/autoattend.py", line 202, in forward
stem_out = bk_gconv(op, data, drop(input))
File "/villa/zhangyp/anaconda3/envs/graproj/lib/python3.7/site-packages/autogl-0.3.0rc0-py3.7.egg/autogl/module/nas/backend.py", line 16, in bk_gconv
return op(data,feat)
File "/villa/zhangyp/anaconda3/envs/graproj/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/villa/zhangyp/anaconda3/envs/graproj/lib/python3.7/site-packages/torch_geometric/nn/conv/arma_conv.py", line 110, in forward
edge_index, edge_weight, x.size(self.node_dim),
AttributeError: 'DGLHeteroGraph' object has no attribute 'size'
It happens in 'random + autoattend' stage、 'rl + autoattend' stage and 'darts + graphnas' stage.
To Reproduce
Steps to reproduce the behavior:
Environment (please complete the following information):
alabaster 0.7.12
astor 0.8.1
attrs 21.4.0
autogl 0.3.0rc0
autopep8 1.6.0
Babel 2.9.1
bayesian-optimization 1.2.0
brotlipy 0.7.0
certifi 2021.10.8
cffi 1.15.0
charset-normalizer 2.0.4
chocolate 0.0.2
click 8.1.2
cloudpickle 2.0.0
colorama 0.4.4
contextlib2 21.6.0
cryptography 36.0.0
cycler 0.11.0
dgl-cu113 0.8.0.post2
dglgo 0.0.1
dill 0.3.4
docutils 0.17.1
filelock 3.6.0
fonttools 4.30.0
future 0.18.2
hyperopt 0.1.2
idna 3.3
imagesize 1.3.0
importlib-metadata 4.11.3
isort 5.10.1
Jinja2 3.0.3
joblib 1.1.0
json-tricks 3.15.5
jsonlines 3.0.0
kiwisolver 1.3.2
lightgbm 3.3.2
littleutils 0.2.2
MarkupSafe 2.1.0
matplotlib 3.5.1
mkl-fft 1.3.1
mkl-random 1.2.2
mkl-service 2.4.0
NetLSD 1.0.2
networkx 2.6.3
nni 2.6.1
numpy 1.21.2
numpydoc 1.2.1
ogb 1.3.3
outdated 0.2.1
packaging 21.3
pandas 1.3.5
Pillow 9.0.1
pip 21.2.2
prettytable 3.2.0
psutil 5.9.0
pycodestyle 2.8.0
pycparser 2.21
pydantic 1.9.0
Pygments 2.11.2
pymongo 4.0.2
pyOpenSSL 22.0.0
pyparsing 3.0.7
PySocks 1.7.1
python-dateutil 2.8.2
PythonWebHDFS 0.2.3
pytz 2021.3
PyYAML 6.0
requests 2.27.1
responses 0.19.0
ruamel.yaml 0.17.21
ruamel.yaml.clib 0.2.6
schema 0.7.5
scikit-learn 1.0.2
scipy 1.7.3
setuptools 58.0.4
simplejson 3.17.6
six 1.16.0
snowballstemmer 2.2.0
Sphinx 4.5.0
sphinxcontrib-applehelp 1.0.2
sphinxcontrib-devhelp 1.0.2
sphinxcontrib-htmlhelp 2.0.0
sphinxcontrib-jsmath 1.0.1
sphinxcontrib-qthelp 1.0.3
sphinxcontrib-serializinghtml 1.1.5
tabulate 0.8.9
threadpoolctl 3.1.0
toml 0.10.2
torch 1.11.0
torch-geometric 2.0.4
torch-scatter 2.0.9
torch-sparse 0.6.13
torchaudio 0.11.0
torchvision 0.12.0
tqdm 4.63.0
typeguard 2.13.3
typer 0.4.1
typing-extensions 3.10.0.2
urllib3 1.26.8
wcwidth 0.2.5
websockets 10.2
wheel 0.37.1
zipp 3.7.0
Additional Info
As I can run this file with AUTOGL_BACKEND=pyg correctly, it may be caused by the mixed use of pyg method and dgl dataset.
i hope there is a requirements.txt , such as pandas <2.0 and lightgbm<4.0 and something else
Good toolkit!
In addition, I wonder if AutoGL supports multi-classification edge prediction tasks. Thanks a lot.
This issue is created to check whether the library has the same performance features with the native implemented models.
WARNING: This is not the evaluation results of this library. For benchmarking of AutoGL, please see the examples provided.
What do we mean when we are checking performance?
First, remember that the performance inconsistency may not be because of our implementations. Sometimes you need to increase the repeat number, or change the range of seeds to see whether the performances match with each other under the "same" setting.
If the rules above do not apply, you need to carefully check whether there are some unwanted implementations in your code. Also, there are still chances that the performance check codes are incorrect, in which case you should point out to @Frozenmad .
All the performance check results are listed below. All the performances inconsistencies are represented as bold in the Table.
Describe the bug
package gensim is seemingly not supported for python3.10 when using pip install autogl
in python3.10
func graph_number_of_cliques is deprecated since networkx3.1
func append for DataFrame in pandas since pandas1.4.0
Environment (please complete the following information):
Package Version
------------------------ ----------------
astor 0.8.1
asttokens 2.4.1
autogl 0.4.0
backcall 0.2.0
bayesian-optimization 1.4.3
certifi 2023.11.17
charset-normalizer 3.3.2
chocolate 0.0.2
cloudpickle 3.0.0
colorama 0.4.6
contextlib2 21.6.0
contourpy 1.2.0
cycler 0.12.1
debugpy 1.6.7
decorator 5.1.1
deeprobust 0.2.9
dill 0.3.7
dnspython 2.5.0
entrypoints 0.4
executing 2.0.1
filelock 3.13.1
fonttools 4.47.2
fsspec 2023.12.2
future 0.18.3
gensim 3.8.3
hyperopt 0.1.2
idna 3.6
imageio 2.33.1
importlib-metadata 7.0.1
importlib-resources 6.1.1
ipykernel 6.14.0
ipython 8.4.0
jedi 0.19.1
Jinja2 3.1.3
joblib 1.3.2
json-tricks 3.17.3
jupyter-client 7.0.6
jupyter_core 5.7.1
kiwisolver 1.4.5
lazy_loader 0.3
lightgbm 4.2.0
littleutils 0.2.2
llvmlite 0.41.1
MarkupSafe 2.1.4
matplotlib 3.8.2
matplotlib-inline 0.1.6
mpmath 1.3.0
nest_asyncio 1.6.0
NetLSD 1.0.2
networkx 3.0
nni 2.8
numba 0.58.1
numpy 1.26.3
nvidia-cublas-cu12 12.1.3.1
nvidia-cuda-cupti-cu12 12.1.105
nvidia-cuda-nvrtc-cu12 12.1.105
nvidia-cuda-runtime-cu12 12.1.105
nvidia-cudnn-cu12 8.9.2.26
nvidia-cufft-cu12 11.0.2.54
nvidia-curand-cu12 10.3.2.106
nvidia-cusolver-cu12 11.4.5.107
nvidia-cusparse-cu12 12.1.0.106
nvidia-nccl-cu12 2.18.1
nvidia-nvjitlink-cu12 12.3.101
nvidia-nvtx-cu12 12.1.105
ogb 1.3.6
outdated 0.2.2
packaging 23.2
pandas 1.3.5
parso 0.8.3
pexpect 4.8.0
pickleshare 0.7.5
pillow 10.2.0
pip 23.3.1
platformdirs 4.1.0
prettytable 3.9.0
prompt-toolkit 3.0.42
protobuf 4.25.2
psutil 5.9.0
ptyprocess 0.7.0
pure-eval 0.2.2
Pygments 2.17.2
pymongo 4.6.1
pyparsing 3.1.1
python-dateutil 2.8.2
PythonWebHDFS 0.2.3
pytz 2023.3.post1
PyYAML 6.0.1
pyzmq 19.0.2
requests 2.31.0
responses 0.24.1
schema 0.7.5
scikit-image 0.22.0
scikit-learn 1.4.0
scipy 1.12.0
setuptools 68.2.2
simplejson 3.19.2
six 1.16.0
smart-open 6.4.0
stack-data 0.6.2
sympy 1.12
tabulate 0.9.0
tensorboardX 2.6.2.2
texttable 1.7.0
threadpoolctl 3.2.0
tifffile 2023.12.9
torch 2.1.2
torch_geometric 2.4.0
torch-scatter 2.1.2+pt21cu121
torch-sparse 0.6.18+pt21cu121
torchvision 0.16.2
tornado 6.1
tqdm 4.66.1
traitlets 5.14.1
triton 2.1.0
typeguard 4.1.5
typing_extensions 4.9.0
tzdata 2023.4
urllib3 2.1.0
wcwidth 0.2.13
websockets 12.0
wheel 0.41.2
zipp 3.17.0
Hello, and thanks for this great autoML framework!
I'm encountering some issues when I try to use my own data with AutoGL. I have a list of PyTorch Geometric's Data objects, I tried to initialise a dataset using your doc, but it doesn't have the same behaviour than prebuilt datasets with random_splits_mask_class():
# Import my own dataset
data_list = [Data(...), ..., Data(...)]
class MyDataset(InMemoryDataset):
def __init__(self, datalist) -> None:
super().__init__()
self.data, self.slices = self.collate(datalist)
myData = MyDataset(data_list)
# Use a prebuilt dataset
cora_dataset = build_dataset_from_name('cora')
# Trying to use AutoNodeClassifier
solver = AutoNodeClassifier(
feature_module='deepgl',
graph_models=['gcn', 'gat'],
hpo_module='anneal',
ensemble_module='voting',
device=device
)
solver.fit(myData, train_split=0.8, val_split=0.2, time_limit=3600)
I obtained an error AssertionError: the total number of samples from every class used for training and validation is larger than the total samples in class 0
with myData, which I believe is coming from a different dataset structure:
# line 119 and 129 of autogl/datasets/utils.py
data = dataset[0]
num_classes = data.y.max().cpu().item() + 1
print(cora_dataset)
# Cora()
print(cora_dataset[0])
# Data(edge_index=[2, 10556], test_mask=[2708], train_mask=[2708], val_mask=[2708], x=[2708, 1433], y=[2708])
print(cora_dataset.data)
# Data(edge_index=[2, 10556], test_mask=[2708], train_mask=[2708], val_mask=[2708], x=[2708, 1433], y=[2708])
print(myData)
# MyDataset(375)
print(myData[0])
# Data(edge_index=[2, 264], idx=[1], pair="XXX_X--XXX_X", x=[39, 24], y=[1])
print(myData.data)
# Data(edge_index=[2, 123456], idx=[375], pair=[375], x=[13950, 24], y=[375])
(pair is a custom metadata from my data)
Am I missing something, or is there an unexpected behaviour from the suggested MyDataset?
Thanks in advance,
Describe the bug
Traceback (most recent call last):
File "/home/xxx/autoGL/ai1.py", line 6, in
custom_static_homogeneous_graph = GeneralStaticGraphGenerator.create_homogeneous_static_graph(
File "/home/xxx/.pyenv/versions/3.10.13/lib/python3.10/site-packages/autogl-0.4.1-py3.10.egg/autogl/data/graph/_general_static_graph/_general_static_graph_generator.py", line 73, in create_homogeneous_static_graph
_heterogeneous_edges_aggregation[('', '', '')] = (
File "/home/xxx/.pyenv/versions/3.10.13/lib/python3.10/site-packages/autogl-0.4.1-py3.10.egg/autogl/data/graph/_general_static_graph/_general_static_graph_default_implementation.py", line 494, in setitem
self._set_edges(edge_t, edges)
File "/home/xxx/.pyenv/versions/3.10.13/lib/python3.10/site-packages/autogl-0.4.1-py3.10.egg/autogl/data/graph/_general_static_graph/_general_static_graph_default_implementation.py", line 683, in _set_edges
self.__heterogeneous_edges_data_frame.append(
File "/home/xxx/.pyenv/versions/3.10.13/lib/python3.10/site-packages/pandas-2.1.4-py3.10-linux-x86_64.egg/pandas/core/generic.py", line 6204, in getattr
return object.getattribute(self, name)
AttributeError: 'DataFrame' object has no attribute 'append'. Did you mean: '_append'?
To Reproduce
Steps to reproduce the behavior:
Use the provided example code from AutoGL documentation to produce a customized dataset
The pip installed old version does not have this problem.
After changing the line in the file" _general_static_graph_default_implementation.py", line 683, in _set_edges:
self.__heterogeneous_edges_data_frame.append(
into:
self.__heterogeneous_edges_data_frame._append(
there's no error message again.
I install AutoGL through pip.
Like this 'pip install auto-graph-learning -i https://pypi.org/simple'
But return shows an error 'ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.'
I want to know how to solve, thank you!
It seems like the documentation website is down?
This problem occurs when I run graphnas.py in examples
<autogl.data.graph._general_static_graph._general_static_graph_default_implementation._HeterogeneousNodeView object at 0x7fb67443d970>
Traceback (most recent call last):
File "/AutoGL/examples/graphnas.py", line 24, in
label = dataset[0].y
AttributeError: 'GeneralStaticGraphImplementation' object has no attribute 'y'
The same goes for using dgl
Namespaces are one honking great idea -- let's do more of those!
Traceback (most recent call last):
File "/AutoGL/examples/graphnas.py", line 27, in
label = dataset[0].ndata['label']
AttributeError: 'GeneralStaticGraphImplementation' object has no attribute 'ndata'
Describe the bug
With NNI V2.9, nni.nas.pytorch
interface is no longer used, causing attribute error when running examples.
Suggestion
lock nni==2.8, works for now.
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Sorry for bothering. I have some problems when I try to use your given NAS examples for semi-supervised node classification.
For example, I tried to run the file: gasso_test.py, all the parameters are default, but I got the accuracy 0.8150. Clearly, it's too low, compared with the accuracy you have given in the GASSO paper.
So I have checked the hyper-parameters you provide in the appendix, but I found no mistakes.
I do not know what leads to the situation. Thanks a lot if you can provide me some solutions.
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