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Mandd/tournament edits for MOGA (#1591)
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* added comment

* edit test files

* removed np.random.shuffle
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@mandd
mandd authored Jun 30, 2021
1 parent 44bbfd3 commit 5377394
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Showing 5 changed files with 348 additions and 20 deletions.
66 changes: 46 additions & 20 deletions framework/Optimizers/parentSelectors/parentSelectors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -86,29 +86,55 @@ def tournamentSelection(population,**kwargs):
fitness = kwargs['fitness']
nParents= kwargs['nParents']
pop = population

popSize = population.values.shape[0]

if 'rank' in kwargs:
# the key rank is used in multi-objective optimization where rank identifies which front the point belongs to
rank = kwargs['rank']
multiObjectiveRanking = True
matrixOperationRaw = np.zeros((popSize,3))
matrixOperationRaw[:,0] = np.transpose(np.arange(popSize))
matrixOperationRaw[:,1] = np.transpose(fitness.data)
matrixOperationRaw[:,2] = np.transpose(rank.data)
matrixOperation = np.zeros((popSize,3))
else:
multiObjectiveRanking = False
matrixOperationRaw = np.zeros((popSize,2))
matrixOperationRaw[:,0] = np.transpose(np.arange(popSize))
matrixOperationRaw[:,1] = np.transpose(fitness.data)
matrixOperation = np.zeros((popSize,2))

indexes = list(np.arange(popSize))
indexesShuffled = randomUtils.randomChoice(indexes, size = popSize, replace = False, engine = None)

for idx, val in enumerate(indexesShuffled):
matrixOperation[idx,:] = matrixOperationRaw[val,:]

selectedParent = xr.DataArray(
np.zeros((nParents,np.shape(pop)[1])),
dims=['chromosome','Gene'],
coords={'chromosome':np.arange(nParents),
'Gene': kwargs['variables']})

if nParents >= popSize/2.0:
# generate combination of 2 with replacement
selectionList = np.atleast_2d(randomUtils.randomChoice(list(range(0,popSize)), 2*nParents, replace=False))
else: # nParents < popSize/2.0
# generate combination of 2 without replacement
selectionList = np.atleast_2d(randomUtils.randomChoice(list(range(0,popSize)), 2*nParents))

selectionList = selectionList.reshape(nParents,2)

for index,pair in enumerate(selectionList):
if fitness[pair[0]]>fitness[pair[1]]:
selectedParent[index,:] = pop.values[pair[0],:]
else: # fitness[pair[1]]>fitness[pair[0]]:
selectedParent[index,:] = pop.values[pair[1],:]
np.zeros((nParents,np.shape(pop)[1])),
dims=['chromosome','Gene'],
coords={'chromosome':np.arange(nParents),
'Gene': kwargs['variables']})

if not multiObjectiveRanking: # single-objective implementation of tournamentSelection
for i in range(nParents):
if matrixOperation[2*i,1] > matrixOperation[2*i+1,1]:
index = int(matrixOperation[i,0])
else:
index = int(matrixOperation[i+1,0])
selectedParent[i,:] = pop.values[index,:]
else: # multi-objective implementation of tournamentSelection
for i in range(nParents-1):
if matrixOperation[2*i,2] > matrixOperation[2*i+1,2]:
index = int(matrixOperation[i,0])
elif matrixOperation[2*i,2] < matrixOperation[2*i+1,2]:
index = int(matrixOperation[i+1,0])
else: # same rank case
if matrixOperation[2*i,1] > matrixOperation[2*i+1,1]:
index = int(matrixOperation[i,0])
else:
index = int(matrixOperation[i+1,0])
selectedParent[i,:] = pop.values[index,:]

return selectedParent

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155 changes: 155 additions & 0 deletions tests/framework/Optimizers/GeneticAlgorithms/GA_knapsackBaseTournament.xml
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Original file line number Diff line number Diff line change
@@ -0,0 +1,155 @@
<?xml version="1.0" ?>
<Simulation verbosity="debug">
<TestInfo>
<name>framework/Optimizers/GeneticAlgorithms/GA_knapsackBaseTournament</name>
<author>mandd</author>
<created>2021-06-22</created>
<classesTested>GeneticAlgorithm</classesTested>
<description>
This test is designed to find the optimal solution of a basic knapsack problem using tournament select
</description>
</TestInfo>

<RunInfo>
<WorkingDir>simpleKnapsack</WorkingDir>
<Sequence>opt, print, printExport</Sequence>
<batchSize>1</batchSize>
<internalParallel>False</internalParallel>
</RunInfo>

<Steps>
<MultiRun name="opt" re-seeding="2286">
<Input class="DataObjects" type="PointSet" >placeholder</Input>
<Model class="Models" type="ExternalModel" >Kbase</Model>
<Optimizer class="Optimizers" type="GeneticAlgorithm" >GAoptimizer</Optimizer>
<SolutionExport class="DataObjects" type="PointSet" >opt_export</SolutionExport>
<Output class="DataObjects" type="PointSet" >optOut</Output>
</MultiRun>
<IOStep name="print">
<Input class="DataObjects" type="PointSet" >optOut</Input>
<Output class="OutStreams" type="Print" >PrintOptOut_1</Output>
</IOStep>
<IOStep name="printExport">
<Input class="DataObjects" type="PointSet" >opt_export</Input>
<Output class="OutStreams" type="Print" >PrintOptOut_export_1</Output>
</IOStep>
</Steps>

<Distributions>
<UniformDiscrete name="unif_dist_wRepl">
<lowerBound>0</lowerBound> <!-- 0 implies not chosen -->
<upperBound>1</upperBound>
<strategy>withReplacement</strategy>
</UniformDiscrete>
</Distributions>

<Optimizers>
<GeneticAlgorithm name="GAoptimizer">
<samplerInit>
<limit>5</limit>
<initialSeed>42</initialSeed>
<writeSteps>final</writeSteps>
</samplerInit>

<GAparams>
<populationSize>14</populationSize>
<reproduction nParents="6">
<crossover type="onePointCrossover">
<crossoverProb>0.9</crossoverProb>
</crossover>
<mutation type="swapMutator">
<mutationProb>0.1</mutationProb>
</mutation>
</reproduction>
<fitness type="logistic">
<a>0.2</a>
<b>13.0</b>
</fitness>
<parentSelection>tournamentSelection</parentSelection>
<survivorSelection>fitnessBased</survivorSelection>
</GAparams>

<convergence>
<objective>-1</objective>
</convergence>

<variable name="proj1">
<distribution>unif_dist_wRepl</distribution>
<initial>0,0,0,0,1,0,0,0,0,1,0,0,0,0</initial>
</variable>
<variable name="proj2">
<distribution>unif_dist_wRepl</distribution>
<initial>0,0,0,1,0,0,0,0,1,0,0,0,0,1</initial>
</variable>
<variable name="proj3">
<distribution>unif_dist_wRepl</distribution>
<initial>0,0,1,0,0,0,0,1,0,0,0,0,1,0</initial>
</variable>
<variable name="proj4">
<distribution>unif_dist_wRepl</distribution>
<initial>0,1,0,0,0,0,1,0,0,0,0,1,0,0</initial>
</variable>
<variable name="proj5">
<distribution>unif_dist_wRepl</distribution>
<initial>1,0,0,0,0,1,0,0,0,0,1,0,0,0</initial>
</variable>
<variable name="proj6">
<distribution>unif_dist_wRepl</distribution>
<initial>0,0,0,0,1,0,0,0,0,1,0,0,0,0</initial>
</variable>
<variable name="proj7">
<distribution>unif_dist_wRepl</distribution>
<initial>0,0,0,1,0,0,0,0,1,0,0,0,0,1</initial>
</variable>
<variable name="proj8">
<distribution>unif_dist_wRepl</distribution>
<initial>0,0,1,0,0,0,0,1,0,0,0,0,1,0</initial>
</variable>
<variable name="proj9">
<distribution>unif_dist_wRepl</distribution>
<initial>0,1,0,0,0,0,1,0,0,0,0,1,0,0</initial>
</variable>
<variable name="proj10">
<distribution>unif_dist_wRepl</distribution>
<initial>1,0,0,0,0,1,0,0,0,0,1,0,0,0</initial>
</variable>

<objective>planValue</objective>
<TargetEvaluation class="DataObjects" type="PointSet">optOut</TargetEvaluation>
</GeneticAlgorithm>
</Optimizers>

<Models>
<ExternalModel ModuleToLoad="KnapsackModel" name="Kbase" subType="">
<variables>proj1,proj2,proj3,proj4,proj5,proj6,proj7,proj8,proj9,proj10,planValue,validPlan</variables>
</ExternalModel>
</Models>

<DataObjects>
<PointSet name="placeholder"/>
<PointSet name="optOut">
<Input>proj1,proj2,proj3,proj4,proj5,proj6,proj7,proj8,proj9,proj10</Input>
<Output>planValue,validPlan</Output>
</PointSet>
<PointSet name="MCOut">
<Input>proj1,proj2,proj3,proj4,proj5,proj6,proj7,proj8,proj9,proj10</Input>
<Output>planValue,validPlan</Output>
</PointSet>
<PointSet name="opt_export">
<Input>trajID</Input>
<Output>proj1,proj2,proj3,proj4,proj5,proj6,proj7,proj8,proj9,proj10,planValue</Output>
</PointSet>
</DataObjects>

<OutStreams>
<Print name="PrintOptOut_1">
<type>csv</type>
<source>optOut</source>
</Print>
<Print name="PrintOptOut_export_1">
<type>csv</type>
<source>opt_export</source>
</Print>
</OutStreams>

</Simulation>
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