| # -*- coding: utf-8 -*- |
| # |
| # Licensed to the Apache Software Foundation (ASF) under one or more |
| # contributor license agreements. See the NOTICE file distributed with |
| # this work for additional information regarding copyright ownership. |
| # The ASF licenses this file to You under the Apache License, Version 2.0 |
| # (the "License"); you may not use this file except in compliance with |
| # the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| |
| import math |
| import random |
| import unittest |
| from builtins import range |
| from collections import defaultdict |
| |
| import hamcrest as hc |
| from parameterized import parameterized |
| from tenacity import retry |
| from tenacity import stop_after_attempt |
| |
| import apache_beam as beam |
| from apache_beam.testing.test_pipeline import TestPipeline |
| from apache_beam.testing.util import BeamAssertException |
| from apache_beam.testing.util import assert_that |
| from apache_beam.testing.util import equal_to |
| from apache_beam.transforms.core import Create |
| from apache_beam.transforms.display import DisplayData |
| from apache_beam.transforms.display_test import DisplayDataItemMatcher |
| from apache_beam.transforms.stats import ApproximateQuantilesCombineFn |
| |
| |
| 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: |
| pipeline = TestPipeline() |
| _ = (pipeline |
| | 'create' |
| >> beam.Create(test_input) |
| | 'get_estimate' |
| >> beam.ApproximateUnique.Globally(size=sample_size)) |
| pipeline.run() |
| |
| 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: |
| pipeline = TestPipeline() |
| _ = (pipeline |
| | 'create' >> beam.Create(test_input) |
| | 'get_estimate' |
| >> beam.ApproximateUnique.Globally(size=sample_size)) |
| pipeline.run() |
| |
| 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: |
| pipeline = TestPipeline() |
| _ = (pipeline |
| | 'create' >> beam.Create(test_input) |
| | 'get_estimate' |
| >> beam.ApproximateUnique.Globally(error=est_err)) |
| pipeline.run() |
| |
| 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: |
| pipeline = TestPipeline() |
| _ = (pipeline |
| | 'create' >> beam.Create(test_input) |
| | 'get_estimate' |
| >> beam.ApproximateUnique.Globally(error=est_err)) |
| pipeline.run() |
| |
| 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: |
| pipeline = TestPipeline() |
| _ = (pipeline |
| | 'create' >> beam.Create(test_input) |
| | 'get_estimate' |
| >> beam.ApproximateUnique.Globally()) |
| pipeline.run() |
| |
| 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: |
| pipeline = TestPipeline() |
| _ = (pipeline |
| | 'create' >> beam.Create(test_input) |
| | 'get_estimate' |
| >> beam.ApproximateUnique.Globally(size=sample_size, error=est_err)) |
| pipeline.run() |
| |
| 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)) |
| |
| pipeline = TestPipeline() |
| 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') |
| pipeline.run() |
| |
| @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)) |
| |
| pipeline = TestPipeline() |
| 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') |
| pipeline.run() |
| |
| @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)) |
| |
| pipeline = TestPipeline() |
| 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') |
| pipeline.run() |
| |
| @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] |
| actual_count = len(set(test_input)) |
| |
| pipeline = TestPipeline() |
| result = (pipeline |
| | 'create' >> beam.Create(test_input) |
| | 'get_estimate' |
| >> beam.ApproximateUnique.Globally(error=est_err) |
| | 'compare' |
| >> beam.FlatMap(lambda x: [abs(x - actual_count) * 1.0 |
| / actual_count <= est_err])) |
| |
| assert_that(result, equal_to([True]), label='assert:global_by_error') |
| pipeline.run() |
| |
| @retry(reraise=True, stop=stop_after_attempt(5)) |
| def test_approximate_unique_global_by_error_with_small_population(self): |
| # test if estimation error from input error of a small dataset is not |
| # greater than input error. Sample size is always not smaller than 16, so |
| # when population size is smaller than 16, estimation should be exactly |
| # same to actual value. |
| est_err = 0.01 |
| test_input = [585, 104, 613, 503, 658, 640, 118, 492, 189, 798, |
| 756, 755, 839, 79, 393] |
| actual_count = len(set(test_input)) |
| |
| pipeline = TestPipeline() |
| result = (pipeline |
| | 'create' >> beam.Create(test_input) |
| | 'get_estimate' |
| >> beam.ApproximateUnique.Globally(error=est_err)) |
| |
| assert_that(result, equal_to([actual_count]), |
| label='assert:global_by_error_with_small_population') |
| pipeline.run() |
| |
| @retry(reraise=True, stop=stop_after_attempt(5)) |
| def test_approximate_unique_perkey_by_size(self): |
| # test if est error per key from sample size is in a expected range. |
| sample_size = 20 |
| max_err = 2 / math.sqrt(sample_size) |
| test_input = [(8, 73), (6, 724), (7, 70), (1, 576), (10, 120), (2, 662), |
| (7, 115), (3, 731), (6, 340), (6, 623), (1, 74), (9, 280), |
| (8, 298), (6, 440), (10, 243), (1, 125), (9, 754), (8, 833), |
| (9, 751), (4, 818), (6, 176), (9, 253), (2, 721), (8, 936), |
| (3, 691), (10, 685), (1, 69), (3, 155), (8, 86), (5, 693), |
| (2, 809), (4, 723), (8, 102), (9, 707), (8, 558), (4, 537), |
| (5, 371), (7, 432), (2, 51), (10, 397)] |
| actual_count_dict = defaultdict(set) |
| for (x, y) in test_input: |
| actual_count_dict[x].add(y) |
| |
| pipeline = TestPipeline() |
| result = (pipeline |
| | 'create' >> beam.Create(test_input) |
| | 'get_estimate' |
| >> beam.ApproximateUnique.PerKey(size=sample_size) |
| | 'compare' |
| >> beam.FlatMap(lambda x: [abs(x[1] |
| - len(actual_count_dict[x[0]])) |
| * 1.0 / len(actual_count_dict[x[0]]) |
| <= max_err])) |
| |
| assert_that(result, equal_to([True] * len(actual_count_dict)), |
| label='assert:perkey_by_size') |
| pipeline.run() |
| |
| @retry(reraise=True, stop=stop_after_attempt(5)) |
| def test_approximate_unique_perkey_by_error(self): |
| # test if estimation error per key from input err is in the expected range. |
| est_err = 0.01 |
| test_input = [(9, 6), (5, 5), (6, 9), (2, 4), (8, 3), (9, 0), (6, 10), |
| (8, 8), (9, 7), (2, 0), (9, 2), (1, 3), (4, 0), (7, 6), |
| (10, 6), (4, 7), (5, 8), (7, 2), (7, 10), (5, 10)] |
| actual_count_dict = defaultdict(set) |
| for (x, y) in test_input: |
| actual_count_dict[x].add(y) |
| |
| pipeline = TestPipeline() |
| result = (pipeline |
| | 'create' >> beam.Create(test_input) |
| | 'get_estimate' |
| >> beam.ApproximateUnique.PerKey(error=est_err) |
| | 'compare' |
| >> beam.FlatMap(lambda x: [abs(x[1] |
| - len(actual_count_dict[x[0]])) |
| * 1.0 / len(actual_count_dict[x[0]]) |
| <= est_err])) |
| |
| assert_that(result, equal_to([True] * len(actual_count_dict)), |
| label='assert:perkey_by_error') |
| pipeline.run() |
| |
| @retry(reraise=True, stop=stop_after_attempt(5)) |
| def test_approximate_unique_globally_by_error_with_skewed_data(self): |
| # test if estimation error is within the expected range with skewed data. |
| est_err = 0.01 |
| test_input = [19, 21, 32, 29, 5, 31, 52, 50, 59, 80, 7, 3, 34, 19, 13, |
| 6, 55, 1, 13, 90, 4, 18, 52, 33, 0, 77, 21, 26, 5, 18] |
| actual_count = len(set(test_input)) |
| |
| pipeline = TestPipeline() |
| result = (pipeline |
| | 'create' >> beam.Create(test_input) |
| | 'get_estimate' |
| >> beam.ApproximateUnique.Globally(error=est_err) |
| | 'compare' |
| >> beam.FlatMap(lambda x: [abs(x - actual_count) * 1.0 |
| / actual_count <= est_err])) |
| |
| assert_that(result, equal_to([True]), |
| label='assert:globally_by_error_with_skewed_data') |
| pipeline.run() |
| |
| |
| class ApproximateQuantilesTest(unittest.TestCase): |
| _kv_data = [("a", 1), ("a", 2), ("a", 3), ("b", 1), ("b", 10), ("b", 10), |
| ("b", 100)] |
| _kv_str_data = [("a", "a"), ("a", "a"*2), ("a", "a"*3), ("b", "b"), |
| ("b", "b"*10), ("b", "b"*10), ("b", "b"*100)] |
| |
| @staticmethod |
| def _quantiles_matcher(expected): |
| l = len(expected) |
| |
| def assert_true(exp): |
| if not exp: |
| raise BeamAssertException('%s Failed assert True' % repr(exp)) |
| |
| def match(actual): |
| actual = actual[0] |
| for i in range(l): |
| if isinstance(expected[i], list): |
| assert_true(expected[i][0] <= actual[i] <= expected[i][1]) |
| else: |
| equal_to([expected[i]])([actual[i]]) |
| |
| return match |
| |
| @staticmethod |
| def _approx_quantile_generator(size, num_of_quantiles, absoluteError): |
| quantiles = [0] |
| k = 1 |
| while k < num_of_quantiles - 1: |
| expected = (size - 1) * k / (num_of_quantiles - 1) |
| quantiles.append([expected - absoluteError, expected + absoluteError]) |
| k = k + 1 |
| quantiles.append(size - 1) |
| return quantiles |
| |
| def test_quantiles_globaly(self): |
| with TestPipeline() as p: |
| pc = p | Create(list(range(101))) |
| |
| quantiles = pc | 'Quantiles globally' >> \ |
| beam.ApproximateQuantiles.Globally(5) |
| quantiles_reversed = pc | 'Quantiles globally reversed' >> \ |
| beam.ApproximateQuantiles.Globally(5, reverse=True) |
| |
| assert_that(quantiles, equal_to([[0, 25, 50, 75, 100]]), |
| label='checkQuantilesGlobally') |
| assert_that(quantiles_reversed, equal_to([[100, 75, 50, 25, 0]]), |
| label='checkReversedQuantiles') |
| |
| def test_quantiles_per_key(self): |
| with TestPipeline() as p: |
| data = self._kv_data |
| pc = p | Create(data) |
| |
| per_key = pc | 'Quantiles PerKey' >> beam.ApproximateQuantiles.PerKey(2) |
| per_key_reversed = (pc | 'Quantiles PerKey Reversed' >> |
| beam.ApproximateQuantiles.PerKey(2, reverse=True)) |
| |
| assert_that(per_key, equal_to([('a', [1, 3]), ('b', [1, 100])]), |
| label='checkQuantilePerKey') |
| assert_that(per_key_reversed, equal_to([('a', [3, 1]), ('b', [100, 1])]), |
| label='checkReversedQuantilesPerKey') |
| |
| def test_quantiles_per_key_with_key_argument(self): |
| with TestPipeline() as p: |
| data = self._kv_str_data |
| pc = p | Create(data) |
| |
| per_key = pc | 'Per Key' >> beam.ApproximateQuantiles.PerKey(2, key=len) |
| per_key_reversed = (pc | 'Per Key Reversed' >> beam.ApproximateQuantiles. |
| PerKey(2, key=len, reverse=True)) |
| |
| assert_that(per_key, equal_to([('a', ['a', 'a' * 3]), |
| ('b', ['b', 'b' * 100])]), |
| label='checkPerKey') |
| assert_that(per_key_reversed, equal_to([('a', ['a'*3, 'a']), |
| ('b', ['b'*100, 'b'])]), |
| label='checkPerKeyReversed') |
| |
| def test_singleton(self): |
| with TestPipeline() as p: |
| data = [389] |
| pc = p | Create(data) |
| qunatiles = pc | beam.ApproximateQuantiles.Globally(5) |
| assert_that(qunatiles, equal_to([[389, 389, 389, 389, 389]])) |
| |
| def test_uneven_quantiles(self): |
| with TestPipeline() as p: |
| pc = p | Create(list(range(5000))) |
| qunatiles = pc | beam.ApproximateQuantiles.Globally(37) |
| aprox_quantiles = self._approx_quantile_generator(size=5000, |
| num_of_quantiles=37, |
| absoluteError=20) |
| assert_that(qunatiles, self._quantiles_matcher(aprox_quantiles)) |
| |
| def test_large_quantiles(self): |
| with TestPipeline() as p: |
| pc = p | Create(list(range(10001))) |
| qunatiles = pc | beam.ApproximateQuantiles.Globally(50) |
| aprox_quantiles = self._approx_quantile_generator(size=10001, |
| num_of_quantiles=50, |
| absoluteError=20) |
| assert_that(qunatiles, self._quantiles_matcher(aprox_quantiles)) |
| |
| def test_random_quantiles(self): |
| with TestPipeline() as p: |
| data = list(range(101)) |
| random.shuffle(data) |
| pc = p | Create(data) |
| quantiles = pc | beam.ApproximateQuantiles.Globally(5) |
| assert_that(quantiles, equal_to([[0, 25, 50, 75, 100]])) |
| |
| def test_duplicates(self): |
| y = list(range(101)) |
| data = [] |
| for _ in range(10): |
| data.extend(y) |
| |
| with TestPipeline() as p: |
| pc = p | Create(data) |
| quantiles = (pc | 'Quantiles Globally' >> |
| beam.ApproximateQuantiles.Globally(5)) |
| quantiles_reversed = (pc | 'Quantiles Reversed' >> |
| beam.ApproximateQuantiles.Globally(5, reverse=True)) |
| |
| assert_that(quantiles, equal_to([[0, 25, 50, 75, 100]]), |
| label="checkQuantilesGlobally") |
| assert_that(quantiles_reversed, equal_to([[100, 75, 50, 25, 0]]), |
| label="checkQuantileReversed") |
| |
| def test_lots_of_duplicates(self): |
| with TestPipeline() as p: |
| data = [1] |
| data.extend([2 for _ in range(299)]) |
| data.extend([3 for _ in range(799)]) |
| pc = p | Create(data) |
| quantiles = pc | beam.ApproximateQuantiles.Globally(5) |
| assert_that(quantiles, equal_to([[1, 2, 3, 3, 3]])) |
| |
| def test_log_distribution(self): |
| with TestPipeline() as p: |
| data = [int(math.log(x)) for x in range(1, 1000)] |
| pc = p | Create(data) |
| quantiles = pc | beam.ApproximateQuantiles.Globally(5) |
| assert_that(quantiles, equal_to([[0, 5, 6, 6, 6]])) |
| |
| def test_zipfian_distribution(self): |
| with TestPipeline() as p: |
| data = [] |
| for i in range(1, 1000): |
| data.append(int(1000 / i)) |
| pc = p | Create(data) |
| quantiles = pc | beam.ApproximateQuantiles.Globally(5) |
| assert_that(quantiles, equal_to([[1, 1, 2, 4, 1000]])) |
| |
| def test_alternate_quantiles(self): |
| data = ["aa", "aaa", "aaaa", "b", "ccccc", "dddd", "zz"] |
| with TestPipeline() as p: |
| pc = p | Create(data) |
| |
| globally = pc | 'Globally' >> beam.ApproximateQuantiles.Globally(3) |
| with_key = (pc | 'Globally with key' >> |
| beam.ApproximateQuantiles.Globally(3, key=len)) |
| key_with_reversed = (pc | 'Globally with key and reversed' >> |
| beam.ApproximateQuantiles.Globally( |
| 3, key=len, reverse=True)) |
| |
| assert_that(globally, equal_to([["aa", "b", "zz"]]), |
| label='checkGlobally') |
| assert_that(with_key, equal_to([["b", "aaa", "ccccc"]]), |
| label='checkGloballyWithKey') |
| assert_that(key_with_reversed, equal_to([["ccccc", "aaa", "b"]]), |
| label='checkWithKeyAndReversed') |
| |
| @staticmethod |
| def _display_data_matcher(instance): |
| expected_items = [ |
| DisplayDataItemMatcher('num_quantiles', instance._num_quantiles), |
| DisplayDataItemMatcher('key', str(instance._key.__name__)), |
| DisplayDataItemMatcher('reverse', str(instance._reverse)) |
| ] |
| return expected_items |
| |
| def test_global_display_data(self): |
| transform = beam.ApproximateQuantiles.Globally(3, key=len, reverse=True) |
| data = DisplayData.create_from(transform) |
| expected_items = self._display_data_matcher(transform) |
| hc.assert_that(data.items, hc.contains_inanyorder(*expected_items)) |
| |
| def test_perkey_display_data(self): |
| transform = beam.ApproximateQuantiles.PerKey(3, key=len, reverse=True) |
| data = DisplayData.create_from(transform) |
| expected_items = self._display_data_matcher(transform) |
| hc.assert_that(data.items, hc.contains_inanyorder(*expected_items)) |
| |
| |
| def _build_quantilebuffer_test_data(): |
| """ |
| Test data taken from "Munro-Paterson Algorithm" reference values table of |
| "Approximate Medians and other Quantiles in One Pass and with Limited Memory" |
| paper. See ApproximateQuantilesCombineFn for paper reference. |
| """ |
| epsilons = [0.1, 0.05, 0.01, 0.005, 0.001] |
| maxElementExponents = [5, 6, 7, 8, 9] |
| expectedNumBuffersValues = [ |
| [11, 14, 17, 21, 24], |
| [11, 14, 17, 20, 23], |
| [9, 11, 14, 17, 21], |
| [8, 11, 14, 17, 20], |
| [6, 9, 11, 14, 17] |
| ] |
| expectedBufferSizeValues = [ |
| [98, 123, 153, 96, 120], |
| [98, 123, 153, 191, 239], |
| [391, 977, 1221, 1526, 954], |
| [782, 977, 1221, 1526, 1908], |
| [3125, 3907, 9766, 12208, 15259] |
| ] |
| test_data = list() |
| i = 0 |
| for epsilon in epsilons: |
| j = 0 |
| for maxElementExponent in maxElementExponents: |
| test_data.append([ |
| epsilon, |
| (10 ** maxElementExponent), |
| expectedNumBuffersValues[i][j], |
| expectedBufferSizeValues[i][j] |
| ]) |
| j += 1 |
| i += 1 |
| return test_data |
| |
| |
| class ApproximateQuantilesBufferTest(unittest.TestCase): |
| """ Approximate Quantiles Buffer Tests to ensure we are calculating the |
| optimal buffers.""" |
| |
| @parameterized.expand(_build_quantilebuffer_test_data) |
| def test_efficiency(self, epsilon, maxInputSize, expectedNumBuffers, |
| expectedBufferSize): |
| """ |
| Verify the buffers are efficiently calculated according to the reference |
| table values. |
| """ |
| |
| combine_fn = ApproximateQuantilesCombineFn.create( |
| num_quantiles=10, max_num_elements=maxInputSize, epsilon=epsilon) |
| self.assertEqual(expectedNumBuffers, combine_fn._num_buffers, |
| "Number of buffers") |
| self.assertEqual(expectedBufferSize, combine_fn._buffer_size, "Buffer size") |
| |
| @parameterized.expand(_build_quantilebuffer_test_data) |
| def test_correctness(self, epsilon, maxInputSize, *args): |
| """ |
| Verify that buffers are correct according to the two constraint equations. |
| """ |
| combine_fn = ApproximateQuantilesCombineFn.create( |
| num_quantiles=10, max_num_elements=maxInputSize, epsilon=epsilon) |
| b = combine_fn._num_buffers |
| k = combine_fn._buffer_size |
| n = maxInputSize |
| self.assertLessEqual((b - 2) * (1 << (b - 2)) + 0.5, (epsilon * n), |
| '(b-2)2^(b-2) + 1/2 <= eN') |
| self.assertGreaterEqual((k * 2) ** (b - 1), n, 'k2^(b-1) >= N') |
| |
| |
| if __name__ == '__main__': |
| unittest.main() |
