Fix CI

lib/DataDrivenSR/Project.toml

@@ -11,7 +11,7 @@ SymbolicRegression = "8254be44-1295-4e6a-a16d-46603ac705cb"
 [compat]
 Reexport = "1.2"
 DataDrivenDiffEq = "1"
-SymbolicRegression = "0.17"
+SymbolicRegression = "1"
 julia = "1.6"
 
 [extras]

lib/DataDrivenSR/src/DataDrivenSR.jl

@@ -22,15 +22,15 @@ using Reexport
 """
 $(TYPEDEF)
 Options for using SymbolicRegression.jl within the `solve` function.
-Automatically creates [`Options`](https://astroautomata.com/SymbolicRegression.jl/stable/api/#Options) with the given specification.
+Automatically creates [`Options`](https://ai.damtp.cam.ac.uk/symbolicregression/stable/api/#Options) with the given specification.
 Sorts the operators stored in `functions` into unary and binary operators on conversion.
 # Fields
 $(FIELDS)
 """
 @with_kw struct EQSearch <: AbstractDataDrivenAlgorithm
     "Optionally weight the loss for each y by this value (same shape as y)"
     weights::Union{AbstractMatrix, AbstractVector, Nothing} = nothing
-    "The number of processes to use, if you want EquationSearch to set this up automatically."
+    "The number of processes to use, if you want `equation_search` to set this up automatically."
     numprocs = nothing
     "If you have set up a distributed run manually with procs = addprocs() and @everywhere, pass the procs to this keyword argument."
     procs::Union{Vector{Int}, Nothing} = nothing
@@ -40,7 +40,7 @@ $(FIELDS)
     parallelism::Union{String, Symbol} = :serial
     "Whether to run (quick) tests before starting the search, to see if there will be any problems during the equation search related to the host environment"
     runtests::Bool = true
-    "Options for 'EquationSearch'"
+    "Options for `equation_search`"
     eq_options::SymbolicRegression.Options = SymbolicRegression.Options()
 end
 
@@ -184,7 +184,7 @@ function (x::EQSearch)(ps::InternalDataDrivenProblem{EQSearch}, X, Y)
     @unpack maxiters, abstol = options
     @unpack weights, eq_options, numprocs, procs, parallelism, runtests = x
 
-    hofs = SymbolicRegression.EquationSearch(X, Y,
+    hofs = SymbolicRegression.equation_search(X, Y;
         niterations = maxiters,
         weights = weights,
         options = eq_options,
@@ -197,7 +197,7 @@ function (x::EQSearch)(ps::InternalDataDrivenProblem{EQSearch}, X, Y)
 
     # Evaluate over the full training data
     paretos = map(enumerate(hofs)) do (i, hof)
-        SymbolicRegression.calculate_pareto_frontier(X, Y[i, :], hof, eq_options)
+        SymbolicRegression.calculate_pareto_frontier(hof)
     end
 
     return SRResult(ps, hofs, paretos)

lib/DataDrivenSR/test/runtests.jl

@@ -9,6 +9,7 @@ X = rand(rng, 2, 50)
 @testset "Simple" begin
     alg = DataDrivenSR.EQSearch(eq_options = Options(unary_operators = [sin, exp],
         binary_operators = [*], maxdepth = 1,
+        seed = 42,
         verbosity = -1, progress = false))
     f(x) = [sin(x[1]); exp(x[2])]
     Y = hcat(map(f, eachcol(X))...)
@@ -21,6 +22,7 @@ end
 @testset "Univariate" begin
     alg = DataDrivenSR.EQSearch(eq_options = Options(unary_operators = [sin, exp],
         binary_operators = [*], maxdepth = 1,
+        seed = 42,
         verbosity = -1, progress = false))
 
     Y = sin.(X[1:1, :])
@@ -33,6 +35,7 @@ end
 @testset "Lifted" begin
     alg = DataDrivenSR.EQSearch(eq_options = Options(unary_operators = [sin, exp],
         binary_operators = [+], maxdepth = 1,
+        seed = 42,
         verbosity = -1, progress = false))
 
     f(x) = [sin(x[1] .^ 2); exp(x[2] * x[1])]

lib/DataDrivenSparse/src/DataDrivenSparse.jl

@@ -25,8 +25,7 @@ abstract type AbstractProximalOperator end
 
 abstract type AbstractSparseRegressionCache <: StatsBase.StatisticalModel end
 
-function _set!(x::AbstractSparseRegressionCache,
-        y::AbstractSparseRegressionCache) where {T <: Number}
+function _set!(x::AbstractSparseRegressionCache, y::AbstractSparseRegressionCache)
     begin
         foreach(eachindex(x.X)) do i
             x.X[i] = y.X[i]

lib/DataDrivenSparse/test/sparse_linear_solve.jl

@@ -64,8 +64,8 @@ end
             cos.(0.5 .* t .^ 2 .- 0.1), exp.(-t))))
     Y = permutedims(0.5 * X[1, :] + 0.22 * X[4, :] - 2.0 * X[3, :])
     X = vcat(X, Y)
-    for alg in [STLSQ, ADMM, SR3]
-        opt = ImplicitOptimizer(alg())
+    for alg in [STLSQ(0.1, 1.0), ADMM(), SR3()]
+        opt = ImplicitOptimizer(alg)
         rescoeff, _... = opt(X, Y, options = DataDrivenCommonOptions(maxiters = 2000))
         @test vec(rescoeff)≈[0.25; 0.0; -1.0; 0.11; 0.0; -0.5] atol=5e-2
     end

src/utils/build_basis.jl

@@ -102,7 +102,7 @@ function _implicit_build_eqs(basis, eqs, p, prob)
         eqs = eqs .~ 0
         try
             # Try to solve the eq for the implicits
-            eqs = ModelingToolkit.solve_for(eqs, implicits)
+            eqs = ModelingToolkit.symbolic_linear_solve(eqs, implicits)
             eqs = implicits .~ eqs
             implicits = Num[]
         catch