Module: Bullshit::EvaluationModule

Included in:

RangeCase, RepeatCase, TimeCase

Defined in:

lib/bullshit.rb

Overview

This module contains methods, that can be used in the evaluation method of benchmark cases.

Instance Method Summary

• #detect_autocorrelation(clock) ⇒ Object
• #detect_outliers(clock) ⇒ Object
• #histogram(clock) ⇒ Object
• #statistics_table(clock) ⇒ Object

Instance Method Details

#detect_autocorrelation(clock) ⇒ Object

# File 'lib/bullshit.rb', line 1925

def detect_autocorrelation(clock)
  result = ''
  clock.case.autocorrelation.enabled or return result
  if r = clock.detect_autocorrelation(clock.case.compare_time)
    result << "\nLjung-Box statistics: q=%.5f (alpha=#{r[:alpha_level]},"\
      " df=#{r[:lags]}).\n" % r[:q]
    if r[:detected]
      result << "%.5f >= %.5f => Autocorrelation was detected.\n" %
        [ r[:p], 1 - r[:alpha_level] ]
    else
      result << "%.5f < %.5f => No autocorrelation was detected.\n" %
        [ r[:p], 1 - r[:alpha_level] ]
    end
  else
      result << "\nDidn't have enough lags to compute Ljung-Box statistics.\n"
  end
  result
end

#detect_outliers(clock) ⇒ Object

# File 'lib/bullshit.rb', line 1908

def detect_outliers(clock)
  result = ''
  if clock.case.detect_outliers and
    outliers = clock.detect_outliers(clock.case.compare_time)
  then
    result << "\nOutliers detected with box plot algo "\
      "(median=%.5f, iqr=%.5f, factor=%.2f):\n" % outliers.values_at(:median, :iqr, :factor)
    result << outliers.select { |n, |
      [ :very_low, :low, :high, :very_high ].include?(n)
    }.map { |n, v| "#{n}=#{v}" } * ' '
    result << "\n"
  else
    result << "\nNo outliers detected with box plot algo.\n"
  end
  result
end

#histogram(clock) ⇒ Object

# File 'lib/bullshit.rb', line 1900

def histogram(clock)
  result = "\n"
  if clock.case.histogram.enabled
    clock.histogram(clock.case.compare_time).display(result, 50)
  end
  result
end

#statistics_table(clock) ⇒ Object

# File 'lib/bullshit.rb', line 1881

def statistics_table(clock)
  result = ' ' * NAME_COLUMN_SIZE << ('%17s ' * 4) % times << "\n"
  result << evaluation_line('sum', times.map { |t| clock.__send__(t) })
  result << evaluation_line('min', times.map { |t| clock.min(t) })
  result << evaluation_line('std-', times.map { |t| clock.arithmetic_mean(t) - clock.sample_standard_deviation(t) })
  result << evaluation_line('mean', times.map { |t| clock.arithmetic_mean(t) })
  result << evaluation_line('std+', times.map { |t| clock.arithmetic_mean(t) + clock.sample_standard_deviation(t) })
  result << evaluation_line('max', times.map { |t| clock.max(t) })
  result << evaluation_line('std', times.map { |t| clock.sample_standard_deviation(t) })
  result << evaluation_line('std%', times.map { |t| clock.sample_standard_deviation_percentage(t) })
  result << evaluation_line('harm', times.map { |t| clock.harmonic_mean(t) })
  result << evaluation_line('geo', times.map { |t| clock.geometric_mean(t) })
  result << evaluation_line('q1', times.map { |t| clock.percentile(t, 25) })
  result << evaluation_line('med', times.map { |t| clock.median(t) })
  result << evaluation_line('q3', times.map { |t| clock.percentile(t, 75) })
  result << ' ' * NAME_COLUMN_SIZE << "%17u %17.5f %17.9f\n" % [ clock.repeat, clock.calls_mean, clock.call_time_mean ]
  result << ' ' * NAME_COLUMN_SIZE << "%17s %17s %17s\n" % %w[calls calls/sec secs/call]
end