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
{{ message }}
Repository navigation
Simplify Pipeline Initalization with Append() that returns the pipeline #7
We can add the ability to add a pipeline item in a fluent fashion. The benefit would be reduction of typing and cleaner API.
This would require pipeline to add the following method:
The user code will look like this:
var pipeline = new LearningPipeline()
.Add(new TextLoader(dataPath, separator: ","))
.Add(new TextFeaturizer("Features", "SentimentText"));
+1 on this change. However, I wonder whether we should return a new copy of the pipeline after each .Append or .Add call. Otherwise, the following code would be very confusing:
It would be also good to make the LearningPipeline a List instead of Collection. This way it would be easy to manipulate the pipeline by adding and replacing items in the arbitrary location.
Example of the scenario is sweep of different learners.
Activity
eerhardt commentedon May 4, 2018
Did you mean to have
.Appendinstead of.Add?markusweimer commentedon May 4, 2018
+1 on this change. However, I wonder whether we should return a new copy of the pipeline after each
.Appendor.Addcall. Otherwise, the following code would be very confusing:We should make sure that
p1refers to[A,B],p2to[A,B,C,D]andp3to[A,B,D].Merge pull request dotnet#7 from dotnet/master
glebuk commentedon May 29, 2018
It would be also good to make the LearningPipeline a List instead of Collection. This way it would be easy to manipulate the pipeline by adding and replacing items in the arbitrary location.
Example of the scenario is sweep of different learners.
shauheen commentedon Jun 26, 2018
closing this issue as the PR has already merged and the API will also be updated in the next release.
comment out fast forest trainer, per discussion on ML.NET open issue d…
code migration from machinelearning-automl to machinelearning repo fe…
comment out fast forest trainer, per discussion on ML.NET open issue d…