Enhancement: Various speedups

[simonmandlik]

Noting down some areas where significant speedups may be achieved:

• vcat in ProductNodes leads to a lot of copying
• data deduplication in leaves may lead to lower memory requirements and also to saving some compute (computing ngrams only once for identical strings in NGramMatrix multiplication)
• deduplicating instances in BagNodes in a similar fashion

[simonmandlik]

PoC for deduplication in NGramMatrices

julia> function Mill._mul(A::AbstractMatrix, S::PooledVector, n, b, m)
           C = zeros(eltype(A), size(A, 1), length(S))
           iz = Mill._init_z(n, b)
           idcs = Dict(r => Queue{Int}() for r in values(S.invpool))
           for (i,r) in S.refs |> enumerate
               enqueue!(idcs[r], i)
           end
           for (s, r) in S.invpool
               z = iz
               for l in 1:Mill._len(s, n)
                   z = Mill._next_ngram(z, l, codeunits(s), n, b)
                   zm = z%m + 1
                   for k in idcs[r]
                       for i in 1:size(C, 1)
                           @inbounds C[i, k] += A[i, zm]
                       end
                   end
               end
           end
           C
       end

julia> ss = [randstring(50), randstring(50)];
julia> S = [rand(ss) for _ in 1:100];
julia> n1 = NGramMatrix(S);
julia> n2 = NGramMatrix(PooledArray(S));
julia> x = randn(100, 2053);
julia> x*n1; @btime x*n1;
  181.145 μs (2 allocations: 78.20 KiB)

julia> x*n2; @btime x*n2;
  108.265 μs (14 allocations: 95.23 KiB)

[pevnak]

I guess that dedup in NGramMatrices will offer the highest benefit. Multiplication of OneHot is essentially a copying, and Dense matrices should not contain that many duplicates (although they might).

[simonmandlik]

Sure, and deduplication of instances in BagNodes as well. That said, it is possible that in some cases the vanilla version will still be faster