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https://issues.apache.org/jira/browse/BEAM-4391?focusedWorklogId=121198&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-121198
]
ASF GitHub Bot logged work on BEAM-4391:
----------------------------------------
Author: ASF GitHub Bot
Created on: 10/Jul/18 04:10
Start Date: 10/Jul/18 04:10
Worklog Time Spent: 10m
Work Description: aaltay commented on a change in pull request #5736:
[BEAM-4391] Example of distributed optimization
URL: https://github.com/apache/beam/pull/5736#discussion_r201211626
##########
File path:
sdks/python/apache_beam/examples/complete/distribopt/distribopt/distribopt.py
##########
@@ -0,0 +1,349 @@
+#
+# 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.
+#
+
+"""
+Example illustrating the use of Apache Beam for distributing optimization
tasks.
+Running this example requires NumPy and SciPy
+"""
+import argparse
+import logging
+import string
+import uuid
+from collections import defaultdict
+
+import numpy as np
+from scipy.optimize import minimize
+
+import apache_beam as beam
+from apache_beam import pvalue
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.options.pipeline_options import SetupOptions
+
+
+class Simulator(object):
+ """
+ Greenhouse simulation
+
+ Disclaimer: this code is an example and does not correspond to any real
greenhouse simulation.
+ """
+
+ def __init__(self, quantities):
+ super(Simulator, self).__init__()
+ self.quantities = np.atleast_1d(quantities)
+
+ self.A = np.array([[3.0, 10, 30],
+ [0.1, 10, 35],
+ [3.0, 10, 30],
+ [0.1, 10, 35]])
+
+ self.P = 1e-4 * np.array([[3689, 1170, 2673],
+ [4699, 4387, 7470],
+ [1091, 8732, 5547],
+ [381, 5743, 8828]])
+
+ a0 = np.array([1.0, 1.2, 3.0, 3.2])
+ coeff = np.sum(np.cos(np.dot(np.atleast_2d(a0).T, self.quantities[None,
:])), axis=1)
+ self.alpha = coeff / np.sum(coeff)
+
+ def simulate(self, xc):
+ # Map the input parameter to a cost for each crop.
+ f = -np.sum(self.alpha * np.exp(-np.sum(self.A * np.square(xc - self.P),
axis=1)))
+ return np.square(f) * np.log(self.quantities)
+
+
+class CreateGrid(beam.PTransform):
+ """
+ A transform for generating the mapping grid.
Review comment:
This could be a single line pydoc.
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Issue Time Tracking
-------------------
Worklog Id: (was: 121198)
Time Spent: 2h (was: 1h 50m)
> Example of distributed optimization
> -----------------------------------
>
> Key: BEAM-4391
> URL: https://issues.apache.org/jira/browse/BEAM-4391
> Project: Beam
> Issue Type: New Feature
> Components: examples-python
> Reporter: Joachim van der Herten
> Assignee: Joachim van der Herten
> Priority: Minor
> Time Spent: 2h
> Remaining Estimate: 0h
>
> Currently, we are writing a blogpost on using the Beam Python SDK for solving
> distributed optimization tasks. It will include an example of a optimization
> problem with both discrete and continuous parameters, which is then solved
> using Apache Beam.
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