def run():
# A 1.6 composition vs commRate
p = Params()
p.nAgents = 20
p.nTeams = 4
p.nDims = 20
p.agentTeams = m.specializedTeams(p.nAgents, p.nTeams)
p.teamDims = m.teamDimensions(p.nDims, p.nTeams) #np.ones([nTeams,nDims])
#p.reps=1
pComms = np.linspace(0, 1, 11)
p.aiScore = 95
#meetingTimes = 100
t0 = timer.time()
resultsA16 = []
for i in range(3):
if i == 0: #homogeneous
p.aiScore = 95
p.aiRange = 0
p.curatedTeams = False
elif i == 1: #hetero70
p.aiScore = 95
p.aiRange = 70
p.curatedTeams = True
elif i == 2: #organic
p.aiScore = None
p.aiRange = None
p.curatedTeams = False
allTeamObjects = []
for pComm in pComms:
p.pComm = pComm
if __name__ == '__main__' or 'kaboom.designScienceStudies.ix_composition_structure':
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()
resultsA16.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))
comps = ['homogeneous', 'heterogeneous70', 'organic']
for i in range(3):
allTeamObjects = resultsA16[i]
allScores = np.array([t.getBestScore() for t in allTeamObjects]) * -1
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)
# name="A1.6_commRate_vStyle_"+comps[i]
# directory = saveResults(allTeamObjects,name)
plt.legend(comps)
myPath = os.path.dirname(__file__)
plt.savefig(myPath + "/results/ix_composition_specialization.pdf")
def run():
t0 = timer.time()
p = Params()
teamSizes = [32] #[8,16,32]
nAgentsPerTeam = [1, 2, 3, 4, 8, 16, 32] #range(1,8+1)
p.nDims = 32
p.pComm = 0.2 # np.linspace(0,.5,10)
#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]
resultMatrixOrganic = []
teamObjectsOrganic = []
for nAgents in teamSizes:
p.nAgents = nAgents
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.iv_specialization_organic':
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()
resultMatrixOrganic.append(scoresA)
teamObjectsOrganic.append(teams)
print("completed one")
print("time to complete: " + str(timer.time() - t0))
#plot score vs structure for different team sizes
for i in range(len(teamSizes)):
nAgents = [
len(team.agents) for teamSet in teamObjectsOrganic[i]
for team in teamSet
]
nTeams = [
len(team.specializations) for teamSet in teamObjectsOrganic[i]
for team in teamSet
]
subTeamSize = [
int(len(team.agents) / len(team.specializations))
for teamSet in teamObjectsOrganic[i] for team in teamSet
]
teamScore = [
team.getBestScore() for teamSet in teamObjectsOrganic[i]
for team in teamSet
]
print("Diverse team, size %s in %s dim space: " %
(teamSizes[i], p.nDims))
# plt.scatter(subTeamSize,teamScore,label='team size: '+str(teamSizes[i]))
invY = np.array(teamScore) * -1
m.plotCategoricalMeans(subTeamSize, invY)
plt.xlabel("subteam size")
plt.xscale('log', basex=2)
plt.xticks([1, 2, 3, 4, 8, 16, 32])
plt.ylabel("performance")
# plt.show()
plt.legend(teamSizes)
#Save results:
# np.savetxt("./results/C1.1_organicSpecialization/params.txt",[makeParamString()], fmt='%s')
# scoreMatrix = [ [ [t.getBestScore() for t in eachStructure] for eachStructure in eachSize] for eachSize in teamObjectsOrganic]
# rFile = './results/C1.1_organicSpecialization/'+'scoreMatrix.obj'
# rPickle = open(rFile, 'wb')
# pickle.dump(scoreMatrix, rPickle)
myPath = os.path.dirname(__file__)
plt.savefig(myPath + "/results/iv_specialization_organicTeams.pdf")