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fix dataframe api changes in concatenating vectors with dataframes #19

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Sep 2, 2021
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fix dataframe api changes in concatenating vectors with dataframes #19

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18 changes: 15 additions & 3 deletions src/pipelines.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -188,13 +188,25 @@ function transform!(pipe::ComboPipeline, features::DataFrame=DataFrame())::Union
new_instances = DataFrame()
for t_index in eachindex(machines)
machine = machines[t_index]
current_instances = transform!(machine, instances)
new_instances = hcat(new_instances,current_instances,makeunique=true)
current_instances = transform!(machine, instances)
new_instances = mcat(new_instances,current_instances)
end

return new_instances
end

# dispatch concat between vectors/dataframes
function mcat(x::DataFrame,y::DataFrame)
hcat(x,y,makeunique=true)
end

function mcat(x::DataFrame,y::Vector)
hcat(x,DataFrame(v=y),makeunique=true)
end

function mcat(x::Vector,y::DataFrame)
hcat(DataFrame(v=x),y,makeunique=true)
end

function transform(pipe::ComboPipeline, features::DataFrame=DataFrame())::Union{Vector,DataFrame}
return transform!(pipe, features)
end
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6 changes: 3 additions & 3 deletions test/test_featureselector.jl
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Expand Up @@ -21,16 +21,16 @@ function iris_test()
@test (fit_transform(catf,X) .== X[:,5]) |> Matrix |> sum == 150
@test (fit_transform!(numf,X) .== X[:,1:4]) |> Matrix |> sum == 600
@test (fit_transform(numf,X) .== X[:,1:4]) |> Matrix |> sum == 600
catnumdata = hcat(X,repeat([1,2,3,4,5],30))
catnumdata = hcat(X,DataFrame(x1=repeat([1,2,3,4,5],30)))
catnum = CatNumDiscriminator()
res = fit_transform!(catnum,catnumdata)
@test infer_eltype(catnumdata[:,[2,4,6]]) <: Number
@test infer_eltype(res[:,[2,4,6]]) <: String
catnumdata = hcat(X,repeat([1,2,3,4,5],30))
catnumdata = hcat(X,DataFrame(x1=repeat([1,2,3,4,5],30)))
catnum = CatNumDiscriminator(0)
res = fit_transform!(catnum,catnumdata)
@test eltype(res[:,6]) <: Number
catnumdata = hcat(X,repeat([1,2,3,4,5],30))
catnumdata = hcat(X,DataFrame(x1=repeat([1,2,3,4,5],30)))
res1 = fit_transform(catnum,catnumdata)
@test eltype(res1[:,6]) <: Number
end
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16 changes: 15 additions & 1 deletion test/test_pipeline.jl
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Expand Up @@ -19,6 +19,10 @@ const noop = Identity()
const rf = RandomForest()
const ada = Adaboost()
const pt = PrunedTree()
const numf = NumFeatureSelector()
const catf = CatFeatureSelector()

acc(X,Y) = score(:accuracy,X,Y)

function test_pipeline()
# test initialization of types
Expand Down Expand Up @@ -58,7 +62,6 @@ end
test_pipeline()
end

acc(X,Y) = score(:accuracy,X,Y)

function test_sympipeline()
pcombo5 = @pipeline :((ohe + noop) |> (ada * rf * pt))
Expand All @@ -78,6 +81,17 @@ end
test_sympipeline()
end

function test_advancedpipeline()
expr = @pipeline ((((ada) + (rf) ) |> ohe) + numf) |> ada;
@test crossvalidate(expr,X,Y,acc,5,false).mean >= 0.90
end
@testset "Advanced Pipeline: Learners as filters" begin
Random.seed!(123)
test_advancedpipeline()
end



function test_pipeline()
# test symbolic pipeline expression
pcombo2 = @pipeline ohe + noop
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