Fix master CI: DataDrivenLux downgrade resolution + docs linkcheck

docs/make.jl

@@ -87,6 +87,9 @@ makedocs(
         "https://royalsocietypublishing.org/doi/10.1098/rspa.2020.0279",
         "https://www.pnas.org/doi/10.1073/pnas.1517384113",
         "https://link.springer.com/article/10.1007/s00332-015-9258-5",
+        # SciML's hosted docs reject Documenter's linkcheck crawler with HTTP 403,
+        # though the cross-doc links resolve fine in a browser.
+        r"^https://docs\.sciml\.ai/.*",
     ],
     format = Documenter.HTML(
         assets = ["assets/favicon.ico"],

docs/src/libs/datadrivensr/example_01.jl

@@ -33,7 +33,7 @@ prob = ContinuousDataDrivenProblem(X, t, U = U)
 
 #md plot(prob)
 
-# To solve our problem, we will use [`EQSearch`](@ref), which provides a wrapper for the [symbolic regression interface](https://ai.damtp.cam.ac.uk/symbolicregression/stable/api/#Options).
+# To solve our problem, we will use [`EQSearch`](@ref), which provides a wrapper for the [symbolic regression interface](https://docs.sciml.ai/SymbolicRegression/stable/api/#Options).
 # We will stick to simple operations, use a `L1DistLoss`, and limit the verbosity of the algorithm.
 
 eqsearch_options = SymbolicRegression.Options(
@@ -46,7 +46,7 @@ eqsearch_options = SymbolicRegression.Options(
 alg = EQSearch(eq_options = eqsearch_options)
 
 # Again, we `solve` the problem to obtain a [`DataDrivenSolution`](@ref). Note that any additional keyword arguments are passed onto
-# symbolic regressions [`EquationSearch`](https://ai.damtp.cam.ac.uk/symbolicregression/stable/api/#EquationSearch) with the exception of `niterations` which
+# symbolic regressions [`equation_search`](https://docs.sciml.ai/SymbolicRegression/stable/api/#equation_search) with the exception of `niterations` which
 # is `maxiters`
 
 res = solve(prob, alg, options = DataDrivenCommonOptions(maxiters = 100))

docs/src/libs/datadrivensr/example_02.jl

@@ -27,7 +27,7 @@ prob = DataDrivenProblem(sol)
 
 #md plot(prob)
 
-# To solve our problem, we will use [`EQSearch`](@ref), which provides a wrapper for the [symbolic regression interface](https://ai.damtp.cam.ac.uk/symbolicregression/stable/api/#Options).
+# To solve our problem, we will use [`EQSearch`](@ref), which provides a wrapper for the [symbolic regression interface](https://docs.sciml.ai/SymbolicRegression/stable/api/#Options).
 # We will stick to simple operations, use a `L1DistLoss`, and limit the verbosity of the algorithm.
 # Note that we do not include `sin`, but rather lift the search space of variables.
 

lib/DataDrivenLux/src/algorithms/common.jl

@@ -1,19 +1,45 @@
-@kwdef @concrete struct CommonAlgOptions
-    populationsize::Int = 100
-    functions = (sin, exp, cos, log, +, -, /, *)
-    arities = (1, 1, 1, 1, 2, 2, 2, 2)
-    n_layers::Int = 1
-    skip::Bool = true
-    simplex <: AbstractSimplex = Softmax()
-    loss = aicc
-    keep <: Union{Real, Int} = 0.1
-    use_protected::Bool = true
-    distributed::Bool = false
-    threaded::Bool = false
-    rng <: AbstractRNG = Random.default_rng()
-    optimizer = LBFGS()
-    optim_options <: Optim.Options = Optim.Options()
-    optimiser <: Union{Nothing, Optimisers.AbstractRule} = nothing
-    observed <: Union{ObservedModel, Nothing} = nothing
-    alpha::Real = 0.999f0
+@concrete struct CommonAlgOptions
+    populationsize::Int
+    functions
+    arities
+    n_layers::Int
+    skip::Bool
+    simplex <: AbstractSimplex
+    loss
+    keep <: Union{Real, Int}
+    use_protected::Bool
+    distributed::Bool
+    threaded::Bool
+    rng <: AbstractRNG
+    optimizer
+    optim_options <: Optim.Options
+    optimiser <: Union{Nothing, Optimisers.AbstractRule}
+    observed <: Union{ObservedModel, Nothing}
+    alpha::Real
+end
+
+function CommonAlgOptions(;
+        populationsize::Int = 100,
+        functions = (sin, exp, cos, log, +, -, /, *),
+        arities = (1, 1, 1, 1, 2, 2, 2, 2),
+        n_layers::Int = 1,
+        skip::Bool = true,
+        simplex::AbstractSimplex = Softmax(),
+        loss = aicc,
+        keep::Union{Real, Int} = 0.1,
+        use_protected::Bool = true,
+        distributed::Bool = false,
+        threaded::Bool = false,
+        rng::AbstractRNG = Random.default_rng(),
+        optimizer = LBFGS(),
+        optim_options::Optim.Options = Optim.Options(),
+        optimiser::Union{Nothing, Optimisers.AbstractRule} = nothing,
+        observed::Union{ObservedModel, Nothing} = nothing,
+        alpha::Real = 0.999f0
+    )
+    return CommonAlgOptions(
+        populationsize, functions, arities, n_layers, skip, simplex, loss, keep,
+        use_protected, distributed, threaded, rng, optimizer, optim_options,
+        optimiser, observed, alpha
+    )
 end

lib/DataDrivenLux/src/caches/candidate.jl

@@ -50,27 +50,27 @@ $(FIELDS)
 """
 @concrete struct Candidate <: StatsBase.StatisticalModel
     "Random seed"
-    rng <: AbstractRNG
+    rng
     "The current state"
-    st <: NamedTuple
+    st
     "The current parameters"
-    ps <: AbstractVector
+    ps
     "Incoming paths"
-    incoming_path <: Vector{<:AbstractPathState}
+    incoming_path
     "Outgoing path"
-    outgoing_path <: Vector{<:AbstractPathState}
+    outgoing_path
     "Statistics"
-    statistics <: PathStatistics
+    statistics
     "The observed model"
-    observed <: ObservedModel
+    observed
     "The parameter distribution"
-    parameterdist <: ParameterDistributions
+    parameterdist
     "The optimal scales"
-    scales <: AbstractVector
+    scales
     "The optimal parameters"
-    parameters <: AbstractVector
+    parameters
     "The component model"
-    model <: ComponentModel
+    model
 end
 
 function (c::Candidate)(dataset::Dataset{T}, ps = c.ps, p = c.parameters) where {T}

lib/DataDrivenLux/src/caches/dataset.jl

@@ -25,7 +25,7 @@ function Dataset(
     x_intervals = interval.(map(extrema, eachrow(X)))
     y_intervals = interval.(map(extrema, eachrow(Y)))
     u_intervals = interval.(map(extrema, eachrow(U)))
-    t_intervals = isempty(t) ? Interval{T}(zero(T), zero(T)) : interval(extrema(t))
+    t_intervals = isempty(t) ? interval(zero(T), zero(T)) : interval(extrema(t))
     return Dataset{T}(X, Y, U, t, x_intervals, y_intervals, u_intervals, t_intervals)
 end
 

lib/DataDrivenLux/src/custom_priors.jl

@@ -47,8 +47,11 @@ function ObservedDistribution(
     return ObservedDistribution{fixed, D}(errormodel, latent_scale, transform)
 end
 
-function Base.summary(io::IO, ::ObservedDistribution{fixed, D}) where {fixed, D}
-    return print(io, "$E : $D() with $(fixed ? "fixed" : "variable") scale.")
+function Base.summary(io::IO, d::ObservedDistribution{fixed, D}) where {fixed, D}
+    return print(
+        io,
+        "$(nameof(typeof(d.errormodel))) : $D() with $(fixed ? "fixed" : "variable") scale."
+    )
 end
 
 get_init(d::ObservedDistribution) = d.latent_scale

lib/DataDrivenLux/src/lux/path_state.jl

@@ -48,11 +48,13 @@ function update_path(::Nothing, id::Tuple{Int, Int}, state::PathState{T}) where
 end
 
 function update_path(
-        f::F where {F <: Function}, id::Tuple{Int, Int}, states::PathState{T}...
+        f::F where {F <: Function}, id::Tuple{Int, Int},
+        state1::PathState{T}, states::PathState{T}...
     ) where {T}
+    allstates = (state1, states...)
     return PathState{T}(
-        f(get_interval.(states)...), (f, tuplejoin(map(get_operators, states)...)...),
-        (id, tuplejoin(map(get_nodes, states)...)...)
+        f(get_interval.(allstates)...), (f, tuplejoin(map(get_operators, allstates)...)...),
+        (id, tuplejoin(map(get_nodes, allstates)...)...)
     )
 end
 

lib/DataDrivenLux/test/qa/Project.toml

@@ -1,10 +1,12 @@
 [deps]
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
+DataDrivenDiffEq = "2445eb08-9709-466a-b3fc-47e12bd697a2"
 DataDrivenLux = "47881146-99d0-492a-8425-8f2f33327637"
 JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [sources]
+DataDrivenDiffEq = {path = "../../../.."}
 DataDrivenLux = {path = "../.."}
 
 [compat]

lib/DataDrivenLux/test/runtests.jl

@@ -10,6 +10,18 @@ const GROUP = get(ENV, "DATADRIVENDIFFEQ_TEST_GROUP", get(ENV, "GROUP", "All"))
 
 function activate_qa_env()
     Pkg.activate(joinpath(@__DIR__, "qa"))
+    # On Julia < 1.11 the qa env's [sources] table is ignored, so the in-repo
+    # DataDrivenLux/DataDrivenDiffEq would resolve as registered packages and QA
+    # would analyze stale released code. Develop the local paths to restore the
+    # 1.11+ [sources] behavior (no-op effect on >= 1.11, which honors [sources]).
+    if VERSION < v"1.11.0-DEV.0"
+        Pkg.develop(
+            [
+                Pkg.PackageSpec(path = joinpath(@__DIR__, "..", "..", "..")),
+                Pkg.PackageSpec(path = joinpath(@__DIR__, "..")),
+            ]
+        )
+    end
     return Pkg.instantiate()
 end
 

lib/DataDrivenSR/src/DataDrivenSR.jl

@@ -22,7 +22,7 @@ using Reexport
 """
 $(TYPEDEF)
 Options for using SymbolicRegression.jl within the `solve` function.
-Automatically creates [`Options`](https://ai.damtp.cam.ac.uk/symbolicregression/stable/api/#Options) with the given specification.
+Automatically creates [`Options`](https://docs.sciml.ai/SymbolicRegression/stable/api/#Options) with the given specification.
 Sorts the operators stored in `functions` into unary and binary operators on conversion.
 
 # Fields
@@ -89,7 +89,7 @@ end
 is_success(k::SRResult) = getfield(k, :retcode) == DDReturnCode(1)
 
 # StatsBase Overload
-StatsBase.coef(x::SRResult) = getfield(x, :k)
+StatsBase.coef(x::SRResult) = get_parameter_values(getfield(x, :basis))
 
 StatsBase.rss(x::SRResult) = getfield(x, :rss)
 

lib/DataDrivenSR/test/qa/Project.toml

@@ -1,10 +1,12 @@
 [deps]
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
+DataDrivenDiffEq = "2445eb08-9709-466a-b3fc-47e12bd697a2"
 DataDrivenSR = "7fed8a53-d475-4873-af3a-ba53cceea094"
 JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [sources]
+DataDrivenDiffEq = {path = "../../../.."}
 DataDrivenSR = {path = "../.."}
 
 [compat]

lib/DataDrivenSR/test/runtests.jl

@@ -8,6 +8,18 @@ const GROUP = get(ENV, "DATADRIVENDIFFEQ_TEST_GROUP", get(ENV, "GROUP", "All"))
 
 function activate_qa_env()
     Pkg.activate(joinpath(@__DIR__, "qa"))
+    # On Julia < 1.11 the qa env's [sources] table is ignored, so the in-repo
+    # DataDrivenSR/DataDrivenDiffEq would resolve as registered packages and QA
+    # would analyze stale released code. Develop the local paths to restore the
+    # 1.11+ [sources] behavior (no-op effect on >= 1.11, which honors [sources]).
+    if VERSION < v"1.11.0-DEV.0"
+        Pkg.develop(
+            [
+                Pkg.PackageSpec(path = joinpath(@__DIR__, "..", "..", "..")),
+                Pkg.PackageSpec(path = joinpath(@__DIR__, "..")),
+            ]
+        )
+    end
     return Pkg.instantiate()
 end
 

src/basis/build_function.jl

@@ -261,6 +261,20 @@ function (f::DataDrivenFunction{true, false})(
     return _apply_vec_function(f, du, u, p, t, __EMPTY_MATRIX)
 end
 
+function (f::DataDrivenFunction{true, true})(
+        du::AbstractMatrix, u::AbstractMatrix, p::P,
+        t::AbstractVector,
+        c::AbstractMatrix
+    ) where {
+        P <:
+        Union{
+            AbstractArray,
+            Tuple,
+        },
+    }
+    return _apply_vec_function(f, du, u, p, t, c)
+end
+
 ## IIP
 
 function (f::DataDrivenFunction{false, false})(
@@ -302,3 +316,18 @@ function (f::DataDrivenFunction{true, false})(
     }
     return _apply_vec_function!(f, res, du, u, p, t, __EMPTY_MATRIX)
 end
+
+function (f::DataDrivenFunction{true, true})(
+        res::AbstractMatrix, du::AbstractMatrix,
+        u::AbstractMatrix, p::P,
+        t::AbstractVector,
+        c::AbstractMatrix
+    ) where {
+        P <:
+        Union{
+            AbstractArray,
+            Tuple,
+        },
+    }
+    return _apply_vec_function!(f, res, du, u, p, t, c)
+end