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https://issues.apache.org/jira/browse/BEAM-7389?focusedWorklogId=268966&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-268966
]
ASF GitHub Bot logged work on BEAM-7389:
----------------------------------------
Author: ASF GitHub Bot
Created on: 28/Jun/19 01:20
Start Date: 28/Jun/19 01:20
Worklog Time Spent: 10m
Work Description: tvalentyn commented on pull request #8904: [BEAM-7389]
Add Python snippet for Partition transform
URL: https://github.com/apache/beam/pull/8904#discussion_r298344639
##########
File path:
sdks/python/apache_beam/examples/snippets/transforms/element_wise/partition.py
##########
@@ -0,0 +1,130 @@
+# 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 print_function
+
+
+def partition_function(test=None):
+ # [START partition_function]
+ import apache_beam as beam
+
+ durations = ['annual', 'biennial', 'perennial']
+
+ def by_duration(plant, num_partitions):
+ return durations.index(plant['duration'])
+
+ with beam.Pipeline() as pipeline:
+ annuals, biennials, perennials = (
+ pipeline
+ | 'Gardening plants' >> beam.Create([
+ {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},
+ {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},
+ {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},
+ {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},
+ {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'},
+ ])
+ | 'Partition' >> beam.Partition(by_duration, len(durations))
+ )
+ _ = (
+ annuals
+ | 'Annuals' >> beam.Map(lambda x: print('annual: ' + str(x)))
+ )
+ _ = (
+ biennials
+ | 'Biennials' >> beam.Map(lambda x: print('biennial: ' + str(x)))
+ )
+ _ = (
+ perennials
+ | 'Perennials' >> beam.Map(lambda x: print('perennial: ' + str(x)))
+ )
+ # [END partition_function]
+ if test:
+ test(annuals, biennials, perennials)
+
+
+def partition_lambda(test=None):
+ # [START partition_lambda]
+ import apache_beam as beam
+
+ durations = ['annual', 'biennial', 'perennial']
+
+ with beam.Pipeline() as pipeline:
+ annuals, biennials, perennials = (
+ pipeline
+ | 'Gardening plants' >> beam.Create([
+ {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},
+ {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},
+ {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},
+ {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},
+ {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'},
+ ])
+ | 'Partition' >> beam.Partition(
+ lambda plant, num_partitions: durations.index(plant['duration']),
+ len(durations),
+ )
+ )
+ _ = (
+ annuals
+ | 'Annuals' >> beam.Map(lambda x: print('annual: ' + str(x)))
+ )
+ _ = (
+ biennials
+ | 'Biennials' >> beam.Map(lambda x: print('biennial: ' + str(x)))
+ )
+ _ = (
+ perennials
+ | 'Perennials' >> beam.Map(lambda x: print('perennial: ' + str(x)))
+ )
+ # [END partition_lambda]
+ if test:
+ test(annuals, biennials, perennials)
+
+
+def partition_multiple_arguments(test=None):
+ # [START partition_multiple_arguments]
+ import apache_beam as beam
+ from random import Random
+
+ random = Random(0)
+ with beam.Pipeline() as pipeline:
+ train_dataset, test_dataset = (
+ pipeline
+ | 'Gardening plants' >> beam.Create([
+ {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},
+ {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},
+ {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},
+ {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},
+ {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'},
+ ])
+ | 'Partition' >> beam.Partition(
+ lambda plant, n, train_ratio: int(random.random() > train_ratio),
Review comment:
This lambda feels a little hacky to me. How about we move it into a separate
function, and call the second parameter something like `unused_num_partitions`,
or, perhaps better, we call it `num_partitions` and `assert num_partitions ==
2` ?
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Issue Time Tracking
-------------------
Worklog Id: (was: 268966)
Time Spent: 18h 10m (was: 18h)
> Colab examples for element-wise transforms (Python)
> ---------------------------------------------------
>
> Key: BEAM-7389
> URL: https://issues.apache.org/jira/browse/BEAM-7389
> Project: Beam
> Issue Type: Improvement
> Components: website
> Reporter: Rose Nguyen
> Assignee: David Cavazos
> Priority: Minor
> Time Spent: 18h 10m
> Remaining Estimate: 0h
>
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