LRGCN_Simulation_reshape

ITSTGCN

Author

SEOYEON CHOI

Published

June 13, 2023

Simulation Study

Import

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

Fivenodes

df1 = pd.read_csv('./simulation_results/2023-06-19_14-50-01.csv')
df2 = pd.read_csv('./simulation_results/2023-06-19_15-28-49.csv')
df3 = pd.read_csv('./simulation_results/2023-06-19_16-07-15.csv')
df4 = pd.read_csv('./simulation_results/2023-06-19_19-11-20.csv')
df5 = pd.read_csv('./simulation_results/2023-06-19_16-43-35.csv')
df6 = pd.read_csv('./simulation_results/2023-06-19_17-20-21.csv')
df7 = pd.read_csv('./simulation_results/2023-06-19_17-55-56.csv')
df8 = pd.read_csv('./simulation_results/2023-06-19_18-31-52.csv')

df9 = pd.read_csv('./simulation_results/2023-07-18_07-04-57.csv')
df10 = pd.read_csv('./simulation_results/2023-07-18_07-45-38.csv')
df11 = pd.read_csv('./simulation_results/2023-07-18_08-27-17.csv')
df12 = pd.read_csv('./simulation_results/2023-07-18_09-09-01.csv')
df13 = pd.read_csv('./simulation_results/2023-07-18_09-49-56.csv')
df14 = pd.read_csv('./simulation_results/2023-07-18_10-31-29.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/LRGCN_fivedones_Simulation.csv',index=False)

data = pd.read_csv('./simulation_results/Real_simulation_reshape/LRGCN_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/2023-06-19_21-16-21.csv') # GNAR random
df2 = pd.read_csv('./simulation_results/2023-06-19_23-17-46.csv') # GNAR block
df3 = pd.read_csv('./simulation_results/2023-06-20_00-19-25.csv') # STGCN, ITSTGCN random 30%
df4 = pd.read_csv('./simulation_results/2023-06-20_01-21-25.csv') 
df5 = pd.read_csv('./simulation_results/2023-06-20_10-19-58.csv') 
df6 = pd.read_csv('./simulation_results/2023-06-20_12-07-01.csv')

df7 = pd.read_csv('./simulation_results/2023-07-18_11-34-37.csv') # GNAR random
df8 = pd.read_csv('./simulation_results/2023-07-18_12-40-07.csv') # GNAR block
df9 = pd.read_csv('./simulation_results/2023-07-18_13-42-11.csv') # STGCN, ITSTGCN random 30%
df10 = pd.read_csv('./simulation_results/2023-07-18_14-46-05.csv')

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

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

data = pd.read_csv('./simulation_results/Real_simulation_reshape/LRGCN_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/2023-06-19_13-57-33.csv') 
df2 = pd.read_csv('./simulation_results/2023-06-19_14-10-25.csv')

df3 = pd.read_csv('./simulation_results/2023-07-18_15-12-36.csv')

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

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

data = pd.read_csv('./simulation_results/Real_simulation_reshape/LRGCN_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/2023-06-20_19-13-42.csv')
df2 = pd.read_csv('./simulation_results/2023-06-20_19-46-03.csv')

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

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

data2 = pd.read_csv('./simulation_results/Real_simulation_reshape/LRGCN_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/2023-06-20_09-26-34.csv')
df2 = pd.read_csv('./simulation_results/2023-06-20_12-04-37.csv')
df3 = pd.read_csv('./simulation_results/2023-06-20_15-15-41.csv')
df4 = pd.read_csv('./simulation_results/2023-06-20_21-03-52.csv')
df5 = pd.read_csv('./simulation_results/2023-06-21_01-43-09.csv')
df6 = pd.read_csv('./simulation_results/2023-06-21_04-30-41.csv')
df7 = pd.read_csv('./simulation_results/2023-06-21_07-09-30.csv')
df8 = pd.read_csv('./simulation_results/2023-06-21_09-20-57.csv')
df9 = pd.read_csv('./simulation_results/2023-06-21_11-51-57.csv') # block
df10 = pd.read_csv('./simulation_results/2023-06-21_14-36-43.csv') # block
df11 = pd.read_csv('./simulation_results/2023-06-21_17-19-18.csv') # block

df12 = pd.read_csv('./simulation_results/2023-08-01_02-30-03.csv')
df13 = pd.read_csv('./simulation_results/2023-08-01_04-38-07.csv')
df14 = pd.read_csv('./simulation_results/2023-08-01_06-53-47.csv')
df15 = pd.read_csv('./simulation_results/2023-08-01_08-43-43.csv')
df16 = pd.read_csv('./simulation_results/2023-08-01_11-09-51.csv')
df17 = pd.read_csv('./simulation_results/2023-08-01_13-19-35.csv')
df18 = pd.read_csv('./simulation_results/2023-08-01_15-26-19.csv')
df19 = pd.read_csv('./simulation_results/2023-08-01_17-49-47.csv')
df20 = pd.read_csv('./simulation_results/2023-08-01_19-29-51.csv') # block
df21 = pd.read_csv('./simulation_results/2023-08-01_20-59-41.csv') # block
df22 = pd.read_csv('./simulation_results/2023-08-01_22-33-11.csv') # block
df23 = pd.read_csv('./simulation_results/2023-08-01_23-55-49.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/LRGCN_wikimath.csv',index=False)

data = pd.read_csv('./simulation_results/Real_simulation_reshape/LRGCN_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/2023-06-21_19-31-38.csv') # STGCN IT-STGCN block
df2 = pd.read_csv('./simulation_results/2023-06-21_22-43-39.csv') # STGCN IT-STGCN
df3 = pd.read_csv('./simulation_results/2023-06-22_02-04-05.csv')

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

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

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

data = pd.read_csv('./simulation_results/Real_simulation_reshape/LRGCN_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/2023-06-20_14-22-23.csv') # lags 8
df2 = pd.read_csv('./simulation_results/2023-06-20_16-44-49.csv') # lags 8
df3 = pd.read_csv('./simulation_results/2023-06-20_19-09-29.csv') # lags 8
df4 = pd.read_csv('./simulation_results/2023-06-20_21-32-02.csv') # lags 8
df5 = pd.read_csv('./simulation_results/2023-06-20_23-35-39.csv') # lags 8
df6 = pd.read_csv('./simulation_results/2023-06-21_01-53-54.csv') # lags 8

df7 = pd.read_csv('./simulation_results/2023-07-24_08-41-55.csv') # lags 8
df8 = pd.read_csv('./simulation_results/2023-07-24_10-33-20.csv') # lags 8
df9 = pd.read_csv('./simulation_results/2023-07-24_12-20-53.csv') # lags 8
df10 = pd.read_csv('./simulation_results/2023-07-24_14-09-27.csv') # lags 8
df11 = pd.read_csv('./simulation_results/2023-07-24_15-55-20.csv') # lags 8
df12 = pd.read_csv('./simulation_results/2023-07-24_17-44-42.csv') # lags 8
df13 = pd.read_csv('./simulation_results/2023-07-24_19-33-32.csv') # lags 8
df14 = pd.read_csv('./simulation_results/2023-07-24_21-18-09.csv') # lags 8
df15 = pd.read_csv('./simulation_results/2023-07-24_23-08-46.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/LRGCN_monte.csv',index=False)

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