[
https://issues.apache.org/jira/browse/BEAM-6693?focusedWorklogId=242915&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-242915
]
ASF GitHub Bot logged work on BEAM-6693:
----------------------------------------
Author: ASF GitHub Bot
Created on: 15/May/19 22:47
Start Date: 15/May/19 22:47
Worklog Time Spent: 10m
Work Description: Hannah-Jiang commented on pull request #8535:
[BEAM-6693] ApproximateUnique transform for Python SDK
URL: https://github.com/apache/beam/pull/8535#discussion_r284481067
##########
File path: sdks/python/apache_beam/transforms/stats_test.py
##########
@@ -0,0 +1,384 @@
+# -*- 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 collections import defaultdict
+
+import numpy as np
+
+import apache_beam as beam
+from apache_beam.testing.test_pipeline import TestPipeline
+from apache_beam.testing.util import assert_that
+from apache_beam.testing.util import equal_to
+
+
+class ApproximateUniqueTest(unittest.TestCase):
+ """Unit tests for ApproximateUniqueGlobally and ApproximateUniquePerKey."""
+
+ 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.ApproximateUniqueGlobally(size=sample_size))
+ pipeline.run()
+
+ expected_msg = beam.ApproximateUniqueGlobally._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.ApproximateUniqueGlobally(size=sample_size))
+ pipeline.run()
+
+ expected_msg = beam.ApproximateUniqueGlobally._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.ApproximateUniqueGlobally(error=est_err))
+ pipeline.run()
+
+ expected_msg = beam.ApproximateUniqueGlobally._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.ApproximateUniqueGlobally(error=est_err))
+ pipeline.run()
+
+ expected_msg = beam.ApproximateUniqueGlobally._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.ApproximateUniqueGlobally())
+ pipeline.run()
+
+ expected_msg = beam.ApproximateUniqueGlobally._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.ApproximateUniqueGlobally(size=sample_size, error=est_err))
+ pipeline.run()
+
+ expected_msg = beam.ApproximateUniqueGlobally._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.ApproximateUniqueGlobally.\
+ _get_sample_size_from_est_error(0.5) == 16
+ assert beam.ApproximateUniqueGlobally.\
+ _get_sample_size_from_est_error(0.4) == 25
+ assert beam.ApproximateUniqueGlobally.\
+ _get_sample_size_from_est_error(0.2) == 100
+ assert beam.ApproximateUniqueGlobally.\
+ _get_sample_size_from_est_error(0.1) == 400
+ assert beam.ApproximateUniqueGlobally.\
+ _get_sample_size_from_est_error(0.05) == 1600
+ assert beam.ApproximateUniqueGlobally.\
+ _get_sample_size_from_est_error(0.01) == 40000
+
+ 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 = 50% of population).
+ sample_size = 50
+ max_err = 2 / math.sqrt(sample_size)
+ test_input = [random.randint(0, 1000) for _ in range(100)]
+ actual_count = len(set(test_input))
+
+ pipeline = TestPipeline()
+ result = (pipeline
+ | 'create' >> beam.Create(test_input)
+ | 'get_estimate'
+ >> beam.ApproximateUniqueGlobally(size=sample_size)
+ | 'compare'
+ >> beam.ParDo(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()
+
+ 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.ApproximateUniqueGlobally(size=sample_size)
+ | 'compare'
+ >> beam.ParDo(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()
+
+ 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 = [random.randint(0, 1000) for _ in range(30)]
+ actual_count = len(set(test_input))
+
+ pipeline = TestPipeline()
+ result = (pipeline
+ | 'create' >> beam.Create(test_input)
+ | 'get_estimate'
+ >> beam.ApproximateUniqueGlobally(size=sample_size))
+
+ assert_that(result, equal_to([actual_count]),
+ label='assert:global_by_sample_size_with_small_population')
+ pipeline.run()
+
+ def test_approximate_unique_global_by_sample_size_with_big_population(self):
+ # test if estimation error is smaller than expected max error with a small
+ # sample and a big population (sample size = 0.3% of population).
+ sample_size = 30
+ max_err = 2 / math.sqrt(sample_size)
+ test_input = [random.randint(0, 1000) for _ in range(10000)]
+ actual_count = len(set(test_input))
+
+ pipeline = TestPipeline()
+ result = (pipeline
+ | 'create' >> beam.Create(test_input)
+ | 'get_estimate'
+ >> beam.ApproximateUniqueGlobally(size=sample_size)
+ | 'compare'
+ >> beam.ParDo(lambda x: [abs(x - actual_count) * 1.0
+ / actual_count <= max_err]))
+
+ assert_that(result, equal_to([True]),
+ label='assert:global_by_sample_size_with_big_population')
+ pipeline.run()
+
+ 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 = [random.randint(0, 1000) for _ in range(100)]
+ actual_count = len(set(test_input))
+
+ pipeline = TestPipeline()
+ result = (pipeline
+ | 'create' >> beam.Create(test_input)
+ | 'get_estimate'
+ >> beam.ApproximateUniqueGlobally(error=est_err)
+ | 'compare'
+ >> beam.ParDo(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()
+
+ def test_approximate_unique_global_by_error_with_samll_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 = [random.randint(0, 1000) for _ in range(15)]
+ actual_count = len(set(test_input))
+
+ pipeline = TestPipeline()
+ result = (pipeline
+ | 'create' >> beam.Create(test_input)
+ | 'get_estimate'
+ >> beam.ApproximateUniqueGlobally(error=est_err))
+
+ assert_that(result, equal_to([actual_count]),
+ label='assert:global_by_error_with_samll_population')
+ pipeline.run()
+
+ def test_approximate_unique_global_by_error_with_big_population(self):
+ # test if estimation error from input error is with in expected range with
+ # a big population.
+ est_err = 0.3
+ test_input = [random.randint(0, 1000) for _ in range(100000)]
+ actual_count = len(set(test_input))
+
+ pipeline = TestPipeline()
+ result = (pipeline
+ | 'create' >> beam.Create(test_input)
+ | 'get_estimate'
+ >> beam.ApproximateUniqueGlobally(error=est_err)
+ | 'compare'
+ >> beam.ParDo(lambda x: [abs(x - actual_count) * 1.0
+ / actual_count <= est_err]))
+
+ assert_that(result, equal_to([True]),
+ label='assert:global_by_error_with_big_population')
+ pipeline.run()
+
+ def test_approximate_unique_perkey_by_size(self):
+ # test if est error per key from sample size is in a expected range.
+ sample_size = 50
+ max_err = 2 / math.sqrt(sample_size)
+ number_of_keys = 10
+ test_input = [(random.randint(1, number_of_keys), random.randint(0, 1000))
+ for _ in range(100)]
+ 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.ApproximateUniquePerKey(size=sample_size)
+ | 'compare'
+ >> beam.ParDo(lambda x: [abs(x[1] - len(actual_count_dict[x[0]]))
Review comment:
It is fixed now.
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
[email protected]
Issue Time Tracking
-------------------
Worklog Id: (was: 242915)
Time Spent: 6h 50m (was: 6h 40m)
> ApproximateUnique transform for Python SDK
> ------------------------------------------
>
> Key: BEAM-6693
> URL: https://issues.apache.org/jira/browse/BEAM-6693
> Project: Beam
> Issue Type: New Feature
> Components: sdk-py-core
> Reporter: Ahmet Altay
> Assignee: Hannah Jiang
> Priority: Minor
> Time Spent: 6h 50m
> Remaining Estimate: 0h
>
> Add a PTransform for estimating the number of distinct elements in a
> PCollection and the number of distinct values associated with each key in a
> PCollection KVs.
> it should offer the same API as its Java counterpart:
> https://github.com/apache/beam/blob/11a977b8b26eff2274d706541127c19dc93131a2/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/ApproximateUnique.java
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
