fix some tests

src/mlj_model_interface.jl

@@ -66,35 +66,35 @@ function MLJModelInterface.fit(model::MLJFluxModel,
     X,
     y)
     # GPU and rng related variables
-    move = Mover(model.acceleration)
+    move = MLJFlux.Mover(model.acceleration)
     rng = true_rng(model)
 
     # Get input properties
     shape = MLJFlux.shape(model, X, y)
-    cat_inds = get_cat_inds(X)
+    cat_inds = MLJFlux.get_cat_inds(X)
     pure_continuous_input = isempty(cat_inds)
 
     # Decide whether to enable entity embeddings (e.g., ImageClassifier won't)
-    enable_entity_embs = is_embedding_enabled(model) && !pure_continuous_input
+    enable_entity_embs = MLJFlux.is_embedding_enabled(model) && !pure_continuous_input
 
     # Prepare entity embeddings inputs and encode X if entity embeddings enabled
     featnames = []
     if enable_entity_embs
-        X = convert_to_table(X)
+        X = MLJFlux.convert_to_table(X)
         featnames = Tables.schema(X).names
     end
 
     # entityprops is (index = cat_inds[i], levels = num_levels[i], newdim = newdims[i])
     # for each categorical feature
     default_embedding_dims = enable_entity_embs ? model.embedding_dims : Dict{Symbol, Real}()
     entityprops, entityemb_output_dim =
-        prepare_entityembs(X, featnames, cat_inds, default_embedding_dims)
-    X, ordinal_mappings = ordinal_encoder_fit_transform(X; featinds = cat_inds)
+        MLJFlux.prepare_entityembs(X, featnames, cat_inds, default_embedding_dims)
+    X, ordinal_mappings = MLJFlux.ordinal_encoder_fit_transform(X; featinds = cat_inds)
 
     ## Construct model chain
     chain =
         (!enable_entity_embs) ? construct_model_chain(model, rng, shape, move) :
-        construct_model_chain_with_entityembs(
+        MLJFlux.construct_model_chain_with_entityembs(
             model,
             rng,
             shape,
@@ -104,7 +104,7 @@ function MLJModelInterface.fit(model::MLJFluxModel,
         )
 
     # Format data as needed by Flux and move to GPU
-    data = move.(collate(model, X, y, verbosity))
+    data = move.(MLJFlux.collate(model, X, y, verbosity))
 
     # Test chain works (as it may be custom)
     x = data[1][1]

test/classifier.jl

@@ -4,18 +4,17 @@ seed!(1234)
 N = 300
 Xm = MLJBase.table(randn(Float32, N, 5));   # purely numeric
 X = (; Tables.columntable(Xm)...,
-    Column1 = repeat([1.0, 2.0, 3.0, 4.0, 5.0], Int(N / 5)),
+    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([1.0, 2.0, 3.0, 4.0, 5.0], Int(N / 5)),
-    Column5 = randn(N),
+    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)),
     ),
 )
 
-
-ycont = 2 * X.x1 - X.x3 + 0.1 * rand(N)
+ycont = 2 * X.x1 - X.x3 + 0.1 * rand(Float32, N)
 m, M = minimum(ycont), maximum(ycont)
 _, a, b, _ = range(m, stop = M, length = 4) |> collect
 y = map(ycont) do η
@@ -126,7 +125,7 @@ end
 seed!(1234)
 N = 300
 X = MLJBase.table(rand(Float32, N, 4));
-ycont = 2 * X.x1 - X.x3 + 0.1 * rand(N)
+ycont = Float32.(2 * X.x1 - X.x3 + 0.1 * rand(N))
 m, M = minimum(ycont), maximum(ycont)
 _, a, _ = range(m, stop = M, length = 3) |> collect
 y = map(ycont) do η

test/regressor.jl

@@ -3,11 +3,11 @@ Random.seed!(123)
 N = 200
 Xm = MLJBase.table(randn(Float32, N, 5));   # purely numeric
 X = (; Tables.columntable(Xm)...,
-    Column1 = repeat([1.0, 2.0, 3.0, 4.0, 5.0], Int(N / 5)),
+    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([1.0, 2.0, 3.0, 4.0, 5.0], Int(N / 5)),
-    Column5 = randn(N),
+    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)),
     ),
@@ -17,7 +17,7 @@ builder = MLJFlux.Short(σ = identity)
 optimiser = Optimisers.Adam()
 
 Random.seed!(123)
-y = 1 .+ X.x1 - X.x2 .- 2X.x4 + X.x5
+y = Float32(1) .+ X.x1 - X.x2 .- 2X.x4 + X.x5
 train, test = MLJBase.partition(1:N, 0.7)
 
 @testset_accelerated "NeuralNetworkRegressor" accel begin