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https://issues.apache.org/jira/browse/BEAM-4391?focusedWorklogId=130301&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-130301
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ASF GitHub Bot logged work on BEAM-4391:
----------------------------------------
Author: ASF GitHub Bot
Created on: 02/Aug/18 15:54
Start Date: 02/Aug/18 15:54
Worklog Time Spent: 10m
Work Description: aaltay closed pull request #5736: [BEAM-4391] Example
of distributed optimization
URL: https://github.com/apache/beam/pull/5736
This is a PR merged from a forked repository.
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diff --git a/sdks/python/apache_beam/examples/complete/distribopt.py
b/sdks/python/apache_beam/examples/complete/distribopt.py
new file mode 100644
index 00000000000..f79489516e9
--- /dev/null
+++ b/sdks/python/apache_beam/examples/complete/distribopt.py
@@ -0,0 +1,385 @@
+#
+# 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 solving distributing
+optimization tasks.
+
+This example solves an optimization problem which consists of distributing a
+number of crops to grow in several greenhouses. The decision where to grow the
+crop has an impact on the production parameters associated with the greenhouse,
+which affects the total cost of production at the greenhouse. Additionally,
+each crop needs to be transported to a customer so the decision where to grow
+the crop has an impact on the transportation costs as well.
+
+This type of optimization problems are known as mixed-integer programs as they
+exist of discrete parameters (do we produce a crop in greenhouse A, B or C?)
+and continuous parameters (the greenhouse production parameters).
+
+Running this example requires NumPy and SciPy. The input consists of a CSV file
+with the following columns (Tx representing the transporation cost/unit if the
+crop is produced in greenhouse x): Crop name, Quantity, Ta, Tb, Tc, ....
+
+Example input file with 5 crops and 3 greenhouses (a transporation cost of 0
+forbids production of the crop in a greenhouse):
+OP01,8,12,0,12
+OP02,30,14,3,12
+OP03,25,7,3,14
+OP04,87,7,2,2
+OP05,19,1,7,10
+
+The pipeline consists of three phases:
+ - Creating a grid of mappings (assignment of each crop to a greenhouse)
+ - For each mapping and each greenhouse, optimization of the production
+ parameters for cost, addition of the transporation costs, and aggregation
+ of the costs for each mapping.
+ - Selecting the mapping with the lowest cost.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+
+import argparse
+import logging
+import string
+import uuid
+from collections import defaultdict
+
+import numpy as np
+
+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
+from scipy.optimize import minimize
+
+
+class Simulator(object):
+ """Greenhouse simulation for the optimization of greenhouse parameters."""
+
+ 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(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.
+ weighted_distance = np.sum(self.A * np.square(xc - self.P), axis=1)
+ f = -np.sum(self.alpha * np.exp(-weighted_distance))
+ return np.square(f) * np.log(self.quantities)
+
+
+class CreateGrid(beam.PTransform):
+ """A transform for generating the mapping grid.
+
+ Input: Formatted records of the input file, e.g.,
+ {
+ 'crop': 'OP009',
+ 'quantity': 102,
+ 'transport_costs': [('A', None), ('B', 3), ('C', 8)]
+ }
+ Output: tuple (mapping_identifier, {crop -> greenhouse})
+ """
+
+ class PreGenerateMappings(beam.DoFn):
+ """ParDo implementation forming based on two elements a small sub grid.
+
+ This facilitates parallellization of the grid generation.
+ Emits two PCollections: the subgrid represented as collection of lists of
+ two tuples, and a list of remaining records. Both serve as an input to
+ GenerateMappings.
+ """
+
+ def process(self, element):
+ records = list(element[1])
+ # Split of 2 crops and pre-generate the subgrid.
+ # Select the crop with highest number of possible greenhouses:
+ # in case two crops with only a single possible greenhouse were selected
+ # the subgrid would consist of only 1 element.
+ best_split = np.argsort([-len(r['transport_costs']) for r 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 mappings.
+
+ Input: output of PreGenerateMappings
+ Output: tuples of the form (mapping_identifier, {crop -> greenhouse})
+ """
+
+ @staticmethod
+ def _coordinates_to_greenhouse(coordinates, greenhouses, crops):
+ # Map the grid coordinates back to greenhouse labels
+ arr = []
+ for coord in coordinates:
+ arr.append(greenhouses[coord])
+ return dict(zip(crops, np.array(arr)))
+
+ def process(self, element, records):
+ # Generate available greenhouses and grid coordinates for each crop.
+ grid_coordinates = []
+ for rec in records:
+ # Get indices for available greenhouses (w.r.t crops)
+ filtered = [i for i, av in enumerate(rec['transport_costs']) if av[1]]
+ grid_coordinates.append(filtered)
+
+ # Generate all mappings
+ grid = np.vstack(list(map(np.ravel, np.meshgrid(*grid_coordinates)))).T
+ crops = [rec['crop'] for rec in records]
+ greenhouses = [rec[0] for rec in records[0]['transport_costs']]
+ for point in grid:
+ # translate back to greenhouse label
+ mapping = self._coordinates_to_greenhouse(point, greenhouses, crops)
+ assert all(rec[0] not in mapping for rec in element)
+ # include the incomplete mapping of 2 crops
+ mapping.update(element)
+ # include identifier
+ yield (uuid.uuid4().hex, mapping)
+
+ def expand(self, records):
+ o = (
+ records
+ | 'pair one' >> beam.Map(lambda x: (1, x))
+ | 'group all records' >> beam.GroupByKey()
+ | 'split one of' >> beam.ParDo(self.PreGenerateMappings())
+ .with_outputs('splitted', 'combine')
+ )
+
+ # Create mappings, and prevent fusion (this limits the parallelization
+ # in the optimization step)
+ mappings = (
+ o.splitted
+ | 'create mappings' >> beam.ParDo(self.GenerateMappings(),
+ pvalue.AsSingleton(o.combine))
+ | 'prevent fusion' >> beam.Reshuffle()
+ )
+
+ return mappings
+
+
+class OptimizeGrid(beam.PTransform):
+ """A transform for optimizing all greenhouses of the mapping grid."""
+
+ class CreateOptimizationTasks(beam.DoFn):
+ """
+ Create tasks for optimization.
+
+ Input: (mapping_identifier, {crop -> greenhouse})
+ Output: ((mapping_identifier, greenhouse), [(crop, quantity),...])
+ """
+
+ def process(self, element, quantities):
+ mapping_identifier, mapping = element
+
+ # Create (crop, quantity) lists for each greenhouse
+ greenhouses = defaultdict(list)
+ for crop, greenhouse in mapping.iteritems():
+ quantity = quantities[crop]
+ greenhouses[greenhouse].append((crop, quantity))
+
+ # Create input for OptimizeProductParameters
+ for greenhouse, crops in greenhouses.iteritems():
+ key = (mapping_identifier, greenhouse)
+ yield (key, crops)
+
+ class OptimizeProductParameters(beam.DoFn):
+ """Solve the optimization task to determine optimal production parameters.
+ Input: ((mapping_identifier, greenhouse), [(crop, quantity),...])
+ Two outputs:
+ - solution: (mapping_identifier, (greenhouse, [production parameters]))
+ - costs: (crop, greenhouse, mapping_identifier, cost)
+ """
+
+ @staticmethod
+ def _optimize_production_parameters(sim):
+ # setup initial starting point & bounds
+ x0 = 0.5 * np.ones(3)
+ bounds = list(zip(np.zeros(3), np.ones(3)))
+
+ # Run L-BFGS-B optimizer
+ result = minimize(lambda x: np.sum(sim.simulate(x)), x0, bounds=bounds)
+ return result.x.tolist(), sim.simulate(result.x)
+
+ def process(self, element):
+ mapping_identifier, greenhouse = element[0]
+ crops, quantities = zip(*element[1])
+ sim = Simulator(quantities)
+ optimum, costs = self._optimize_production_parameters(sim)
+ solution = (mapping_identifier, (greenhouse, optimum))
+ yield pvalue.TaggedOutput('solution', solution)
+ for crop, cost, quantity in zip(crops, costs, quantities):
+ costs = (crop, greenhouse, mapping_identifier, cost * quantity)
+ yield pvalue.TaggedOutput('costs', costs)
+
+ def expand(self, inputs):
+ mappings, quantities = inputs
+ opt = (
+ mappings
+ | 'optimization tasks' >> beam.ParDo(self.CreateOptimizationTasks(),
+ pvalue.AsDict(quantities))
+ | 'optimize' >> beam.ParDo(self.OptimizeProductParameters())
+ .with_outputs('costs', 'solution')
+ )
+ return opt
+
+
+class CreateTransportData(beam.DoFn):
+ """Transform records to pvalues ((crop, greenhouse), transport_cost)"""
+
+ def process(self, record):
+ crop = record['crop']
+ for greenhouse, transport_cost in record['transport_costs']:
+ yield ((crop, greenhouse), transport_cost)
+
+
+def add_transport_costs(element, transport, quantities):
+ """Adds the transport cost for the crop to the production cost.
+
+ elements are of the form (crop, greenhouse, mapping, cost), the cost only
+ corresponds to the production cost. Return the same format, but including
+ the transport cost.
+ """
+ crop = element[0]
+ cost = element[3]
+ # lookup & compute cost
+ transport_key = element[:2]
+ transport_cost = transport[transport_key] * quantities[crop]
+ return element[:3] + (cost + transport_cost,)
+
+
+def parse_input(line):
+ # Process each line of the input file to a dict representing each crop
+ # and the transport costs
+ columns = line.split(',')
+
+ # Assign each greenhouse a character
+ transport_costs = []
+ for greenhouse, cost in zip(string.ascii_uppercase, columns[2:]):
+ info = (greenhouse, int(cost) if cost else None)
+ transport_costs.append(info)
+
+ return {
+ 'crop': columns[0],
+ 'quantity': int(columns[1]),
+ 'transport_costs': transport_costs
+ }
+
+
+def format_output(element):
+ """Transforms the datastructure (unpack lists introduced by CoGroupByKey)
+ before writing the result to file.
+ """
+ result = element[1]
+ result['cost'] = result['cost'][0]
+ result['production'] = dict(result['production'])
+ result['mapping'] = result['mapping'][0]
+ return result
+
+
+def run(argv=None):
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--input',
+ dest='input',
+ required=True,
+ help='Input description to process.')
+ parser.add_argument('--output',
+ dest='output',
+ required=True,
+ help='Output file to write results to.')
+ known_args, pipeline_args = parser.parse_known_args(argv)
+ pipeline_options = PipelineOptions(pipeline_args)
+ pipeline_options.view_as(SetupOptions).save_main_session = True
+
+ with beam.Pipeline(options=pipeline_options) as p:
+ # Parse input file
+ records = (
+ p
+ | 'read' >> beam.io.ReadFromText(known_args.input)
+ | 'process input' >> beam.Map(parse_input)
+ )
+
+ # Create two pcollections, used as side inputs
+ transport = (
+ records
+ | 'create transport' >> beam.ParDo(CreateTransportData())
+ )
+
+ quantities = (
+ records
+ | 'create quantities' >> beam.Map(lambda r: (r['crop'], r['quantity']))
+ )
+
+ # Generate all mappings and optimize greenhouse production parameters
+ mappings = records | CreateGrid()
+ opt = (mappings, quantities) | OptimizeGrid()
+
+ # Then add the transport costs and sum costs per crop.
+ costs = (
+ opt.costs
+ | 'include transport' >> beam.Map(add_transport_costs,
+ pvalue.AsDict(transport),
+ pvalue.AsDict(quantities))
+ | 'drop crop and greenhouse' >> beam.Map(lambda x: (x[2], x[3]))
+ | 'aggregate crops' >> beam.CombinePerKey(sum)
+ )
+
+ # Join cost, mapping and production settings solution on mapping
identifier.
+ # Then select best.
+ join_operands = {
+ 'cost': costs,
+ 'production': opt.solution,
+ 'mapping': mappings
+ }
+ best = (
+ join_operands
+ | 'join' >> beam.CoGroupByKey()
+ | 'select best' >> beam.CombineGlobally(min, key=lambda x:
x[1]['cost'])
+ .without_defaults()
+ | 'format output' >> beam.Map(format_output)
+ )
+
+ # pylint: disable=expression-not-assigned
+ best | 'write optimum' >> beam.io.WriteToText(known_args.output)
+
+
+if __name__ == '__main__':
+ logging.getLogger().setLevel(logging.INFO)
+ run()
diff --git a/sdks/python/apache_beam/examples/complete/distribopt_test.py
b/sdks/python/apache_beam/examples/complete/distribopt_test.py
new file mode 100644
index 00000000000..eb8ff53a90f
--- /dev/null
+++ b/sdks/python/apache_beam/examples/complete/distribopt_test.py
@@ -0,0 +1,95 @@
+#
+# 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.
+#
+
+"""Test for the distrib_optimization example."""
+
+from __future__ import absolute_import
+
+import logging
+import os
+import tempfile
+import unittest
+from ast import literal_eval as make_tuple
+
+import numpy as np
+from mock import MagicMock
+from mock import patch
+
+from apache_beam.testing.util import open_shards
+
+FILE_CONTENTS = 'OP01,8,12,0,12\n' \
+ 'OP02,30,14,3,12\n' \
+ 'OP03,25,7,3,14\n' \
+ 'OP04,87,7,2,2\n' \
+ 'OP05,19,1,7,10'
+
+EXPECTED_MAPPING = {
+ 'OP01': 'A',
+ 'OP02': 'B',
+ 'OP03': 'B',
+ 'OP04': 'C',
+ 'OP05': 'A'
+}
+
+
+class DistribOptimizationTest(unittest.TestCase):
+
+ def create_file(self, path, contents):
+ logging.info('Creating temp file: %s', path)
+ with open(path, 'w') as f:
+ f.write(contents)
+
+ def test_basics(self):
+ # Setup the files with expected content.
+ temp_folder = tempfile.mkdtemp()
+ self.create_file(os.path.join(temp_folder, 'input.txt'), FILE_CONTENTS)
+
+ # Run pipeline
+ # Avoid dependency on SciPy
+ scipy_mock = MagicMock()
+ result_mock = MagicMock(x=np.ones(3))
+ scipy_mock.optimize.minimize = MagicMock(return_value=result_mock)
+ modules = {
+ 'scipy': scipy_mock,
+ 'scipy.optimize': scipy_mock.optimize
+ }
+
+ with patch.dict('sys.modules', modules):
+ from apache_beam.examples.complete import distribopt
+ distribopt.run([
+ '--input=%s/input.txt' % temp_folder,
+ '--output', os.path.join(temp_folder, 'result')])
+
+ # Load result file and compare.
+ with open_shards(os.path.join(temp_folder, 'result-*-of-*')) as
result_file:
+ lines = result_file.readlines()
+
+ # Only 1 result
+ self.assertEqual(len(lines), 1)
+
+ # parse result line and verify optimum
+ optimum = make_tuple(lines[0])
+ self.assertAlmostEqual(optimum['cost'], 454.39597, places=3)
+ self.assertDictEqual(optimum['mapping'], EXPECTED_MAPPING)
+ production = optimum['production']
+ for plant in ['A', 'B', 'C']:
+ np.testing.assert_almost_equal(production[plant], np.ones(3))
+
+
+if __name__ == '__main__':
+ logging.getLogger().setLevel(logging.INFO)
+ unittest.main()
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Issue Time Tracking
-------------------
Worklog Id: (was: 130301)
Time Spent: 2h 50m (was: 2h 40m)
> 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 50m
> 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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