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

1 points by Jashwanth01 8mo ago

Jashwanth01 8mo 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 https://github.com/thatipamula-jashwanth/smart-knn I’d appreciate feedback, especially from people who have deployed nearest-neighbor systems in production. Thanks. - Jashwanth
verdverm 8mo 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.
- Jashwanth01 8mo 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.
  - verdverm 8mo 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
    - Jashwanth01 8mo ago
      Thanks for clarifying... I appreciate you taking the time to explain. I understand the sensitivity around spam lately. I’m new here and still learning the culture, so the context helps a lot. I’ll take the feedback on presentation and positioning, polish it properly, and resubmit in a better form. Thanks again for pointing me in the right direction.
- deleted 8mo ago
  [deleted]

1 points by Jashwanth01 8mo 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 https://github.com/thatipamula-jashwanth/smart-knn I’d appreciate feedback, especially from people who have deployed nearest-neighbor systems in production. Thanks. - Jashwanth

verdverm 8mo 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.

Jashwanth01 8mo 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.
- verdverm 8mo 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
  - Jashwanth01 8mo ago
    Thanks for clarifying... I appreciate you taking the time to explain. I understand the sensitivity around spam lately. I’m new here and still learning the culture, so the context helps a lot. I’ll take the feedback on presentation and positioning, polish it properly, and resubmit in a better form. Thanks again for pointing me in the right direction.
deleted 8mo ago
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