Change isapprox(a,b) to a \approx b in tests (#2419)

* change isapprox(bar,foo) to bar \approx foo * fix formatting Co-authored-by: Rouleau, Michael <mike.rouleau@gatech.edu>
jump-dev · Jan 4, 2021 · b562ae8 · b562ae8
1 parent 2b0d910
commit b562ae8
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diff --git a/test/derivatives.jl b/test/derivatives.jl
@@ -15,14 +15,14 @@ struct ΦEvaluator <: MOI.AbstractNLPEvaluator end
     nd, const_values = expr_to_nodedata(ex)
     adj = adjmat(nd)
 
-    #@show nd
+    # @show nd
 
     storage = zeros(length(nd))
     partials_storage = zeros(length(nd))
     reverse_storage = zeros(length(nd))
 
     x = [2.0, 3.0]
-    #@show x
+    # @show x
     fval = forward_eval(
         storage,
         partials_storage,
@@ -37,14 +37,14 @@ struct ΦEvaluator <: MOI.AbstractNLPEvaluator end
         NO_USER_OPS,
     )
     true_val = sin(x[1]^2) + cos(x[2] * 4) / 5 - 2.0
-    @test isapprox(fval, true_val)
+    @test fval ≈ true_val
 
     grad = zeros(2)
     reverse_eval(reverse_storage, partials_storage, nd, adj)
     reverse_extract(grad, reverse_storage, nd, adj, [], 1.0)
 
     true_grad = [2 * x[1] * cos(x[1]^2), -4 * sin(x[2] * 4) / 5]
-    @test isapprox(grad, true_grad)
+    @test grad ≈ true_grad
 
     # Testing view
     xx = [1.0, 2.0, 3.0, 4.0, 5.0]
@@ -128,7 +128,7 @@ struct ΦEvaluator <: MOI.AbstractNLPEvaluator end
         [],
         NO_USER_OPS,
     )
-    @test isapprox(fval, true_val)
+    @test fval ≈ true_val
 
     outer_reverse_storage = zeros(1)
     fill!(grad, 0.0)
@@ -150,7 +150,7 @@ struct ΦEvaluator <: MOI.AbstractNLPEvaluator end
     @assert subexpr_output[1] == 1.0
     reverse_eval(reverse_storage, partials_storage, nd, adj)
     reverse_extract(grad, reverse_storage, nd, adj, [], subexpr_output[1])
-    @test isapprox(grad, true_grad)
+    @test grad ≈ true_grad
 
     ex = :((1 / x[1])^x[2] - x[3])
 
@@ -162,7 +162,7 @@ struct ΦEvaluator <: MOI.AbstractNLPEvaluator end
     reverse_storage = zeros(length(nd))
 
     x = [2.5, 3.5, 1.0]
-    #@show x
+    # @show x
     fval = forward_eval(
         storage,
         partials_storage,
@@ -177,14 +177,14 @@ struct ΦEvaluator <: MOI.AbstractNLPEvaluator end
         NO_USER_OPS,
     )
     true_val = (1 / x[1])^x[2] - x[3]
-    @test isapprox(fval, true_val)
+    @test fval ≈ true_val
 
     grad = zeros(3)
     reverse_eval(reverse_storage, partials_storage, nd, adj)
     reverse_extract(grad, reverse_storage, nd, adj, [], 1.0)
 
     true_grad = [-x[2] * x[1]^(-x[2] - 1), -((1 / x[1])^x[2]) * log(x[1]), -1]
-    @test isapprox(grad, true_grad)
+    @test grad ≈ true_grad
 
     # logical expressions
     ex = :(x[1] > 0.5 && x[1] < 0.9)
@@ -538,7 +538,7 @@ struct ΦEvaluator <: MOI.AbstractNLPEvaluator end
         r,
     )
     true_val = Φ(x[2], x[1] - 1) * cos(x[3])
-    @test isapprox(fval, true_val)
+    @test fval ≈ true_val
     grad = zeros(3)
     reverse_eval(reverse_storage, partials_storage, nd, adj)
     reverse_extract(grad, reverse_storage, nd, adj, [], 1.0)
@@ -548,7 +548,7 @@ struct ΦEvaluator <: MOI.AbstractNLPEvaluator end
         -4cos(x[3]) * x[2],
         -sin(x[3]) * Φ(x[2], x[1] - 1),
     ]
-    @test isapprox(grad, true_grad)
+    @test grad ≈ true_grad
 
     # dual forward test
     function dualforward(ex, x; ignore_nan = false)
@@ -605,7 +605,7 @@ struct ΦEvaluator <: MOI.AbstractNLPEvaluator end
             2.0,
             zero_ϵ,
         )
-        @test isapprox(fval_ϵ[1], dot(grad, ones(length(x))))
+        @test fval_ϵ[1] ≈ dot(grad, ones(length(x)))
 
         # compare with running dual numbers
         _epsilon(x::ForwardDiff.Dual{Nothing,Float64,1}) = x.partials[1]
@@ -643,30 +643,24 @@ struct ΦEvaluator <: MOI.AbstractNLPEvaluator end
             ForwardDiff.Dual(2.0, 0.0),
         )
         for k in 1:length(nd)
-            @test isapprox(
-                _epsilon(forward_dual_storage[k]),
-                forward_storage_ϵ[k][1],
-            )
+            @test _epsilon(forward_dual_storage[k]) ≈ forward_storage_ϵ[k][1]
+
             if !(isnan(_epsilon(partials_dual_storage[k])) && ignore_nan)
-                @test isapprox(
-                    _epsilon(partials_dual_storage[k]),
-                    partials_storage_ϵ[k][1],
-                )
+                @test _epsilon(partials_dual_storage[k]) ≈
+                      partials_storage_ϵ[k][1]
             else
                 @test !isnan(forward_storage_ϵ[k][1])
             end
             if !(isnan(_epsilon(reverse_dual_storage[k])) && ignore_nan)
-                @test isapprox(
-                    _epsilon(reverse_dual_storage[k]),
-                    reverse_storage_ϵ[k][1] / 2,
-                )
+                @test _epsilon(reverse_dual_storage[k]) ≈
+                      reverse_storage_ϵ[k][1] / 2
             else
                 @test !isnan(reverse_storage_ϵ[k][1])
             end
         end
         for k in 1:length(x)
             if !(isnan(_epsilon(output_dual_storage[k])) && ignore_nan)
-                @test isapprox(_epsilon(output_dual_storage[k]), output_ϵ[k][1])
+                @test _epsilon(output_dual_storage[k]) ≈ output_ϵ[k][1]
             else
                 @test !isnan(output_ϵ[k][1])
             end

diff --git a/test/nlp.jl b/test/nlp.jl
@@ -1,5 +1,3 @@
-# TODO: Replace isapprox with ≈ everywhere.
-
 using JuMP
 using LinearAlgebra
 using SparseArrays
@@ -254,10 +252,8 @@ end
         V = zeros(length(hessian_sparsity))
         values = [1.0, 2.0, 3.0] # Values for a, b, and c, respectively.
         MOI.eval_hessian_lagrangian(d, V, values, 1.0, Float64[])
-        @test isapprox(
-            dense_hessian(hessian_sparsity, V, 3),
-            [0.0 1.0 0.0; 1.0 0.0 0.0; 0.0 0.0 2.0],
-        )
+        @test dense_hessian(hessian_sparsity, V, 3) ≈
+              [0.0 1.0 0.0; 1.0 0.0 0.0; 0.0 0.0 2.0]
 
         # make sure we don't get NaNs in this case
         @NLobjective(m, Min, a * b + 3 * c^2)
@@ -266,19 +262,15 @@ end
         values = [1.0, 2.0, -1.0]
         V = zeros(length(hessian_sparsity))
         MOI.eval_hessian_lagrangian(d, V, values, 1.0, Float64[])
-        @test isapprox(
-            dense_hessian(hessian_sparsity, V, 3),
-            [0.0 1.0 0.0; 1.0 0.0 0.0; 0.0 0.0 6.0],
-        )
+        @test dense_hessian(hessian_sparsity, V, 3) ≈
+              [0.0 1.0 0.0; 1.0 0.0 0.0; 0.0 0.0 6.0]
 
         # Initialize again
         MOI.initialize(d, [:Hess])
         V = zeros(length(hessian_sparsity))
         MOI.eval_hessian_lagrangian(d, V, values, 1.0, Float64[])
-        @test isapprox(
-            dense_hessian(hessian_sparsity, V, 3),
-            [0.0 1.0 0.0; 1.0 0.0 0.0; 0.0 0.0 6.0],
-        )
+        @test dense_hessian(hessian_sparsity, V, 3) ≈
+              [0.0 1.0 0.0; 1.0 0.0 0.0; 0.0 0.0 6.0]
     end
 
     @testset "NaN corner case (Issue #695)" begin
@@ -297,7 +289,7 @@ end
         values = [1.0, 2.0] # For x and y.
         MOI.eval_hessian_lagrangian_product(d, h, values, v, 1.0, Float64[])
         correct = [0.0 -1/(2*2^(3/2)); -1/(2*2^(3/2)) 3/(4*2^(5/2))] * v
-        @test isapprox(h, correct)
+        @test h ≈ correct
     end
 
     @testset "NaN corner case (Issue #1205)" begin
@@ -312,7 +304,7 @@ end
         V = zeros(length(hessian_sparsity))
         values = zeros(1)
         MOI.eval_hessian_lagrangian(d, V, values, 1.0, Float64[])
-        @test isapprox(dense_hessian(hessian_sparsity, V, 1), [0.0])
+        @test dense_hessian(hessian_sparsity, V, 1) ≈ [0.0]
     end
 
     @testset "NaN corner case - ifelse (Issue #1205)" begin
@@ -327,7 +319,7 @@ end
         V = zeros(length(hessian_sparsity))
         values = zeros(1)
         MOI.eval_hessian_lagrangian(d, V, values, 1.0, Float64[])
-        @test isapprox(dense_hessian(hessian_sparsity, V, 1), [0.0])
+        @test dense_hessian(hessian_sparsity, V, 1) ≈ [0.0]
     end
 
     @testset "Product corner case (issue #1181)" begin
@@ -371,12 +363,12 @@ end
         V = zeros(length(hessian_sparsity))
         MOI.eval_hessian_lagrangian(d, V, values, 1.0, [2.0, 3.0])
         correct_hessian = [3.0 1.0 0.0; 1.0 0.0 2.0; 0.0 2.0 2.0]
-        @test isapprox(dense_hessian(hessian_sparsity, V, 3), correct_hessian)
+        @test dense_hessian(hessian_sparsity, V, 3) ≈ correct_hessian
 
         h = ones(3) # The input values should be overwritten.
         v = [2.4, 3.5, 1.2]
         MOI.eval_hessian_lagrangian_product(d, h, values, v, 1.0, [2.0, 3.0])
-        @test isapprox(h, correct_hessian * v)
+        @test h ≈ correct_hessian * v
     end
 
     @testset "Hessians and Hess-vec with subexpressions" begin
@@ -599,20 +591,15 @@ end
         V = zeros(length(hessian_sparsity))
         values = ones(18)
         MOI.eval_hessian_lagrangian(d, V, values, 1.0, Float64[])
-        @test isapprox(
-            dense_hessian(hessian_sparsity, V, 18),
-            ones(18, 18) - diagm(0 => ones(18)),
-        )
+        @test dense_hessian(hessian_sparsity, V, 18) ≈
+              ones(18, 18) - diagm(0 => ones(18))
 
         values[1] = 0.5
         MOI.eval_hessian_lagrangian(d, V, values, 1.0, Float64[])
-        @test isapprox(
-            dense_hessian(hessian_sparsity, V, 18),
-            [
-                0 ones(17)'
-                ones(17) (ones(17, 17)-diagm(0 => ones(17)))/2
-            ],
-        )
+        @test dense_hessian(hessian_sparsity, V, 18) ≈ [
+            0 ones(17)'
+            ones(17) (ones(17, 17)-diagm(0 => ones(17)))/2
+        ]
     end
 
     @testset "eval_objective and eval_objective_gradient" begin
@@ -630,13 +617,11 @@ end
         d = JuMP.NLPEvaluator(m)
         MOI.initialize(d, [:Grad])
         variable_values = fill(2.0, (4,))
-        @test isapprox(
-            MOI.eval_objective(d, variable_values),
-            variable_values[1] + 1 + variable_values[3] + 3variable_values[4],
-        )
+        @test MOI.eval_objective(d, variable_values) ≈
+              variable_values[1] + 1 + variable_values[3] + 3variable_values[4]
         grad = zeros(4)
         MOI.eval_objective_gradient(d, grad, variable_values)
-        @test isapprox(grad, [1.0, 0.0, 1.0, 3.0])
+        @test grad ≈ [1.0, 0.0, 1.0, 3.0]
     end
 
     @testset "eval_constraint and Jacobians" begin
@@ -660,10 +645,8 @@ end
         variable_values = fill(2.0, (4,))
         constraint_value = zeros(1)
         MOI.eval_constraint(d, constraint_value, variable_values)
-        @test isapprox(
-            constraint_value[1],
-            variable_values[1] + 1 + variable_values[3] + 3variable_values[4],
-        )
+        @test constraint_value[1] ≈
+              variable_values[1] + 1 + variable_values[3] + 3variable_values[4]
         jacobian_sparsity = MOI.jacobian_structure(d)
         I = [i for (i, j) in jacobian_sparsity]
         J = [j for (i, j) in jacobian_sparsity]
@@ -672,7 +655,7 @@ end
         MOI.eval_constraint_jacobian(d, jac_nonzeros, variable_values)
         jac_values = zeros(4)
         jac_values[J] = jac_nonzeros
-        @test isapprox(jac_values, [1.0, 0.0, 1.0, 3.0])
+        @test jac_values ≈ [1.0, 0.0, 1.0, 3.0]
     end
 
     @testset "set_NL_objective and add_NL_constraint" begin
@@ -856,7 +839,7 @@ end
         err = ErrorException(
             "Encountered an error parsing nonlinear expression: we don't support " *
             "models of type $(typeof(model)). In general, JuMP's nonlinear features " *
-            "don't work with JuMP-extensions."
+            "don't work with JuMP-extensions.",
         )
         @test_throws(err, @NLexpression(model, sqrt(x)))
     end