Support Numo Gem for performing SVD

.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

docs/index.md

@@ -60,12 +60,34 @@ The only runtime dependency of this gem is Roman Shterenzon's `fast-stemmer` gem
 gem install fast-stemmer
 ```
 
-To speed up `LSI` classification by at least 10x consider installing following libraries.
+In addition, it is **recommended** to install either Numo or GSL to speed up LSI classification by at least 10x.
 
-* [GSL - GNU Scientific Library](http://www.gnu.org/software/gsl)
-* [Ruby/GSL Gem](https://rubygems.org/gems/gsl)
+Note that LSI will work without these libraries, but as soon as they are installed, classifier will make use of them. No configuration changes are needed, we like to keep things ridiculously easy for you.
+
+### Install Numo Gems
+
+[Numo](https://ruby-numo.github.io/narray/) is a set of Numerical Module gems for Ruby that provide a Ruby interface to [LAPACK](http://www.netlib.org/lapack/). If classifier detects that the required Numo gems are installed, it will make use of them to perform LSI faster. 
+
+* Install [LAPACKE](https://www.netlib.org/lapack/lapacke.html)
+  * Ubuntu: `apt-get install liblapacke-dev`
+  * macOS: (Help wanted to verify installation steps) https://stackoverflow.com/questions/38114201/installing-lapack-and-blas-libraries-for-c-on-mac-os
+* Install [OpenBLAS](https://www.openblas.net/)
+  * Ubuntu: `apt-get install libopenblas-dev`
+  * macOS: (Help wanted to verify installation steps) https://stackoverflow.com/questions/38114201/installing-lapack-and-blas-libraries-for-c-on-mac-os
+* Install the [Numo::NArray](https://ruby-numo.github.io/narray/) and [Numo::Linalg](https://ruby-numo.github.io/linalg/) gems
+  * `gem install numo-narray numo-linalg`
+
+### Install GSL Gem
+
+**Note:** The `gsl` gem is currently incompatible with Ruby 3. It is recommended to use Numo instead with Ruby 3.
+
+The [GNU Scientific Library (GSL)](http://www.gnu.org/software/gsl) is an alternative to Numo/LAPACK that can be used to improve LSI performance. (You should install one or the other, but both are not required.)
+
+* Install the [GNU Scientific Library](http://www.gnu.org/software/gsl)
+  * Ubuntu: `apt-get install libgsl-dev`
+* Install the [Ruby/GSL Gem](https://rubygems.org/gems/gsl) (or add it to your Gemfile)
+  * `gem install gsl`
 
-Note that `LSI` will work without these libraries, but as soon as they are installed, classifier will make use of them. No configuration changes are needed, we like to keep things ridiculously easy for you.
 
 ## Further Readings
 

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'