Merge pull request #17 from TensorKitchen/fix-decomp-comp-accessor

Fix DecompositionComponent accessor

Author: sChoiKr

src/api/btd.jl

@@ -50,6 +50,9 @@ end
     return solver == :als ? BTDHOSVDMultistartInit() : :alswarm
 end
 
+@inline _btd_solver_symbol(solver::AbstractSolver) =
+    solver isa ALSSolver ? :als : solver_symbol(solver)
+
 function _btd_effective_init(
     solver::Symbol,
     init,
@@ -246,7 +249,9 @@ function btd(
     restart_seed = nothing,
     kwargs...,
 ) where {T<:AbstractFloat,N}
-    init_resolved = _resolve_btd_init(init, solver)
+    solver_obj = _solver_object(solver, stepsize; kwargs...)
+    solver_sym = _btd_solver_symbol(solver_obj)
+    init_resolved = _resolve_btd_init(init, solver_sym)
     init_eff =
         init_resolved == :alswarm ?
         BTDALSWarmStartInit(
@@ -264,12 +269,12 @@ function btd(
     warm_info = (;)
     short_circuited = Ref(false)
     result = with_phase_progress() do
-        if solver ∉ (:als,) && init_eff isa BTDALSWarmStartInit
+        if solver_sym ∉ (:als,) && init_eff isa BTDALSWarmStartInit
             warm = _btd_warm_start_result(
                 model,
                 b,
                 init_eff,
-                solver;
+                solver_sym;
                 verbose = verbose,
                 warm_rel_error_gate,
             )
@@ -285,7 +290,7 @@ function btd(
             model;
             init = solve_init,
             p0 = solve_p0,
-            solver = solver,
+            solver = solver_obj,
             maxiter = maxiter,
             stepsize = stepsize,
             tol = tol,
@@ -311,15 +316,15 @@ function btd(
         isempty(propertynames(warm_info)) ? result :
         _merge_btd_solver_info(result, warm_info)
     polish_n = if btd_als_polish_maxiter === nothing
-        solver == :als ? 0 : clamp(maxiter ÷ 2, 20, 500)
+        solver_sym == :als ? 0 : clamp(maxiter ÷ 2, 20, 500)
     else
         btd_als_polish_maxiter
     end
-    if polish_n > 0 && solver ∉ (:als,)
+    if polish_n > 0 && solver_sym ∉ (:als,)
         result = _polish_btd_with_als(
             b,
             result,
-            solver;
+            solver_sym;
             block_method = block_method,
             block_maxiter = block_maxiter,
             polish_maxiter = polish_n,

src/core/types.jl

@@ -1,6 +1,7 @@
 # core/types.jl — RankOneTensor, CPDResult, ApproxResult, TuckerResult
 export RankOneTensor,
     CPDResult,
+    CPDComponent,
     ApproxResult,
     BTDResult,
     TuckerResult,
@@ -37,6 +38,20 @@ struct RankOneTensor{T<:AbstractFloat}
     vectors::Vector{Vector{T}}
 end
 
+"""
+    CPDComponent{T}
+
+Lazy public component wrapper for CPD-backed join results.
+
+Each component stores its component-specific rank-one point and decoded
+rank-one tensor data. Dense tensors are reconstructed on demand through
+`component.tensor` or [`tensor(component)`](@ref).
+"""
+struct CPDComponent{T<:AbstractFloat,P}
+    point::P
+    component::RankOneTensor{T}
+end
+
 """
     CPDResult{T}
 
@@ -244,7 +259,8 @@ end
 Return the factor vectors of a rank-one tensor component.
 """
 vectors(c::RankOneTensor) = c.vectors
-kind(c::DecompositionComponent) = c.kind
+kind(::CPDComponent) = :Segre
+kind(c::DecompositionComponent) = typeof(c.manifold)
 
 """
     point(x)
@@ -254,28 +270,42 @@ Return the optimization point stored in a result or component.
 Supported inputs include [`CPDResult`](@ref), [`ApproxResult`](@ref),
 [`BTDResult`](@ref), and [`DecompositionComponent`](@ref).
 """
+point(c::CPDComponent) = c.point
 point(c::DecompositionComponent) = c.point
 
 """
     tensor(c::DecompositionComponent)
 
 Reconstruct a component into its ambient tensor representation.
 """
+reconstruct(c::CPDComponent) = reconstruct_cp_rank1(λ(c.component), vectors(c.component))
+tensor(c::CPDComponent) = reconstruct(c)
 tensor(c::DecompositionComponent) = c.tensor
 
 """
     core(x)
 
 Return the Tucker core stored in a Tucker component.
 """
-core(c::DecompositionComponent) = c.core
+core(c::DecompositionComponent) = _component_core(c.point)
 
 """
     factors(x)
 
 Return factor matrices for a CP or Tucker result/component.
 """
-factors(c::DecompositionComponent) = c.factors
+weights(c::CPDComponent) = [λ(c.component)]
+factors(c::CPDComponent) = [reshape(v, :, 1) for v in vectors(c.component)]
+factors(c::DecompositionComponent) = _component_factors(c.point)
+
+function Base.getproperty(c::CPDComponent, name::Symbol)
+    name === :kind && return kind(c)
+    name === :weights && return weights(c)
+    name === :factors && return factors(c)
+    name === :tensor && return reconstruct(c)
+    return getfield(c, name)
+end
+
 point(r::CPDResult) = cpd_point(r)
 point(r::ApproxResult) = r.point
 point(r::BTDResult) = r.point

src/join/cpd_backend.jl

@@ -29,7 +29,7 @@ function JoinModel(
             scale_by_lambda = scale_by_lambda,
             lambda_eps = lambda_eps,
             nonnegative = nonnegative,
-            use_pullback_metric = use_pullback_metric,
+            use_pullback_metric = (geometry_eff == :squaring_metric) || use_pullback_metric,
             pullback_eps = pullback_eps,
         )
     b = CPDBackend(inner)
@@ -172,15 +172,7 @@ function extract_components(model::JoinModel{<:AbstractFloat,<:CPDBackend}, p)
                 λ, U = _decode_nonnegative_cpd(m, λ̃, Ũ)
                 components_from_factors(λ, U)
             end : unpack_point_rankr_components(p, m.dims, m.r)
-        return [
-            (
-                kind = :Segre,
-                point = p,
-                weights = [c.λ],
-                factors = [reshape(c.vectors[j], :, 1) for j = 1:length(c.vectors)],
-                tensor = reconstruct_cp_rank1(c.λ, c.vectors),
-            ) for c in comps
-        ]
+        return [CPDComponent(pack_point_rank1(c.λ, c.vectors), c) for c in comps]
     elseif m isa Rank1CPDModel
         λ, U = unpack_point_rank1(p, m.dims)
         if m.nonnegative
@@ -192,13 +184,8 @@ function extract_components(model::JoinModel{<:AbstractFloat,<:CPDBackend}, p)
                 U = [u .^ 2 for u in U]
             end
         end
-        return [(
-            kind = :Segre,
-            point = p,
-            weights = [λ],
-            factors = [reshape(U[j], :, 1) for j = 1:length(U)],
-            tensor = reconstruct_cp_rank1(λ, U),
-        )]
+        c = RankOneTensor(λ, U)
+        return [CPDComponent(pack_point_rank1(λ, U), c)]
     end
     throw(
         ArgumentError(

test/basic_tests.jl

@@ -223,6 +223,21 @@ end
     @test size(Ahat) == size(A)
     @test rel_error(A, res) == TensorKitchen.relative_frobenius_error(A, Ahat)
     @test rel_error(A, Ahat) == rel_error(A, res)
+
+    model = JoinModel(A, r; geometry = :canonical)
+    p = TensorKitchen.initial_point(model, :random)
+    comps = TensorKitchen.extract_components(model, p)
+    @test length(comps) == r
+    @test comps[1] isa TensorKitchen.CPDComponent
+    @test !(:tensor in fieldnames(typeof(comps[1])))
+    @test comps[1].point !== p
+    @test comps[1].kind == :Segre
+    @test size(comps[1].tensor) == size(A)
+    Xparts = zero(A)
+    for c in comps
+        Xparts .+= c.tensor
+    end
+    @test TensorKitchen.cost(model, p) ≈ 0.5 * sum(abs2, A .- Xparts)
 end
 
 @testset "frontend defaults through public APIs" begin
@@ -562,6 +577,10 @@ end
     @test all(
         isapprox(norm(q_sep.factors[m][:, k]), 1; atol = 1e-10) for m = 1:3 for k = 1:r
     )
+    U_sep, λ_sep = normalize_components(U, λ, SeparateLambdaNormalization())
+    @test U_sep isa Vector{Matrix{Float64}}
+    @test λ_sep isa Vector{Float64}
+    @test reconstruct_cpd_rankr(λ_sep, U_sep) ≈ A_ref
 
     q_last = normalize_components(CPDPoint(λ, U), :last_mode)
     @test reconstruct_cpd_rankr(q_last.lambda, q_last.factors) ≈ A_ref
@@ -886,6 +905,11 @@ end
     )
     @test TensorKitchen.manifold(model_sm) isa ProductManifold
     @test all(m -> m isa SqEuclidean, TensorKitchen.manifold(model_sm).manifolds)
+    join_model_sm = JoinModel(A, r; nonnegative = true, geometry = :squaring_metric)
+    @test all(
+        m -> m isa SqEuclidean,
+        TensorKitchen.manifold(TensorKitchen.cpd_model(join_model_sm)).manifolds,
+    )
     @test getproperty(model_sm, :scale_by_lambda) == false
     res_nn_sm = cpd(
         A,
@@ -1712,6 +1736,28 @@ end
           length(res_btd_tsd.solver_info.line_search_trial_history)
     @test isfinite(res_btd_tsd.rel_error)
     @test res_btd_tsd.rel_error ≈ norm(A - reconstruct(res_btd_tsd)) / norm(A)
+    res_btd_tsd_object = btd(
+        A,
+        2,
+        (2, 2, 2);
+        solver = BTDTSDSolver(stepsize = 1.0),
+        init = :hosvd_multistart,
+        maxiter = 1,
+        schedule = :cyclic,
+        btd_als_polish_maxiter = 0,
+        tol = 1e-6,
+        verbose = false,
+    )
+    @test res_btd_tsd_object isa BTDResult
+    @test res_btd_tsd_object.solver == :btd_tsd
+    @test_throws ArgumentError btd(
+        A,
+        2,
+        (2, 2, 2);
+        solver = :tsd,
+        maxiter = 1,
+        verbose = false,
+    )
 
     manifolds = TensorKitchen._as_join_manifold_tuple(TuckerJoin(size(A), (2, 2, 2), 2))
     backend = TensorKitchen._sum_backend_instance(TensorKitchen.BTDBackend, manifolds, A)