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get rid of CategoricalArrays #157

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merged 8 commits into from
Nov 11, 2019
Merged

get rid of CategoricalArrays #157

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0130fa1
get rid of CategoricalArrays
kleinschmidt Oct 2, 2019
53a131c
bump version to 0.6.6 and add DataAPI (compat=1.1)
kleinschmidt Oct 2, 2019
03b4290
DataAPI doesn't export anything so we need to import levels
kleinschmidt Oct 3, 2019
a28dfb6
use broadcast to hit optimized method for CategoricalArrays
kleinschmidt Oct 16, 2019
e6dd17f
0.6.6 was already tagged, so this would be 0.6.7
kleinschmidt Oct 16, 2019
1361cfc
actually do the broadcasting correctly
kleinschmidt Oct 16, 2019
457c800
Merge remote-tracking branch 'origin/master' into drop-categorical-ar…
kleinschmidt Oct 16, 2019
b566f35
restore fix that I clobbered trying to merge master
kleinschmidt Oct 16, 2019
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10 changes: 6 additions & 4 deletions Project.toml
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Original file line number Diff line number Diff line change
@@ -1,9 +1,9 @@
name = "StatsModels"
uuid = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
version = "0.6.4"
version = "0.6.7"

[deps]
CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
DataAPI = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a"
DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
ShiftedArrays = "1277b4bf-5013-50f5-be3d-901d8477a67a"
Expand All @@ -12,13 +12,15 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"

[compat]
StatsBase = "≥0.22.0"
julia = "1"
DataAPI = "1.1"
StatsBase = "≥0.22.0"

[extras]
CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[targets]
test = ["DataFrames", "Statistics", "Test"]
test = ["CategoricalArrays", "DataFrames", "Statistics", "Test"]
2 changes: 1 addition & 1 deletion src/StatsModels.jl
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Expand Up @@ -3,8 +3,8 @@ module StatsModels
using Tables
using StatsBase
using ShiftedArrays: lag, lead
using CategoricalArrays
using DataStructures
using DataAPI: levels

using SparseArrays
using LinearAlgebra
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10 changes: 1 addition & 9 deletions src/modelframe.jl
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Expand Up @@ -47,17 +47,9 @@ end
function _nonmissing!(res, col)
# workaround until JuliaLang/julia#21256 is fixed
eltype(col) >: Missing || return

@inbounds for (i, el) in enumerate(col)
res[i] &= !ismissing(el)
end
res .&= .!ismissing.(col)
end

function _nonmissing!(res, col::CategoricalArray{>: Missing})
@inbounds for (i, el) in enumerate(col.refs)
res[i] &= el > 0
end
end

function missing_omit(d::T) where T<:ColumnTable
nonmissings = trues(length(first(d)))
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6 changes: 3 additions & 3 deletions src/statsmodel.jl
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Expand Up @@ -132,19 +132,19 @@ StatsBase.r2(mm::TableRegressionModel, variant::Symbol) = r2(mm.model, variant)
StatsBase.adjr2(mm::TableRegressionModel, variant::Symbol) = adjr2(mm.model, variant)

function _return_predictions(yp::AbstractVector, nonmissings, len)
out = missings(eltype(yp), len)
out = Vector{Union{eltype(yp),Missing}}(missing, len)
out[nonmissings] = yp
out
end

function _return_predictions(yp::AbstractMatrix, nonmissings, len)
out = missings(eltype(yp), (len, 3))
out = Matrix{Union{eltype(yp),Missing}}(missing, len, 3)
out[nonmissings, :] = yp
DataFrame(prediction = out[:,1], lower = out[:,2], upper = out[:,3])
end

function _return_predictions(yp::NamedTuple, nonmissings, len)
y = missings(eltype(yp[:prediction]), len)
y = Vector{Union{eltype(yp.prediction),Missing}}(missing, len)
l, h = similar(y), similar(y)
out = (prediction = y, lower = l, upper = h)
for key in (:prediction, :lower, :upper)
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