import matplotlib.pyplot as plt
import pandas as pd
import numpy as npEvolveGCNO_Simulation_reshape
ITSTGCN
Simulation Study
Import
Fivenodes
df1 = pd.read_csv('./simulation_results/2023-06-26_00-55-36.csv')
df2 = pd.read_csv('./simulation_results/2023-06-26_01-09-23.csv')
df3 = pd.read_csv('./simulation_results/2023-06-26_01-23-14.csv')
df4 = pd.read_csv('./simulation_results/2023-06-26_01-36-42.csv')
df5 = pd.read_csv('./simulation_results/2023-06-26_01-49-58.csv')
df6 = pd.read_csv('./simulation_results/2023-06-26_02-03-37.csv')
df7 = pd.read_csv('./simulation_results/2023-06-26_02-17-20.csv')
df8 = pd.read_csv('./simulation_results/2023-06-26_02-31-49.csv')df9 = pd.read_csv('./simulation_results/2023-07-19_06-44-18.csv')
df10 = pd.read_csv('./simulation_results/2023-07-19_06-58-20.csv')
df11 = pd.read_csv('./simulation_results/2023-07-19_07-12-30.csv')
df12 = pd.read_csv('./simulation_results/2023-07-19_07-28-02.csv')
df13 = pd.read_csv('./simulation_results/2023-07-19_07-43-42.csv')
df14 = pd.read_csv('./simulation_results/2023-07-19_07-58-52.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/EvolveGCNO_fivedones_Simulation.csv',index=False)data = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNO_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/2023-06-26_03-09-21.csv') # GNAR random
df2 = pd.read_csv('./simulation_results/2023-06-26_03-47-41.csv') # GNAR block
df3 = pd.read_csv('./simulation_results/2023-06-26_04-10-16.csv') # STGCN, ITSTGCN random 30%
df4 = pd.read_csv('./simulation_results/2023-06-26_04-33-08.csv')
df5 = pd.read_csv('./simulation_results/2023-06-26_05-18-31.csv')
df6 = pd.read_csv('./simulation_results/2023-06-26_06-01-38.csv') df7 = pd.read_csv('./simulation_results/2023-07-19_08-23-06.csv') # GNAR random
df8 = pd.read_csv('./simulation_results/2023-07-19_08-48-36.csv') # GNAR block
df9 = pd.read_csv('./simulation_results/2023-07-19_09-34-53.csv') # STGCN, ITSTGCN random 30%
df10 = pd.read_csv('./simulation_results/2023-07-19_09-11-10.csv') data = pd.concat([df1,df2,df3,df4,df5,df6,df7,df8,df9,df10],axis=0)data.to_csv('./simulation_results/Real_simulation_reshape/EvolveGCNO_chikenpox_Simulation.csv',index=False)data = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNO_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_col='nof_filters',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/2023-06-26_06-16-39.csv')
df2 = pd.read_csv('./simulation_results/2023-06-26_06-22-00.csv') df3 = pd.read_csv('./simulation_results/2023-07-19_09-45-34.csv')data = pd.concat([df1,df2,df3],axis=0)data.to_csv('./simulation_results/Real_simulation_reshape/EvolveGCNO_pedalme_Simulation.csv',index=False)data = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNO_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_col='nof_filters',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/2023-06-27_00-26-15.csv')
df2 = pd.read_csv('./simulation_results/2023-06-27_00-42-14.csv')data2 = pd.concat([df1,df2],axis=0)data2.to_csv('./simulation_results/Real_simulation_reshape/EvolveGCNO_pedalme_Simulation_itstgcnsnd.csv',index=False)data2 = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNO_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/2023-06-28_08-28-01.csv')
df2 = pd.read_csv('./simulation_results/2023-06-28_10-55-29.csv')
df3 = pd.read_csv('./simulation_results/2023-06-28_13-20-19.csv')
df4 = pd.read_csv('./simulation_results/2023-06-28_16-32-40.csv')
df5 = pd.read_csv('./simulation_results/2023-06-28_19-10-50.csv')
df6 = pd.read_csv('./simulation_results/2023-06-28_21-48-52.csv')
df7 = pd.read_csv('./simulation_results/2023-06-29_00-26-13.csv')
df8 = pd.read_csv('./simulation_results/2023-06-29_03-08-16.csv')
df9 = pd.read_csv('./simulation_results/2023-06-29_05-55-03.csv') # block
df10 = pd.read_csv('./simulation_results/2023-06-29_08-41-22.csv') # block
df11 = pd.read_csv('./simulation_results/2023-06-29_11-05-52.csv') # blockdf12 = pd.read_csv('./simulation_results/2023-08-05_00-22-49.csv')
df13 = pd.read_csv('./simulation_results/2023-08-05_02-06-06.csv')
df14 = pd.read_csv('./simulation_results/2023-08-05_03-32-39.csv')
df15 = pd.read_csv('./simulation_results/2023-08-05_05-04-11.csv')
df16 = pd.read_csv('./simulation_results/2023-08-05_06-40-47.csv')
df17 = pd.read_csv('./simulation_results/2023-08-05_08-19-46.csv')
df18 = pd.read_csv('./simulation_results/2023-08-05_10-01-56.csv')
df19 = pd.read_csv('./simulation_results/2023-08-05_13-12-32.csv')
df20 = pd.read_csv('./simulation_results/2023-08-05_16-04-31.csv') # block
df21 = pd.read_csv('./simulation_results/2023-08-05_18-53-42.csv') # block
df22 = pd.read_csv('./simulation_results/2023-08-05_21-32-52.csv') # block
df23 = pd.read_csv('./simulation_results/2023-08-05_23-04-58.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/EvolveGCNO_wikimath.csv',index=False)data = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNO_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/2023-06-29_18-21-43.csv') # STGCN IT-STGCN block
df2 = pd.read_csv('./simulation_results/2023-06-29_13-25-16.csv') # STGCN IT-STGCN
df3 = pd.read_csv('./simulation_results/2023-06-29_15-50-57.csv') data = pd.concat([df1,df2,df3],axis=0)data.to_csv('./simulation_results/Real_simulation_reshape/EvolveGCNO_wikimath_GSO_st.csv',index=False)data = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNO_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
df1 = pd.read_csv('./simulation_results/2023-09-03_15-33-53.csv') # STGCN IT-STGCN 70%
df2 = pd.read_csv('./simulation_results/2023-09-03_23-30-43.csv') # STGCN IT-STGCN
df3 = pd.read_csv('./simulation_results/2023-09-04_07-24-56.csv') # STGCN IT-STGCN
df4 = pd.read_csv('./simulation_results/2023-09-04_15-39-20.csv') # STGCN IT-STGCN
df5 = pd.read_csv('./simulation_results/2023-09-04_23-36-51.csv') # STGCN IT-STGCN
df6 = pd.read_csv('./simulation_results/2023-09-05_07-31-59.csv') # GNAR 30%, 50%, 70% # 뭔가 일단 필요없어서 데이터셋에서 뺌
df7 = pd.read_csv('./simulation_results/2023-09-05_14-29-50.csv') # STGCN IT-STGCN
df8 = pd.read_csv('./simulation_results/2023-09-05_20-43-00.csv') # STGCN IT-STGCN
df9 = pd.read_csv('./simulation_results/2023-09-06_02-56-11.csv') # STGCN IT-STGCN
df10 = pd.read_csv('./simulation_results/2023-09-06_09-56-21.csv') # STGCN IT-STGCN
df11 = pd.read_csv('./simulation_results/2023-09-06_18-13-36.csv') # STGCN IT-STGCN
df12 = pd.read_csv('./simulation_results/2023-09-07_02-31-44.csv') # STGCN IT-STGCN
df13 = pd.read_csv('./simulation_results/2023-09-07_10-52-05.csv') # STGCN IT-STGCN
df14 = pd.read_csv('./simulation_results/2023-09-07_19-05-50.csv') # STGCN IT-STGCN
df15 = pd.read_csv('./simulation_results/2023-09-08_03-32-59.csv') # STGCN IT-STGCN
df16 = pd.read_csv('./simulation_results/2023-09-08_11-43-54.csv') # STGCN IT-STGCN
df17 = pd.read_csv('./simulation_results/2023-09-08_20-02-50.csv') # STGCN IT-STGCN
df18 = pd.read_csv('./simulation_results/2023-09-09_03-11-49.csv') # STGCN IT-STGCN
df19 = pd.read_csv('./simulation_results/2023-09-09_10-07-13.csv') # STGCN IT-STGCN
df20 = pd.read_csv('./simulation_results/2023-09-09_18-30-46.csv') # STGCN IT-STGCN
df21 = pd.read_csv('./simulation_results/2023-09-10_02-55-37.csv') # STGCN IT-STGCN
df22 = pd.read_csv('./simulation_results/2023-09-10_11-15-23.csv') # STGCN IT-STGCN
df23 = pd.read_csv('./simulation_results/2023-09-10_19-29-52.csv') # STGCN IT-STGCN
df24 = pd.read_csv('./simulation_results/2023-09-11_03-57-15.csv') # STGCN IT-STGCN
df25 = pd.read_csv('./simulation_results/2023-09-11_12-31-37.csv') # STGCN IT-STGCN
df26 = pd.read_csv('./simulation_results/2023-09-11_20-47-33.csv') # STGCN IT-STGCN
df27 = pd.read_csv('./simulation_results/2023-09-12_05-13-58.csv') # STGCN IT-STGCN
df28 = pd.read_csv('./simulation_results/2023-09-12_13-40-21.csv') # STGCN IT-STGCN
df29 = pd.read_csv('./simulation_results/2023-09-12_22-03-52.csv') # STGCN IT-STGCN
df30 = pd.read_csv('./simulation_results/2023-09-13_06-30-48.csv') # STGCN IT-STGCN
# block
df31 = pd.read_csv('./simulation_results/2023-09-06_15-10-18.csv') # GNAR 70%
df32 = pd.read_csv('./simulation_results/2023-09-06_23-26-16.csv') # GNAR
df33 = pd.read_csv('./simulation_results/2023-09-07_07-46-04.csv')
df34 = pd.read_csv('./simulation_results/2023-09-07_15-51-53.csv')
df35 = pd.read_csv('./simulation_results/2023-09-08_00-21-28.csv')
df36 = pd.read_csv('./simulation_results/2023-09-08_08-36-46.csv')
df37 = pd.read_csv('./simulation_results/2023-09-08_16-52-18.csv')
df38 = pd.read_csv('./simulation_results/2023-09-09_00-54-24.csv')
df39 = pd.read_csv('./simulation_results/2023-09-09_07-11-05.csv')
df40 = pd.read_csv('./simulation_results/2023-09-16_12-29-11.csv')
df41 = pd.read_csv('./simulation_results/2023-09-09_15-41-53.csv')
df42 = pd.read_csv('./simulation_results/2023-09-10_00-05-13.csv')
df43 = pd.read_csv('./simulation_results/2023-09-10_08-34-45.csv')
df44 = pd.read_csv('./simulation_results/2023-09-10_17-02-15.csv')
df45 = pd.read_csv('./simulation_results/2023-09-11_01-37-07.csv')
df46 = pd.read_csv('./simulation_results/2023-09-11_09-58-13.csv')
df47 = pd.read_csv('./simulation_results/2023-09-11_18-31-25.csv')
df48 = pd.read_csv('./simulation_results/2023-09-12_03-04-08.csv')
df49 = pd.read_csv('./simulation_results/2023-09-12_11-37-15.csv')
df50 = pd.read_csv('./simulation_results/2023-09-12_20-06-38.csv')
df51 = pd.read_csv('./simulation_results/2023-09-13_04-38-09.csv')
df52 = pd.read_csv('./simulation_results/2023-09-13_13-07-29.csv')
df53 = pd.read_csv('./simulation_results/2023-09-13_20-39-32.csv')
df54 = pd.read_csv('./simulation_results/2023-09-14_03-54-29.csv')
df55 = pd.read_csv('./simulation_results/2023-09-14_11-33-26.csv')
df56 = pd.read_csv('./simulation_results/2023-09-14_19-51-19.csv')
df57 = pd.read_csv('./simulation_results/2023-09-15_02-17-07.csv')
df58 = pd.read_csv('./simulation_results/2023-09-15_08-43-50.csv')
df59 = pd.read_csv('./simulation_results/2023-09-15_16-54-42.csv')
df60 = pd.read_csv('./simulation_results/2023-09-16_02-24-06.csv')
# baseline
df61 = pd.read_csv('./simulation_results/2023-09-09_15-49-48.csv')
df62 = pd.read_csv('./simulation_results/2023-09-10_00-21-01.csv')
df63 = pd.read_csv('./simulation_results/2023-09-10_08-50-32.csv')
df64 = pd.read_csv('./simulation_results/2023-09-10_17-17-29.csv')
df65 = pd.read_csv('./simulation_results/2023-09-11_01-52-53.csv')
df66 = pd.read_csv('./simulation_results/2023-09-11_10-24-01.csv')
df67 = pd.read_csv('./simulation_results/2023-09-11_18-57-21.csv')
df68 = pd.read_csv('./simulation_results/2023-09-12_03-30-06.csv') # 확인 후 block 만 넣기!
df69 = pd.read_csv('./simulation_results/2023-09-12_12-03-13.csv')
df70 = pd.read_csv('./simulation_results/2023-09-12_20-32-38.csv')
df71 = pd.read_csv('./simulation_results/2023-09-13_05-04-05.csv')
df72 = pd.read_csv('./simulation_results/2023-09-13_13-33-57.csv')
df73 = pd.read_csv('./simulation_results/2023-09-13_21-02-18.csv')
df74 = pd.read_csv('./simulation_results/2023-09-14_04-10-58.csv')
df75 = pd.read_csv('./simulation_results/2023-09-14_12-05-32.csv')
df76 = pd.read_csv('./simulation_results/2023-09-14_20-08-52.csv')
df77 = pd.read_csv('./simulation_results/2023-09-15_02-34-54.csv')
df78 = pd.read_csv('./simulation_results/2023-09-15_09-01-39.csv')
df79 = pd.read_csv('./simulation_results/2023-09-15_17-20-42.csv')
df80 = pd.read_csv('./simulation_results/2023-09-16_02-51-31.csv')
df81 = pd.read_csv('./simulation_results/2023-09-16_13-18-15.csv')
df82 = pd.read_csv('./simulation_results/2023-09-16_22-36-14.csv')
df83 = pd.read_csv('./simulation_results/2023-09-17_05-16-28.csv')
df84 = pd.read_csv('./simulation_results/2023-09-17_13-28-50.csv')
df85 = pd.read_csv('./simulation_results/2023-09-17_22-21-07.csv')
df86 = pd.read_csv('./simulation_results/2023-09-18_07-09-08.csv')
df87 = pd.read_csv('./simulation_results/2023-09-18_15-58-50.csv')
df88 = pd.read_csv('./simulation_results/2023-09-19_01-20-55.csv')
df89 = pd.read_csv('./simulation_results/2023-09-19_10-48-04.csv')
df90 = pd.read_csv('./simulation_results/2023-09-19_20-21-06.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/EvolveGCNO_windmillsmall.csv',index=False)data = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNO_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/2023-06-26_07-43-06.csv') # lags 8
df2 = pd.read_csv('./simulation_results/2023-06-26_09-04-24.csv') # lags 8
df3 = pd.read_csv('./simulation_results/2023-06-26_10-20-16.csv') # lags 8
df4 = pd.read_csv('./simulation_results/2023-06-26_11-33-26.csv') # lags 8
df5 = pd.read_csv('./simulation_results/2023-06-27_01-11-52.csv') # lags 8
df6 = pd.read_csv('./simulation_results/2023-06-27_02-33-33.csv') # lags 8df7 = pd.read_csv('./simulation_results/2023-07-25_19-32-08.csv') # lags 8
df8 = pd.read_csv('./simulation_results/2023-07-25_20-35-49.csv') # lags 8
df9 = pd.read_csv('./simulation_results/2023-07-25_21-38-32.csv') # lags 8
df10 = pd.read_csv('./simulation_results/2023-07-25_22-43-56.csv') # lags 8
df11 = pd.read_csv('./simulation_results/2023-07-25_23-47-06.csv') # lags 8
df12 = pd.read_csv('./simulation_results/2023-07-26_00-49-54.csv') # lags 8
df13 = pd.read_csv('./simulation_results/2023-07-26_01-51-39.csv') # lags 8
df14 = pd.read_csv('./simulation_results/2023-07-26_02-55-30.csv') # lags 8
df15 = pd.read_csv('./simulation_results/2023-07-26_03-59-13.csv') # lags 8data = pd.concat([df1,df2,df3,df4,df5,df6,df7,df8,df9,df10,df11,df12,df13,df14,df15],axis=0)data.to_csv('./simulation_results/Real_simulation_reshape/EvolveGCNO_monte.csv',index=False)data = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNO_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)