More sophisticated example of learning networks #9

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1. tlienart commented on Oct 22, 2019

   

   tlienart

   on Oct 22, 2019

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   Hmm the KNNRidge blend example is actually just that. But I'd still be curious to understand what I did wrong above
2. ablaom commented on Oct 23, 2019

   

   ablaom

   on Oct 23, 2019

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   • Your problem: LinearRegressor() expects a table but it is getting a matrix here. So you need to insert an MLJTable.

   • Another problem: You can't apply the inverse_transform directly to ẑ because it makes probablisitic predictions. So you need to insert a mean. Sadly, mean is not overloaded for nodes, so you need to use the node method to do this yourself

   The following code works:

   using MLJ
   
   Xr, yr = @load_boston
   zr = rand(length(yr))
   
   X = source(Xr)
   y = source(yr)
   z = source(zr)
   
   W = X |> Standardizer()
   z = y |> UnivariateBoxCoxTransformer()
   ẑ₁ = (W, z) |> @load RidgeRegressor pkg=MultivariateStats
   ẑ₂ = (W, z) |> @load DecisionTreeRegressor
   R = MLJ.table(hcat(ẑ₁, ẑ₂))
   ẑ_prob = (R, z) |> @load LinearRegressor pkg=GLM
   ẑ = node(v->mean.(v), ẑ_prob)
   ŷ = ẑ |> inverse_transform(z)
   fit!(ŷ)
   ŷ()
   

   Side comment: A great non-trivial example is stacking, as suggested by the diagram on the MLJ Readme.
3. ablaom commented on Oct 23, 2019

   

   ablaom

   on Oct 23, 2019

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   Or you could have used predict_mean but then you couldn't use the arrow syntax, I guess.
4. tlienart

   added

   tutorial

   on Nov 29, 2019