BetaML v11.0 Gaussian Mixture Model not compatible with MLJ about betaml.jl HOT 7 CLOSED

(temp) pkg> activate --temp

julia> using MLJ
 │ Package MLJ not found, but a package named MLJ is available from a registry. 
 │ Install package?
 │   (jl_TgwIzK) pkg> add MLJ 
 └ (y/n/o) [y]: y

[...]

julia> model2 = BetaML.Bmlj.GaussianMixtureClusterer()
GaussianMixtureClusterer(
  n_classes = 3, 
  initial_probmixtures = Float64[], 
  mixtures = BetaML.GMM.DiagonalGaussian{Float64}[BetaML.GMM.DiagonalGaussian{Float64}(nothing, nothing), BetaML.GMM.DiagonalGaussian{Float64}(nothing, nothing), BetaML.GMM.DiagonalGaussian{Float64}(nothing, nothing)], 
  tol = 1.0e-6, 
  minimum_variance = 0.05, 
  minimum_covariance = 0.0, 
  initialisation_strategy = "kmeans", 
  maximum_iterations = 9223372036854775807, 
  rng = Random._GLOBAL_RNG())

julia> modelMachine = machine(model2, rand(100, 10))
┌ Warning: The number and/or types of data arguments do not match what the specified model
│ supports. Suppress this type check by specifying `scitype_check_level=0`.
│ 
│ Run `@doc BetaML.GaussianMixtureClusterer` to learn more about your model's requirements.
│ 
│ Commonly, but non exclusively, supervised models are constructed using the syntax
│ `machine(model, X, y)` or `machine(model, X, y, w)` while most other models are
│ constructed with `machine(model, X)`.  Here `X` are features, `y` a target, and `w`
│ sample or class weights.
│ 
│ In general, data in `machine(model, data...)` is expected to satisfy
│ 
│     scitype(data) <: MLJ.fit_data_scitype(model)
│ 
│ In the present case:
│ 
│ scitype(data) = Tuple{AbstractMatrix{Continuous}}
│ 
│ fit_data_scitype(model) = Tuple{Table{<:AbstractVector{<:Union{Missing, Continuous}}}}
└ @ MLJBase ~/.julia/packages/MLJBase/mIaqI/src/machines.jl:231
untrained Machine; caches model-specific representations of data
  model: GaussianMixtureClusterer(n_classes = 3, …)
  args: 
    1:	Source @867 ⏎ AbstractMatrix{Continuous}


julia> fit!(modelMachine)
[ Info: Training machine(GaussianMixtureClusterer(n_classes = 3, …), …).
Iter. 1:	Var. of the post  8.609516054119508 	  Log-likelihood -164.88840388654208
trained Machine; caches model-specific representations of data
  model: GaussianMixtureClusterer(n_classes = 3, …)
  args: 
    1:	Source @867 ⏎ AbstractMatrix{Continuous}

julia> classes_est = predict(modelMachine,rand(10,10))
10-element CategoricalDistributions.UnivariateFiniteVector{Multiclass{3}, Int64, UInt32, Float64}:
 UnivariateFinite{Multiclass{3}}(1=>0.853, 2=>0.00419, 3=>0.142)
 UnivariateFinite{Multiclass{3}}(1=>0.951, 2=>0.0433, 3=>0.00529)
 UnivariateFinite{Multiclass{3}}(1=>0.101, 2=>0.287, 3=>0.613)
 UnivariateFinite{Multiclass{3}}(1=>0.224, 2=>0.69, 3=>0.0857)
 UnivariateFinite{Multiclass{3}}(1=>0.0488, 2=>0.0195, 3=>0.932)
 UnivariateFinite{Multiclass{3}}(1=>0.656, 2=>0.129, 3=>0.215)
 UnivariateFinite{Multiclass{3}}(1=>0.386, 2=>0.46, 3=>0.154)
 UnivariateFinite{Multiclass{3}}(1=>0.888, 2=>0.0113, 3=>0.101)
 UnivariateFinite{Multiclass{3}}(1=>0.617, 2=>0.238, 3=>0.145)
 UnivariateFinite{Multiclass{3}}(1=>0.621, 2=>0.0495, 3=>0.329)

input_scitype = Union{
    MMI.Table(Union{MMI.Continuous,MMI.Missing}),
    AbstractMatrix{<:Union{MMI.Continuous,MMI.Missing}},
}