https://github.com/javierbarbero/DataEnvelopmentAnalysis.jl
Tip revision: d4c8831ef80541606f746c2896179cf2d227f6bd authored by Javier Barbero on 12 January 2022, 19:01:53 UTC
Version 0.7.0
Version 0.7.0
Tip revision: d4c8831
deabigdata.jl
# Tests for Big Data Radial DEA Models
@testset "BigData RadialDEAModel" begin
## Test Radial DEA Models with FLS Book data
X = [5 13; 16 12; 16 26; 17 15; 18 14; 23 6; 25 10; 27 22; 37 14; 42 25; 5 17]
Y = [12; 14; 25; 26; 8; 9; 27; 30; 31; 26; 12]
# Input oriented CRS
deaio = deabigdata(X, Y, orient = :Input, rts = :CRS)
@test typeof(deaio) == RadialDEAModel
@test nobs(deaio) == 11
@test ninputs(deaio) == 2
@test noutputs(deaio) == 1
@test efficiency(deaio) ≈ [1.0000000000;
0.6222896791;
0.8198562444;
1.0000000000;
0.3103709311;
0.5555555556;
1.0000000000;
0.7576690896;
0.8201058201;
0.4905660377;
1.0000000000]
@test convert(Matrix, peers(deaio)) ≈
[1.000000000 0 0 0.0000000000 0 0 0.00000000000 0 0 0 0;
0.000000000 0 0 0.4249783174 0 0 0.10928013877 0 0 0 0;
1.134321653 0 0 0.4380053908 0 0 0.00000000000 0 0 0 0;
0.000000000 0 0 1.0000000000 0 0 0.00000000000 0 0 0 0;
0.000000000 0 0 0.2573807721 0 0 0.04844814534 0 0 0 0;
0.000000000 0 0 0.0000000000 0 0 0.33333333333 0 0 0 0;
0.000000000 0 0 0.0000000000 0 0 1.00000000000 0 0 0 0;
0.000000000 0 0 1.0348650979 0 0 0.11457435013 0 0 0 0;
0.000000000 0 0 0.0000000000 0 0 1.14814814815 0 0 0 0;
0.000000000 0 0 0.4905660377 0 0 0.49056603774 0 0 0 0;
0.000000000 0 0 0.0000000000 0 0 0.00000000000 0 0 0 1.000000000] atol = 1e-8
@test slacks(deaio, :X) ≈ [0.000000000 0;
0.000000000 0;
0.000000000 0;
0.000000000 0;
0.000000000 0;
4.444444444 0;
0.000000000 0;
0.000000000 0;
1.640211640 0;
0.000000000 0;
0.000000000 4]
@test slacks(deaio, :Y) ≈ zeros(11)
@test efficiency(deabigdata(targets(deaio, :X), targets(deaio, :Y), orient = :Input, rts = :CRS, slack = false)) ≈ ones(11)
@test efficiency(deaadd(targets(deaio, :X), targets(deaio, :Y))) ≈ zeros(11) atol=1e-14
# Otuput oriented CRS
deaoo = deabigdata(X, Y, orient = :Output, rts = :CRS)
@test nobs(deaoo) == 11
@test ninputs(deaoo) == 2
@test noutputs(deaoo) == 1
@test efficiency(deaoo) ≈ [1.0000000000;
1.606968641;
1.219726027;
1.0000000000;
3.221951220;
1.800000000;
1.0000000000;
1.319837398;
1.219354839;
2.038461538;
1.0000000000]
@test convert(Matrix, peers(deaoo)) ≈
[1.000000000 0 0 0.0000000000 0 0 0.00000000000 0 0 0 0;
0.000000000 0 0 0.6829268293 0 0 0.1756097561 0 0 0 0;
1.383561644 0 0 0.5342465753 0 0 0.00000000000 0 0 0 0;
0.000000000 0 0 1.0000000000 0 0 0.00000000000 0 0 0 0;
0.000000000 0 0 0.8292682927 0 0 0.1560975610 0 0 0 0;
0.000000000 0 0 0.0000000000 0 0 0.6000000000 0 0 0 0;
0.000000000 0 0 0.0000000000 0 0 1.00000000000 0 0 0 0;
0.000000000 0 0 1.3658536585 0 0 0.1512195122 0 0 0 0;
0.000000000 0 0 0.0000000000 0 0 1.4000000000 0 0 0 0;
0.000000000 0 0 1.0000000000 0 0 1.0000000000 0 0 0 0;
1.000000000 0 0 0.0000000000 0 0 0.00000000000 0 0 0 0]
@test slacks(deaoo, :X) ≈ [0.000000000 0;
0.000000000 0;
0.000000000 0;
0.000000000 0;
0.000000000 0;
8 0;
0.000000000 0;
0.000000000 0;
2 0;
0.000000000 0;
0.000000000 4]
@test slacks(deaoo, :Y) ≈ zeros(11)
@test efficiency(deabigdata(targets(deaoo, :X), targets(deaoo, :Y), orient = :Output, rts = :CRS, slack = false)) ≈ ones(11)
@test efficiency(deaadd(targets(deaoo, :X), targets(deaoo, :Y))) ≈ zeros(11) atol=1e-10
# Input oriented VRS
deaiovrs = deabigdata(X, Y, orient = :Input, rts = :VRS)
@test nobs(deaiovrs) == 11
@test ninputs(deaiovrs) == 2
@test noutputs(deaiovrs) == 1
@test efficiency(deaiovrs) ≈ [1.0000000000;
0.8699861687;
1.0000000000;
1.0000000000;
0.7116402116;
1.0000000000;
1.0000000000;
1.0000000000;
1.0000000000;
0.4931209269;
1.0000000000]
@test convert(Matrix, peers(deaiovrs)) ≈
[1.000000000 0 0 0.0000000000 0 0.00000000000 0.00000000000 0 0 0 0;
0.52558782849 0 0 0.0000000000 0 0.2842323651 0.1901798064 0 0 0 0;
0.000000000 0 1 0.0000000000 0 0.00000000000 0.00000000000 0 0 0 0;
0.000000000 0 0 1.0000000000 0 0.00000000000 0.00000000000 0 0 0 0;
0.56613756614 0 0 0.0000000000 0 0.4338624339 0.00000000000 0 0 0 0;
0.000000000 0 0 0.0000000000 0 1.00000000000 0.00000000000 0 0 0 0;
0.000000000 0 0 0.0000000000 0 0.00000000000 1.00000000000 0 0 0 0;
0.000000000 0 0 0.0000000000 0 0.00000000000 0.00000000000 1 0 0 0;
0.000000000 0 0 0.0000000000 0 0.00000000000 0.00000000000 0 1 0 0;
0.03711078928 0 0 0.4433381608 0 0.00000000000 0.5195510500 0 0 0 0;
0.000000000 0 0 0.0000000000 0 0.00000000000 0.00000000000 0 0 0 1.000000000]
@test slacks(deaiovrs, :X) ≈ [0.000000000 0;
0.000000000 0;
0.000000000 0;
0.000000000 0;
0.000000000 0;
0 0;
0.000000000 0;
0.000000000 0;
0 0;
0.000000000 0;
0.000000000 4]
@test slacks(deaiovrs, :Y) ≈ [0.000000000;
0.000000000;
0.000000000;
0.000000000;
2.698412698;
0.000000000;
0.000000000;
0.000000000;
0.000000000;
0.000000000;
0.000000000]
@test efficiency(deabigdata(targets(deaiovrs, :X), targets(deaiovrs, :Y), orient = :Input, rts = :VRS, slack = false)) ≈ ones(11)
@test efficiency(deaadd(targets(deaiovrs, :X), targets(deaiovrs, :Y))) ≈ zeros(11) atol=1e-12
# Output oriented VRS
deaoovrs = deabigdata(X, Y, orient = :Output, rts = :VRS)
@test nobs(deaoovrs) == 11
@test ninputs(deaoovrs) == 2
@test noutputs(deaoovrs) == 1
@test efficiency(deaoovrs) ≈ [1.0000000000;
1.507518797;
1.0000000000;
1.0000000000;
3.203947368;
1.000000000;
1.0000000000;
1.000000000;
1.000000000;
1.192307692;
1.0000000000]
@test convert(Matrix, peers(deaoovrs)) ≈
[1.000000000 0 0 0.0000000000 0 0 0.00000000000 0 0 0 0;
0.38157894737 0 0 0.1710526316 0 0 0.4473684211 0 0 0 0;
0.000000000 0 1 0.0000000000 0 0 0.00000000000 0 0 0 0;
0.000000000 0 0 1.0000000000 0 0 0.00000000000 0 0 0 0;
0.03947368421 0 0 0.7763157895 0 0 0.1842105263 0 0 0 0;
0.000000000 0 0 0.0000000000 0 1 0.00000000000 0 0 0 0;
0.000000000 0 0 0.0000000000 0 0 1.00000000000 0 0 0 0;
0.000000000 0 0 0.0000000000 0 0 0.00000000000 1 0 0 0;
0.000000000 0 0 0.0000000000 0 0 0.00000000000 0 1 0 0;
0.000000000 0 0 0.0000000000 0 0 0.00000000000 0 1 0 0;
1.000000000 0 0 0.0000000000 0 0 0.00000000000 0 0 0 0]
@test slacks(deaoovrs, :X) ≈ [0.000000000 0;
0.000000000 0;
0.000000000 0;
0.000000000 0;
0.000000000 0;
0.000000000 0;
0.000000000 0;
0.000000000 0;
0.000000000 0;
5 11;
0.000000000 4]
@test slacks(deaoovrs, :Y) ≈ zeros(11) atol=1e-10
@test efficiency(deabigdata(targets(deaoovrs, :X), targets(deaoovrs, :Y), orient = :Output, rts = :VRS, slack = false)) ≈ ones(11)
@test efficiency(deaadd(targets(deaoovrs, :X), targets(deaoovrs, :Y))) ≈ zeros(11) atol=1e-12
# Test no slacks
deaionoslack = deabigdata(X, Y, slack = false)
@test efficiency(deaionoslack) == efficiency(deaio)
@test isempty(slacks(deaionoslack, :X)) == 1
@test isempty(slacks(deaionoslack, :Y)) == 1
@test efficiency(deabigdata(targets(deaionoslack, :X), targets(deaionoslack, :Y), slack = false)) ≈ ones(11)
@test efficiency(deaadd(targets(deaionoslack, :X), targets(deaionoslack, :Y))) != zeros(11) # Different as there is no slacks in first model
# Print
show(IOBuffer(), deaio)
show(IOBuffer(), deaionoslack)
# Test errors
@test_throws DimensionMismatch deabigdata([1; 2 ; 3], [4 ; 5]) # Different number of observations
@test_throws ArgumentError deabigdata([1; 2; 3], [4; 5; 6], orient = :Error) # Invalid orientation
@test_throws ArgumentError deabigdata([1; 2; 3], [4; 5; 6], rts = :Error) # Invalid returns to scale
@test_throws ErrorException deabigdata(X, Y, atol = 0.0, optimizer = DEAOptimizer(:NLP))
# ------------------
# Test if no exteriors
# ------------------
Xnoext = [1 1; 1.5 1; 2 1]
Ynoext = [2 2; 1.5 1.5; 1 0.5]
deanoext = dea(Xnoext, Ynoext, orient = :Input)
deabignoext = deabigdata(Xnoext, Ynoext, orient = :Input)
@test efficiency(deanoext) ≈ efficiency(deabignoext)
@test slacks(deanoext, :X) ≈ slacks(deabignoext, :X)
@test slacks(deanoext, :X) ≈ slacks(deabignoext, :X)
@test targets(deanoext, :X) ≈ targets(deabignoext, :X)
@test targets(deanoext, :Y) ≈ targets(deabignoext, :Y)
@test peersmatrix(deanoext) ≈ peersmatrix(deabignoext)
# ------------------
# Test with random data
# ------------------
rng = StableRNG(1234567)
X = rand(Uniform(10, 20), 500, 6)
Y = rand(Uniform(10, 20), 500, 4)
rdea = dea(X, Y, progress = false)
rdeabig = deabigdata(X, Y, progress = false)
@test efficiency(rdeabig) ≈ efficiency(rdea)
@test slacks(rdeabig, :X) ≈ slacks(rdea, :X)
@test slacks(rdeabig, :Y) ≈ slacks(rdea, :Y)
@test targets(rdeabig, :X) ≈ targets(rdea, :X)
@test targets(rdeabig, :Y) ≈ targets(rdea, :Y)
@test peersmatrix(rdeabig) ≈ peersmatrix(rdea)
# ------------------
# Test Vector and Matrix inputs and outputs
# ------------------
# Tests against results in R
# Inputs is Matrix, Outputs is Vector
X = [2 2; 1 4; 4 1; 4 3; 5 5; 6 1; 2 5; 1.6 8]
Y = [1; 1; 1; 1; 1; 1; 1; 1]
@test efficiency(deabigdata(X, Y, orient = :Input)) ≈ [1; 1; 1; 0.6; 0.4; 1; 0.6666666667; 0.625]
# Inputs is Vector, Output is Matrix
X = [1; 1; 1; 1; 1; 1; 1; 1]
Y = [7 7; 4 8; 8 4; 3 5; 3 3; 8 2; 6 4; 1.5 5]
@test efficiency(deabigdata(X, Y, orient = :Output)) ≈ [1; 1; 1; 1.555555556; 2.333333333; 1; 1.272727273; 1.6]
# Inputs is Vector, Output is Vector
X = [2; 4; 8; 12; 6; 14; 14; 9.412]
Y = [1; 5; 8; 9; 3; 7; 9; 2.353]
@test efficiency(deabigdata(X, Y, orient = :Input)) ≈ [0.4; 1; 0.8; 0.6; 0.4; 0.4; 0.5142857143; 0.2]
end