Smart-KNN: A production-focused, feature-weighted KNN optimized for CPU

https://github.com/thatipamula-jashwanth/smart-knn

by Jashwanth01 • 5 months ago

1 5 5 months ago

Jashwanth013• 5 months ago

Hi HN,

I’ve been working on SmartKNN, a nearest-neighbor system designed specifically for production deployment rather than academic experimentation.

The goal was not to slightly tweak classical KNN, but to restructure it into a deployable, latency-aware system while preserving interpretability.

What it does differently

Traditional KNN is simple and interpretable, but in practice it struggles with:

Inference latency as datasets grow

Equal treatment of all features

Fixed distance metrics

Unpredictable performance under load

SmartKNN addresses these issues through:

1. Learned Feature Weighting

Feature importance is learned automatically and incorporated into the distance computation. This reduces noise and improves neighbor quality without manual tuning.

2. Adaptive Distance Behavior

Distance computation adapts to learned feature relevance instead of relying on a fixed metric like plain Euclidean.

3. Backend Selection

SmartKNN supports both brute-force and approximate nearest-neighbor strategies.

Small datasets → brute-force

Larger datasets → approximate candidate retrieval

Approximate search is used only to retrieve candidates. Final prediction always uses the learned distance function.

4. CPU-Focused Design

The system is optimized for predictable CPU inference performance rather than GPU-heavy workflows. The focus is stable latency characteristics suitable for production workloads.

5. Unified API

Supports both classification and regression through a scikit-learn compatible interface.

Performance

On structured/tabular datasets with strong local structure, SmartKNN achieves competitive accuracy against tree-based models.

It does not aim to replace tree models or neural networks universally. It performs best where neighborhood structure is meaningful and interpretability is desired.

Limitations

- Requires dataset to remain in memory - High-dimensional dense data can still challenge nearest-neighbor methods - No online/incremental updates yet - Backend preparation adds setup time for large datasets

Project Status

- Public release: 0.2.2 - Stable API - Open source - CPU-optimized core Repository: https://github.com/thatipamula-jashwanth/smart-knn I’d appreciate feedback, especially from people who have deployed nearest-neighbor systems in production.

Thanks.

- Jashwanth

verdverm2• 5 months ago

you posted this 5 hours ago, from the FAQ

Are reposts ok?
If a story has not had significant attention in the last year or so, a small number of reposts is ok. Otherwise we bury reposts as duplicates.
Please don't delete and repost the same story. Deletion is for things that shouldn't have been submitted in the first place.

Jashwanth013• 5 months ago

Thanks for pointing that out.. I appreciate it. I’m still learning how HN submissions work and didn’t realize how reposts are handled here. I’ll be more careful going forward. Thanks for the clarification.

verdverm2• 5 months ago

I may have been a little overzealous with this one. There is a significant bot spam problem on HN right now, re: openclaw and friends

It's good to review all the guidelines and rules from time to time. Submitting the same post a few times over a week is generally going to be ok, despite what the rules say about 1 year. I recall there being other timing guidelines written somewhere else (perhaps a @dang comment, dang is the OG mod for HN, recently a second has joined, forget handle) By and large, HN is community moderated via interactions like this (ideally), but in reality it is mainly through downvotes and flagging.

This is generally an interesting topic, a little polish on presentation and positioning will help. Once you do that, submit again

Smart-KNN: A production-focused, feature-weighted KNN optimized for CPU

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