KMeans singnificantly slower on 0.23 #17208
Description
Describe the bug
With the latest changes, KMeans is significantly slower on small datasets. The time needed to compute clusters is around ten times longer.
Steps/Code to Reproduce
Times with the following code are:
scikit-lern 0.22: ~0.015
scikit-learn 0.23: ~0.15
import time
import sklearn.cluster
from sklearn import datasets
data = datasets.load_iris()['data']
t = time.time()
sklearn.cluster.KMeans(n_clusters=2).fit(data)
print(time.time() - t)
I also tried on a bigger dataset with shape (300, 25) where clustering with the new version needed 3-4s while before it happened in miliseconds.
Expected Results
Clusters would be computed as fast as before.
Versions
System:
python: 3.7.6 | packaged by conda-forge | (default, Jan 7 2020, 22:05:27) [Clang 9.0.1 ]
executable: /Users/primoz/miniconda3/envs/orange/bin/python
machine: Darwin-19.0.0-x86_64-i386-64bit
Python dependencies:
pip: 20.1
setuptools: 46.1.3
sklearn: 0.23.0
numpy: 1.18.4
scipy: 1.4.1
Cython: None
pandas: 1.0.3
matplotlib: 3.2.1
joblib: 0.14.1
Built with OpenMP: True