feat: Add LumpFeatures transfomer
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)
- Find the corresponding markdown file for your new feature in
website/docs/documentationfolder. Make sure you choose the correct classestimators/transformersand namespace. - Follow the pattern in markdown file and add another section for your new API, including pyspark, scala (and .NET potentially) samples.
- Make sure the
DocTablepoints to correct API link. - Navigate to website folder, and run
yarn run startto make sure the website renders correctly. - Don't forget to add
<!--pytest-codeblocks:cont-->before each python code blocks to enable auto-tests for python samples. - Make sure the
WebsiteSamplesTestsjob pass in the pipeline.
Hey @dylanw-oss :wave:! Thank you so much for contributing to our repository :raised_hands:. Someone from SynapseML Team will be reviewing this pull request soon.
We use semantic commit messages to streamline the release process. Before your pull request can be merged, you should make sure your first commit and PR title start with a semantic prefix. This helps us to create release messages and credit you for your hard work!
Examples of commit messages with semantic prefixes:
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fix: Fix LightGBM crashes with empty partitions -
feat: Make HTTP on Spark back-offs configurable -
docs: Update Spark Serving usage -
build: Add codecov support -
perf: improve LightGBM memory usage -
refactor: make python code generation rely on classes -
style: Remove nulls from CNTKModel -
test: Add test coverage for CNTKModel
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if there is no objective for this feature, I'll add document. @sarahshy @memoryz