Class: Eps::Evaluators::LinearRegression

Inherits:
Object
  • Object
show all
Defined in:
lib/eps/evaluators/linear_regression.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(coefficients:, features:, text_features:) ⇒ LinearRegression

Returns a new instance of LinearRegression.



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# File 'lib/eps/evaluators/linear_regression.rb', line 6

def initialize(coefficients:, features:, text_features:)
  @coefficients = Hash[coefficients.map { |k, v| [k.is_a?(Array) ? [k[0].to_s, k[1]] : k.to_s, v] }]
  @features = features
  @text_features = text_features || {}
end

Instance Attribute Details

#features ⇒ Object (readonly)

Returns the value of attribute features.



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# File 'lib/eps/evaluators/linear_regression.rb', line 4

def features
  @features
end

Instance Method Details

#coefficients ⇒ Object 



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# File 'lib/eps/evaluators/linear_regression.rb', line 49

def coefficients
  Hash[@coefficients.map { |k, v| [Array(k).join.to_sym, v] }]
end

#predict(x) ⇒ Object 



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# File 'lib/eps/evaluators/linear_regression.rb', line 12

def predict(x)
  intercept = @coefficients["_intercept"]
  scores = [intercept] * x.size

  @features.each do |k, type|
    raise "Missing data in #{k}" if !x.columns[k] || x.columns[k].any?(&:nil?)

    case type
    when "categorical"
      x.columns[k].each_with_index do |xv, i|
        scores[i] += @coefficients[[k, xv]].to_f
      end
    when "text"
      encoder = TextEncoder.new(@text_features[k])
      counts = encoder.transform(x.columns[k])
      coef = {}
      @coefficients.each do |k2, v|
        next unless k2.is_a?(Array) && k2.first == k
        coef[k2.last] = v
      end

      counts.each_with_index do |xc, i|
        xc.each do |word, count|
          scores[i] += coef[word] * count if coef[word]
        end
      end
    else
      coef = @coefficients[k].to_f
      x.columns[k].each_with_index do |xv, i|
        scores[i] += coef * xv
      end
    end
  end

  scores
end