GConvGRU_Simulation Tables_reshape

ITSTGCN
Author

SEOYEON CHOI

Published

May 25, 2023

Simulation Tables

import

import pandas as pd
data_fivenodes = pd.read_csv('./simulation_results/Real_simulation_reshape/fivedones_Simulation.csv')
data_chickenpox = pd.read_csv('./simulation_results/Real_simulation_reshape/chikenpox_Simulation.csv')
data_pedal = pd.read_csv('./simulation_results/Real_simulation_reshape/pedalme_Simulation.csv')
data_pedal2 = pd.read_csv('./simulation_results/Real_simulation_reshape/pedalme_Simulation_itstgcnsnd.csv')
data__wiki = pd.read_csv('./simulation_results/Real_simulation_reshape/wikimath.csv')
data_wiki_GSO = pd.read_csv('./simulation_results/Real_simulation_reshape/wikimath_GSO_st.csv')
data_windmillsmall = pd.read_csv('./simulation_results/Real_simulation_reshape/windmillsmall.csv')
data_monte = pd.read_csv('./simulation_results/Real_simulation_reshape/monte.csv')
data = pd.concat([data_fivenodes,data_chickenpox,data_pedal,data__wiki,data_windmillsmall,data_monte]);data
dataset method mrate mtype lags nof_filters inter_method epoch mse calculation_time
0 fivenodes STGCN 0.000000 NaN 2 12 NaN 50 0.729374 80.985221
1 fivenodes STGCN 0.000000 NaN 2 12 NaN 50 0.729082 80.891788
2 fivenodes STGCN 0.700000 rand 2 12 linear 50 1.892262 81.976547
3 fivenodes STGCN 0.700000 rand 2 12 nearest 50 2.211288 87.803869
4 fivenodes STGCN 0.800000 rand 2 12 linear 50 2.072818 103.648742
... ... ... ... ... ... ... ... ... ... ...
385 monte IT-STGCN 0.149142 block 4 12 nearest 50 0.932036 472.003806
386 monte STGCN 0.149142 block 4 12 nearest 50 0.936046 384.706071
387 monte IT-STGCN 0.149142 block 4 12 nearest 50 0.933899 449.077441
388 monte STGCN 0.149142 block 4 12 nearest 50 0.933985 332.606736
389 monte IT-STGCN 0.149142 block 4 12 nearest 50 0.931460 476.834988

2034 rows × 10 columns

Fivenodes

Baseline

pd.merge(data.query("dataset=='fivenodes' and mtype!='rand' and mtype!='block'").groupby(['nof_filters','method','lags'])['mse'].mean().reset_index(),
         data.query("dataset=='fivenodes' and mtype!='rand' and mtype!='block'").groupby(['nof_filters','method','lags'])['mse'].std().reset_index(),
         on=['method','nof_filters','lags']).rename(columns={'mse_x':'mean','mse_y':'std'}).round(3)
nof_filters method lags mean std
0 12 IT-STGCN 2 0.732 0.005
1 12 STGCN 2 0.732 0.005

Random

pd.merge(data.query("dataset=='fivenodes' and mtype=='rand'").groupby(['mrate','nof_filters','method','lags'])['mse'].mean().reset_index(),
         data.query("dataset=='fivenodes' and mtype=='rand'").groupby(['mrate','nof_filters','method','lags'])['mse'].std().reset_index(),
         on=['method','nof_filters','mrate','lags']).rename(columns={'mse_x':'mean','mse_y':'std'}).round(3).query("nof_filters==12")
mrate nof_filters method lags mean std
0 0.7 12 IT-STGCN 2 1.167 0.059
1 0.7 12 STGCN 2 2.077 0.252
2 0.8 12 IT-STGCN 2 1.371 0.097
3 0.8 12 STGCN 2 2.432 0.263

Block

pd.merge(data.query("dataset=='fivenodes' and mtype=='block'").groupby(['mrate','nof_filters','method'])['mse'].mean().reset_index(),
         data.query("dataset=='fivenodes' and mtype=='block'").groupby(['mrate','nof_filters','method'])['mse'].std().reset_index(),
         on=['method','nof_filters','mrate']).rename(columns={'mse_x':'mean','mse_y':'std'}).round(3)
mrate nof_filters method mean std
0 0.125 12 IT-STGCN 1.160 0.042
1 0.125 12 STGCN 1.215 0.036

ChickenpoxDatasetLoader(lags=4)

Baseline

pd.merge(data.query("dataset=='chickenpox' and mtype!='rand' and mtype!='block'").groupby(['nof_filters','method'])['mse'].mean().reset_index(),
         data.query("dataset=='chickenpox' and mtype!='rand' and mtype!='block'").groupby(['nof_filters','method'])['mse'].std().reset_index(),
         on=['method','nof_filters']).rename(columns={'mse_x':'mean','mse_y':'std'}).round(3).query("nof_filters==16")
nof_filters method mean std
0 16 IT-STGCN 0.752 0.013
1 16 STGCN 0.752 0.012

Random

pd.merge(data.query("dataset=='chickenpox' and mtype=='rand'").groupby(['mrate','inter_method','nof_filters','method'])['mse'].mean().reset_index(),
         data.query("dataset=='chickenpox' and mtype=='rand'").groupby(['mrate','inter_method','nof_filters','method'])['mse'].std().reset_index(),
         on=['method','inter_method','mrate','nof_filters']).rename(columns={'mse_x':'mean','mse_y':'std'}).round(3)
mrate inter_method nof_filters method mean std
0 0.3 linear 16 IT-STGCN 0.851 0.031
1 0.3 linear 16 STGCN 1.087 0.046
2 0.8 linear 16 IT-STGCN 1.586 0.199
3 0.8 linear 16 STGCN 2.529 0.292

Block

pd.merge(data.query("dataset=='chickenpox' and mtype=='block'").groupby(['inter_method','mrate','nof_filters','method'])['mse'].mean().reset_index(),
         data.query("dataset=='chickenpox' and mtype=='block'").groupby(['inter_method','mrate','nof_filters','method'])['mse'].std().reset_index(),
         on=['method','inter_method','mrate','nof_filters']).rename(columns={'mse_x':'mean','mse_y':'std'})
inter_method mrate nof_filters method mean std
0 linear 0.28777 16 IT-STGCN 0.807041 0.016362
1 linear 0.28777 16 STGCN 0.828224 0.021919
2 nearest 0.28777 16 IT-STGCN 0.823756 0.022918
3 nearest 0.28777 16 STGCN 0.828498 0.022007

PedalMeDatasetLoader (lags=4)

Baseline

pd.merge(data.query("dataset=='pedalme' and mtype!='rand' and mtype!='block'").groupby(['lags','nof_filters','method'])['mse'].mean().reset_index(),
         data.query("dataset=='pedalme' and mtype!='rand' and mtype!='block'").groupby(['lags','nof_filters','method'])['mse'].std().reset_index(),
         on=['method','lags','nof_filters']).rename(columns={'mse_x':'mean','mse_y':'std'}).round(3).query("lags==4")
lags nof_filters method mean std
0 4 12 IT-STGCN 1.233 0.115
1 4 12 STGCN 1.233 0.099

Random

pd.merge(data.query("dataset=='pedalme' and mtype=='rand'").groupby(['mrate','lags','inter_method','method'])['mse'].mean().reset_index(),
         data.query("dataset=='pedalme' and mtype=='rand'").groupby(['mrate','lags','inter_method','method'])['mse'].std().reset_index(),
         on=['method','mrate','lags','inter_method']).rename(columns={'mse_x':'mean','mse_y':'std'}).round(3)
mrate lags inter_method method mean std
0 0.3 4 linear IT-STGCN 1.354 0.134
1 0.3 4 linear STGCN 1.575 0.198
2 0.3 4 nearest IT-STGCN 1.385 0.173
3 0.3 4 nearest STGCN 1.527 0.342
4 0.6 4 linear IT-STGCN 1.516 0.211
5 0.6 4 linear STGCN 1.655 0.179
6 0.6 4 nearest IT-STGCN 1.625 0.324
7 0.6 4 nearest STGCN 1.851 0.254

Block

pd.merge(data.query("dataset=='pedalme' and mtype=='block'").groupby(['mrate','lags','inter_method','method'])['mse'].mean().reset_index(),
         data.query("dataset=='pedalme' and mtype=='block'").groupby(['mrate','lags','inter_method','method'])['mse'].std().reset_index(),
         on=['method','mrate','lags','inter_method']).rename(columns={'mse_x':'mean','mse_y':'std'}).round(3).query("lags==4")
mrate lags inter_method method mean std
0 0.286 4 linear IT-STGCN 1.329 0.131
1 0.286 4 linear STGCN 1.320 0.111
2 0.286 4 nearest IT-STGCN 1.289 0.115
3 0.286 4 nearest STGCN 1.270 0.114

W_st

pd.merge(data_pedal2.query("mtype=='rand'").groupby(['mrate','lags','inter_method','method'])['mse'].mean().reset_index(),
         data_pedal2.query("mtype=='rand'").groupby(['mrate','lags','inter_method','method'])['mse'].std().reset_index(),
         on=['method','mrate','lags','inter_method']).rename(columns={'mse_x':'mean','mse_y':'std'}).round(3).query("lags==4")
mrate lags inter_method method mean std
0 0.3 4 linear IT-STGCN 1.270 0.163
1 0.3 4 linear STGCN 1.556 0.264
2 0.3 4 nearest IT-STGCN 1.324 0.163
3 0.3 4 nearest STGCN 1.520 0.206
4 0.6 4 linear IT-STGCN 1.434 0.222
5 0.6 4 linear STGCN 1.678 0.211
6 0.6 4 nearest IT-STGCN 1.410 0.208
7 0.6 4 nearest STGCN 1.771 0.220 
pd.merge(data_pedal2.query("mtype=='block'").groupby(['mrate','lags','inter_method','method'])['mse'].mean().reset_index(),
         data_pedal2.query("mtype=='block'").groupby(['mrate','lags','inter_method','method'])['mse'].std().reset_index(),
         on=['method','mrate','lags','inter_method']).rename(columns={'mse_x':'mean','mse_y':'std'}).round(3).query("lags==4")
mrate lags inter_method method mean std
0 0.286 4 linear IT-STGCN 1.391 0.151
1 0.286 4 linear STGCN 1.420 0.110
2 0.286 4 nearest IT-STGCN 1.361 0.114
3 0.286 4 nearest STGCN 1.430 0.145

WikiMathsDatasetLoader (lags=8)

Baseline

pd.merge(data.query("dataset=='wikimath' and mrate==0").groupby(['lags','nof_filters','method'])['mse'].mean().reset_index(),
         data.query("dataset=='wikimath' and mrate==0").groupby(['lags','nof_filters','method'])['mse'].std().reset_index(),
         on=['lags','nof_filters','method']).rename(columns={'mse_x':'mean','mse_y':'std'}).round(3)
lags nof_filters method mean std
0 8 12 IT-STGCN 0.529 0.003
1 8 12 STGCN 0.528 0.003

Random

pd.merge(data.query("dataset=='wikimath' and mtype=='rand'").groupby(['mrate','lags','method'])['mse'].mean().reset_index(),
         data.query("dataset=='wikimath' and mtype=='rand'").groupby(['mrate','lags','method'])['mse'].std().reset_index(),
         on=['method','mrate','lags']).rename(columns={'mse_x':'mean','mse_y':'std'}).round(3)
mrate lags method mean std
0 0.3 8 IT-STGCN 0.518 0.002
1 0.3 8 STGCN 0.570 0.006
2 0.8 8 IT-STGCN 0.687 0.021
3 0.8 8 STGCN 0.932 0.043

Block

pd.merge(data.query("dataset=='wikimath' and mtype=='block'").groupby(['mrate','lags','method'])['mse'].mean().reset_index(),
         data.query("dataset=='wikimath' and mtype=='block'").groupby(['mrate','lags','method'])['mse'].std().reset_index(),
         on=['method','mrate','lags']).rename(columns={'mse_x':'mean','mse_y':'std'})
mrate lags method mean std
0 0.003835 8 IT-STGCN 0.528737 0.002806
1 0.003835 8 STGCN 0.527871 0.002606
2 0.095870 8 IT-STGCN 0.529440 0.003820
3 0.095870 8 STGCN 0.544176 0.010772

missing values on the same nodes

pd.merge(data_wiki_GSO.groupby(['mrate','lags','method'])['mse'].mean().reset_index(),
        data_wiki_GSO.groupby(['mrate','lags','method'])['mse'].std().reset_index(),
         on=['method','mrate','lags']).rename(columns={'mse_x':'mean','mse_y':'std'}).round(3)
mrate lags method mean std
0 0.512 8 IT-STGCN 0.531 0.002
1 0.512 8 STGCN 0.720 0.013

WindmillOutputSmallDatasetLoader (lags=8)

Baseline

pd.merge(data.query("dataset=='windmillsmall' and mrate==0").groupby(['lags','method'])['mse'].mean().reset_index(),
         data.query("dataset=='windmillsmall' and mrate==0").groupby(['lags','method'])['mse'].std().reset_index(),
         on=['method','lags']).rename(columns={'mse_x':'mean','mse_y':'std'}).round(3)
lags method mean std
0 8 IT-STGCN 1.004 0.004
1 8 STGCN 1.003 0.004

Random

pd.merge(data.query("dataset=='windmillsmall' and mtype=='rand'").groupby(['mrate','lags','method'])['mse'].mean().reset_index(),
         data.query("dataset=='windmillsmall' and mtype=='rand'").groupby(['mrate','lags','method'])['mse'].std().reset_index(),
         on=['method','mrate','lags']).rename(columns={'mse_x':'mean','mse_y':'std'}).round(3)
mrate lags method mean std
0 0.7 8 IT-STGCN 1.193 0.045
1 0.7 8 STGCN 1.661 0.076

Block

pd.merge(data.query("dataset=='windmillsmall' and mtype=='block'").groupby(['mrate','lags','method'])['mse'].mean().reset_index(),
         data.query("dataset=='windmillsmall' and mtype=='block'").groupby(['mrate','lags','method'])['mse'].std().reset_index(),
         on=['method','mrate','lags']).rename(columns={'mse_x':'mean','mse_y':'std'}).round(3)
mean mrate lags method std

Montevideobus (lags=4)

Baseline

pd.merge(data.query("dataset=='monte' and mrate==0").groupby(['lags','method'])['mse'].mean().reset_index(),
         data.query("dataset=='monte' and mrate==0").groupby(['lags','method'])['mse'].std().reset_index(),
         on=['method','lags']).rename(columns={'mse_x':'mean','mse_y':'std'}).round(3)
lags method mean std
0 4 IT-STGCN 0.931 0.001
1 4 STGCN 0.931 0.002

Random

pd.merge(data.query("dataset=='monte' and mtype=='rand'").groupby(['mrate','lags','inter_method','method'])['mse'].mean().reset_index(),
         data.query("dataset=='monte' and mtype=='rand'").groupby(['mrate','lags','inter_method','method'])['mse'].std().reset_index(),
         on=['mrate','inter_method','method','mrate','lags']).rename(columns={'mse_x':'mean','mse_y':'std'})
mrate lags inter_method method mean std
0 0.8 4 nearest IT-STGCN 1.09556 0.018743
1 0.8 4 nearest STGCN 1.51600 0.039793

Block

pd.merge(data.query("dataset=='monte' and mtype=='block'").groupby(['mrate','lags','inter_method','method'])['mse'].mean().reset_index(),
         data.query("dataset=='monte' and mtype=='block'").groupby(['mrate','lags','inter_method','method'])['mse'].std().reset_index(),
         on=['method','mrate','inter_method','lags']).rename(columns={'mse_x':'mean','mse_y':'std'})
mrate lags inter_method method mean std
0 0.149142 4 cubic IT-STGCN 1.022866 0.021048
1 0.149142 4 cubic STGCN 1.028363 0.031275
2 0.149142 4 linear IT-STGCN 0.930156 0.001956
3 0.149142 4 linear STGCN 0.934719 0.004724
4 0.149142 4 nearest IT-STGCN 0.931785 0.002158
5 0.149142 4 nearest STGCN 0.934596 0.003562