feat: Add LumpFeatures transfomer

[dylanw-oss]

Related Issues/PRs

#1891

What changes are proposed in this pull request?

A transformer can be used to handle data with high cardinality skewed categorical before doing other featurization processing.

How is this patch tested?

unit test

Does this PR add a new feature? If so, have you added samples on website?

will add document in next commit (I'd like to ensure it makes sense before doing the next step)

1. Find the corresponding markdown file for your new feature in website/docs/documentation folder.
   Make sure you choose the correct class estimators/transformers and namespace.
2. Follow the pattern in markdown file and add another section for your new API, including pyspark, scala (and .NET potentially) samples.
3. Make sure the DocTable points to correct API link.
4. Navigate to website folder, and run yarn run start to make sure the website renders correctly.
5. Don't forget to add <!--pytest-codeblocks:cont--> before each python code blocks to enable auto-tests for python samples.
6. Make sure the WebsiteSamplesTests job pass in the pipeline.

[github-actions]

Hey dylanw-oss 👋!
Thank you so much for contributing to our repository 🙌.
Someone from SynapseML Team will be reviewing this pull request soon.

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[dylanw-oss]

if there is no objective for this feature, I'll add document. sarahshy Jason Wang (@memoryz)

[github-actions]

Summary by GPT-4

The LumpFeatures transformer is a custom transformer that takes a DataFrame and a list of lumping rules as input and returns a DataFrame comprised of the original columns, but the columns defined in lumping rules will be indexed and lumped to top k. This transformer can be used to handle high cardinality skewed categorical features before doing encoding.

In the given code, the LumpFeatures class extends Transformer and implements the following methods:

1. transform: This method takes an input dataset and applies the lumping rules to it. It first creates a pipeline with StringIndexer transformers for each column specified in the lumping rules. Then, it fits and transforms the input dataset using this pipeline. Finally, it keeps only the top k levels for each categorical column according to the lumping rules.

2. transformSchema: This method returns the schema of the output DataFrame after applying the transformation.

3. copy: This method creates a copy of this instance with extra parameters.

The test suite LumpFeaturesSuite tests this transformer's basic functionality by creating an input DataFrame with categorical columns, applying lumping rules using an instance of LumpFeatures, and comparing the output DataFrame with an expected result.

In summary, this custom transformer helps in handling high cardinality skewed categorical features by indexing and lumping them according to specified rules before encoding them.

Suggestions

The changes in this PR look good and no suggestions are needed.