vaex.from_arrays(s=['a,b']).s.str.replace(r'(\w+)',r'--\g<1>==',regex=True)

when using capture group in str, it fails, while str_pandas.replace() is correct

Name: vaex
Version: 4.6.0
Summary: Out-of-Core DataFrames to visualize and explore big tabular datasets
Home-page:

Related: awslabs/autogluon#1479

Add a scikit-learn compatible API wrapper of TabularPredictor:

• TabularClassifier
• TabularRegressor

Required functionality (may need more than listed):

• init API
• fit API
• predict API
• works in sklearn pipelines

I published a new v0.1.12 release of HCrystalBall, that updated some package dependencies and fixed some bugs in cross validation.

Should the original pin for 0.1.10 be updated? Unfortunately won't have time soon to submit a PR for this.

Feature request

As requested by some, and as @ekamioka started on this PR #244. It might be interesting to get some helper functions to use embeddings as it's not the simplest concept in deep learning.

What is the expected behavior?
Calling a few helper function to get all the correct parameters before using TabNet

🐛 Bug

found another paper-cut with instance segmentation...
it is not trivial/intuitive how to match mask with input image as output can be any size and mask is 128x128
so it is just scaled equally in each dimension is applied padding uniformly?

To Reproduce

https://www.kaggle.com/jirkaborovec/cell-instance-segm-lightning-flash#Training-with-Flash-Lightning

Additional cont

Is there a way to stabilise the results of the algorithm spot the diff drift detection?
In each run with same configuration and data the results of diff and p values are different.

Describe the bug
Not necessarily a bug. The dependency of pandas == 1.1.5 is a really obsolete version, (and also many other packages)
For pandas I read through the source and didn't find a version specific usage. Considering change it to pandas >=1.1.5?

🚀 Feature request

The original PyTorch implementation of TabularDropout transformation is available at transformers4rec/torch/tabular/transformations.py