Segmentation fault

[ChangbingChen]

when try the sample code:

import autosklearn.classification
import sklearn.model_selection
import sklearn.datasets
import sklearn.metrics
X, y = sklearn.datasets.load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, random_state=1)
automl = autosklearn.classification.AutoSklearnClassifier()
automl.fit(X_train, y_train)
y_hat = automl.predict(X_test)
print("Accuracy score", sklearn.metrics.accuracy_score(y_test, y_hat))

something wrong with the automl.fit line, and got "Segmentation fault", the python command console exit.

>automl.fit(X_train, y_train)
/home/work/.pyenv/versions/py3_env/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
[WARNING] [2019-05-29 16:26:32,672:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-29 16:26:32,680:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
Segmentation fault
(py3_env) [work@*** ~]$ [WARNING] [2019-05-29 16:26:34,685:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-29 16:26:36,689:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!

does anyone come across this problem?

[mfeurer]

I'm afraid that this is a very uncommon problem. Could you please provide further information on the OS you're using and how you installed Auto-sklearn?

Also, could you please upload the log file created by Auto-sklearn?

[ChangbingChen]

os info：

LSB Version:    :core-4.1-amd64:core-4.1-noarch
Distributor ID: CentOS
Description:    CentOS Linux release 7.2.1511 (Core) 
Release:        7.2.1511
Codename:       Core

python version:

Python 3.6.6 (default, Oct 24 2018, 14:44:15) 
[GCC 4.8.5 20150623 (Red Hat 4.8.5-28)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> 

installation：
a）make compile and install swig-4.0.0.tar.gz
b）pip install auto-sklearn

pip list:

Package                       Version 
----------------------------- --------
alabaster                     0.7.12  
auto-sklearn                  0.5.2   
Babel                         2.7.0   
certifi                       2019.3.9
chardet                       3.0.4   
ConfigSpace                   0.4.10  
Cython                        0.29.9  
docutils                      0.14    
idna                          2.8     
imagesize                     1.1.0   
Jinja2                        2.10.1  
joblib                        0.13.2  
liac-arff                     2.4.0   
lockfile                      0.12.2  
MarkupSafe                    1.1.1   
nose                          1.3.7   
numpy                         1.16.4  
packaging                     19.0    
pandas                        0.24.2  
pip                           10.0.1  
psutil                        5.6.2   
Pygments                      2.4.2   
pynisher                      0.5.0   
pyparsing                     2.4.0   
pyrfr                         0.7.4   
python-dateutil               2.8.0   
pytz                          2019.1  
PyYAML                        5.1     
requests                      2.22.0  
scikit-learn                  0.19.2  
scipy                         1.3.0   
setuptools                    39.0.1  
six                           1.12.0  
smac                          0.8.0   
snowballstemmer               1.2.1   
Sphinx                        2.0.1   
sphinx-rtd-theme              0.4.3   
sphinxcontrib-applehelp       1.0.1   
sphinxcontrib-devhelp         1.0.1   
sphinxcontrib-htmlhelp        1.0.2   
sphinxcontrib-jsmath          1.0.1   
sphinxcontrib-qthelp          1.0.2   
sphinxcontrib-serializinghtml 1.1.3   
typing                        3.6.6   
urllib3                       1.25.3  
xgboost                       0.90   

test.py:

import autosklearn.classification
import sklearn.model_selection
import sklearn.datasets
import sklearn.metrics
X, y = sklearn.datasets.load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, random_state=1)
automl = autosklearn.classification.AutoSklearnClassifier()
automl.fit(X_train, y_train)
y_hat = automl.predict(X_test)
print("Accuracy score", sklearn.metrics.accuracy_score(y_test, y_hat))

nohup python test.py &
nohup file cotennt:

/home/work/.pyenv/versions/py3_env/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.
  from numpy.core.umath_tests import inner1d
/home/work/.pyenv/versions/py3_env/lib/python3.6/site-packages/autosklearn/evaluation/train_evaluator.py:197: RuntimeWarning: Mean of empty slice
  Y_train_pred = np.nanmean(Y_train_pred_full, axis=0)
[WARNING] [2019-05-30 11:11:50,584:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:11:50,592:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:11:52,596:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:11:54,600:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:11:56,604:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:11:58,608:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:00,612:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:02,616:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:04,620:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:06,624:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:08,628:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:10,631:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:12,635:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:14,639:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:16,643:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:18,647:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:20,651:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:22,654:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:24,658:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:26,662:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:28,666:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:30,670:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:32,673:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:34,677:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:36,681:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:38,685:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:40,689:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:42,692:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:44,696:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:46,700:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:48,704:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:50,707:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:52,711:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:54,715:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:56,718:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:12:58,722:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:13:00,727:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:13:02,730:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:13:04,734:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:13:06,738:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!
[WARNING] [2019-05-30 11:13:08,741:EnsembleBuilder(1):d74860caaa557f473ce23908ff7ba369] No models better than random - using Dummy Score!

[ChangbingChen]

automl_d74860caaa557f473ce23908ff7ba369.txt

The attached file is the detailed log file with debug level.

[mfeurer]

Thanks for the additional information. Unfortunately, the logs do not provide any further detail except that Auto-sklearn did not start training a single machine learning model. To further understand the problem, for how long does Auto-sklearn run, and when does the segfault happen? Also, would you be able to test Auto-sklearn in a conda environment to ensure that there's no issue with the installation?

[wangmoyu]

we meet the same problem also: in 2nd round, we meet segment error

[ChangbingChen]

It seems to work, thx~

1. install anaconda3, wget https://repo.anaconda.com/archive/Anaconda3-2019.03-Linux-x86_64.sh and run it
2. conda activate base, and conda install gxx_linux-64 gcc_linux-64 swig

(base) [work@** anaconda3]$ which python
~/anaconda3/bin/python
(base) [work@** anaconda3]$ python
Python 3.7.3 (default, Mar 27 2019, 22:11:17) 
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> 

3. pip install auto-sklearn
4. run the sample code, and about one hour later got the result 0.9911

[mfeurer]

Thank you very reporting a solution to this issue.

Could you please do me a favor and check the swig version installed with conda? I suspect that this is an issue of the recent SWIG4.

[ChangbingChen]

perhaps, this swig version is 3.0.12

(base) [work@** ~]$ which swig
~/anaconda3/bin/swig
(base) [work@** ~]$ swig -version

SWIG Version 3.0.12

Compiled with x86_64-conda_cos6-linux-gnu-c++ [x86_64-conda_cos6-linux-gnu]

Configured options: +pcre

Please see http://www.swig.org for reporting bugs and further information

[mfeurer]

Thanks a lot for checking. I'll close this issue as we have found a solution.

@sfalkner do you happen to know if the pyrfr behaves differently with SWIG4?

[IncubatorShokuhou]

@mfeurer I also have the "Segmentation fault" problem , and it remains unfixed after I install the newest anaconda3 and downgrade the version of swig to 3.0.12.

[weitongseu]

I also encounter this problem, and unfortunately, the above solution seems doesn't work for me.

[adithyabsk]

I can add another data point on this issue, running into the same issue here. Tried downgrading to swig3 as well. (I am on a Mac, though)

[adithyabsk]

As an update, installing through conda seems to work (I'd be happy to post instructions if anyone needs it). My follow up question would be one for @mfeurer. Why is that so? If swig is installed through brew, I would assume that the package should be no difference no?

[adithyabsk]

I think I've isolated the issue, the segfault seems to be occuring when using the pyrfr version that the package currently specifies (0.7.4). If the package version is bumped to the latest version (0.8.0) I am not getting a segfault. @mfeurer could we get an intermediate patch with a bumped pyrfr version?