Segmentation Fault in KMeans on OSX

Describe the bug

Hi when I run this code

import numpy as np      
from sklearn.cluster import KMeans

X_train = np.random.random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)

I get this error

zsh: segmentation fault python debugging.py

This can’t be a memory error, it’s only a 10x2 matrix, any idea what’s wrong?

Python 3.10.4 Anaconda installation

sklearn.version ‘1.0.2’ numpy.version ‘1.22.3’

Steps/Code to Reproduce

import numpy as np      
from sklearn.cluster import KMeans

X_train = np.random.random((10, 2))
kmeans = KMeans(n_clusters=3, random_state=0).fit(X_train)

Expected Results

KMeans clusters

Actual Results

zsh: segmentation fault python debugging.py

This can’t be a memory error, it’s only a 10x2 matrix, any idea what’s wrong?

Versions

Python 3.10.4
Anaconda installation
>>> sklearn.__version__
'1.0.2'
>>> numpy.__version__
'1.22.3'

[lesteve]

It would be very helpful if you could:

• post the output of conda list for future reference
• try if the problem persists in a new conda environment using the conda-forge channel, conda create -n test-env -c conda-forge scikit-learn
• try to use a gdb or ldb to get a stack-trace from the segmentation fault, see https://github.com/scikit-learn/scikit-learn/issues/23574#issuecomment-1152379936 for gdb

[lesteve]

@EoinKenny as a work-around you may want to try to create a new environment from conda-forge and see whether the problem persists, i.e. something like this

conda create -c conda-forge -n test-env scikit-learn -y
conda activate test-env
python debugging.py

You could also use gdb to see whether it gives some useful additional info

gdb run --args python debugging.py

Then type r (for run) and then bt (for backtrace). There maybe more info about gdb and python in https://wiki.python.org/moin/DebuggingWithGdb.