fix: turn off progress bars in CI

SciML · Sep 20, 2024 · 1100ab4 · 1100ab4
1 parent 2545c2f
commit 1100ab4
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Showing 5 changed files with 13 additions and 9 deletions.
diff --git a/lib/DataDrivenLux/src/algorithms/reinforce.jl b/lib/DataDrivenLux/src/algorithms/reinforce.jl
@@ -10,7 +10,7 @@ $(SIGNATURES)
 Uses the REINFORCE algorithm to search over the space of possible solutions to the
 symbolic regression problem.
 """
-function Reinforce(reward = RelativeReward(false); populationsize = 100,
+function Reinforce(; reward = RelativeReward(false), populationsize = 100,
         functions = (sin, exp, cos, log, +, -, /, *), arities = (1, 1, 1, 1, 2, 2, 2, 2),
         n_layers = 1, skip = true, loss = aicc, keep = 0.1, use_protected = true,
         distributed = false, threaded = false, rng = Random.default_rng(),

diff --git a/lib/DataDrivenLux/test/candidate.jl b/lib/DataDrivenLux/test/candidate.jl
@@ -37,7 +37,7 @@ end
     Y = sin.(2.0 * X)
     @variables x
     @parameters p [bounds = (1.0, 2.5), dist = Normal(1.75, 1.0)]
-    basis = Basis([sin(p * x)], [x], parameters = [p])
+    basis = Basis([sin(p * x)], [x], parameters = [p])  # NaNMath.sin causes issues
 
     dataset = Dataset(X, Y)
     rng = StableRNG(2)

diff --git a/lib/DataDrivenLux/test/crossentropy_solve.jl b/lib/DataDrivenLux/test/crossentropy_solve.jl
@@ -39,7 +39,8 @@ alg = CrossEntropy(populationsize = 2_00, functions = (sin, exp, +), arities = (
     threaded = true, optim_options = Optim.Options(time_limit = 0.2))
 
 res = solve(dummy_problem, b, alg,
-    options = DataDrivenCommonOptions(maxiters = 1_000, progress = true, abstol = 0.0))
+    options = DataDrivenCommonOptions(
+        maxiters = 1_000, progress = parse(Bool, get(ENV, "CI", "false")), abstol = 0.0))
 @test rss(res) <= 1e-2
 @test aicc(res) <= -100.0
 @test r2(res) >= 0.95

diff --git a/lib/DataDrivenLux/test/randomsearch_solve.jl b/lib/DataDrivenLux/test/randomsearch_solve.jl
@@ -34,11 +34,13 @@ for (data, _interval) in zip((X, Y, 1:size(X, 2)),
 end
 
 # We have 1 Choices in the first layer, 2 in the last 
-alg = RandomSearch(populationsize = 10, functions = (sin, exp, *),
-    arities = (1, 1, 2), rng = rng, n_layers = 2, loss = rss, keep = 2)
+alg = RandomSearch(;
+    populationsize = 10, functions = (sin, exp, *), arities = (1, 1, 2), rng,
+    n_layers = 2, loss = rss, keep = 2)
 
 res = solve(dummy_problem, alg,
-    options = DataDrivenCommonOptions(maxiters = 50, progress = true, abstol = 0.0))
+    options = DataDrivenCommonOptions(
+        maxiters = 50, progress = parse(Bool, get(ENV, "CI", "false")), abstol = 0.0))
 @test rss(res) <= 1e-2
 @test aicc(res) <= -100.0
 @test r2(res) >= 0.95

diff --git a/lib/DataDrivenLux/test/reinforce_solve.jl b/lib/DataDrivenLux/test/reinforce_solve.jl
@@ -34,13 +34,14 @@ dummy_dataset = DataDrivenLux.Dataset(dummy_problem)
 
 b = Basis([x; exp.(x)], x)
 # We have 1 Choices in the first layer, 2 in the last 
-alg = Reinforce(
-    populationsize = 200, functions = (sin, exp, +), arities = (1, 1, 2), rng = rng,
+alg = Reinforce(;
+    populationsize = 200, functions = (sin, exp, +), arities = (1, 1, 2), rng,
     n_layers = 3, use_protected = true, loss = bic, keep = 10, threaded = true,
     optim_options = Optim.Options(time_limit = 0.2), optimiser = AdamW(1e-2))
 
 res = solve(dummy_problem, b, alg,
-    options = DataDrivenCommonOptions(maxiters = 1000, progress = true, abstol = 0.0))
+    options = DataDrivenCommonOptions(
+        maxiters = 1000, progress = parse(Bool, get(ENV, "CI", "false")), abstol = 0.0))
 
 @test rss(res) <= 1e-2
 @test aicc(res) <= -100.0