Add SymbolicRegression integration test (#2965)

* Add SymbolicRegression integration test

* Relax compat bounds for SymbolicRegression integration test

* Rewrite SymbolicRegression integration test

* Simplify SymbolicRegression integration test deps

* Remove unnecessary Dataset extra field in SR integration test

Author: MilesCranmerBot

.github/workflows/Integration.yml

@@ -50,6 +50,7 @@ jobs:
           - DifferentiationInterface
           - Distributions
           - DynamicExpressions
+          - SymbolicRegression
           - Lux
           - SciML
           - KernelAbstractions

test/integration/SymbolicRegression/Project.toml

@@ -0,0 +1,12 @@
+[deps]
+Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
+EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869"
+SymbolicRegression = "8254be44-1295-4e6a-a16d-46603ac705cb"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[sources]
+Enzyme = {path = "../../.."}
+EnzymeCore = {path = "../../../lib/EnzymeCore"}
+
+[compat]
+SymbolicRegression = "1.12.0"

test/integration/SymbolicRegression/runtests.jl

@@ -0,0 +1,47 @@
+using Test
+using Random: MersenneTwister
+using SymbolicRegression
+using Enzyme
+
+rng = MersenneTwister(0)
+X = rand(rng, 2, 16)
+# Choose a constant target so constant-optimization is guaranteed to run,
+# exercising `autodiff_backend=:Enzyme` deterministically.
+y = fill(1.0, size(X, 2))
+
+dataset = Dataset(
+    X,
+    y;
+    variable_names=["x1", "x2"],
+)
+
+options = Options(
+    binary_operators=[+, *, -],
+    unary_operators=[],
+    populations=1,
+    population_size=20,
+    ncycles_per_iteration=5,
+    maxsize=8,
+    autodiff_backend=:Enzyme,
+    optimizer_probability=1.0,
+    seed=0,
+    deterministic=true,
+    verbosity=0,
+    save_to_file=false,
+)
+
+hall_of_fame = equation_search(
+    dataset;
+    niterations=2,
+    options=options,
+    parallelism=:serial,
+    runtests=false,
+    progress=false,
+)
+
+best_loss = minimum(
+    member.loss for (member, exists) in zip(hall_of_fame.members, hall_of_fame.exists) if
+    exists
+)
+
+@test best_loss < 1e-8