You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
is there a way to add a calculated feature?
I get the data in a CSV-File which is loaded with a TextLoader. I'm not able to affect the data in the file. For My model I would use a calculated feature (e. g. NewFeature = Feature1 + Feature2) or any other calculations like manual binning (e. g. if Feature1 < 10 then NewFeature = 1 else if Feature1 >= 10 and Feature1 < 20 then NewFeature = 2 else NewFeature = 3).
In the Azure Machine Learning Studio are some tasks which can be used for e. g. SQL Transformation, R-Scripts ...
Reacted by antiufo, Jon Wood, Andre W. and WladdGorshenin
When it is done, you will be able to add your own transformations to the pipeline, and mix&match them with the standard ones provided by ML.NET.
Right now, even though MapTransform is available as a public class, you will have to rewrite your entire experiment to use low-level API (and not LearningPipeline) in order to use it.
Once we resolve #371 and #581, we will reconcile the two.
We definitely do not support dictionaries as sources of data right now. Nor do we have plans to do this in the near future, unless there is a compelling argument that outweighs the perf and data consistency implications of this potential feature.
Activity
WladdGorshenin commentedon Jul 30, 2018
Hi, I'm also curious how can I convert a date field to a set of new fields like: year, month, day_of_month, day_of_week, etc?
AbhiOnGithub commentedon Jul 30, 2018
for dynamic fields using .NET Dictionaries Dictionary<"YourNewField",Value>
Zruty0 commentedon Jul 30, 2018
Hi @gironymo ,
Your and @WladdGorshenin 's cases are perfect examples of why we want to have https://github.com/dotnet/machinelearning/blob/master/src/Microsoft.ML.Api/MapTransform.cs available through the user API.
When it is done, you will be able to add your own transformations to the pipeline, and mix&match them with the standard ones provided by ML.NET.
Right now, even though
MapTransformis available as a public class, you will have to rewrite your entire experiment to use low-level API (and notLearningPipeline) in order to use it.Once we resolve #371 and #581, we will reconcile the two.
WladdGorshenin commentedon Aug 1, 2018
Hi @AbhiOnGithub could you please elaborate a bit more?
Zruty0 commentedon Aug 1, 2018
@WladdGorshenin , the comment from @AbhiOnGithub seems to me more like a feature request.
We definitely do not support dictionaries as sources of data right now. Nor do we have plans to do this in the near future, unless there is a compelling argument that outweighs the perf and data consistency implications of this potential feature.
WladdGorshenin commentedon Aug 2, 2018
@Zruty0 is there any workaround?
Zruty0 commentedon Aug 2, 2018
@WladdGorshenin I assume you are referring to this:
My best recommendation at the moment would be to perform this pre-processing prior to giving this object to ML.NET.
Actually, now that we're merging #616 , you should be able to get away with something like this:
Zruty0 commentedon Aug 9, 2018
I think this should be closed. Feel free to reopen to add more comments.