fix: preserve state when niterations=0

Author: MilesCranmer

src/SymbolicRegression.jl

@@ -802,12 +802,32 @@ function _initialize_search!(
     end
     return nothing
 end
+
+function _preserve_loaded_state!(
+    state::AbstractSearchState{T,L,N},
+    ropt::AbstractRuntimeOptions,
+    options::AbstractOptions,
+) where {T,L,N}
+    nout = length(state.worker_output)
+    for j in 1:nout, i in 1:(options.populations)
+        (pop, _, _, _) = extract_from_worker(
+            state.worker_output[j][i], Population{T,L,N}, HallOfFame{T,L,N}
+        )
+        state.last_pops[j][i] = copy(pop)
+    end
+    return nothing
+end
+
 function _warmup_search!(
     state::AbstractSearchState{T,L,N},
     datasets,
     ropt::AbstractRuntimeOptions,
     options::AbstractOptions,
 ) where {T,L,N}
+    if ropt.niterations == 0
+        return _preserve_loaded_state!(state, ropt, options)
+    end
+
     nout = length(datasets)
     for j in 1:nout, i in 1:(options.populations)
         dataset = datasets[j]

test/runtests.jl

@@ -144,9 +144,7 @@ end
     include("test_deterministic.jl")
 end
 
-@testitem "Testing whether early stop criteria works." tags = [:part2] begin
-    include("test_early_stop.jl")
-end
+include("test_early_stop.jl")
 
 include("test_mlj.jl")
 

test/test_early_stop.jl

@@ -1,19 +1,69 @@
-using SymbolicRegression
+@testitem "Early stop condition" tags = [:part2] begin
+    using SymbolicRegression
 
-X = randn(Float32, 5, 100)
-y = 2 * cos.(X[4, :]) + X[1, :] .^ 2
+    X = randn(Float32, 5, 100)
+    y = 2 * cos.(X[4, :]) + X[1, :] .^ 2
 
-early_stop(loss, c) = ((loss <= 1e-10) && (c <= 10))
+    early_stop(loss, c) = ((loss <= 1e-10) && (c <= 10))
 
-options = SymbolicRegression.Options(;
-    binary_operators=(+, *, /, -),
-    unary_operators=(cos, exp),
-    populations=20,
-    early_stop_condition=early_stop,
-)
+    options = SymbolicRegression.Options(;
+        binary_operators=(+, *, /, -),
+        unary_operators=(cos, exp),
+        populations=20,
+        early_stop_condition=early_stop,
+    )
 
-hof = equation_search(X, y; options=options, niterations=1_000_000_000)
+    hof = equation_search(X, y; options=options, niterations=1_000_000_000)
 
-@test any(
-    early_stop(member.loss, count_nodes(member.tree)) for member in hof.members[hof.exists]
-)
+    @test any(
+        early_stop(member.loss, count_nodes(member.tree)) for
+        member in hof.members[hof.exists]
+    )
+end
+
+@testitem "State preservation with niterations=0" tags = [:part2] begin
+    using SymbolicRegression
+    using Random
+
+    # Regression test for https://github.com/MilesCranmer/SymbolicRegression.jl/issues/178
+
+    rng = MersenneTwister(42)
+    X = randn(rng, 2, 10)
+    y = X[1, :] .+ X[2, :]
+
+    options = Options(;
+        binary_operators=(+,),
+        unary_operators=(),
+        verbosity=0,
+        progress=false,
+        population_size=5,
+        populations=2,
+        maxsize=5,
+        tournament_selection_n=2,
+    )
+
+    # Manually create saved state
+    dataset = Dataset(X, y)
+    pop1 = Population(dataset; population_size=5, nlength=3, options=options, nfeatures=2)
+    pop2 = Population(dataset; population_size=5, nlength=3, options=options, nfeatures=2)
+    hof = HallOfFame(options, dataset)
+
+    saved_pops = [[pop1, pop2]]
+    saved_hof = [hof]
+    saved_state = (saved_pops, saved_hof)
+
+    # Run with niterations=0 - should preserve populations
+    result_pops, result_hof = equation_search(
+        X,
+        y;
+        niterations=0,
+        saved_state=saved_state,
+        options=options,
+        parallelism=:serial,
+        return_state=true,
+    )
+
+    # Verify populations are preserved (not reset to size 1)
+    @test length(result_pops[1]) == 2
+    @test all(pop -> length(pop.members) == 5, result_pops[1])
+end