copy predicted array so we dont overwrite used memory location (#188)

* copy predicted array so we dont overwrite used memory location

* use mersenee instead of xoshiro for 1.6

* add predict_nocopy and stop using transpose

Author: ExpandingMan

Project.toml

@@ -1,6 +1,6 @@
 name = "XGBoost"
 uuid = "009559a3-9522-5dbb-924b-0b6ed2b22bb9"
-version = "2.3.1"
+version = "2.3.2"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"

docs/src/api.md

@@ -39,6 +39,7 @@ Booster
 updateone!
 update!
 predict
+predict_nocopy
 setparam!
 setparams!
 getnrounds

src/booster.jl

@@ -275,29 +275,18 @@ function deserialize(::Type{Booster}, buf::AbstractVector{UInt8}, data=DMatrix[]
     deserialize!(b, buf)
 end
 
-# sadly this is type unstable because we might return a transpose
 """
-    predict(b::Booster, data; margin=false, training=false, ntree_limit=0)
+    predict_nocopy(b::Booster, data; kw...)
 
-Use the model `b` to run predictions on `data`.  This will return a `Vector{Float32}` which can be compared
-to training or test target data.
-
-If `ntree_limit > 0` only the first `ntree_limit` trees will be used in prediction.
-
-## Examples
-```julia
-(X, y) = (randn(100,3), randn(100))
-b = xgboost((X, y), 10)
-
-ŷ = predict(b, X)
-```
+Same as [`predict`](@ref), but the output array is not copied.  Data in the array output
+by this function may be overwritten by future calls to `predict_nocopy` or `predict`.
 """
-function predict(b::Booster, Xy::DMatrix;
-                 margin::Bool=false,  # whether to output margin
-                 training::Bool=false,
-                 ntree_lower_limit::Integer=0,
-                 ntree_limit::Integer=0,  # 0 corresponds to no limit
-                )
+function predict_nocopy(b::Booster, Xy::DMatrix;
+                        margin::Bool=false,  # whether to output margin
+                        training::Bool=false,
+                        ntree_lower_limit::Integer=0,
+                        ntree_limit::Integer=0,  # 0 corresponds to no limit
+                       )
     opts = Dict("type"=>(margin ? 1 : 0),
                 "iteration_begin"=>ntree_lower_limit,
                 "iteration_end"=>ntree_limit,
@@ -309,9 +298,31 @@ function predict(b::Booster, Xy::DMatrix;
     o = Ref{Ptr{Cfloat}}()
     xgbcall(XGBoosterPredictFromDMatrix, b.handle, Xy.handle, opts, oshape, odim, o)
     dims = reverse(unsafe_wrap(Array, oshape[], odim[]))
+    # this `copy` is needed because libxgboost re-uses the pointer
     o = unsafe_wrap(Array, o[], tuple(dims...))
-    length(dims) > 1 ? transpose(o) : o
+    length(dims) > 1 ? permutedims(o) : o
 end
+
+predict_nocopy(b::Booster, Xy; kw...) = predict_nocopy(b, DMatrix(Xy); kw...)
+
+"""
+    predict(b::Booster, data; margin=false, training=false, ntree_limit=0)
+
+Use the model `b` to run predictions on `data`.  This will return a `Vector{Float32}` which can be compared
+to training or test target data.
+
+If `ntree_limit > 0` only the first `ntree_limit` trees will be used in prediction.
+
+## Examples
+```julia
+(X, y) = (randn(100,3), randn(100))
+b = xgboost((X, y), 10)
+
+ŷ = predict(b, X)
+```
+"""
+predict(b::Booster, Xy::DMatrix; kw...) = copy(predict_nocopy(b, Xy; kw...))
+
 predict(b::Booster, Xy; kw...) = predict(b, DMatrix(Xy); kw...)
 
 function evaliter(b::Booster, watch, n::Integer=1)

test/runtests.jl

@@ -179,7 +179,7 @@ end
     @test Term.Panel(bst) isa Term.Panel
 end
 
-@testset "Booster Save/Load/Serialize" begin
+@testset "Booster" begin
     dtrain = XGBoost.load(DMatrix, testfilepath("agaricus.txt.train"))
     dtest = XGBoost.load(DMatrix, testfilepath("agaricus.txt.test"))
 
@@ -217,6 +217,14 @@ end
     bst2 = Booster(DMatrix[])
     XGBoost.deserialize!(bst2, bin)
     @test preds == predict(bst2, dtest)
+
+    # libxgboost re-uses the prediction memory location,
+    # so we are testing to make sure we don't do that
+    rng = MersenneTwister(999)  # note that Xoshiro is not available on 1.6
+    (X, y) = (randn(rng, 10,2), randn(rng, 10))
+    b = xgboost((X, y))
+    ŷ = predict(b, X)
+    @test predict(b, randn(MersenneTwister(998), 10,2)) ≠ ŷ
 end
 
 has_cuda() && @testset "cuda" begin