[BUG]: PyCall.jlwrap error-- dimensional constraint

[Sshamekh]

What happened?

Hi Miles,
I am trying out the new feature where we can impose the dimensional constraint. I just copied the example available from the collection of toy examples (number ten), and I run to the following error:

Version

0.16.3

Operating System

Linux

Package Manager

Conda

Interface

Jupyter Notebook

Relevant log output

RuntimeError                              Traceback (most recent call last)
Cell In[5], line 1
----> 1 model.fit(
      2     X,
      3     y,
      4     X_units=["Constants.M_sun", "kg", "Constants.R_earth"],
      5     y_units="kg * m / s^2"
      6 )

File /glade/user/opt/miniconda/envs/eq_new/lib/python3.11/site-packages/pysr/sr.py:1970, in PySRRegressor.fit(self, X, y, Xresampled, weights, variable_names, X_units, y_units)
   1967     self._checkpoint()
   1969 # Perform the search:
-> 1970 self._run(X, y, mutated_params, weights=weights, seed=seed)
   1972 # Then, after fit, we save again, so the pickle file contains
   1973 # the equations:
   1974 if not self.temp_equation_file:

File /glade/user/opt/miniconda/envs/eq_new/lib/python3.11/site-packages/pysr/sr.py:1800, in PySRRegressor._run(self, X, y, mutated_params, weights, seed)
   1796     y_variable_names = [f"y{_subscriptify(i)}" for i in range(y.shape[1])]
   1798 # Call to Julia backend.
   1799 # See https://github.com/MilesCranmer/SymbolicRegression.jl/blob/master/src/SymbolicRegression.jl
-> 1800 self.raw_julia_state_ = SymbolicRegression.equation_search(
   1801     Main.X,
   1802     Main.y,
   1803     weights=Main.weights,
   1804     niterations=int(self.niterations),
   1805     variable_names=self.feature_names_in_.tolist(),
   1806     display_variable_names=self.display_feature_names_in_.tolist(),
   1807     y_variable_names=y_variable_names,
   1808     X_units=self.X_units_,
   1809     y_units=self.y_units_,
   1810     options=options,
   1811     numprocs=cprocs,
   1812     parallelism=parallelism,
   1813     saved_state=self.raw_julia_state_,
   1814     return_state=True,
   1815     addprocs_function=cluster_manager,
   1816     progress=progress and self.verbosity > 0 and len(y.shape) == 1,
   1817     verbosity=int(self.verbosity),
   1818 )
   1820 # Set attributes
   1821 self.equations_ = self.get_hof()

RuntimeError: <PyCall.jlwrap (in a Julia function called from Python)
JULIA: MethodError: no method matching _method_instances(::Type{typeof(*)}, ::Type{Tuple{SymbolicRegression.DimensionalAnalysisModule.WildcardQuantity{DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, SymbolicRegression.DimensionalAnalysisModule.WildcardQuantity{DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}}})
The applicable method may be too new: running in world age 45056, while current world is 54697.

Closest candidates are:
  _method_instances(::Any, ::Any) (method too new to be called from this world context.)
   @ Tricks /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/Tricks/7oAyo/src/Tricks.jl:150

Stacktrace:
  [1] #s1778#1
    @ /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/Tricks/7oAyo/src/Tricks.jl:16 [inlined]
  [2] var"#s1778#1"(T::Any, ::Any, f::Any, t::Any)
    @ Tricks ./none:0
  [3] (::Core.GeneratedFunctionStub)(::Any, ::Vararg{Any})
    @ Core ./boot.jl:602
  [4] deg2_eval(op::typeof(*), l::SymbolicRegression.DimensionalAnalysisModule.WildcardQuantity{DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, r::SymbolicRegression.DimensionalAnalysisModule.WildcardQuantity{DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}})
    @ SymbolicRegression.DimensionalAnalysisModule /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/DimensionalAnalysis.jl:142
  [5] violates_dimensional_constraints_dispatch(tree::Node{Float32}, x_units::Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, x::SubArray{Float32, 1, Matrix{Float32}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}, operators::DynamicExpressions.OperatorEnumModule.OperatorEnum)
    @ SymbolicRegression.DimensionalAnalysisModule /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/DimensionalAnalysis.jl:166
  [6] violates_dimensional_constraints_dispatch(tree::Node{Float32}, x_units::Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, x::SubArray{Float32, 1, Matrix{Float32}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}, operators::DynamicExpressions.OperatorEnumModule.OperatorEnum)
    @ SymbolicRegression.DimensionalAnalysisModule /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/DimensionalAnalysis.jl:164
  [7] violates_dimensional_constraints_dispatch(tree::Node{Float32}, x_units::Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, x::SubArray{Float32, 1, Matrix{Float32}, Tuple{Base.Slice{Base.OneTo{Int64}}, Int64}, true}, operators::DynamicExpressions.OperatorEnumModule.OperatorEnum)
    @ SymbolicRegression.DimensionalAnalysisModule /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/DimensionalAnalysis.jl:161
  [8] violates_dimensional_constraints
    @ /glade/useropt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/DimensionalAnalysis.jl:191 [inlined]
  [9] violates_dimensional_constraints
    @ /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/DimensionalAnalysis.jl:177 [inlined]
 [10] dimensional_regularization
    @ /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/LossFunctions.jl:215 [inlined]
 [11] _eval_loss(tree::Node{Float32}, dataset::Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}, Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}, Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.SymbolicDimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, DynamicQuantities.Quantity{Float32, DynamicQuantities.SymbolicDimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, options::Options{Int64, DynamicExpressions.OperatorEnumModule.OperatorEnum, false, Optim.Options{Float64, Nothing}, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, regularization::Bool, idx::Nothing)
    @ SymbolicRegression.LossFunctionsModule /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/LossFunctions.jl:67
 [12] eval_loss(tree::Node{Float32}, dataset::Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}, Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}, Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.SymbolicDimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, DynamicQuantities.Quantity{Float32, DynamicQuantities.SymbolicDimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, options::Options{Int64, DynamicExpressions.OperatorEnumModule.OperatorEnum, false, Optim.Options{Float64, Nothing}, StatsBase.Weights{Float64, Float64, Vector{Float64}}}; regularization::Bool, idx::Nothing)
    @ SymbolicRegression.LossFunctionsModule /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/LossFunctions.jl:101
 [13] eval_loss
    @ /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/LossFunctions.jl:93 [inlined]
 [14] #score_func#5
    @ /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/LossFunctions.jl:160 [inlined]
 [15] score_func
    @ /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/LossFunctions.jl:157 [inlined]
 [16] PopMember(dataset::Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}, Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}, Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.SymbolicDimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, DynamicQuantities.Quantity{Float32, DynamicQuantities.SymbolicDimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, t::Node{Float32}, options::Options{Int64, DynamicExpressions.OperatorEnumModule.OperatorEnum, false, Optim.Options{Float64, Nothing}, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, complexity::Nothing; ref::Int64, parent::Int64, deterministic::Bool)
    @ SymbolicRegression.PopMemberModule /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/PopMember.jl:99
 [17] PopMember (repeats 2 times)
    @ /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/PopMember.jl:88 [inlined]
 [18] #2
    @ ./none:0 [inlined]
 [19] iterate
    @ ./generator.jl:47 [inlined]
 [20] collect(itr::Base.Generator{UnitRange{Int64}, SymbolicRegression.PopulationModule.var"#2#3"{Float32, Int64, Options{Int64, DynamicExpressions.OperatorEnumModule.OperatorEnum, false, Optim.Options{Float64, Nothing}, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, Int64, Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}, Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}, Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.SymbolicDimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, DynamicQuantities.Quantity{Float32, DynamicQuantities.SymbolicDimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}}})
    @ Base ./array.jl:782
 [21] #Population#1
    @ /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/Population.jl:51 [inlined]
 [22] Population
    @ /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/Population.jl:37 [inlined]
 [23] #6
    @ ./none:0 [inlined]
 [24] iterate
    @ ./generator.jl:47 [inlined]
 [25] collect(itr::Base.Generator{UnitRange{Int64}, SymbolicRegression.SearchUtilsModule.var"#6#8"{Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}, Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}, Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.SymbolicDimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, DynamicQuantities.Quantity{Float32, DynamicQuantities.SymbolicDimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, Options{Int64, DynamicExpressions.OperatorEnumModule.OperatorEnum, false, Optim.Options{Float64, Nothing}, StatsBase.Weights{Float64, Float64, Vector{Float64}}}}})
    @ Base ./array.jl:782
 [26] #5
    @ ./none:0 [inlined]
 [27] iterate
    @ ./generator.jl:47 [inlined]
 [28] collect(itr::Base.Generator{Vector{Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}, Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}, Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.SymbolicDimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, DynamicQuantities.Quantity{Float32, DynamicQuantities.SymbolicDimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}}, SymbolicRegression.SearchUtilsModule.var"#5#7"{Int64, Options{Int64, DynamicExpressions.OperatorEnumModule.OperatorEnum, false, Optim.Options{Float64, Nothing}, StatsBase.Weights{Float64, Float64, Vector{Float64}}}}})
    @ Base ./array.jl:782
 [29] init_dummy_pops
    @ /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/SearchUtils.jl:51 [inlined]
 [30] _equation_search(#unused#::Val{:multithreading}, #unused#::Val{1}, datasets::Vector{Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}, Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}, Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.SymbolicDimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, DynamicQuantities.Quantity{Float32, DynamicQuantities.SymbolicDimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}}, niterations::Int64, options::Options{Int64, DynamicExpressions.OperatorEnumModule.OperatorEnum, false, Optim.Options{Float64, Nothing}, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, numprocs::Nothing, procs::Nothing, addprocs_function::Nothing, runtests::Bool, saved_state::Nothing, verbosity::Int64, progress::Bool, #unused#::Val{true})
    @ SymbolicRegression /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/SymbolicRegression.jl:601
 [31] equation_search(datasets::Vector{Dataset{Float32, Float32, Matrix{Float32}, Vector{Float32}, Nothing, NamedTuple{(), Tuple{}}, Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, DynamicQuantities.Quantity{Float32, DynamicQuantities.Dimensions{DynamicQuantities.FixedRational{Int32, 25200}}}, Vector{DynamicQuantities.Quantity{Float32, DynamicQuantities.SymbolicDimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}, DynamicQuantities.Quantity{Float32, DynamicQuantities.SymbolicDimensions{DynamicQuantities.FixedRational{Int32, 25200}}}}}; niterations::Int64, options::Options{Int64, DynamicExpressions.OperatorEnumModule.OperatorEnum, false, Optim.Options{Float64, Nothing}, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, parallelism::String, numprocs::Nothing, procs::Nothing, addprocs_function::Nothing, runtests::Bool, saved_state::Nothing, return_state::Bool, verbosity::Int64, progress::Bool, v_dim_out::Val{1})
    @ SymbolicRegression /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/SymbolicRegression.jl:507
 [32] equation_search(X::Matrix{Float32}, y::Matrix{Float32}; niterations::Int64, weights::Nothing, options::Options{Int64, DynamicExpressions.OperatorEnumModule.OperatorEnum, false, Optim.Options{Float64, Nothing}, StatsBase.Weights{Float64, Float64, Vector{Float64}}}, variable_names::Vector{String}, display_variable_names::Vector{String}, y_variable_names::Nothing, parallelism::String, numprocs::Nothing, procs::Nothing, addprocs_function::Nothing, runtests::Bool, saved_state::Nothing, return_state::Bool, loss_type::Type{Nothing}, verbosity::Int64, progress::Bool, X_units::Vector{String}, y_units::String, v_dim_out::Val{1}, multithreaded::Nothing, varMap::Nothing)
    @ SymbolicRegression /glade/user/opt/miniconda/envs/eq_new/share/pysr/depot/packages/SymbolicRegression/XKtla/src/SymbolicRegression.jl:385

Extra Info

The installed packages are:
Julia 1.9.3
Pysr 0.16.3
python 3.11.5

When I remove the dimensional constraints the code runs with no issue.

I very much appreciate any advice

[MilesCranmer]

Hi @Sshamekh,

Thanks for the bug report. This will be a bit lengthy to fix but I’ll get on it asap. In the meantime you can fix this issue by upgrading to Juli 1.10 (it’s currently in beta, but it will fix things).

Cheers,
Miles

[drcassar]

Can confirm, updating to Julia 1.10 fixed it for me.