online-ml/river

Online GapEncoder

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#1,439 建立於 2023年11月3日

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 (1 留言) (0 反應) (0 負責人)Python (4,574 star) (553 fork)batch import
Good first issueNew feature

描述

skrub is a wonderful new project related to scikit-learn. You can see Gaël Varoquaux present it here. They have a transformer called GapEncoder: it's a way to embed fuzzy strings. This could be really powerful online, say for classifying Tweets or Twitch messages, where typos are aplenty.

We already have a way to do online TD-IDF/count vectorization. But we don't have Gamma-Poisson matrix factorization. It is doable online though. Once we have it, we could assemble the two into a nice GapEncoder class. See paper here.

This is related to #1412. Indeed, maybe this works well without Gamma-Poisson matrix factorization. For instance, we could use decomposition.LDA, which we already have.

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技術棧
python
領域
machine learningdata
議題類型
feature
難度面向新貢獻者的預計實作難度，1 表示很小改動，5 表示專家級工作。
5
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over 1 week
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needs maintainer response
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mostly clear
前置要求
Gamma Poisson matrix factorizationonline learningPython
新手友善度1-100 的估計分數，表示該議題對首次貢獻者的友善程度。
15
研究方向
The issue proposes implementing an online version of GapEncoder, which involves Gamma Poisson matrix factorization. Start by reviewing the referenced paper (https://inria.hal.science/hal 02171256v4) and the existing River implementation for online TF IDF. Consider adapting decomposition.LDA as suggested, or implement the Gamma Poisson model from scratch. Check related issue #1412 for potential overlap. Key files to look at are the decomposition module in River and the skrub GapEncoder source for reference. The main challenge is ensuring the factor update rules are online and computationally efficient.