DYGRENCODER_Simulation_reshape

ITSTGCN

Author

SEOYEON CHOI

Published

June 28, 2023

Simulation Study

Import

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

Fivenodes

df1 = pd.read_csv('./simulation_results/2023-06-29_18-32-37.csv')
df2 = pd.read_csv('./simulation_results/2023-06-29_19-03-08.csv')
df3 = pd.read_csv('./simulation_results/2023-06-29_19-30-06.csv')
df4 = pd.read_csv('./simulation_results/2023-06-29_19-56-06.csv')
df5 = pd.read_csv('./simulation_results/2023-06-29_20-24-32.csv')
df6 = pd.read_csv('./simulation_results/2023-06-29_20-51-36.csv')
df7 = pd.read_csv('./simulation_results/2023-06-29_21-23-07.csv')
df8 = pd.read_csv('./simulation_results/2023-06-29_21-51-08.csv')

df9 = pd.read_csv('./simulation_results/2023-07-19_11-51-08.csv')
df10 = pd.read_csv('./simulation_results/2023-07-19_12-40-54.csv')
df11 = pd.read_csv('./simulation_results/2023-07-19_13-33-52.csv')
df12 = pd.read_csv('./simulation_results/2023-07-19_14-21-16.csv')
df13 = pd.read_csv('./simulation_results/2023-07-19_15-14-51.csv')
df14 = pd.read_csv('./simulation_results/2023-07-19_16-04-07.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/DYGRENCODER_fivedones_Simulation.csv',index=False)

data = pd.read_csv('./simulation_results/Real_simulation_reshape/DYGRENCODER_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/2023-06-29_22-35-38.csv') # GNAR random
df2 = pd.read_csv('./simulation_results/2023-06-29_23-18-24.csv') # GNAR block
df3 = pd.read_csv('./simulation_results/2023-06-30_01-07-15.csv')
df4 = pd.read_csv('./simulation_results/2023-06-30_02-53-54.csv') 
df5 = pd.read_csv('./simulation_results/2023-06-30_20-40-01.csv')
df6 = pd.read_csv('./simulation_results/2023-06-30_22-28-57.csv')

df7 = pd.read_csv('./simulation_results/2023-07-19_17-15-37.csv') # GNAR random
df8 = pd.read_csv('./simulation_results/2023-07-19_18-36-12.csv') # GNAR block
df9 = pd.read_csv('./simulation_results/2023-07-19_19-50-51.csv')
df10 = pd.read_csv('./simulation_results/2023-07-19_21-33-18.csv')

data = pd.concat([df1,df2,df3,df4,df5,df6,df7,df8,df9,df10],axis=0)

data.to_csv('./simulation_results/Real_simulation_reshape/DYGRENCODER_chikenpox_Simulation.csv',index=False)

data = pd.read_csv('./simulation_results/Real_simulation_reshape/DYGRENCODER_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/2023-06-30_03-34-45.csv') 
df2 = pd.read_csv('./simulation_results/2023-06-30_03-48-08.csv')

df3 = pd.read_csv('./simulation_results/2023-07-19_22-14-42.csv')

data = pd.concat([df1,df2,df3],axis=0)

data.to_csv('./simulation_results/Real_simulation_reshape/DYGRENCODER_pedalme_Simulation.csv',index=False)

data = pd.read_csv('./simulation_results/Real_simulation_reshape/DYGRENCODER_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/2023-06-30_13-25-56.csv')
df2 = pd.read_csv('./simulation_results/2023-06-30_14-00-19.csv')

data2 = pd.concat([df1,df2],axis=0)

data2.to_csv('./simulation_results/Real_simulation_reshape/DYGRENCODER_pedalme_Simulation_itstgcnsnd.csv',index=False)

data2 = pd.read_csv('./simulation_results/Real_simulation_reshape/DYGRENCODER_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/2023-06-30_02-53-36.csv')
df2 = pd.read_csv('./simulation_results/2023-06-30_06-31-21.csv')
df3 = pd.read_csv('./simulation_results/2023-06-30_10-03-24.csv')
df4 = pd.read_csv('./simulation_results/2023-06-30_15-50-56.csv')
df5 = pd.read_csv('./simulation_results/2023-06-30_21-17-48.csv')
df6 = pd.read_csv('./simulation_results/2023-07-01_00-13-15.csv')
df7 = pd.read_csv('./simulation_results/2023-07-01_03-01-56.csv')
df8 = pd.read_csv('./simulation_results/2023-07-01_05-42-24.csv')
df9 = pd.read_csv('./simulation_results/2023-07-01_08-30-04.csv') # block
df10 = pd.read_csv('./simulation_results/2023-07-01_11-32-33.csv') # block
df11 = pd.read_csv('./simulation_results/2023-07-01_14-38-22.csv') # block

df12 = pd.read_csv('./simulation_results/2023-08-06_04-42-31.csv')
df13 = pd.read_csv('./simulation_results/2023-08-06_09-09-26.csv')
df14 = pd.read_csv('./simulation_results/2023-08-06_12-31-54.csv')
df15 = pd.read_csv('./simulation_results/2023-08-06_14-41-11.csv')
df16 = pd.read_csv('./simulation_results/2023-08-06_17-12-58.csv')
df17 = pd.read_csv('./simulation_results/2023-08-06_20-50-45.csv')
df18 = pd.read_csv('./simulation_results/2023-08-07_00-36-51.csv')
df19 = pd.read_csv('./simulation_results/2023-08-07_04-21-24.csv')
df20 = pd.read_csv('./simulation_results/2023-08-07_06-15-25.csv') # block
df21 = pd.read_csv('./simulation_results/2023-08-07_08-07-18.csv') # block
df22 = pd.read_csv('./simulation_results/2023-08-07_10-21-36.csv') # block
df23 = pd.read_csv('./simulation_results/2023-08-07_14-07-17.csv') # block

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],axis=0)

data.to_csv('./simulation_results/Real_simulation_reshape/DYGRENCODER_wikimath.csv',index=False)

data = pd.read_csv('./simulation_results/Real_simulation_reshape/DYGRENCODER_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/2023-07-01_17-41-40.csv') # STGCN IT-STGCN block
df2 = pd.read_csv('./simulation_results/2023-07-01_21-00-26.csv') # STGCN IT-STGCN
df3 = pd.read_csv('./simulation_results/2023-07-02_00-17-30.csv')

data = pd.concat([df1,df2,df3],axis=0)

data.to_csv('./simulation_results/Real_simulation_reshape/DYGRENCODER_wikimath_GSO_st.csv',index=False)

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

data = pd.read_csv('./simulation_results/Real_simulation_reshape/DYGRENCODER_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/2023-06-30_06-15-55.csv') # lags 8
df2 = pd.read_csv('./simulation_results/2023-06-30_08-46-05.csv') # lags 8
df3 = pd.read_csv('./simulation_results/2023-06-30_11-09-20.csv') # lags 8
df4 = pd.read_csv('./simulation_results/2023-06-30_14-00-17.csv') # lags 8
df5 = pd.read_csv('./simulation_results/2023-06-30_16-23-53.csv') # lags 8
df6 = pd.read_csv('./simulation_results/2023-06-30_18-47-55.csv') # lags 8

df7 = pd.read_csv('./simulation_results/2023-07-26_09-18-59.csv') # lags 8
df8 = pd.read_csv('./simulation_results/2023-07-26_11-40-12.csv') # lags 8
df9 = pd.read_csv('./simulation_results/2023-07-26_13-53-51.csv') # lags 8
df10 = pd.read_csv('./simulation_results/2023-07-26_16-08-33.csv') # lags 8
df11 = pd.read_csv('./simulation_results/2023-07-26_18-03-51.csv') # lags 8
df12 = pd.read_csv('./simulation_results/2023-07-26_19-59-34.csv') # lags 8
df13 = pd.read_csv('./simulation_results/2023-07-26_21-55-45.csv') # lags 8
df14 = pd.read_csv('./simulation_results/2023-07-27_00-15-20.csv') # lags 8
df15 = pd.read_csv('./simulation_results/2023-07-27_02-39-45.csv') # lags 8

data = 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/DYGRENCODER_monte.csv',index=False)

data = pd.read_csv('./simulation_results/Real_simulation_reshape/DYGRENCODER_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)