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, 6)
roughnesses = np.logspace(-1, .7, num=6, base=10)
p.amplitude = roughnesses[3]
speeds = np.logspace(-1, .7, num=6, base=10) / 100
p.AVG_SPEED = speeds[3]
allTeamObjects = []
for pComm in pComms:
if __name__ == '__main__' or 'kaboom.designScienceStudies.i_optimalCommRate':
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()
print('finished one communication frequency')
else:
print(__name__)
# allTeams = [t for tl in allTeamObjects for t in tl]
print("time to complete: " + str(timer.time() - t0))
#save results object to file
# name="sharingRate"
# directory = saveResults(allTeamObjects,name)
r = allTeamObjects
pC = [prob for prob in pComms for i in range(p.reps)]
perf = np.array([t.getBestScore() for t in r]) * -1
nMeetings = [t.nMeetings for t in r]
plt.scatter(pC, perf, c=[.9, .9, .9])
m.plotCategoricalMeans(pC, perf)
plt.xlabel('prob of communication (c)')
plt.ylabel('performance')
myPath = os.path.dirname(__file__)
plt.savefig(myPath + "/results/i_communicationFrequency.pdf")
plt.show()
#alternatively, plot performance vs the actual number of pairwise interactions
plt.scatter(nMeetings, perf)
def run():
#Strategy 4: Homogenous teams of 3 styles
t0 = timer.time()
p=Params()
# teamSizes =[32]
p.nAgents = 32
nAgentsPerTeam = [32,16,8,4,3,2,1]
# nTeams = [1,2,4,8,16,32]
p.nDims = 32
#p.reps=1
# choose one Team allocation strategy
p.aiRange = 0#0
aiScores = [55,95,135]#140# 300
#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]
resultMatrixH3 = []
teamObjectsH3 = []
for aiScore in aiScores:
p.aiScore = aiScore
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.v_structure_style':
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()
resultMatrixH3.append(scoresA)
teamObjectsH3.append(teams)
print("completed one")
print("time to complete: "+str(timer.time()-t0))
# In[ ]:
#plot score vs structure for different team sizes
for i in range(len(aiScores)):
nAgents = [len(team.agents) for teamSet in teamObjectsH3[i] for team in teamSet]
nTeams = [len(team.specializations) for teamSet in teamObjectsH3[i] for team in teamSet]
subTeamSize = [int(len(team.agents)/len(team.specializations)) for teamSet in teamObjectsH3[i] for team in teamSet]
teamScore = [team.getBestScore() for teamSet in teamObjectsH3[i] for team in teamSet]
# print("homgeneous team, size %s in %s dim space: " % (teamSizes[i],nDims))
# plt.scatter(subTeamSize,teamScore,label='team size: '+str(teamSizes[i]))
m.plotCategoricalMeans(subTeamSize,np.array(teamScore)*-1)
# plt.xscale('log')
plt.xlabel("subteam size")
plt.xticks(nAgentsPerTeam)
plt.ylabel("performance")
# plt.show()
plt.legend(aiScores)
plt.title("homogeneous team of 3 styles")
#np.savetxt("./results/C_3homog/params.txt",[makeParamString()], fmt='%s')
#scoreMatrix = [ [ [t.getBestScore() for t in eachStructure] for eachStructure in eachStyle] for eachStyle in teamObjectsH3]
#rFile = './results/C_3homog/'+'scoreMatrix_homo_3styles_32.obj'
#rPickle = open(rFile, 'wb')
#pickle.dump(scoreMatrix, rPickle)
myPath = os.path.dirname(__file__)
plt.savefig(myPath+"/results/v_structure_3styles.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")
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():
t0 = timer.time()
p = Params()
p.curatedTeams = True
# pComm = 0.2 # np.linspace(0,.5,10)
aiScores = np.linspace(60, 140, 7)
p.aiRange = 0
#pick 5 logarithmically spaced values of roughness (amplitude) and speed
roughnesses = np.logspace(-1, .7, num=6, base=10)
roughnesses = roughnesses[1:]
speeds = np.logspace(-1, .7, num=6, base=10) / 100
speeds = speeds[1:]
count = 1
roughness_ai_speed_matrix = []
for i in range(len(roughnesses)):
p.amplitude = roughnesses[i] #,8,16,32,64]:
ai_speed_matrix = []
for j in range(len(speeds)):
p.AVG_SPEED = speeds[j]
scoresForAI = []
teams = []
for aiScore in aiScores:
p.aiScore = aiScore
if __name__ == '__main__' or 'kaboom.designScienceStudies.vi_problem_matrix':
pool = multiprocessing.Pool(processes=4)
allTeams = pool.starmap(
teamWorkProcess, zip(range(p.reps),
itertools.repeat(p)))
scoresForAI.append([t.getBestScore() for t in allTeams])
for t in allTeams:
teams.append(t)
pool.close()
pool.join()
ai_speed_matrix.append(scoresForAI)
print("completed %s" % count)
count += 1
roughness_ai_speed_matrix.append(ai_speed_matrix)
print("time to complete: " + str(timer.time() - t0))
# In[416]:
#
#f = open('/Users/samlapp/SAE_ABM/results/B1_matrix_of_problems/ProblemMatrixScores.obj','rb')
#roughness_ai_speed_matrix = pickle.load(f)
#np.shape(roughness_ai_speed_matrix)
#aiScores = np.linspace(60,140,7)
#aiRanges = np.linspace(0,50,5)
#reps = 8
# In[420]:
index = 0
for i in range(len(roughness_ai_speed_matrix)):
r = roughness_ai_speed_matrix[i]
for j in range(len(r)):
index += 1
plt.subplot(len(roughness_ai_speed_matrix), len(r), index)
s = r[j]
allScores = [sc for row in s for sc in row]
allScores = np.array(allScores) * -1
myKai = [ai for ai in aiScores for i in range(p.reps)]
# plt.scatter(myKai,allScores,c=[.9,.9,.9])
# plotCategoricalMeans(myKai,allScores)
pl = np.polyfit(myKai, allScores, 2)
z = np.poly1d(pl)
x1 = np.linspace(min(myKai), max(myKai), 100)
plt.plot(x1, z(x1), color='red')
# print(roughnesses[i])
# plt.title("roughness %s speed %s" % (roughnesses[i], speeds[i]))
# plt.show()
# break
plt.savefig('./results/vi_problemMatrixDetail' + str(timer.time()) +
'.pdf')
# In[29]:
index = 0
bestScoreImg = np.zeros([len(roughnesses), len(speeds)])
for i in range(len(roughness_ai_speed_matrix)):
r = roughness_ai_speed_matrix[i]
for j in range(len(r)):
index += 1
s = r[j]
allScores = [sc for row in s for sc in row]
myKai = [ai for ai in aiScores for i in range(p.reps)]
# plt.scatter(myKai,allScores,c=[.9,.9,.9])
# plotCategoricalMeans(myKai,allScores)
pl = np.polyfit(myKai, allScores, 2)
z = np.poly1d(pl)
x1 = np.linspace(min(myKai), max(myKai), 100)
y = z(x1)
bestScore = round(x1[np.argmin(y)])
bestScoreImg[i, j] = bestScore
# plt.plot(x1,z(x1),color='red')
# plt.title("roughness %s speed %s" % (roughnesses[i], speeds[i]))
# plt.show()
# break
plt.subplot(1, 1, 1)
cmapRB = matplotlib.colors.LinearSegmentedColormap.from_list(
"", ["blue", "red"])
plt.imshow(bestScoreImg, cmap=cmapRB)
plt.xlabel('decreasing solution space size')
plt.ylabel('decreasing amplitude of objective function sinusoid')
plt.colorbar()
myPath = os.path.dirname(__file__)
plt.savefig(myPath + "/results/vi_matrixOfProblems_bestStyle.pdf")