- minitest/benchmark.rb

Object

- ::bench_exp
- ::bench_linear
- ::bench_range
- ::benchmark_suites
- #assert_performance
- #assert_performance_constant
- #assert_performance_exponential
- #assert_performance_linear
- #assert_performance_logarithmic
- #assert_performance_power
- #fit_error
- #fit_exponential
- #fit_linear
- #fit_logarithmic
- #fit_power
- #sigma
- #validation_for_fit

bench_exp(min, max, base = 10)
click to toggle source

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]

# File minitest/benchmark.rb, line 26 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

bench_linear(min, max, step = 10)
click to toggle source

Returns a set of ranges stepped linearly from `min`

to
`max`

by `step`

. Eg:

bench_linear(20, 40, 10) # => [20, 30, 40]

# File minitest/benchmark.rb, line 39 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

bench_range()
click to toggle source

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.

# File minitest/benchmark.rb, line 67 def self.bench_range bench_exp 1, 10_000 end

assert_performance(validation, &work)
click to toggle source

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

# File minitest/benchmark.rb, line 89 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

assert_performance_constant(threshold = 0.99, &work)
click to toggle source

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 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

# File minitest/benchmark.rb, line 133 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

assert_performance_exponential(threshold = 0.99, &work)
click to toggle source

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

# File minitest/benchmark.rb, line 159 def assert_performance_exponential threshold = 0.99, &work assert_performance validation_for_fit(:exponential, threshold), &work end

assert_performance_linear(threshold = 0.99, &work)
click to toggle source

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

# File minitest/benchmark.rb, line 199 def assert_performance_linear threshold = 0.99, &work assert_performance validation_for_fit(:linear, threshold), &work end

assert_performance_logarithmic(threshold = 0.99, &work)
click to toggle source

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

# File minitest/benchmark.rb, line 179 def assert_performance_logarithmic threshold = 0.99, &work assert_performance validation_for_fit(:logarithmic, threshold), &work end

assert_performance_power(threshold = 0.99, &work)
click to toggle source

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

# File minitest/benchmark.rb, line 219 def assert_performance_power threshold = 0.99, &work assert_performance validation_for_fit(:power, threshold), &work end

fit_error(xys)
click to toggle source

Takes an array of x/y pairs and calculates the general R^2 value.

See: en.wikipedia.org/wiki/Coefficient_of_determination

# File minitest/benchmark.rb, line 228 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

fit_exponential(xs, ys)
click to toggle source

To fit a functional form: y = ae^(bx).

Takes x and y values and returns [a, b, r^2].

See: mathworld.wolfram.com/LeastSquaresFittingExponential.html

# File minitest/benchmark.rb, line 243 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

fit_linear(xs, ys)
click to toggle source

Fits the functional form: a + bx.

Takes x and y values and returns [a, b, r^2].

See: mathworld.wolfram.com/LeastSquaresFitting.html

# File minitest/benchmark.rb, line 288 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

fit_logarithmic(xs, ys)
click to toggle source

To fit a functional form: y = a + b*ln(x).

Takes x and y values and returns [a, b, r^2].

See: mathworld.wolfram.com/LeastSquaresFittingLogarithmic.html

# File minitest/benchmark.rb, line 265 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

fit_power(xs, ys)
click to toggle source

To fit a functional form: y = ax^b.

Takes x and y values and returns [a, b, r^2].

See: mathworld.wolfram.com/LeastSquaresFittingPowerLaw.html

# File minitest/benchmark.rb, line 310 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

sigma(enum, &block)
click to toggle source

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

# File minitest/benchmark.rb, line 331 def sigma enum, &block enum = enum.map(&block) if block enum.inject { |sum, n| sum + n } end

validation_for_fit(msg, threshold)
click to toggle source

Returns a proc that calls the specified fit method and asserts that the error is within a tolerable threshold.

# File minitest/benchmark.rb, line 340 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

Commenting is here to help enhance the documentation. For example, code samples, or clarification of the documentation.

If you have questions about Ruby or the documentation, please post to one of the Ruby mailing lists. You will get better, faster, help that way.

If you wish to post a correction of the docs, please do so, but also file bug report so that it can be corrected for the next release. Thank you.

If you want to help improve the Ruby documentation, please see Improve the docs, or visit Documenting-ruby.org.