| # -*- 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. |
| # |
| |
| # pytype: skip-file |
| |
| import math |
| import random |
| import sys |
| import unittest |
| from collections import defaultdict |
| |
| import hamcrest as hc |
| from parameterized import parameterized |
| from parameterized import parameterized_class |
| |
| import apache_beam as beam |
| from apache_beam.coders import coders |
| 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 |
| from apache_beam.transforms.stats import ApproximateUniqueCombineFn |
| |
| try: |
| import mmh3 |
| mmh3_options = [(mmh3, ), (None, )] |
| except ImportError: |
| mmh3_options = [(None, )] |
| |
| |
| @parameterized_class(('mmh3_option', ), mmh3_options) |
| class ApproximateUniqueTest(unittest.TestCase): |
| """Unit tests for ApproximateUnique.Globally and ApproximateUnique.PerKey.""" |
| random.seed(0) |
| |
| def setUp(self): |
| sys.modules['mmh3'] = self.mmh3_option |
| |
| @parameterized.expand([ |
| ( |
| 'small_population_by_size', |
| list(range(30)), |
| 32, |
| None, |
| 'assert:global_by_sample_size_with_small_population'), |
| ( |
| 'large_population_by_size', |
| list(range(100)), |
| 16, |
| None, |
| 'assert:global_by_sample_size_with_large_population'), |
| ( |
| 'with_duplicates_by_size', [10] * 50 + [20] * 50, |
| 30, |
| None, |
| 'assert:global_by_sample_size_with_duplicates'), |
| ( |
| 'small_population_by_error', |
| list(range(30)), |
| None, |
| 0.3, |
| 'assert:global_by_error_with_small_population'), |
| ( |
| 'large_population_by_error', |
| [random.randint(1, 1000) for _ in range(500)], |
| None, |
| 0.1, |
| 'assert:global_by_error_with_large_population'), |
| ]) |
| def test_approximate_unique_global( |
| self, name, test_input, sample_size, est_error, label): |
| # check that only either sample_size or est_error is not None |
| assert bool(sample_size) != bool(est_error) |
| if sample_size: |
| error = 2 / math.sqrt(sample_size) |
| else: |
| error = est_error |
| random.shuffle(test_input) |
| 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, error=est_error) |
| | 'compare' >> beam.FlatMap( |
| lambda x: [abs(x - actual_count) * 1.0 / actual_count <= error])) |
| |
| assert_that(result, equal_to([True]), label=label) |
| |
| @parameterized.expand([ |
| ('by_size', 20, None, 'assert:unique_perkey_by_sample_size'), |
| ('by_error', None, 0.02, 'assert:unique_perkey_by_error') |
| ]) |
| def test_approximate_unique_perkey(self, name, sample_size, est_error, label): |
| # check that only either sample_size or est_error is set |
| assert bool(sample_size) != bool(est_error) |
| if sample_size: |
| error = 2 / math.sqrt(sample_size) |
| else: |
| error = est_error |
| |
| 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) |
| |
| with TestPipeline() as pipeline: |
| result = ( |
| pipeline |
| | 'create' >> beam.Create(test_input) |
| | 'get_estimate' >> beam.ApproximateUnique.PerKey( |
| size=sample_size, error=est_error) |
| | 'compare' >> beam.FlatMap( |
| lambda x: [ |
| abs(x[1] - len(actual_count_dict[x[0]])) * 1.0 / len( |
| actual_count_dict[x[0]]) <= error |
| ])) |
| |
| assert_that( |
| result, equal_to([True] * len(actual_count_dict)), label=label) |
| |
| @parameterized.expand([ |
| ( |
| 'invalid_input_size', |
| list(range(30)), |
| 10, |
| None, |
| beam.ApproximateUnique._INPUT_SIZE_ERR_MSG % 10), |
| ( |
| 'invalid_type_size', |
| list(range(30)), |
| 100.0, |
| None, |
| beam.ApproximateUnique._INPUT_SIZE_ERR_MSG % 100.0), |
| ( |
| 'invalid_small_error', |
| list(range(30)), |
| None, |
| 0.0, |
| beam.ApproximateUnique._INPUT_ERROR_ERR_MSG % 0.0), |
| ( |
| 'invalid_big_error', |
| list(range(30)), |
| None, |
| 0.6, |
| beam.ApproximateUnique._INPUT_ERROR_ERR_MSG % 0.6), |
| ( |
| 'no_input', |
| list(range(30)), |
| None, |
| None, |
| beam.ApproximateUnique._NO_VALUE_ERR_MSG), |
| ( |
| 'both_input', |
| list(range(30)), |
| 30, |
| 0.2, |
| beam.ApproximateUnique._MULTI_VALUE_ERR_MSG % (30, 0.2)), |
| ]) |
| def test_approximate_unique_global_value_error( |
| self, name, test_input, sample_size, est_error, expected_msg): |
| 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_error)) |
| |
| assert e.exception.args[0] == expected_msg |
| |
| def test_approximate_unique_combine_fn_requires_nondeterministic_coder(self): |
| sample_size = 30 |
| coder = coders.Base64PickleCoder() |
| |
| with self.assertRaises(ValueError) as e: |
| _ = ApproximateUniqueCombineFn(sample_size, coder) |
| |
| self.assertRegex( |
| e.exception.args[0], |
| 'The key coder "Base64PickleCoder" ' |
| 'for ApproximateUniqueCombineFn is not deterministic.') |
| |
| def test_approximate_unique_combine_fn_requires_compatible_coder(self): |
| test_input = 'a' |
| sample_size = 30 |
| coder = coders.FloatCoder() |
| combine_fn = ApproximateUniqueCombineFn(sample_size, coder) |
| accumulator = combine_fn.create_accumulator() |
| with self.assertRaises(RuntimeError) as e: |
| accumulator = combine_fn.add_input(accumulator, test_input) |
| |
| self.assertRegex(e.exception.args[0], 'Runtime exception') |
| |
| 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 |
| |
| |
| 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_globally_weighted(self): |
| num_inputs = 1e3 |
| a = -3 |
| b = 3 |
| |
| # Weighting function coincides with the pdf of the standard normal |
| # distribution up to a constant. Since 99.7% of the probability mass for |
| # this pdf is concentrated in the interval [a, b] = [-3, 3], the quantiles |
| # for a sample from this interval with the given weight function are |
| # expected to be close to the quantiles of the standard normal distribution. |
| def weight(x): |
| return math.exp(-(x**2) / 2) |
| |
| input_data = [ |
| (a + (b - a) * i / num_inputs, weight(a + (b - a) * i / num_inputs)) |
| for i in range(int(num_inputs) + 1) |
| ] |
| with TestPipeline() as p: |
| pc = p | Create(input_data) |
| |
| weighted_quantiles = pc | "Quantiles globally weighted" >> \ |
| beam.ApproximateQuantiles.Globally(5, weighted=True) |
| reversed_weighted_quantiles = ( |
| pc | 'Quantiles globally weighted reversed' >> |
| beam.ApproximateQuantiles.Globally(5, reverse=True, weighted=True)) |
| |
| assert_that( |
| weighted_quantiles, |
| equal_to([[-3., -0.6720000000000002, 0., 0.6720000000000002, 3.]]), |
| label="checkWeightedQuantilesGlobally") |
| assert_that( |
| reversed_weighted_quantiles, |
| equal_to([[3., 0.6720000000000002, 0., -0.6720000000000002, -3.]]), |
| label="checkWeightedReversedQuantilesGlobally") |
| |
| 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_weighted(self): |
| with TestPipeline() as p: |
| data = [(k, (v, 2.)) for k, v in self._kv_data] |
| pc = p | Create(data) |
| |
| per_key = pc | 'Weighted Quantiles PerKey' >> \ |
| beam.ApproximateQuantiles.PerKey(2, weighted=True) |
| per_key_reversed = pc | 'Weighted Quantiles PerKey Reversed' >> \ |
| beam.ApproximateQuantiles.PerKey( |
| 2, reverse=True, weighted=True) |
| |
| assert_that( |
| per_key, |
| equal_to([('a', [1, 3]), ('b', [1, 100])]), |
| label='checkWeightedQuantilesPerKey') |
| assert_that( |
| per_key_reversed, |
| equal_to([('a', [3, 1]), ('b', [100, 1])]), |
| label='checkWeightedReversedQuantilesPerKey') |
| |
| 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) |
| quantiles = pc | beam.ApproximateQuantiles.Globally(5) |
| assert_that(quantiles, equal_to([[389, 389, 389, 389, 389]])) |
| |
| def test_uneven_quantiles(self): |
| with TestPipeline() as p: |
| pc = p | Create(list(range(5000))) |
| quantiles = pc | beam.ApproximateQuantiles.Globally(37) |
| approx_quantiles = self._approx_quantile_generator( |
| size=5000, num_of_quantiles=37, absoluteError=20) |
| assert_that(quantiles, self._quantiles_matcher(approx_quantiles)) |
| |
| def test_large_quantiles(self): |
| with TestPipeline() as p: |
| pc = p | Create(list(range(10001))) |
| quantiles = pc | beam.ApproximateQuantiles.Globally(50) |
| approx_quantiles = self._approx_quantile_generator( |
| size=10001, num_of_quantiles=50, absoluteError=20) |
| assert_that(quantiles, self._quantiles_matcher(approx_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') |
| |
| def test_batched_quantiles(self): |
| with TestPipeline() as p: |
| data = [] |
| for i in range(100): |
| data.append([(j / 10, abs(j - 500)) |
| for j in range(i * 10, (i + 1) * 10)]) |
| pc = p | Create(data) |
| globally = ( |
| pc | 'Globally' >> beam.ApproximateQuantiles.Globally( |
| 3, input_batched=True)) |
| with_key = ( |
| pc | 'Globally with key' >> beam.ApproximateQuantiles.Globally( |
| 3, key=sum, input_batched=True)) |
| key_with_reversed = ( |
| pc | 'Globally with key and reversed' >> |
| beam.ApproximateQuantiles.Globally( |
| 3, key=sum, reverse=True, input_batched=True)) |
| assert_that( |
| globally, |
| equal_to([[(0.0, 500), (49.9, 1), (99.9, 499)]]), |
| label='checkGlobally') |
| assert_that( |
| with_key, |
| equal_to([[(50.0, 0), (72.5, 225), (99.9, 499)]]), |
| label='checkGloballyWithKey') |
| assert_that( |
| key_with_reversed, |
| equal_to([[(99.9, 499), (72.5, 225), (50.0, 0)]]), |
| label='checkGloballyWithKeyAndReversed') |
| |
| def test_batched_weighted_quantiles(self): |
| with TestPipeline() as p: |
| data = [] |
| for i in range(100): |
| data.append([[(i / 10, abs(i - 500)) |
| for i in range(i * 10, (i + 1) * 10)], [i] * 10]) |
| pc = p | Create(data) |
| globally = ( |
| pc | 'Globally' >> beam.ApproximateQuantiles.Globally( |
| 3, weighted=True, input_batched=True)) |
| with_key = ( |
| pc | 'Globally with key' >> beam.ApproximateQuantiles.Globally( |
| 3, key=sum, weighted=True, input_batched=True)) |
| key_with_reversed = ( |
| pc | 'Globally with key and reversed' >> |
| beam.ApproximateQuantiles.Globally( |
| 3, key=sum, reverse=True, weighted=True, input_batched=True)) |
| assert_that( |
| globally, |
| equal_to([[(0.0, 500), (70.8, 208), (99.9, 499)]]), |
| label='checkGlobally') |
| assert_that( |
| with_key, |
| equal_to([[(50.0, 0), (21.0, 290), (99.9, 499)]]), |
| label='checkGloballyWithKey') |
| assert_that( |
| key_with_reversed, |
| equal_to([[(99.9, 499), (21.0, 290), (50.0, 0)]]), |
| label='checkGloballyWithKeyAndReversed') |
| |
| def test_quantiles_merge_accumulators(self): |
| # This test exercises merging multiple buffers and approximation accuracy. |
| # The max_num_elements is set to a small value to trigger buffers collapse |
| # and interpolation. Under the conditions below, buffer_size=125 and |
| # num_buffers=4, so we're only allowed to keep half of the input values. |
| num_accumulators = 100 |
| num_quantiles = 5 |
| eps = 0.01 |
| max_num_elements = 1000 |
| combine_fn = ApproximateQuantilesCombineFn.create( |
| num_quantiles, eps, max_num_elements) |
| combine_fn_weighted = ApproximateQuantilesCombineFn.create( |
| num_quantiles, eps, max_num_elements, weighted=True) |
| data = list(range(1000)) |
| weights = list(reversed(range(1000))) |
| step = math.ceil(len(data) / num_accumulators) |
| accumulators = [] |
| accumulators_weighted = [] |
| for i in range(num_accumulators): |
| accumulator = combine_fn.create_accumulator() |
| accumulator_weighted = combine_fn_weighted.create_accumulator() |
| for element, weight in zip(data[i*step:(i+1)*step], |
| weights[i*step:(i+1)*step]): |
| accumulator = combine_fn.add_input(accumulator, element) |
| accumulator_weighted = combine_fn_weighted.add_input( |
| accumulator_weighted, (element, weight)) |
| accumulators.append(accumulator) |
| accumulators_weighted.append(accumulator_weighted) |
| accumulator = combine_fn.merge_accumulators(accumulators) |
| accumulator_weighted = combine_fn_weighted.merge_accumulators( |
| accumulators_weighted) |
| quantiles = combine_fn.extract_output(accumulator) |
| quantiles_weighted = combine_fn_weighted.extract_output( |
| accumulator_weighted) |
| |
| # In fact, the final accuracy is much higher than eps, but we test for a |
| # minimal accuracy here. |
| for q, actual_q in zip(quantiles, [0, 249, 499, 749, 999]): |
| self.assertAlmostEqual(q, actual_q, delta=max_num_elements * eps) |
| for q, actual_q in zip(quantiles_weighted, [0, 133, 292, 499, 999]): |
| self.assertAlmostEqual(q, actual_q, delta=max_num_elements * eps) |
| |
| @staticmethod |
| def _display_data_matcher(instance): |
| expected_items = [ |
| DisplayDataItemMatcher('num_quantiles', instance._num_quantiles), |
| DisplayDataItemMatcher('weighted', str(instance._weighted)), |
| DisplayDataItemMatcher('key', str(instance._key.__name__)), |
| DisplayDataItemMatcher('reverse', str(instance._reverse)), |
| DisplayDataItemMatcher('input_batched', str(instance._input_batched)), |
| ] |
| return expected_items |
| |
| def test_global_display_data(self): |
| transform = beam.ApproximateQuantiles.Globally( |
| 3, weighted=True, 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, weighted=True, 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._spec.num_buffers, "Number of buffers") |
| self.assertEqual( |
| expectedBufferSize, combine_fn._spec.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._spec.num_buffers |
| k = combine_fn._spec.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() |
