# Class: Minitest::Benchmark

Inherits:
Test
show all
Defined in:
lib/minitest/benchmark.rb

## Overview

Subclass Benchmark to create your own benchmark runs. Methods starting with “bench_” get executed on a per-class.

See Minitest::Assertions

BenchSpec

## Constant Summary

### Constants inherited from Runnable

Runnable::SIGNALS

## Class Method Summary collapse

• Returns a set of ranges stepped exponentially from `min` to `max` by powers of `base`.

• Returns a set of ranges stepped linearly from `min` to `max` by `step`.

• Specifies the ranges used for benchmarking for that class.

• :nodoc:.

• :nodoc:.

• :nodoc:.

## Instance Method Summary collapse

• Runs the given `work`, gathering the times of each run.

• Runs the given `work` and asserts that the times gathered fit to match a constant rate (eg, linear slope == 0) within a given `threshold`.

• Runs the given `work` and asserts that the times gathered fit to match a exponential curve within a given error `threshold`.

• Runs the given `work` and asserts that the times gathered fit to match a straight line within a given error `threshold`.

• Runs the given `work` and asserts that the times gathered fit to match a logarithmic curve within a given error `threshold`.

• Runs the given `work` and asserts that the times gathered curve fit to match a power curve within a given error `threshold`.

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

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

• Fits the functional form: a + bx.

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

• To fit a functional form: y = ax^b.

• :nodoc:.

• Enumerates over `enum` mapping `block` if given, returning the sum of the result.

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

## Constructor Details

This class inherits a constructor from Minitest::Runnable

## Class Method Details

### .bench_exp(min, max, base = 10) ⇒ Object

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]
``````
 ``` 35 36 37 38 39 40``` ```# File 'lib/minitest/benchmark.rb', line 35 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) ⇒ Object

Returns a set of ranges stepped linearly from `min` to `max` by `step`. Eg:

``````bench_linear(20, 40, 10) # => [20, 30, 40]
``````
 ``` 48 49 50 51 52``` ```# File 'lib/minitest/benchmark.rb', line 48 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 ⇒ Object

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.

 ``` 61 62 63``` ```# File 'lib/minitest/benchmark.rb', line 61 def self.bench_range bench_exp 1, 10_000 end```

### .io ⇒ Object

:nodoc:

 ``` 12 13 14``` ```# File 'lib/minitest/benchmark.rb', line 12 def self.io # :nodoc: @io end```

### .run(reporter, options = {}) ⇒ Object

:nodoc:

 ``` 20 21 22 23``` ```# File 'lib/minitest/benchmark.rb', line 20 def self.run reporter, options = {} # :nodoc: @io = reporter.io super end```

### .runnable_methods ⇒ Object

:nodoc:

 ``` 25 26 27``` ```# File 'lib/minitest/benchmark.rb', line 25 def self.runnable_methods # :nodoc: methods_matching(/^bench_/) end```

## Instance Method Details

### #assert_performance(validation, &work) ⇒ Object

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
``````
 ``` 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102``` ```# File 'lib/minitest/benchmark.rb', line 83 def assert_performance validation, &work range = self.class.bench_range io.print "#{self.name}" times = [] range.each do |x| GC.start t0 = Minitest.clock_time instance_exec(x, &work) t = Minitest.clock_time - t0 io.print "\t%9.6f" % t times << t end io.puts validation[range, times] end```

### #assert_performance_constant(threshold = 0.99, &work) ⇒ Object

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.

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
``````
 ``` 127 128 129 130 131 132 133 134 135``` ```# File 'lib/minitest/benchmark.rb', line 127 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) ⇒ Object

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
``````
 ``` 153 154 155``` ```# File 'lib/minitest/benchmark.rb', line 153 def assert_performance_exponential threshold = 0.99, &work assert_performance validation_for_fit(:exponential, threshold), &work end```

### #assert_performance_linear(threshold = 0.99, &work) ⇒ Object

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
``````
 ``` 193 194 195``` ```# File 'lib/minitest/benchmark.rb', line 193 def assert_performance_linear threshold = 0.99, &work assert_performance validation_for_fit(:linear, threshold), &work end```

### #assert_performance_logarithmic(threshold = 0.99, &work) ⇒ Object

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
``````
 ``` 173 174 175``` ```# File 'lib/minitest/benchmark.rb', line 173 def assert_performance_logarithmic threshold = 0.99, &work assert_performance validation_for_fit(:logarithmic, threshold), &work end```

### #assert_performance_power(threshold = 0.99, &work) ⇒ Object

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
``````
 ``` 213 214 215``` ```# File 'lib/minitest/benchmark.rb', line 213 def assert_performance_power threshold = 0.99, &work assert_performance validation_for_fit(:power, threshold), &work end```

### #fit_error(xys) ⇒ Object

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

 ``` 222 223 224 225 226 227 228``` ```# File 'lib/minitest/benchmark.rb', line 222 def fit_error xys y_bar = sigma(xys) { |_, y| y } / xys.size.to_f ss_tot = sigma(xys) { |_, 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) ⇒ Object

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

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

 ``` 237 238 239 240 241 242 243 244 245 246 247 248 249 250``` ```# File 'lib/minitest/benchmark.rb', line 237 def fit_exponential xs, ys n = xs.size xys = xs.zip(ys) sxlny = sigma(xys) { |x, y| x * Math.log(y) } slny = sigma(xys) { |_, y| Math.log(y) } sx2 = sigma(xys) { |x, _| 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) ⇒ Object

Fits the functional form: a + bx.

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

 ``` 281 282 283 284 285 286 287 288 289 290 291 292 293 294``` ```# File 'lib/minitest/benchmark.rb', line 281 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) ⇒ Object

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

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

 ``` 259 260 261 262 263 264 265 266 267 268 269 270 271 272``` ```# File 'lib/minitest/benchmark.rb', line 259 def fit_logarithmic xs, ys n = xs.size xys = xs.zip(ys) slnx2 = sigma(xys) { |x, _| Math.log(x) ** 2 } slnx = sigma(xys) { |x, _| Math.log(x) } sylnx = sigma(xys) { |x, y| y * Math.log(x) } sy = sigma(xys) { |_, 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) ⇒ Object

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

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

 ``` 303 304 305 306 307 308 309 310 311 312 313 314 315``` ```# File 'lib/minitest/benchmark.rb', line 303 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```

### #io ⇒ Object

:nodoc:

 ``` 16 17 18``` ```# File 'lib/minitest/benchmark.rb', line 16 def io # :nodoc: self.class.io end```

### #sigma(enum, &block) ⇒ Object

Enumerates over `enum` mapping `block` if given, returning the sum of the result. Eg:

``````sigma([1, 2, 3])                # => 1 + 2 + 3 => 6
sigma([1, 2, 3]) { |n| n ** 2 } # => 1 + 4 + 9 => 14
``````
 ``` 324 325 326 327``` ```# File 'lib/minitest/benchmark.rb', line 324 def sigma enum, &block enum = enum.map(&block) if block enum.inject { |sum, n| sum + n } end```

### #validation_for_fit(msg, threshold) ⇒ Object

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

 ``` 333 334 335 336 337 338 339``` ```# File 'lib/minitest/benchmark.rb', line 333 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```