- minitest/benchmark.rb

Object

bench_exp(min, max, base = 10)
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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 28 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)
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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 41 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()
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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 69 def self.bench_range bench_exp 1, 10_000 end

assert_performance(validation, &work)
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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 |x| @obj.algorithm end end

# File minitest/benchmark.rb, line 91 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)
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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 |x| @obj.algorithm end end

# File minitest/benchmark.rb, line 135 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)
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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 |x| @obj.algorithm end end

# File minitest/benchmark.rb, line 161 def assert_performance_exponential threshold = 0.99, &work assert_performance validation_for_fit(:exponential, threshold), &work end

assert_performance_linear(threshold = 0.99, &work)
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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 |x| @obj.algorithm end end

# File minitest/benchmark.rb, line 181 def assert_performance_linear threshold = 0.99, &work assert_performance validation_for_fit(:linear, threshold), &work end

assert_performance_power(threshold = 0.99, &work)
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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 201 def assert_performance_power threshold = 0.99, &work assert_performance validation_for_fit(:power, threshold), &work end

fit_error(xys)
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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 210 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)
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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 225 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)
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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 247 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_power(xs, ys)
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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 269 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)
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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 290 def sigma enum, &block enum = enum.map(&block) if block enum.inject { |sum, n| sum + n } end

validation_for_fit(msg, threshold)
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Returns a proc that calls the specified fit method and asserts that the error is within a tolerable threshold.

# File minitest/benchmark.rb, line 299 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

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