Support Numo Gem for performing SVD

**Background:**
The slow step of LSI is computing the SVD (singular value decomposition)
of a matrix. Even with a relatively small collection of documents (say,
about 20 blog posts), the native ruby implementation is too slow to be
usable (taking hours to complete).

To work around this problem, classifier-reborn allows you to optionally
use the `gsl` gem to make use of the [Gnu Scientific
Library](https://www.gnu.org/software/gsl/) when performing matrix
calculations. Computations with this gem perform orders of magnitude
faster than the ruby-only matrix implementation, and they're fast enough
that using LSI with Jekyll finishes in a reasonable amount of time
(seconds).

Unfortunately, [rb-gsl](https://github.com/SciRuby/rb-gsl) is
unmaintained -- there's a commit on main that makes it compatible with
Ruby 3, but nobody has released the gem so the only way to use rb-gsl
with Ruby 3 right now is to specify the git hash in your Gemfile. See
SciRuby/rb-gsl#67. This will be increasingly
problematic because Ruby 2.7 is now in [security
maintenance](https://www.ruby-lang.org/en/news/2022/04/12/ruby-2-7-6-released/)
and will become end of life in less than a year.

Notably, `rb-gsl` depends on the
[narray](https://github.com/masa16/narray#new-version-is-under-development---rubynumonarray)
gem. `narray` is deprecated, and the readme suggests using
`Numo::NArray` instead.

**Changes:**
In this PR, my goal is to provide an alternative matrix implementation
that can perform singular value decomposition quickly and works with
Ruby 3. Doing so will make classifier-reborn compatible with Ruby 3
without depending on the unmaintained/unreleased gsl gem. There aren't
many gems that provide fast matrix support for ruby, but
[Numo](https://github.com/ruby-numo) seems to be more actively
maintained than rb-gsl, and Numo has a working Ruby 3 implementation
that can perform a singular value decomposition, which is exactly what
we need. This requires
[numo-narray](https://github.com/ruby-numo/numo-narray) and
[numo-linalg](https://github.com/ruby-numo/numo-linalg).

My goal is to allow users to (optionally) use classifier-reborn with
Numo/Lapack the same way they'd use it with GSL. That is, the user
should install the `numo-narray` and `numo-linalg` gems (with their
required C libraries), and classifier-reborn will detect and use these
if they are found.

.github/workflows/ci.yml

@@ -14,17 +14,17 @@ on:
 
 jobs:
   ci:
-    name: "Run Tests (Ruby ${{ matrix.ruby_version }}, GSL: ${{ matrix.gsl }})"
+    name: "Run Tests (Ruby ${{ matrix.ruby_version }}, Linalg: ${{ matrix.linalg_gem }})"
     runs-on: "ubuntu-latest"
     env:
       # See https://github.com/marketplace/actions/setup-ruby-jruby-and-truffleruby#matrix-of-gemfiles
       BUNDLE_GEMFILE: ${{ matrix.gemfile }}
-      LOAD_GSL: ${{ matrix.gsl }}
+      LINALG_GEM: ${{ matrix.linalg_gem }}
     strategy:
       fail-fast: false
       matrix:
         ruby_version: ["2.7", "3.0", "3.1", "jruby-9.3.4.0"]
-        gsl: [true, false]
+        linalg_gem: ["none", "gsl", "numo"]
         # We use `include` to assign the correct Gemfile for each ruby_version
         include:
           - ruby_version: "2.7"
@@ -39,17 +39,23 @@ jobs:
           # Ruby 3.0 does not work with the latest released gsl gem
           # https://github.com/SciRuby/rb-gsl/issues/67
           - ruby_version: "3.0"
-            gsl: true
+            linalg_gem: "gsl"
           # Ruby 3.1 does not work with the latest released gsl gem
           # https://github.com/SciRuby/rb-gsl/issues/67
           - ruby_version: "3.1"
-            gsl: true
+            linalg_gem: "gsl"
           # jruby-9.3.4.0 doesn't easily build the gsl gem on a GitHub worker. Skipping for now.
           - ruby_version: "jruby-9.3.4.0"
-            gsl: true
+            linalg_gem: "gsl"
+          # jruby-9.3.4.0 doesn't easily build the numo gems on a GitHub worker. Skipping for now.
+          - ruby_version: "jruby-9.3.4.0"
+            linalg_gem: "numo"
     steps:
       - name: Checkout Repository
         uses: actions/checkout@v3
+      - name: Install Lapack
+        if: ${{ matrix.linalg_gem == 'numo' }}
+        run: sudo apt-get install -y liblapacke-dev libopenblas-dev
       - name: "Set up ${{ matrix.label }}"
         uses: ruby/setup-ruby@v1
         with:

.rubocop.yml

@@ -1,7 +1,7 @@
 inherit_from: .rubocop_todo.yml
 
 Style/GlobalVars:
-  AllowedVariables: [$GSL]
+  AllowedVariables: [$SVD]
 
 Naming/MethodName:
   Exclude:

Gemfile

@@ -4,4 +4,9 @@ source 'https://rubygems.org'
 gemspec name: 'classifier-reborn'
 
 # For testing with GSL support & bundle exec
-gem 'gsl' if ENV['LOAD_GSL'] == 'true'
+gem 'gsl' if ENV['LINALG_GEM'] == 'gsl'
+
+if ENV['LINALG_GEM'] == 'numo'
+  gem 'numo-narray'
+  gem 'numo-linalg'
+end

lib/classifier-reborn/lsi.rb

@@ -4,16 +4,28 @@
 # Copyright:: Copyright (c) 2005 David Fayram II
 # License::   LGPL
 
+# Try to load Numo first - it's the most current and the most well-supported.
+# Fall back to GSL.
+# Fall back to native vector.
 begin
   raise LoadError if ENV['NATIVE_VECTOR'] == 'true' # to test the native vector class, try `rake test NATIVE_VECTOR=true`
+  raise LoadError if ENV['GSL'] == 'true' # to test with gsl, try `rake test GSL=true`
 
-  require 'gsl' # requires https://github.com/SciRuby/rb-gsl
-  require_relative 'extensions/vector_serialize'
-  $GSL = true
+  require 'numo/narray' # https://ruby-numo.github.io/narray/
+  require 'numo/linalg' # https://ruby-numo.github.io/linalg/
+  $SVD = :numo
 rescue LoadError
-  $GSL = false
-  require_relative 'extensions/vector'
-  require_relative 'extensions/zero_vector'
+  begin
+    raise LoadError if ENV['NATIVE_VECTOR'] == 'true' # to test the native vector class, try `rake test NATIVE_VECTOR=true`
+
+    require 'gsl' # requires https://github.com/SciRuby/rb-gsl
+    require_relative 'extensions/vector_serialize'
+    $SVD = :gsl
+  rescue LoadError
+    $SVD = :ruby
+    require_relative 'extensions/vector'
+    require_relative 'extensions/zero_vector'
+  end
 end
 
 require_relative 'lsi/word_list'
@@ -140,7 +152,15 @@ def build_index(cutoff = 0.75)
       doc_list = @items.values
       tda = doc_list.collect { |node| node.raw_vector_with(@word_list) }
 
-      if $GSL
+      if $SVD == :numo
+        tdm = Numo::NArray.asarray(tda.map(&:to_a)).transpose
+        ntdm = numo_build_reduced_matrix(tdm, cutoff)
+
+        ntdm.each_over_axis(1).with_index do |col_vec, i|
+          doc_list[i].lsi_vector = col_vec
+          doc_list[i].lsi_norm = col_vec / Numo::Linalg.norm(col_vec)
+        end
+      elsif $SVD == :gsl
         tdm = GSL::Matrix.alloc(*tda).trans
         ntdm = build_reduced_matrix(tdm, cutoff)
 
@@ -201,7 +221,9 @@ def proximity_array_for_content(doc, &block)
       content_node = node_for_content(doc, &block)
       result =
         @items.keys.collect do |item|
-          val = if $GSL
+          val = if $SVD == :numo
+                  content_node.search_vector.dot(@items[item].transposed_search_vector)
+                elsif $SVD == :gsl
                   content_node.search_vector * @items[item].transposed_search_vector
                 else
                   (Matrix[content_node.search_vector] * @items[item].search_vector)[0]
@@ -220,7 +242,8 @@ def proximity_norms_for_content(doc, &block)
       return [] if needs_rebuild?
 
       content_node = node_for_content(doc, &block)
-      if $GSL && content_node.raw_norm.isnan?.all?
+      if ($SVD == :gsl && content_node.raw_norm.isnan?.all?) ||
+          ($SVD == :numo && content_node.raw_norm.isnan.all?)
         puts "There are no documents that are similar to #{doc}"
       else
         content_node_norms(content_node)
@@ -230,7 +253,9 @@ def proximity_norms_for_content(doc, &block)
     def content_node_norms(content_node)
       result =
         @items.keys.collect do |item|
-          val = if $GSL
+          val = if $SVD == :numo
+                  content_node.search_norm.dot(@items[item].search_norm)
+                elsif $SVD == :gsl
                   content_node.search_norm * @items[item].search_norm.col
                 else
                   (Matrix[content_node.search_norm] * @items[item].search_norm)[0]
@@ -332,7 +357,20 @@ def build_reduced_matrix(matrix, cutoff = 0.75)
         s[ord] = 0.0 if s[ord] < s_cutoff
       end
       # Reconstruct the term document matrix, only with reduced rank
-      u * ($GSL ? GSL::Matrix : ::Matrix).diag(s) * v.trans
+      u * ($SVD == :gsl ? GSL::Matrix : ::Matrix).diag(s) * v.trans
+    end
+
+    def numo_build_reduced_matrix(matrix, cutoff = 0.75)
+      s, u, vt = Numo::Linalg.svd(matrix, driver: 'svd', job: 'S')
+
+      # TODO: Better than 75% term (as above)
+      s_cutoff = s.sort.reverse[(s.size * cutoff).round - 1]
+      s.size.times do |ord|
+        s[ord] = 0.0 if s[ord] < s_cutoff
+      end
+
+      # Reconstruct the term document matrix, only with reduced rank
+      u.dot(::Numo::DFloat.eye(s.size) * s).dot(vt)
     end
 
     def node_for_content(item, &block)

lib/classifier-reborn/lsi/content_node.rb

@@ -29,7 +29,11 @@ def search_vector
 
     # Method to access the transposed search vector
     def transposed_search_vector
-      search_vector.col
+      if $SVD == :numo
+        search_vector
+      else
+        search_vector.col
+      end
     end
 
     # Use this to fetch the appropriate search vector in normalized form.
@@ -40,7 +44,9 @@ def search_norm
     # Creates the raw vector out of word_hash using word_list as the
     # key for mapping the vector space.
     def raw_vector_with(word_list)
-      vec = if $GSL
+      vec = if $SVD == :numo
+              Numo::DFloat.zeros(word_list.size)
+            elsif $SVD == :gsl
               GSL::Vector.alloc(word_list.size)
             else
               Array.new(word_list.size, 0)
@@ -51,7 +57,9 @@ def raw_vector_with(word_list)
       end
 
       # Perform the scaling transform and force floating point arithmetic
-      if $GSL
+      if $SVD == :numo
+        total_words = vec.sum.to_f
+      elsif $SVD == :gsl
         sum = 0.0
         vec.each { |v| sum += v }
         total_words = sum
@@ -61,7 +69,7 @@ def raw_vector_with(word_list)
 
       total_unique_words = 0
 
-      if $GSL
+      if [:numo, :gsl].include?($SVD)
         vec.each { |word| total_unique_words += 1 if word != 0.0 }
       else
         total_unique_words = vec.count { |word| word != 0 }
@@ -85,12 +93,15 @@ def raw_vector_with(word_list)
           hash[val] = Math.log(val + 1) / -weighted_total
         end
 
-        vec.collect! do |val|
+        vec = vec.map do |val|
           cached_calcs[val]
         end
       end
 
-      if $GSL
+      if $SVD == :numo
+        @raw_norm   = vec / Numo::Linalg.norm(vec)
+        @raw_vector = vec
+      elsif $SVD == :gsl
         @raw_norm   = vec.normalize
         @raw_vector = vec
       else

test/extensions/matrix_test.rb

@@ -2,7 +2,7 @@
 
 class MatrixTest < Minitest::Test
   def test_zero_division
-    skip "extensions/vector is only used by non-GSL implementation" if $GSL
+    skip "extensions/vector is only used by non-GSL implementation" if $SVD != :ruby
 
     matrix = Matrix[[1, 0], [0, 1]]
     matrix.SV_decomp

test/extensions/zero_vector_test.rb

@@ -2,7 +2,7 @@
 
 class ZeroVectorTest < Minitest::Test
   def test_zero?
-    skip "extensions/zero_vector is only used by non-GSL implementation" if $GSL
+    skip "extensions/zero_vector is only used by non-GSL implementation" if $SVD != :ruby
 
     vec0 = Vector[]
     vec1 = Vector[0]

test/lsi/lsi_test.rb

@@ -163,7 +163,7 @@ def test_cached_content_node_option
   end
 
   def test_clears_cached_content_node_cache
-    skip "transposed_search_vector is only used by GSL implementation" unless $GSL
+    skip "transposed_search_vector is only used by GSL implementation" if $SVD == :ruby
 
     lsi = ClassifierReborn::LSI.new(cache_node_vectors: true)
     lsi.add_item @str1, 'Dog'
@@ -191,8 +191,8 @@ def test_keyword_search
     assert_equal %i[dog text deal], lsi.highest_ranked_stems(@str1)
   end
 
-  def test_invalid_searching_when_using_gsl
-    skip "Only GSL currently raises invalid search error" unless $GSL
+  def test_invalid_searching_with_linalg_lib
+    skip "Only GSL currently raises invalid search error" if $SVD == :ruby
 
     lsi = ClassifierReborn::LSI.new
     lsi.add_item @str1, 'Dog'