[good first issue - beginner][examples] regenerate all visualizations

[skrawcz]

What

With https://github.com/DAGWorks-Inc/hamilton/pull/169 our visualizations will be more discerning.

Task

We now have stale images. Task is to regenerate all the ones we have in documentation and in the examples folders.

Examples

For each directory please:

1. Identify what images need to be regenerated. E.g. if the image has nodes with "UD:" in it instead of "Input:". Please mark it and note which image it is if there's several.
2. Then regenerate the image(s) for those examples, using visualize_execution using the given code. You will need to install dependencies to make the example(s) work.

Examples

• [ ] LLM_Workflows
• [ ] airflow
• [ ] async
• [ ] caching_nodes
• [ ] dask
• [ ] data_loaders
• [x] data_quality
• [ ] dbt
• [ ] feast
• [ ] feature_engineering
• [ ] hello_world
• [ ] lineage
• [ ] materialization
• [ ] model_examples
• [ ] numpy/air-quality-analysis
• [ ] pandas/materialization
• [ ] parallelism
• [ ] polars
• [ ] prefect
• [ ] ray
• [ ] reusing_functions
• [x] scikit-learn
• [ ] spark

[bovem]

Hey @skrawcz can I work on this issue? Are you referring to the visulatization inside example/ folder?

[skrawcz]

Sure @bovem. To confirm, yes I am talking about regenerating all the images that are in the examples folders.

Most of them were generated with the visualize_execution() function, but some weren't -- so if you get stuck let me know.

[subhamc1]

Hey @skrawcz, can I work on this issue ?

[skrawcz]

@subhamc1 sure -- does the description make sense?

[subhamc1]

@skrawcz yes, it does.

[skrawcz]

@skrawcz yes, it does.

Assigned!

[skrawcz]

@subhamc1 let me know what should be checked off please :)

[subhamc1]

I did data_quality and scikit-learn.