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Fix defect ensemble model (with Code) and genetic algorithm #2317

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merged 19 commits into from
May 16, 2024
Merged

Fix defect ensemble model (with Code) and genetic algorithm #2317

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a0ea3b5
Closes #2304
alfoa May 8, 2024
e9feed6
added test
alfoa May 8, 2024
336bae3
Update tests/framework/Optimizers/GeneticAlgorithms/continuous/uncons…
alfoa May 8, 2024
da32086
removed trailing
alfoa May 8, 2024
70a5354
Merge branch 'alfoa/genetic_ensemble_model' of github.com:idaholab/ra…
alfoa May 8, 2024
6077f56
added missing input file
alfoa May 8, 2024
c7f27bf
Update tests/framework/Optimizers/GeneticAlgorithms/continuous/uncons…
alfoa May 8, 2024
af23869
isinstance
alfoa May 9, 2024
ee2f53f
fixed parallel execution
alfoa May 9, 2024
7fba807
readded heron and baycal?
alfoa May 9, 2024
9f077b8
removed trailing spaces
alfoa May 9, 2024
ab60b73
some cleaning
alfoa May 9, 2024
54f43a4
fixed test name and rook error message
alfoa May 9, 2024
a522493
module ensemble doc
alfoa May 10, 2024
5856f6b
Apply suggestions from code review
alfoa May 13, 2024
f83fb30
removed trailing whitespaces
alfoa May 13, 2024
91a23d9
Merge branch 'devel' into alfoa/genetic_ensemble_model
alfoa May 13, 2024
d3c7069
fix for 'cvxpy'...this is not a very maintainable way to handle such …
alfoa May 13, 2024
7ff7659
changed approach
alfoa May 13, 2024
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12 changes: 10 additions & 2 deletions ravenframework/JobHandler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -702,7 +702,7 @@ def reAddJob(self, runner):
runner.trackTime('queue')
self.__submittedJobs.append(runner.identifier)

def addClientJob(self, args, functionToRun, identifier, metadata=None, uniqueHandler="any"):
def addClientJob(self, args, functionToRun, identifier, metadata=None, uniqueHandler="any", groupInfo = None):
"""
Method to add an internal run (function execution), without consuming
resources (free spots). This can be used for client handling (see
Expand All @@ -719,11 +719,19 @@ def addClientJob(self, args, functionToRun, identifier, metadata=None, uniqueHan
this runner. For example, if present, to retrieve this runner using the
method jobHandler.getFinished, the uniqueHandler needs to be provided.
If uniqueHandler == 'any', every "client" can get this runner.
@ In, groupInfo, dict, optional, {id:string, size:int}.
- "id": it is a special keyword attached to
this runner to identify that this runner belongs to a special set of runs that need to be
grouped together (all will be retrievable only when all the runs ended).
- "size", number of runs in this group self.__batching
NOTE: If the "size" of the group is only set the first time a job of this group is added.
Consequentially the size is immutable
@ Out, None
"""
self.addJob(args, functionToRun, identifier, metadata,
forceUseThreads = True, uniqueHandler = uniqueHandler,
clientQueue = True)
clientQueue = True, groupInfo = groupInfo)


def addFinishedJob(self, data, metadata=None, uniqueHandler="any", profile=False):
"""
Expand Down
9 changes: 8 additions & 1 deletion ravenframework/Models/EnsembleModel.py
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Expand Up @@ -563,7 +563,14 @@ def submit(self,myInput,samplerType,jobHandler,**kwargs):
uniqueHandler=uniqueHandler, forceUseThreads=forceThreads,
groupInfo={'id': kwargs['batchInfo']['batchId'], 'size': nRuns} if batchMode else None)
else:
jobHandler.addClientJob((self, myInput, samplerType, kwargs), self.__class__.evaluateSample, prefix, kwargs)
# for parallel strategy 2, the ensemble model works as a step => it needs the jobHandler
kw['jobHandler'] = jobHandler
# for parallel strategy 2, we need to make sure that the batchMode is set to False in the inner runs since only the
# ensemble model evaluation should be batched (THIS IS REQUIRED because the CODE does not submit runs like the other models)
kw['batchMode'] = False
jobHandler.addClientJob((self, myInput, samplerType, kw), self.__class__.evaluateSample, prefix, metadata=metadata,
uniqueHandler=uniqueHandler,
groupInfo={'id': kwargs['batchInfo']['batchId'], 'size': nRuns} if batchMode else None)

def __retrieveDependentOutput(self,modelIn,listOfOutputs, typeOutputs):
"""
Expand Down
16 changes: 16 additions & 0 deletions ...ticAlgorithms/continuous/unconstrained/metaModelWithCodeAndFunctionsAndGenetic/ABCDsum.py
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@@ -0,0 +1,16 @@
# Copyright 2017 Battelle Energy Alliance, LLC
#
# Licensed 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.

def run(self, Input):
self.sum = self.A + self.B + self.C + self.D
6 changes: 6 additions & 0 deletions ...rithms/continuous/unconstrained/metaModelWithCodeAndFunctionsAndGenetic/decayConstantB.py
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@@ -0,0 +1,6 @@
# Copyright Nucube Energy, Inc.

def evaluate(self):
return self.decay_A+0.0001


180 changes: 180 additions & 0 deletions ...neticAlgorithms/continuous/unconstrained/test_ensembleModel_withGA_Code_and_Functions.xml
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<?xml version="1.0" ?>
<Simulation verbosity="debug">
<TestInfo>
<name>framework/Optimizers/GeneticAlgorithms.GAwithEnsembleModelIncludingCode</name>
<author>mandd</author>
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<created>2024-05-07</created>
<classesTested>Models.EnsembleModel, Optimizers.GeneticAlgorithm</classesTested>
<description>
This test is aimed to test the usage of the GeneticAlgorithm with an EnsembleModel, including
a model Code. This test has been created to test the modifications performed to address issue
#2304 (see \\url{https://github.com/idaholab/raven/issues/2304}).
</description>
</TestInfo>

<RunInfo>
<JobName>metaModelWithCodeAndFunctionsAndGenetic</JobName>
<Sequence>
optimize
</Sequence>
<WorkingDir>metaModelWithCodeAndFunctionsAndGenetic</WorkingDir>
<batchSize>1</batchSize>
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Have you tested it with different batch size?

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ops. Not yet. let me test it

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@wangcj05 I fixed the parallel execution and converted the test into a parallel (batchSize > 1) to test it.

Ready for re-review

</RunInfo>

<Files>
<Input name="referenceInput.xml" type="input">referenceInput.xml</Input>
</Files>

<Models>
<Code name="testModel" subType="GenericCode">
<executable>../../../../user_guide/physicalCode/analyticalbateman/AnalyticalDplMain.py</executable>
<clargs arg="python" type="prepend"/>
<clargs arg="" extension=".xml" type="input"/>
<clargs arg=" " extension=".csv" type="output"/>
<prepend>python</prepend>
</Code>
<ExternalModel ModuleToLoad="ABCDsum" name="sumOfABCD" subType="">
<variables>A,B,C,D,sum</variables>
</ExternalModel>
<EnsembleModel name="codeAndExtModel" subType="">
<Model class="Models" type="ExternalModel">
sumOfABCD
<Input class="DataObjects" type="PointSet">inputPlaceHolderForInternalObjects</Input>
<TargetEvaluation class="DataObjects" type="PointSet">sumData</TargetEvaluation>
</Model>
<Model class="Models" type="Code">
testModel
<Input class="Files" type="">referenceInput.xml</Input>
<TargetEvaluation class="DataObjects" type="PointSet">samplesMC</TargetEvaluation>
</Model>
</EnsembleModel>
</Models>

<Distributions>
<Uniform name="sigma">
<lowerBound>0</lowerBound>
<upperBound>1000</upperBound>
</Uniform>
<Uniform name="decayConstant">
<lowerBound>0.00000001</lowerBound>
<upperBound>0.0000001</upperBound>
</Uniform>
</Distributions>

<Optimizers>
<GeneticAlgorithm name="GAopt">
<samplerInit>
<limit>20</limit>
<initialSeed>42</initialSeed>
<writeSteps>every</writeSteps>
</samplerInit>

<GAparams>
<populationSize>2</populationSize>
<parentSelection>rouletteWheel</parentSelection>
<reproduction>
<crossover type="onePointCrossover">
<crossoverProb>0.8</crossoverProb>
</crossover>
<mutation type="swapMutator">
<mutationProb>0.9</mutationProb>
</mutation>
</reproduction>
<fitness type="feasibleFirst"></fitness>
<survivorSelection>fitnessBased</survivorSelection>
</GAparams>

<convergence>
<AHDp>0.0</AHDp>
</convergence>

<variable name="sigma-A">
<distribution>sigma</distribution>
</variable>
<variable name="decay_A">
<distribution>decayConstant</distribution>
</variable>
<variable name="sigma-B">
<distribution>sigma</distribution>
</variable>
<variable name="decay_B">
<function>decayConstantB</function>
</variable>

<objective>sum</objective>
<TargetEvaluation class="DataObjects" type="PointSet">finalResponses</TargetEvaluation>
<Sampler class="Samplers" type="MonteCarlo">MC_samp</Sampler>
</GeneticAlgorithm>
</Optimizers>

<Functions>
<External file="decayConstantB.py" name="decayConstantB">
<variables>decay_A</variables>
</External>
</Functions>

<Samplers>
<MonteCarlo name="MC_samp">
<samplerInit>
<limit>2</limit>
<initialSeed>20021986</initialSeed>
</samplerInit>
<variable name="sigma-A">
<distribution>sigma</distribution>
</variable>
<variable name="decay_A">
<distribution>decayConstant</distribution>
</variable>
<variable name="sigma-B">
<distribution>sigma</distribution>
</variable>
<variable name="decay_B">
<function>decayConstantB</function>
</variable>
</MonteCarlo>
</Samplers>

<Steps>
<MultiRun name="optimize">
<Input class="Files" type="">referenceInput.xml</Input>
<Input class="DataObjects" type="PointSet">inputPlaceHolderForInternalObjects</Input>
<Model class="Models" type="EnsembleModel">codeAndExtModel</Model>
<Optimizer class="Optimizers" type="GeneticAlgorithm">GAopt</Optimizer>
<SolutionExport class="DataObjects" type="PointSet">opt_export</SolutionExport>
<Output class="DataObjects" type="PointSet">finalResponses</Output>
<Output class="OutStreams" type="Print">opt_export</Output>
</MultiRun>
</Steps>

<OutStreams>
<Print name="opt_export">
<type>csv</type>
<source>opt_export</source>
<what>input,output</what>
</Print>
</OutStreams>

<DataObjects>
<PointSet name="samplesMC">
<Input>sigma-A,sigma-B,decay_A,decay_B</Input>
<Output>A,B,C,D</Output>
</PointSet>
<PointSet name="inputPlaceHolderForInternalObjects">
<Input>A,B,C,D</Input>
<Output>OutputPlaceHolder</Output>
</PointSet>
<PointSet name="sumData">
<Input>A,B,C,D</Input>
<Output>sum</Output>
</PointSet>
<PointSet name="finalResponses">
<Input>sigma-A,sigma-B,decay_A,decay_B</Input>
<Output>A,B,C,D,sum</Output>
</PointSet>
<PointSet name="opt_export">
<Input>trajID</Input>
<Output>sigma-A,sigma-B,decay_A,decay_B,sum,age,batchId,fitness,iteration,accepted,AHDp,conv_AHDp</Output>
</PointSet>
</DataObjects>

</Simulation>
39 changes: 39 additions & 0 deletions ...thms/gold/continuous/unconstrained/metaModelWithCodeAndFunctionsAndGenetic/opt_export.csv
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trajID,sigma-A,sigma-B,decay_A,decay_B,sum,age,batchId,fitness,iteration,accepted,AHDp,conv_AHDp
0,812.997460787,464.508307042,1.255650268e-08,0.000100012556503,3.15760954156,0,1,-3.15760954156,0,first,,False
0,759.507928453,912.545952926,8.14640085566e-08,0.000100081464009,2.90476176926,0,1,-2.90476176926,0,first,,False
0,912.545952926,812.997460787,1.255650268e-08,0.000100012556503,3.15873736407,0,2,-3.15873736407,1,accepted,299.428022512,False
0,464.508307042,759.507928453,8.14640085566e-08,0.000100081464009,2.83929690449,0,2,-2.83929690449,1,accepted,299.428022512,False
0,794.044539517,912.545952926,5.18057476338e-08,0.000100051805748,2.98393998882,1,3,-2.98393998882,2,accepted,261.653404312,False
0,794.044539517,759.507928453,7.83557135608e-08,0.000100078355714,2.9174761674,1,3,-2.9174761674,2,accepted,261.653404312,False
0,912.545952926,464.508307042,8.14640085566e-08,0.000100081464009,2.93136915288,2,4,-2.93136915288,3,accepted,379.964041465,False
0,759.507928453,759.507928453,8.14640085566e-08,0.000100081464009,2.90491165751,2,4,-2.90491165751,3,accepted,379.964041465,False
0,464.508307042,912.545952926,8.14640085566e-08,0.000100081464009,2.83915821533,3,5,-2.83915821533,4,accepted,154.98621892,False
0,794.044539517,759.507928453,7.83557135608e-08,0.000100078355714,2.9174761674,3,5,-2.9174761674,4,accepted,154.98621892,False
0,912.545952926,464.508307042,8.14640085566e-08,0.000100081464009,2.93136915288,3,6,-2.93136915288,5,accepted,484.155218746,False
0,759.507928453,464.508307042,8.14640085566e-08,0.000100081464009,2.90520071484,3,6,-2.90520071484,5,accepted,484.155218746,False
0,912.545952926,464.508307042,8.14640085566e-08,0.000100081464009,2.93136915288,4,7,-2.93136915288,6,accepted,484.155218746,False
0,759.507928453,464.508307042,8.14640085566e-08,0.000100081464009,2.90520071484,4,7,-2.90520071484,6,accepted,484.155218746,False
0,794.044539517,759.507928453,5.18057476338e-08,0.000100051805748,2.98410104597,5,8,-2.98410104597,7,accepted,329.536232476,False
0,794.044539517,912.545952926,5.18057476338e-08,0.000100051805748,2.98393998882,5,8,-2.98393998882,7,accepted,329.536232476,False
0,794.044539517,759.507928453,5.18057476338e-08,0.000100051805748,2.98410104597,6,9,-2.98410104597,8,accepted,261.553081047,False
0,464.508307042,912.545952926,8.14640085566e-08,0.000100081464009,2.83915821533,6,9,-2.83915821533,8,accepted,261.553081047,False
0,794.044539517,912.545952926,5.18057476338e-08,0.000100051805748,2.98393998882,4,10,-2.98393998882,9,accepted,329.536232476,False
0,794.044539517,912.545952926,5.18057476338e-08,0.000100051805748,2.98393998882,4,10,-2.98393998882,9,accepted,329.536232476,False
0,464.508307042,794.044539517,9.21291357633e-08,0.000100092129136,2.81925891819,5,11,-2.81925891819,10,accepted,118.501413408,False
0,464.508307042,794.044539517,9.21291357633e-08,0.000100092129136,2.81925891819,5,11,-2.81925891819,10,accepted,118.501413408,False
0,464.508307042,912.545952926,8.14640085566e-08,0.000100081464009,2.83915821533,0,12,-2.83915821533,11,accepted,118.501413408,False
0,464.508307042,912.545952926,8.14640085566e-08,0.000100081464009,2.83915821533,0,12,-2.83915821533,11,accepted,118.501413408,False
0,464.508307042,794.044539517,9.21291357633e-08,0.000100092129136,2.81925891819,1,13,-2.81925891819,12,accepted,94.054634152,False
0,464.508307042,912.545952926,8.14640085566e-08,0.000100081464009,2.83915821533,1,13,-2.83915821533,12,accepted,94.054634152,False
0,912.545952926,794.044539517,5.18057476338e-08,0.000100051805748,3.00110960171,2,14,-3.00110960171,13,accepted,448.037645884,False
0,912.545952926,794.044539517,5.18057476338e-08,0.000100051805748,3.00110960171,2,14,-3.00110960171,13,accepted,448.037645884,False
0,912.545952926,794.044539517,5.18057476338e-08,0.000100051805748,3.00110960171,3,15,-3.00110960171,14,accepted,448.037645884,False
0,912.545952926,794.044539517,5.18057476338e-08,0.000100051805748,3.00110960171,3,15,-3.00110960171,14,accepted,448.037645884,False
0,794.044539517,464.508307042,9.21291357633e-08,0.000100092129136,2.89183632446,4,16,-2.89183632446,15,accepted,369.891914497,False
0,464.508307042,794.044539517,9.21291357633e-08,0.000100092129136,2.81925891819,4,16,-2.81925891819,15,accepted,369.891914497,False
0,794.044539517,464.508307042,9.21291357633e-08,0.000100092129136,2.89183632446,3,17,-2.89183632446,16,accepted,466.03460926,False
0,794.044539517,464.508307042,9.21291357633e-08,0.000100092129136,2.89183632446,3,17,-2.89183632446,16,accepted,466.03460926,False
0,794.044539517,464.508307042,9.21291357633e-08,0.000100092129136,2.89183632446,4,18,-2.89183632446,17,accepted,466.03460926,False
0,794.044539517,464.508307042,9.21291357633e-08,0.000100092129136,2.89183632446,4,18,-2.89183632446,17,accepted,466.03460926,False
0,464.508307042,912.545952926,8.14640085566e-08,0.000100081464009,2.83915821533,5,19,-2.83915821533,18,accepted,94.054634152,False
0,464.508307042,794.044539517,9.21291357633e-08,0.000100092129136,2.81925891819,5,19,-2.81925891819,18,accepted,94.054634152,False
10 changes: 10 additions & 0 deletions tests/framework/Optimizers/GeneticAlgorithms/tests
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Expand Up @@ -384,4 +384,14 @@
rel_err = 0.001
[../]
[../]

[./GAwithEnsembleModelIncludingCode]
type = 'RavenFramework'
input = 'continuous/unconstrained/test_ensembleModel_withGA_Code_and_Functions.xml'
[./csv]
type = OrderedCSV
output = 'continuous/unconstrained/metaModelWithCodeAndFunctionsAndGenetic/opt_export.csv'
rel_err = 0.001
[../]
[../]
[]