Module: OpenTox::Algorithm::Similarity

Defined in:
lib/utils.rb

Overview

Similarity calculations

Class Method Summary collapse

Class Method Details

.cosine(fingerprints_a, fingerprints_b, weights = nil) ⇒ Float

Cosine similarity

Parameters:

  • properties_a (Hash) —

    key-value properties of first compound

  • properties_b (Hash) —

    key-value properties of second compound

Returns:

  • (Float) —

    cosine of angle enclosed between vectors induced by keys present in both a and b



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# File 'lib/utils.rb', line 550

def self.cosine(fingerprints_a,fingerprints_b,weights=nil)

  # fingerprints are hashes
  if fingerprints_a.class == Hash && fingerprints_b.class == Hash
    a = []; b = []
    common_features = fingerprints_a.keys & fingerprints_b.keys
    if common_features.size > 1
      common_features.each do |p|
        a << fingerprints_a[p]
        b << fingerprints_b[p]
      end
    end

  # fingerprints are arrays
  elsif fingerprints_a.class == Array && fingerprints_b.class == Array
    a = fingerprints_a
    b = fingerprints_b
  end

  (a.size > 0 && b.size > 0) ? self.cosine_num(a.to_gv, b.to_gv) : 0.0

end

.cosine_num(a, b) ⇒ Float

Cosine similarity

Parameters:

  • a (GSL::Vector)
  • b (GSL::Vector)

Returns:

  • (Float) —

    cosine of angle enclosed between a and b



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# File 'lib/utils.rb', line 578

def self.cosine_num(a, b)
  if a.size>12 && b.size>12
    a = a[0..11]
    b = b[0..11]
  end
  a.dot(b) / (a.norm * b.norm)
end

.outliers(params) ⇒ Object

Outlier detection based on Mahalanobis distances Multivariate detection on X, univariate detection on y Uses an existing Rinruby instance, if possible @param Keys query_matrix, data_matrix, acts are required; r, p_outlier optional @return indices identifying outliers (may occur several times, this is intended)



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# File 'lib/utils.rb', line 592

def self.outliers(params)
  outlier_array = []
  data_matrix = params[:data_matrix]
  query_matrix = params[:query_matrix]
  acts = params[:acts]
  begin
    LOGGER.debug "Outliers (p=#{params[:p_outlier] || 0.9999})..."
    r = ( params[:r] || RinRuby.new(false,false) )
    r.eval "suppressPackageStartupMessages(library(\"robustbase\"))"
    r.eval "outlier_threshold = #{params[:p_outlier] || 0.999}"
    nr_cases, nr_features = data_matrix.to_a.size, data_matrix.to_a[0].size
    r.odx = data_matrix.to_a.flatten
    r.q = query_matrix.to_a.flatten
    r.y = acts.to_a.flatten
    r.eval "odx = matrix(odx, #{nr_cases}, #{nr_features}, byrow=T)"
    r.eval 'odx = rbind(q,odx)' # query is nr 0 (1) in ruby (R)
    r.eval 'mah = covMcd(odx)$mah' # run MCD alg
    r.eval "mah = pchisq(mah,#{nr_features})"
    r.eval 'outlier_array = which(mah>outlier_threshold)'  # multivariate outliers using robust mahalanobis
    outlier_array = r.outlier_array.to_a.collect{|v| v-2 }  # translate to ruby index (-1 for q, -1 due to ruby)
    r.eval 'fqu = matrix(summary(y))[2]'
    r.eval 'tqu = matrix(summary(y))[5]'
    r.eval 'outlier_array = which(y>(tqu+1.5*IQR(y)))'     # univariate outliers due to Tukey (http://goo.gl/mwzNH)
    outlier_array += r.outlier_array.to_a.collect{|v| v-1 } # translate to ruby index (-1 due to ruby)
    r.eval 'outlier_array = which(y<(fqu-1.5*IQR(y)))'
    outlier_array += r.outlier_array.to_a.collect{|v| v-1 }
  rescue Exception => e
    LOGGER.debug "#{e.class}: #{e.message}"
    #LOGGER.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
  end
  outlier_array
end

.tanimoto(fingerprints_a, fingerprints_b, weights = nil, params = nil) ⇒ Float

Tanimoto similarity

Parameters:

  • fingerprints (Hash, Array) —

    of first compound

  • fingerprints (Hash, Array) —

    of second compound

Returns:

  • (Float) —

    (Weighted) tanimoto similarity



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# File 'lib/utils.rb', line 517

def self.tanimoto(fingerprints_a,fingerprints_b,weights=nil,params=nil)

  common_p_sum = 0.0
  all_p_sum = 0.0

  # fingerprints are hashes
  if fingerprints_a.class == Hash && fingerprints_b.class == Hash
    common_features = fingerprints_a.keys & fingerprints_b.keys
    all_features = (fingerprints_a.keys + fingerprints_b.keys).uniq
    if common_features.size > 0
      common_features.each{ |f| common_p_sum += [ fingerprints_a[f], fingerprints_b[f] ].min }
      all_features.each{ |f| all_p_sum += [ fingerprints_a[f],fingerprints_b[f] ].compact.max } # compact, since one fp may be empty at that pos
    end

  # fingerprints are arrays
  elsif fingerprints_a.class == Array && fingerprints_b.class == Array
    size = [ fingerprints_a.size, fingerprints_b.size ].min
    LOGGER.warn "fingerprints don't have equal size" if fingerprints_a.size != fingerprints_b.size
    (0...size).each { |idx|
      common_p_sum += [ fingerprints_a[idx], fingerprints_b[idx] ].min
      all_p_sum += [ fingerprints_a[idx], fingerprints_b[idx] ].max
    }
  end

  (all_p_sum > 0.0) ? (common_p_sum/all_p_sum) : 0.0

end