pyspark.ml.linalg data types support?
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Domain
- data, machine-learning
Research direction
Start by examining the pyspark.ml.linalg dense vector, sparse vector, and matrix types mentioned in the issue, along with TensorFlow's sparse vector support. Clarify which TensorFrames conversions are required and define completion around supporting the agreed data types without the current array and expand_dims workaround.
Written by the indexing model from the issue text.
Description
some how for a dense vector i need to convert to array and expand_dims to do the work around
but we also have sparse vector and matrix.
since tensorflow do have sparse vector type, thoughts ?
yes.. i'm working on a spark linear algebra utils project using tensorframes.
- Dominant language
- Scala
- Stars
- 743
- Forks
- 158
- PR merge metrics
- No merged PRs in 30d
Getting set up
- Ships a Dockerfile or Docker Compose file
- No pull request template
- No contributing guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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