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Add handling of an empty iterator for mean and var #29033

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merged 5 commits into from
Oct 11, 2018
Merged

Add handling of an empty iterator for mean and var #29033

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4f9028c
add handling of an empty iterator for mean and var
bkamins Sep 4, 2018
40b2ea7
additional tests
bkamins Sep 4, 2018
9e0b0ba
further cleanup of tests
bkamins Sep 4, 2018
0487d17
change to oftype and define mean of empty range
bkamins Sep 14, 2018
bff28ee
remove module qualifier
bkamins Sep 14, 2018
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6 changes: 4 additions & 2 deletions stdlib/Statistics/src/Statistics.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -57,7 +57,8 @@ julia> mean([√1, √2, √3])
function mean(f::Base.Callable, itr)
y = iterate(itr)
if y === nothing
throw(ArgumentError("mean of empty collection undefined: $(repr(itr))"))
return Base.mapreduce_empty_iter(f, Base.add_sum, itr,
Base.IteratorEltype(itr)) / 0
end
count = 1
value, state = y
Expand Down Expand Up @@ -148,7 +149,8 @@ var(iterable; corrected::Bool=true, mean=nothing) = _var(iterable, corrected, me
function _var(iterable, corrected::Bool, mean)
y = iterate(iterable)
if y === nothing
throw(ArgumentError("variance of empty collection undefined: $(repr(iterable))"))
T = eltype(iterable)
return typeof((abs2(zero(T)) + abs2(zero(T)))/2)(NaN)
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@nalimilan nalimilan Sep 4, 2018 •
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So this throws an error when calling zero if eltype returns Any. Maybe better call throw a more explicit error similar to the existing one?

Could use oftype.

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I would leave it as is, because this is how var for AbstractArray is implemented and we want to be non-breaking (so it should have 100% the same behavior - the same outputs and the same errors - so using array or iterator is transparent).

For sure in Julia 2.0 approach we could consider cleaning it up in both places.

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I just mean that instead of letting zero throw the error, it would be clearer to check T === Any and throw a more explicit error.

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The problem is that T does not have to be Any. It can be an arbitrary type that is or is not a subtype of Number. And upfront we do not know if this type:

  • defines zero
  • has abs2 defined
  • implements a constructor from NaN

Any of those may or may not be true so the only way to be consistent is to repeat the same code that is used for a version of var defined for arrays (otherwise I will be able to create a situation where array and iterator var differs).

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OK. We could still print a nice error in the most common case, but then we'd lose consistency.

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This is the point for now. As discussed in Julia 2.0 we can probably clean it up in both methods.

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personally, would use oftype as @nalimilan suggested for clarity

end
count = 1
value, state = y
Expand Down
7 changes: 7 additions & 0 deletions stdlib/Statistics/test/runtests.jl
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Expand Up @@ -86,6 +86,10 @@ end
@test ismissing(mean([missing, NaN]))
@test isequal(mean([missing 1.0; 2.0 3.0], dims=1), [missing 2.0])
@test mean(skipmissing([1, missing, 2])) === 1.5
@test isequal(mean(Complex{Float64}[]), NaN+NaN*im)
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It would also make sense to test that an empty Int input gives a Float64 output, and that an error is thrown when the element type isn't known (e.g. using a generator).

BTW, you could probably use ===.

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This PR is in muddy waters :) You cannot use === as we have different NaN in Julia and here it would fail. Notice that:

julia> NaN === -NaN
false

and this is exactly the case here, that is why I had to use isequal.

I will add the tests.

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I must be missing something: you really expect NaN::Float64 here, right?

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The situation is more complex (and that is why I have used this as a test):

julia> x = mean(Complex{Float64}[])
NaN + NaN*im

julia> bitstring(x.re)
"1111111111111000000000000000000000000000000000000000000000000000"

julia> bitstring(x.im)
"1111111111111000000000000000000000000000000000000000000000000000"

julia> bitstring(NaN) # see that the bitstring is different
"0111111111111000000000000000000000000000000000000000000000000000"

also you have:

julia> x = mean(Int[])
NaN

julia> x === NaN
false

For the same reason that we have different NaNs in Julia (and as you see they actually happen in practice).

@test isequal(mean(skipmissing(Complex{Float64}[])), NaN+NaN*im)
@test isequal(mean(abs, Complex{Float64}[]), NaN)
@test isequal(mean(abs, skipmissing(Complex{Float64}[])), NaN)

# Check that small types are accumulated using wider type
for T in (Int8, UInt8)
Expand Down Expand Up @@ -245,6 +249,9 @@ end
@test ismissing(f([missing, NaN], missing))
@test f(skipmissing([1, missing, 2]), 0) === f([1, 2], 0)
end

@test isequal(mean(Complex{Float64}[]), NaN)
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I guess you intended to test var?

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Sure. Fixed.

@test isequal(mean(skipmissing(Complex{Float64}[])), NaN)
end

function safe_cov(x, y, zm::Bool, cr::Bool)
Expand Down