Class: Eps::Evaluators::NaiveBayes

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

Constant Summary collapse

SQRT_2PI =
Math.sqrt(2 * Math::PI)

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(probabilities:, features:, derived: nil, legacy: false) ⇒ NaiveBayes


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

def initialize(probabilities:, features:, derived: nil, legacy: false)
  @probabilities = probabilities
  @features = features
  @derived = derived
  @legacy = legacy
end

Instance Attribute Details

#featuresObject (readonly)

Returns the value of attribute features


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

def features
  @features
end

#probabilitiesObject (readonly)

Returns the value of attribute probabilities


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

def probabilities
  @probabilities
end

Instance Method Details

#calculate_class_probabilities(x) ⇒ Object


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# File 'lib/eps/evaluators/naive_bayes.rb', line 26

def calculate_class_probabilities(x)
  probs = Eps::DataFrame.new

  # assign very small probability if probability is 0
  tiny_p = @legacy ? 0.0001 : 0

  total = probabilities[:prior].values.sum.to_f
  probabilities[:prior].each do |c, cv|
    prior = Math.log(cv / total)
    px = [prior] * x.size

    @features.each do |k, type|
      case type
      when "categorical"
        x.columns[k].each_with_index do |xi, i|
          vc = probabilities[:conditional][k][xi]

          # unknown value if not vc
          if vc
            denom = probabilities[:conditional][k].map { |k, v| v[c] }.sum.to_f
            p2 = vc[c].to_f / denom

            # TODO use proper smoothing instead
            p2 = tiny_p if p2 == 0

            px[i] += Math.log(p2)
          end
        end
      when "derived"
        @derived[k].each do |k2, v2|
          vc = probabilities[:conditional][k2][c]

          x.columns[k].each_with_index do |xi, i|
            px[i] += Math.log(calculate_probability(xi == v2 ? 1 : 0, vc[:mean], vc[:stdev]))
          end
        end
      else
        vc = probabilities[:conditional][k][c]

        if vc[:stdev] != 0 && !vc[:stdev].nil?
          x.columns[k].each_with_index do |xi, i|
            px[i] += Math.log(calculate_probability(xi, vc[:mean], vc[:stdev]))
          end
        else
          x.columns[k].each_with_index do |xi, i|
            if xi != vc[:mean]
              # TODO use proper smoothing instead
              px[i] += Math.log(tiny_p)
            end
          end
        end
      end

      probs.columns[c] = px
    end
  end

  probs
end

#calculate_probability(x, mean, stdev) ⇒ Object

TODO memoize for performance


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# File 'lib/eps/evaluators/naive_bayes.rb', line 89

def calculate_probability(x, mean, stdev)
  exponent = Math.exp(-((x - mean)**2) / (2 * (stdev**2)))
  (1 / (SQRT_2PI * stdev)) * exponent
end

#predict(x) ⇒ Object


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# File 'lib/eps/evaluators/naive_bayes.rb', line 13

def predict(x)
  probs = calculate_class_probabilities(x)
  probs.map do |xp|
    # convert probabilities
    # not needed when just returning label
    # sum = xp.values.map { |v| Math.exp(v) }.sum.to_f
    # p xp.map { |k, v| [k, Math.exp(v) / sum] }.to_h
    xp.sort_by { |k, v| [-v, k] }[0][0]
  end
end