Toy Example Figure(Intro)

edit

교수님 설정(수정 안 한 것)

T = 100
t = np.arange(T)/T * 5

x = 0.1*np.sin(2*t)+0.1*np.sin(4*t)+0.1*np.sin(8*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}
df1 = pd.DataFrame({'x':x,'y':y,'xer':x,'yer':y})

loader1 = itstgcn.DatasetLoader(data_dict1)

dataset_DCRNN = loader1.get_dataset(lags=1)

mindex = [[np.array(list(range(20,35)))],random.sample(range(0, T), int(T*0.5))]
dataset_miss_DCRNN = itstgcn.miss(dataset_DCRNN,mindex,mtype='block')

dataset_padded_DCRNN = itstgcn.padding(dataset_miss_DCRNN,interpolation_method='linear')

lrnr_DCRNN = itstgcn.StgcnLearner(dataset_padded_DCRNN)

lrnr_DCRNN.learn(filters=1,epoch=10,lr=0.01,RecurrentGCN='GConvLSTM')

evtor_DCRNN = Eval_csy(lrnr_DCRNN,dataset_padded_DCRNN)

lrnr_DCRNN2 = itstgcn.ITStgcnLearner(dataset_padded_DCRNN)

lrnr_DCRNN2.learn(filters=1,epoch=10,lr=0.01,RecurrentGCN='GConvLSTM')

---

초기 설정(수정 안 한 것)

T = 200
t = np.arange(T)/T * 10
x = 0.1*np.sin(2*t)+0.2*np.sin(4*t)+0.1*np.sin(8*t)+0.2*np.sin(16*t)
eps_x  = np.random.normal(size=T)*0
y = x.copy()
eps_y  = np.random.normal(size=T)*0
x = x*0.35
y = y*0.3
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],[1,0]]).tolist()

data_dict1 = {'edges':_edges1, 'node_ids':_node_ids, 'FX':_FX1}
df1 = pd.DataFrame({'x':x,'y':y,'xer':x,'yer':y})

loader1 = itstgcn.DatasetLoader(data_dict1)

dataset_GConvLSTM = loader1.get_dataset(lags=1)

mindex = [random.sample(range(0, T), int(T*0.5)),[np.array(list(range(100,120)))]]
dataset_miss_GConvLSTM = itstgcn.miss(dataset_GConvLSTM,mindex,mtype='block')

dataset_padded_cubic_GConvLSTM = itstgcn.padding(dataset_miss_GConvLSTM,interpolation_method='cubic')

lrnr_GConvLSTM = itstgcn.StgcnLearner(dataset_padded_cubic_GConvLSTM)

lrnr_GConvLSTM.learn(filters=8,epoch=50,RecurrentGCN='GConvLSTM')

---

import

import itstgcn 
import torch
import numpy as np

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()

import pickle
import pandas as pd

def load_data(fname):
    with open(fname, 'rb') as outfile:
        data_dict = pickle.load(outfile)
    return data_dict

def save_data(data_dict,fname):
    with open(fname,'wb') as outfile:
        pickle.dump(data_dict,outfile)

from plotnine import *

Example

T = 500
t = np.arange(T)/T * 5

x = 1*np.sin(2*t)+np.random.rand(T)+np.sin(4*t)+1*np.sin(8*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')

_x = np.array(dataset_padded.targets)[:,0]

plt.plot(_x)
plt.plot(itstgcn.trim(_x))

# T = 100
# t = np.arange(T)/T * 5

# x = 0.01*np.sin(2*t)+0.1*np.sin(4*t)+0.1*np.sin(8*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')

data_dict1 = itstgcn.load_data('./data/toy_example1.pkl')
loader1 = itstgcn.DatasetLoader(data_dict1)

loader1 = itstgcn.DatasetLoader(data_dict1)

dataset = loader1.get_dataset(lags=4)

mindex = [random.sample(range(0, T), int(T*0.9)),[np.array(list(range(20,35)))]]
dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

# mindex = [[np.array(list(range(181,300)))],[np.array(list(range(20,35)))]]
# dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

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

lrnr = itstgcn.StgcnLearner(dataset_padded)

lrnr.learn(filters=16,epoch=5)

5/5

evtor = Eval_csy(lrnr,dataset_padded)

lrnr_2 = itstgcn.ITStgcnLearner(dataset_padded)

lrnr_2.learn(filters=16,epoch=5)

5/5

evtor_2 = Eval_csy(lrnr_2,dataset_padded)

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['x'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,0]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,0],'--o',color='C3',alpha=0.8,label='Interpolarion')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,0],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,0],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 2)',fontsize=40)
    ax1.plot(data1['y'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    ax2.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.tensor(dataset_miss.targets).reshape(-1,2)[:,1],'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    ax3.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,1],'--o',color='C3',alpha=0.8,label='Interpolarion')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,1],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,1],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot((mindex[1][0][0]+mindex[1][0][len(mindex[1][0])-1])/2, 0.1,'s', markersize=110, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)

data_dict1 = itstgcn.load_data('./data/toy_example1.pkl')
loader1 = itstgcn.DatasetLoader(data_dict1)

loader1 = itstgcn.DatasetLoader(data_dict1)

dataset = loader1.get_dataset(lags=4)

mindex = [[np.array(list(range(40,55)))],[np.array(list(range(20,35)))]]
dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

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

lrnr = itstgcn.StgcnLearner(dataset_padded)

lrnr.learn(filters=1,epoch=10)

10/10

evtor = Eval_csy(lrnr,dataset_padded)

lrnr_2 = itstgcn.ITStgcnLearner(dataset_padded)

lrnr_2.learn(filters=1,epoch=10)

10/10

evtor_2 = Eval_csy(lrnr_2,dataset_padded)

np.array(dataset_miss.features).shape

(96, 2, 4)

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['x'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,0]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,0],'--o',color='C3',alpha=0.8,label='Interpolarion')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,0],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,0],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)

시도 1

T = 500
t = np.arange(T)/T * 5

x = np.sin(2*t)+0.5*np.random.rand(T)+np.sin(4*t)+1.5*np.sin(8*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 = itstgcn.DatasetLoader(data_dict1)

dataset = loader1.get_dataset(lags=4)

mindex = [random.sample(range(0, T), int(T*0.8)),[np.array(list(range(20,35)))]]
dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

# mindex = [[np.array(list(range(181,300)))],[np.array(list(range(20,35)))]]
# dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

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

lrnr = itstgcn.StgcnLearner(dataset_padded)

lrnr.learn(filters=4,epoch=5)

5/5

evtor = Eval_csy(lrnr,dataset_padded)

lrnr_2 = itstgcn.ITStgcnLearner(dataset_padded)

lrnr_2.learn(filters=4,epoch=5)

5/5

evtor_2 = Eval_csy(lrnr_2,dataset_padded)

# with plt.style.context('seaborn-white'):
#     fig, ax1 = plt.subplots(figsize=(40,15))
#     # fig.suptitle('Figure 1(node 1)',fontsize=40)
#     ax1.plot(data1['x'][:],'-',color='C3',label='Complete Data')
#     ax1.legend(fontsize=40,loc='lower left',facecolor='white', frameon=True)
#     ax1.tick_params(axis='y', labelsize=40)
#     ax1.tick_params(axis='x', labelsize=40)
# plt.savefig('node1_fst.png')

# with plt.style.context('seaborn-white'):
#     fig, ax2 = plt.subplots(figsize=(40,15))
#     # fig.suptitle('Figure 1(node 1)',fontsize=40)
#     ax2.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
#     ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,0]],dim=0),'--o',color='C3',label='Observed Data',markersize=15)
#     ax2.legend(fontsize=40,loc='lower left',facecolor='white', frameon=True)
#     ax2.tick_params(axis='y', labelsize=40)
#     ax2.tick_params(axis='x', labelsize=40)
# plt.savefig('node1_snd.png')

# with plt.style.context('seaborn-white'):
#     fig, ax3 = plt.subplots(figsize=(40,15))
#     # fig.suptitle('Figure 1(node 1)',fontsize=40)    
#     ax3.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
#     ax3.plot(evtor_2.f_tr[:,0],'--o',color='C3',alpha=0.8,label='Interpolarion')
#     ax3.legend(fontsize=40,loc='lower left',facecolor='white', frameon=True)
#     ax3.tick_params(axis='y', labelsize=40)
#     ax3.tick_params(axis='x', labelsize=40)
# plt.savefig('node1_3rd.png')

# with plt.style.context('seaborn-white'):
#     fig, ax4 = plt.subplots(figsize=(40,15))
#     # fig.suptitle('Figure 1(node 1)',fontsize=40)
#     ax4.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
#     ax4.plot(evtor.fhat_tr[:,0],color='brown',lw=3,label='STGCN')
#     ax4.plot(evtor_2.fhat_tr[:,0],color='blue',lw=3,label='ITSTGCN')
#     ax4.plot(138, -1.2, 'o', markersize=230, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
#     ax4.plot(220, -1.5, 'o', markersize=200, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
#     ax4.plot(290, -1.2, 'o', markersize=310, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
#     ax4.plot(455, -0.9, 'o', markersize=280, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
#     ax4.legend(fontsize=40,loc='lower left',facecolor='white', frameon=True)
#     ax4.tick_params(axis='y', labelsize=40)
#     ax4.tick_params(axis='x', labelsize=40)
# plt.savefig('node1_4th_1.png')

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['x'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,0]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,0],'--o',color='C3',alpha=0.8,label='Interpolarion')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,0],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,0],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try2_node1.png')

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 2)',fontsize=40)
    ax1.plot(data1['y'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,1]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,1],'--o',color='C3',alpha=0.8,label='Interpolarion')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,1],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,1],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try2_node2.png')

시도 2

T = 500
t = np.arange(T)/T * 5

x = 1*np.sin(2*t)+0.4*np.random.rand(T)+np.sin(4*t)+1.3*np.sin(8*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 = itstgcn.DatasetLoader(data_dict1)

dataset = loader1.get_dataset(lags=4)

mindex = [random.sample(range(0, T), int(T*0.8)),[np.array(list(range(20,35)))]]
dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

# mindex = [[np.array(list(range(181,300)))],[np.array(list(range(20,35)))]]
# dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

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

lrnr = itstgcn.StgcnLearner(dataset_padded)

lrnr.learn(filters=8,epoch=5)

5/5

evtor = Eval_csy(lrnr,dataset_padded)

lrnr_2 = itstgcn.ITStgcnLearner(dataset_padded)

lrnr_2.learn(filters=8,epoch=5)

5/5

evtor_2 = Eval_csy(lrnr_2,dataset_padded)

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['x'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,0]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,0],'--o',color='C3',alpha=0.8,label='Interpolation')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,0],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,0],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try1_node1.png')

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['y'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,1]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,1],'--o',color='C3',alpha=0.8,label='Interpolation')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,1],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,1],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try1_node2.png')

시도 3

T = 500
t = np.arange(T)/T * 5

x = 1*np.sin(2*t)+0.3*np.random.rand(T)+np.sin(4*t)+1.5*np.sin(8*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')

_data_dict = itstgcn.load_data('./data/fivenodes.pkl')
_loader = itstgcn.DatasetLoader(_data_dict)

_dataset = _loader.get_dataset(lags=2)

mindex = [random.sample(range(0, 200), int(200*0.8)),[np.array(list(range(40,100)))]]
_dataset_miss = itstgcn.miss(_dataset,mindex,mtype='block')

# mindex = itstgcn.rand_mindex(dataset,mrate=0.8)
# dataset_miss = itstgcn.miss(dataset,mindex,mtype='rand')

# mindex = [[np.array(list(range(181,300)))],[np.array(list(range(20,35)))]]
# dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

_dataset_padded = itstgcn.padding(_dataset_miss,interpolation_method='linear')

_lrnr = itstgcn.StgcnLearner(_dataset_padded)

_lrnr.learn(filters=12,epoch=10)

10/10

_evtor = Eval_csy(_lrnr,_dataset_padded)

_lrnr_2 = itstgcn.ITStgcnLearner(_dataset_padded)

_lrnr_2.learn(filters=12,epoch=10)

10/10

_evtor_2 = Eval_csy(_lrnr_2,_dataset_padded)

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['x'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(torch.tensor(_dataset.features)[:,0,0],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(_dataset.features)[:2,0,0],torch.tensor(_dataset_miss.targets).reshape(-1,5)[:,0]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(torch.tensor(_dataset.features)[:,0,0],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(_evtor_2.f_tr[:,0],'--o',color='C3',alpha=0.8,label='Interpolation')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(torch.tensor(_dataset.features)[:,0,0],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(_evtor.fhat_tr[:,0],color='brown',lw=3,label='STGCN')
    ax4.plot(_evtor_2.fhat_tr[:,0],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try2_node1.png')

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 2)',fontsize=40)
    ax1.plot(torch.tensor(_dataset.features)[:,1,0],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(torch.tensor(_dataset.features)[:,1,0],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(_dataset.features)[:2,1,0],torch.tensor(_dataset_miss.targets).reshape(-1,5)[:,1]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(torch.tensor(_dataset.features)[:,1,0],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(_evtor_2.f_tr[:,1],'--o',color='C3',alpha=0.8,label='Interpolation')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(torch.tensor(_dataset.features)[:,1,0],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(_evtor.fhat_tr[:,1],color='brown',lw=3,label='STGCN')
    ax4.plot(_evtor_2.fhat_tr[:,1],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try2_node2.png')

시도 4 noise 0

T = 500
t = np.arange(T)/T * 5

x = 1*np.sin(2*t)+np.sin(4*t)+1.2*np.sin(8*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 = itstgcn.DatasetLoader(data_dict1)

dataset = loader1.get_dataset(lags=4)

mindex = [random.sample(range(0, T), int(T*0.5)),[np.array(list(range(50,95)))]]
dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

# mindex = [[np.array(list(range(181,300)))],[np.array(list(range(20,35)))]]
# dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

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

lrnr = itstgcn.StgcnLearner(dataset_padded)

lrnr.learn(filters=16,epoch=5)

5/5

evtor = Eval_csy(lrnr,dataset_padded)

lrnr_2 = itstgcn.ITStgcnLearner(dataset_padded)

lrnr_2.learn(filters=16,epoch=5)

5/5

evtor_2 = Eval_csy(lrnr_2,dataset_padded)

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['x'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,0]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,0],'--o',color='C3',alpha=0.8,label='Interpolation')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,0],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,0],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(135, -1.5, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(220, -1.5, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(290, -1.8, 'o', markersize=120, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(450, -1.5, 'o', markersize=120, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try2_node1_2.png')

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 2)',fontsize=40)
    ax1.plot(data1['y'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,1]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,1],'--o',color='C3',alpha=0.8,label='Interpolation')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,1],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,1],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(75, 0.75, 'o', markersize=150, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try2_node2_2.png')

시도 5 noise 10%

T = 500
t = np.arange(T)/T * 5

x = 1.5*np.sin(2*t)+0.1*np.random.rand(T)+np.sin(4*t)+1.5*np.sin(8*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 = itstgcn.DatasetLoader(data_dict1)

dataset = loader1.get_dataset(lags=4)

mindex = [random.sample(range(0, T), int(T*0.8)),[np.array(list(range(20,35)))]]
dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

# mindex = [[np.array(list(range(181,300)))],[np.array(list(range(20,35)))]]
# dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

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

lrnr = itstgcn.StgcnLearner(dataset_padded)

lrnr.learn(filters=8,epoch=5)

5/5

evtor = Eval_csy(lrnr,dataset_padded)

lrnr_2 = itstgcn.ITStgcnLearner(dataset_padded)

lrnr_2.learn(filters=8,epoch=5)

5/5

evtor_2 = Eval_csy(lrnr_2,dataset_padded)

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['x'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,0]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,0],'--o',color='C3',alpha=0.8,label='Interpolation')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,0],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,0],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try3_node1_noise10.png')

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['y'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,1]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,1],'--o',color='C3',alpha=0.8,label='Interpolation')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,1],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,1],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try3_node2_noise10.png')

시도 6 noise 20%

T = 500
t = np.arange(T)/T * 5

x = 1.5*np.sin(2*t)+0.2*np.random.rand(T)+np.sin(4*t)+1.5*np.sin(8*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 = itstgcn.DatasetLoader(data_dict1)

dataset = loader1.get_dataset(lags=4)

mindex = [random.sample(range(0, T), int(T*0.75)),[np.array(list(range(20,35)))]]
dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

# mindex = [[np.array(list(range(181,300)))],[np.array(list(range(20,35)))]]
# dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

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

lrnr = itstgcn.StgcnLearner(dataset_padded)

lrnr.learn(filters=8,epoch=5)

5/5

evtor = Eval_csy(lrnr,dataset_padded)

lrnr_2 = itstgcn.ITStgcnLearner(dataset_padded)

lrnr_2.learn(filters=8,epoch=5)

5/5

evtor_2 = Eval_csy(lrnr_2,dataset_padded)

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['x'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,0]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,0],'--o',color='C3',alpha=0.8,label='Interpolation')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,0],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,0],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try4_node1_noise20.png')

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['y'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,1]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,1],'--o',color='C3',alpha=0.8,label='Interpolation')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,1],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,1],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try2_node2_noise20.png')

시도 7 noise 30%

T = 500
t = np.arange(T)/T * 5

x = 1.5*np.sin(2*t)+0.3*np.random.rand(T)+np.sin(4*t)+1.5*np.sin(8*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 = itstgcn.DatasetLoader(data_dict1)

dataset = loader1.get_dataset(lags=4)

mindex = [random.sample(range(0, T), int(T*0.75)),[np.array(list(range(20,35)))]]
dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

# mindex = [[np.array(list(range(181,300)))],[np.array(list(range(20,35)))]]
# dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

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

lrnr = itstgcn.StgcnLearner(dataset_padded)

lrnr.learn(filters=8,epoch=5)

5/5

evtor = Eval_csy(lrnr,dataset_padded)

lrnr_2 = itstgcn.ITStgcnLearner(dataset_padded)

lrnr_2.learn(filters=8,epoch=5)

5/5

evtor_2 = Eval_csy(lrnr_2,dataset_padded)

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['x'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,0]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,0],'--o',color='C3',alpha=0.8,label='Interpolation')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,0],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,0],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try5_node1_noise30.png')

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['y'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,1]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,1],'--o',color='C3',alpha=0.8,label='Interpolation')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,1],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,1],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try5_node2_noise20.png')

시도 8 noise 30% only random

T = 500
t = np.arange(T)/T * 5

x = 1*np.sin(2*t)+0.3*np.random.rand(T)+np.sin(4*t)+1.5*np.sin(8*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 = itstgcn.DatasetLoader(data_dict1)

dataset = loader1.get_dataset(lags=4)

# mindex = [random.sample(range(0, T), int(T*0.7)),[np.array(list(range(20,35)))]]
# dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

mindex = itstgcn.rand_mindex(dataset,mrate=0.75)
dataset_miss = itstgcn.miss(dataset,mindex,mtype='rand')

# mindex = [[np.array(list(range(181,300)))],[np.array(list(range(20,35)))]]
# dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

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

lrnr = itstgcn.StgcnLearner(dataset_padded)

lrnr.learn(filters=8,epoch=5)

5/5

evtor = Eval_csy(lrnr,dataset_padded)

lrnr_2 = itstgcn.ITStgcnLearner(dataset_padded)

lrnr_2.learn(filters=8,epoch=5)

5/5

evtor_2 = Eval_csy(lrnr_2,dataset_padded)

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['x'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,0]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,0],'--o',color='C3',alpha=0.8,label='Interpolation')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,0],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,0],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try2_node1.png')

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 2)',fontsize=40)
    ax1.plot(data1['y'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,1]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,1],'--o',color='C3',alpha=0.8,label='Interpolation')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,1],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,1],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try2_node2.png')

시도 9 noise 40%

T = 500
t = np.arange(T)/T * 5

x = 1.5*np.sin(2*t)+0.4*np.random.rand(T)+np.sin(4*t)+1.5*np.sin(8*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 = itstgcn.DatasetLoader(data_dict1)

dataset = loader1.get_dataset(lags=4)

mindex = [[np.array(list(range(40,85)))],random.sample(range(0, T), int(T*0.7))]
dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

# mindex = [[np.array(list(range(181,300)))],[np.array(list(range(20,35)))]]
# dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

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

lrnr = itstgcn.StgcnLearner(dataset_padded)

lrnr.learn(filters=32,epoch=5)

5/5

evtor = Eval_csy(lrnr,dataset_padded)

lrnr_2 = itstgcn.ITStgcnLearner(dataset_padded)

lrnr_2.learn(filters=32,epoch=5)

5/5

evtor_2 = Eval_csy(lrnr_2,dataset_padded)

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['x'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,0]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,0],'--o',color='C3',alpha=0.8,label='Interpolation')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,0],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,0],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try6_node1_noise40.png')

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['y'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,1]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,1],'--o',color='C3',alpha=0.8,label='Interpolarion')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,1],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,1],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try6_node2_noise40.png')

시도 10 noise 50%

T = 500
t = np.arange(T)/T * 5

x = 1.5*np.sin(2*t)+0.1*np.random.rand(T)+np.sin(4*t)+1.5*np.sin(8*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 = itstgcn.DatasetLoader(data_dict1)

dataset = loader1.get_dataset(lags=4)

mindex = [random.sample(range(0, T), int(T*0.7)),[np.array(list(range(40,85)))]]
dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

# mindex = [[np.array(list(range(181,300)))],[np.array(list(range(20,35)))]]
# dataset_miss = itstgcn.miss(dataset,mindex,mtype='block')

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

lrnr = itstgcn.StgcnLearner(dataset_padded)

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

5/5

evtor = Eval_csy(lrnr,dataset_padded)

lrnr_2 = itstgcn.ITStgcnLearner(dataset_padded)

lrnr_2.learn(filters=12,epoch=5)

5/5

evtor_2 = Eval_csy(lrnr_2,dataset_padded)

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['x'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,0]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,0],'--o',color='C3',alpha=0.8,label='Interpolarion')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['x'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,0],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,0],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try6_node1_noise40.png')

with plt.style.context('seaborn-white'):
    # plt.rcParams['font.family'] = 'xkcd'
    # plt.xkcd(scale=0,length=200)
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,figsize=(40,15))
    fig.suptitle('Figure 1(node 1)',fontsize=40)
    ax1.plot(data1['y'][:],'-',color='C3',label='Complete Data')
    ax1.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax1.tick_params(axis='y', labelsize=20)
    ax1.tick_params(axis='x', labelsize=20)
    
    ax2.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax2.plot(torch.cat([torch.tensor(data1['x'][:4]),torch.tensor(dataset_miss.targets).reshape(-1,2)[:,1]],dim=0),'--o',color='C3',label='Observed Data')
    ax2.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax2.tick_params(axis='y', labelsize=20)
    ax2.tick_params(axis='x', labelsize=20)
    
    ax3.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax3.plot(evtor_2.f_tr[:,1],'--o',color='C3',alpha=0.8,label='Interpolarion')
    ax3.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax3.tick_params(axis='y', labelsize=20)
    ax3.tick_params(axis='x', labelsize=20)
    
    ax4.plot(data1['y'][:],'--',color='C5',alpha=0.5,label='Complete Data')
    ax4.plot(evtor.fhat_tr[:,1],color='brown',lw=3,label='STGCN')
    ax4.plot(evtor_2.fhat_tr[:,1],color='blue',lw=3,label='ITSTGCN')
    # ax4.plot(55, 0, 'o', markersize=100, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(150, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    # ax4.plot(185, 0, 'o', markersize=80, markerfacecolor='none', markeredgecolor='red',markeredgewidth=3)
    ax4.legend(fontsize=20,loc='lower left',facecolor='white', frameon=True)
    ax4.tick_params(axis='y', labelsize=20)
    ax4.tick_params(axis='x', labelsize=20)
# plt.savefig('try6_node2_noise40.png')