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generatePlots.py
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generatePlots.py
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import pickle
import numpy as np
from Evaluation import EvaluationClassIterationMCPlot
from Evaluation import EvaluationClassIterationMCPlot_time
# importing this so that Regret accuracy metric can be calculated
from FaultChainSolver import FaultChainSovlerClass
iteration_track = 1
three_nine_bus = False
alreadyHaveData = True
if alreadyHaveData:
with open('118bus_precision_data.npy', 'rb') as f:
dataPrecision = np.load(f)
finalPlot = EvaluationClassIterationMCPlot([], [], [], [], [], [], [], [], [], [])
pivot = 25
finalPlot.plot_precision(dataPrecision[:, :, pivot:], 1600-pivot)
if three_nine_bus:
selectCase = "39" # "14, "30", "39", "118"
LOADING_FACTOR = 0.55
dataSetString = "loading055_39bus.h5"
M = 5
FCsovler = FaultChainSovlerClass(LOADING_FACTOR, selectCase) # instantiated object!
dataSet = FCsovler.load_dataset(dataSetString)
FCsovler.print_datatset_information(dataSet)
optimalSequenceRisk = np.sort(dataSet[:, 3])[::-1] # sorting the entire dataset in decreasing order
if iteration_track:
# old-results
finalPlot = EvaluationClassIterationMCPlot([], [], [], [], [], [], [], [], [], [])
dontPlotOld = False
with open("39-bus Results/Final Results/1200_risky_data_list", "rb") as fp:
risky_data_list, M_episodes_qlearning = pickle.load(fp)
with open("39-bus Results/Final Results/1200_num_data_list", "rb") as fp:
num_data_list, M_episodes_qlearning = pickle.load(fp)
if dontPlotOld:
finalPlot.plot_fc_risk(risky_data_list, M_episodes_qlearning)
finalPlot.plot_num_risky_fcs(num_data_list, M_episodes_qlearning)
# new-results (Accuracy Metrics)
# Cumulative Regret
checkRisky = np.array(risky_data_list)
optimalSequenceRisk1200 = np.cumsum( optimalSequenceRisk[:1200] )
#optimalSequenceRisk1200[-1] # the sum of the entire sequences of risks
dataRegret = optimalSequenceRisk1200[-1]*np.ones((checkRisky.shape[1], checkRisky.shape[2])) - checkRisky
finalPlot.plot_regret(dataRegret, M_episodes_qlearning)
# Precision
checkNumdata = np.array(num_data_list)
lengthArray = np.array( range(1, checkNumdata.shape[2]+1) )
lengthArrayMatrix = np.vstack([lengthArray]*checkNumdata.shape[1])
dataPrecision = (checkNumdata/lengthArrayMatrix)
pivot = 6
finalPlot.plot_precision(dataPrecision[:, :, pivot:], 1200-pivot)
finalPlot.display_statistics_for_list(dataRegret, dataPrecision)
else:
finalPlot = EvaluationClassIterationMCPlot_time([], [], [], [], [], [], [], [], [], [])
with open("39-bus Results/Final Results/time_risky_data_list", "rb") as fp:
risky_data_list, M_episodes_qlearning = pickle.load(fp)
for approach in range(5):
algorithm = risky_data_list[approach]
MC_iterations = np.array(algorithm).shape[0]
numberofFCsList, totalRiskList, regretList = [], [], []
numRiskyFCs, precisionList = [], []
for iteration in range(MC_iterations):
temp = np.array( algorithm[iteration] ) # FC Risk, time taken
numberofFCs, _ = temp.shape
#print(" Number of FCs: ", numberofFCs, " Total Risk: ", temp[:, 0][-1], " Optimal Sequence Risk: ", sum(optimalSequenceRisk[:numberofFCs]) )
numberofFCsList.append(numberofFCs)
cumu = temp[:, 0]
normalArray = cumu.copy()
normalArray[1:] = np.diff(cumu)
#print( sum( normalArray > 34.3982*5 ) )
numRiskyFCs.append( sum( normalArray >= 34*5 ) )
precisionList.append( sum( normalArray >= 34*5 )/numberofFCs )
totalRiskList.append( temp[:, 0][-1] )
regretList.append( sum(optimalSequenceRisk[:numberofFCs]) - temp[:, 0][-1] )
print("Avg No. of FCs found: ", np.average( np.array(numberofFCsList) ))
print("Range Total Risk found: ", np.average( np.array(totalRiskList) ), " +- " , np.std( np.array(totalRiskList) ), " also in percentage ", 100*np.std( np.array(totalRiskList) )/np.average( np.array(totalRiskList) ))
print("Range Regret found: ", np.average( np.array(regretList) ) , " +- " , np.std( np.array(regretList) ), " also in percentage ", 100*np.std( np.array(regretList) )/np.average( np.array(regretList) ))
print("Range No. of Risky FCs found: ", np.average( np.array(numRiskyFCs) ), " +- " , np.std( np.array(numRiskyFCs) ), " also in percentage ", 100*np.std( np.array(numRiskyFCs) )/np.average( np.array(numRiskyFCs) ))
print("Range Precision found: ", np.average( np.array(precisionList) ), " +- " , np.std( np.array(precisionList) ), " also in percentage ", 100*np.std( np.array(precisionList) )/np.average( np.array(precisionList) ) )
print(" ")
"""
for mc_iteration in approach:
y.append([sublist[0] for sublist in mc_iteration]) # rewards
x.append([sublist[1] for sublist in mc_iteration]) # time
a = np.array(x)
b = np.array(y)
#print(y)
print(a.mean(axis=0))
print(b.mean(axis=0))
finalPlot.plot_fc_risk(risky_data_list, M_episodes_qlearning)
"""
else:
selectCase = "118" # "14, "30", "39", "118"
LOADING_FACTOR = 0.6
dataSetString = "118bus_loading06.h5"
M = 5
FCsovler = FaultChainSovlerClass(LOADING_FACTOR, selectCase) # instantiated object!
dataSet = FCsovler.load_dataset(dataSetString)
FCsovler.print_datatset_information(dataSet)
riskyFaultChainDict = FCsovler.find_risky_fault_chains(dataSet, M)
optimalSequenceRisk = np.sort(dataSet[:, 3])[::-1] # sorting the entire dataset in decreasing order
if iteration_track:
finalPlot = EvaluationClassIterationMCPlot([], [], [], [], [], [], [], [], [], [])
with open("118-bus Results/Final Results/First MC/118bus_risky_data_list", "rb") as fp:
risky_data_list1, M_episodes_qlearning = pickle.load(fp)
with open("118-bus Results/Final Results/Second MC/118bus_risky_data_list", "rb") as fp:
risky_data_list2, M_episodes_qlearning = pickle.load(fp)
# Cumulative Regret
checkRisky1 = np.array(risky_data_list1)
checkRisky2 = np.array(risky_data_list2)
checkRisky = np.concatenate((checkRisky1, checkRisky2), axis=1)
optimalSequenceRisk1600 = np.cumsum( optimalSequenceRisk[:1600] )
dataRegret = optimalSequenceRisk1600[-1]*np.ones((checkRisky.shape[1], checkRisky.shape[2])) - checkRisky
finalPlot.plot_regret(dataRegret, M_episodes_qlearning)
# Precision
with open("118-bus Results/Final Results/Second MC/118bus_num_data_list", "rb") as fp:
num_data_list2, M_episodes_qlearning = pickle.load(fp)
checkNumdata = np.array(num_data_list2)
lengthArray = np.array( range(1, checkNumdata.shape[2]+1) )
lengthArrayMatrix = np.vstack([lengthArray]*checkNumdata.shape[1])
dataPrecision = (checkNumdata/lengthArrayMatrix)
pivot = 6
finalPlot.plot_precision(dataPrecision[:, :, pivot:], 1600-pivot)
finalPlot.display_statistics_for_list(dataRegret, dataPrecision)