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-# encoding: utf-8
-# frozen_string_literal: true
-
-require 'minitest/unit'
-
-class MiniTest::Unit # :nodoc:
- def run_benchmarks # :nodoc:
- _run_anything :benchmark
- end
-
- def benchmark_suite_header suite # :nodoc:
- "\n#{suite}\t#{suite.bench_range.join("\t")}"
- end
-
- class TestCase
- ##
- # Returns a set of ranges stepped exponentially from +min+ to
- # +max+ by powers of +base+. Eg:
- #
- # bench_exp(2, 16, 2) # => [2, 4, 8, 16]
-
- def self.bench_exp min, max, base = 10
- min = (Math.log10(min) / Math.log10(base)).to_i
- max = (Math.log10(max) / Math.log10(base)).to_i
-
- (min..max).map { |m| base ** m }.to_a
- end
-
- ##
- # Returns a set of ranges stepped linearly from +min+ to +max+ by
- # +step+. Eg:
- #
- # bench_linear(20, 40, 10) # => [20, 30, 40]
-
- def self.bench_linear min, max, step = 10
- (min..max).step(step).to_a
- rescue LocalJumpError # 1.8.6
- r = []; (min..max).step(step) { |n| r << n }; r
- end
-
- ##
- # Returns the benchmark methods (methods that start with bench_)
- # for that class.
-
- def self.benchmark_methods # :nodoc:
- public_instance_methods(true).grep(/^bench_/).map { |m| m.to_s }.sort
- end
-
- ##
- # Returns all test suites that have benchmark methods.
-
- def self.benchmark_suites
- TestCase.test_suites.reject { |s| s.benchmark_methods.empty? }
- end
-
- ##
- # Specifies the ranges used for benchmarking for that class.
- # Defaults to exponential growth from 1 to 10k by powers of 10.
- # Override if you need different ranges for your benchmarks.
- #
- # See also: ::bench_exp and ::bench_linear.
-
- def self.bench_range
- bench_exp 1, 10_000
- end
-
- ##
- # Runs the given +work+, gathering the times of each run. Range
- # and times are then passed to a given +validation+ proc. Outputs
- # the benchmark name and times in tab-separated format, making it
- # easy to paste into a spreadsheet for graphing or further
- # analysis.
- #
- # Ranges are specified by ::bench_range.
- #
- # Eg:
- #
- # def bench_algorithm
- # validation = proc { |x, y| ... }
- # assert_performance validation do |n|
- # @obj.algorithm(n)
- # end
- # end
-
- def assert_performance validation, &work
- range = self.class.bench_range
-
- io.print "#{__name__}"
-
- times = []
-
- range.each do |x|
- GC.start
- t0 = Time.now
- instance_exec(x, &work)
- t = Time.now - t0
-
- io.print "\t%9.6f" % t
- times << t
- end
- io.puts
-
- validation[range, times]
- end
-
- ##
- # Runs the given +work+ and asserts that the times gathered fit to
- # match a constant rate (eg, linear slope == 0) within a given
- # +threshold+. Note: because we're testing for a slope of 0, R^2
- # is not a good determining factor for the fit, so the threshold
- # is applied against the slope itself. As such, you probably want
- # to tighten it from the default.
- #
- # See http://www.graphpad.com/curvefit/goodness_of_fit.htm for
- # more details.
- #
- # Fit is calculated by #fit_linear.
- #
- # Ranges are specified by ::bench_range.
- #
- # Eg:
- #
- # def bench_algorithm
- # assert_performance_constant 0.9999 do |n|
- # @obj.algorithm(n)
- # end
- # end
-
- def assert_performance_constant threshold = 0.99, &work
- validation = proc do |range, times|
- a, b, rr = fit_linear range, times
- assert_in_delta 0, b, 1 - threshold
- [a, b, rr]
- end
-
- assert_performance validation, &work
- end
-
- ##
- # Runs the given +work+ and asserts that the times gathered fit to
- # match a exponential curve within a given error +threshold+.
- #
- # Fit is calculated by #fit_exponential.
- #
- # Ranges are specified by ::bench_range.
- #
- # Eg:
- #
- # def bench_algorithm
- # assert_performance_exponential 0.9999 do |n|
- # @obj.algorithm(n)
- # end
- # end
-
- def assert_performance_exponential threshold = 0.99, &work
- assert_performance validation_for_fit(:exponential, threshold), &work
- end
-
- ##
- # Runs the given +work+ and asserts that the times gathered fit to
- # match a logarithmic curve within a given error +threshold+.
- #
- # Fit is calculated by #fit_logarithmic.
- #
- # Ranges are specified by ::bench_range.
- #
- # Eg:
- #
- # def bench_algorithm
- # assert_performance_logarithmic 0.9999 do |n|
- # @obj.algorithm(n)
- # end
- # end
-
- def assert_performance_logarithmic threshold = 0.99, &work
- assert_performance validation_for_fit(:logarithmic, threshold), &work
- end
-
- ##
- # Runs the given +work+ and asserts that the times gathered fit to
- # match a straight line within a given error +threshold+.
- #
- # Fit is calculated by #fit_linear.
- #
- # Ranges are specified by ::bench_range.
- #
- # Eg:
- #
- # def bench_algorithm
- # assert_performance_linear 0.9999 do |n|
- # @obj.algorithm(n)
- # end
- # end
-
- def assert_performance_linear threshold = 0.99, &work
- assert_performance validation_for_fit(:linear, threshold), &work
- end
-
- ##
- # Runs the given +work+ and asserts that the times gathered curve
- # fit to match a power curve within a given error +threshold+.
- #
- # Fit is calculated by #fit_power.
- #
- # Ranges are specified by ::bench_range.
- #
- # Eg:
- #
- # def bench_algorithm
- # assert_performance_power 0.9999 do |x|
- # @obj.algorithm
- # end
- # end
-
- def assert_performance_power threshold = 0.99, &work
- assert_performance validation_for_fit(:power, threshold), &work
- end
-
- ##
- # Takes an array of x/y pairs and calculates the general R^2 value.
- #
- # See: http://en.wikipedia.org/wiki/Coefficient_of_determination
-
- def fit_error xys
- y_bar = sigma(xys) { |x, y| y } / xys.size.to_f
- ss_tot = sigma(xys) { |x, y| (y - y_bar) ** 2 }
- ss_err = sigma(xys) { |x, y| (yield(x) - y) ** 2 }
-
- 1 - (ss_err / ss_tot)
- end
-
- ##
- # To fit a functional form: y = ae^(bx).
- #
- # Takes x and y values and returns [a, b, r^2].
- #
- # See: http://mathworld.wolfram.com/LeastSquaresFittingExponential.html
-
- def fit_exponential xs, ys
- n = xs.size
- xys = xs.zip(ys)
- sxlny = sigma(xys) { |x,y| x * Math.log(y) }
- slny = sigma(xys) { |x,y| Math.log(y) }
- sx2 = sigma(xys) { |x,y| x * x }
- sx = sigma xs
-
- c = n * sx2 - sx ** 2
- a = (slny * sx2 - sx * sxlny) / c
- b = ( n * sxlny - sx * slny ) / c
-
- return Math.exp(a), b, fit_error(xys) { |x| Math.exp(a + b * x) }
- end
-
- ##
- # To fit a functional form: y = a + b*ln(x).
- #
- # Takes x and y values and returns [a, b, r^2].
- #
- # See: http://mathworld.wolfram.com/LeastSquaresFittingLogarithmic.html
-
- def fit_logarithmic xs, ys
- n = xs.size
- xys = xs.zip(ys)
- slnx2 = sigma(xys) { |x,y| Math.log(x) ** 2 }
- slnx = sigma(xys) { |x,y| Math.log(x) }
- sylnx = sigma(xys) { |x,y| y * Math.log(x) }
- sy = sigma(xys) { |x,y| y }
-
- c = n * slnx2 - slnx ** 2
- b = ( n * sylnx - sy * slnx ) / c
- a = (sy - b * slnx) / n
-
- return a, b, fit_error(xys) { |x| a + b * Math.log(x) }
- end
-
-
- ##
- # Fits the functional form: a + bx.
- #
- # Takes x and y values and returns [a, b, r^2].
- #
- # See: http://mathworld.wolfram.com/LeastSquaresFitting.html
-
- def fit_linear xs, ys
- n = xs.size
- xys = xs.zip(ys)
- sx = sigma xs
- sy = sigma ys
- sx2 = sigma(xs) { |x| x ** 2 }
- sxy = sigma(xys) { |x,y| x * y }
-
- c = n * sx2 - sx**2
- a = (sy * sx2 - sx * sxy) / c
- b = ( n * sxy - sx * sy ) / c
-
- return a, b, fit_error(xys) { |x| a + b * x }
- end
-
- ##
- # To fit a functional form: y = ax^b.
- #
- # Takes x and y values and returns [a, b, r^2].
- #
- # See: http://mathworld.wolfram.com/LeastSquaresFittingPowerLaw.html
-
- def fit_power xs, ys
- n = xs.size
- xys = xs.zip(ys)
- slnxlny = sigma(xys) { |x, y| Math.log(x) * Math.log(y) }
- slnx = sigma(xs) { |x | Math.log(x) }
- slny = sigma(ys) { | y| Math.log(y) }
- slnx2 = sigma(xs) { |x | Math.log(x) ** 2 }
-
- b = (n * slnxlny - slnx * slny) / (n * slnx2 - slnx ** 2);
- a = (slny - b * slnx) / n
-
- return Math.exp(a), b, fit_error(xys) { |x| (Math.exp(a) * (x ** b)) }
- end
-
- ##
- # Enumerates over +enum+ mapping +block+ if given, returning the
- # sum of the result. Eg:
- #
- # sigma([1, 2, 3]) # => 1 + 2 + 3 => 7
- # sigma([1, 2, 3]) { |n| n ** 2 } # => 1 + 4 + 9 => 14
-
- def sigma enum, &block
- enum = enum.map(&block) if block
- enum.inject { |sum, n| sum + n }
- end
-
- ##
- # Returns a proc that calls the specified fit method and asserts
- # that the error is within a tolerable threshold.
-
- def validation_for_fit msg, threshold
- proc do |range, times|
- a, b, rr = send "fit_#{msg}", range, times
- assert_operator rr, :>=, threshold
- [a, b, rr]
- end
- end
- end
-end
-
-class MiniTest::Spec
- ##
- # This is used to define a new benchmark method. You usually don't
- # use this directly and is intended for those needing to write new
- # performance curve fits (eg: you need a specific polynomial fit).
- #
- # See ::bench_performance_linear for an example of how to use this.
-
- def self.bench name, &block
- define_method "bench_#{name.gsub(/\W+/, '_')}", &block
- end
-
- ##
- # Specifies the ranges used for benchmarking for that class.
- #
- # bench_range do
- # bench_exp(2, 16, 2)
- # end
- #
- # See Unit::TestCase.bench_range for more details.
-
- def self.bench_range &block
- return super unless block
-
- meta = (class << self; self; end)
- meta.send :define_method, "bench_range", &block
- end
-
- ##
- # Create a benchmark that verifies that the performance is linear.
- #
- # describe "my class" do
- # bench_performance_linear "fast_algorithm", 0.9999 do |n|
- # @obj.fast_algorithm(n)
- # end
- # end
-
- def self.bench_performance_linear name, threshold = 0.99, &work
- bench name do
- assert_performance_linear threshold, &work
- end
- end
-
- ##
- # Create a benchmark that verifies that the performance is constant.
- #
- # describe "my class" do
- # bench_performance_constant "zoom_algorithm!" do |n|
- # @obj.zoom_algorithm!(n)
- # end
- # end
-
- def self.bench_performance_constant name, threshold = 0.99, &work
- bench name do
- assert_performance_constant threshold, &work
- end
- end
-
- ##
- # Create a benchmark that verifies that the performance is exponential.
- #
- # describe "my class" do
- # bench_performance_exponential "algorithm" do |n|
- # @obj.algorithm(n)
- # end
- # end
-
- def self.bench_performance_exponential name, threshold = 0.99, &work
- bench name do
- assert_performance_exponential threshold, &work
- end
- end
-end