added tests

src/shapley_sensitivity.jl

@@ -79,6 +79,7 @@ end
 
 using Copulas, Distributions, Combinatorics, LinearAlgebra, Random
 
+Random.seed!(1234)
 ## Data structures
 struct Shapley
     n_boot::Int
@@ -138,7 +139,7 @@ function find_cond_mean_var(cov::Matrix, dependent_ind::Vector{Int}, given_ind::
 
     mat_cdinv = transpose(mat_c) * inv(mat_d);
     conditional_mean = mat_cdinv * X_given;
-    contional_var = mat_b - CDinmat_cdinvv * mat_c;
+    contional_var = mat_b - mat_cdinv * mat_c;
 
     return conditional_mean, contional_var
 

test/shapley_method.jl

@@ -0,0 +1,117 @@
+using Copulas, Distributions, Combinatorics, LinearAlgebra, Random, GlobalSensitivity
+
+Random.seed!(1234)
+
+function ishi(X)
+    A= 7
+    B= 0.1
+    sin(X[1]) + A*sin(X[2])^2+ B*X[3]^4 *sin(X[1])
+end
+
+function ishi_batch(X)
+    A= 7
+    B= 0.1
+    @. sin(X[:,1]) + A*sin(X[:,2])^2+ B*X[:,3]^4 *sin(X[:,1])
+end
+
+function linear_batch(X)
+    A = 7
+    B = 0.1
+    @. A * X[:, 1] + B * X[:, 2]
+end
+function linear(X)
+    A = 7
+    B = 0.1
+    A * X[1] + B * X[2]
+end
+
+######################### Test ishi #########################
+
+n_perms = -1; 
+n_var = 1_000; 
+n_outer = 100; 
+n_inner = 3; 
+n_boot = 60_000;
+dim = 4;
+margins = (Uniform(-pi, pi), Uniform(-pi, pi), Uniform(-pi, pi), Uniform(-pi, pi));
+dependency_matrix = Matrix{Int}(I, dim, dim);
+C = GaussianCopula(dependency_matrix);
+input_distribution = SklarDist(C,margins);
+
+method = Shapley(dim=dim, n_perms=n_perms, n_var = n_var, n_outer = n_outer, n_inner = n_inner, n_boot=n_boot);
+
+#---> non batch
+result = gsa(ishi, method, input_distribution, batch=false);
+
+shapley_effects = []
+for idx in range(1, dim)
+    push!(shapley_effects, median(result.Shapley_indices[idx, :]));
+end
+
+@test shapley_effects[1]≈0.4541102166987666 atol=1e-4
+@test shapley_effects[2]≈0.4291427404161051 atol=1e-4
+@test shapley_effects[3]≈0.09943782238327539 atol=1e-4
+@test shapley_effects[4]≈0.017521253643223787 atol=1e-4
+#<---- non batch
+
+#---> batch 
+result = gsa(ishi_batch, method, input_distribution, batch=true);
+
+shapley_effects = []
+for idx in range(1, dim)
+    push!(shapley_effects, median(result.Shapley_indices[idx, :]));
+end
+
+@test shapley_effects[1]≈0.4541102166987666 atol=1e-4
+@test shapley_effects[2]≈0.4291427404161051 atol=1e-4
+@test shapley_effects[3]≈0.09943782238327539 atol=1e-4
+@test shapley_effects[4]≈0.017521253643223787 atol=1e-4
+#<--- batch 
+
+######################### Test ishi #########################
+
+
+######################### Test linear #########################
+
+n_perms = -1; 
+n_var = 1_000; 
+n_outer = 100; 
+n_inner = 3; 
+n_boot = 60_000;
+dim = 2;
+margins = (Uniform(-pi, pi), Uniform(-pi, pi));
+dependency_matrix = Matrix{Int}(I, dim, dim);
+C = GaussianCopula(dependency_matrix);
+input_distribution = SklarDist(C,margins);
+
+method = Shapley(dim=dim, n_perms=n_perms, n_var = n_var, n_outer = n_outer, n_inner = n_inner, n_boot=n_boot);
+
+#---> non batch 
+result = gsa(linear, method, input_distribution, batch=false);
+
+shapley_effects = []
+for idx in range(1, dim)
+    push!(shapley_effects, median(result.Shapley_indices[idx, :]));
+end
+
+@test shapley_effects[1]≈0.9337925131867066 atol=1e-4
+@test shapley_effects[2]≈0.06620748681329328 atol=1e-4
+#<--- non batch 
+
+#---> batch 
+result = gsa(linear_batch, method, input_distribution, batch=true);
+
+shapley_effects = []
+for idx in range(1, dim)
+    push!(shapley_effects, median(result.Shapley_indices[idx, :]));
+end
+
+@test shapley_effects[1]≈0.9337925131867066 atol=1e-4
+@test shapley_effects[2]≈0.06620748681329328 atol=1e-4
+#<--- batch
+
+######################### Test linear #########################
+
+
+
+