Transformer

Transformer(supervised_model)

Wrap supervised_model so that it is treated as a transformer in MLJ pipelines. It is assumed that supervised_model isa Supervised and that transform is implemented for the model type.

For Supervised models in an MLJ pipeline, it is the output of predict that is propagated by default to the next model in the pipeline. By wrapping in Transform, the output of transform is propagated instead.

The original hyperparameters of supervised_model are nested hyperparameters in Transformer(supervised_model), but in most other respects the latter behaves like supervised_model.

Example

Below reducer is a supervised model implementing transform which selects features using Recursive Feature Elimination. Because it is supervised, we obtain an error when we follow it with another supervised model in a pipeline:

using MLJ
RandomForestClassifier = @load RandomForestClassifier pkg=DecisionTree
KNNClassifier = @load KNNClassifier pkg=NearestNeighborModels

reducer = RecursiveFeatureElimination(RandomForestClassifier(), n_features=2)
reducer |> KNNClassifier()
## ERROR: ArgumentError: More than one supervised model in a pipeline is not permitted

The following, however, works as expected, passing the reduced training features to the K-nearest neighbor classifier, when pipe is trained.

pipe = Transformer(reducer) |> KNNClassifier()