Merge pull request #309 from FluxML/doc-touch

✨ Small improvement to EntityEmbedder docs.

Author: ablaom

src/mlj_embedder_interface.jl

@@ -110,6 +110,7 @@ In the following example we wrap a `NeuralNetworkClassifier` as an `EntityEmbedd
 that it can be used to supply continuously encoded features to a nearest neighbor model,
 which does not support categorical features.
 
+## Simple Example
 ```julia
 using MLJ
 
@@ -129,21 +130,46 @@ EntityEmbedder = @load EntityEmbedder pkg=MLJFlux
 # Flux model to do learn the entity embeddings:
 NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux
 
-# Other supervised model type, requiring `Continuous` features:
-KNNClassifier = @load KNNClassifier pkg=NearestNeighborModels
-
 # Instantiate the models:
 clf = NeuralNetworkClassifier(embedding_dims=Dict(:b => 2, :c => 3))
 emb = EntityEmbedder(clf)
 
-# For illustrative purposes, train the embedder on its own:
+# Train and transform the data using the embedder:
 mach = machine(emb, X, y)
 fit!(mach)
 Xnew = transform(mach, X)
 
-# And compare feature scitypes:
+# Compare schemas before and after transformation
 schema(X)
 schema(Xnew)
+```
+
+## Using with Downstream Models (Pipeline)
+```julia
+using MLJ
+
+# Setup some data
+N = 400
+X = (
+  a = rand(Float32, N),
+  b = categorical(rand("abcde", N)),
+  c = categorical(rand("ABCDEFGHIJ", N), ordered = true),
+)
+
+y = categorical(rand("YN", N));
+
+# Initiate model
+EntityEmbedder = @load EntityEmbedder pkg=MLJFlux
+
+# Flux model to do learn the entity embeddings:
+NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux
+
+# Other supervised model type, requiring `Continuous` features:
+KNNClassifier = @load KNNClassifier pkg=NearestNeighborModels
+
+# Instantiate the models:
+clf = NeuralNetworkClassifier(embedding_dims=Dict(:b => 2, :c => 3))
+emb = EntityEmbedder(clf)
 
 # Now construct the pipeline:
 pipe = emb |> KNNClassifier()