⭐️ Entity embedder interface is here

src/MLJFlux.jl

@@ -30,9 +30,11 @@ include("image.jl")
 include("fit_utils.jl")
 include("entity_embedding_utils.jl")
 include("mlj_model_interface.jl")
+include("mlj_embedder_interface.jl")
 
 export NeuralNetworkRegressor, MultitargetNeuralNetworkRegressor
 export NeuralNetworkClassifier, NeuralNetworkBinaryClassifier, ImageClassifier
+export EntityEmbedder
 export CUDALibs, CPU1
 
 include("deprecated.jl")

src/entity_embedding.jl

@@ -25,7 +25,7 @@ entityprops = [
 numfeats = 4
 
 # Run it through the categorical embedding layer
-embedder = EntityEmbedder(entityprops, 4)
+embedder = EntityEmbedderLayer(entityprops, 4)
 julia> output = embedder(batch)
 5×10 Matrix{Float64}:
   0.2        0.3        0.4        0.5       …   0.8        0.9         1.0        1.1
@@ -35,18 +35,18 @@ julia> output = embedder(batch)
  -0.847354  -0.847354  -1.66261   -1.66261      -1.66261   -1.66261    -0.847354  -0.847354
 ```
 """ # 1. Define layer struct to hold parameters
-struct EntityEmbedder{A1 <: AbstractVector, A2 <: AbstractVector, I <: Integer}
+struct EntityEmbedderLayer{A1 <: AbstractVector, A2 <: AbstractVector, I <: Integer}
     embedders::A1
     modifiers::A2       # applied on the input before passing it to the embedder
     numfeats::I
 end
 
 # 2. Define the forward pass (i.e., calling an instance of the layer)
-(m::EntityEmbedder)(x) =
+(m::EntityEmbedderLayer)(x) =
     (vcat([m.embedders[i](m.modifiers[i](x, i)) for i in 1:m.numfeats]...))
 
 # 3. Define the constructor which initializes the parameters and returns the instance
-function EntityEmbedder(entityprops, numfeats; init = Flux.randn32)
+function EntityEmbedderLayer(entityprops, numfeats; init = Flux.randn32)
     embedders = []
     modifiers = []
     # Setup entityprops
@@ -66,8 +66,8 @@ function EntityEmbedder(entityprops, numfeats; init = Flux.randn32)
         end
     end
 
-    EntityEmbedder(embedders, modifiers, numfeats)
+    EntityEmbedderLayer(embedders, modifiers, numfeats)
 end
 
 # 4. Register it as layer with Flux
-Flux.@layer EntityEmbedder
+Flux.@layer EntityEmbedderLayer

src/entity_embedding_utils.jl

@@ -100,7 +100,7 @@ function construct_model_chain_with_entityembs(
 )
     chain = try
         Flux.Chain(
-            EntityEmbedder(entityprops, shape[1]; init = Flux.glorot_uniform(rng)),
+            EntityEmbedderLayer(entityprops, shape[1]; init = Flux.glorot_uniform(rng)),
             build(model, rng, (entityemb_output_dim, shape[2])),
         ) |> move
     catch ex

src/mlj_embedder_interface.jl

@@ -0,0 +1,128 @@
+### EntityEmbedder with MLJ Interface
+
+# 1. Interface Struct
+mutable struct EntityEmbedder{M <: MLJFluxModel} <: Unsupervised
+    model::M
+end;
+
+
+# 2. Constructor
+function EntityEmbedder(model;)
+    return EntityEmbedder(model)
+end;
+
+
+# 4. Fitted parameters (for user access)
+MMI.fitted_params(::EntityEmbedder, fitresult) = fitresult
+
+# 5. Fit method
+function MMI.fit(transformer::EntityEmbedder, verbosity::Int, X, y)
+    return MLJModelInterface.fit(transformer.model, verbosity, X, y)
+end;
+
+
+# 6. Transform method
+function MMI.transform(transformer::EntityEmbedder, fitresult, Xnew)
+    Xnew_transf = MLJModelInterface.transform(transformer.model, fitresult, Xnew)
+    return Xnew_transf
+end
+
+# 8. Extra metadata
+MMI.metadata_pkg(
+    EntityEmbedder,
+    package_name = "MLJTransforms",
+    package_uuid = "23777cdb-d90c-4eb0-a694-7c2b83d5c1d6",
+    package_url = "https://github.com/JuliaAI/MLJTransforms.jl",
+    is_pure_julia = true,
+)
+
+MMI.metadata_model(
+    EntityEmbedder,
+    input_scitype = Table,
+    output_scitype = Table,
+    load_path = "MLJTransforms.EntityEmbedder",
+)
+
+MMI.target_in_fit(::Type{<:EntityEmbedder}) = true
+
+
+
+
+
+"""
+$(MMI.doc_header(EntityEmbedder))
+
+`EntityEmbedder` implements entity embeddings as in the "Entity Embeddings of Categorical Variables" paper by Cheng Guo, Felix Berkhahn.
+
+# Training data
+
+In MLJ (or MLJBase) bind an instance unsupervised `model` to data with
+
+    mach = machine(model, X, y)
+
+Here:
+
+
+- `X` is any table of input features (eg, a `DataFrame`). Features to be transformed must
+   have element scitype `Multiclass` or `OrderedFactor`. Use `schema(X)` to 
+   check scitypes. 
+
+- `y` is the target, which can be any `AbstractVector` whose element
+  scitype is `Continuous` or `Count` for regression problems and 
+  `Multiclass` or `OrderedFactor` for classification problems; check the scitype with `schema(y)`
+
+Train the machine using `fit!(mach)`.
+
+# Hyper-parameters
+
+- `model`: The underlying deep learning model to be used for entity embedding. So far this supports `NeuralNetworkClassifier`, `NeuralNetworkRegressor`, and `MultitargetNeuralNetworkRegressor`.
+
+# Operations
+
+- `transform(mach, Xnew)`: Transform the categorical features of `Xnew` into dense `Continuous` vectors using the trained `MLJFlux.EntityEmbedderLayer` layer present in the network.
+    Check relevant documentation [here](https://fluxml.ai/MLJFlux.jl/dev/) and in particular, the `embedding_dims` hyperparameter.
+
+
+# Examples
+
+```julia
+using MLJFlux
+using MLJ
+using CategoricalArrays
+
+# Setup some data
+N = 200
+X = (;
+    Column1 = repeat(Float32[1.0, 2.0, 3.0, 4.0, 5.0], Int(N / 5)),
+    Column2 = categorical(repeat(['a', 'b', 'c', 'd', 'e'], Int(N / 5))),
+    Column3 = categorical(repeat(["b", "c", "d", "f", "f"], Int(N / 5)), ordered = true),
+    Column4 = repeat(Float32[1.0, 2.0, 3.0, 4.0, 5.0], Int(N / 5)),
+    Column5 = randn(Float32, N),
+    Column6 = categorical(
+        repeat(["group1", "group1", "group2", "group2", "group3"], Int(N / 5)),
+    ),
+)
+y = categorical([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])           # Classification
+
+# Initiate model
+NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux
+
+clf = NeuralNetworkClassifier(embedding_dims=Dict(:Column2 => 2, :Column3 => 2))
+
+emb = EntityEmbedder(clf)
+
+# Construct machine
+mach = machine(emb, X, y)
+
+# Train model
+fit!(mach)
+
+# Transform data using model to encode categorical columns
+Xnew = transform(mach, X)
+Xnew
+```
+
+See also
+[`TargetEncoder`](@ref)
+"""
+EntityEmbedder

src/types.jl

@@ -194,7 +194,7 @@ MMI.metadata_pkg.(
 const MODELSUPPORTDOC = """
 In addition to features with `Continuous` scientific element type, this model supports
 categorical features in the input table. If present, such features are embedded into dense
-vectors by the use of an additional `EntityEmbedder` layer after the input, as described in
+vectors by the use of an additional `EntityEmbedderLayer` layer after the input, as described in
 Entity Embeddings of Categorical Variables by Cheng Guo, Felix Berkhahn arXiv, 2016.
 """
 
@@ -204,7 +204,7 @@ const XDOC = """
   scitype (typically `Float32`); or (ii) a table of input features (eg, a `DataFrame`)
   whose columns have `Continuous`, `Multiclass` or `OrderedFactor` element scitype; check
   column scitypes with `schema(X)`.  If any `Multiclass` or `OrderedFactor` features
-  appear, the constructed network will use an `EntityEmbedder` layer to transform
+  appear, the constructed network will use an `EntityEmbedderLayer` layer to transform
   them into dense vectors. If `X` is a `Matrix`, it is assumed that columns correspond to
   features and rows corresponding to observations.
 
@@ -222,7 +222,7 @@ const EMBDOC = """
 const TRANSFORMDOC = """
 - `transform(mach, Xnew)`: Assuming `Xnew` has the same schema as `X`, transform the
   categorical features of `Xnew` into dense `Continuous` vectors using the
-  `MLJFlux.EntityEmbedder` layer present in the network. Does nothing in case the model
+  `MLJFlux.EntityEmbedderLayer` layer present in the network. Does nothing in case the model
   was trained on an input `X` that lacks categorical features.
 """
 

test/entity_embedding.jl

@@ -22,7 +22,7 @@ entityprops = [
         (index = 4, levels = 2, newdim = 2),
     ]
 
-    embedder = MLJFlux.EntityEmbedder(entityprops, 4)
+    embedder = MLJFlux.EntityEmbedderLayer(entityprops, 4)
 
     output = embedder(batch)
 
@@ -68,7 +68,7 @@ end
         ]
 
         cat_model = Chain(
-            MLJFlux.EntityEmbedder(entityprops, 4),
+            MLJFlux.EntityEmbedderLayer(entityprops, 4),
             Dense(9 => (ind == 1) ? 10 : 1),
             finalizer[ind],
         )
@@ -143,7 +143,7 @@ end
 @testset "Transparent when no categorical variables" begin
     entityprops = []
     numfeats = 4
-    embedder = MLJFlux.EntityEmbedder(entityprops, 4)
+    embedder = MLJFlux.EntityEmbedderLayer(entityprops, 4)
     output = embedder(batch)
     @test output ≈ batch
     @test eltype(output) == Float32
@@ -187,21 +187,37 @@ end
         3 4
     ])
 
+    stable_rng=StableRNG(123)
+
     for j in eachindex(embedding_dims)
         for i in eachindex(models)
+            # Without lightweight wrapper
             clf = models[1](
-                builder = MLJFlux.Short(n_hidden = 5, dropout = 0.2),
+                builder = MLJFlux.MLP(hidden=(10, 10)),
                 optimiser = Optimisers.Adam(0.01),
                 batch_size = 8,
                 epochs = 100,
                 acceleration = CUDALibs(),
                 optimiser_changes_trigger_retraining = true,
                 embedding_dims = embedding_dims[3],
+                rng=42
             )
-
             mach = machine(clf, X, ys[1])
-
             fit!(mach, verbosity = 0)
+            Xnew = transform(mach, X)
+            # With lightweight wrapper
+            clf2 = deepcopy(clf)
+            emb = MLJFlux.EntityEmbedder(clf2)
+            mach_emb = machine(emb, X, ys[1])
+            fit!(mach_emb, verbosity = 0)
+            Xnew_emb = transform(mach_emb, X)
+            @test Xnew == Xnew_emb
+
+            # Pipeline doesn't throw an error
+            pipeline = emb |> clf
+            mach_pipe = machine(pipeline, X, y)
+            fit!(mach_pipe, verbosity = 0)
+            y = predict_mode(mach_pipe, X)
 
             mapping_matrices = MLJFlux.get_embedding_matrices(
                 fitted_params(mach).chain,