Most functions in scipy.linalg functions (e.g. svd, qr, eig, eigh, pinv, pinv2 ...) have a default kwarg check_finite=True that we typically leave to the default value in scikit-learn.

As we already validate the input data for most estimators in scikit-learn, this check is redundant and can cause significant overhead, especially at predict / transform time. We should probably a

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Description

Menu items in superset show a

Just noticed that this is poorly documented.

It has signature (target, method=None, level=None, limit=None, tolerance=None) and only documents the behaviour of target

Is a good candidate to revisit.

When you run a Streamlit app, you get a few messages printed on the terminal. In some extreme cases, you'll see these:

We should flip the order of the following CLI messages to:

1. Welcome to Streamlit (...)
2. For bette

Is your feature request related to a problem? Please describe.
When working with a big piece of text, I sometimes scroll down and copy some text into another tab. When switching back to the first tab, both the input and the output pane is back on top. So I don't know where I was working just now.

Describe the solution you'd like
After tab switching, scroll position should be remembere

To Reproduce

Steps to reproduce the behavior:

1. Switch the language to another language than the default language of OpenRefine (English), e.g. German.
2. Note that the captions for facet selection (most-left c

Collection of follow-ups to #5827. These can/should be broken out into individual PRs. Many are relatively straightforward and would make a good first PR.

General

• Documentation (none was added in original PR).
• Release notes.
• Example notebook.
• Double-check how sm.tsa.arima.ARIMA works with fix_params (it should fail except when the fit method is statespace

In some figure, the axis name is cropped. We should probably used tight_layout explicitly:

https://imbalanced-learn.org/stable/auto_examples/over-sampling/plot_shrinkage_effect.html#sphx-glr-auto-examples-over-sampling-plot-shrinkage-effect-py

See:
gonum/gonum#845 (comment)

The official instructions say to use joblib for pickling PyOD models.

This fails for AutoEncoders, or any other TensorFlow-backed model as far as I can tell. The error is:

>>> dump(model, 'model.joblib')
...
TypeError: can't pickle _thread.RLock objects

Note that it's not sufficient to save the underlying Keras S

** 由于每天都有新股上市，要是能有一个关于每个股票的行业、地域、概念分类相关的接口就好了，类比于tushare中的那些分类接口,参考此处可惜tushare已经不维护了
**

https://igel.readthedocs.io/en/latest/_sources/readme.rst.txt includes a link to the assets/igel-help.gif, but that path is broken on readthedocs.

readme.rst is included as ../readme.rst in the sphinx build.
The gifs are in asses/igel-help.gif

The sphinx build needs to point to the asset directory, absolutely:

.. image:: /assets/igel-help.gif

I haven't made a patch, because I haven't