Commit a67a8401933ce41a867fed5558beebf03d6112b5
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33831508
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python script to plot 3d pareto frontiers
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| 1 | +import numpy as np | |
| 2 | +import matplotlib.pyplot as plt | |
| 3 | + | |
| 4 | + | |
| 5 | +#val = input("1:WoodYield/SoilLoss \n 2:WoodYield/RiskPercentile \n 3:SoilLoss/RiskPercentile \n 4:All three | |
| 6 | + | |
| 7 | +f=open("paretoWSR.csv","r") | |
| 8 | +#lines=sorted(list(map(str, f.readlines()))) | |
| 9 | + | |
| 10 | +f2 =open("nonParetoWSR.csv","r") | |
| 11 | +#lines2 = sorted(list(map(str, f2.readlines()))) | |
| 12 | + | |
| 13 | +lines=f.readlines() | |
| 14 | +lines2 = f2.readlines() | |
| 15 | + | |
| 16 | + | |
| 17 | +paretoPoints = [] | |
| 18 | +dominatedPoints = [] | |
| 19 | +x = [] | |
| 20 | +y = [] | |
| 21 | +z = [] | |
| 22 | +lst = [] | |
| 23 | +scatterx = [] | |
| 24 | +scattery = [] | |
| 25 | + | |
| 26 | +for i in lines: | |
| 27 | + result = i.split(',') | |
| 28 | + x.append(int(result[0])) | |
| 29 | + y.append(int(result[1])) | |
| 30 | + paretoPoints.append([int(result[0]),int(result[1]),int(result[2])]) | |
| 31 | +f.close() | |
| 32 | + | |
| 33 | +for i in lines2: | |
| 34 | + result = i.split(',') | |
| 35 | + x.append(int(result[0])) | |
| 36 | + y.append(int(result[1])) | |
| 37 | + z.append(int(result[2])) | |
| 38 | + if([int(result[0]),int(result[1]),int(result[2])] not in paretoPoints): | |
| 39 | + dominatedPoints.append([int(result[0]),int(result[1]),int(result[2])]) | |
| 40 | +f2.close() | |
| 41 | + | |
| 42 | +#print(dominatedPoints) | |
| 43 | + | |
| 44 | +def simple_cull(inputPoints, dominates): | |
| 45 | + paretoPoints = set() | |
| 46 | + candidateRowNr = 0 | |
| 47 | + dominatedPoints = set() | |
| 48 | + while True: | |
| 49 | + candidateRow = inputPoints[candidateRowNr] | |
| 50 | + inputPoints.remove(candidateRow) | |
| 51 | + rowNr = 0 | |
| 52 | + nonDominated = True | |
| 53 | + while len(inputPoints) != 0 and rowNr < len(inputPoints): | |
| 54 | + row = inputPoints[rowNr] | |
| 55 | + if dominates(candidateRow, row): | |
| 56 | + # If it is worse on all features remove the row from the array | |
| 57 | + inputPoints.remove(row) | |
| 58 | + dominatedPoints.add(tuple(row)) | |
| 59 | + elif dominates(row, candidateRow): | |
| 60 | + nonDominated = False | |
| 61 | + dominatedPoints.add(tuple(candidateRow)) | |
| 62 | + rowNr += 1 | |
| 63 | + else: | |
| 64 | + rowNr += 1 | |
| 65 | + | |
| 66 | + if nonDominated: | |
| 67 | + # add the non-dominated point to the Pareto frontier | |
| 68 | + paretoPoints.add(tuple(candidateRow)) | |
| 69 | + | |
| 70 | + if len(inputPoints) == 0: | |
| 71 | + break | |
| 72 | + return paretoPoints, dominatedPoints | |
| 73 | + | |
| 74 | +def dominates(row, candidateRow): | |
| 75 | + return sum([row[x] >= candidateRow[x] for x in range(len(row))]) == len(row) | |
| 76 | + | |
| 77 | +import random | |
| 78 | +inputPoints = [[random.randint(70,100) for i in range(3)] for j in range(500)] | |
| 79 | +#paretoPoints, dominatedPoints = simple_cull(inputPoints, dominates) | |
| 80 | + | |
| 81 | +#print() | |
| 82 | + | |
| 83 | +#print(paretoPoints) | |
| 84 | + | |
| 85 | +import matplotlib.pyplot as plt | |
| 86 | +from mpl_toolkits.mplot3d import Axes3D | |
| 87 | + | |
| 88 | +fig = plt.figure() | |
| 89 | +ax = fig.add_subplot(111, projection='3d') | |
| 90 | +dp = np.array(dominatedPoints) | |
| 91 | +pp = np.array(paretoPoints) | |
| 92 | +#print(dp) | |
| 93 | +ax.scatter(dp[:,0],dp[:,1],dp[:,2]) | |
| 94 | +ax.scatter(pp[:,0],pp[:,1],pp[:,2],color='red') | |
| 95 | + | |
| 96 | +import matplotlib.tri as mtri | |
| 97 | +triang = mtri.Triangulation(pp[:,0],pp[:,1]) | |
| 98 | +ax.plot_trisurf(triang,pp[:,2],color='red') | |
| 99 | + | |
| 100 | +ax.set_xlabel("Wood Yield",linespacing=5) | |
| 101 | +ax.set_ylabel("Soil Loss",linespacing=5) | |
| 102 | +ax.set_zlabel("Fire Risk Protection",linespacing=5) | |
| 103 | +ax.xaxis.labelpad=7 | |
| 104 | +ax.yaxis.labelpad=7 | |
| 105 | +ax.zaxis.labelpad=7 | |
| 106 | +#plt.xlabel("Wood Yield") | |
| 107 | +#plt.ylabel("Soil Loss") | |
| 108 | +#plt.clabel("Fire Risk Protection") | |
| 109 | + | |
| 110 | +plt.show() | |
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