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

That is a good suggestion. Another option is to have a keyword argument on fit which is a dictionary of estimator to kwargs to eliminate any potential for unnamed kwargs.

Originally posted by @camer314 in microsoft/FLAML#451 (comment)

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

It does not help users view the data when all that is printed on the screen is column names.

Here is pillar output where the number of columns goes into the thousands

🐛 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.

🚀 Feature request

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