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Use std of 0 for singleton vectors #90

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Use std of 0 for singleton vectors #90

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05b2113
Use std of 0 for singleton vectors
bencottier May 11, 2021
1a8183b
Add test for singleton vector
bencottier May 11, 2021
4b6b514
Bump version
bencottier May 11, 2021
e402c78
Add bracket
bencottier May 11, 2021
5ce668d
Expose corrected kwarg as alternative
bencottier May 12, 2021
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2 changes: 1 addition & 1 deletion Project.toml
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
name = "FeatureTransforms"
uuid = "8fd68953-04b8-4117-ac19-158bf6de9782"
authors = ["Invenia Technical Computing Corporation"]
version = "0.3.6"
version = "0.3.7"

[deps]
Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
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3 changes: 2 additions & 1 deletion src/scaling.jl
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Expand Up @@ -68,7 +68,8 @@ struct MeanStdScaling <: AbstractScaling
end
end

compute_stats(x) = (mean(x), std(x))
# Set std to 0 using corrected=false if x is a singleton
compute_stats(x) = (mean(x), std(x; corrected=(length(x) != 1)))
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maybe it's better to pass this via MeanStdScaling as a kwarg? since it's already exposed via Statistics.std anyway?

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@bencottier bencottier May 11, 2021 •
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I see where you're coming from by leaving it up to the user. I guess it would just be inconvenient in the case that came up for me. I would rather change my downstream test case so that the data doesn't end up being one row, than add corrected=(length(x) != 1) to MeanStdScaling in the transform pipeline, in the source code.

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yeah I think it's better to not make those kinds of assumptions in the code here, especially when Statistics.std doesn't. In the instance of edge-case we just have to handle it on that end.


function _apply(A::AbstractArray, scaling::MeanStdScaling; inverse=false, eps=1e-3, kwargs...)
inverse && return scaling.μ .+ scaling.σ .* A
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6 changes: 6 additions & 0 deletions test/scaling.jl
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Expand Up @@ -312,6 +312,12 @@
scaling = MeanStdScaling(x)
@test FeatureTransforms.apply_append(x, scaling, append_dim=1) == vcat(x, expected)
end

@testset "singleton" begin
x = [2.]
scaling = MeanStdScaling(x)
@test FeatureTransforms.apply(x, scaling) == [0.]
end
end

@testset "Matrix" begin
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