https://github.com/javierbarbero/DataEnvelopmentAnalysis.jl
Tip revision: 0de97613808801b3bcfd1e598c5667bd9338662b authored by Javier Barbero on 06 May 2023, 10:10:10 UTC
Bump version to 0.8.1
Bump version to 0.8.1
Tip revision: 0de9761
deaddf.jl
"""
DirectionalDEAModel
An data structure representing a directional distance function DEA model.
"""
struct DirectionalDEAModel <: AbstractTechnicalDEAModel
n::Int64
m::Int64
s::Int64
Gx::Symbol
Gy::Symbol
rts::Symbol
dmunames::Union{Vector{AbstractString},Nothing}
eff::Vector
slackX::Matrix
slackY::Matrix
lambda::SparseMatrixCSC{Float64, Int64}
Xtarget::Matrix
Ytarget::Matrix
end
"""
deaddf(X, Y; Gx, Gy)
Compute data envelopment analysis directional distance function model for inputs
`X` and outputs `Y`, using directions `Gx` and `Gy`.
# Direction specification:
The directions `Gx` and `Gy` can be one of the following symbols.
- `:Zeros`: use zeros.
- `:Ones`: use ones.
- `:Observed`: use observed values.
- `:Mean`: use column means.
Alternatively, a vector or matrix with the desired directions can be supplied.
# Optional Arguments
- `rts=:CRS`: chooses constant returns to scale. For variable returns to scale choose `:VRS`.
- `slack=true`: computes input and output slacks.
- `Xref=X`: Identifies the reference set of inputs against which the units are evaluated.
- `Yref=Y`: Identifies the reference set of outputs against which the units are evaluated.
- `names`: a vector of strings with the names of the decision making units.
"""
function deaddf(X::Union{Matrix,Vector}, Y::Union{Matrix,Vector};
Gx::Union{Symbol, Matrix, Vector}, Gy::Union{Symbol, Matrix, Vector},
rts::Symbol = :CRS, slack::Bool = true,
Xref::Union{Matrix,Vector,Nothing} = nothing, Yref::Union{Matrix,Vector,Nothing} = nothing,
names::Union{Vector{<: AbstractString},Nothing} = nothing,
optimizer::Union{DEAOptimizer,Nothing} = nothing)::DirectionalDEAModel
# Check parameters
nx, m = size(X, 1), size(X, 2)
ny, s = size(Y, 1), size(Y, 2)
if Xref === nothing Xref = X end
if Yref === nothing Yref = Y end
nrefx, mref = size(Xref, 1), size(Xref, 2)
nrefy, sref = size(Yref, 1), size(Yref, 2)
if nx != ny
throw(DimensionMismatch("number of rows in X and Y ($nx, $ny) are not equal"));
end
if nrefx != nrefy
throw(DimensionMismatch("number of rows in Xref and Yref ($nrefx, $nrefy) are not equal"));
end
if m != mref
throw(DimensionMismatch("number of columns in X and Xref ($m, $mref) are not equal"));
end
if s != sref
throw(DimensionMismatch("number of columns in Y and Yref ($s, $sref) are not equal"));
end
# Build or get user directions
if typeof(Gx) == Symbol
Gxsym = Gx
if Gx == :Zeros
Gx = zeros(size(X))
elseif Gx == :Ones
Gx = ones(size(X))
elseif Gx == :Observed
Gx = X
elseif Gx == :Mean
Gx = repeat(mean(X, dims = 1), size(X, 1))
else
throw(ArgumentError("Invalid `Gx`"));
end
else
Gxsym = :Custom
end
if typeof(Gy) == Symbol
Gysym = Gy
if Gy == :Zeros
Gy = zeros(size(Y))
elseif Gy == :Ones
Gy = ones(size(Y))
elseif Gy == :Observed
Gy = Y
elseif Gy == :Mean
Gy = repeat(mean(Y, dims = 1), size(Y, 1))
else
throw(ArgumentError("Invalid `Gy`"));
end
else
Gysym = :Custom
end
nGx, mGx = size(Gx, 1), size(Gx, 2)
nGy, sGy = size(Gy, 1), size(Gy, 2)
if (size(Gx, 1) != size(X, 1)) | (size(Gx, 2) != size(X, 2))
throw(DimensionMismatch("size of Gx and X ($(size(Gx)), $(size(X))) are not equal"));
end
if (size(Gy, 1) != size(Y, 1)) | (size(Gy, 2) != size(Y, 2))
throw(DimensionMismatch("size of Gy and Y ($(size(Gy)), $(size(Y))) are not equal"));
end
# Default optimizer
if optimizer === nothing
optimizer = DEAOptimizer(:LP)
end
# Compute efficiency for each DMU
n = nx
nref = nrefx
effi = zeros(n)
lambdaeff = spzeros(n, nref)
for i=1:n
# Value of inputs and outputs to evaluate
x0 = X[i,:]
y0 = Y[i,:]
# Directions to use
Gx0 = Gx[i,:]
Gy0 = Gy[i,:]
# Solve if any direction is different from zero
if any(Gx0 .!= 0) | any(Gy0 .!= 0)
# Create the optimization model
deamodel = newdeamodel(optimizer)
@variable(deamodel, eff)
@variable(deamodel, lambda[1:nref] >= 0)
@objective(deamodel, Max, eff)
@constraint(deamodel, [j in 1:m], sum(Xref[t,j] * lambda[t] for t in 1:nref) <= x0[j] - eff * Gx0[j])
@constraint(deamodel, [j in 1:s], sum(Yref[t,j] * lambda[t] for t in 1:nref) >= y0[j] + eff * Gy0[j])
# Add return to scale constraints
if rts == :CRS
# No contraint to add for constant returns to scale
elseif rts == :VRS
@constraint(deamodel, sum(lambda) == 1)
else
throw(ArgumentError("`rts` must be :CRS or :VRS"));
end
# Optimize and return results
JuMP.optimize!(deamodel)
effi[i] = JuMP.objective_value(deamodel)
lambdaeff[i,:] = JuMP.value.(lambda)
# Check termination status
if (termination_status(deamodel) != MOI.OPTIMAL) && (termination_status(deamodel) != MOI.LOCALLY_SOLVED)
@warn ("DMU $i termination status: $(termination_status(deamodel)). Primal status: $(primal_status(deamodel)). Dual status: $(dual_status(deamodel))")
end
else
effi[i] = 0.0
lambdaeff[i,:] .= 0.0
lambdaeff[i,i] = 1.0
end
end
# Get first-stage X and Y targets
Xtarget = X .- effi .* Gx
Ytarget = Y .+ effi .* Gy
# Compute slacks
if slack == true
# Use additive model with X and Y targets to get slacks
slacksmodel = deaadd(Xtarget, Ytarget, :Ones, rts = rts, Xref = Xref, Yref = Yref, optimizer = optimizer)
slackX = slacks(slacksmodel, :X)
slackY = slacks(slacksmodel, :Y)
# Get second-stage X and Y targets
Xtarget = Xtarget - slackX
Ytarget = Ytarget + slackY
else
if typeof(Xtarget) <: AbstractVector Xtarget = Xtarget[:,:] end
if typeof(Ytarget) <: AbstractVector Ytarget = Ytarget[:,:] end
slackX = Array{Float64}(undef, 0, 0)
slackY = Array{Float64}(undef, 0, 0)
end
return DirectionalDEAModel(n, m, s, Gxsym, Gysym, rts, names, effi, slackX, slackY, lambdaeff, Xtarget, Ytarget)
end
function Base.show(io::IO, x::DirectionalDEAModel)
compact = get(io, :compact, false)
n = nobs(x)
m = ninputs(x)
s = noutputs(x)
eff = efficiency(x)
dmunames = names(x)
slackX = slacks(x, :X)
slackY = slacks(x, :Y)
hasslacks = ! isempty(slackX)
if !compact
print(io, "Directional DF DEA Model \n")
print(io, "DMUs = ", n)
print(io, "; Inputs = ", m)
print(io, "; Outputs = ", s)
print(io, "\n")
print(io, "Returns to Scale = ", string(x.rts))
print(io, "\n")
print(io, "Gx = ", string(x.Gx), "; Gy = ", string(x.Gy))
print(io, "\n")
if hasslacks == true
show(io, CoefTable(hcat(eff, slackX, slackY), ["efficiency"; ["slackX$i" for i in 1:m ]; ["slackY$i" for i in 1:s ]], dmunames))
else
show(io, CoefTable(hcat(eff), ["efficiency"], dmunames))
end
end
end