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