Add the option to ignore ConvergenceExceptions (#233)

src/ica.jl

@@ -107,6 +107,7 @@ Invoke the Fast ICA algorithm[^1].
 - `fun`: The approximate neg-entropy functor of type [`ICAGDeriv`](@ref).
 - `maxiter`: Maximum number of iterations.
 - `tol`: Tolerable change of `W` at convergence.
+- `omit_convergence_exception`: Whether to omit an exception if the function did not converge.
 
 Returns the updated `W`.
 
@@ -116,7 +117,8 @@ function fastica!(W::DenseMatrix{T},         # initialized component matrix, siz
                   X::DenseMatrix{T},         # (whitened) observation sample matrix, size(m, n)
                   fun::ICAGDeriv,            # approximate neg-entropy functor
                   maxiter::Int,              # maximum number of iterations
-                  tol::Real) where {T<:Real} # convergence tolerance
+                  tol::Real,                 # convergence tolerance
+                  omit_convergence_exception::Bool) where {T<:Real}
 
     # argument checking
     m = size(W, 1)
@@ -173,7 +175,11 @@ function fastica!(W::DenseMatrix{T},         # initialized component matrix, siz
 
         @debug "Iteration $t" change=chg tolerance=tol
     end
-    converged || throw(ConvergenceException(maxiter, chg, oftype(chg, tol)))
+
+    if !omit_convergence_exception && !converged
+        throw(ConvergenceException(maxiter, chg, oftype(chg, tol)))
+    end
+
     return W
 end
 
@@ -194,6 +200,7 @@ while each column corresponds to an observation (*e.g* all signal value at a par
 - `do_whiten`: Whether to perform pre-whitening (*default* `true`)
 - `maxiter`: Maximum number of iterations (*default* `100`)
 - `tol`: Tolerable change of ``W`` at convergence (*default* `1.0e-6`)
+- `omit_convergence_exception`: Whether to omit an exception if the function did not converge (*default* `false`).
 - `mean`: The mean vector, which can be either of:
     - `0`: the input data has already been centralized
     - `nothing`: this function will compute the mean (*default*)
@@ -216,6 +223,7 @@ function fit(::Type{ICA}, X::AbstractMatrix{T},# sample matrix, size (m, n)
              do_whiten::Bool=true,             # whether to perform pre-whitening
              maxiter::Integer=100,             # maximum number of iterations
              tol::Real=1.0e-6,                 # convergence tolerance
+             omit_convergence_exception::Bool=false,
              mean=nothing,                     # pre-computed mean
              winit::Matrix{T}=zeros(T,0,0)     # init guess of W, size (m, k)
             ) where {T<:Real}
@@ -247,7 +255,7 @@ function fit(::Type{ICA}, X::AbstractMatrix{T},# sample matrix, size (m, n)
     W = (isempty(winit) ? randn(T, size(Z,1), k) : copy(winit))
 
     # invoke core algorithm
-    fastica!(W, Z, fun, maxiter, tol)
+    fastica!(W, Z, fun, maxiter, tol, omit_convergence_exception)
 
     # construct model
     if do_whiten

src/mmds.jl

@@ -113,6 +113,7 @@ Let `(d, n) = size(X)` be respectively the input dimension and the number of obs
     - any two parameter disparity transformation function, where the first parameter is a vector of proximities (i.e. dissimilarities) and the second parameter is a vector of distances, e.g. `(p,d)->b*p` for some `b` is a transformation function for *ratio* MDS.
 - `tol`: Convergence tolerance (*default* `1.0e-3`)
 - `maxiter`: Maximum number of iterations (*default* `300`)
+- `omit_convergence_exception`: Whether to omit an exception if the function did not converge (*default* `false`).
 - `initial`: an initial reduced space point configuration
     - `nothing`: then an initial configuration is randomly generated (*default*)
     - pre-defined matrix
@@ -129,7 +130,8 @@ function fit(::Type{MetricMDS}, X::AbstractMatrix{T};
              maxiter::Int = 300,
              initial::Union{Nothing,AbstractMatrix{<:Real}} = nothing,
              weights::Union{Nothing,AbstractMatrix{<:Real}} = nothing,
-             distances::Bool) where {T<:Real}
+             distances::Bool,
+             omit_convergence_exception::Bool = false) where {T<:Real}
 
     # get distance matrix and space dimension
     Δ, d = if !distances
@@ -204,7 +206,10 @@ function fit(::Type{MetricMDS}, X::AbstractMatrix{T};
         σ′ = σ
         i += 1
     end
-    converged || throw(ConvergenceException(maxiter, chg, oftype(chg, tol)))
+
+    if !omit_convergence_exception && !converged
+        throw(ConvergenceException(maxiter, chg, oftype(chg, tol)))
+    end
 
     MetricMDS(d, Z, σ′)
 end

src/ppca.jl

@@ -153,12 +153,14 @@ or an empty vector indicating a zero mean.
 
 Returns the resultant [`PPCA`](@ref) model.
 
-**Note:** This function accepts three keyword arguments: `maxoutdim`, `tol`, and `maxiter`.
+**Note:** This function accepts four keyword arguments: `maxoutdim`, `tol`,
+`maxiter` and `omit_convergence_exception`.
 """
 function ppcaem(S::AbstractMatrix{T}, mean::Vector{T}, n::Int;
                 maxoutdim::Int=size(S,1)-1,
                 tol::Real=1.0e-6,   # convergence tolerance
-                maxiter::Integer=1000) where {T<:Real}
+                maxiter::Integer=1000,
+                omit_convergence_exception::Bool = false) where {T<:Real}
 
     check_pcaparams(size(S,1), mean, maxoutdim, 1.)
 
@@ -197,7 +199,10 @@ function ppcaem(S::AbstractMatrix{T}, mean::Vector{T}, n::Int;
         L_old = L
         i += 1
     end
-    converged || throw(ConvergenceException(maxiter, chg, oftype(chg, tol)))
+
+    if !omit_convergence_exception && !converged
+        throw(ConvergenceException(maxiter, chg, oftype(chg, tol)))
+    end
 
     return PPCA(mean, W, σ²)
 end
@@ -217,14 +222,16 @@ or an empty vector indicating a zero mean.
 Returns the resultant [`PPCA`](@ref) model.
 
 **Notes:**
-- This function accepts three keyword arguments: `maxoutdim`, `tol`, and `maxiter`.
+- **Note:** This function accepts four keyword arguments: `maxoutdim`, `tol`,
+`maxiter` and `omit_convergence_exception`.
 - Function uses the `maxoutdim` parameter as an upper boundary when it automatically
 determines the latent space dimensionality.
 """
 function bayespca(S::AbstractMatrix{T}, mean::Vector{T}, n::Int;
                  maxoutdim::Int=size(S,1)-1,
                  tol::Real=1.0e-6,   # convergence tolerance
-                 maxiter::Integer=1000) where {T<:Real}
+                 maxiter::Integer=1000,
+                omit_convergence_exception::Bool = false) where {T<:Real}
 
     check_pcaparams(size(S,1), mean, maxoutdim, 1.)
 
@@ -271,7 +278,10 @@ function bayespca(S::AbstractMatrix{T}, mean::Vector{T}, n::Int;
         L_old = L
         i += 1
     end
-    converged || throw(ConvergenceException(maxiter, chg, oftype(chg, tol)))
+
+    if !omit_convergence_exception && !converged
+        throw(ConvergenceException(maxiter, chg, oftype(chg, tol)))
+    end
 
     return PPCA(mean, W[:,wnorm .> 0.], σ²)
 end
@@ -299,6 +309,7 @@ Let `(d, n) = size(X)` be respectively the input dimension and the number of obs
     - a pre-computed mean vector
 - `tol`: Convergence tolerance (*default* `1.0e-6`)
 - `maxiter`: Maximum number of iterations (*default* `1000`)
+- `omit_convergence_exception`: Whether to omit an exception if the function did not converge (*default* `false`).
 
 **Notes:** This function calls [`ppcaml`](@ref), [`ppcaem`](@ref) or
 [`bayespca`](@ref) internally, depending on the choice of method.
@@ -308,7 +319,8 @@ function fit(::Type{PPCA}, X::AbstractMatrix{T};
              maxoutdim::Int=size(X,1)-1,
              mean=nothing,
              tol::Real=1.0e-6,   # convergence tolerance
-             maxiter::Integer=1000) where {T<:Real}
+             maxiter::Integer=1000,
+                omit_convergence_exception::Bool = false) where {T<:Real}
 
     @assert !SparseArrays.issparse(X) "Use Kernel PCA for sparse arrays"
 
@@ -326,9 +338,25 @@ function fit(::Type{PPCA}, X::AbstractMatrix{T};
     elseif method == :em || method == :bayes
         S = covm(X, isempty(mv) ? 0 : mv, 2)
         if method == :em
-            M = ppcaem(S, mv, n, maxoutdim=maxoutdim, tol=tol, maxiter=maxiter)
+            M = ppcaem(
+                S,
+                mv,
+                n,
+                maxoutdim=maxoutdim,
+                tol=tol,
+                maxiter=maxiter,
+                omit_convergence_exception=omit_convergence_exception
+            )
         elseif method == :bayes
-            M = bayespca(S, mv, n, maxoutdim=maxoutdim, tol=tol, maxiter=maxiter)
+            M = bayespca(
+                S,
+                mv,
+                n,
+                maxoutdim=maxoutdim,
+                tol=tol,
+                maxiter=maxiter,
+                omit_convergence_exception=omit_convergence_exception
+            )
         end
     else
         throw(ArgumentError("Invalid method name $(method)"))

test/ica.jl

@@ -85,6 +85,7 @@ using StatsBase: ConvergenceException
         @test W'C * W ≈ Matrix(I, k, k)
 
         @test_throws ConvergenceException fit(ICA, X, k; do_whiten=true, tol=1e-8, maxiter=2)
+        _ = fit(ICA, X, k; do_whiten=true, tol=1e-8, maxiter=2, omit_convergence_exception=true)
 
         # Use data of different type
         XX = convert(Matrix{Float32}, X)

test/ppca.jl

@@ -113,6 +113,7 @@ import StatsBase
     @test P'P ≈ Matrix(I, 3, 3)
 
     @test_throws StatsBase.ConvergenceException fit(PPCA, X; method=:em, maxiter=1)
+    _ = fit(PPCA, X; method=:em, maxiter=1, omit_convergence_exception=true)
 
     # bayespca
     M0 = fit(PCA, X; mean=mval, maxoutdim = 3)
@@ -139,6 +140,7 @@ import StatsBase
     @test P'P ≈ Matrix(I, 2, 2)
 
     @test_throws StatsBase.ConvergenceException fit(PPCA, X; method=:bayes, maxiter=1)
+    _ = fit(PPCA, X; method=:bayes, maxiter=1, omit_convergence_exception=true)
 
     # Different data types
     # --------------------