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New release on crates.io #2

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nishaq503 opened this issue May 29, 2023 · 2 comments
Closed

New release on crates.io #2

nishaq503 opened this issue May 29, 2023 · 2 comments

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@nishaq503
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Hello,

I have found this crate useful in some of my research work.
I noticed that the last commit to this crate is about a year old but has not yet been published to crates.io.
Are there any blockers to publishing this?
If so, I would be interested in helping clear them?
If not, could you please release a new version on crates.io?

Thank you!

@cmccomb
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cmccomb commented May 29, 2023

I'm so glad you're getting some use out of it! Would you mind sharing a bit more about what you use it for?

Honestly, the biggest blocker has been a lack of time! I'll do a quick onceover and then push a new release to crates for you.

I would welcome your contributions to this project in the future. Here are a few things I've had in mind:

  1. Hyperparameter Tuning - I've been keeping an eye on some of the work in Smartcore that is establishing hyperparameter tuning capabilities. Move to enum Kernels instead of Structs for hypertuning smartcorelib/smartcore#169
  2. Jank elimination - The current implementation is straight up bad (for some of the same reasons mentioned in that PR) and relies on serializing/deserializing models frequently. I'd love to find a better approach to that as well.

cmccomb added a commit that referenced this issue May 29, 2023
cmccomb added a commit that referenced this issue May 29, 2023
@cmccomb cmccomb closed this as completed May 29, 2023
@nishaq503
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I'm using this crate for research in anomaly detection (repo). We had a python implementation some time ago and wanted to migrate to Rust.

In very broad strokes, we have a clustering algorithm and we train ML models on geometric properties of the clusters to select those clusters which would build a neighborhood graph which provides the best anomaly classification from a suite of anomaly detection algorithms.

I have forked this repo and will first take a look at jank elimination, before moving on to hyperparameter tuning.

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