microsoft/SynapseML

Featurizer should provide option to pass through missing values as Double.NaN instead of removing rows (currently the default)

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#304 opened on May 18, 2018

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 (5 comments) (0 reactions) (1 assignee)Scala (861 forks)batch import
enhancementgood first issuehelp wanted

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Description

Hi! Using lightGBM I faced another problem. I'm not sure if it is bug or feature :) but in our data we have a lot of empty values, so before we used sparse vector to store features, and it worked fine with our previous lib. But when i tried to use featurizer, that you provide - i mentioned, that you skip all raws if any nulls are presents as a feature. you can see it in example in attachment. So is it possible to have sparse feature vector for lightGBM training?

https://gist.github.com/ekaterina-sereda-rf/929183b9bcbbf5baf15eec3e81329992

Contributor guide

Research direction
Examine the Featurizer class in the SynapseML codebase to understand how missing values are currently handled. Identify where rows are removed due to nulls and design a new parameter (e.g., 'handleMissing') that can be set to 'drop' (current) or 'keep' (to keep NaNs). Then implement the parameter and modify the logic to optionally pass through Double.NaN.
Tech stack
scala
Domain
datamachine learning
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
Half day
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
ScalaApache Spark
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
40