lambdo
go-featureprocessing
lambdo | go-featureprocessing | |
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3 | 6 | |
22 | 114 | |
- | - | |
0.0 | 5.2 | |
over 3 years ago | about 2 months ago | |
Python | Go | |
MIT License | MIT License |
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lambdo
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Why isn't differential dataflow more popular?
It will return the sum of all values in column A. For large tables it will take some time to compute the result. Now assume we append a new record and want to get the new result. The traditional approach is execute this query again. A better approach is to process this new record only by adding its value in A to the result of the previous query. It is important in (stateful) stream processing.
Something similar is implemented in these libraries which however rely on a different data processing conception (alternative to map-reduce):
https://github.com/asavinov/prosto - Functions matter! No join-groupby, No map-reduce.
https://github.com/asavinov/lambdo - Feature engineering and machine learning: together at last!
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Feature Processing in Go
I find this project quite interesting because sklearn has a good general design including data transformations and it does make sense to provide compatible functionality for Go.
Feature engineering in general is a hot topic and especially if features are not simple hard-coded transformations but rather can be learned from data. For example, I developed a toolkit intended for combining feature engineering and ML:
https://github.com/asavinov/lambdo - Feature engineering and machine learning: together at last!
go-featureprocessing
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Machine learning with GOlang
Tabular data is well supported in Go. Tabular data and small sample sizes is a sweet spot for Go. You may squeze in some meaningful performance and good looking code and integrations. Some entry points: (1) https://github.com/nikolaydubina/go-ml-benchmarks (2) https://github.com/dmitryikh/leaves (3) https://github.com/nikolaydubina/go-featureprocessing
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Show HN: Go-Featureprocessing v1.0.0
What is this?
Fast feature preprocessing in Go with feature parity to sklearn
https://github.com/nikolaydubina/go-featureprocessing
What is new?
* Added batch processing
- Feature Processing in Go
- [P] fast and convenient feature processing in Go! I am sure many backend teams are running Go services, if so this should help integration better!
- Fast and convenient library for feature processing in pure Go! I am focusing on single sample processing time, benchmarks and ease of use. Try it out!
What are some alternatives?
differential-dataflow - An implementation of differential dataflow using timely dataflow on Rust.
m2cgen - Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
ballista - Distributed compute platform implemented in Rust, and powered by Apache Arrow.
bayesian - Naive Bayesian Classification for Golang.
rslint - A (WIP) Extremely fast JavaScript and TypeScript linter and Rust crate
gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
tablespoon - 🥄✨Time-series Benchmark methods that are Simple and Probabilistic
ocrserver - A simple OCR API server, seriously easy to be deployed by Docker, on Heroku as well
openHistorian - The Open Source Time-Series Data Historian
gago - :four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)
sliding-window-aggregators - Reference implementations of sliding window aggregation algorithms
sklearn - bits of sklearn ported to Go #golang