EvolveGCNH_Simulation Tables_reshape

Simulation Tables

import

import pandas as pd

data_fivenodes = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_fivedones_Simulation.csv')

data_chickenpox = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_chikenpox_Simulation.csv')

data_pedal = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_pedalme_Simulation.csv')

data_pedal2 = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_pedalme_Simulation_itstgcnsnd.csv')

data__wiki = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_wikimath.csv')

data_wiki_GSO = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_wikimath_GSO_st.csv')

data_windmillsmall = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_windmillsmall.csv')

data_monte = pd.read_csv('./simulation_results/Real_simulation_reshape/EvolveGCNH_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	12	linear	50	1.197773	11.067156
1	fivenodes	STGCN	0.7	rand	2	12	nearest	50	1.123908	11.630244
2	fivenodes	IT-STGCN	0.7	rand	2	12	linear	50	1.164585	16.001837
3	fivenodes	IT-STGCN	0.7	rand	2	12	nearest	50	1.251060	23.003865
4	fivenodes	STGCN	0.7	rand	2	12	linear	50	1.199062	20.373322
...	...	...	...	...	...	...	...	...	...	...
535	monte	IT-STGCN	0.7	rand	4	12	nearest	50	2.402173	274.951623
536	monte	STGCN	0.7	rand	4	12	nearest	50	3.650682	78.908180
537	monte	IT-STGCN	0.7	rand	4	12	nearest	50	1.702123	334.897944
538	monte	STGCN	0.7	rand	4	12	nearest	50	2.133708	127.980580
539	monte	IT-STGCN	0.7	rand	4	12	nearest	50	1.962617	244.929669

3060 rows × 10 columns

data.to_csv('./simulation_results/Real_simulation_reshape/Final_Simulation_EvolveGCNH.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_EvolveGCNH_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	12	IT-STGCN	2	1.174	0.074
1	12	STGCN	2	1.175	0.062

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	0.7	12	IT-STGCN	2	1.197	0.057
1	0.7	12	STGCN	2	1.217	0.064
2	0.8	12	IT-STGCN	2	1.210	0.060
3	0.8	12	STGCN	2	1.217	0.060

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	1.170	0.055
1	0.125	12	STGCN	1.189	0.060

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)

	nof_filters	method	mean	std
0	32	IT-STGCN	0.996	0.019
1	32	STGCN	1.004	0.021

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	32	IT-STGCN	1.011	0.019
1	0.3	linear	32	STGCN	1.058	0.015
2	0.8	linear	32	IT-STGCN	1.140	0.042
3	0.8	linear	32	STGCN	1.203	0.061

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	32	IT-STGCN	1.007400	0.020847
1	linear	0.28777	32	STGCN	1.027340	0.022523
2	nearest	0.28777	32	IT-STGCN	1.011141	0.017937
3	nearest	0.28777	32	STGCN	1.030143	0.016657

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	2	IT-STGCN	1.223	0.053
1	4	2	STGCN	1.204	0.060

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	1.245	0.069
1	0.3	4	linear	STGCN	1.273	0.057
2	0.3	4	nearest	IT-STGCN	1.238	0.046
3	0.3	4	nearest	STGCN	1.244	0.054
4	0.6	4	linear	IT-STGCN	1.279	0.057
5	0.6	4	linear	STGCN	1.301	0.061
6	0.6	4	nearest	IT-STGCN	1.262	0.091
7	0.6	4	nearest	STGCN	1.284	0.066

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	1.259	0.085
1	0.286	4	linear	STGCN	1.246	0.073
2	0.286	4	nearest	IT-STGCN	1.222	0.040
3	0.286	4	nearest	STGCN	1.265	0.072

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	1.218	0.058
1	0.3	4	linear	STGCN	1.237	0.051
2	0.3	4	nearest	IT-STGCN	1.197	0.068
3	0.3	4	nearest	STGCN	1.237	0.058
4	0.6	4	linear	IT-STGCN	1.229	0.070
5	0.6	4	linear	STGCN	1.278	0.066
6	0.6	4	nearest	IT-STGCN	1.246	0.067
7	0.6	4	nearest	STGCN	1.291	0.063

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	1.188	0.042
1	0.286	4	linear	STGCN	1.230	0.056
2	0.286	4	nearest	IT-STGCN	1.195	0.037
3	0.286	4	nearest	STGCN	1.240	0.062

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	0.784	0.027
1	8	12	STGCN	0.771	0.028

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	0.775	0.021
1	0.3	8	STGCN	0.787	0.024
2	0.5	8	IT-STGCN	0.791	0.029
3	0.5	8	STGCN	0.804	0.026
4	0.6	8	IT-STGCN	0.795	0.025
5	0.6	8	STGCN	0.843	0.042
6	0.8	8	IT-STGCN	0.877	0.045
7	0.8	8	STGCN	0.915	0.063

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	0.775854	0.027860
1	0.119837	8	STGCN	0.773374	0.020599

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	0.794	0.031
1	0.512	8	STGCN	0.818	0.031

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	0.986	0.002
1	8	STGCN	0.986	0.002

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	1.129	0.035
1	0.7	8	STGCN	1.330	0.137

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	0.986	0.003
1	0.081	8	STGCN	0.993	0.003

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	1.358	0.107
1	4	STGCN	1.006	0.018

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	0.3	4	nearest	IT-STGCN	1.398504	0.135477
1	0.3	4	nearest	STGCN	1.563461	0.134200
2	0.5	4	nearest	IT-STGCN	1.441138	0.110318
3	0.5	4	nearest	STGCN	1.730591	0.311464
4	0.7	4	nearest	IT-STGCN	1.700290	0.328235
5	0.7	4	nearest	STGCN	2.118390	0.528367
6	0.8	4	nearest	IT-STGCN	1.845349	0.504113
7	0.8	4	nearest	STGCN	2.158357	0.545361

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	1.391881	0.110351
1	0.149142	4	nearest	STGCN	1.612466	0.216224

Check

import itstgcnEvolveGCNH
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 = itstgcnEvolveGCNH.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*0.7)),[np.array(list(range(20,30)))]]
dataset_miss = itstgcnEvolveGCNH.miss(dataset,mindex,mtype='block')

dataset_padded = itstgcnEvolveGCNH.padding(dataset_miss,interpolation_method='linear')

- 학습

lrnr = itstgcnEvolveGCNH.StgcnLearner(dataset_padded)

lrnr.learn(filters= 12,epoch=5)

lrnr1 = itstgcnEvolveGCNH.ITStgcnLearner(dataset_padded)

lrnr1.learn(filters=32,epoch=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)

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=32,epoch=5)

lrnr1 = itstgcnsnd.ITStgcnLearner(dataset_padded)

lrnr1.learn(filters=32,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

EvolveGCNH?

Init signature:
EvolveGCNH(
    num_of_nodes: int,
    in_channels: int,
    improved: bool = False,
    cached: bool = False,
    normalize: bool = True,
    add_self_loops: bool = True,
)
Docstring:     
An implementation of the Evolving Graph Convolutional Hidden Layer.
For details see this paper: `"EvolveGCN: Evolving Graph Convolutional
Networks for Dynamic Graph." <https://arxiv.org/abs/1902.10191>`_
Args:
    num_of_nodes (int): Number of vertices.
    in_channels (int): Number of filters.
    improved (bool, optional): If set to :obj:`True`, the layer computes
        :math:`\mathbf{\hat{A}}` as :math:`\mathbf{A} + 2\mathbf{I}`.
        (default: :obj:`False`)
    cached (bool, optional): If set to :obj:`True`, the layer will cache
        the computation of :math:`\mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}}
        \mathbf{\hat{D}}^{-1/2}` on first execution, and will use the
        cached version for further executions.
        This parameter should only be set to :obj:`True` in transductive
        learning scenarios. (default: :obj:`False`)
    normalize (bool, optional): Whether to add self-loops and apply
        symmetric normalization. (default: :obj:`True`)
    add_self_loops (bool, optional): If set to :obj:`False`, will not add
        self-loops to the input graph. (default: :obj:`True`)
Init docstring: Initializes internal Module state, shared by both nn.Module and ScriptModule.
File:           ~/anaconda3/envs/temp_csy/lib/python3.8/site-packages/torch_geometric_temporal/nn/recurrent/evolvegcnh.py
Type:           type
Subclasses:     

import torch
import torch.nn.functional as F
from torch_geometric_temporal.nn.recurrent import EvolveGCNH

# 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)

_a = itstgcn.load_data('./data/Windmillsmall.pkl')

dataset6 = _a.get_dataset(lags=8)

train_dataset6, test_dataset6 = temporal_signal_split(dataset6, train_ratio=0.2)

class RecurrentGCN(torch.nn.Module):
    def __init__(self, num_of_nodes, node_features, filters):
        super(RecurrentGCN, self).__init__()
        self.recurrent = EvolveGCNH(num_of_nodes, node_features)
        self.linear = torch.nn.Linear(node_features, 1)

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

fivenodes Nodes = 2, Filters =

model = RecurrentGCN(num_of_nodes=5, node_features=2, filters= 12)

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

model.train()

for epoch in tqdm(range(200)):
    cost = 0
    _b=[]
    _d=[]
    for time, snapshot in enumerate(train_dataset):
        y_hat = model(snapshot.x, snapshot.edge_index, snapshot.edge_attr).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%|██████████| 200/200 [00:13<00:00, 14.93it/s]

model.eval()
cost = 0
_a=[]
_a1=[]
for time, snapshot in enumerate(test_dataset):
    y_hat = model(snapshot.x, snapshot.edge_index, snapshot.edge_attr).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: 1.1026

_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)

Chickenpox Nodes = 4, Filters =

model1 = RecurrentGCN(num_of_nodes=20 ,node_features=4, filters=12)

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

model1.train()

for epoch in tqdm(range(200)):
    cost = 0
    _b=[]
    _d=[]
    for time, snapshot in enumerate(train_dataset1):
        y_hat = model1(snapshot.x, snapshot.edge_index, snapshot.edge_attr).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%|██████████| 200/200 [00:50<00:00,  3.95it/s]

model1.eval()
cost = 0
_a=[]
_a1=[]
for time, snapshot in enumerate(test_dataset1):
    y_hat = model1(snapshot.x, snapshot.edge_index, snapshot.edge_attr).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: 0.9892

_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 = 12

model2 = RecurrentGCN(num_of_nodes=15, node_features=4, filters=12)

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

model2.train()
    
for epoch in tqdm(range(200)):
    cost = 0
    _b=[]
    _d=[]
    for time, snapshot in enumerate(train_dataset2):
        y_hat = model2(snapshot.x, snapshot.edge_index, snapshot.edge_attr).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%|██████████| 200/200 [00:04<00:00, 48.87it/s]

model2.eval()
cost = 0
_a=[]
_a1=[]
for time, snapshot in enumerate(test_dataset2):
    y_hat = model2(snapshot.x, snapshot.edge_index, snapshot.edge_attr).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: 0.5801

_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 =

model3 = RecurrentGCN(num_of_nodes=1068,node_features=8, filters=12)

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

model3.train()

for epoch in tqdm(range(50)):
    cost = 0
    _b=[]
    _d=[]
    for time, snapshot in enumerate(train_dataset3):
        y_hat = model3(snapshot.x, snapshot.edge_index, snapshot.edge_attr).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%|██████████| 50/50 [03:07<00:00,  3.75s/it]

model3.eval()
cost = 0
_a=[]
_a1=[]
for time, snapshot in enumerate(test_dataset3):
    y_hat = model3(snapshot.x, snapshot.edge_index, snapshot.edge_attr).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: 0.7479

_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 =

model6 = RecurrentGCN(num_of_nodes=11, node_features=8, filters=12)

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

model6.train()

for epoch in tqdm(range(10)):
    cost = 0
    _b=[]
    _d=[]
    for time, snapshot in enumerate(train_dataset6):
        y_hat = model6(snapshot.x, snapshot.edge_index, snapshot.edge_attr).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 [01:19<00:00,  7.99s/it]

model6.eval()
cost = 0
_a=[]
_a1=[]
for time, snapshot in enumerate(test_dataset6):
    y_hat = model6(snapshot.x, snapshot.edge_index, snapshot.edge_attr).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: 1.0017

_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 =

model10 = RecurrentGCN(num_of_nodes=675, node_features=4, filters=16)

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

model10.train()

for epoch in tqdm(range(200)):
    cost = 0
    _b=[]
    _d=[]
    for time, snapshot in enumerate(train_dataset10):
        y_hat = model10(snapshot.x, snapshot.edge_index, snapshot.edge_attr).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%|██████████| 200/200 [01:12<00:00,  2.74it/s]

model10.eval()
cost = 0
_a=[]
_a1=[]
for time, snapshot in enumerate(test_dataset10):
    y_hat = model10(snapshot.x, snapshot.edge_index, snapshot.edge_attr).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: 0.9892

_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)