This MR introduces a new application, TrainVectorRegression
for training a regression machine learning model from vector data, in the same fashion as TrainVectorClassifier
See #1799 (closed)
This MR includes two major changes:
The application TrainVectorBase
(the base class for TrainVectorClassifier
), is now template on TInputValue
(features) and TOutputValues
(class). Before the MR it only works on float
as feature type and int
as class type (classification case), but now it can also be used for other type like float,float
(regression case).
A new application TrainVectorRegression
deriving from TrainVectorBase
A test has been added for the new application, using a rf classifier as regression algorithm. In the end all regression algorithm should be tested, but I think we can do that in the (future) TrainImagesRegression, to keep the same testing strategy as for classification.
This is not exactly the workflow described in the issue, because I don't think the first step is relevent (removing sampling from TrainRegrssion
), as TrainRegression
will be deprecated at the end of the refactoring.
The next step of the refactoring is to create a TrainImagesRegression
application: it could be a composite application that chains ImageEnvelope
to create a polygon on the extent of the image, SampleSelection
to select random points over this polygon, SampleExtraction
to extract feature and predictor values over two input images and finally TrainVectorRegression
to extract the model (this is the workflow used in the KMeansClassification
composite application), what do you think ? Anyway I think this is out of the scope of this MR.
In the issue we talked about CSV input compatibility. It is hard to add it in TrainVectorBase
because of the design of the application, the best way (given the design of the learning applications) would probably to create a new application TrainCSVBase
inheriting from LearningApplicationBase
doing the CSV input reading, and then create a TrainCSVRegression
from it, and maybe also a TrainCSVClassifier
. But it there really a need for such functionality ?
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