fix #274 (#275)

* fix #274

fix GPU preds

* fix #274

fix GPU preds

* fix #274

fix GPU preds

* fix #274

fix GPU preds

Author: jeremiedb

Project.toml

@@ -1,7 +1,7 @@
 name = "EvoTrees"
 uuid = "f6006082-12f8-11e9-0c9c-0d5d367ab1e5"
 authors = ["jeremiedb <[email protected]>"]
-version = "0.16.8"
+version = "0.16.9"
 
 [deps]
 BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"

ext/EvoTreesCUDAExt/init.jl

@@ -23,7 +23,7 @@ function EvoTrees.init_core(params::EvoTrees.EvoTypes{L}, ::Type{<:EvoTrees.GPU}
     elseif L == EvoTrees.MLogLoss
         if eltype(y_train) <: EvoTrees.CategoricalValue
             target_levels = EvoTrees.CategoricalArrays.levels(y_train)
-            target_isordered = isordered(y_train)
+            target_isordered = EvoTrees.isordered(y_train)
             y = UInt32.(EvoTrees.CategoricalArrays.levelcode.(y_train))
         elseif eltype(y_train) <: Integer || eltype(y_train) <: Bool || eltype(y_train) <: String || eltype(y_train) <: Char
             target_levels = sort(unique(y_train))

ext/EvoTreesCUDAExt/predict.jl

@@ -150,13 +150,13 @@ function EvoTrees.predict!(
 end
 
 # prediction for EvoTree model
-function predict(
-    m::EvoTree{L,K},
+function EvoTrees._predict(
+    m::EvoTrees.EvoTree{L,K},
     data,
     ::Type{<:EvoTrees.GPU};
     ntree_limit=length(m.trees)) where {L,K}
 
-    Tables.istable(data) ? data = Tables.columntable(data) : nothing
+    EvoTrees.Tables.istable(data) ? data = EvoTrees.Tables.columntable(data) : nothing
     ntrees = length(m.trees)
     ntree_limit > ntrees && error("ntree_limit is larger than number of trees $ntrees.")
     x_bin = CuArray(EvoTrees.binarize(data; fnames=m.info[:fnames], edges=m.info[:edges]))

src/models.jl

@@ -70,6 +70,10 @@ function EvoTreeRegressor(; kwargs...)
         :rng => 123,
     )
 
+    args_ignored = setdiff(keys(kwargs), keys(args))
+    length(args_ignored) > 0 &&
+        @info "The following kwargs are not supported and will be ignored: $(args_ignored)."
+
     args_override = intersect(keys(args), keys(kwargs))
     for arg in args_override
         args[arg] = kwargs[arg]
@@ -163,6 +167,10 @@ function EvoTreeCount(; kwargs...)
         :rng => 123,
     )
 
+    args_ignored = setdiff(keys(kwargs), keys(args))
+    length(args_ignored) > 0 &&
+        @info "The following kwargs are not supported and will be ignored: $(args_ignored)."
+
     args_override = intersect(keys(args), keys(kwargs))
     for arg in args_override
         args[arg] = kwargs[arg]
@@ -231,6 +239,10 @@ function EvoTreeClassifier(; kwargs...)
         :rng => 123,
     )
 
+    args_ignored = setdiff(keys(kwargs), keys(args))
+    length(args_ignored) > 0 &&
+        @info "The following kwargs are not supported and will be ignored: $(args_ignored)."
+
     args_override = intersect(keys(args), keys(kwargs))
     for arg in args_override
         args[arg] = kwargs[arg]
@@ -301,6 +313,10 @@ function EvoTreeMLE(; kwargs...)
         :rng => 123,
     )
 
+    args_ignored = setdiff(keys(kwargs), keys(args))
+    length(args_ignored) > 0 &&
+        @info "The following kwargs are not supported and will be ignored: $(args_ignored)."
+
     args_override = intersect(keys(args), keys(kwargs))
     for arg in args_override
         args[arg] = kwargs[arg]
@@ -387,6 +403,10 @@ function EvoTreeGaussian(; kwargs...)
         :rng => 123,
     )
 
+    args_ignored = setdiff(keys(kwargs), keys(args))
+    length(args_ignored) > 0 &&
+        @info "The following kwargs are not supported and will be ignored: $(args_ignored)."
+
     args_override = intersect(keys(args), keys(kwargs))
     for arg in args_override
         args[arg] = kwargs[arg]

src/predict.jl

@@ -74,16 +74,23 @@ function predict!(pred::Matrix{T}, tree::Tree{L,K}, x_bin::Matrix{UInt8}, featty
     return nothing
 end
 
+
 """
-    predict(model::EvoTree, X::AbstractMatrix; ntree_limit = length(model.trees))
+    predict(m::EvoTree, data; ntree_limit=length(m.trees), device=:cpu)
 
 Predictions from an EvoTree model - sums the predictions from all trees composing the model.
 Use `ntree_limit=N` to only predict with the first `N` trees.
 """
-function predict(
+function predict(m::EvoTree, data; ntree_limit=length(m.trees), device=:cpu)
+    @assert Symbol(device) ∈ [:cpu, :gpu]
+    _device = Symbol(device) == :cpu ? CPU : GPU
+    _predict(m, data, _device; ntree_limit)
+end
+
+function _predict(
     m::EvoTree{L,K},
     data,
-    ::Type{<:Device}=CPU;
+    ::Type{<:CPU};
     ntree_limit=length(m.trees)) where {L,K}
 
     Tables.istable(data) ? data = Tables.columntable(data) : nothing

src/structs.jl

@@ -87,12 +87,10 @@ struct EvoTree{L,K}
     trees::Vector{Tree{L,K}}
     info::Dict
 end
-# (m::EvoTree)(data, device::Type{D}=CPU; ntree_limit=length(m.trees)) where {D<:Device} =
-#     predict(m, data, device; ntree_limit)
-function (m::EvoTree)(data; ntree_limit=length(m.trees), device="cpu")
-    @assert string(device) ∈ ["cpu", "gpu"]
-    _device = string(device) == "cpu" ? CPU : GPU
-    return predict(m, data, _device; ntree_limit)
+function (m::EvoTree)(data; ntree_limit=length(m.trees), device=:cpu)
+    @assert Symbol(device) ∈ [:cpu, :gpu]
+    _device = Symbol(device) == :cpu ? CPU : GPU
+    return _predict(m, data, _device; ntree_limit)
 end
 
 _get_struct_loss(::EvoTree{L,K}) where {L,K} = L

test/issue-274.jl

@@ -0,0 +1,9 @@
+using CUDA
+using EvoTrees
+using CategoricalArrays
+
+x_train = x=rand(10, 2)
+y_train = categorical(rand("abc", 10), ordered=true)
+config = EvoTreeClassifier(; device=:gpu, L2=123)
+m = fit_evotree(config; x_train, y_train, device=:gpu)
+yhat = m(x_train; device=:gpu)