NitroFE
lambdo
NitroFE | lambdo | |
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1 | 3 | |
106 | 22 | |
- | - | |
0.0 | 0.0 | |
almost 2 years ago | over 3 years ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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NitroFE
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[D] Feature engineering automation?
https://github.com/NITRO-AI/NitroFE for any time-dependent feature engineering
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!
What are some alternatives?
upgini - Data search & enrichment library for Machine Learning → Easily find and add relevant features to your ML & AI pipeline from hundreds of public and premium external data sources, including open & commercial LLMs
differential-dataflow - An implementation of differential dataflow using timely dataflow on Rust.
zoofs - zoofs is a python library for performing feature selection using a variety of nature-inspired wrapper algorithms. The algorithms range from swarm-intelligence to physics-based to Evolutionary. It's easy to use , flexible and powerful tool to reduce your feature size.
ballista - Distributed compute platform implemented in Rust, and powered by Apache Arrow.
deeplearning
rslint - A (WIP) Extremely fast JavaScript and TypeScript linter and Rust crate
neural_prophet - NeuralProphet: A simple forecasting package
tablespoon - 🥄✨Time-series Benchmark methods that are Simple and Probabilistic
feast - Feature Store for Machine Learning
openHistorian - The Open Source Time-Series Data Historian
sliding-window-aggregators - Reference implementations of sliding window aggregation algorithms
timely-dataflow - A modular implementation of timely dataflow in Rust