import matplotlib.pyplot as plt
import pandas as pd
import numpy as npEvolveGCNH_Simulation_reshape
ITSTGCN
Simulation Study
Import
Fivenodes
df1 = pd.read_csv('./simulation_results/2023-07-01_09-36-19.csv')
df2 = pd.read_csv('./simulation_results/2023-07-01_09-54-35.csv')
df3 = pd.read_csv('./simulation_results/2023-07-01_10-13-37.csv')
df4 = pd.read_csv('./simulation_results/2023-07-01_10-32-11.csv')
df5 = pd.read_csv('./simulation_results/2023-07-01_10-51-03.csv')
df6 = pd.read_csv('./simulation_results/2023-07-01_11-09-55.csv')
df7 = pd.read_csv('./simulation_results/2023-07-01_11-27-48.csv')
df8 = pd.read_csv('./simulation_results/2023-07-01_11-46-45.csv')df9 = pd.read_csv('./simulation_results/2023-07-20_00-39-07.csv')
df10 = pd.read_csv('./simulation_results/2023-07-20_01-02-29.csv')
df11 = pd.read_csv('./simulation_results/2023-07-20_01-25-27.csv')
df12 = pd.read_csv('./simulation_results/2023-07-20_01-47-51.csv')
df13 = pd.read_csv('./simulation_results/2023-07-20_02-10-50.csv')
df14 = pd.read_csv('./simulation_results/2023-07-20_02-33-09.csv')data = pd.concat([df1,df2,df3,df4,df5,df6,df7,df8,df9,df10,df11,df12,df13,df14],axis=0)data.to_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_fivedones_Simulation.csv',index=False)data = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_fivedones_Simulation.csv')Baseline
data.query("method!='GNAR' and mrate==0").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_col='lags',facet_row='nof_filters',height=600)Random
data.query("method!='GNAR' and mtype =='rand'").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_col='inter_method',facet_row='nof_filters',height=600)
Block
data.query("method!='GNAR' and mtype =='block' ").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_col='inter_method',facet_row='nof_filters',height=600)chickenpox
df1 = pd.read_csv('./simulation_results/2023-07-01_12-47-57.csv') # GNAR random
df2 = pd.read_csv('./simulation_results/2023-07-01_13-48-35.csv') # GNAR block
df3 = pd.read_csv('./simulation_results/2023-07-01_14-17-12.csv')
df4 = pd.read_csv('./simulation_results/2023-07-01_14-46-07.csv')
df5 = pd.read_csv('./simulation_results/2023-07-01_15-43-30.csv')
df6 = pd.read_csv('./simulation_results/2023-07-01_16-43-22.csv') df7 = pd.read_csv('./simulation_results/2023-07-20_03-10-09.csv') # GNAR random
df8 = pd.read_csv('./simulation_results/2023-07-20_03-46-59.csv') # GNAR block
df9 = pd.read_csv('./simulation_results/2023-07-20_04-22-14.csv')
df10 = pd.read_csv('./simulation_results/2023-07-20_04-58-38.csv') data = pd.concat([df1,df2,df3,df4,df5,df6,df7,df8,df9,df10],axis=0)data.to_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_chikenpox_Simulation.csv',index=False)data = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_chikenpox_Simulation.csv')Baseline
data.query("method!='GNAR' and mrate ==0 ").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_col='nof_filters',height=600)Random
data.query("method!='GNAR' and mtype =='rand'").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_row='inter_method',height=800)Block
data.query("method!='GNAR' and mtype =='block'").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_col='inter_method',facet_row='lags',height=800)Pedalme
df1 = pd.read_csv('./simulation_results/2023-07-01_17-09-48.csv')
df2 = pd.read_csv('./simulation_results/2023-07-01_17-03-22.csv') df3 = pd.read_csv('./simulation_results/2023-07-20_05-13-15.csv') data = pd.concat([df1,df2,df3],axis=0)data.to_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_pedalme_Simulation.csv',index=False)data = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_pedalme_Simulation.csv')Baseline
data.query("mrate ==0 and lags!=2").plot.box(backend='plotly',x='epoch',color='method',y='mse',facet_col='nof_filters',facet_row='lags',height=400)random
data.query("method!='GNAR' and mtype =='rand' and lags!=2").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_row='inter_method',height=800)block
data.query("method!='GNAR' and mtype =='block' and lags!=2 ").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_col='nof_filters',facet_row='inter_method',height=800)weight matrix time, node 고려한 결과
df1 = pd.read_csv('./simulation_results/2023-07-02_07-01-12.csv')
df2 = pd.read_csv('./simulation_results/2023-07-02_07-19-21.csv')data2 = pd.concat([df1,df2],axis=0)data2.to_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_pedalme_Simulation_itstgcnsnd.csv',index=False)data2 = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_pedalme_Simulation_itstgcnsnd.csv')data2.query("mtype=='rand'").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_col='inter_method',facet_row='nof_filters',height=1000)data2.query("mtype=='block'").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_col='inter_method',facet_row='nof_filters',height=1000)Wikimath
df1 = pd.read_csv('./simulation_results/2023-07-02_03-31-06.csv')
df2 = pd.read_csv('./simulation_results/2023-07-02_06-13-56.csv')
df3 = pd.read_csv('./simulation_results/2023-07-02_09-31-51.csv')
df4 = pd.read_csv('./simulation_results/2023-07-02_14-14-23.csv')
df5 = pd.read_csv('./simulation_results/2023-07-02_18-46-53.csv')
df6 = pd.read_csv('./simulation_results/2023-07-02_21-31-18.csv')
df7 = pd.read_csv('./simulation_results/2023-07-03_00-09-30.csv')
df8 = pd.read_csv('./simulation_results/2023-07-03_02-53-00.csv')
df9 = pd.read_csv('./simulation_results/2023-07-03_05-33-07.csv') # block
df10 = pd.read_csv('./simulation_results/2023-07-03_05-33-07.csv') # block
df11 = pd.read_csv('./simulation_results/2023-07-03_10-59-17.csv') # blockdf12 = pd.read_csv('./simulation_results/2023-08-07_18-04-46.csv')
df13 = pd.read_csv('./simulation_results/2023-08-07_21-44-30.csv')
df14 = pd.read_csv('./simulation_results/2023-08-07_23-51-44.csv')
df15 = pd.read_csv('./simulation_results/2023-08-08_01-52-40.csv')
df16 = pd.read_csv('./simulation_results/2023-08-08_03-44-44.csv')
df17 = pd.read_csv('./simulation_results/2023-08-08_05-36-09.csv')
df18 = pd.read_csv('./simulation_results/2023-08-08_07-39-50.csv')
df19 = pd.read_csv('./simulation_results/2023-08-08_09-29-29.csv')
df20 = pd.read_csv('./simulation_results/2023-08-08_11-16-26.csv') # block
df21 = pd.read_csv('./simulation_results/2023-08-08_13-06-23.csv') # block
df22 = pd.read_csv('./simulation_results/2023-08-08_14-57-34.csv') # block
df23 = pd.read_csv('./simulation_results/2023-08-08_16-55-16.csv') # blockdata = pd.concat([df1,df2,df3,df4,df5,df6,df7,df8,df9,df10,df11,df12,df13,df14,df15,df16,df17,df18,df19,df20,df21,df22,df23],axis=0)data.to_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_wikimath.csv',index=False)data = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_wikimath.csv')Baseline
data.query("mrate==0 and method!='GNAR'").plot.box(backend='plotly',x='mrate',color='method',y='mse',height=600)random
data.query("mtype=='rand' and mrate !=0 and method!='GNAR'").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_col='lags',facet_row='nof_filters',height=800)block
data.query("mtype=='block' and method!='GNAR'").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_col='lags',facet_row='nof_filters',height=1200)missing values on the same nodes
df1 = pd.read_csv('./simulation_results/2023-07-03_13-39-23.csv') # STGCN IT-STGCN block
df2 = pd.read_csv('./simulation_results/2023-07-03_16-17-40.csv') # STGCN IT-STGCN
df3 = pd.read_csv('./simulation_results/2023-07-03_19-00-05.csv') data = pd.concat([df1,df2,df3],axis=0)data.to_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_wikimath_GSO_st.csv',index=False)data = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_wikimath_GSO_st.csv')data.query("method!='GNAR' and mtype =='block' ").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_col='inter_method',facet_row='lags',height=800)Windmilsmall
# random
df1 = pd.read_csv('./simulation_results/2023-09-29_04-06-19.csv') # STGCN IT-STGCN 70%
df2 = pd.read_csv('./simulation_results/2023-09-29_13-18-49.csv') # STGCN IT-STGCN
df3 = pd.read_csv('./simulation_results/2023-09-29_22-39-46.csv') # STGCN IT-STGCN
df4 = pd.read_csv('./simulation_results/2023-09-30_07-57-28.csv') # STGCN IT-STGCN
df5 = pd.read_csv('./simulation_results/2023-09-30_17-16-58.csv') # STGCN IT-STGCN
df6 = pd.read_csv('./simulation_results/2023-10-01_01-49-41.csv') # GNAR 30%, 50%, 70% # 뭔가 일단 필요없어서 데이터셋에서 뺌
df7 = pd.read_csv('./simulation_results/2023-10-01_10-55-03.csv') # STGCN IT-STGCN
df8 = pd.read_csv('./simulation_results/2023-10-01_20-13-11.csv') # STGCN IT-STGCN
df9 = pd.read_csv('./simulation_results/2023-10-02_05-24-27.csv') # STGCN IT-STGCN
df10 = pd.read_csv('./simulation_results/2023-10-02_14-35-29.csv') # STGCN IT-STGCN
df11 = pd.read_csv('./simulation_results/2023-10-02_23-46-10.csv') # STGCN IT-STGCN
df12 = pd.read_csv('./simulation_results/2023-10-03_08-48-03.csv') # STGCN IT-STGCN
df13 = pd.read_csv('./simulation_results/2023-10-03_16-09-34.csv') # STGCN IT-STGCN
df14 = pd.read_csv('./simulation_results/2023-10-04_00-42-21.csv') # STGCN IT-STGCN
df15 = pd.read_csv('./simulation_results/2023-10-04_09-39-49.csv') # STGCN IT-STGCN
df16 = pd.read_csv('./simulation_results/2023-10-04_18-40-35.csv') # STGCN IT-STGCN
df17 = pd.read_csv('./simulation_results/2023-10-05_03-47-37.csv') # STGCN IT-STGCN
df18 = pd.read_csv('./simulation_results/2023-10-05_12-34-55.csv') # STGCN IT-STGCN
df19 = pd.read_csv('./simulation_results/2023-10-05_21-39-26.csv') # STGCN IT-STGCN
df20 = pd.read_csv('./simulation_results/2023-10-06_06-41-45.csv') # STGCN IT-STGCN
df21 = pd.read_csv('./simulation_results/2023-10-06_15-32-09.csv') # STGCN IT-STGCN
df22 = pd.read_csv('./simulation_results/2023-10-07_00-30-57.csv') # STGCN IT-STGCN
df23 = pd.read_csv('./simulation_results/2023-10-07_09-28-12.csv') # STGCN IT-STGCN
df24 = pd.read_csv('./simulation_results/2023-10-07_18-34-57.csv') # STGCN IT-STGCN
df25 = pd.read_csv('./simulation_results/2023-10-08_03-34-18.csv') # STGCN IT-STGCN
df26 = pd.read_csv('./simulation_results/2023-10-08_12-42-49.csv') # STGCN IT-STGCN
df27 = pd.read_csv('./simulation_results/2023-10-08_21-41-05.csv') # STGCN IT-STGCN
df28 = pd.read_csv('./simulation_results/2023-10-09_06-35-11.csv') # STGCN IT-STGCN
df29 = pd.read_csv('./simulation_results/2023-10-09_15-29-16.csv') # STGCN IT-STGCN
df30 = pd.read_csv('./simulation_results/2023-10-10_00-26-35.csv') # STGCN IT-STGCN
# block
df31 = pd.read_csv('./simulation_results/2023-10-01_04-34-06.csv') # GNAR 70%
df32 = pd.read_csv('./simulation_results/2023-10-01_13-23-02.csv') # GNAR
df33 = pd.read_csv('./simulation_results/2023-10-01_22-09-40.csv')
df34 = pd.read_csv('./simulation_results/2023-10-02_07-00-56.csv')
df35 = pd.read_csv('./simulation_results/2023-10-02_15-50-54.csv')
df36 = pd.read_csv('./simulation_results/2023-10-03_00-50-23.csv')
df37 = pd.read_csv('./simulation_results/2023-10-03_09-42-40.csv')
df38 = pd.read_csv('./simulation_results/2023-10-03_16-36-42.csv')
df39 = pd.read_csv('./simulation_results/2023-10-04_00-50-14.csv')
df40 = pd.read_csv('./simulation_results/2023-10-04_09-37-38.csv')
df41 = pd.read_csv('./simulation_results/2023-10-04_18-24-25.csv')
df42 = pd.read_csv('./simulation_results/2023-10-05_03-09-53.csv')
df43 = pd.read_csv('./simulation_results/2023-10-05_11-43-27.csv')
df44 = pd.read_csv('./simulation_results/2023-10-05_20-20-36.csv')
df45 = pd.read_csv('./simulation_results/2023-10-06_04-47-48.csv')
df46 = pd.read_csv('./simulation_results/2023-10-06_13-29-37.csv')
df47 = pd.read_csv('./simulation_results/2023-10-06_22-01-52.csv')
df48 = pd.read_csv('./simulation_results/2023-10-07_06-36-34.csv')
df49 = pd.read_csv('./simulation_results/2023-10-07_15-05-09.csv')
df50 = pd.read_csv('./simulation_results/2023-10-07_23-36-17.csv')
df51 = pd.read_csv('./simulation_results/2023-10-08_08-04-28.csv')
df52 = pd.read_csv('./simulation_results/2023-10-08_16-26-25.csv')
df53 = pd.read_csv('./simulation_results/2023-10-09_01-08-27.csv')
df54 = pd.read_csv('./simulation_results/2023-10-09_09-43-42.csv')
df55 = pd.read_csv('./simulation_results/2023-10-09_18-16-00.csv')
df56 = pd.read_csv('./simulation_results/2023-10-10_02-20-10.csv')
df57 = pd.read_csv('./simulation_results/2023-10-10_09-02-03.csv')
df58 = pd.read_csv('./simulation_results/2023-10-10_15-41-16.csv')
df59 = pd.read_csv('./simulation_results/2023-10-10_22-21-23.csv')
df60 = pd.read_csv('./simulation_results/2023-10-11_05-01-42.csv')
# baseline
df61 = pd.read_csv('./simulation_results/2023-10-04_02-44-29.csv')
df62 = pd.read_csv('./simulation_results/2023-10-04_11-25-44.csv')
df63 = pd.read_csv('./simulation_results/2023-10-04_20-02-18.csv')
df64 = pd.read_csv('./simulation_results/2023-10-05_04-41-21.csv')
df65 = pd.read_csv('./simulation_results/2023-10-05_13-16-12.csv')
df66 = pd.read_csv('./simulation_results/2023-10-05_21-48-14.csv')
df67 = pd.read_csv('./simulation_results/2023-10-06_06-17-46.csv')
df68 = pd.read_csv('./simulation_results/2023-10-06_14-52-21.csv') # 확인 후 block 만 넣기!
df69 = pd.read_csv('./simulation_results/2023-10-06_23-18-03.csv')
df70 = pd.read_csv('./simulation_results/2023-10-07_07-54-40.csv')
df71 = pd.read_csv('./simulation_results/2023-10-07_16-17-49.csv')
df72 = pd.read_csv('./simulation_results/2023-10-08_00-45-12.csv')
df73 = pd.read_csv('./simulation_results/2023-10-08_09-15-34.csv')
df74 = pd.read_csv('./simulation_results/2023-10-08_17-42-43.csv')
df75 = pd.read_csv('./simulation_results/2023-10-09_02-27-37.csv')
df76 = pd.read_csv('./simulation_results/2023-10-09_11-02-16.csv')
df77 = pd.read_csv('./simulation_results/2023-10-09_19-35-02.csv')
df78 = pd.read_csv('./simulation_results/2023-10-10_03-16-57.csv')
df79 = pd.read_csv('./simulation_results/2023-10-10_09-59-59.csv')
df80 = pd.read_csv('./simulation_results/2023-10-10_16-39-27.csv')
df81 = pd.read_csv('./simulation_results/2023-10-10_23-19-13.csv')
df82 = pd.read_csv('./simulation_results/2023-10-11_05-43-18.csv')
df83 = pd.read_csv('./simulation_results/2023-10-11_09-56-28.csv')
df84 = pd.read_csv('./simulation_results/2023-10-11_14-04-57.csv')
df85 = pd.read_csv('./simulation_results/2023-10-11_18-15-26.csv')
df86 = pd.read_csv('./simulation_results/2023-10-11_22-24-47.csv')
df87 = pd.read_csv('./simulation_results/2023-10-12_02-33-17.csv')
df88 = pd.read_csv('./simulation_results/2023-10-12_06-42-05.csv')
df89 = pd.read_csv('./simulation_results/2023-10-12_10-52-51.csv')
df90 = pd.read_csv('./simulation_results/2023-10-12_15-01-35.csv')data = pd.concat([df1,df2,df3,df4,df5,df6,df7,df8,df9,df10,df11,df12,df13,df14,df15,df16,df17,df18,df19,df20,df21,df22,df23,df24,df25,df26,df27,df28,df29,df30,
df31,df32,df33,df34,df35,df36,df37,df38,df39,df40,df41,df42,df43,df44,df45,df46,df47,df48,df49,df50,df51,df52,df53,df54,df55,df56,df57,df58,df59,df60,
df61,df62,df63,df64,df65,df66,df67,df68,df69,df70,df71,df72,df73,df74,df75,df76,df77,df78,df79,df80,df81,df82,df83,df84,df85,df86,df87,df88,df89,df90
],axis=0)data.to_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_windmillsmall.csv',index=False)data = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_windmillsmall.csv')Baseline
data.query("method!='GNAR' and mrate ==0 ").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_col='lags',height=800)random
data.query("method!='GNAR' and mtype =='rand' ").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_col='lags',facet_row='inter_method',height=800)block
data.query("method!='GNAR' and mtype =='block' ").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_col='lags',facet_row='inter_method',height=800)Montevideobus
df1 = pd.read_csv('./simulation_results/2023-07-01_18-46-28.csv') # lags 8
df2 = pd.read_csv('./simulation_results/2023-07-01_20-21-33.csv') # lags 8
df3 = pd.read_csv('./simulation_results/2023-07-01_21-54-40.csv') # lags 8
df4 = pd.read_csv('./simulation_results/2023-07-01_23-23-55.csv') # lags 8
df5 = pd.read_csv('./simulation_results/2023-07-02_02-26-37.csv') # lags 8
df6 = pd.read_csv('./simulation_results/2023-07-02_00-53-47.csv') # lags 8
df7 = pd.read_csv('./simulation_results/2023-07-02_09-46-49.csv') # lags 8
df8 = pd.read_csv('./simulation_results/2023-07-02_11-18-51.csv') # lags 8
df9 = pd.read_csv('./simulation_results/2023-07-02_12-46-16.csv') # lags 8
df10 = pd.read_csv('./simulation_results/2023-07-02_14-20-34.csv') # lags 8
df11 = pd.read_csv('./simulation_results/2023-07-02_15-52-23.csv') # lags 8
df12 = pd.read_csv('./simulation_results/2023-07-02_17-21-46.csv') # lags 8df13 = pd.read_csv('./simulation_results/2023-07-27_08-19-01.csv') # lags 8
df14 = pd.read_csv('./simulation_results/2023-07-27_09-39-49.csv') # lags 8
df15 = pd.read_csv('./simulation_results/2023-07-27_10-45-40.csv') # lags 8
df16 = pd.read_csv('./simulation_results/2023-07-27_11-50-23.csv') # lags 8
df17 = pd.read_csv('./simulation_results/2023-07-27_12-59-27.csv') # lags 8
df18 = pd.read_csv('./simulation_results/2023-07-27_14-09-43.csv') # lags 8
df19 = pd.read_csv('./simulation_results/2023-07-27_15-18-30.csv') # lags 8
df20 = pd.read_csv('./simulation_results/2023-07-27_16-21-30.csv') # lags 8
df21 = pd.read_csv('./simulation_results/2023-07-27_17-30-16.csv') # lags 8data = pd.concat([df1,df2,df3,df4,df5,df6,df7,df8,df9,df10,df11,df12,df13,df14,df15,df16,df17,df18,df19,df20,df21],axis=0)data.to_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_monte.csv',index=False)data = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_monte.csv')Baseline
data.query("mrate==0 and method!='GNAR'").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_col='lags',facet_row='nof_filters',height=600)random
data.query("mtype=='rand' and mrate !=0 and method!='GNAR'").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_col='lags',facet_row='inter_method',height=800)block
data.query("mtype=='block' and method!='GNAR'").plot.box(backend='plotly',x='mrate',color='method',y='mse',facet_col='lags',facet_row='nof_filters',height=1200)