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ASF GitHub Bot logged work on BEAM4391:

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: [BEAM4391] Example
of distributed optimization
URL: https://github.com/apache/beam/pull/5736#discussion_r201211811
##########
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/LICENSE2.0
+#
+# 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 = 1e4 * 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.
+ """
+
+ class PreGenerateMappings(beam.DoFn):
+ """
+ ParDo implementation which splits of 2 records and generated a sub grid.
+
+ This facilitates parallellization of the grid generation.
+ Emits both the PCollection representing the subgrid, as well as the list
+ of remaining records. Both serve as an input to GenerateMappings
+ """
+
+ def process(self, element):
+ records = list(element[1])
+ # Split of 2 crops and pregenerate all combinations to facilitate parallellism
+ # No point splitting of a crop which can only be created in 1 greenhouse,
+ # split of crops with highest number of options.
+ best_split = np.argsort([len(rec['transport_costs']) for rec in records])[:2]
+ rec1 = records[best_split[0]]
+ rec2 = records[best_split[1]]
+
+ # Generate & emit all combinations
+ for a in rec1['transport_costs']:
+ if a[1]:
+ for b in rec2['transport_costs']:
+ if b[1]:
+ combination = [(rec1['crop'], a[0]), (rec2['crop'], b[0])]
+ yield pvalue.TaggedOutput('splitted', combination)
+
+ # Pass on remaining records
+ remaining = [rec for i, rec in enumerate(records) if i not in best_split]
+ yield pvalue.TaggedOutput('combine', remaining)
+
+ class GenerateMappings(beam.DoFn):
+ """
+ ParDo implementation to generate all possible assignments of crops to greenhouses.
Review comment:
You can start pydoc comments in the first line with """. (For examples see: https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/complete/tfidf.py#L52)

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Issue Time Tracking

Worklog Id: (was: 121193)
Time Spent: 1.5h (was: 1h 20m)
> Example of distributed optimization
> 
>
> Key: BEAM4391
> URL: https://issues.apache.org/jira/browse/BEAM4391
> Project: Beam
> Issue Type: New Feature
> Components: examplespython
> Reporter: Joachim van der Herten
> Assignee: Joachim van der Herten
> Priority: Minor
> Time Spent: 1.5h
> 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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