monicadsong commented on a change in pull request #12756:
URL: https://github.com/apache/beam/pull/12756#discussion_r486076366
##########
File path: sdks/python/apache_beam/transforms/stats_test.py
##########
@@ -41,355 +41,88 @@
from apache_beam.transforms.display import DisplayData
from apache_beam.transforms.display_test import DisplayDataItemMatcher
from apache_beam.transforms.stats import ApproximateQuantilesCombineFn
+from apache_beam.transforms.stats import ApproximateUniqueCombineFn
+try:
+ import mmh3
+ mmh3_options = [(mmh3, ), (None, )]
+except ImportError:
+ mmh3_options = [(None, )]
-class ApproximateUniqueTest(unittest.TestCase):
- """Unit tests for ApproximateUnique.Globally and ApproximateUnique.PerKey.
- Hash() with Python3 is nondeterministic, so Approximation algorithm generates
- different result each time and sometimes error rate is out of range, so add
- retries for all tests who actually running approximation algorithm."""
- def test_approximate_unique_global_by_invalid_size(self):
- # test if the transformation throws an error as expected with an invalid
- # small input size (< 16).
- sample_size = 10
- test_input = [random.randint(0, 1000) for _ in range(100)]
-
- with self.assertRaises(ValueError) as e:
- with TestPipeline() as pipeline:
- _ = (
- pipeline
- | 'create' >> beam.Create(test_input)
- |
- 'get_estimate' >>
beam.ApproximateUnique.Globally(size=sample_size))
-
- expected_msg = beam.ApproximateUnique._INPUT_SIZE_ERR_MSG % (sample_size)
-
- assert e.exception.args[0] == expected_msg
-
- def test_approximate_unique_global_by_invalid_type_size(self):
- # test if the transformation throws an error as expected with an invalid
- # type of input size (not int).
- sample_size = 100.0
- test_input = [random.randint(0, 1000) for _ in range(100)]
-
- with self.assertRaises(ValueError) as e:
- with TestPipeline() as pipeline:
- _ = (
- pipeline
- | 'create' >> beam.Create(test_input)
- |
- 'get_estimate' >>
beam.ApproximateUnique.Globally(size=sample_size))
-
- expected_msg = beam.ApproximateUnique._INPUT_SIZE_ERR_MSG % (sample_size)
-
- assert e.exception.args[0] == expected_msg
-
- def test_approximate_unique_global_by_invalid_small_error(self):
- # test if the transformation throws an error as expected with an invalid
- # small input error (< 0.01).
- est_err = 0.0
- test_input = [random.randint(0, 1000) for _ in range(100)]
-
- with self.assertRaises(ValueError) as e:
- with TestPipeline() as pipeline:
- _ = (
- pipeline
- | 'create' >> beam.Create(test_input)
- | 'get_estimate' >> beam.ApproximateUnique.Globally(error=est_err))
-
- expected_msg = beam.ApproximateUnique._INPUT_ERROR_ERR_MSG % (est_err)
-
- assert e.exception.args[0] == expected_msg
-
- def test_approximate_unique_global_by_invalid_big_error(self):
- # test if the transformation throws an error as expected with an invalid
- # big input error (> 0.50).
- est_err = 0.6
- test_input = [random.randint(0, 1000) for _ in range(100)]
-
- with self.assertRaises(ValueError) as e:
- with TestPipeline() as pipeline:
- _ = (
- pipeline
- | 'create' >> beam.Create(test_input)
- | 'get_estimate' >> beam.ApproximateUnique.Globally(error=est_err))
-
- expected_msg = beam.ApproximateUnique._INPUT_ERROR_ERR_MSG % (est_err)
-
- assert e.exception.args[0] == expected_msg
-
- def test_approximate_unique_global_by_invalid_no_input(self):
- # test if the transformation throws an error as expected with no input.
- test_input = [random.randint(0, 1000) for _ in range(100)]
-
- with self.assertRaises(ValueError) as e:
- with TestPipeline() as pipeline:
- _ = (
- pipeline
- | 'create' >> beam.Create(test_input)
- | 'get_estimate' >> beam.ApproximateUnique.Globally())
-
- expected_msg = beam.ApproximateUnique._NO_VALUE_ERR_MSG
- assert e.exception.args[0] == expected_msg
-
- def test_approximate_unique_global_by_invalid_both_input(self):
- # test if the transformation throws an error as expected with multi input.
- test_input = [random.randint(0, 1000) for _ in range(100)]
- est_err = 0.2
- sample_size = 30
-
- with self.assertRaises(ValueError) as e:
- with TestPipeline() as pipeline:
- _ = (
- pipeline
- | 'create' >> beam.Create(test_input)
- | 'get_estimate' >> beam.ApproximateUnique.Globally(
- size=sample_size, error=est_err))
-
- expected_msg = beam.ApproximateUnique._MULTI_VALUE_ERR_MSG % (
- sample_size, est_err)
-
- assert e.exception.args[0] == expected_msg
-
- def test_get_sample_size_from_est_error(self):
- # test if get correct sample size from input error.
- assert beam.ApproximateUnique._get_sample_size_from_est_error(0.5) == 16
- assert beam.ApproximateUnique._get_sample_size_from_est_error(0.4) == 25
- assert beam.ApproximateUnique._get_sample_size_from_est_error(0.2) == 100
- assert beam.ApproximateUnique._get_sample_size_from_est_error(0.1) == 400
- assert beam.ApproximateUnique._get_sample_size_from_est_error(0.05) == 1600
- assert beam.ApproximateUnique._get_sample_size_from_est_error(0.01) ==
40000
-
- @unittest.skip(
- 'Skip it because hash function is not good enough. '
- 'TODO: BEAM-7654')
- def test_approximate_unique_global_by_sample_size(self):
- # test if estimation error with a given sample size is not greater than
- # expected max error.
- sample_size = 16
- max_err = 2 / math.sqrt(sample_size)
- test_input = [
- 4,
- 34,
- 29,
- 46,
- 80,
- 66,
- 51,
- 81,
- 31,
- 9,
- 26,
- 36,
- 10,
- 41,
- 90,
- 35,
- 33,
- 19,
- 88,
- 86,
- 28,
- 93,
- 38,
- 76,
- 15,
- 87,
- 12,
- 39,
- 84,
- 13,
- 32,
- 49,
- 65,
- 100,
- 16,
- 27,
- 23,
- 30,
- 96,
- 54
- ]
-
- actual_count = len(set(test_input))
-
- with TestPipeline() as pipeline:
- result = (
- pipeline
- | 'create' >> beam.Create(test_input)
- | 'get_estimate' >> beam.ApproximateUnique.Globally(size=sample_size)
- | 'compare' >> beam.FlatMap(
- lambda x: [abs(x - actual_count) * 1.0 / actual_count <=
max_err])
- )
-
- assert_that(result, equal_to([True]), label='assert:global_by_size')
-
- @retry(reraise=True, stop=stop_after_attempt(5))
- def test_approximate_unique_global_by_sample_size_with_duplicates(self):
- # test if estimation error with a given sample size is not greater than
- # expected max error with duplicated input.
- sample_size = 30
- max_err = 2 / math.sqrt(sample_size)
- test_input = [10] * 50 + [20] * 50
- actual_count = len(set(test_input))
-
- with TestPipeline() as pipeline:
- result = (
- pipeline
- | 'create' >> beam.Create(test_input)
- | 'get_estimate' >> beam.ApproximateUnique.Globally(size=sample_size)
- | 'compare' >> beam.FlatMap(
- lambda x: [abs(x - actual_count) * 1.0 / actual_count <=
max_err])
- )
-
- assert_that(
- result,
- equal_to([True]),
- label='assert:global_by_size_with_duplicates')
-
- @retry(reraise=True, stop=stop_after_attempt(5))
- def
test_approximate_unique_global_by_sample_size_with_small_population(self):
- # test if estimation is exactly same to actual value when sample size is
- # not smaller than population size (sample size > 100% of population).
- sample_size = 31
- test_input = [
- 144,
- 160,
- 229,
- 923,
- 390,
- 756,
- 674,
- 769,
- 145,
- 888,
- 809,
- 159,
- 222,
- 101,
- 943,
- 901,
- 876,
- 194,
- 232,
- 631,
- 221,
- 829,
- 965,
- 729,
- 35,
- 33,
- 115,
- 894,
- 827,
- 364
- ]
- actual_count = len(set(test_input))
- with TestPipeline() as pipeline:
- result = (
- pipeline
- | 'create' >> beam.Create(test_input)
- | 'get_estimate' >>
beam.ApproximateUnique.Globally(size=sample_size))
-
- assert_that(
- result,
- equal_to([actual_count]),
- label='assert:global_by_sample_size_with_small_population')
-
- @unittest.skip(
- 'Skip because hash function is not good enough. '
- 'TODO: BEAM-7654')
- def test_approximate_unique_global_by_error(self):
- # test if estimation error from input error is not greater than input
error.
- est_err = 0.3
- test_input = [
- 291,
- 371,
- 271,
- 126,
- 762,
- 391,
- 222,
- 565,
- 428,
- 786,
- 801,
- 867,
- 337,
- 690,
- 261,
- 436,
- 311,
- 568,
- 946,
- 722,
- 973,
- 386,
- 506,
- 546,
- 991,
- 450,
- 226,
- 889,
- 514,
- 693
- ]
+@parameterized_class(('sys.modules[\'mmh3\']', ), mmh3_options)
+class ApproximateUniqueTest(unittest.TestCase):
+ """Unit tests for ApproximateUnique.Globally, ApproximateUnique.PerKey,
+ and ApproximateUniqueCombineFn.
+ """
+ random.seed(0)
+ sys.modules['mmh3'] = None
Review comment:
that was deliberate, but it bothered me as well. done.
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