upgrade to MS 0.9

Author: testercwt

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJMultivariateStatsInterface"
 uuid = "1b6a4a23-ba22-4f51-9698-8599985d3728"
 authors = ["Anthony D. Blaom <[email protected]>", "Thibaut Lienart <[email protected]>", "Okon Samuel <[email protected]>"]
-version = "0.2.2"
+version = "0.2.3"
 
 [deps]
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
@@ -13,7 +13,7 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 [compat]
 Distances = "^0.9,^0.10"
 MLJModelInterface = "^0.3.5,^0.4, 1.0"
-MultivariateStats = "0.7, 0.8"
+MultivariateStats = "0.9"
 StatsBase = "0.32, 0.33"
 julia = "1"
 

src/models/decomposition_models.jl

@@ -44,11 +44,11 @@ function MMI.fit(model::PCA, verbosity::Int, X)
     )
     cache = nothing
     report = (
-        indim=MS.indim(fitresult),
-        outdim=MS.outdim(fitresult),
+        indim=MS.size(fitresult,1),
+        outdim=MS.size(fitresult,2),
         tprincipalvar=MS.tprincipalvar(fitresult),
         tresidualvar=MS.tresidualvar(fitresult),
-        tvar=MS.tvar(fitresult),
+        tvar=MS.var(fitresult),
         mean=copy(MS.mean(fitresult)),
         principalvars=copy(MS.principalvars(fitresult))
     )
@@ -113,9 +113,9 @@ function MMI.fit(model::KernelPCA, verbosity::Int, X)
     )
     cache  = nothing
     report = (
-        indim=MS.indim(fitresult),
-        outdim=MS.outdim(fitresult),
-        principalvars=copy(MS.principalvars(fitresult))
+        indim=MS.size(fitresult,1),
+        outdim=MS.size(fitresult,2),
+        principalvars=copy(MS.eigvals(fitresult))
     )
     return fitresult, cache, report
 end
@@ -168,14 +168,19 @@ $ICA_DESCR
 end
 
 function MMI.fit(model::ICA, verbosity::Int, X)
+    icagfun(fname::Symbol, ::Type{T} = Float64) where T<:Real=
+    fname == :tanh ? MS.Tanh{T}(1.0) :
+    fname == :gaus ? MS.Gaus{T}() :
+    error("Unknown gfun $(fname)")
+
     Xarray = MMI.matrix(X)
     n, p = size(Xarray)
     m = min(n, p)
     k = ifelse(model.k ≤ m, model.k, m)
     fitresult = MS.fit(
         MS.ICA, transpose(Xarray), k;
         alg=model.alg,
-        fun=MS.icagfun(model.fun, eltype(Xarray)),
+        fun=icagfun(model.fun, eltype(Xarray)),
         do_whiten=model.do_whiten,
         maxiter=model.maxiter,
         tol=model.tol,
@@ -184,8 +189,8 @@ function MMI.fit(model::ICA, verbosity::Int, X)
     )
     cache = nothing
     report = (
-        indim=MS.indim(fitresult),
-        outdim=MS.outdim(fitresult),
+        indim=MS.size(fitresult,1),
+        outdim=MS.size(fitresult,2),
         mean=copy(MS.mean(fitresult))
     )
     return fitresult, cache, report
@@ -244,8 +249,8 @@ function MMI.fit(model::PPCA, verbosity::Int, X)
     )
     cache = nothing
     report = (
-        indim=MS.indim(fitresult),
-        outdim=MS.outdim(fitresult),
+        indim=MS.size(fitresult,1),
+        outdim=MS.size(fitresult,2),
         tvar=MS.var(fitresult),
         mean=copy(MS.mean(fitresult)),
         loadings=MS.loadings(fitresult)
@@ -308,8 +313,8 @@ function MMI.fit(model::FactorAnalysis, verbosity::Int, X)
     )
     cache = nothing
     report = (
-        indim=MS.indim(fitresult),
-        outdim=MS.outdim(fitresult),
+        indim=MS.size(fitresult,1),
+        outdim=MS.size(fitresult,2),
         variance=MS.var(fitresult),
         covariance_matrix=MS.cov(fitresult),
         mean=MS.mean(fitresult),
@@ -346,7 +351,7 @@ for (M, MFitResultType) in model_types
     @eval function MMI.transform(::$M, fr::$MFitResultType, X)
         # X is n x d, need to transpose twice
         Xarray = MMI.matrix(X)
-        Xnew = transpose(MS.transform(fr, transpose(Xarray)))
+        Xnew = transpose(MS.predict(fr, transpose(Xarray)))
         return MMI.table(Xnew, prototype=X)
     end
 

src/models/discriminant_analysis.jl

@@ -55,7 +55,7 @@ function MMI.fit(model::LDA, ::Int, X, y)
     cache = nothing
     report = (
         classes=classes_seen,
-        out_dim=MS.outdim(core_res),
+        out_dim=MS.size(core_res)[2],
         class_means=MS.classmeans(core_res),
         mean=MS.mean(core_res),
         class_weights=MS.classweights(core_res),
@@ -221,7 +221,7 @@ function MMI.fit(model::BayesianLDA, ::Int, X, y)
     cache     = nothing
     report    = (
         classes=classes_seen,
-        out_dim=MS.outdim(core_res),
+        out_dim=MS.size(core_res)[2],
         class_means=MS.classmeans(core_res),
         mean=MS.mean(core_res),
         class_weights=MS.classweights(core_res),

test/testutils.jl

@@ -22,7 +22,7 @@ end
 function test_composition_model(ms_model, mlj_model, X, X_array ; test_inverse=true)
     mlj_model_type = typeof(mlj_model)
     Xtr_ms = permutedims(
-        MultivariateStats.transform(ms_model, permutedims(X_array))
+        MultivariateStats.predict(ms_model, permutedims(X_array))
     )  
     fitresult, _, _ = fit(mlj_model, 1, X)
     Xtr_mlj_table = transform(mlj_model, fitresult, X)