-
Notifications
You must be signed in to change notification settings - Fork 1
Expand file tree
/
Copy pathrefset_parallel.py
More file actions
201 lines (165 loc) · 12.4 KB
/
refset_parallel.py
File metadata and controls
201 lines (165 loc) · 12.4 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
import seaborn as sns
from matplotlib import ticker
from matplotlib import cm
import matplotlib.colors as colors
objs_dvs = pd.read_csv('NC_dvs_objs.csv', sep=',', index_col=False)
cols = ['REL_C', 'RF_C', 'INF_NPC_C', 'PFC_C', 'WCC_C', \
'REL_D', 'RF_D', 'INF_NPC_D', 'PFC_D', 'WCC_D', \
'REL_R', 'RF_R', 'INF_NPC_R', 'PFC_R', 'WCC_R', \
'REL_rg', 'RF_rg', 'INF_NPC_rg', 'PFC_rg', 'WCC_rg', \
'RT_C', 'RT_D', 'RT_R', 'TT_D', 'TT_R', 'JLA_C', \
'JLA_D', 'JLA_R', 'RC_C', 'RC_D', 'RC_R', 'IT_C', \
'IT_D', 'IT_R', 'IP_C', 'IP_D', 'IP_R', 'INF_C', 'INF_D', 'INF_R']
objs_dvs[['REL_C','REL_D','REL_R', 'REL_rg']] = objs_dvs[['REL_C','REL_D','REL_R', 'REL_rg']]*100
objs_dvs[['PFC_C','PFC_D','PFC_R', 'PFC_rg']] = objs_dvs[['PFC_C','PFC_D','PFC_R', 'PFC_rg']]*100
objs_dvs[['WCC_C','WCC_D','WCC_R', 'WCC_rg']] = objs_dvs[['WCC_C','WCC_D','WCC_R', 'WCC_rg']]*100
# objectives
objs = objs_dvs[['REL_C','RF_C','INF_NPC_C', 'PFC_C', 'WCC_C', \
'REL_D','RF_D','INF_NPC_D', 'PFC_D', 'WCC_D', \
'REL_R','RF_R','INF_NPC_R', 'PFC_R', 'WCC_R', \
'REL_rg','RF_rg','INF_NPC_rg', 'PFC_rg', 'WCC_rg']]
normalized_objs = objs
# reliability
normalized_objs.iloc[:,0] = 1-(normalized_objs.iloc[:,0] - min(normalized_objs.iloc[:,0]))/(max(normalized_objs.iloc[:,0])-min(normalized_objs.iloc[:,0]))
normalized_objs.iloc[:,5] = 1-(normalized_objs.iloc[:,5] - min(normalized_objs.iloc[:,5]))/(max(normalized_objs.iloc[:,5])-min(normalized_objs.iloc[:,5]))
normalized_objs.iloc[:,10] = 1-(normalized_objs.iloc[:,10] - min(normalized_objs.iloc[:,10]))/(max(normalized_objs.iloc[:,10])-min(normalized_objs.iloc[:,10]))
normalized_objs.iloc[:,15] = 1-(normalized_objs.iloc[:,15] - min(normalized_objs.iloc[:,15]))/(max(normalized_objs.iloc[:,15])-min(normalized_objs.iloc[:,15]))
# restriction frequency
normalized_objs.iloc[:,1] = (normalized_objs.iloc[:,1] - min(normalized_objs.iloc[:,1]))/(max(normalized_objs.iloc[:,1])-min(normalized_objs.iloc[:,1]))
normalized_objs.iloc[:,6] = (normalized_objs.iloc[:,6] - min(normalized_objs.iloc[:,6]))/(max(normalized_objs.iloc[:,6])-min(normalized_objs.iloc[:,6]))
normalized_objs.iloc[:,11] = (normalized_objs.iloc[:,11] - min(normalized_objs.iloc[:,11]))/(max(normalized_objs.iloc[:,11])-min(normalized_objs.iloc[:,11]))
normalized_objs.iloc[:,16] = (normalized_objs.iloc[:,16] - min(normalized_objs.iloc[:,16]))/(max(normalized_objs.iloc[:,16])-min(normalized_objs.iloc[:,16]))
# infrastructure NPC
normalized_objs.iloc[:,2] = (normalized_objs.iloc[:,2] - min(normalized_objs.iloc[:,2]))/(max(normalized_objs.iloc[:,2])-min(normalized_objs.iloc[:,2]))
normalized_objs.iloc[:,7] = (normalized_objs.iloc[:,7] - min(normalized_objs.iloc[:,7]))/(max(normalized_objs.iloc[:,7])-min(normalized_objs.iloc[:,7]))
normalized_objs.iloc[:,12] = (normalized_objs.iloc[:,12] - min(normalized_objs.iloc[:,12]))/(max(normalized_objs.iloc[:,12])-min(normalized_objs.iloc[:,12]))
normalized_objs.iloc[:,17] = (normalized_objs.iloc[:,17] - min(normalized_objs.iloc[:,17]))/(max(normalized_objs.iloc[:,17])-min(normalized_objs.iloc[:,17]))
# peak financial cost
normalized_objs.iloc[:,3] = (normalized_objs.iloc[:,3] - min(normalized_objs.iloc[:,3]))/(max(normalized_objs.iloc[:,3])-min(normalized_objs.iloc[:,3]))
normalized_objs.iloc[:,8] = (normalized_objs.iloc[:,8] - min(normalized_objs.iloc[:,8]))/(max(normalized_objs.iloc[:,8])-min(normalized_objs.iloc[:,8]))
normalized_objs.iloc[:,13] = (normalized_objs.iloc[:,13] - min(normalized_objs.iloc[:,13]))/(max(normalized_objs.iloc[:,13])-min(normalized_objs.iloc[:,13]))
normalized_objs.iloc[:,18] = (normalized_objs.iloc[:,18] - min(normalized_objs.iloc[:,18]))/(max(normalized_objs.iloc[:,18])-min(normalized_objs.iloc[:,18]))
# worst-case cost
normalized_objs.iloc[:,4] = (normalized_objs.iloc[:,4] - min(normalized_objs.iloc[:,4]))/(max(normalized_objs.iloc[:,4])-min(normalized_objs.iloc[:,4]))
normalized_objs.iloc[:,9] = (normalized_objs.iloc[:,9] - min(normalized_objs.iloc[:,9]))/(max(normalized_objs.iloc[:,9])-min(normalized_objs.iloc[:,9]))
normalized_objs.iloc[:,14] = (normalized_objs.iloc[:,14] - min(normalized_objs.iloc[:,14]))/(max(normalized_objs.iloc[:,14])-min(normalized_objs.iloc[:,14]))
normalized_objs.iloc[:,19] = (normalized_objs.iloc[:,19] - min(normalized_objs.iloc[:,19]))/(max(normalized_objs.iloc[:,19])-min(normalized_objs.iloc[:,19]))
objs_names = ['REL', 'RF', 'INF_NPC', 'PFC', 'WCC']
objs_C = normalized_objs[['REL_C','RF_C','INF_NPC_C', 'PFC_C', 'WCC_C']]
objs_D = normalized_objs[['REL_D','RF_D','INF_NPC_D', 'PFC_D', 'WCC_D']]
objs_R = normalized_objs[['REL_R','RF_R','INF_NPC_R', 'PFC_R', 'WCC_R']]
objs_rg = normalized_objs[['REL_rg','RF_rg','INF_NPC_rg', 'PFC_rg', 'WCC_rg']]
objs_REL = normalized_objs[['REL_C','REL_D','REL_R', 'REL_rg']]
objs_RF = normalized_objs[['RF_C','RF_D','RF_R', 'RF_rg']]
objs_INF = normalized_objs[['INF_NPC_C','INF_NPC_D','INF_NPC_R', 'INF_NPC_rg']]
objs_PFC = normalized_objs[['PFC_C','PFC_D','PFC_R', 'PFC_rg']]
objs_WCC = normalized_objs[['WCC_C','WCC_D','WCC_R', 'WCC_rg']]
# decision variables
dvs_all = objs_dvs[['RT_C', 'RT_D', 'RT_R', 'TT_D', 'TT_R', 'JLA_C', \
'JLA_D', 'JLA_R', 'RC_C', 'RC_D', 'RC_R', 'IT_C', \
'IT_D', 'IT_R', 'IP_C', 'IP_D', 'IP_R', 'INF_C', 'INF_D', 'INF_R', \
'INF_NPC_C', 'INF_NPC_D', 'INF_NPC_R']]
normalized_dvs = dvs_all
# restriction trigger
normalized_dvs.iloc[:,0] = (normalized_dvs.iloc[:,0] - min(normalized_dvs.iloc[:,0]))/(max(normalized_dvs.iloc[:,0])-min(normalized_dvs.iloc[:,0]))
normalized_dvs.iloc[:,1] = (normalized_dvs.iloc[:,1] - min(normalized_dvs.iloc[:,1]))/(max(normalized_dvs.iloc[:,1])-min(normalized_dvs.iloc[:,1]))
normalized_dvs.iloc[:,2] = (normalized_dvs.iloc[:,2] - min(normalized_dvs.iloc[:,2]))/(max(normalized_dvs.iloc[:,2])-min(normalized_dvs.iloc[:,2]))
# transfer trigger
normalized_dvs.iloc[:,3] = (normalized_dvs.iloc[:,3] - min(normalized_dvs.iloc[:,3]))/(max(normalized_dvs.iloc[:,3])-min(normalized_dvs.iloc[:,3]))
normalized_dvs.iloc[:,4] = (normalized_dvs.iloc[:,4] - min(normalized_dvs.iloc[:,4]))/(max(normalized_dvs.iloc[:,4])-min(normalized_dvs.iloc[:,4]))
# Lake Michael allocation
normalized_dvs.iloc[:,5] = (normalized_dvs.iloc[:,5] - min(normalized_dvs.iloc[:,5]))/(max(normalized_dvs.iloc[:,5])-min(normalized_dvs.iloc[:,5]))
normalized_dvs.iloc[:,6] = (normalized_dvs.iloc[:,6] - min(normalized_dvs.iloc[:,6]))/(max(normalized_dvs.iloc[:,6])-min(normalized_dvs.iloc[:,6]))
normalized_dvs.iloc[:,7] = (normalized_dvs.iloc[:,7] - min(normalized_dvs.iloc[:,7]))/(max(normalized_dvs.iloc[:,7])-min(normalized_dvs.iloc[:,7]))
# reserve fund contribution
normalized_dvs.iloc[:,8] = (normalized_dvs.iloc[:,8] - min(normalized_dvs.iloc[:,8]))/(max(normalized_dvs.iloc[:,8])-min(normalized_dvs.iloc[:,8]))
normalized_dvs.iloc[:,9] = (normalized_dvs.iloc[:,9] - min(normalized_dvs.iloc[:,9]))/(max(normalized_dvs.iloc[:,9])-min(normalized_dvs.iloc[:,9]))
normalized_dvs.iloc[:,10] = (normalized_dvs.iloc[:,10] - min(normalized_dvs.iloc[:,10]))/(max(normalized_dvs.iloc[:,10])-min(normalized_dvs.iloc[:,10]))
# insurance trigger
normalized_dvs.iloc[:,11] = (normalized_dvs.iloc[:,11] - min(normalized_dvs.iloc[:,11]))/(max(normalized_dvs.iloc[:,11])-min(normalized_dvs.iloc[:,11]))
normalized_dvs.iloc[:,12] = (normalized_dvs.iloc[:,12] - min(normalized_dvs.iloc[:,12]))/(max(normalized_dvs.iloc[:,12])-min(normalized_dvs.iloc[:,12]))
normalized_dvs.iloc[:,13] = (normalized_dvs.iloc[:,13] - min(normalized_dvs.iloc[:,13]))/(max(normalized_dvs.iloc[:,13])-min(normalized_dvs.iloc[:,13]))
# insurance payment
normalized_dvs.iloc[:,14] = (normalized_dvs.iloc[:,14] - min(normalized_dvs.iloc[:,14]))/(max(normalized_dvs.iloc[:,14])-min(normalized_dvs.iloc[:,14]))
normalized_dvs.iloc[:,15] = (normalized_dvs.iloc[:,15] - min(normalized_dvs.iloc[:,15]))/(max(normalized_dvs.iloc[:,15])-min(normalized_dvs.iloc[:,15]))
normalized_dvs.iloc[:,16] = (normalized_dvs.iloc[:,16] - min(normalized_dvs.iloc[:,16]))/(max(normalized_dvs.iloc[:,16])-min(normalized_dvs.iloc[:,16]))
# infrastructure trigger
normalized_dvs.iloc[:,17] = (normalized_dvs.iloc[:,17] - min(normalized_dvs.iloc[:,17]))/(max(normalized_dvs.iloc[:,17])-min(normalized_dvs.iloc[:,17]))
normalized_dvs.iloc[:,18] = (normalized_dvs.iloc[:,18] - min(normalized_dvs.iloc[:,18]))/(max(normalized_dvs.iloc[:,18])-min(normalized_dvs.iloc[:,18]))
normalized_dvs.iloc[:,19] = (normalized_dvs.iloc[:,19] - min(normalized_dvs.iloc[:,19]))/(max(normalized_dvs.iloc[:,19])-min(normalized_dvs.iloc[:,19]))
dvC_names = ['RT', 'JLA', 'RC', 'IT', 'IP', 'INF']
dv_names = ['RT', 'TT', 'JLA', 'RC', 'IT', 'IP', 'INF']
n_dvs_C = normalized_dvs[['RT_C', 'JLA_C', 'RC_C', 'IT_C', 'IP_C', 'INF_C', 'INF_NPC_C']]
n_dvs_D = normalized_dvs[['RT_D', 'TT_D', 'JLA_D', 'RC_D', 'IT_D', 'IP_D', 'INF_D', 'INF_NPC_D']]
n_dvs_R = normalized_dvs[['RT_R', 'TT_R', 'JLA_R', 'RC_R', 'IT_R', 'IP_R', 'INF_R','INF_NPC_R']]
dvs_C = objs_dvs[['RT_C', 'JLA_C', 'RC_C', 'IT_C', 'IP_C', 'INF_C', 'INF_NPC_C']]
dvs_D = objs_dvs[['RT_D', 'TT_D', 'JLA_D', 'RC_D', 'IT_D', 'IP_D', 'INF_D', 'INF_NPC_D']]
dvs_R = objs_dvs[['RT_R', 'TT_R', 'JLA_R', 'RC_R', 'IT_R', 'IP_R', 'INF_R','INF_NPC_R']]
dvs_C=dvs_C.sort_values(by=['INF_NPC_C'], ascending=False)
dvs_D=dvs_D.sort_values(by=['INF_NPC_D'], ascending=False)
dvs_R=dvs_R.sort_values(by=['INF_NPC_R'], ascending=False)
print(dvs_R)
dvs_rgT = normalized_dvs[['RT_C', 'RT_D', 'RT_R']]
dvs_TT = normalized_dvs[['TT_D', 'TT_R']]
dvs_JLA = normalized_dvs[['JLA_C', 'JLA_D', 'JLA_R']]
dvs_rgC = normalized_dvs[['RC_C', 'RC_D', 'RC_R']]
dvs_IT = normalized_dvs[['IT_C', 'IT_D', 'IT_R']]
dvs_IP = normalized_dvs[['IP_C', 'IP_D', 'IP_R']]
dvs_IP = normalized_dvs[['INF_C', 'INF_D', 'INF_R']]
## Parallel axis plots
# sharey=False indicates that all the subplot y-axes will be set to different values
x_objs = [i for i, _ in enumerate(objs_names)]
x_dvs = [i for i, _ in enumerate(dvC_names)]
fig1, ax1 = plt.subplots(1,len(x_dvs)-1, sharey=False, figsize=(20,4))
# enumerate through all the axes in the figure and plot the data
# only the first line of each
# blue for the nondominated solutions
# grey or everthing else
cmap = plt.get_cmap('pink_r')
cNorm = colors.Normalize(vmin=min(objs_INF['INF_NPC_C']), vmax=max(objs_INF['INF_NPC_C']))
scalarMap = cm.ScalarMappable(norm=cNorm, cmap=cmap)
for i, ax_i in enumerate(ax1):
for d in range(len(n_dvs_C)):
ax_i.plot(dvC_names, dvs_C.iloc[d, :6], color=scalarMap.to_rgba(objs_INF.iloc[d,0]), alpha=0.5, linewidth=3)
ax_i.set_xlim([x_dvs[i], x_dvs[i+1]])
# function for setting ticks and tick_lables along the y-axis of each subplot
def set_ticks_for_axis(dim, ax_i, arr, ticks, rev):
min_val = min(arr)
max_val = max(arr)
v_rgange = abs(max_val - min_val)
step = v_rgange/float(ticks)
tick_labels = [round(min_val + step*i, 2) for i in range(ticks)]
if (rev == True):
tick_labels.sort(reverse=True)
print(tick_labels)
norm_min = 0
norm_range = 1.0
norm_step =(norm_range/float(ticks-1))
ticks = [round(norm_min + norm_step*i, 2) for i in range(ticks)]
ax_i.set_ylim([0,1])
ax_i.yaxis.set_ticks(ticks)
ax_i.set_yticklabels(tick_labels)
# enumerating over each axis in fig2
for i in range(len(ax1)):
ax1[i].xaxis.set_major_locator(ticker.FixedLocator([i])) # set tick locations along the x-axis
if i == 1:
set_ticks_for_axis(i, ax1[i], dvs_C.iloc[:,1], ticks=5, rev=True)
else:
set_ticks_for_axis(i, ax1[i], dvs_C.iloc[:,i], ticks=5, rev=False) # set ticks along the y-axis
# create a twin axis on the last subplot of fig2
# this will enable you to label the last axis with y-ticks
ax2 = plt.twinx(ax1[-1])
dim = len(ax1)
ax2.xaxis.set_major_locator(ticker.FixedLocator([x_dvs[-2], x_dvs[-1]]))
set_ticks_for_axis(dim, ax2, dvs_C.iloc[:,i], ticks=5, rev=False)
ax2.set_xticklabels([dv_names[-2], dv_names[-1]])
plt.subplots_adjust(wspace=0, hspace=0.5, left=0.1, right=0.85, bottom=0.1, top=0.9)
cbar=plt.colorbar(cm.ScalarMappable(norm=cNorm, cmap=cmap), ax=ax1)
cbar.set_label(r'$\leftarrow$ Infrastructure NPC')
ax1[0].set_ylabel("$\leftarrow$ Direction of preference ", fontsize=12)
plt.suptitle("Decision variables across Cary", fontsize=12, y=1.0)
plt.show()