GCLSTM_Simulation Tables_reshape

ITSTGCN
Author

SEOYEON CHOI

Published

June 6, 2023

Simulation Tables

import

import pandas as pd
data_fivenodes = pd.read_csv('./simulation_results/Real_simulation_reshape/GCLSTM_fivedones_Simulation.csv')
data_chickenpox = pd.read_csv('./simulation_results/Real_simulation_reshape/GCLSTM_chikenpox_Simulation.csv')
data_pedal = pd.read_csv('./simulation_results/Real_simulation_reshape/GCLSTM_pedalme_Simulation.csv')
data_pedal2 = pd.read_csv('./simulation_results/Real_simulation_reshape/GCLSTM_pedalme_Simulation_itstgcnsnd.csv')
data__wiki = pd.read_csv('./simulation_results/Real_simulation_reshape/GCLSTM_wikimath.csv')
data_wiki_GSO = pd.read_csv('./simulation_results/Real_simulation_reshape/GCLSTM_wikimath_GSO_st.csv')
data_windmillsmall = pd.read_csv('./simulation_results/Real_simulation_reshape/GCLSTM_windmillsmall.csv')
data_monte = pd.read_csv('./simulation_results/Real_simulation_reshape/GCLSTM_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 0.7 rand 2 4 linear 50 1.299148 25.236690
1 fivenodes STGCN 0.7 rand 2 4 nearest 50 1.335855 24.400978
2 fivenodes IT-STGCN 0.7 rand 2 4 linear 50 1.305905 33.300703
3 fivenodes IT-STGCN 0.7 rand 2 4 nearest 50 1.248720 33.621689
4 fivenodes STGCN 0.7 rand 2 4 linear 50 1.288252 24.205301
... ... ... ... ... ... ... ... ... ... ...
385 monte IT-STGCN 0.7 rand 4 12 nearest 50 1.004984 744.126746
386 monte STGCN 0.7 rand 4 12 nearest 50 1.012916 579.258597
387 monte IT-STGCN 0.7 rand 4 12 nearest 50 1.005659 793.337650
388 monte STGCN 0.7 rand 4 12 nearest 50 1.158421 481.330722
389 monte IT-STGCN 0.7 rand 4 12 nearest 50 1.020264 843.310333

2912 rows × 10 columns

data.to_csv('./simulation_results/Real_simulation_reshape/Final_Simulation_GCLSTM.csv',index=False)
pedal_wiki_GSO = pd.concat([data_pedal2,data_wiki_GSO])
pedal_wiki_GSO.to_csv('./simulation_results/Real_simulation_reshape/Final_Simulation_GCLSTM_pedal_wiki_GSO.csv',index=False)

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 4 IT-STGCN 2 1.212 0.026
1 4 STGCN 2 1.206 0.020

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)
mrate nof_filters method lags mean std
0 0.3 4 IT-STGCN 2 1.219 0.022
1 0.3 4 STGCN 2 1.221 0.029
2 0.5 4 IT-STGCN 2 1.220 0.027
3 0.5 4 STGCN 2 1.236 0.035
4 0.6 4 IT-STGCN 2 1.215 0.024
5 0.6 4 STGCN 2 1.242 0.041
6 0.7 4 IT-STGCN 2 1.226 0.033
7 0.7 4 STGCN 2 1.245 0.052
8 0.8 4 IT-STGCN 2 1.236 0.028
9 0.8 4 STGCN 2 1.261 0.048

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 4 IT-STGCN 1.217 0.023
1 0.125 4 STGCN 1.246 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 0.878 0.047
1 16 STGCN 0.892 0.054

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 0.850 0.022
1 0.3 linear 16 STGCN 1.050 0.036
2 0.5 linear 16 IT-STGCN 0.899 0.023
3 0.5 linear 16 STGCN 1.514 0.050
4 0.6 linear 16 IT-STGCN 0.997 0.030
5 0.6 linear 16 STGCN 1.807 0.064
6 0.8 linear 16 IT-STGCN 1.371 0.072
7 0.8 linear 16 STGCN 2.172 0.186

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 0.883365 0.045500
1 linear 0.28777 16 STGCN 0.889922 0.033144
2 nearest 0.28777 16 IT-STGCN 0.901308 0.054389
3 nearest 0.28777 16 STGCN 0.884887 0.041756

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 4 IT-STGCN 1.170 0.040
1 4 4 STGCN 1.191 0.036

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 1.202 0.029
1 0.3 4 linear STGCN 1.267 0.041
2 0.3 4 nearest IT-STGCN 1.211 0.039
3 0.3 4 nearest STGCN 1.256 0.038
4 0.6 4 linear IT-STGCN 1.278 0.040
5 0.6 4 linear STGCN 1.364 0.068
6 0.6 4 nearest IT-STGCN 1.259 0.042
7 0.6 4 nearest STGCN 1.365 0.064

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 1.182 0.031
1 0.286 4 linear STGCN 1.211 0.023
2 0.286 4 nearest IT-STGCN 1.195 0.029
3 0.286 4 nearest STGCN 1.248 0.019

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 1.191 0.041
1 0.3 4 linear STGCN 1.264 0.041
2 0.3 4 nearest IT-STGCN 1.193 0.033
3 0.3 4 nearest STGCN 1.250 0.049
4 0.6 4 linear IT-STGCN 1.260 0.084
5 0.6 4 linear STGCN 1.340 0.059
6 0.6 4 nearest IT-STGCN 1.231 0.044
7 0.6 4 nearest STGCN 1.355 0.068 
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 1.182 0.045
1 0.286 4 linear STGCN 1.225 0.030
2 0.286 4 nearest IT-STGCN 1.185 0.035
3 0.286 4 nearest STGCN 1.249 0.027

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 64 IT-STGCN 0.643 0.024
1 8 64 STGCN 0.645 0.018

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 0.628 0.020
1 0.3 8 STGCN 0.674 0.020
2 0.8 8 IT-STGCN 0.815 0.058
3 0.8 8 STGCN 1.407 0.117

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 0.119837 8 IT-STGCN 0.640461 0.019198
1 0.119837 8 STGCN 0.637772 0.012983

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 0.617 0.011
1 0.512 8 STGCN 0.823 0.048

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 0.992 0.011
1 8 STGCN 0.992 0.009

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 1.116 0.021
1 0.7 8 STGCN 1.573 0.105

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)
mrate lags method mean std
0 0.081 8 IT-STGCN 0.985 0.003
1 0.081 8 STGCN 0.985 0.002

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 0.969 0.012
1 4 STGCN 0.970 0.011

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 1.031532 0.028135
1 0.8 4 nearest STGCN 1.140193 0.061301

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 nearest IT-STGCN 0.958620 0.007766
1 0.149142 4 nearest STGCN 0.955762 0.005461

Check

import itstgcnGCLSTM
import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import random
class Eval_csy:
    def __init__(self,learner,train_dataset):
        self.learner = learner
        # self.learner.model.eval()
        try:self.learner.model.eval()
        except:pass
        self.train_dataset = train_dataset
        self.lags = self.learner.lags
        rslt_tr = self.learner(self.train_dataset) 
        self.X_tr = rslt_tr['X']
        self.y_tr = rslt_tr['y']
        self.f_tr = torch.concat([self.train_dataset[0].x.T,self.y_tr],axis=0).float()
        self.yhat_tr = rslt_tr['yhat']
        self.fhat_tr = torch.concat([self.train_dataset[0].x.T,self.yhat_tr],axis=0).float()
from plotnine import *
T = 500
t = np.arange(T)/T * 5

x = 1*np.sin(2*t)+np.sin(4*t)+1.5*np.sin(7*t)
eps_x  = np.random.normal(size=T)*0
y = x.copy()
for i in range(2,T):
    y[i] = 0.35*x[i-1] - 0.15*x[i-2] + 0.5*np.cos(0.4*t[i]) 
eps_y  = np.random.normal(size=T)*0
x = x
y = y
plt.plot(t,x,color='C0',lw=5)
plt.plot(t,x+eps_x,alpha=0.5,color='C0')
plt.plot(t,y,color='C1',lw=5)
plt.plot(t,y+eps_y,alpha=0.5,color='C1')
_node_ids = {'node1':0, 'node2':1}

_FX1 = np.stack([x+eps_x,y+eps_y],axis=1).tolist()

_edges1 = torch.tensor([[0,1]]).tolist()

data_dict1 = {'edges':_edges1, 'node_ids':_node_ids, 'FX':_FX1}

# save_data(data_dict1, './data/toy_example1.pkl')

data1 = pd.DataFrame({'x':x,'y':y,'xer':x,'yer':y})

# save_data(data1, './data/toy_example_true1.csv')

loader1 = itstgcnGCLSTM.DatasetLoader(data_dict1)
dataset = loader1.get_dataset(lags=4)

mindex = itstgcn.rand_mindex(dataset,mrate=0) dataset_miss = itstgcn.miss(dataset,mindex,mtype=‘rand’)

mindex = [random.sample(range(0, T), int(T*0.8)),[np.array(list(range(20,30)))]]
dataset_miss = itstgcnGCLSTM.miss(dataset,mindex,mtype='block')
/home/csy/Dropbox/blog/posts/GCN/itstgcnGCLSTM/utils.py:71: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /opt/conda/conda-bld/pytorch_1682343998658/work/torch/csrc/utils/tensor_new.cpp:245.)
dataset_padded = itstgcnGCLSTM.padding(dataset_miss,interpolation_method='linear')

- 학습

lrnr = itstgcnGCLSTM.StgcnLearner(dataset_padded)
lrnr.learn(filters=32,epoch=10)
10/10
lrnr1 = itstgcnGCLSTM.ITStgcnLearner(dataset_padded)
lrnr1.learn(filters=16,epoch=10)
10/10
evtor = Eval_csy(lrnr,dataset_padded)
evtor1 = Eval_csy(lrnr1,dataset_padded)
with plt.style.context('seaborn-white'):
    fig, ax = plt.subplots(figsize=(20,10))
    
    ax.plot(evtor.f_tr[:,0],'--o',color='black',alpha=0.5,label='Imputation')
    ax.plot(data1['x'][:],'-',color='grey',label='Complete Data')
    ax.plot(evtor.fhat_tr[:,0],color='brown',lw=3,label='STGCN')
    ax.plot(evtor1.fhat_tr[:,0],color='blue',lw=3,label='ITSTGCN')
    
    ax.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax.tick_params(axis='y', labelsize=20)
    ax.tick_params(axis='x', labelsize=20)

with plt.style.context('seaborn-white'):
    fig, ax = plt.subplots(figsize=(20,10))
    
    ax.plot(evtor.f_tr[:,1],'--o',color='black',alpha=0.5,label='Imputation')
    ax.plot(data1['y'][:],'-',color='grey',label='Complete Data')
    ax.plot(evtor.fhat_tr[:,1],color='brown',lw=3,label='STGCN')
    ax.plot(evtor1.fhat_tr[:,1],color='blue',lw=3,label='ITSTGCN')
    
    ax.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax.tick_params(axis='y', labelsize=20)
    ax.tick_params(axis='x', labelsize=20)

import itstgcnsnd
import torch
import numpy as np
loader1 = itstgcnsnd.DatasetLoader(data_dict1)
NameError: name 'data_dict1' is not defined
dataset = loader1.get_dataset(lags=2)

mindex = itstgcn.rand_mindex(dataset,mrate=0) dataset_miss = itstgcn.miss(dataset,mindex,mtype=‘rand’)

mindex = [random.sample(range(0, T), int(T*0.5)),[np.array(list(range(20,30)))]]
dataset_miss = itstgcnsnd.miss(dataset,mindex,mtype='block')
dataset_padded = itstgcnsnd.padding(dataset_miss,interpolation_method='linear')

- 학습

lrnr = itstgcnsnd.StgcnLearner(dataset_padded)
lrnr.learn(filters=2,epoch=5)
lrnr1 = itstgcnsnd.ITStgcnLearner(dataset_padded)
lrnr1.learn(filters=2,epoch=5)
evtor = Eval_csy(lrnr,dataset_padded)
evtor1 = Eval_csy(lrnr1,dataset_padded)
with plt.style.context('seaborn-white'):
    fig, ax = plt.subplots(figsize=(20,10))
    
    ax.plot(evtor.f_tr[:,0],'--o',color='black',alpha=0.5,label='Imputation')
    ax.plot(data1['x'][:],'-',color='grey',label='Complete Data')
    ax.plot(evtor.fhat_tr[:,0],color='brown',lw=3,label='STGCN')
    ax.plot(evtor1.fhat_tr[:,0],color='blue',lw=3,label='ITSTGCN')
    
    ax.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax.tick_params(axis='y', labelsize=20)
    ax.tick_params(axis='x', labelsize=20)
with plt.style.context('seaborn-white'):
    fig, ax = plt.subplots(figsize=(20,10))
    
    ax.plot(evtor.f_tr[:,1],'--o',color='black',alpha=0.5,label='Imputation')
    ax.plot(data1['y'][:],'-',color='grey',label='Complete Data')
    ax.plot(evtor.fhat_tr[:,1],color='brown',lw=3,label='STGCN')
    ax.plot(evtor1.fhat_tr[:,1],color='blue',lw=3,label='ITSTGCN')
    
    ax.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax.tick_params(axis='y', labelsize=20)
    ax.tick_params(axis='x', labelsize=20)

hyperparameter

import itstgcn

data_dict = itstgcn.load_data('./data/fivenodes.pkl')
loader = itstgcn.DatasetLoader(data_dict)

from torch_geometric_temporal.dataset import ChickenpoxDatasetLoader
loader1 = ChickenpoxDatasetLoader()

from torch_geometric_temporal.dataset import PedalMeDatasetLoader
loader2 = PedalMeDatasetLoader()

from torch_geometric_temporal.dataset import WikiMathsDatasetLoader
loader3 = WikiMathsDatasetLoader()

from torch_geometric_temporal.dataset import WindmillOutputSmallDatasetLoader
loader6 = WindmillOutputSmallDatasetLoader()

from torch_geometric_temporal.dataset import MontevideoBusDatasetLoader
loader10 = MontevideoBusDatasetLoader()
try:
    from tqdm import tqdm
except ImportError:
    def tqdm(iterable):
        return iterable
Dataset RecurrentGCN Method Missing Rate Filters Lags Mean SD
fivenodes GConvGRU IT-STGCN 0.7 12 2 1.167 0.059
fivenodes GConvGRU STGCN 0.7 12 2 2.077 0.252
chickenpox GConvGRU IT-STGCN 0.8 16 4 1.586 0.199
chickenpox GConvGRU STGCN 0.8 16 4 2.529 0.292
pedalme GConvGRU IT-STGCN 0.6 12 4 1.571 0.277
pedalme GConvGRU STGCN 0.6 12 4 1.753 0.239
wikimath GConvGRU IT-STGCN 0.8 12 8 0.687 0.021
wikimath GConvGRU STGCN 0.8 12 8 0.932 0.04
windmillsmall GConvGRU IT-STGCN 0.7 12 8 1.180 0.035
windmillsmall GConvGRU STGCN 0.7 12 8 1.636 0.088
monte GConvGRU IT-STGCN 0.8 12 4 1.096 0.019
monte GConvGRU STGCN 0.8 12 4 1.516 0.040 
import torch
import torch.nn.functional as F
from torch_geometric_temporal.nn.recurrent import GCLSTM

# from torch_geometric_temporal.dataset import ChickenpoxDatasetLoader
from torch_geometric_temporal.signal import temporal_signal_split

# loader1 = ChickenpoxDatasetLoader()

dataset = loader.get_dataset(lags=2)
dataset1 = loader1.get_dataset(lags=4)
dataset2 = loader2.get_dataset(lags=4)
dataset3 = loader3.get_dataset(lags=8)
dataset6 = loader6.get_dataset(lags=8)
dataset10 = loader10.get_dataset(lags=4)

train_dataset, test_dataset = temporal_signal_split(dataset, train_ratio=0.2)
train_dataset1, test_dataset1 = temporal_signal_split(dataset1, train_ratio=0.2)
train_dataset2, test_dataset2 = temporal_signal_split(dataset2, train_ratio=0.2)
train_dataset3, test_dataset3 = temporal_signal_split(dataset3, train_ratio=0.2)
train_dataset6, test_dataset6 = temporal_signal_split(dataset6, train_ratio=0.2)
train_dataset10, test_dataset10 = temporal_signal_split(dataset10, train_ratio=0.2)
class RecurrentGCN(torch.nn.Module):
    def __init__(self, node_features, filters):
        super(RecurrentGCN, self).__init__()
        self.recurrent = GCLSTM(node_features, filters, 1)
        self.linear = torch.nn.Linear(filters, 1)

    def forward(self, x, edge_index, edge_weight, h, c):
        h_0, c_0 = self.recurrent(x, edge_index, edge_weight, h, c)
        h = F.relu(h_0)
        h = self.linear(h)
        return h, h_0, c_0

fivenodes Nodes = 2, Filters = 4

model = RecurrentGCN(node_features=2, filters=4)

optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

model.train()

for epoch in tqdm(range(50)): #200
    cost = 0
    h, c = None, None
    _b = []
    _d=[]
    for time, snapshot in enumerate(train_dataset):
        y_hat, h, c = model(snapshot.x, snapshot.edge_index, snapshot.edge_attr, h, c)
        y_hat = y_hat.reshape(-1)
        cost = cost + torch.mean((y_hat-snapshot.y)**2)
        _b.append(y_hat)
        _d.append(cost)
    cost = cost / (time+1)
    cost.backward()
    optimizer.step()
    optimizer.zero_grad()
100%|██████████| 50/50 [00:06<00:00,  7.93it/s]
model.eval()
cost = 0
_a=[]
_a1=[]
for time, snapshot in enumerate(test_dataset):
    y_hat, h, c = model(snapshot.x, snapshot.edge_index, snapshot.edge_attr, h, c)
    y_hat = y_hat.reshape(-1)
    cost = cost + torch.mean((y_hat-snapshot.y)**2)
    _a.append(y_hat)
    _a1.append(cost)
cost = cost / (time+1)
cost = cost.item()
print("MSE: {:.4f}".format(cost))
MSE: 1.2507
_c = [_a1[i].detach() for i in range(len(_a1))]

_e = [_d[i].detach() for i in range(len(_d))]
fig, (( ax1,ax2),(ax3,ax4),(ax5,ax6)) = plt.subplots(3,2,figsize=(30,20))

ax1.set_title('train node1')
ax1.plot([train_dataset.targets[i][0] for i in range(train_dataset.snapshot_count)])
ax1.plot(torch.tensor([_b[i].detach()[0] for i in range(train_dataset.snapshot_count)]))

ax2.set_title('test node1')
ax2.plot([test_dataset.targets[i][0] for i in range(test_dataset.snapshot_count)])
ax2.plot(torch.tensor([_a[i].detach()[0] for i in range(test_dataset.snapshot_count)]))

ax3.set_title('train node2')
ax3.plot([train_dataset.targets[i][1] for i in range(train_dataset.snapshot_count)])
ax3.plot(torch.tensor([_b[i].detach()[1] for i in range(train_dataset.snapshot_count)]))


ax4.set_title('test node2')
ax4.plot([test_dataset.targets[i][1] for i in range(test_dataset.snapshot_count)])
ax4.plot(torch.tensor([_a[i].detach()[1] for i in range(test_dataset.snapshot_count)]))

ax5.set_title('train cost')
ax5.plot(_e)

ax6.set_title('test cost')
ax6.plot(_c)

import itstgcnGCLSTM
import torch
import itstgcnGCLSTM.planner 
import pandas as pd

import numpy as np
import random
data_dict = itstgcnGCLSTM.load_data('./data/fivenodes.pkl')
loader = itstgcnGCLSTM.DatasetLoader(data_dict)
plans_stgcn_rand = {
    'max_iteration': 15, 
    'method': ['STGCN', 'IT-STGCN'], 
    'mrate': [0.7],
    'lags': [2], 
    'nof_filters': [4], 
    'inter_method': ['linear','nearest'],
    'epoch': [50]
}

plnr = itstgcnGCLSTM.planner.PLNR_STGCN_RAND(plans_stgcn_rand,loader,dataset_name=‘fivenodes’)

plnr.simulate()

plnr = itstgcnGCLSTM.planner.PLNR_STGCN_RAND(plans_stgcn_rand,loader,dataset_name='fivenodes')

plnr.simulate()
plans_stgcn_rand = {
    'max_iteration': 15, 
    'method': ['STGCN', 'IT-STGCN'], 
    'mrate': [0.8],
    'lags': [2], 
    'nof_filters': [4], 
    'inter_method': ['linear','nearest'],
    'epoch': [50]
}
plnr = itstgcnGCLSTM.planner.PLNR_STGCN_RAND(plans_stgcn_rand,loader,dataset_name='fivenodes')

plnr.simulate()
plnr = itstgcnGCLSTM.planner.PLNR_STGCN_RAND(plans_stgcn_rand,loader,dataset_name='fivenodes')

plnr.simulate()
plans_stgcn_rand = {
    'max_iteration': 15, 
    'method': ['STGCN', 'IT-STGCN'], 
    'mrate': [0],
    'lags': [2], 
    'nof_filters': [4], 
    'inter_method': ['linear','nearest'],
    'epoch': [50]
}
plnr = itstgcnGCLSTM.planner.PLNR_STGCN_RAND(plans_stgcn_rand,loader,dataset_name='fivenodes')

plnr.simulate()
plnr = itstgcnGCLSTM.planner.PLNR_STGCN_RAND(plans_stgcn_rand,loader,dataset_name='fivenodes')

plnr.simulate()
mindex= [[],[],[],list(range(50,150)),[]]
# mindex= [list(range(50,150)),[],list(range(50,90)),list(range(50,150)),[]] # node 2
plans_stgcn_block = {
    'max_iteration': 15, 
    'method': ['STGCN', 'IT-STGCN'],
    'mindex': [mindex],
    'lags': [2], 
    'nof_filters': [12], 
    'inter_method': ['linear','nearest'],
    'epoch': [50]
}
plnr = itstgcnGCLSTM.planner.PLNR_STGCN_MANUAL(plans_stgcn_block,loader1,dataset_name='fivenodes')

plnr.simulate(mindex=mindex,mtype='block')
plnr = itstgcnGCLSTM.planner.PLNR_STGCN_MANUAL(plans_stgcn_block,loader1,dataset_name='fivenodes')

plnr.simulate(mindex=mindex,mtype='block')

Chickenpox Nodes = 4, Filters = 16

model1 = RecurrentGCN(node_features=4, filters=16)

optimizer1 = torch.optim.Adam(model1.parameters(), lr=0.01)

model1.train()

for epoch in tqdm(range(50)): #200
    cost = 0
    h, c = None, None
    _b = []
    _d=[]
    for time, snapshot in enumerate(train_dataset1):
        y_hat, h, c = model1(snapshot.x, snapshot.edge_index, snapshot.edge_attr, h, c)
        y_hat = y_hat.reshape(-1)
        cost = cost + torch.mean((y_hat-snapshot.y)**2)
        _b.append(y_hat)
        _d.append(cost)
    cost = cost / (time+1)
    cost.backward()
    optimizer1.step()
    optimizer1.zero_grad()
100%|██████████| 50/50 [00:17<00:00,  2.79it/s]
model1.eval()
cost = 0
_a=[]
_a1=[]
for time, snapshot in enumerate(test_dataset1):
    y_hat, h, c = model1(snapshot.x, snapshot.edge_index, snapshot.edge_attr, h, c)
    y_hat = y_hat.reshape(-1)
    cost = cost + torch.mean((y_hat-snapshot.y)**2)
    _a.append(y_hat)
    _a1.append(cost)
cost = cost / (time+1)
cost = cost.item()
print("MSE: {:.4f}".format(cost))
MSE: 0.6817
_e = [_d[i].detach() for i in range(len(_d))]

_c = [_a1[i].detach() for i in range(len(_a1))]
fig, (( ax1,ax2),(ax3,ax4),(ax5,ax6)) = plt.subplots(3,2,figsize=(30,20))

ax1.set_title('train node1')
ax1.plot([train_dataset1.targets[i][0] for i in range(train_dataset1.snapshot_count)])
ax1.plot(torch.tensor([_b[i].detach()[0] for i in range(train_dataset1.snapshot_count)]))

ax2.set_title('test node1')
ax2.plot([test_dataset1.targets[i][0] for i in range(test_dataset1.snapshot_count)])
ax2.plot(torch.tensor([_a[i].detach()[0] for i in range(test_dataset1.snapshot_count)]))

ax3.set_title('train node2')
ax3.plot([train_dataset1.targets[i][1] for i in range(train_dataset1.snapshot_count)])
ax3.plot(torch.tensor([_b[i].detach()[1] for i in range(train_dataset1.snapshot_count)]))


ax4.set_title('test node2')
ax4.plot([test_dataset1.targets[i][1] for i in range(test_dataset1.snapshot_count)])
ax4.plot(torch.tensor([_a[i].detach()[1] for i in range(test_dataset1.snapshot_count)]))

ax5.set_title('train cost')
ax5.plot(_e)

ax6.set_title('test cost')
ax6.plot(_c)

Pedalme Nodes = 4, Filters = 4

model2 = RecurrentGCN(node_features=4, filters=4)

optimizer2 = torch.optim.Adam(model2.parameters(), lr=0.01)

model2.train()

for epoch in tqdm(range(50)): #200
    cost = 0
    h, c = None, None
    _b = []
    _d=[]
    for time, snapshot in enumerate(train_dataset2):
        y_hat, h, c = model2(snapshot.x, snapshot.edge_index, snapshot.edge_attr, h, c)
        y_hat = y_hat.reshape(-1)
        cost = cost + torch.mean((y_hat-snapshot.y)**2)
        _b.append(y_hat)
        _d.append(cost)
    cost = cost / (time+1)
    cost.backward()
    optimizer2.step()
    optimizer2.zero_grad()
100%|██████████| 50/50 [00:01<00:00, 46.16it/s]
model2.eval()
cost = 0
_a=[]
_a1=[]
for time, snapshot in enumerate(test_dataset2):
    y_hat, h, c = model2(snapshot.x, snapshot.edge_index, snapshot.edge_attr, h, c)
    y_hat = y_hat.reshape(-1)
    cost = cost + torch.mean((y_hat-snapshot.y)**2)
    _a.append(y_hat)
    _a1.append(cost)
cost = cost / (time+1)
cost = cost.item()
print("MSE: {:.4f}".format(cost))
MSE: 0.4983
_e = [_d[i].detach() for i in range(len(_d))]
_c = [_a1[i].detach() for i in range(len(_a1))]
]

fig, (( ax1,ax2),(ax3,ax4),(ax5,ax6)) = plt.subplots(3,2,figsize=(30,20))

ax1.set_title('train node1')
ax1.plot([train_dataset2.targets[i][0] for i in range(train_dataset2.snapshot_count)])
ax1.plot(torch.tensor([_b[i].detach()[0] for i in range(train_dataset2.snapshot_count)]))

ax2.set_title('test node1')
ax2.plot([test_dataset2.targets[i][0] for i in range(test_dataset2.snapshot_count)])
ax2.plot(torch.tensor([_a[i].detach()[0] for i in range(test_dataset2.snapshot_count)]))

ax3.set_title('train node2')
ax3.plot([train_dataset2.targets[i][1] for i in range(train_dataset2.snapshot_count)])
ax3.plot(torch.tensor([_b[i].detach()[1] for i in range(train_dataset2.snapshot_count)]))

ax4.set_title('test node2')
ax4.plot([test_dataset2.targets[i][1] for i in range(test_dataset2.snapshot_count)])
ax4.plot(torch.tensor([_a[i].detach()[1] for i in range(test_dataset2.snapshot_count)]))

ax5.set_title('train cost')
ax5.plot(_e)

ax6.set_title('test cost')
ax6.plot(_c)

Wikimaths Nodes = 8, Filters = 64

model3 = RecurrentGCN(node_features=8, filters=64)

optimizer3 = torch.optim.Adam(model3.parameters(), lr=0.01)

model3.train()

for epoch in tqdm(range(10)): #200
    cost = 0
    h, c = None, None
    _b = []
    _d=[]
    for time, snapshot in enumerate(train_dataset3):
        y_hat, h, c = model3(snapshot.x, snapshot.edge_index, snapshot.edge_attr, h, c)
        y_hat = y_hat.reshape(-1)
        cost = cost + torch.mean((y_hat-snapshot.y)**2)
        _b.append(y_hat)
        _d.append(cost)
    cost = cost / (time+1)
    cost.backward()
    optimizer3.step()
    optimizer3.zero_grad()
100%|██████████| 10/10 [00:16<00:00,  1.66s/it]
model3.eval()
cost = 0
_a=[]
_a1=[]
for time, snapshot in enumerate(test_dataset3):
    y_hat, h, c = model3(snapshot.x, snapshot.edge_index, snapshot.edge_attr, h, c)
    y_hat = y_hat.reshape(-1)
    cost = cost + torch.mean((y_hat-snapshot.y)**2)
    _a.append(y_hat)
    _a1.append(cost)
cost = cost / (time+1)
cost = cost.item()
print("MSE: {:.4f}".format(cost))
MSE: 0.5909
_e = [_d[i].detach() for i in range(len(_d))]
_c = [_a1[i].detach() for i in range(len(_a1))]
fig, (( ax1,ax2),(ax3,ax4),(ax5,ax6)) = plt.subplots(3,2,figsize=(30,20))

ax1.set_title('train node1')
ax1.plot([train_dataset3.targets[i][0] for i in range(train_dataset3.snapshot_count)])
ax1.plot(torch.tensor([_b[i].detach()[0] for i in range(train_dataset3.snapshot_count)]))

ax2.set_title('test node1')
ax2.plot([test_dataset3.targets[i][0] for i in range(test_dataset3.snapshot_count)])
ax2.plot(torch.tensor([_a[i].detach()[0] for i in range(test_dataset3.snapshot_count)]))

ax3.set_title('train node2')
ax3.plot([train_dataset3.targets[i][1] for i in range(train_dataset3.snapshot_count)])
ax3.plot(torch.tensor([_b[i].detach()[1] for i in range(train_dataset3.snapshot_count)]))


ax4.set_title('test node2')
ax4.plot([test_dataset3.targets[i][1] for i in range(test_dataset3.snapshot_count)])
ax4.plot(torch.tensor([_a[i].detach()[1] for i in range(test_dataset3.snapshot_count)]))

ax5.set_title('train cost')
ax5.plot(_e)

ax6.set_title('test cost')
ax6.plot(_c)

Windmillsmall Nodes = 8, Filters = 16

model6 = RecurrentGCN(node_features=8, filters=4)

optimizer6 = torch.optim.Adam(model6.parameters(), lr=0.01)

model6.train()

for epoch in tqdm(range(10)): #200
    cost = 0
    h, c = None, None
    _b = []
    _d=[]
    for time, snapshot in enumerate(train_dataset6):
        y_hat, h, c = model6(snapshot.x, snapshot.edge_index, snapshot.edge_attr, h, c)
        y_hat = y_hat.reshape(-1)
        cost = cost + torch.mean((y_hat-snapshot.y)**2)
        _b.append(y_hat)
        _d.append(cost)
    cost = cost / (time+1)
    cost.backward()
    optimizer6.step()
    optimizer6.zero_grad()
100%|██████████| 10/10 [01:23<00:00,  8.34s/it]
model6.eval()
cost = 0
_a=[]
_a1=[]
for time, snapshot in enumerate(test_dataset6):
    y_hat, h, c = model6(snapshot.x, snapshot.edge_index, snapshot.edge_attr, h, c)
    y_hat = y_hat.reshape(-1)
    cost = cost + torch.mean((y_hat-snapshot.y)**2)
    _a.append(y_hat)
    _a1.append(cost)
cost = cost / (time+1)
cost = cost.item()
print("MSE: {:.4f}".format(cost))
MSE: 1.0018
_e = [_d[i].detach() for i in range(len(_d))]
_c = [_a1[i].detach() for i in range(len(_a1))]
fig, (( ax1,ax2),(ax3,ax4),(ax5,ax6)) = plt.subplots(3,2,figsize=(30,20))

ax1.set_title('train node1')
ax1.plot([train_dataset6.targets[i][0] for i in range(train_dataset6.snapshot_count)])
ax1.plot(torch.tensor([_b[i].detach()[0] for i in range(train_dataset6.snapshot_count)]))

ax2.set_title('test node1')
ax2.plot([test_dataset6.targets[i][0] for i in range(test_dataset6.snapshot_count)])
ax2.plot(torch.tensor([_a[i].detach()[0] for i in range(test_dataset6.snapshot_count)]))

ax3.set_title('train node2')
ax3.plot([train_dataset6.targets[i][1] for i in range(train_dataset6.snapshot_count)])
ax3.plot(torch.tensor([_b[i].detach()[1] for i in range(train_dataset6.snapshot_count)]))


ax4.set_title('test node2')
ax4.plot([test_dataset6.targets[i][1] for i in range(test_dataset6.snapshot_count)])
ax4.plot(torch.tensor([_a[i].detach()[1] for i in range(test_dataset6.snapshot_count)]))

ax5.set_title('train cost')
ax5.plot(_e)

ax6.set_title('test cost')
ax6.plot(_c)

Monte Nodes = 4, Filters = 12

model10 = RecurrentGCN(node_features=4, filters=12)

optimizer10 = torch.optim.Adam(model10.parameters(), lr=0.01)

model10.train()

for epoch in tqdm(range(50)): #200
    cost = 0
    h, c = None, None
    _b = []
    _d=[]
    for time, snapshot in enumerate(train_dataset10):
        y_hat, h, c = model10(snapshot.x, snapshot.edge_index, snapshot.edge_attr, h, c)
        y_hat = y_hat.reshape(-1)
        cost = cost + torch.mean((y_hat-snapshot.y)**2)
        _b.append(y_hat)
        _d.append(cost)
    cost = cost / (time+1)
    cost.backward()
    optimizer10.step()
    optimizer10.zero_grad()
100%|██████████| 50/50 [00:30<00:00,  1.66it/s]
model10.eval()
cost = 0
_a=[]
_a1=[]
for time, snapshot in enumerate(test_dataset10):
    y_hat, h, c = model10(snapshot.x, snapshot.edge_index, snapshot.edge_attr, h, c)
    y_hat = y_hat.reshape(-1)
    cost = cost + torch.mean((y_hat-snapshot.y)**2)
    _a.append(y_hat)
    _a1.append(cost)
cost = cost / (time+1)
cost = cost.item()
print("MSE: {:.4f}".format(cost))
MSE: 0.8881
_e = [_d[i].detach() for i in range(len(_d))]

_c = [_a1[i].detach() for i in range(len(_a1))]
fig, (( ax1,ax2),(ax3,ax4),(ax5,ax6)) = plt.subplots(3,2,figsize=(30,20))

ax1.set_title('train node1')
ax1.plot([train_dataset10.targets[i][0] for i in range(train_dataset10.snapshot_count)])
ax1.plot(torch.tensor([_b[i].detach()[0] for i in range(train_dataset10.snapshot_count)]))

ax2.set_title('test node1')
ax2.plot([test_dataset10.targets[i][0] for i in range(test_dataset10.snapshot_count)])
ax2.plot(torch.tensor([_a[i].detach()[0] for i in range(test_dataset10.snapshot_count)]))

ax3.set_title('train node2')
ax3.plot([train_dataset10.targets[i][10] for i in range(train_dataset10.snapshot_count)])
ax3.plot(torch.tensor([_b[i].detach()[10] for i in range(train_dataset10.snapshot_count)]))


ax4.set_title('test node2')
ax4.plot([test_dataset10.targets[i][10] for i in range(test_dataset10.snapshot_count)])
ax4.plot(torch.tensor([_a[i].detach()[10] for i in range(test_dataset10.snapshot_count)]))

ax5.set_title('train cost')
ax5.plot(_e)

ax6.set_title('test cost')
ax6.plot(_c)