Issue 1:
Describe the bug

When doing model selection on very high-dimensional data using GridSearchCV, I found that when my estimator design has a ColumnTransformer where one of the transformers has a columns spec that is a numpy array of e.g. 50k column names, that GridSearchCV and the embedded joblib Parallel calls consume a great deal of shared memory (/dev/shm) that's proportional to the number of GridSearchCV fits. I always set ColumnTransfomer n_jobs=1 in this setup.

When troubleshooting, I found that for every fit GridSearchCV is doing, for some reason joblib Parallel is creating a duplicate pickle file of the same column name numpy array in /dev/shm. I realized this by setting JOBLIB_TEMP_FOLDER and seeing all the duplicate pickle files and loading some of them to check. The shared memory usage can add up to many GBs since for e.g. each pickle file might be 4 MB * 5000 fits = 20 GB.

What I then found out which is stranger, is that if I instead make the ColumnTransformer columns spec a Python list instead of a numpy array then there isn't any joblib Parallel /dev/shm pickling issue but this causes major GridSearchCV and joblib Parallel performance degradation. I see that each parallel job isn't even using close 100% CPU as if it's spending a lot of time with I/O or overhead. This is not typical at all, in my experience model selection with most model designs in sklearn is very CPU bound with low overhead. So changing the ColumnTransformer column spec dtype for a very large list of column names from a numpy array to Python list with everything else being the exactly same results in a substantial change in GridSearchCV behavior.

Steps/Code to Reproduce

I need to still come up with a minimal example using sklearn.datasets or other public one, does anyone know of any very high-dimensional example dataset that can be imported for use with sklearn?

TODO

Expected Results

Actual Results

Versions

System:
python: 3.8.1 | packaged by conda-forge | (default, Jan 29 2020, 14:55:04) [GCC 7.3.0]
executable: /home/hermidalc/soft/miniconda3/envs/sklearn-workflows/bin/python
machine: Linux-5.5.8-200.fc31.x86_64-x86_64-with-glibc2.10

Python dependencies:
pip: 20.0.2
setuptools: 45.2.0.post20200209
sklearn: 0.22.1
numpy: 1.18.1
scipy: 1.4.1
Cython: None
pandas: 1.0.1
matplotlib: 3.1.3
joblib: 0.14.1

Built with OpenMP: True

Issue 2:
Description

Vising the scikit-learn website using https is not possible and gives an error. Github pages now allows custom domains to use https so this error can be resolved. See https://blog.github.com/2018-05-01-github-pages-custom-domains-https/ on how to set this up.

Steps/Code to Reproduce

Manually type in "https://scikit-learn.org" into a web browser.

Expected Results

I should be able to visit the website using HTTPS without an error.

Actual Results

Going to https://scikit-learn.org generates the following error page and does not direct me to the scikit learn website (this message's wording comes from Firefox specifically):

"""

Your connection is not secure

The owner of scikit-learn.org has configured their website improperly. To protect your information from being stolen, Firefox has not connected to this website.

scikit-learn.org uses an invalid security certificate. The certificate is only valid for the following names: *.github.com, github.com, *.github.io, github.io Error code: SSL_ERROR_BAD_CERT_DOMAIN

"""

A similar error is shown in Chromium.

Versions

N/A