def runBeamDesignProblem():
"""
Run an example case of the beam designer problem
"""
p = Params()
p.nAgents = 8
p.nDims = 4
p.nTeams = 2
p.reps = 32
p.steps = 100
p.agentTeams = m.specializedTeams(p.nAgents, p.nTeams)
p.teamDims = m.teamDimensions(p.nDims, p.nTeams)
t = teamWorkSharing(p, BeamDesigner)
return t
def run():
t0 = timer.time()
p = Params()
# p.reps=2
# selfBias = 0.5
# curatedTeams = False
# shareAcrossTeams = True
#change team size and specialization
p.nAgents = 12
p.nTeams = 4
p.nDims = 56
p.agentTeams = m.specializedTeams(p.nAgents, p.nTeams)
p.teamDims = m.teamDimensions(p.nDims, p.nTeams)
p.reps = 16
p.steps = 500
pComms = np.linspace(0, 1, 6)
allTeamObjects = []
for pComm in pComms:
if __name__ == '__main__' or 'kaboom.designScienceStudies.i_optimalCommRate':
pool = multiprocessing.Pool(processes=16)
allTeams = pool.starmap(carTeamWorkProcess,
zip(range(p.reps), itertools.repeat(p)))
print('next. time: ' + str(timer.time() - t0))
for team in allTeams:
allTeamObjects.append(team)
pool.close()
pool.join()
print('finished a pComm round')
else:
print(__name__)
# allTeams = [t for tl in allTeamObjects for t in tl]
print("time to complete: " + str(timer.time() - t0))
# name="nShares_long_smallTeam"
# directory = saveResults(allTeamObjects,name)
# plt.savefig(directory+"/"+name+".pdf")
# plt.savefig(directory+"/"+name+".png",dpi=300)
return allTeamObjects, pComms, p
def run():
# ## B 2.3
# Robustness on two diagonals
#
# PROBLEM SET 1
#problems favor a range of styles from adaptive to mid to innovative
t0 = timer.time()
p = Params()
p.curatedTeams = True
p.reps = 32
allTeams = []
allTeamScores = []
p.aiScore = 100
aiRanges = np.linspace(0, 100, 6)
# pComm = 0.2
#the diagonal across preferred style:
roughnesses = np.logspace(-1, .7, num=6, base=10) #[.2,.6,1.8,5.4]
roughnesses = roughnesses[1:]
speeds = np.logspace(-1, .7, num=6, base=10) / 100 # [.001,.004,.016,.048]
speeds = speeds[1:]
problemSet = [roughnesses, speeds]
for aiRange in aiRanges:
p.aiRange = aiRange
teams = []
teamScores = []
for i in range(p.reps):
scores, t = robustnessTest(p, problemSet)
teams.append(t)
teamScores.append(scores)
allTeams.append(teams)
allTeamScores.append(teamScores)
print('next')
print('time: %s' % (timer.time() - t0))
#
#teams = [ t for tt in allTeams for t in tt]
#directory = saveResults(teams,'robustnessTest_diag1')
#rFile = directory+'/'+'scoreMatrix.obj'
#rPickle = open(rFile, 'wb')
#pickle.dump(allTeamScores,rPickle)
# f = open('/Users/samlapp/SAE_ABM/results/1542634807.0205839robustnessTest/scoreMatrix.obj','rb')
# sm = pickle.load(f)
# In[67]:
#STADARDIZE for each problem!
ats = np.array(allTeamScores)
problemMeans = [np.mean(ats[:, :, i]) for i in range(len(problemSet[0]))]
problemSds = [np.std(ats[:, :, i]) for i in range(len(problemSet[0]))]
allTeamScoresStandardized = ats
for j in range(len(allTeamScoresStandardized)):
for i in range(len(problemSet[0])):
for k in range(p.reps):
allTeamScoresStandardized[
j, k, i] = (ats[j, k, i] - problemMeans[i]) / problemSds[i]
np.shape(allTeamScoresStandardized)
meanScores = [
np.mean(t) for teamSet in allTeamScoresStandardized for t in teamSet
]
meanGrouped = [[np.mean(t) for t in teamSet]
for teamSet in allTeamScoresStandardized]
sdScores = [
np.std(t) for teamSet in allTeamScoresStandardized for t in teamSet
]
sdGrouped = [[np.std(t) for t in teamSet]
for teamSet in allTeamScoresStandardized]
ranges = [t.dAI for teamSet in allTeams for t in teamSet]
robustness = np.array(meanScores) - np.array(sdScores)
robustnessGrouped = np.array(meanGrouped) - np.array(sdGrouped)
# meanScores = np.array(meanScores)*-1
plt.scatter(ranges, np.array(meanScores), c=[.9, .9, .9])
# plt.title("9 problem matrix")
cms = m.plotCategoricalMeans(ranges, meanScores)
#plt.savefig('results/vii_set1_robustnessInv.pdf')
stat, pscore = scipy.stats.ttest_ind(meanGrouped[0], meanGrouped[2])
print("significance: p= " + str(pscore))
corr, _ = scipy.stats.pearsonr(ranges[0:p.reps * 4],
meanScores[0:p.reps * 4])
print('Pearsons correlation: %.3f' % corr)
# In[2]
# PROBLEM SET 2
# Now the other diagonal (all 5 problems prefer mid-range style)
allTeamsD2 = []
allTeamScoresD2 = []
# aiScore = 100
aiRanges = np.linspace(0, 100, 6)
#the diagonal across preferred style:
roughnesses = np.logspace(-1, .7, num=6, base=10) #[.2,.6,1.8,5.4]
roughnesses = roughnesses[1:]
speeds = np.logspace(-1, .7, num=6, base=10) / 100 # [.001,.004,.016,.048]
speeds = speeds[1:]
#reverse the order: pair large speed (small space) with small roughness
speeds = speeds[::-1]
problemSet = [roughnesses, speeds]
for aiRange in aiRanges:
p.aiRange = aiRange
teams = []
teamScores = []
for i in range(p.reps):
scores, t = robustnessTest(p, problemSet)
teams.append(t)
teamScores.append(scores)
allTeamsD2.append(teams)
allTeamScoresD2.append(teamScores)
print('next')
print('time: %s' % (timer.time() - t0))
#teams = [ t for tt in allTeamsD2 for t in tt]
#directory = saveResults(teams,'robustnessTest_diag2')
#rFile = directory+'/'+'scoreMatrix.obj'
#rPickle = open(rFile, 'wb')
#pickle.dump(allTeamScores,rPickle)
# f = open('/Users/samlapp/SAE_ABM/results/1542634807.0205839robustnessTest/scoreMatrix.obj','rb')
# sm = pickle.load(f)
#STADARDIZE for each problem!
ats = np.array(allTeamScoresD2)
problemMeans = [np.mean(ats[:, :, i]) for i in range(len(problemSet[0]))]
problemSds = [np.std(ats[:, :, i]) for i in range(len(problemSet[0]))]
allTeamScoresStandardized = ats
for j in range(len(allTeamScoresStandardized)):
for i in range(len(problemSet[0])):
for k in range(p.reps):
allTeamScoresStandardized[
j, k, i] = (ats[j, k, i] - problemMeans[i]) / problemSds[i]
np.shape(allTeamScoresStandardized)
meanScoresD2 = [
np.mean(t) for teamSet in allTeamScoresStandardized for t in teamSet
]
meanGroupedD2 = [[np.mean(t) for t in teamSet]
for teamSet in allTeamScoresStandardized]
sdScoresD2 = [
np.std(t) for teamSet in allTeamScoresStandardized for t in teamSet
]
sdGroupedD2 = [[np.std(t) for t in teamSet]
for teamSet in allTeamScoresStandardized]
ranges = [t.dAI for teamSet in allTeams for t in teamSet]
# robustness = np.array(meanScores)-np.array(sdScores)
# robustnessGrouped = np.array(meanGrouped)-np.array(sdGrouped)
#flip the scores
meanScoresD2 = np.array(meanScoresD2) * -1
plt.scatter(ranges, np.array(meanScoresD2), c=[.9, .9, .9])
cms = m.plotCategoricalMeans(ranges, np.array(meanScoresD2))
stat, p = scipy.stats.ttest_ind(meanGroupedD2[0], meanGroupedD2[3])
print("significance: p= " + str(p))
corr, _ = scipy.stats.pearsonr(ranges[0:p.reps * 4],
meanScoresD2[0:p.reps * 4])
print('Pearsons correlation: %.3f' % corr)
# plt.scatter(ranges,np.array(meanScoresD2),c=[.9,.9,.9])
cms = m.plotCategoricalMeans(ranges, np.array(meanScoresD2))
cms2 = m.plotCategoricalMeans(ranges, np.array(meanScores))
plt.xlabel('maximum cognitive gap (style diversity)')
plt.ylabel('performance')
plt.legend(['probem set 1', 'problem set 2'])
myPath = os.path.dirname(__file__)
plt.savefig(myPath + "/results/vii_diverseTeams_2problemsets.pdf")
from kaboom import modelFunctions as m
#from kaboom.kaboom import teamWorkProcess
from kaboom.carMakers import carTeamWorkProcess
# Structure vs Composition
# Optimal structure of 32-agent team for 3 allocation strategies
t0 = timer.time()
p = Params()
#change team size and specialization
p.nAgents = 16 #32
p.nDims = 56
p.steps = 3 #00
p.reps = 4
nAgentsPerTeam = [1, 2, 3, 4] #,8,16,32]#[32,16]# [8,4,3,2,1]
resultMatrix = []
teamObjects = []
for i in range(3): #range(3):
if i == 0: #homogeneous
p.aiRange = 0
p.aiScore = 95
p.curatedTeams = True
elif i == 1:
p.aiRange = 70
p.aiScore = 95
p.curatedTeams = False
elif i == 2:
def run(numberOfCores=4):
t0 = timer.time()
p = Params()
#change team size and specialization
p.nAgents = 33
p.nDims = 56
p.steps = 100 #100
p.reps = 16
myPath = os.path.dirname(__file__)
parentPath = os.path.dirname(myPath)
paramsDF = pd.read_csv(parentPath + "/SAE/paramDBreduced.csv")
paramsDF = paramsDF.drop(["used"], axis=1)
paramsDF.head()
teams = ['brk', 'c', 'e', 'ft', 'fw', 'ia', 'fsp', 'rsp', 'rt', 'rw', 'sw']
teamsDict = {i: teams[i] for i in range(10)}
paramTeams = paramsDF.team
p.nTeams = len(teams)
#in the semantic division of the problem, variables are grouped by parts of
#the car (eg, wheel dimensions; engine; brakes)
teamDimensions_semantic = [[
1 if paramTeam == thisTeam else 0 for paramTeam in paramTeams
] for thisTeam in teams]
p.agentTeams = m.specializedTeams(p.nAgents, p.nTeams)
p.teamDims = teamDimensions_semantic
if __name__ == '__main__' or 'kaboom.IDETC_studies.iv_problemDecomposition':
pool = multiprocessing.Pool(processes=4)
teamObjectsSemantic = pool.starmap(
carTeamWorkProcess,
zip(range(p.reps), itertools.repeat(p),
itertools.repeat(CarDesignerWeighted)))
pool.close()
pool.join()
print("finished semantic: " + str(timer.time() - t0))
# name="allocation_semantic"
# directory = saveResults(teamObjectsSemantic,p,name)
#NOW WITH BLIND TEAM DIMENSIONS INSTEAD OF SEMANTIC
#assign dimensions blindly to teams,with even # per team (as possible)
teamDimensions_blind = m.teamDimensions(p.nDims, p.nTeams)
p.teamDims = teamDimensions_blind
if __name__ == '__main__' or 'kaboom.IDETC_studies.iv_problemDecomposition':
pool = multiprocessing.Pool(processes=numberOfCores)
teamObjectsBlind = pool.starmap(
carTeamWorkProcess,
zip(range(p.reps), itertools.repeat(p),
itertools.repeat(CarDesignerWeighted)))
pool.close()
pool.join()
# name="allocation_blind"
# directory = saveResults(teamObjectsBlind,p,name)
print("finished blind: " + str(timer.time() - t0))
#inverted scores so that its a maximization problem
#(in the plot, higher scores are better)
semanticScores = [t.getBestScore() * -1 for t in teamObjectsSemantic]
blindScores = [t.getBestScore() * -1 for t in teamObjectsBlind]
#Plot results:
plt.boxplot([semanticScores, blindScores],
labels=["semantic", "blind"],
showfliers=True)
plt.ylabel("car design performance")
plt.savefig(myPath + "/results/iv_problemDecomposition.pdf")
plt.show()
plt.clf()
print("Results figure saved to " + myPath +
"/results/iv_problemDecomposition.pdf")
print("effect size:")
print(h.effectSize(semanticScores, blindScores))
print("ANOVA p score: ")
print(h.pScore(semanticScores, blindScores))
def run():
""" Experiment to test how KAI style affects car design performance
and beam design performance """
t0 = timer.time()
p = Params()
#change team size and specialization
p.nAgents = 33
p.nDims = 56
p.steps = 100
p.reps = 16
myPath = os.path.dirname(__file__)
parentPath = os.path.dirname(myPath)
paramsDF = pd.read_csv(parentPath + "/SAE/paramDBreduced.csv")
paramsDF = paramsDF.drop(["used"], axis=1)
paramsDF.head()
teams = ['brk', 'c', 'e', 'ft', 'fw', 'ia', 'fsp', 'rsp', 'rt', 'rw', 'sw']
paramTeams = paramsDF.team
p.nTeams = len(teams)
#in the semantic division of the problem, variables are grouped by parts of
#the car (eg, wheel dimensions; engine; brakes)
teamDimensions_semantic = [[
1 if paramTeam == thisTeam else 0 for paramTeam in paramTeams
] for thisTeam in teams]
p.agentTeams = m.specializedTeams(p.nAgents, p.nTeams)
p.teamDims = teamDimensions_semantic
styleTeams = []
flatTeamObjects = []
aiScores = np.linspace(45, 145, 9)
for aiScore in aiScores:
#use a homogeneous team with KAI style of [aiScore]
p.aiScore = aiScore
p.aiRange = 0
teamObjects = [] #save results
if __name__ == '__main__' or 'kaboom.IDETC_STUDIES.i_teamStyle':
pool = multiprocessing.Pool(processes=4)
allTeams = pool.starmap(
carTeamWorkProcess,
zip(range(p.reps), itertools.repeat(p),
itertools.repeat(CarDesignerWeighted)))
for t in allTeams:
teamObjects.append(t)
flatTeamObjects.append(t)
pool.close()
pool.join()
print("time to complete: " + str(timer.time() - t0))
styleTeams.append(teamObjects)
# saveResults(flatTeamObjects, p, "carProblem_KaiStyle")
#invert the scores *-1 to show a maximization (rather than minimization)
#objective. (Then, in this plot, higher scores are better)
allScores = [t.getBestScore() * -1 for s in styleTeams for t in s]
allkai = [kai for kai in aiScores for i in range(p.reps)]
m.plotCategoricalMeans(allkai, allScores)
plt.scatter(allkai, allScores, c=[0.9, 0.9, 0.9])
qFit = np.polyfit(allkai, allScores, 2)
q = np.poly1d(qFit)
x = np.linspace(45, 145, 100)
plt.plot(x, q(x), c='red')
plt.xticks([int(i) for i in aiScores])
plt.xlabel("KAI score of homogeneous team")
plt.ylabel("Car Design Performance")
plt.savefig(myPath + '/results/i_teamStyle_carProblem.pdf')
plt.clf()
#Now test the performance on the beam design problem
p = Params()
p.nAgents = 8
p.nDims = 4
p.nTeams = 2
p.reps = 16
p.steps = 100
p.agentTeams = m.specializedTeams(p.nAgents, p.nTeams)
p.teamDims = m.teamDimensions(p.nDims, p.nTeams)
beamTeams = []
for aiScore in aiScores:
teamSet = []
for i in range(p.reps):
t = teamWorkSharing(p, BeamDesigner)
teamSet.append(t)
beamTeams.append(teamSet)
print('next')
#flip scores so that higher is better in the plot
allScores = [[t.getBestScore() * -1 for t in teams] for teams in beamTeams]
allAiScores = [ai for ai in aiScores for i in range(p.reps)]
allScoresFlat = [s for r in allScores for s in r]
plt.scatter(allAiScores, allScoresFlat, c=[.9, .9, .9])
m.plotCategoricalMeans(allAiScores, allScoresFlat)
#quadratic fit
qm = np.polyfit(allAiScores, allScoresFlat, 2)
qmodel = np.poly1d(qm)
x = np.linspace(45, 145, 101)
plt.plot(x, qmodel(x), c='red')
plt.xlabel("KAI score of homogeneous team")
plt.ylabel("Beam Design Performance")
plt.savefig(myPath + '/results/i_teamStyle_beamProblem.pdf')
plt.show()
plt.clf()
print("Results figure saved to " + myPath +
"/results/i_teamStyle_beamProblem.pdf")
def run():
t0 = timer.time()
p = Params()
#change team size and specialization
p.nAgents = 12
p.nTeams = 4
p.nDims = 12
p.agentTeams = m.specializedTeams(p.nAgents, p.nTeams)
p.teamDims = m.teamDimensions(p.nDims, p.nTeams)
pComms = np.linspace(0, 1, 11)
p.reps = 32 # 10 #$40 #5
aiScores = [60, 95, 130] #100#300
p.aiRange = 0
# aiRanges = np.linspace(0,100,10)
# meetingTimes = 100
resultsA14 = []
for aiScore in aiScores:
p.aiScore = aiScore
allTeamObjects = []
for pComm in pComms:
if __name__ == '__main__' or 'kaboom.designScienceStudies.ii_comm_v_style':
pool = multiprocessing.Pool(processes=4)
allTeams = pool.starmap(
teamWorkProcess, zip(range(p.reps), itertools.repeat(p)))
print('next. time: ' + str(timer.time() - t0))
for team in allTeams:
allTeamObjects.append(team)
pool.close()
pool.join()
resultsA14.append(allTeamObjects)
scores = [t.getBestScore() for t in allTeamObjects]
pcs = [pc for pc in pComms for i in range(p.reps)]
m.plotCategoricalMeans(pcs, np.array(scores) * -1)
plt.show()
# allTeams = [t for tl in allTeamObjects for t in tl]
print("time to complete: " + str(timer.time() - t0))
for allTeamObjects in resultsA14:
allScores = np.array([t.getBestScore() for t in allTeamObjects]) * -1
kai = allTeamObjects[0].agents[0].kai.KAI
nS = [t.nMeetings for t in allTeamObjects]
# plt.scatter(nS,allScores, c=[.9,.9,.9])
pC = [pc for pc in pComms for i in range(p.reps)]
# plt.show()
# plt.scatter(pC,allScores, label=kai)
c = m.plotCategoricalMeans(pC, allScores)
plt.plot(pComms, c)
# name="A1.5_commRate_vStyle32_kai"+str(kai)
# directory = saveResults(allTeamObjects,name)
plt.legend(aiScores)
plt.xlabel('prob of communication (c)')
plt.ylabel('performance')
myPath = os.path.dirname(__file__)
plt.savefig(myPath + "/results/ii_commRate_vStyle32_plot.pdf")
from kaboom import helperFunctions as h
from kaboom import modelFunctions as m
import itertools
import os
from kaboom.params import Params
from kaboom import modelFunctions as m
from kaboom.kaboom import teamWorkSharing
p= Params()
p.nAgents = 8
p.nDims = 4
p.nTeams = 2
p.reps = 32
p.steps = 50
#p.steps = 50
p.agentTeams = m.specializedTeams(p.nAgents,p.nTeams)
p.teamDims = m.teamDimensions(p.nDims,p.nTeams)
aiScores = np.linspace(45,145,9)
allScores = []
for aiScore in aiScores:
scores = []
for i in range(p.reps):
t= teamWorkSharing(p,BeamDesigner)
scores.append(t.getBestScore())
allScores.append(scores)
print('next')
i += 1
return myTeam
# Do certain sub-teams have style preference adaptive/innovative?
t0 = timer.time()
p=Params()
#change team size and one sub-teams style:
p.nAgents = 33
p.nDims = 56
p.steps = 100 #100
p.reps = 4#16
myPath = os.path.dirname(__file__)
paramsDF = pd.read_csv("../SAE/paramDBreduced.csv")
paramsDF = paramsDF.drop(["used"],axis=1)
paramsDF.head()
#assign the actual specialized teams:
teams = ['brk', 'c', 'e', 'ft', 'fw', 'ia','fsp','rsp', 'rt', 'rw', 'sw']
teamsDict = { i:teams[i] for i in range(10)}
paramTeams = paramsDF.team
p.nTeams = len(teams)
teamDimensions_semantic = [[ 1 if paramTeam == thisTeam else 0 for paramTeam in paramTeams] for thisTeam in teams]
#teamDimensions_blind = m.specializedTeams(p.nAgents,p.nTeams)
p.agentTeams = m.specializedTeams(p.nAgents,p.nTeams)
def run():
t0 = timer.time()
p=Params()
p.nAgents = 32
nAgentsPerTeam = [1,2,3,4,8,16,32]#[32,16]# [8,4,3,2,1]
p.nDims = 32
p.reps = 32
#choose one problem (middle, favors mid-range)
roughnesses = np.logspace(-1,.7,num=6,base=10)
speeds = np.logspace(-1,.7,num=6,base=10) / 100
p.amplitude = roughnesses[3]
p.AVG_SPEED = speeds[3]
resultMatrix = []
teamObjects = []
for i in range(3):
if i == 0: #homogeneous
p.aiRange = 0
p.aiScore = 95
p.curatedTeams = True
elif i == 1:
p.aiRange = 70
p.aiScore = 95
p.curatedTeams = False
elif i == 2:
p.aiScore = None
p.aiRange = None
p.curatedTeams = False
scoresA = []
teams = []
for subteamSize in nAgentsPerTeam:
p.nTeams = int(p.nAgents/subteamSize)
p.agentTeams = m.specializedTeams(p.nAgents,p.nTeams)
p.teamDims = m.teamDimensions(p.nDims,p.nTeams)
if __name__ == '__main__' or 'kaboom.designScienceStudies.viii_specialization_composition':
pool = multiprocessing.Pool(processes = 4)
allTeams = pool.starmap(teamWorkProcess, zip(range(p.reps),itertools.repeat(p)))
scoresA.append([t.getBestScore() for t in allTeams])
teams.append(allTeams)
pool.close()
pool.join()
resultMatrix.append(scoresA)
teamObjects.append(teams)
print("completed one")
print("time to complete: "+str(timer.time()-t0))
for i in range(3):
nAgents = [len(team.agents) for teamSet in teamObjects[i] for team in teamSet]
nTeams = [len(team.specializations) for teamSet in teamObjects[i] for team in teamSet]
subTeamSize = [int(len(team.agents)/len(team.specializations)) for teamSet in teamObjects[i] for team in teamSet]
teamScore = [team.getBestScore() for teamSet in teamObjects[i] for team in teamSet]
print("Diverse team, size %s in %s dim space: " % (32,p.nDims))
# plt.scatter(subTeamSize,teamScore,label='team size: '+str(teamSizes[i]))
m.plotCategoricalMeans(subTeamSize,np.array(teamScore)*-1)
plt.xlabel("subteam size")
# plt.xticks([1,4,8,16,32])
plt.ylabel("performance")
# plt.show()
plt.legend(['homogeneous','heterogeneous70','organic'])
plt.title("composition vs structure")
myPath = os.path.dirname(__file__)
plt.savefig(myPath+"/results/viii_structure_composition.pdf")
def run(numberOfCores=4):
t0 = timer.time()
p = Params()
#change team size and one sub-teams style:
p.nAgents = 33
p.nDims = 56
p.steps = 100 #100
p.reps = 16
#organic composition: select agents randomly from population
p.aiScore = None
p.aiRange = None
myPath = os.path.dirname(__file__)
parentDir = os.path.dirname(myPath)
paramsDF = pd.read_csv(parentDir + "/SAE/paramDBreduced.csv")
paramsDF = paramsDF.drop(["used"], axis=1)
paramsDF.head()
#assign the actual specialized teams:
teams = ['brk', 'c', 'e', 'ft', 'fw', 'ia', 'fsp', 'rsp', 'rt', 'rw', 'sw']
paramTeams = paramsDF.team
p.nTeams = len(teams)
teamDimensions_semantic = [[
1 if paramTeam == thisTeam else 0 for paramTeam in paramTeams
] for thisTeam in teams]
#teamDimensions_blind = m.specializedTeams(p.nAgents,p.nTeams)
p.agentTeams = m.specializedTeams(p.nAgents, p.nTeams)
p.teamDims = teamDimensions_semantic
#First run the control group: teams with organic composition
if __name__ == '__main__' or 'kaboom.IDETC_studies.iii_strategicTeams':
pool = multiprocessing.Pool(processes=numberOfCores)
controlTeams = pool.starmap(
carTeamWorkProcess,
zip(range(p.reps), itertools.repeat(p),
itertools.repeat(CarDesignerWeighted)))
# scoresA.append([t.getBestScore() for t in allTeams])
# teams.append(allTeams)
pool.close()
pool.join()
controlScores = [t.getBestScore() * -1 for t in controlTeams]
#Run strategic teams
subteamsSortedByStyle = [7, 8, 0, 10, 3, 2, 6, 4, 1, 5, 9]
# namedSortedTeams = [teams[i] for i in subteamsSortedByStyle]
strategicTeamObjects = []
if __name__ == '__main__' or 'kaboom.IDETC_studies.iii_strategicTeams':
pool = multiprocessing.Pool(processes=4)
allTeams = pool.starmap(
teamWorkOrganicSuperteam,
zip(range(p.reps), itertools.repeat(p),
itertools.repeat(subteamsSortedByStyle)))
# scoresA.append([t.getBestScore() for t in allTeams])
# teams.append(allTeams)
for t in allTeams:
strategicTeamObjects.append(t)
pool.close()
pool.join()
print("time to complete: " + str(timer.time() - t0))
strategicScores = [t.getBestScore() * -1 for t in strategicTeamObjects]
plt.boxplot([np.array(controlScores),
np.array(strategicScores)],
labels=["control", "strategic allocation"],
showfliers=True)
plt.ylabel("car design performance")
plt.savefig(myPath + "/results/iii_carStrategicTeamAssignment.pdf")
plt.show()
plt.clf()
print("Results figure saved to " + myPath +
"/results/iii_carStrategicTeamAssignment.pdf")
print("effect size:")
print(h.effectSize(controlScores, strategicScores))
print("ANOVA p score: ")
print(h.pScore(controlScores, strategicScores))