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plotsGeneration.py
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import pandas as pd
import configuration
import pickle
import math
import copy
import matplotlib.pyplot as plt
import os
# Libraries for radar plots
from math import pi
def getExperimentStorageFolder(nodeNumber,appNumber,repNum):
global storageFoldershort
storageFoldershort = "./plots2/"
return storageFoldershort+str(nodeNumber)+"nodes"+str(appNumber)+"apps"+str(repNum)+"repetition/"
def calculateHypercube():
getExperimentStorageFolder(0, 0, 0) # dummy execution just to set the value of storageFoldershort
with open(storageFoldershort + 'radarplot.pickle', 'rb') as f:
radar2 = pickle.load(f)
radarLabels = radar2['radarLabels']
finalHyperVolume = radar2['finalHyperVolume']
import pandas as pd
data4radarplots = {'radarLabels': radarLabels, 'finalHyperVolume': finalHyperVolume}
df = pd.DataFrame(data4radarplots)
aggregatedColumns = ['finalHyperVolume']
mean_df = df.groupby('radarLabels')[aggregatedColumns].mean()
mean_df.reset_index(inplace=True)
std_df = df.groupby('radarLabels')[aggregatedColumns].std(ddof=1)
std_df.reset_index(inplace=True)
# df.groupby('name')['fsColonyOptTime','oneColonyOptTime', 'selectedFromParetoOptTime', 'fsColonyNetTime', 'oneColonyNetTime', 'selectedFromParetoNetTime'].sem()
radarLabels = mean_df["radarLabels"].values.tolist()
hyperVolume = mean_df["finalHyperVolume"].values.tolist()
std_radarLabels = std_df["radarLabels"].values.tolist()
std_hyperVolume = std_df["finalHyperVolume"].values.tolist()
print("Values of the average hypervolumen and the standard deviation")
for i in range(len(radarLabels)):
j=0
while j<len(std_radarLabels) and std_radarLabels[j]!=radarLabels[i]:
j+=1
print(radarLabels[i]+" & "+str(hyperVolume[i]) + " ("+str(std_hyperVolume[j])+")")
def drawAllRadarPlots():
# ******************************************************************************************
# Calculation of the generation number where the pareto set of the GA dominates the two control solutions
# This is an independent code that can be executed isolated, since the data needed is readed from a file
# that was generated in the whole previous execution of all this script.
# ******************************************************************************************
getExperimentStorageFolder(0, 0, 0) # dummy execution just to set the value of storageFoldershort
with open(storageFoldershort + 'radarplot.pickle', 'rb') as f:
radar2 = pickle.load(f)
radarLabels = radar2['radarLabels']
fsColonyOptTime = radar2['fsColonyOptTime']
oneColonyOptTime = radar2['oneColonyOptTime']
selectedFromParetoOptTime = radar2['selectedFromParetoOptTime']
fsColonyNetTime = radar2['fsColonyNetTime']
oneColonyNetTime = radar2['oneColonyNetTime']
selectedFromParetoNetTime = radar2['selectedFromParetoNetTime']
fsColonyDistance = radar2['fsColonyDistance']
oneColonyDistance = radar2['oneColonyDistance']
selectedFromParetoDistance = radar2['selectedFromParetoDistance']
finalHyperVolume = radar2['finalHyperVolume']
finalCoverageFSGA = radar2['finalCoverageFSGA']
finalCoverage1CGA = radar2['finalCoverage1CGA']
finalCoverageGAFS = radar2['finalCoverageGAFS']
finalCoverageGA1C = radar2['finalCoverageGA1C']
#
# radarLabels=radar2['radarLabels']
# fsColonyOptTime=radar2['fsColonyOptTime']
# oneColoyOptTime=radar2['oneColoyOptTime']
# selectedFromParetoOptTime=radar2['selectedFromParetoOptTime']
# fsColonyNetTime=radar2['fsColonyNetTime']
# oneColoyNetTime=radar2['oneColoyNetTime']
# selectedFromParetoNetTime=radar2['selectedFromParetoNetTime']
# finalHyperVolume=radar2['finalHyperVolume']
# finalCoverageFSGA=radar2['finalCoverageFSGA']
# finalCoverage1CGA=radar2['finalCoverage1CGA']
# finalCoverageGAFS=radar2['finalCoverageGAFS']
# finalCoverageGA1C=radar2['finalCoverageGA1C']
#
# radarLabels.pop(-1)
# fsColonyOptTime.pop(-1)
# oneColoyOptTime.pop(-1)
# selectedFromParetoOptTime.pop(-1)
# fsColonyNetTime.pop(-1)
# oneColoyNetTime.pop(-1)
# selectedFromParetoNetTime.pop(-1)
# finalHyperVolume.pop(-1)
# finalCoverageFSGA.pop(-1)
# finalCoverage1CGA.pop(-1)
# finalCoverageGAFS.pop(-1)
# finalCoverageGA1C.pop(-1)
import pandas as pd
data4radarplots = {'radarLabels': radarLabels, 'fsColonyOptTime': fsColonyOptTime,
'oneColonyOptTime': oneColonyOptTime, 'selectedFromParetoOptTime': selectedFromParetoOptTime,
'fsColonyNetTime': fsColonyNetTime, 'oneColonyNetTime': oneColonyNetTime,
'selectedFromParetoNetTime': selectedFromParetoNetTime,'fsColonyDistance': fsColonyDistance,
'oneColonyDistance': oneColonyDistance,
'selectedFromParetoDistance': selectedFromParetoDistance}
df = pd.DataFrame(data4radarplots)
aggregatedColumns = ['fsColonyOptTime', 'oneColonyOptTime', 'selectedFromParetoOptTime', 'fsColonyNetTime',
'oneColonyNetTime', 'selectedFromParetoNetTime', 'fsColonyDistance',
'oneColonyDistance', 'selectedFromParetoDistance']
mean_df = df.groupby('radarLabels')[aggregatedColumns].mean()
mean_df.reset_index(inplace=True)
std_df = df.groupby('radarLabels')[aggregatedColumns].std(ddof=1)
std_df.reset_index(inplace=True)
# df.groupby('name')['fsColonyOptTime','oneColonyOptTime', 'selectedFromParetoOptTime', 'fsColonyNetTime', 'oneColonyNetTime', 'selectedFromParetoNetTime'].sem()
radarLabels = mean_df["radarLabels"].values.tolist()
fsColonyOptTime = mean_df["fsColonyOptTime"].values.tolist()
oneColonyOptTime = mean_df["oneColonyOptTime"].values.tolist()
selectedFromParetoOptTime = mean_df["selectedFromParetoOptTime"].values.tolist()
fsColonyNetTime = mean_df["fsColonyNetTime"].values.tolist()
oneColonyNetTime = mean_df["oneColonyNetTime"].values.tolist()
selectedFromParetoNetTime = mean_df["selectedFromParetoNetTime"].values.tolist()
fsColonyDistance = mean_df["fsColonyDistance"].values.tolist()
oneColonyDistance = mean_df["oneColonyDistance"].values.tolist()
selectedFromParetoDistance = mean_df["selectedFromParetoDistance"].values.tolist()
std_radarLabels = std_df["radarLabels"].values.tolist()
std_fsColonyOptTime = std_df["fsColonyOptTime"].values.tolist()
std_oneColonyOptTime = std_df["oneColonyOptTime"].values.tolist()
std_selectedFromParetoOptTime = std_df["selectedFromParetoOptTime"].values.tolist()
std_fsColonyNetTime = std_df["fsColonyNetTime"].values.tolist()
std_oneColonyNetTime = std_df["oneColonyNetTime"].values.tolist()
std_selectedFromParetoNetTime = std_df["selectedFromParetoNetTime"].values.tolist()
std_fsColonyDistance = std_df["fsColonyDistance"].values.tolist()
std_oneColonyDistance = std_df["oneColonyDistance"].values.tolist()
std_selectedFromParetoDistance = std_df["selectedFromParetoDistance"].values.tolist()
series2Plot = list()
std_series2Plot = list()
ind1 = copy.copy(oneColonyDistance)
ind1.append(oneColonyDistance[0])
series2Plot.append(ind1)
std_ind1 = copy.copy(std_oneColonyDistance)
std_ind1.append(std_oneColonyDistance[0])
std_series2Plot.append(std_ind1)
ind2 = copy.copy(fsColonyDistance)
ind2.append(fsColonyDistance[0])
series2Plot.append(ind2)
std_ind2 = copy.copy(std_fsColonyDistance)
std_ind2.append(std_fsColonyDistance[0])
std_series2Plot.append(std_ind2)
ind3 = copy.copy(selectedFromParetoDistance)
ind3.append(selectedFromParetoDistance[0])
series2Plot.append(ind3)
std_ind3 = copy.copy(std_selectedFromParetoDistance)
std_ind3.append(std_selectedFromParetoDistance[0])
std_series2Plot.append(std_ind3)
paletteValues = [0, 1, 2]
radarTitle = 'Euclidean distance to origin'
theLabels = ["one-colony", "fixed-size", "smallED-GA"]
fileName2Store = "radarDistance.pdf"
plotRadarFigures(radarLabels, series2Plot, std_series2Plot, paletteValues, radarTitle, theLabels, fileName2Store,with_Error_Bars=False, with_Bars=False)
series2Plot = list()
std_series2Plot = list()
ind2 = copy.copy(fsColonyDistance)
ind2.append(fsColonyDistance[0])
series2Plot.append(ind2)
std_ind2 = copy.copy(std_fsColonyDistance)
std_ind2.append(std_fsColonyDistance[0])
std_series2Plot.append(std_ind2)
ind3 = copy.copy(selectedFromParetoDistance)
ind3.append(selectedFromParetoDistance[0])
series2Plot.append(ind3)
std_ind3 = copy.copy(std_selectedFromParetoDistance)
std_ind3.append(std_selectedFromParetoDistance[0])
std_series2Plot.append(std_ind3)
paletteValues = [1, 2]
radarTitle = 'Euclidean distance to origin'
theLabels = ["fixed-size", "smallED-GA"]
fileName2Store = "radarDistanceOnly2.pdf"
plotRadarFigures(radarLabels, series2Plot, std_series2Plot, paletteValues, radarTitle, theLabels, fileName2Store,with_Error_Bars=False, with_Bars=False)
series2Plot = list()
std_series2Plot = list()
ind1 = copy.copy(oneColonyOptTime)
ind1.append(oneColonyOptTime[0])
series2Plot.append(ind1)
std_ind1 = copy.copy(std_oneColonyOptTime)
std_ind1.append(std_oneColonyOptTime[0])
std_series2Plot.append(std_ind1)
ind2 = copy.copy(fsColonyOptTime)
ind2.append(fsColonyOptTime[0])
series2Plot.append(ind2)
std_ind2 = copy.copy(std_fsColonyOptTime)
std_ind2.append(std_fsColonyOptTime[0])
std_series2Plot.append(std_ind2)
ind3 = copy.copy(selectedFromParetoOptTime)
ind3.append(selectedFromParetoOptTime[0])
series2Plot.append(ind3)
std_ind3 = copy.copy(std_selectedFromParetoOptTime)
std_ind3.append(std_selectedFromParetoOptTime[0])
std_series2Plot.append(std_ind3)
paletteValues = [0, 1, 2]
radarTitle = 'placement_time (ms)'
theLabels = ["one-colony", "fixed-size", "smallED-GA"]
fileName2Store = "radarOptTime.pdf"
plotRadarFigures(radarLabels, series2Plot, std_series2Plot, paletteValues, radarTitle, theLabels, fileName2Store)
fileName2Store = "radarOptTime3.pdf"
plotRadarFigures(radarLabels, series2Plot, std_series2Plot, paletteValues, radarTitle, theLabels, fileName2Store,
y_limit=(0, 50))
series2Plot = list()
ind2 = copy.copy(fsColonyOptTime)
ind2.append(fsColonyOptTime[0])
series2Plot.append(ind2)
std_ind2 = copy.copy(std_fsColonyOptTime)
std_ind2.append(std_fsColonyOptTime[0])
std_series2Plot.append(std_ind2)
ind3 = copy.copy(selectedFromParetoOptTime)
ind3.append(selectedFromParetoOptTime[0])
series2Plot.append(ind3)
std_ind3 = copy.copy(std_selectedFromParetoOptTime)
std_ind3.append(std_selectedFromParetoOptTime[0])
std_series2Plot.append(std_ind3)
paletteValues = [1, 2]
radarTitle = 'placement_time (ms)'
theLabels = ["one-colony", "fixed-size", "smallED-GA"]
fileName2Store = "radarOptTime2.pdf"
plotRadarFigures(radarLabels,series2Plot, std_series2Plot, paletteValues, radarTitle, theLabels, fileName2Store)
series2Plot = list()
ind1 = copy.copy(oneColonyNetTime)
ind1.append(oneColonyNetTime[0])
series2Plot.append(ind1)
std_ind1 = copy.copy(std_oneColonyNetTime)
std_ind1.append(std_oneColonyNetTime[0])
std_series2Plot.append(std_ind1)
ind2 = copy.copy(fsColonyNetTime)
ind2.append(fsColonyNetTime[0])
series2Plot.append(ind2)
std_ind2 = copy.copy(std_fsColonyNetTime)
std_ind2.append(std_fsColonyNetTime[0])
std_series2Plot.append(std_ind2)
ind3 = copy.copy(selectedFromParetoNetTime)
ind3.append(selectedFromParetoNetTime[0])
series2Plot.append(ind3)
std_ind3 = copy.copy(std_selectedFromParetoNetTime)
std_ind3.append(std_selectedFromParetoNetTime[0])
std_series2Plot.append(std_ind3)
paletteValues = [0, 1, 2]
radarTitle = 'response_time (ms)'
theLabels = ["one-colony", "fixed-size", "smallED-GA"]
fileName2Store = "radarNetTime.pdf"
plotRadarFigures(radarLabels, series2Plot, std_series2Plot, paletteValues, radarTitle, theLabels, fileName2Store)
series2Plot = list()
ind2 = copy.copy(fsColonyNetTime)
ind2.append(fsColonyNetTime[0])
series2Plot.append(ind2)
std_ind2 = copy.copy(std_fsColonyNetTime)
std_ind2.append(std_fsColonyNetTime[0])
std_series2Plot.append(std_ind2)
ind3 = copy.copy(selectedFromParetoNetTime)
ind3.append(selectedFromParetoNetTime[0])
series2Plot.append(ind3)
std_ind3 = copy.copy(std_selectedFromParetoNetTime)
std_ind3.append(std_selectedFromParetoNetTime[0])
std_series2Plot.append(std_ind3)
paletteValues = [1, 2]
radarTitle = 'response_time (ms)'
theLabels = ["fixed-size", "smallED-GA"]
fileName2Store = "radarNetTime2.pdf"
plotRadarFigures(radarLabels, series2Plot, std_series2Plot, paletteValues, radarTitle, theLabels, fileName2Store)
def calculateSmallestGenerationDomination():
# ******************************************************************************************
# Calculation of the generation number where the pareto set of the GA dominates the two control solutions
# This is an independent code that can be executed isolated, since the data needed is readed from a file
# that was generated in the whole previous execution of all this script.
# ******************************************************************************************
experimentLabels = list()
experimentSmallestGeneration = list()
for oneExperiment in configuration.experiments2execute:
appNumber = oneExperiment[0]
nodeNumber = oneExperiment[1]
for repNum in configuration.rangeOfExperimentsRepetitions:
storageFolder = getExperimentStorageFolder(nodeNumber, appNumber, repNum)
with open(storageFolder + 'resultdata.pickle', 'rb') as f:
oneIterationData = pickle.load(f)
f.close()
controlCases = oneIterationData['controlCases']
smallestGenerationDominates = 1000000
for i in range(configuration.numberGenerations):
minX = min(oneIterationData['meanMinX'])
minY = min(oneIterationData['meanMinY'])
maxX = max(oneIterationData['meanMaxX'])
maxY = max(oneIterationData['meanMaxY'])
diffX = maxX - minX
diffY = maxY - minY
hyperVolume = list()
coverage1CGA = list()
coverageFSGA = list()
coverageGA1C = list()
coverageGAFS = list()
paretoMaxX = -1.0
paretoMaxY = -1.0
paretoMinX = float('inf')
paretoMinY = float('inf')
SOLdominates1C = 0
SOLdominatesFS = 0
one1CdominatesSOL = 0
FSdominatesSOL = 0
for j in range(len(oneIterationData['all_paretox'][i])):
x_value = oneIterationData['all_paretox'][i][j]
y_value = oneIterationData['all_paretoy'][i][j]
if (controlCases['otCc'] <= x_value) and (controlCases['rtCc'] <= y_value):
FSdominatesSOL += 1
if (controlCases['ot1c'] <= x_value) and (controlCases['rt1c'] <= y_value):
one1CdominatesSOL += 1
if (x_value <= controlCases['otCc']) and (y_value <= controlCases['rtCc']):
SOLdominatesFS = 1
if (x_value <= controlCases['ot1c']) and (y_value <= controlCases['rt1c']):
SOLdominates1C = 1
# print ("otCc "+str(otCc)+" x_value "+str(x_value)+" rtCc "+str(rtCc)+" y_value "+str(y_value)+" ")
# ot1c,rt1c
#
# otCc,rtCc
#
# #LAS X son los optimization times
# #las Y son los estimated response times
# x_value = (x_value - minY) / diffX
x_value = (x_value - minX) / diffX
y_value = (y_value - minY) / diffY
paretoMinX = min(paretoMinX, x_value)
paretoMinY = min(paretoMinY, y_value)
paretoMaxX = max(paretoMaxX, x_value)
paretoMaxY = max(paretoMaxY, y_value)
value4HyperVolume = (float(paretoMaxX) - float(paretoMinX)) * (float(paretoMaxY) - float(paretoMinY))
hyperVolume.append(value4HyperVolume)
perc_dominanceFSGA = float(float(FSdominatesSOL) / float(len(oneIterationData['all_paretox'][i])))
coverageFSGA.append(perc_dominanceFSGA)
perc_dominance1CGA = float(float(one1CdominatesSOL) / float(len(oneIterationData['all_paretox'][i])))
coverage1CGA.append(perc_dominance1CGA)
if (SOLdominates1C == 1) and (SOLdominatesFS == 1):
smallestGenerationDominates = min(smallestGenerationDominates, i + 1)
coverageGA1C.append(SOLdominates1C)
coverageGAFS.append(SOLdominatesFS)
experimentLabels.append(oneExperiment)
experimentSmallestGeneration.append(smallestGenerationDominates)
data4TableAnalysis = {'experimentLabels': experimentLabels,
'experimentSmallestGeneration': experimentSmallestGeneration}
df = pd.DataFrame(data4TableAnalysis)
aggregatedColumns = ['experimentSmallestGeneration']
mean_df = df.groupby('experimentLabels')[aggregatedColumns].mean()
mean_df.reset_index(inplace=True)
std_df = df.groupby('experimentLabels')[aggregatedColumns].std()
std_df.reset_index(inplace=True)
print(mean_df)
print(std_df)
def calculateSmallestGenerationDomination2():
# ******************************************************************************************
# Calculation of the generation number where the pareto set of the GA dominates the two control solutions
# This is an independent code that can be executed isolated, since the data needed is readed from a file
# that was generated in the whole previous execution of all this script.
# ******************************************************************************************
experimentLabels = list()
experimentSmallestGeneration = list()
MYMAXVALUE = 1000000
for oneExperiment in configuration.experiments2execute:
appNumber = oneExperiment[0]
nodeNumber = oneExperiment[1]
for repNum in configuration.rangeOfExperimentsRepetitions:
storageFolder = getExperimentStorageFolder(nodeNumber, appNumber, repNum)
with open(storageFolder + 'resultdata.pickle', 'rb') as f:
oneIterationData = pickle.load(f)
f.close()
controlCases = oneIterationData['controlCases']
smallestGenerationDominates = MYMAXVALUE
for i in range(configuration.numberGenerations):
SOLdominates1C = 0
SOLdominatesFS = 0
for j in range(len(oneIterationData['all_paretox'][i])):
x_value = oneIterationData['all_paretox'][i][j]
y_value = oneIterationData['all_paretoy'][i][j]
if (x_value <= controlCases['otCc']) and (y_value <= controlCases['rtCc']):
SOLdominatesFS = 1
if (x_value <= controlCases['ot1c']) and (y_value <= controlCases['rt1c']):
SOLdominates1C = 1
if (SOLdominates1C == 1) and (SOLdominatesFS == 1):
smallestGenerationDominates = min(smallestGenerationDominates, i + 1)
experimentLabels.append(oneExperiment)
experimentSmallestGeneration.append(smallestGenerationDominates)
data4TableAnalysis = {'experimentLabels': experimentLabels,
'experimentSmallestGeneration': experimentSmallestGeneration}
df = pd.DataFrame(data4TableAnalysis)
df = df[df.experimentSmallestGeneration != MYMAXVALUE]
aggregatedColumns = ['experimentSmallestGeneration']
mean_df = df.groupby('experimentLabels')[aggregatedColumns].mean()
mean_df.reset_index(inplace=True)
std_df = df.groupby('experimentLabels')[aggregatedColumns].std()
std_df.reset_index(inplace=True)
total_df = df.groupby('experimentLabels')[aggregatedColumns].count()
total_df.reset_index(inplace=True)
experimentLabels = mean_df["experimentLabels"].values.tolist()
experimentSmallestGenerationValues = mean_df["experimentSmallestGeneration"].values.tolist()
std_experimentLabels = std_df["experimentLabels"].values.tolist()
std_experimentSmallestGeneration = std_df["experimentSmallestGeneration"].values.tolist()
total_experimentLabels = total_df["experimentLabels"].values.tolist()
total_experimentSmallestGeneration = total_df["experimentSmallestGeneration"].values.tolist()
print("Values of the average hypervolumen and the standard deviation")
for i in range(len(experimentLabels)):
j=0
while j<len(std_experimentLabels) and std_experimentLabels[j]!=experimentLabels[i]:
j+=1
k=0
while k<len(total_experimentLabels) and total_experimentLabels[k]!=experimentLabels[i]:
k+=1
print(str(experimentLabels[i])+" & "+str(experimentSmallestGenerationValues[i]) + " ("+str(std_experimentSmallestGeneration[j])+") in "+str(total_experimentSmallestGeneration[j])+" samples")
def generateScatter4AllGenerations(oneIterationData,meanCloserTo00X,meanCloserTo00Y,xlimitmin,ylimitmin,xlimit,ylimit):
#PARA poder hacer los cálculos normalizados, esta función necesita que se haya
# calculado anteriormente:
# minX diffX
# minY diffY
#
# ###Recorremos todas las generaciones que se han ejecutado en este experimento concreto (para un num de nodos,
# #un numero de apps y una rep concreta.
for i in range(len(oneIterationData['all_paretox'])):
#
# minDistance = float('inf')
# indxMin = -1
#
# paretoMaxX = -1.0
# paretoMaxY = -1.0
# paretoMinX = float('inf')
# paretoMinY = float('inf')
#
# SOLdominates1C = 0
# SOLdominatesFS = 0
#
# one1CdominatesSOL = 0
# FSdominatesSOL = 0
#
# print("Generation " + str(i))
# print("Pareto size " + str(len(oneIterationData['all_paretox'][i])))
#
#
# #Para cada una de las generaciones de este experimento, buscamos cual es la solucion que
# #esta mas cerca del centro de coordenadas, es decir aplicamos una regla de seleccion
# #de una instruccion del parteo front y buscamos cual es la solucion que la cumple
# #para posteriormente poder marcar esta solucion con un * en el scatter plot
# for j in range(len(oneIterationData['all_paretox'][i])):
# x_value = oneIterationData['all_paretox'][i][j]
# y_value = oneIterationData['all_paretoy'][i][j]
#
#
# #TODO, hoy 24/feb he cambiado lo que creo que es un error, ya que se restaba por el minY en lugar de por el minX
# #x_value = (x_value - minY) / diffX
# x_value = (x_value - minX) / diffX
# y_value = (y_value - minY) / diffY
#
# paretoMinX = min(paretoMinX, x_value)
# paretoMinY = min(paretoMinY, y_value)
#
# paretoMaxX = min(paretoMaxX, x_value)
# paretoMaxY = min(paretoMaxY, y_value)
#
# distanceToCeroCero = math.sqrt(x_value ** 2 + y_value ** 2)
# if distanceToCeroCero < minDistance:
# minDistance = distanceToCeroCero
# indxMin = j
#
# # meanCloserTo00X.append(oneIterationData['all_paretox'][i][j])
# # meanCloserTo00Y.append(oneIterationData['all_paretoy'][i][j])
# # TODO: SEGURO QUE NO TENGO QUE ESCOGER LA QUE ES INDICE J EN LUGAR DE indxMin ????
# # TODO: RESUELTO, LA QUE HAY QUE COGER ES indxMin
# meanCloserTo00X.append(oneIterationData['all_paretox'][i][indxMin])
# meanCloserTo00Y.append(oneIterationData['all_paretoy'][i][indxMin])
# meanCloserTo00.append(minDistance)
ax = plt.subplot()
plt.text(0.05, 0.98, "Generation " + str(i + 1), horizontalalignment='left',
verticalalignment='top', transform=ax.transAxes, fontsize=12, fontweight="bold")
plt.text(0.95, 0.98, str(nodeNumber) + "nodes\n" + str(appNumber) + "apps", horizontalalignment='right',
verticalalignment='top', transform=ax.transAxes, fontsize=12, fontweight="bold")
plt.xlim(xlimitmin, xlimit)
plt.ylim(ylimitmin, ylimit)
plt.scatter(oneIterationData['all_nonparetox'][i], oneIterationData['all_nonparetoy'][i])
plt.scatter(oneIterationData['all_paretox'][i], oneIterationData['all_paretoy'][i])
plt.scatter(controlCases['ot1c'], controlCases['rt1c'], marker='x', color='g')
plt.scatter(controlCases['otCc'], controlCases['rtCc'], marker='x', color='r')
plt.scatter(meanCloserTo00X[i], meanCloserTo00Y[i],
marker='*',
color='#000000')
plt.title('Objective space')
plt.xlabel("placement_time (ms)")
if configuration.ILP_METHOD:
plt.ylabel("Free resources")
else:
plt.ylabel("response_time (ms)")
# plt.xlim(min_x, max_x)
# plt.ylim(min_y, max_y)
plt.savefig(storageFolder + "scaled_scatterPlot_GEN" + str(i) + "_APPS" + str(appNumber) + "_NODES" + str(nodeNumber) + ".pdf", format='pdf')
plt.close()
def getParetoComparativeMetrics(oneIterationData):
hyperVolume = list()
coverageFSGA = list()
coverage1CGA = list()
coverageGAFS = list()
coverageGA1C = list()
for i in range(len(oneIterationData['all_paretox'])):
paretoMaxX = -1.0
paretoMaxY = -1.0
paretoMinX = float('inf')
paretoMinY = float('inf')
paretoMaxXnoNormalized = -1.0
paretoMaxYnoNormalized = -1.0
paretoMinXnoNormalized = float('inf')
paretoMinYnoNormalized = float('inf')
SOLdominates1C = 0
SOLdominatesFS = 0
one1CdominatesSOL = 0
FSdominatesSOL = 0
print("Generation " + str(i))
print("Pareto size " + str(len(oneIterationData['all_paretox'][i])))
for j in range(len(oneIterationData['all_paretox'][i])):
x_value = oneIterationData['all_paretox'][i][j]
y_value = oneIterationData['all_paretoy'][i][j]
if (controlCases['otCc'] <= x_value) and (controlCases['rtCc'] <= y_value):
FSdominatesSOL += 1
if (controlCases['ot1c'] <= x_value) and (controlCases['rt1c'] <= y_value):
one1CdominatesSOL += 1
if (x_value <= controlCases['otCc']) and (y_value <= controlCases['rtCc']):
SOLdominatesFS = 1
if (x_value <= controlCases['ot1c']) and (y_value <= controlCases['rt1c']):
SOLdominates1C = 1
#x_value = (x_value - minY) / diffX
x_value = (x_value - minX) / diffX
y_value = (y_value - minY) / diffY
paretoMinX = min(paretoMinX, x_value)
paretoMinY = min(paretoMinY, y_value)
paretoMaxX = max(paretoMaxX, x_value)
paretoMaxY = max(paretoMaxY, y_value)
paretoMinXnoNormalized = min(paretoMinXnoNormalized, x_value)
paretoMinYnoNormalized = min(paretoMinYnoNormalized, y_value)
paretoMaxXnoNormalized = max(paretoMaxXnoNormalized, x_value)
paretoMaxYnoNormalized = max(paretoMaxYnoNormalized, y_value)
value4HyperVolume = (float(paretoMaxX) - float(paretoMinX)) * (float(paretoMaxY) - float(paretoMinY))
value4HyperVolume = (float(paretoMaxX) - float(paretoMinX)) * (float(paretoMaxY) - float(paretoMinY))
hyperVolume.append(value4HyperVolume)
perc_dominance = float(float(FSdominatesSOL) / float(len(oneIterationData['all_paretox'][i])))
coverageFSGA.append(perc_dominance)
perc_dominance = float(float(one1CdominatesSOL) / float(len(oneIterationData['all_paretox'][i])))
coverage1CGA.append(perc_dominance)
coverageGA1C.append(SOLdominates1C)
coverageGAFS.append(SOLdominatesFS)
return hyperVolume[-1], coverageFSGA[-1], coverage1CGA[-1], coverageGAFS[-1], coverageGA1C[-1]
def plotRadarFigures(radarLabels, series2Plot, std_series2Plot, paletteValues, radarTitle, theLabels, fileName2Store,y_limit=None, with_Error_Bars=True, with_Bars=False):
# Set data
# df = pd.DataFrame({
# 'group': radarLabels,
# 'var1': fsColonyOptTime,
# 'var2': oneColoyOptTime,
# 'var3': selectedFromParetoOptTime
# })
#
my_palette = plt.cm.get_cmap("Set2", 4)
# ------- PART 1: Create background
# number of variable
categories = radarLabels
N = len(categories)
# What will be the angle of each axis in the plot? (we divide the plot / number of variable)
angles = [n / float(N) * 2 * pi for n in range(N)]
angles += angles[:1]
# Initialise the spider plot
ax = plt.subplot(111, polar=True)
#fig = plt.figure(figsize=(10, 10))
#ax = fig.add_subplot(projection='polar')
# If you want the first axis to be on top:
ax.set_theta_offset(pi / 2)
ax.set_theta_direction(-1)
# Draw one axe per variable + add labels
plt.xticks(angles[:-1], categories)
plt.yticks(fontsize=6)
plt.xticks(fontsize=8)
if y_limit!=None:
plt.ylim(y_limit[0], y_limit[1])
ax.tick_params(axis='x', which='major', pad=10)
# Draw ylabels
# ax.set_rlabel_position(0)
# plt.yticks([10,20,30], ["10","20","30"], color="grey", size=7)
# plt.ylim(0,40)
# ------- PART 2: Add plots
# Plot each individual = each line of the data
# I don't make a loop, because plotting more than 3 groups makes the chart unreadable
# Ind1
# for i in range(len(series2Plot)):
# errorSerieMax = []
# errorSerieMin = []
# for j in range(len(series2Plot[i])):
# errorSerieMax.append(series2Plot[i][j]*1.1)
# errorSerieMin.append(series2Plot[i][j]*0.9)
# ax.plot(angles, series2Plot[i], linewidth=1, color="black", linestyle='solid', label=theLabels[i])
# ax.fill(angles, series2Plot[i], color=my_palette(paletteValues[i]), alpha=0.4)
#
# ax.fill_between(angles, errorSerieMax, errorSerieMin, facecolor=my_palette(paletteValues[i]), alpha=1)
for i in range(len(series2Plot)):
errorSerieMax = []
errorSerieMin = []
for j in range(len(series2Plot[i])):
# errorSerieMax.append(series2Plot[i][j]*1.1)
# errorSerieMin.append(series2Plot[i][j]*0.9)
#errorSerieMax.append(series2Plot[i][j] + std_series2Plot[i][j])
#errorSerieMin.append(series2Plot[i][j] - std_series2Plot[i][j])
errorSerieMax.append(std_series2Plot[i][j])
errorSerieMin.append(std_series2Plot[i][j])
if with_Bars:
ax.bar(angles, series2Plot[i], linewidth=1, color=my_palette(paletteValues[i]), linestyle='solid',
label=theLabels[i])
else:
ax.plot(angles, series2Plot[i], linewidth=1, color=my_palette(paletteValues[i]), linestyle='solid', label=theLabels[i])
ax.fill(angles, series2Plot[i], color=my_palette(paletteValues[i]), alpha=0.4)
#ax.errorbar(angles, series2Plot[i], yerr=[errorSerieMax, errorSerieMin], xerr=0.2,
# fmt='o', ecolor='g', capthick=2)
#ax.errorbar(angles, series2Plot[i], xerr=0.2, yerr=0.4)
#ax.errorbar(angles, series2Plot[i], yerr=errorSerieAvg, fmt='o', color=my_palette(paletteValues[i]), ecolor="black", capsize=7)
if with_Error_Bars:
ax.errorbar(angles, series2Plot[i], yerr=[errorSerieMin, errorSerieMax], fmt='o', color=my_palette(paletteValues[i]), ecolor=my_palette(paletteValues[i]), elinewidth=3)
# plt.title('Values of the placement_time objective for the three algorithms')
# Add legend
# plt.legend(loc='upper right', bbox_to_anchor=(0.1, 0.1))
plt.title(radarTitle, y=1.2, fontweight="bold")
# Add legend
handles, labels = plt.gca().get_legend_handles_labels()
plt.legend([plt.Rectangle((0, 0), 1, 1, fc=my_palette(paletteValues[i])) for i, handle in enumerate(handles)],
[label for i, label in enumerate(labels)],
handlelength=0.8, handleheight=0.8, fontsize=6, loc='upper right', bbox_to_anchor=(0.1, 0.1))
plt.tight_layout()
# Show the graph
plt.savefig(storageFoldershort + fileName2Store, format='pdf')
plt.close()
def getDistanceToReferencePoint(minX,minY,maxX,maxY,xval,yval):
#Caculate the disntance to the point (0,0), the origin of coordinates
diffX = maxX - minX
diffY = maxY - minY
x_value = (xval - minX) / diffX
y_value = (yval - minY) / diffY
distanceToCeroCero = math.sqrt(x_value ** 2 + y_value ** 2)
return distanceToCeroCero
def getBestSolution4EachGeneration(oneIterationData,minX,minY,maxX,maxY):
#PARA poder hacer los cálculos normalizados, esta función necesita que se haya
# calculado anteriormente:
# minX maxX diffX
# minY maxY diffY
meanCloserTo00 = list()
meanCloserTo00X = list()
meanCloserTo00Y = list()
diffX = maxX - minX
diffY = maxY - minY
###Recorremos todas las generaciones que se han ejecutado en este experimento concreto (para un num de nodos,
#un numero de apps y una rep concreta.
for i in range(len(oneIterationData['all_paretox'])):
minDistance = float('inf')
indxMin = -1
print("Generation " + str(i))
print("Pareto size " + str(len(oneIterationData['all_paretox'][i])))
#Para cada una de las generaciones de este experimento, buscamos cual es la solucion que
#esta mas cerca del centro de coordenadas, es decir aplicamos una regla de seleccion
#de una instruccion del parteo front y buscamos cual es la solucion que la cumple
#para posteriormente poder marcar esta solucion con un * en el scatter plot
for j in range(len(oneIterationData['all_paretox'][i])):
x_value = oneIterationData['all_paretox'][i][j]
y_value = oneIterationData['all_paretoy'][i][j]
#TODO, hoy 24/feb he cambiado lo que creo que es un error, ya que se restaba por el minY en lugar de por el minX
#x_value = (x_value - minY) / diffX
x_value = (x_value - minX) / diffX
y_value = (y_value - minY) / diffY
distanceToCeroCero = math.sqrt(x_value ** 2 + y_value ** 2)
if distanceToCeroCero < minDistance:
minDistance = distanceToCeroCero
indxMin = j
# meanCloserTo00X.append(oneIterationData['all_paretox'][i][j])
# meanCloserTo00Y.append(oneIterationData['all_paretoy'][i][j])
# TODO: SEGURO QUE NO TENGO QUE ESCOGER LA QUE ES INDICE J EN LUGAR DE indxMin ????
# TODO: RESUELTO, LA QUE HAY QUE COGER ES indxMin
meanCloserTo00X.append(oneIterationData['all_paretox'][i][indxMin])
meanCloserTo00Y.append(oneIterationData['all_paretoy'][i][indxMin])
meanCloserTo00.append(minDistance)
return meanCloserTo00, meanCloserTo00X, meanCloserTo00Y
finalHyperVolume = list()
finalCoverage1CGA = list()
finalCoverageFSGA = list()
finalCoverageGA1C = list()
finalCoverageGAFS = list()
radarLabels = list()
fsColonyNetTime = list()
oneColonyNetTime = list()
selectedFromParetoNetTime = list()
fsColonyOptTime = list()
oneColonyOptTime = list()
selectedFromParetoOptTime = list()
fsColonyDistance = list()
oneColonyDistance = list()
selectedFromParetoDistance = list()
for oneExperiment in configuration.experiments2execute:
appNumber = oneExperiment[0]
nodeNumber = oneExperiment[1]
for repNum in configuration.rangeOfExperimentsRepetitions:
storageFolder = getExperimentStorageFolder(nodeNumber,appNumber,repNum)
with open(storageFolder + 'alldata.pickle', 'rb') as f:
data2Store = pickle.load(f)
oneIterationData = data2Store['oneIterationData']
GAstructure4partition = data2Store['GAstructure4partition']
#domainConfiguration = data2Store['domainConfiguration']
controlCases = data2Store['controlCases']
# ******************************************************************************************
# Plots creation
# ******************************************************************************************
# LAS X son los optimization times
# las Y son los estimated execution (responses) times
# xlimit =sum(oneIterationData['meanMaxX'])/len(oneIterationData['meanMaxX'])
# ylimit =sum(oneIterationData['meanMaxY'])/len(oneIterationData['meanMaxY'])
xlimit =max(sum(oneIterationData['meanMaxX']) / len(oneIterationData['meanMaxX']), controlCases['otCc'])
ylimit = max(sum(oneIterationData['meanMaxY']) / len(oneIterationData['meanMaxY']), controlCases['rtCc'])