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Optimize completecases to process only missingable columns #2726

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May 7, 2021
Merged

Optimize completecases to process only missingable columns #2726

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e642415
completecases process only missingable cols
pstorozenko Apr 17, 2021
c4e07f7
Split oprations in completecases
pstorozenko Apr 18, 2021
79e34df
Some tests for completecases added
pstorozenko May 2, 2021
7bb1ee5
Comment added
pstorozenko May 2, 2021
0cbf724
Update test/data.jl
bkamins May 2, 2021
1f2fb7f
Apply suggestions from code review
bkamins May 3, 2021
920bf79
Update test/data.jl
bkamins May 5, 2021
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22 changes: 18 additions & 4 deletions src/abstractdataframe/abstractdataframe.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -761,14 +761,28 @@ function completecases(df::AbstractDataFrame, col::Colon=:)
"data frame with no columns"))
end
res = trues(size(df, 1))
aux = BitVector(undef, size(df, 1))
for i in 1:size(df, 2)
res .&= .!ismissing.(df[!, i])
v = df[!, i]
if Missing <: eltype(v)
# Disable fused broadcasting as it happens to be much slower
aux .= .!ismissing.(v)
res .&= aux
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end
end
res
return res
end

completecases(df::AbstractDataFrame, col::ColumnIndex) =
.!ismissing.(df[!, col])
function completecases(df::AbstractDataFrame, col::ColumnIndex)
v = df[!, col]
if Missing <: eltype(v)
res = BitVector(undef, size(df, 1))
res .= .!ismissing.(v)
return res
Comment on lines +779 to +781
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Why not just this?

Suggested change
res = BitVector(undef, size(df, 1))
res .= .!ismissing.(v)
return res
return .!ismissing.(v)

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because it is not type stable. I have just reversed this. The current design (with res) has no performance penalty, but pasess @inferred.

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That's surprising. How about just adding ::BitVector in the function definition? That would make it clearer that the goal is to fix inference.

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I thought you would ask about it :).

The ::BitVector annotation could potentially allocate once more by converting Vector{Bool} to BitVector while what I do should guarantee only that only one allocation happens because broadcasting assignment does copyto! of Broadcasted into target.

Here is an example:

julia> f1(x::AbstractVector)::BitVector = .!ismissing.(x)
f1 (generic function with 1 method)

julia> f2(x::AbstractVector) = (res = BitVector(undef, length(x)); res .= .!ismissing.(x); return x)
f2 (generic function with 1 method)

julia> using SparseArrays

julia> x = sparse(1:10^7);

julia> using BenchmarkTools

julia> f1(x::AbstractVector)::BitVector = .!ismissing.(x)
f1 (generic function with 1 method)

julia> @btime f1($x);
  568.808 ms (7 allocations: 87.02 MiB)

julia> @btime f2($x);
  525.133 ms (4 allocations: 1.20 MiB)

else
return trues(size(df, 1))
end
end

completecases(df::AbstractDataFrame, cols::MultiColumnIndex) =
completecases(df[!, cols])
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15 changes: 13 additions & 2 deletions test/data.jl
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Expand Up @@ -114,8 +114,17 @@ end
:auto)
df2 = DataFrame([Union{Int, Missing}[1, 2, 3, 4], ["one", "two", missing, "four"]],
:auto)

@test df2[completecases(df2), :] == df2[[1, 2, 4], :]
df3 = DataFrame(x = Int[1, 2, 3, 4], y = Union{Int, Missing}[1, missing, 2, 3],
z = Missing[missing, missing, missing, missing])

@test completecases(df2) == .!ismissing.(df2.x2)
@test @inferred(completecases(df3, :x)) == trues(nrow(df3))
@test completecases(df3, :y) == .!ismissing.(df3.y)
@test completecases(df3, :z) == completecases(df3, [:z, :x]) ==
completecases(df3, [:x, :z]) == completecases(df3, [:y, :x, :z]) ==
falses(nrow(df3))
@test @inferred(completecases(df3, [:y, :x])) ==
completecases(df3, [:x, :y]) == .!ismissing.(df3.y)
@test dropmissing(df2) == df2[[1, 2, 4], :]
returned = dropmissing(df1)
@test df1 == returned && df1 !== returned
Expand All @@ -127,7 +136,9 @@ end
@test df1b == df1

@test_throws ArgumentError completecases(DataFrame())
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@test_throws ArgumentError completecases(DataFrame(x=1:3), Cols())
@test_throws MethodError completecases(DataFrame(x=1), true)
@test_throws ArgumentError completecases(df3, :a)

for cols in (:x2, "x2", [:x2], ["x2"], [:x1, :x2], ["x1", "x2"], 2, [2], 1:2,
[true, true], [false, true], :,
Expand Down