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Make covariance and correlation work for iterators, skipmissing in particular. #34

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Make covariance and correlation work for iterators, skipmissing in particular. #34

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1cdf046
Initial commit, `collects` everywhere
pdeffebach Apr 27, 2020
f3e9641
Add tests
pdeffebach Apr 27, 2020
2f9c4f8
Respond to comments
pdeffebach Apr 28, 2020
52c18ea
Apply suggestions from code review
pdeffebach Apr 28, 2020
4620247
more comments -- ready for review
pdeffebach Apr 28, 2020
b86ddba
fix deleted line
pdeffebach Apr 28, 2020
0221557
many more tests
pdeffebach Apr 29, 2020
e3bc3cc
Apply suggestions from code review
pdeffebach Apr 29, 2020
3493ed2
Polish up tests
pdeffebach Apr 29, 2020
b940ae1
Merge branch 'iterator_cor2' of https://github.com/pdeffebach/Statist…
pdeffebach Apr 29, 2020
8b49745
Errors with matrices
pdeffebach Apr 30, 2020
2b28908
Add _return_one method for DRY
pdeffebach Apr 30, 2020
e42c0b0
Put pack uuid
pdeffebach Apr 30, 2020
cb3020c
_lazycollect solution
pdeffebach Apr 30, 2020
36734bf
Apply suggestions from code review
pdeffebach Apr 30, 2020
2f1c404
change name of lazy_collect
pdeffebach Apr 30, 2020
4279703
get rid of unnecesary _vmean
pdeffebach Apr 30, 2020
b9f8f96
simplify error, add back uuid
pdeffebach Apr 30, 2020
14c5701
Futher simplify error
pdeffebach Apr 30, 2020
11bd8f5
add back uuid
pdeffebach Apr 30, 2020
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80 changes: 73 additions & 7 deletions src/Statistics.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -479,6 +479,17 @@ end
_vmean(x::AbstractVector, vardim::Int) = mean(x)
_vmean(x::AbstractMatrix, vardim::Int) = mean(x, dims=vardim)

_collect_if_itr(x::Any) = collect(x)
_collect_if_itr(x::AbstractVector) = x

function _matrix_error(x, y)
if !(x isa AbstractVector || y isa AbstractVector) && (x isa AbstractArray || y isa AbstractArray)
s = "Covariance and correlation between a non-vector array and a non-vector iterator" *
"is currently disallowed. `collect` one of the arguments."
throw(ArgumentError(s))
end
end

# core functions

unscaled_covzm(x::AbstractVector{<:Number}) = sum(abs2, x)
Expand All @@ -495,6 +506,7 @@ unscaled_covzm(x::AbstractMatrix, y::AbstractMatrix, vardim::Int) =

# covzm (with centered data)

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covzm(itr::Any; corrected::Bool = true) = covzm(collect(itr); corrected = corrected)
covzm(x::AbstractVector; corrected::Bool=true) = unscaled_covzm(x) / (length(x) - Int(corrected))
function covzm(x::AbstractMatrix, vardim::Int=1; corrected::Bool=true)
C = unscaled_covzm(x, vardim)
Expand All @@ -504,6 +516,10 @@ function covzm(x::AbstractMatrix, vardim::Int=1; corrected::Bool=true)
A .= A .* b
return A
end
function covzm(x::Any, y::Any; corrected::Bool = true)
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In what case covzm can get Any? It is an internal method and I thought it can only get already processed data.

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The same question applies to covm below.

_matrix_error(x, y)
covzm(_collect_if_itr(x), _collect_if_itr(y); corrected = corrected)
end
covzm(x::AbstractVector, y::AbstractVector; corrected::Bool=true) =
unscaled_covzm(x, y) / (length(x) - Int(corrected))
function covzm(x::AbstractVecOrMat, y::AbstractVecOrMat, vardim::Int=1; corrected::Bool=true)
Expand All @@ -518,22 +534,34 @@ end
# covm (with provided mean)
## Use map(t -> t - xmean, x) instead of x .- xmean to allow for Vector{Vector}
## which can't be handled by broadcast
covm(itr::Any, itrmean; corrected::Bool=true) =
covm(collect(itr), itrmean; corrected=corrected)
covm(x::AbstractVector, xmean; corrected::Bool=true) =
covzm(map(t -> t - xmean, x); corrected=corrected)
covm(x::AbstractMatrix, xmean, vardim::Int=1; corrected::Bool=true) =
covzm(x .- xmean, vardim; corrected=corrected)
function covm(x::Any, xmean, y::Any, ymean; corrected::Bool=true)
_matrix_error(x, y)
covzm(map(t -> t - xmean, x), map(t -> t - ymean, y); corrected=corrected)
end
covm(x::AbstractVector, xmean, y::AbstractVector, ymean; corrected::Bool=true) =
covzm(map(t -> t - xmean, x), map(t -> t - ymean, y); corrected=corrected)
covm(x::AbstractVecOrMat, xmean, y::AbstractVecOrMat, ymean, vardim::Int=1; corrected::Bool=true) =
covzm(x .- xmean, y .- ymean, vardim; corrected=corrected)

# cov (API)
"""
cov(x::AbstractVector; corrected::Bool=true)
cov(itr::Any; corrected::Bool=true)
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Compute the variance of the vector `x`. If `corrected` is `true` (the default) then the sum
is scaled with `n-1`, whereas the sum is scaled with `n` if `corrected` is `false` where `n = length(x)`.
Compute the variance of the iterator `itr`. If `corrected` is `true` (the default) then the sum
is scaled with `n-1`, whereas the sum is scaled with `n` if `corrected` is `false` where
``n`` is the number of elements.
"""
function cov(itr::Any; corrected::Bool=true)
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do we want to allow 0 or more than 2 dimensional arrays here?

x = collect(itr)
meanx = mean(x)
covzm(map!(t -> t - meanx, x, x); corrected=corrected)
end
cov(x::AbstractVector; corrected::Bool=true) = covm(x, mean(x); corrected=corrected)

"""
Expand All @@ -547,13 +575,23 @@ cov(X::AbstractMatrix; dims::Int=1, corrected::Bool=true) =
covm(X, _vmean(X, dims), dims; corrected=corrected)

"""
cov(x::AbstractVector, y::AbstractVector; corrected::Bool=true)
cov(x::Any, y::Any; corrected::Bool=true)

Compute the covariance between the vectors `x` and `y`. If `corrected` is `true` (the
Compute the covariance between the iterators `x` and `y`. If `corrected` is `true` (the
default), computes ``\\frac{1}{n-1}\\sum_{i=1}^n (x_i-\\bar x) (y_i-\\bar y)^*`` where
``*`` denotes the complex conjugate and `n = length(x) = length(y)`. If `corrected` is
``*`` denotes the complex conjugate and ``n`` the number of elements. If `corrected` is
`false`, computes ``\\frac{1}{n}\\sum_{i=1}^n (x_i-\\bar x) (y_i-\\bar y)^*``.
"""
function cov(x::Any, y::Any; corrected::Bool=true)
_matrix_error(x, y)
cx = collect(x)
cy = collect(y)
meanx = mean(cx)
meany = mean(cy)
dx = map!(t -> t - meanx, cx, cx)
dy = map!(t -> t - meany, cy, cy)
covzm(dx, dy; corrected=corrected)
end
cov(x::AbstractVector, y::AbstractVector; corrected::Bool=true) =
covm(x, mean(x), y, mean(y); corrected=corrected)

Expand Down Expand Up @@ -629,8 +667,17 @@ function cov2cor!(C::AbstractMatrix, xsd::AbstractArray, ysd::AbstractArray)
return C
end

function _return_one(itr)
if Base.IteratorEltype(itr) isa Base.HasEltype && isconcrete(eltype(itr))
return one(real(eltype(itr)))
else
return one(real(eltype(collect(itr))))
end
end

# corzm (non-exported, with centered data)

corzm(itr::Any) = _return_one(itr)
corzm(x::AbstractVector{T}) where {T} = one(real(T))
function corzm(x::AbstractMatrix, vardim::Int=1)
c = unscaled_covzm(x, vardim)
Expand All @@ -645,8 +692,13 @@ corzm(x::AbstractMatrix, y::AbstractMatrix, vardim::Int=1) =

# corm

corm(itr::Any, itrmean) = _return_one(itr)
corm(x::AbstractVector{T}, xmean) where {T} = one(real(T))
corm(x::AbstractMatrix, xmean, vardim::Int=1) = corzm(x .- xmean, vardim)
function corm(x::Any, mx, y::Any, my)
_matrix_error(x, y)
corm(_collect_if_itr(x), mx, _collect_if_itr(y), my)
end
function corm(x::AbstractVector, mx, y::AbstractVector, my)
require_one_based_indexing(x, y)
n = length(x)
Expand Down Expand Up @@ -675,10 +727,11 @@ corm(x::AbstractVecOrMat, xmean, y::AbstractVecOrMat, ymean, vardim::Int=1) =

# cor
"""
cor(x::AbstractVector)
cor(itr::Any)

Return the number one.
"""
cor(itr::Any) = _return_one(itr)
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If we touch this part of code then I do not understand the following (this is in general a separate PR, but we implement this method here, so I would like to clarify what is intended):

julia> x = rand(10, 2)
10×2 Array{Float64,2}:
 0.281236  0.0338547
 0.691944  0.830649
 0.627939  0.62187
 0.251539  0.162161
 0.649065  0.627302
 0.67754   0.227709
 0.904292  0.481443
 0.768511  0.439196
 0.56268   0.885131
 0.520348  0.0185026

julia> y = collect(eachrow(x))
10-element Array{SubArray{Float64,1,Array{Float64,2},Tuple{Int64,Base.Slice{Base.OneTo{Int64}}},true},1}:
 [0.28123641854321346, 0.03385469973171129]
 [0.6919437304763723, 0.8306494675149141]
 [0.6279387501997036, 0.6218700109315964]
 [0.2515386883173856, 0.16216070470557398]
 [0.6490648176650291, 0.6273015737594985]
 [0.6775400549397448, 0.22770867432380815]
 [0.9042917317032804, 0.4814426050177454]
 [0.7685107010600403, 0.4391959677108144]
 [0.562680390776801, 0.8851305746997942]
 [0.5203479821836228, 0.018502575073925165]

julia> cov(x)
2×2 Array{Float64,2}:
 0.0409994  0.0321085
 0.0321085  0.0983311

julia> cov(y)
2×2 Array{Float64,2}:
 0.0409994  0.0321085
 0.0321085  0.0983311

julia> cor(x)
2×2 Array{Float64,2}:
 1.0       0.505691
 0.505691  1.0

julia> cor(y)
ERROR: MethodError: no method matching zero(::Type{SubArray{Float64,1,Array{Float64,2},Tuple{Int64,Base.Slice{Base.OneTo{Int64}}},true}})

and I do not understand why cov and cor behave differently in how they handle x and y.

cor(x::AbstractVector) = one(real(eltype(x)))

"""
Expand All @@ -688,6 +741,19 @@ Compute the Pearson correlation matrix of the matrix `X` along the dimension `di
"""
cor(X::AbstractMatrix; dims::Int=1) = corm(X, _vmean(X, dims), dims)

"""
cor(x::Any, y::Any)

Compute the Pearson correlation between iterators `x` and `y`.
"""
function cor(x::Any, y::Any)
_matrix_error(x, y)
cx = _collect_if_itr(x)
cy = _collect_if_itr(y)

corm(cx, mean(cx), cy, mean(cy))
end

"""
cor(x::AbstractVector, y::AbstractVector)
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Remove this docstring which is a special case of the previous one.


Expand Down
49 changes: 45 additions & 4 deletions test/runtests.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -339,12 +339,17 @@ Y = [6.0 2.0;
x1 = vec(X[1,:])
y1 = vec(Y[1,:])
end
x1_itr = (x1i for x1i in x1)
y1_itr = skipmissing(y1)

c = zm ? Statistics.covm(x1, 0, corrected=cr) :
cov(x1, corrected=cr)
c_itr = zm ? Statistics.covm(x1_itr, 0, corrected=cr) :
cov(x1_itr, corrected=cr)
@test isa(c, Float64)
@test c ≈ Cxx[1,1]
@test c == c_itr == Cxx[1,1]
@inferred cov(x1, corrected=cr)
@inferred cov(x1_itr, corrected=cr)

@test cov(X) == Statistics.covm(X, mean(X, dims=1))
C = zm ? Statistics.covm(X, 0, vd, corrected=cr) :
Expand All @@ -356,9 +361,16 @@ Y = [6.0 2.0;
@test cov(x1, y1) == Statistics.covm(x1, mean(x1), y1, mean(y1))
c = zm ? Statistics.covm(x1, 0, y1, 0, corrected=cr) :
cov(x1, y1, corrected=cr)
c_itr = zm ? Statistics.covm(x1_itr, 0, y1_itr, 0, corrected=cr) :
cov(x1_itr, y1_itr, corrected=cr)
c_itrx = zm ? Statistics.covm(x1_itr, 0, y1, 0, corrected=cr) :
cov(x1_itr, y1, corrected=cr)
c_itry = zm ? Statistics.covm(x1, 0, y1_itr, 0, corrected=cr) :
cov(x1, y1_itr, corrected=cr)
@test isa(c, Float64)
@test c ≈ Cxy[1,1]
@test c == c_itr == c_itrx == c_itry == Cxy[1,1]
@inferred cov(x1, y1, corrected=cr)
@inferred cov(x1_itr, y1_itr, corrected=cr)

if vd == 1
@test cov(x1, Y) == Statistics.covm(x1, mean(x1), Y, mean(Y, dims=1))
Expand Down Expand Up @@ -386,6 +398,17 @@ Y = [6.0 2.0;
@inferred cov(X, Y, dims=vd, corrected=cr)
end

@testset "errors for `cov` with non-array iterators and matrices" begin
x1_itr = (xi for xi in X[:, 1])
y1_itr = skipmissing(Y[:, 1])
@test_throws ArgumentError Statistics.covzm(X, y1_itr)
@test_throws ArgumentError Statistics.covzm(x1_itr, Y)
@test_throws ArgumentError Statistics.covm(X, mean(X, dims = 1), y1_itr, mean(y1_itr))
@test_throws ArgumentError Statistics.covm(x1_itr, mean(x1_itr), Y, mean(Y, dims = 1))
@test_throws ArgumentError cov(X, y1_itr)
@test_throws ArgumentError cov(x1_itr, Y)
end

@testset "floating point accuracy for `cov` of large numbers" begin
A = [4.0, 7.0, 13.0, 16.0]
C = A .+ 1.0e10
Expand Down Expand Up @@ -426,11 +449,15 @@ end
x1 = vec(X[1,:])
y1 = vec(Y[1,:])
end
x1_itr = (x1i for x1i in x1)
y1_itr = skipmissing(y1)

c = zm ? Statistics.corm(x1, 0) : cor(x1)
c_itr = zm ? Statistics.corm(x1_itr, 0) : cor(x1_itr)
@test isa(c, Float64)
@test c ≈ Cxx[1,1]
@test c ≈ c_itr ≈ Cxx[1,1]
@inferred cor(x1)
@inferred cor(x1_itr)

@test cor(X) == Statistics.corm(X, mean(X, dims=1))
C = zm ? Statistics.corm(X, 0, vd) : cor(X, dims=vd)
Expand All @@ -440,9 +467,14 @@ end

@test cor(x1, y1) == Statistics.corm(x1, mean(x1), y1, mean(y1))
c = zm ? Statistics.corm(x1, 0, y1, 0) : cor(x1, y1)
c_itr = zm ? Statistics.corm(x1_itr, 0, y1_itr, 0) : cor(x1_itr, y1_itr)
c_itrx = zm ? Statistics.corm(x1_itr, 0, y1, 0) : cor(x1_itr, y1)
c_itry = zm ? Statistics.corm(x1, 0, y1_itr, 0) : cor(x1, y1_itr)

@test isa(c, Float64)
@test c ≈ Cxy[1,1]
@test c == c_itr == c_itrx == c_itry ≈ Cxy[1,1]
@inferred cor(x1, y1)
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@inferred cor(x1_itr, y1_itr)

if vd == 1
@test cor(x1, Y) == Statistics.corm(x1, mean(x1), Y, mean(Y, dims=1))
Expand Down Expand Up @@ -477,6 +509,15 @@ end
@test cor(tmp, tmp) <= 1.0
@test cor(tmp, tmp2) <= 1.0
end

@testset "errors for `cor` with non-array iterators and matrices" begin
x1_itr = (xi for xi in X[:, 1])
y1_itr = skipmissing(Y[:, 1])
@test_throws ArgumentError Statistics.corm(X, mean(X, dims = 1), y1_itr, mean(y1_itr))
@test_throws ArgumentError Statistics.corm(x1_itr, mean(x1_itr), Y, mean(Y, dims = 1))
@test_throws ArgumentError cor(X, y1_itr)
@test_throws ArgumentError cor(x1_itr, Y)
end
end

@testset "quantile" begin
Expand Down