Merge pull request #469 from SciML/symbolicsv5

Complete Symbolics v5 update

.github/workflows/CI.yml

@@ -10,6 +10,7 @@ jobs:
   test:
     runs-on: ubuntu-latest
     strategy:
+      fail-fast: false
       matrix:
         group:
           - Core

Project.toml

@@ -20,6 +20,8 @@ Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
+Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
+SymbolicUtils = "d1185830-fcd6-423d-90d6-eec64667417b"
 
 [compat]
 CommonSolve = "0.2"
@@ -35,6 +37,8 @@ RecipesBase = "1"
 Reexport = "1.0"
 Setfield = "1"
 StatsBase = "0.32.0, 0.33"
+Symbolics = "5"
+SymbolicUtils = "1"
 julia = "1.6"
 
 [extras]

lib/DataDrivenLux/src/custom_priors.jl

@@ -53,38 +53,38 @@ function Base.summary(io::IO, d::ObservedDistribution{fixed, D, E}) where {fixed
 end
 
 get_init(d::ObservedDistribution) = d.latent_scale
-get_scale(d::ObservedDistribution) = transform(d.scale_transformation, d.latent_scale)
+get_scale(d::ObservedDistribution) = TransformVariables.transform(d.scale_transformation, d.latent_scale)
 get_dist(d::ObservedDistribution{<:Any, D}) where {D} = D
 
 Base.show(io::IO, d::ObservedDistribution) = summary(io, d)
 
 function Distributions.logpdf(d::ObservedDistribution{false}, x::X, x̂::Y,
                               scale::S) where {X, Y, S <: Number}
-    sum(map(xs -> d.errormodel(get_dist(d), xs..., transform(d.scale_transformation, scale)),
+    sum(map(xs -> d.errormodel(get_dist(d), xs..., TransformVariables.transform(d.scale_transformation, scale)),
             zip(x, x̂)))
 end
 
 function Distributions.logpdf(d::ObservedDistribution{true}, x::X, x̂::Y,
                               scale::S) where {X, Y, S <: Number}
     sum(map(xs -> d.errormodel(get_dist(d), xs...,
-                               transform(d.scale_transformation, d.latent_scale)),
+    TransformVariables.transform(d.scale_transformation, d.latent_scale)),
             zip(x, x̂)))
 end
 
 function Distributions.logpdf(d::ObservedDistribution{false}, x::X, x̂::Number,
                               scale::S) where {X, S <: Number}
     sum(map(xs -> d.errormodel(get_dist(d), xs, x̂,
-                               transform(d.scale_transformation, scale)), x))
+    TransformVariables.transform(d.scale_transformation, scale)), x))
 end
 
 function Distributions.logpdf(d::ObservedDistribution{true}, x::X, x̂::Number,
                               scale::S) where {X, S <: Number}
     sum(map(xs -> d.errormodel(get_dist(d), xs, x̂,
-                               transform(d.scale_transformation, d.latent_scale)), x))
+    TransformVariables.transform(d.scale_transformation, d.latent_scale)), x))
 end
 
 function transform_scales(d::ObservedDistribution, scale::T) where {T <: Number}
-    transform(d.scale_transformation, scale)
+    TransformVariables.transform(d.scale_transformation, scale)
 end
 
 """
@@ -159,7 +159,7 @@ Base.show(io::IO, p::ParameterDistribution) = summary(io, p)
 
 get_init(p::ParameterDistribution) = p.init
 function transform_parameter(p::ParameterDistribution, pval::T) where {T <: Number}
-    transform(p.transformation, pval)
+    TransformVariables.transform(p.transformation, pval)
 end
 get_interval(p::ParameterDistribution) = p.interval
 

lib/DataDrivenSR/Project.toml

@@ -11,7 +11,7 @@ SymbolicRegression = "8254be44-1295-4e6a-a16d-46603ac705cb"
 [compat]
 Reexport = "1.2"
 DataDrivenDiffEq = "1"
-SymbolicRegression = "0.14"
+SymbolicRegression = "0.17"
 julia = "1.6"
 
 [extras]

src/DataDrivenDiffEq.jl

@@ -14,12 +14,11 @@ using Setfield
 
 @reexport using ModelingToolkit
 using ModelingToolkit: AbstractSystem
-using ModelingToolkit: value, operation, arguments, istree, get_observed
-using ModelingToolkit.Symbolics
-using ModelingToolkit.SymbolicUtils
-using ModelingToolkit.Symbolics: scalarize, variable
+using SymbolicUtils: operation, arguments, istree, issym
+using Symbolics
+using Symbolics: scalarize, variable, value
 @reexport using ModelingToolkit: states, parameters, independent_variable, observed,
-                                 controls, get_iv
+                                 controls, get_iv, get_observed
 
 using Random
 using QuadGK

src/basis/utils.jl

@@ -1,12 +1,7 @@
 ## Create linear independent basis
 count_operation(x::Number, op::Function, nested::Bool = true) = 0
-count_operation(x::Sym, op::Function, nested::Bool = true) = 0
-count_operation(x::SymbolicUtils.BasicSymbolic, op::Function, nested::Bool = true) = 0
-function count_operation(x::Num, op::Function, nested::Bool = true)
-    count_operation(value(x), op, nested)
-end
-
-function count_operation(x, op::Function, nested::Bool = true)
+function count_operation(x::SymbolicUtils.BasicSymbolic, op::Function, nested::Bool = true)
+    issym(x) && return 0
     if operation(x) == op
         if is_unary(op)
             # Handles sin, cos and stuff
@@ -23,6 +18,10 @@ function count_operation(x, op::Function, nested::Bool = true)
     return 0
 end
 
+function count_operation(x::Num, op::Function, nested::Bool = true)
+    count_operation(value(x), op, nested)
+end
+
 function count_operation(x, ops::AbstractArray, nested::Bool = true)
     return sum([count_operation(x, op, nested) for op in ops])
 end