Comments (3)
hmm... going to look tomorrow, but that's the idea... the multitarget one does support y as a nxd matrix...
from betaml.jl.
Indeed, corrected.... MultitargetNeuralNetworkRegressor
works only for tabular Y, while vector Y are estimated using NeuralNetworkRegressor
.
In the code it was:
target_scitype = Union{AbstractVector{<: Union{MMI.Continuous,MMI.Count}},AbstractMatrix{<: Union{MMI.Continuous,MMI.Count}}}
now it is:
target_scitype = AbstractMatrix{<: Union{MMI.Continuous,MMI.Count}}
The above example works:
julia> using BetaML
julia> import MLJBase
julia> const Mlj = MLJBase
MLJBase
julia> model = BetaML.Nn.MultitargetNeuralNetworkRegressor();
julia> X, y = Mlj.make_regression(); # y is vector here
julia> mach = Mlj.machine(model, X, [y y]) # [y y ] is a Matrix
untrained Machine; caches model-specific representations of data
model: MultitargetNeuralNetworkRegressor(layers = nothing, …)
args:
1: Source @151 ⏎ ScientificTypesBase.Table{AbstractVector{ScientificTypesBase.Continuous}}
2: Source @448 ⏎ AbstractMatrix{ScientificTypesBase.Continuous}
julia> Mlj.fit!(mach)
[ Info: Training machine(MultitargetNeuralNetworkRegressor(layers = nothing, …), …).
***
*** Training for 100 epochs with algorithm ADAM.
Training.. avg ϵ on (Epoch 1 Batch 3): 2.5587421021877805
Training of 100 epoch completed. Final epoch error: 2.7066215590008897.
trained Machine; caches model-specific representations of data
model: MultitargetNeuralNetworkRegressor(layers = nothing, …)
args:
1: Source @151 ⏎ ScientificTypesBase.Table{AbstractVector{ScientificTypesBase.Continuous}}
2: Source @448 ⏎ AbstractMatrix{ScientificTypesBase.Continuous}
from betaml.jl.
Okay. That sounds like a resolution to me, thanks.
from betaml.jl.
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from betaml.jl.