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Ruby Vector Space Model (VSM) with tf*idf weights

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Calculates the similarity between texts using a bag-of-words Vector Space Model with Term Frequency-Inverse Document Frequency (tf*idf) weights. If your use case demands performance, use Lucene (see below).

Usage

require 'matrix'
require 'tf-idf-similarity'

Create a set of documents:

document1 = TfIdfSimilarity::Document.new("Lorem ipsum dolor sit amet...")
document2 = TfIdfSimilarity::Document.new("Pellentesque sed ipsum dui...")
document3 = TfIdfSimilarity::Document.new("Nam scelerisque dui sed leo...")
corpus = [document1, document2, document3]

Create a document-term matrix using Term Frequency-Inverse Document Frequency function:

model = TfIdfSimilarity::TfIdfModel.new(corpus)

Or, create a document-term matrix using the Okapi BM25 ranking function:

model = TfIdfSimilarity::BM25Model.new(corpus)

Create a similarity matrix:

matrix = model.similarity_matrix

Find the similarity of two documents in the matrix:

matrix[model.document_index(document1), model.document_index(document2)]

Print the tf*idf values for terms in a document:

tfidf_by_term = {}
document1.terms.each do |term|
  tfidf_by_term[term] = model.tfidf(document1, term)
end
puts tfidf_by_term.sort_by{|_,tfidf| -tfidf}

Tokenize a document yourself, for example by excluding stop words:

require 'unicode_utils'
text = "Lorem ipsum dolor sit amet..."
tokens = UnicodeUtils.each_word(text).to_a - ['and', 'the', 'to']
document1 = TfIdfSimilarity::Document.new(text, :tokens => tokens)

Provide, by yourself, the number of times each term appears and the number of tokens in the document:

require 'unicode_utils'
text = "Lorem ipsum dolor sit amet..."
tokens = UnicodeUtils.each_word(text).to_a - ['and', 'the', 'to']
term_counts = Hash.new(0)
size = 0
tokens.each do |token|
  # Unless the token is numeric.
  unless token[/\A\d+\z/]
    # Remove all punctuation from tokens.
    term_counts[token.gsub(/\p{Punct}/, '')] += 1
    size += 1
  end
end
document1 = TfIdfSimilarity::Document.new(text, :term_counts => term_counts, :size => size)

Or, use your own classes for the tokenizer and tokens, like in this example.

Read the documentation at RubyDoc.info.

Troubleshooting

NoMethodError: undefined method `[]' for Matrix:Module

The matrix gem conflicts with Ruby's internal Matrix module. Don't use the matrix gem.

Speed

Instead of using the Ruby Standard Library's Matrix class, you can use one of the GNU Scientific Library (GSL), NArray or NMatrix (0.0.9 or greater) gems for faster matrix operations. For example:

require 'narray'
model = TfIdfSimilarity::TfIdfModel.new(corpus, :library => :narray)

NArray seems to have the best performance of the three libraries.

The NMatrix gem gives access to Automatically Tuned Linear Algebra Software (ATLAS), which you may know of through Linear Algebra PACKage (LAPACK) or Basic Linear Algebra Subprograms (BLAS). Follow these instructions to install the NMatrix gem.

Extras

You can access more term frequency, document frequency, and normalization formulas with:

require 'tf-idf-similarity/extras/document'
require 'tf-idf-similarity/extras/tf_idf_model'

The default tf*idf formula follows the Lucene Conceptual Scoring Formula.

Why?

At the time of writing, no other Ruby gem implemented the tf*idf formula used by Lucene, Sphinx and Ferret.

Term frequencies

Document frequencies

Normalization

Additional adapters

Adapters for the following projects were also considered:

Reference

Further Reading

Lucene implements many more similarity functions, such as:

Lucene can even combine similarity measures.

Copyright (c) 2012 James McKinney, released under the MIT license