suppress uninformative warnings during package testing

ext/SEMProximalOptExt/ProximalAlgorithms.jl

@@ -61,7 +61,6 @@ function ProximalAlgorithms.value_and_gradient(model::AbstractSem, params)
     return obj, grad
 end
 
-
 mutable struct ProximalResult
     result::Any
 end

src/frontend/fit/summary.jl

@@ -1,9 +1,4 @@
-function details(
-    sem_fit::SemFit;
-    show_fitmeasures = false,
-    color = :light_cyan,
-    digits = 2,
-)
+function details(sem_fit::SemFit; show_fitmeasures = false, color = :light_cyan, digits = 2)
     print("\n")
     println("Fitted Structural Equation Model")
     print("\n")
@@ -51,7 +46,7 @@ function details(
     secondary_color = :light_yellow,
     digits = 2,
     show_variables = true,
-    show_columns = nothing
+    show_columns = nothing,
 )
     if show_variables
         print("\n")
@@ -150,7 +145,8 @@ function details(
         check_round(partable.columns[c][regression_indices]; digits = digits) for
         c in regression_columns
     )
-    regression_columns[2] = regression_columns[2] == :relation ? Symbol("") : regression_columns[2]
+    regression_columns[2] =
+        regression_columns[2] == :relation ? Symbol("") : regression_columns[2]
 
     print("\n")
     pretty_table(
@@ -222,7 +218,8 @@ function details(
         printstyled("Means: \n"; color = color)
 
         if isnothing(show_columns)
-            sorted_columns = [:from, :relation, :to, :estimate, :param, :value_fixed, :start]
+            sorted_columns =
+                [:from, :relation, :to, :estimate, :param, :value_fixed, :start]
             mean_columns = sort_partially(sorted_columns, columns)
         else
             mean_columns = copy(show_columns)
@@ -256,7 +253,7 @@ function details(
     secondary_color = :light_yellow,
     digits = 2,
     show_variables = true,
-    show_columns = nothing
+    show_columns = nothing,
 )
     if show_variables
         print("\n")
@@ -297,7 +294,7 @@ function details(
             secondary_color = secondary_color,
             digits = digits,
             show_variables = false,
-            show_columns = show_columns
+            show_columns = show_columns,
         )
     end
 

src/frontend/specification/EnsembleParameterTable.jl

@@ -19,7 +19,7 @@ EnsembleParameterTable(::Nothing; params::Union{Nothing, Vector{Symbol}} = nothi
     )
 
 # convert pairs to dict
-EnsembleParameterTable(ps::Pair{K, V}...; params = nothing) where {K, V} = 
+EnsembleParameterTable(ps::Pair{K, V}...; params = nothing) where {K, V} =
     EnsembleParameterTable(Dict(ps...); params = params)
 
 # dictionary of SEM specifications
@@ -148,8 +148,6 @@ end
 ############################################################################################
 
 function Base.:(==)(p1::EnsembleParameterTable, p2::EnsembleParameterTable)
-    out = 
-        (p1.tables == p2.tables) &&
-        (p1.params == p2.params)
+    out = (p1.tables == p2.tables) && (p1.params == p2.params)
     return out
-end
+end

src/frontend/specification/ParameterTable.jl

@@ -128,7 +128,7 @@ end
 
 # Equality --------------------------------------------------------------------------------
 function Base.:(==)(p1::ParameterTable, p2::ParameterTable)
-    out = 
+    out =
         (p1.columns == p2.columns) &&
         (p1.observed_vars == p2.observed_vars) &&
         (p1.latent_vars == p2.latent_vars) &&

src/frontend/specification/RAMMatrices.jl

@@ -133,8 +133,10 @@ function RAMMatrices(
         @assert length(partable.sorted_vars) == nvars(partable)
         vars_sorted = copy(partable.sorted_vars)
     else
-        vars_sorted = [partable.observed_vars
-                       partable.latent_vars]
+        vars_sorted = [
+            partable.observed_vars
+            partable.latent_vars
+        ]
     end
 
     # indices of the vars (A/S/M rows or columns)
@@ -216,13 +218,20 @@ function RAMMatrices(
         sort!(M_consts, by = first)
     end
 
-    return RAMMatrices(ParamsMatrix{T}(A_inds, A_consts, (n_vars, n_vars)),
-                       ParamsMatrix{T}(S_inds, S_consts, (n_vars, n_vars)),
-                       sparse(1:n_observed,
-                              [vars_index[var] for var in partable.observed_vars],
-                              ones(T, n_observed), n_observed, n_vars),
-                       !isnothing(M_inds) ? ParamsVector{T}(M_inds, M_consts, (n_vars,)) : nothing,
-                       params, vars_sorted)
+    return RAMMatrices(
+        ParamsMatrix{T}(A_inds, A_consts, (n_vars, n_vars)),
+        ParamsMatrix{T}(S_inds, S_consts, (n_vars, n_vars)),
+        sparse(
+            1:n_observed,
+            [vars_index[var] for var in partable.observed_vars],
+            ones(T, n_observed),
+            n_observed,
+            n_vars,
+        ),
+        !isnothing(M_inds) ? ParamsVector{T}(M_inds, M_consts, (n_vars,)) : nothing,
+        params,
+        vars_sorted,
+    )
 end
 
 Base.convert(
@@ -360,10 +369,7 @@ function append_rows!(
             arr_ix = arr_ixs[arr.linear_indices[j]]
             skip_symmetric && (arr_ix ∈ visited_indices) && continue
 
-            push!(
-                partable,
-                partable_row(par, arr_ix, arr_name, varnames, free = true),
-            )
+            push!(partable, partable_row(par, arr_ix, arr_name, varnames, free = true))
             if skip_symmetric
                 # mark index and its symmetric as visited
                 push!(visited_indices, arr_ix)

src/implied/abstract.jl

@@ -25,8 +25,9 @@ function check_acyclic(A::AbstractMatrix; verbose::Bool = false)
         # check if non-triangular matrix is acyclic
         acyclic = isone(det(I - A))
         if acyclic
-            verbose && @info "The matrix is acyclic. Reordering variables in the model to make the A matrix either Upper or Lower Triangular can significantly improve performance.\n" maxlog =
-                1
+            verbose &&
+                @info "The matrix is acyclic. Reordering variables in the model to make the A matrix either Upper or Lower Triangular can significantly improve performance.\n" maxlog =
+                    1
         end
         return A
     end

src/loss/ML/FIML.jl

@@ -161,7 +161,9 @@ function ∇F_fiml_outer!(G, JΣ, Jμ, implied, model, semfiml)
 
     ∇Σ = P * (implied.∇S + Q * implied.∇A)
 
-    ∇μ = implied.F⨉I_A⁻¹ * implied.∇M + kron((implied.I_A⁻¹ * implied.M)', implied.F⨉I_A⁻¹) * implied.∇A
+    ∇μ =
+        implied.F⨉I_A⁻¹ * implied.∇M +
+        kron((implied.I_A⁻¹ * implied.M)', implied.F⨉I_A⁻¹) * implied.∇A
 
     mul!(G, ∇Σ', JΣ) # actually transposed
     mul!(G, ∇μ', Jμ, -1, 1)

src/objective_gradient_hessian.jl

@@ -28,16 +28,26 @@ evaluate!(objective, gradient, hessian, loss::SemLossFunction, model::AbstractSe
     evaluate!(objective, gradient, hessian, loss, implied(model), model, params)
 
 # fallback method
-function evaluate!(obj, grad, hess, loss::SemLossFunction, implied::SemImplied, model, params)
+function evaluate!(
+    obj,
+    grad,
+    hess,
+    loss::SemLossFunction,
+    implied::SemImplied,
+    model,
+    params,
+)
     isnothing(obj) || (obj = objective(loss, implied, model, params))
     isnothing(grad) || copyto!(grad, gradient(loss, implied, model, params))
     isnothing(hess) || copyto!(hess, hessian(loss, implied, model, params))
     return obj
 end
 
 # fallback methods
-objective(f::SemLossFunction, implied::SemImplied, model, params) = objective(f, model, params)
-gradient(f::SemLossFunction, implied::SemImplied, model, params) = gradient(f, model, params)
+objective(f::SemLossFunction, implied::SemImplied, model, params) =
+    objective(f, model, params)
+gradient(f::SemLossFunction, implied::SemImplied, model, params) =
+    gradient(f, model, params)
 hessian(f::SemLossFunction, implied::SemImplied, model, params) = hessian(f, model, params)
 
 # fallback method for SemImplied that calls update_xxx!() methods

test/Project.toml

@@ -15,4 +15,5 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+Suppressor = "fd094767-a336-5f1f-9728-57cf17d0bbfb"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

test/examples/multigroup/build_models.jl

@@ -17,10 +17,9 @@ model_ml_multigroup2 = SemEnsemble(
     data = dat,
     column = :school,
     groups = [:Pasteur, :Grant_White],
-    loss = SemML
+    loss = SemML,
 )
 
-
 # gradients
 @testset "ml_gradients_multigroup" begin
     test_gradient(model_ml_multigroup, start_test; atol = 1e-9)
@@ -206,11 +205,19 @@ end
 # GLS estimation
 ############################################################################################
 
-model_ls_g1 =
-    Sem(specification = specification_g1, data = dat_g1, implied = RAMSymbolic, loss = SemWLS)
+model_ls_g1 = Sem(
+    specification = specification_g1,
+    data = dat_g1,
+    implied = RAMSymbolic,
+    loss = SemWLS,
+)
 
-model_ls_g2 =
-    Sem(specification = specification_g2, data = dat_g2, implied = RAMSymbolic, loss = SemWLS)
+model_ls_g2 = Sem(
+    specification = specification_g2,
+    data = dat_g2,
+    implied = RAMSymbolic,
+    loss = SemWLS,
+)
 
 model_ls_multigroup = SemEnsemble(model_ls_g1, model_ls_g2; optimizer = semoptimizer)
 
@@ -239,7 +246,7 @@ end
         atol = 1e-5,
     )
 
-    update_se_hessian!(partable, solution_ls)
+    @suppress update_se_hessian!(partable, solution_ls)
     test_estimates(
         partable,
         solution_lav[:parameter_estimates_ls];
@@ -283,7 +290,7 @@ if !isnothing(specification_miss_g1)
         groups = [:Pasteur, :Grant_White],
         loss = SemFIML,
         observed = SemObservedMissing,
-        meanstructure = true
+        meanstructure = true,
     )
 
     ############################################################################################

test/examples/multigroup/multigroup.jl

@@ -1,4 +1,4 @@
-using StructuralEquationModels, Test, FiniteDiff
+using StructuralEquationModels, Test, FiniteDiff, Suppressor
 using LinearAlgebra: diagind, LowerTriangular
 
 const SEM = StructuralEquationModels
@@ -71,10 +71,8 @@ specification_g2 = RAMMatrices(;
     vars = [:x1, :x2, :x3, :x4, :x5, :x6, :x7, :x8, :x9, :visual, :textual, :speed],
 )
 
-partable = EnsembleParameterTable(
-    :Pasteur => specification_g1,
-    :Grant_White => specification_g2
-)
+partable =
+    EnsembleParameterTable(:Pasteur => specification_g1, :Grant_White => specification_g2)
 
 specification_miss_g1 = nothing
 specification_miss_g2 = nothing

test/examples/political_democracy/by_parts.jl

@@ -133,7 +133,7 @@ end
     )
     @test (fm[:AIC] === missing) & (fm[:BIC] === missing) & (fm[:minus2ll] === missing)
 
-    update_se_hessian!(partable, solution_ls)
+    @suppress update_se_hessian!(partable, solution_ls)
     test_estimates(
         partable,
         solution_lav[:parameter_estimates_ls];
@@ -158,7 +158,8 @@ if opt_engine == :Optim
         ),
     )
 
-    implied_sym_hessian_vech = RAMSymbolic(specification = spec, vech = true, hessian = true)
+    implied_sym_hessian_vech =
+        RAMSymbolic(specification = spec, vech = true, hessian = true)
 
     implied_sym_hessian = RAMSymbolic(specification = spec, hessian = true)
 
@@ -294,7 +295,7 @@ end
     )
     @test (fm[:AIC] === missing) & (fm[:BIC] === missing) & (fm[:minus2ll] === missing)
 
-    update_se_hessian!(partable_mean, solution_ls)
+    @suppress update_se_hessian!(partable_mean, solution_ls)
     test_estimates(
         partable_mean,
         solution_lav[:parameter_estimates_ls_mean];

test/examples/political_democracy/constructor.jl

@@ -141,7 +141,7 @@ end
     )
     @test ismissing(fm[:AIC]) && ismissing(fm[:BIC]) && ismissing(fm[:minus2ll])
 
-    update_se_hessian!(partable, solution_ls)
+    @suppress update_se_hessian!(partable, solution_ls)
     test_estimates(
         partable,
         solution_lav[:parameter_estimates_ls];
@@ -337,7 +337,7 @@ end
     )
     @test ismissing(fm[:AIC]) && ismissing(fm[:BIC]) && ismissing(fm[:minus2ll])
 
-    update_se_hessian!(partable_mean, solution_ls)
+    @suppress update_se_hessian!(partable_mean, solution_ls)
     test_estimates(
         partable_mean,
         solution_lav[:parameter_estimates_ls_mean];

test/examples/political_democracy/political_democracy.jl

@@ -1,4 +1,4 @@
-using StructuralEquationModels, Test, FiniteDiff
+using StructuralEquationModels, Test, Suppressor, FiniteDiff
 
 SEM = StructuralEquationModels
 
@@ -78,22 +78,7 @@ spec = RAMMatrices(;
     S = S,
     F = F,
     params = x,
-    vars = [
-        :x1,
-        :x2,
-        :x3,
-        :y1,
-        :y2,
-        :y3,
-        :y4,
-        :y5,
-        :y6,
-        :y7,
-        :y8,
-        :ind60,
-        :dem60,
-        :dem65,
-    ],
+    vars = [:x1, :x2, :x3, :y1, :y2, :y3, :y4, :y5, :y6, :y7, :y8, :ind60, :dem60, :dem65],
 )
 
 partable = ParameterTable(spec)
@@ -110,22 +95,7 @@ spec_mean = RAMMatrices(;
     F = F,
     M = M,
     params = [SEM.params(spec); Symbol.("x", string.(32:38))],
-    vars = [
-        :x1,
-        :x2,
-        :x3,
-        :y1,
-        :y2,
-        :y3,
-        :y4,
-        :y5,
-        :y6,
-        :y7,
-        :y8,
-        :ind60,
-        :dem60,
-        :dem65,
-    ],
+    vars = [:x1, :x2, :x3, :y1, :y2, :y3, :y4, :y5, :y6, :y7, :y8, :ind60, :dem60, :dem65],
 )
 
 partable_mean = ParameterTable(spec_mean)

test/examples/proximal/ridge.jl

@@ -1,4 +1,4 @@
-using StructuralEquationModels, Test, ProximalAlgorithms, ProximalOperators
+using StructuralEquationModels, Test, ProximalAlgorithms, ProximalOperators, Suppressor
 
 # load data
 dat = example_data("political_democracy")
@@ -54,7 +54,7 @@ solution_ridge = sem_fit(model_ridge)
 
 model_prox = Sem(specification = partable, data = dat, loss = SemML)
 
-solution_prox = sem_fit(model_prox, engine = :Proximal, operator_g = SqrNormL2(λ))
+solution_prox = @suppress sem_fit(model_prox, engine = :Proximal, operator_g = SqrNormL2(λ))
 
 @testset "ridge_solution" begin
     @test isapprox(solution_prox.solution, solution_ridge.solution; rtol = 1e-4)