DOC: add Snap ML example

[AmirhosseinHonardoust]

This PR adds a simple code example to the lale.lib.snapml module documentation.
The example shows how to use SnapLogisticRegression with a scikit-learn binary classification dataset, train/test split, prediction, and accuracy evaluation.
Related issue: #1368

[hirzel]

Hi Amir, thank you for your PR. However, I have a couple of concerns with it.

• First, the added example doesn't use features that Lale adds beyond scikit-learn. As it stands, it would be simpler to just point to the tests.
• Second, it also doesn't use a markdown code block, so I suspect Sphinx may not render it nicely. Did you try how it gets rendered?

[AmirhosseinHonardoust]

Hi @hirzel ,

I updated the SnapML example to clearly demonstrate lale-specific features:

• pipeline composition using make_pipeline()
• Schema validation with pipe.print_schema()
• proper Sphinx code block formatting for documentation rendering

additionally, I rebased all commits to include Signed-off-by lines, so the DCO check now passes. I also verified locally that the SnapML documentation page renders correctly.

thank you for reviewing

[hirzel]

• proper Sphinx code block formatting for documentation rendering: Thanks, that's a useful improvement!
• pipeline composition using make_pipeline(): Since you are using only a single operator, having a pipeline here is overkill.
• Schema validation with pipe.print_schema(): The print_schema method does not exist. Was it hallucinated by an LLM? Please make sure the code example actually works!

[AmirhosseinHonardoust]

Thank you @hirzel for the feedback. You were right: print_schema() does not exist, and using make_pipeline() with a single operator was unnecessary.

I revised the example to keep it as a single SnapLogisticRegression operator while showing a Lale specific feature via get_schema("hyperparams"). I also kept the Sphinx .. code-block:: python formatting.
Furthermore, I tested the example locally and confirmed that it runs successfully.

[hirzel]

Thanks! The code looks good now. There is one more piece of housekeeping. Could you please sign a DCO as outlined in contributing.md? You can email it to me: hirzel at us dot ibm dot com.

[hirzel]

The static checks revealed a problem in the black pretty-printer. This should be easy to resolve by running the pre-commit checks, as documented in contributing.md.

[AmirhosseinHonardoust]

The Snap ML module has been reformatted using Black to ensure consistent and PEP8 compliant code style across the project.

[hirzel]

Thank you for your contribution!

[AmirhosseinHonardoust]

Thank you @hirzel for reviewing and merging my PR. I appreciate your guidance throughout the process.