Revision

**a9ad709c106fcaa6c21d03edad7c83fa02a3fbf0**authored by Javier Barbero on**14 September 2022, 17:40:23 UTC**, committed by GitHub on**14 September 2022, 17:40:23 UTC****1 parent**63f7300

deabigdata.jl

```
# This file contains functions for the Radial Big Data Model, following Khezrimotlagh et al. (2019) algorithm (we use KZCT as acronym here)
# D. Khezrimotlagh, J. Zhu and W.D. Cook et al. / European Journal of Operational Research 274 (2019) 1047–1054
"""
initialsubset(X,Y)
Creates the initial subset as in KZCT algorithm:
1. Determines p the size of the subset and create an empty vector or matrix to fill out
2. Determines the minimum value for each input m and the maximum value for each output s
3. Select a subset with one DMU per input, with DMU's input equal to the minimum value for this input m and one DMU per output
with DMU's output equal to the maximum value for this output s: m + s DMU's selected
4. To fill out the subset up to p DMUs, implement the following algorithm:
For each unselected DMU_j,
{u = 0,
{For each i and k_i,
If x_ij <= k_ith percentile of x^i then
u = u + 1}
{For each r and k'_r
If y_rj >= k'th percentile of y^r then
u = u + 1}
pre_score_j = u }
Where x^i and y^r represent the ith inputs and the rth outputs of all DMUS.
The indexes k_i and k'_r are changed from 0 to 100 and refer to the percentiles of x^i and y^r respectively.
Sorting DMUs in descending order by the assigned pre-scores, the remaining DMUs (to construct the subsample with size p)
are selected as those having the greatest pre-scores.
"""
function initialsubset(X::Union{Vector,Matrix},Y::Union{Vector,Matrix}, n::Int64, s::Int64, m::Int64)
# Determines the size of the initial subset Dˢ
p = convert(Int64, round(sqrt(n)))
# Select DMU's with the minimum input or the maximum output
dmus_selected = Vector{Int64}()
for x in 1:m
push!(dmus_selected, argmin(X[:,x]))
end
for y in 1:s
push!(dmus_selected, argmax(Y[:,y]))
end
unique!(dmus_selected)
initial_index = collect(1:n)
dmus_unselected = initial_index[Not(dmus_selected)]
# Algorithm implementation to fill out the subsample up to the size p
# select unselected DMUs
X_unselected = @view X[Not(dmus_selected), :]
Y_unselected = @view Y[Not(dmus_selected), :]
# create the pre-scores matrix (one column with the pre-score and the other to store the initial index)
pre_scores = zeros(length(dmus_unselected))
@inbounds for i in 1:size(X_unselected, 1)
for x in 1:m
xquant = quantile!(X[:,x], 0.01:0.01:1)
for k in 1:100
if X_unselected[i,x] <= xquant[k]
pre_scores[i] = pre_scores[i] + 1
end
end
end
for y in 1:s
yquant = quantile!(Y[:,y], 0.01:0.01:1)
for k in 1:100
if Y_unselected[i,y] >= yquant[k]
pre_scores[i] = pre_scores[i] + 1
end
end
end
end
# sort pre_scores to get the best DMUs in unselected DMUs
score_perm = sortperm(pre_scores, rev = true)
dmus_unselected = dmus_unselected[score_perm]
pre_scores = pre_scores[score_perm, :]
# add the number of DMUs needed to fill out the subsample
z = p - length(dmus_selected)
new_dmus = dmus_unselected[1:z]
dmus_selected = vcat(dmus_selected, new_dmus)
dmus_unselected = initial_index[Not(dmus_selected)]
# Creates the subsets Dˢ and D_excluding_Dˢ
return dmus_selected, dmus_unselected
end
"""
bestpracticesfinder(Subset)
Identify best-practices in the subsample selected
"""
function bestpracticesfinder(scores::Vector, orient::Symbol, atol::Float64)
index_bestpractices = Vector{Int64}()
for i in 1:length(scores)
if orient == :Input
if scores[i] >= 1 - atol
push!(index_bestpractices, i)
end
elseif orient == :Output
if scores[i] <= 1 + atol
push!(index_bestpractices, i)
end
end
end
return index_bestpractices
end
"""
deabigdata(X, Y)
Compute the big data radial model using data envelopment analysis for inputs X and outputs Y.
# Optional Arguments
- `orient=:Input`: chooses the radially oriented input mode. For the radially oriented output model choose `:Output`.
- `rts=:CRS`: chooses constant returns to scale. For variable returns to scale choose `:VRS`.
- `atol=1e-6`: tolerance for DMU to be considered efficient.
- `names`: a vector of strings with the names of the decision making units.
"""
function deabigdata(X::Union{Matrix,Vector}, Y::Union{Matrix,Vector};
orient::Symbol = :Input, rts::Symbol = :CRS, slack::Bool = true, atol::Float64 = 1e-6,
names::Union{Vector{<: AbstractString},Nothing} = nothing,
optimizer::Union{DEAOptimizer,Nothing} = nothing, progress::Bool = true)
#=
The algorithm is such as:
1. Start
2. D <- get a sample of DMUs
3. D^S <- select a subsample of D
4. B^S <- find best-practices in D^S
5. E <- find exterior DMUs in D excluding D^S respect to the hull of B^S
6. If E = {}, then F = B^S and all DMUs are altready evaluated (go to step 7)
Otherwise
6.1. F <- find best-practice DMUs in D^S Union E
6.2. Evaluate DMUs in D excluding (D^S Union E) respoect to the F's hull
7. End
=#
# Check parameters
nx, m = size(X, 1), size(X, 2)
ny, s = size(Y, 1), size(Y, 2)
if nx != ny
throw(DimensionMismatch("number of@rows in X and Y ($nx, $ny) are not equal"));
end
# Default optimizer
if optimizer === nothing
optimizer = DEAOptimizer(:LP)
end
n = nx
disposX = :Strong
disposY = :Strong
# Select initial subset Dˢ using Khezrimotlagh et al. (2019) algorithm
Dˢ, D_excluding_Dˢ = initialsubset(X, Y, n, s, m)
# Find the best-practices Bˢ in Dˢ
Dˢ_evaluation = dea(X[Dˢ, :], Y[Dˢ, :], orient = orient, rts = rts,slack = slack,
disposX = disposX, disposY = disposY, optimizer = optimizer, progress = progress)
index_bestpractices = bestpracticesfinder(efficiency(Dˢ_evaluation), orient, atol)
length(index_bestpractices) > 0 || throw(ErrorException("No efficient DMUs found in the initial subset. Consider increasing the tolerance `atol`"))
Bˢ = Dˢ[index_bestpractices]
# Find exterior DMUs in D excluding Dˢ respect to the hull of Bˢ
D_excluding_Dˢ_evaluation = dea(X[D_excluding_Dˢ, :], Y[D_excluding_Dˢ, :], orient = orient,
rts = rts, slack = slack, Xref = X[Bˢ, :], Yref = Y[Bˢ, :],
disposX = disposX, disposY = disposY, optimizer = optimizer, progress = progress)
index_exteriors = bestpracticesfinder(efficiency(D_excluding_Dˢ_evaluation), orient, atol)
E = D_excluding_Dˢ[index_exteriors]
# If E is not empty, then we have to find best practice DMUs in D^S Union E, otherwise, F = B_S and all DMUs are already evaluated
if size(E,1) > 0
# Create the subset Dˢ_union_E
Dˢ_union_E = vcat(Dˢ, E)
sort!(Dˢ_union_E, rev = false)
Dˢ_union_E_evaluation = dea(X[Dˢ_union_E, :], Y[Dˢ_union_E, :], orient = orient, rts = rts,slack = slack,
disposX = disposX, disposY = disposY,optimizer = optimizer, progress = progress)
# Find the index of best practices DMUs
index_bestpractices = bestpracticesfinder(efficiency(Dˢ_union_E_evaluation), orient, atol)
# Best practices DMUs are the efficient DMUs F of the sample D
F = Dˢ_union_E[index_bestpractices]
# # We then evaluate the rest of DMUs
initial_index = collect(1:n)
D_excluding_Dˢ_union_E = initial_index[Not(Dˢ_union_E)]
D_excluding_Dˢ_union_E_evaluation = dea(X[D_excluding_Dˢ_union_E, :], Y[D_excluding_Dˢ_union_E, :], orient = orient,
rts = rts, slack = slack, Xref = X[F, :], Yref = Y[F, :],
disposX = disposX, disposY = disposY, optimizer = optimizer, progress = progress)
subset1 = Dˢ_union_E
results1 = Dˢ_union_E_evaluation
subset2 = D_excluding_Dˢ_union_E
results2 = D_excluding_Dˢ_union_E_evaluation
else
F = Bˢ
subset1 = Dˢ
results1 = Dˢ_evaluation
subset2 = D_excluding_Dˢ
results2 = D_excluding_Dˢ_evaluation
end
# Get results
resultsperm = sortperm(vcat(subset1, subset2), rev = false)
effi = vcat(efficiency(results1), efficiency(results2))
effi = effi[resultsperm]
if slack
slackX = vcat(slacks(results1, :X), slacks(results2, :X))
slackY = vcat(slacks(results1, :Y), slacks(results2, :Y))
slackX = slackX[resultsperm, :]
slackY = slackY[resultsperm, :]
else
slackX = Array{Float64}(undef, 0, 0)
slackY = Array{Float64}(undef, 0, 0)
end
Xtarget = vcat(targets(results1, :X), targets(results2, :X))
Ytarget = vcat(targets(results1, :Y), targets(results2, :Y))
Xtarget = Xtarget[resultsperm, :]
Ytarget = Ytarget[resultsperm, :]
lambdas1 = peersmatrix(results1)
lambdas2 = peersmatrix(results2)
index_lambda_to_keep = findall(x -> x in F, subset1)
lambdaeff = vcat(lambdas1[:,index_lambda_to_keep],lambdas2)
lambdaindex = vcat(subset1, subset2)
lambda_matrix = lambdaeff[resultsperm, :]
# Create the sparse matrix for lambdas
lambdaeff = spzeros(n, n)
lambdaeff[:, convert.(Int, lambdaindex[index_lambda_to_keep])] = lambda_matrix
# return results
return RadialDEAModel(n, m, s, orient, rts, disposX, disposY, names, effi, slackX, slackY, lambdaeff, Xtarget, Ytarget)
end
```

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