summaryrefslogtreecommitdiff
path: root/test/lib/minitest/benchmark.rb
blob: e233282b0a4e2258ab70c568d3741bd8224bdff7 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
# encoding: utf-8
######################################################################
# This file is imported from the minitest project.
# DO NOT make modifications in this repo. They _will_ be reverted!
# File a patch instead and assign it to Ryan Davis.
######################################################################

require 'minitest/unit'
require 'minitest/spec'

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