Class: ClassifierReborn::LSI

### Home Classes Methods

### In Files

- lib/classifier-reborn/lsi.rb

### Parent

Object

### Methods

- ::new
- #<<
- #add_item
- #build_index
- #categories_for
- #classify
- #find_related
- #highest_ranked_stems
- #highest_relative_content
- #items
- #needs_rebuild?
- #proximity_array_for_content
- #proximity_norms_for_content
- #remove_item
- #search

### Files

- LICENSE

### Class/Module Index

Quicksearch

- ClassifierReborn
- ClassifierReborn::Bayes
- ClassifierReborn::CategoryNamer
- ClassifierReborn::ContentNode
- ClassifierReborn::Hasher
- ClassifierReborn::LSI
- ClassifierReborn::Summarizer
- ClassifierReborn::WordList
- GSL
- GSL::Matrix
- GSL::Vector
- Matrix

No matching classes.

# ClassifierReborn::LSI

This class implements a Latent Semantic Indexer, which can search, classify and cluster data based on underlying semantic relations. For more information on the algorithms used, please consult Wikipedia.

### Attributes

auto_rebuild[RW]
word_list[R]

### Public Class Methods

new(options = {}) click to toggle source

Create a fresh index. If you want to call build_index manually, use

    ClassifierReborn::LSI.new :auto_rebuild => false

    # File lib/classifier-reborn/lsi.rb, line 34
    def initialize(options = {})
      @auto_rebuild = true unless options[:auto_rebuild] == false
      @word_list, @items = WordList.new, {}
      @version, @built_at_version = 0, -1
    end

### Public Instance Methods

<<( item ) click to toggle source

A less flexible shorthand for add_item that assumes you are passing in a string with no categorries. item will be duck typed via to_s .

    # File lib/classifier-reborn/lsi.rb, line 71
    def <<( item )
      add_item item
    end

add_item( item, *categories, &block ) click to toggle source

Adds an item to the index. item is assumed to be a string, but any item may be indexed so long as it responds to to_s or if you provide an optional block explaining how the indexer can fetch fresh string data. This optional block is passed the item, so the item may only be a reference to a URL or file name.

For example:

    lsi = ClassifierReborn::LSI.new
    lsi.add_item "This is just plain text"
    lsi.add_item "/home/me/filename.txt" { |x| File.read x }
    ar = ActiveRecordObject.find( :all )
    lsi.add_item ar, *ar.categories { |x| ar.content }

    # File lib/classifier-reborn/lsi.rb, line 60
    def add_item( item, *categories, &block )
      clean_word_hash = Hasher.clean_word_hash(block ? block.call(item) : item.to_s)
      @items[item] = ContentNode.new(clean_word_hash, *categories)
      @version += 1
      build_index if @auto_rebuild
    end

build_index( cutoff=0.75 ) click to toggle source

This function rebuilds the index if needs_rebuild? returns true. For very large document spaces, this indexing operation may take some time to complete, so it may be wise to place the operation in another thread.

As a rule, indexing will be fairly swift on modern machines until you have well over 500 documents indexed, or have an incredibly diverse vocabulary for your documents.

The optional parameter "cutoff" is a tuning parameter. When the index is built, a certain number of s-values are discarded from the system. The cutoff parameter tells the indexer how many of these values to keep. A value of 1 for cutoff means that no semantic analysis will take place, turning the LSI class into a simple vector search engine.

    # File lib/classifier-reborn/lsi.rb, line 117
    def build_index( cutoff=0.75 )
      return unless needs_rebuild?
      make_word_list

      doc_list = @items.values
      tda = doc_list.collect { |node| node.raw_vector_with( @word_list ) }

      if $GSL
         tdm = GSL::Matrix.alloc(*tda).trans
         ntdm = build_reduced_matrix(tdm, cutoff)

         ntdm.size[1].times do |col|
           vec = GSL::Vector.alloc( ntdm.column(col) ).row
           doc_list[col].lsi_vector = vec
           doc_list[col].lsi_norm = vec.normalize
         end
      else
         tdm = Matrix.rows(tda).trans
         ntdm = build_reduced_matrix(tdm, cutoff)

         ntdm.row_size.times do |col|
           doc_list[col].lsi_vector = ntdm.column(col) if doc_list[col]
           doc_list[col].lsi_norm = ntdm.column(col).normalize  if doc_list[col]
         end
      end

      @built_at_version = @version
    end

categories_for(item) click to toggle source

Returns the categories for a given indexed items. You are free to add and remove items from this as you see fit. It does not invalide an index to change its categories.

    # File lib/classifier-reborn/lsi.rb, line 77
    def categories_for(item)
      return [] unless @items[item]
      return @items[item].categories
    end

classify( doc, cutoff=0.30, &block ) click to toggle source

This function uses a voting system to categorize documents, based on the categories of other documents. It uses the same logic as the find_related function to find related documents, then returns the most obvious category from this list.

cutoff signifies the number of documents to consider when clasifying text. A cutoff of 1 means that every document in the index votes on what category the document is in. This may not always make sense.

    # File lib/classifier-reborn/lsi.rb, line 251
    def classify( doc, cutoff=0.30, &block )
      icutoff = (@items.size * cutoff).round
      carry = proximity_array_for_content( doc, &block )
      carry = carry[0..icutoff-1]
      votes = {}
      carry.each do |pair|
        categories = @items[pair[0]].categories
        categories.each do |category|
          votes[category] ||= 0.0
          votes[category] += pair[1]
        end
      end

      ranking = votes.keys.sort_by { |x| votes[x] }
      return ranking[-1]
    end

find_related( doc, max_nearest=3, &block ) click to toggle source

This function takes content and finds other documents that are semantically "close", returning an array of documents sorted from most to least relavant. max_nearest specifies the number of documents to return. A value of 0 means that it returns all the indexed documents, sorted by relavence.

This is particularly useful for identifing clusters in your document space. For example you may want to identify several "What's Related" items for weblog articles, or find paragraphs that relate to each other in an essay.

    # File lib/classifier-reborn/lsi.rb, line 235
    def find_related( doc, max_nearest=3, &block )
      carry =
        proximity_array_for_content( doc, &block ).reject { |pair| pair[0] == doc }
      result = carry.collect { |x| x[0] }
      return result[0..max_nearest-1]
    end

highest_ranked_stems( doc, count=3 ) click to toggle source

Prototype, only works on indexed documents. I have no clue if this is going to work, but in theory it's supposed to.

    # File lib/classifier-reborn/lsi.rb, line 271
    def highest_ranked_stems( doc, count=3 )
      raise "Requested stem ranking on non-indexed content!" unless @items[doc]
      arr = node_for_content(doc).lsi_vector.to_a
      top_n = arr.sort.reverse[0..count-1]
      return top_n.collect { |x| @word_list.word_for_index(arr.index(x))}
    end

highest_relative_content( max_chunks=10 ) click to toggle source

This method returns max_chunks entries, ordered by their average semantic rating. Essentially, the average distance of each entry from all other entries is calculated, the highest are returned.

This can be used to build a summary service, or to provide more information about your dataset's general content. For example, if you were to use categorize on the results of this data, you could gather information on what your dataset is generally about.

    # File lib/classifier-reborn/lsi.rb, line 154
    def highest_relative_content( max_chunks=10 )
       return [] if needs_rebuild?

       avg_density = Hash.new
       @items.each_key { |x| avg_density[x] = proximity_array_for_content(x).inject(0.0) { |x,y| x + y[1]} }

       avg_density.keys.sort_by { |x| avg_density[x] }.reverse[0..max_chunks-1].map
    end

items() click to toggle source

Returns an array of items that are indexed.

    # File lib/classifier-reborn/lsi.rb, line 92
    def items
      @items.keys
    end

needs_rebuild?() click to toggle source

Returns true if the index needs to be rebuilt. The index needs to be built after all informaton is added, but before you start using it for search, classification and cluster detection.

    # File lib/classifier-reborn/lsi.rb, line 43
    def needs_rebuild?
      (@items.keys.size > 1) && (@version != @built_at_version)
    end

proximity_array_for_content( doc, &block ) click to toggle source

This function is the primitive that find_related and classify build upon. It returns an array of 2-element arrays. The first element of this array is a document, and the second is its "score", defining how "close" it is to other indexed items.

These values are somewhat arbitrary, having to do with the vector space created by your content, so the magnitude is interpretable but not always meaningful between indexes.

The parameter doc is the content to compare. If that content is not indexed, you can pass an optional block to define how to create the text data. See add_item for examples of how this works.

    # File lib/classifier-reborn/lsi.rb, line 175
    def proximity_array_for_content( doc, &block )
      return [] if needs_rebuild?

      content_node = node_for_content( doc, &block )
      result =
        @items.keys.collect do |item|
          if $GSL
             val = content_node.search_vector * @items[item].search_vector.col
          else
             val = (Matrix[content_node.search_vector] * @items[item].search_vector)[0]
          end
          [item, val]
        end
      result.sort_by { |x| x[1] }.reverse
    end

proximity_norms_for_content( doc, &block ) click to toggle source

Similar to proximity_array_for_content, this function takes similar arguments and returns a similar array. However, it uses the normalized calculated vectors instead of their full versions. This is useful when you're trying to perform operations on content that is much smaller than the text you're working with. search uses this primitive.

    # File lib/classifier-reborn/lsi.rb, line 196
    def proximity_norms_for_content( doc, &block )
      return [] if needs_rebuild?

      content_node = node_for_content( doc, &block )
      result =
        @items.keys.collect do |item|
          if $GSL
            val = content_node.search_norm * @items[item].search_norm.col
          else
            val = (Matrix[content_node.search_norm] * @items[item].search_norm)[0]
          end
          [item, val]
        end
      result.sort_by { |x| x[1] }.reverse
    end

remove_item( item ) click to toggle source

Removes an item from the database, if it is indexed.

    # File lib/classifier-reborn/lsi.rb, line 84
    def remove_item( item )
      if @items.key? item
        @items.delete item
        @version += 1
      end
    end

search( string, max_nearest=3 ) click to toggle source

This function allows for text-based search of your index. Unlike other functions like find_related and classify, search only takes short strings. It will also ignore factors like repeated words. It is best for short, google-like search terms. A search will first priortize lexical relationships, then semantic ones.

While this may seem backwards compared to the other functions that LSI supports, it is actually the same algorithm, just applied on a smaller document.

    # File lib/classifier-reborn/lsi.rb, line 219
    def search( string, max_nearest=3 )
      return [] if needs_rebuild?
      carry = proximity_norms_for_content( string )
      result = carry.collect { |x| x[0] }
      return result[0..max_nearest-1]
    end

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