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Plot_phase_transition.py
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Plot_phase_transition.py
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import matplotlib.pyplot as plt
import json
import os
import numpy as np
# with open('maxind_rand_200_tot.log') as f:
# Log=f.readlines()
with open('/Users/nasimeh/Documents/distributed_GCN-main-6/Oct12_2023/log/maxind_rand_200_tot.log') as f:
Log=f.readlines()
log_d={}
for lines in Log:
temp=lines.split(',')
temp1=temp[1].split(':')
temp2 = temp[2].split(':')
temp3 = temp[3].split(':')
name=temp[0][10:-1]
time=float(temp1[1][1:-1])
res=float(temp2[1][10:-4])
res_th=float(temp3[1][10:-4])
log_d[name]={}
log_d[name]['time']=time
log_d[name]['res'] = abs(int(res))
log_d[name]['res_th'] = abs(int(res_th))
n=200
for p in np.linspace(0.001,0.01,10):
name = 'Random_' + str(int(1000 * p)) + '_Over1000_' + str(n) + '.txt'
log_d[name]['p']=p
for p in np.linspace(0.01,0.1,10):
name = 'Random_' + str(int(1000 * p)) + '_Over1000_' + str(n) + '.txt'
log_d[name]['p']=p
y_axis=np.array([log_d[name]['res_th']/log_d[name]['res'] for name in log_d])
x_axis=np.array([np.log(log_d[name]['p']) for name in log_d])
nd=len(y_axis)
plotm=np.zeros([2,nd])
plotm[0,:]=x_axis
plotm[1,:]=y_axis
plotms=plotm[:, plotm[0].argsort()]
plt.plot(plotms[0,:],plotms[1,:], marker='o', label='N=200')
plt.ylabel('GNN outcome over HypOp outcome')
plt.xlabel('ln(p)')
plt.legend(loc='upper right')
#plt.show()
# with open('maxind_rand_1000_tot.log') as f:
# Log=f.readlines()
with open('/Users/nasimeh/Documents/distributed_GCN-main-6/Oct12_2023/log/maxind_rand_1000_tot.log') as f:
Log=f.readlines()
log_d={}
for lines in Log:
temp=lines.split(',')
temp1=temp[1].split(':')
temp2 = temp[2].split(':')
temp3 = temp[3].split(':')
name=temp[0][10:-1]
time=float(temp1[1][1:-1])
res=float(temp2[1][10:-4])
res_th=float(temp3[1][10:-4])
log_d[name]={}
log_d[name]['time']=time
log_d[name]['res'] = abs(int(res))
log_d[name]['res_th'] = abs(int(res_th))
n=1000
for p in np.linspace(0.001,0.01,10):
name = 'Random_' + str(int(1000 * p)) + '_Over1000_' + str(n) + '.txt'
log_d[name]['p']=p
for p in np.linspace(0.01,0.1,10):
name = 'Random_' + str(int(1000 * p)) + '_Over1000_' + str(n) + '.txt'
log_d[name]['p']=p
y_axis=np.array([log_d[name]['res_th']/log_d[name]['res'] for name in log_d])
x_axis=np.array([np.log(log_d[name]['p']) for name in log_d])
nd=len(y_axis)
plotm=np.zeros([2,nd])
plotm[0,:]=x_axis
plotm[1,:]=y_axis
plotms=plotm[:, plotm[0].argsort()]
plt.plot(plotms[0,:],plotms[1,:], marker='o', label='N=1000')
plt.ylabel('GNN outcome over HypOp outcome')
plt.xlabel('ln(p)')
plt.legend(loc='upper right')
# with open('maxind_rand_2000.log') as f:
# Log=f.readlines()
with open('/Users/nasimeh/Documents/distributed_GCN-main-6/Oct12_2023/log/maxind_rand_2000.log') as f:
Log=f.readlines()
log_d={}
for lines in Log:
temp=lines.split(',')
temp1=temp[1].split(':')
temp2 = temp[2].split(':')
temp3 = temp[3].split(':')
name=temp[0][10:-1]
time=float(temp1[1][1:-1])
res=float(temp2[1][10:-4])
res_th=float(temp3[1][10:-4])
log_d[name]={}
log_d[name]['time']=time
log_d[name]['res'] = abs(int(res))
log_d[name]['res_th'] = abs(int(res_th))
n=2000
for p in np.linspace(0.001,0.01,10):
name = 'Random_' + str(int(1000 * p)) + '_Over1000_' + str(n) + '.txt'
log_d[name]['p']=p
y_axis=np.array([log_d[name]['res_th']/log_d[name]['res'] for name in log_d])
x_axis=np.array([np.log(log_d[name]['p']) for name in log_d])
nd=len(y_axis)
plotm=np.zeros([2,nd])
plotm[0,:]=x_axis
plotm[1,:]=y_axis
plotms=plotm[:, plotm[0].argsort()]
plt.plot(plotms[0,:],plotms[1,:], marker='o', label='N=2000')
plt.ylabel('GNN outcome over HypOp outcome')
plt.xlabel('ln(p)')
plt.legend(loc='upper right')
# with open('maxind_rand_3000.log') as f:
# Log=f.readlines()
with open('/Users/nasimeh/Documents/distributed_GCN-main-6/Oct12_2023/log/maxind_rand_3000.log') as f:
Log=f.readlines()
log_d={}
for lines in Log:
temp=lines.split(',')
temp1=temp[1].split(':')
temp2 = temp[2].split(':')
temp3 = temp[3].split(':')
name=temp[0][10:-1]
time=float(temp1[1][1:-1])
res=float(temp2[1][10:-4])
res_th=float(temp3[1][10:-4])
log_d[name]={}
log_d[name]['time']=time
log_d[name]['res'] = abs(int(res))
log_d[name]['res_th'] = abs(int(res_th))
n=3000
for p in np.linspace(0.001,0.01,10):
name = 'Random_' + str(int(1000 * p)) + '_Over1000_' + str(n) + '.txt'
log_d[name]['p']=p
y_axis=np.array([log_d[name]['res_th']/log_d[name]['res'] for name in log_d])
x_axis=np.array([np.log(log_d[name]['p']) for name in log_d])
nd=len(y_axis)
plotm=np.zeros([2,nd])
plotm[0,:]=x_axis
plotm[1,:]=y_axis
plotms=plotm[:, plotm[0].argsort()]
plt.plot(plotms[0,:],plotms[1,:], marker='o', label='N=3000')
plt.ylabel('GNN outcome over HypOp outcome')
plt.xlabel('ln(p)')
plt.legend(loc='upper right')
# path='./res/plots/'
# os.chdir(path)
plt.savefig("/Users/nasimeh/Documents/distributed_GCN-main-6/Oct12_2023/res/plots/phase_transit_random_lr_1e-4.pdf")
plt.show()
#lr=1e-4
n_vec=[200, 1000, 2000, 3000]
p_phase=[0.03, 0.009, 0.004, 0.003]
p_erdos=[np.log(item)/item for item in n_vec]
plt.plot(n_vec,p_phase, marker='o', label='Phase Transition Curve')
plt.ylabel('Transition Probability')
plt.xlabel('n')
plt.legend(loc='upper right')
plt.plot(n_vec,p_erdos, marker='o', label='ln(N)/N Curve')
plt.ylabel('Transition Probability')
plt.xlabel('Number of Nodes')
plt.legend(loc='upper right')
plt.savefig("/Users/nasimeh/Documents/distributed_GCN-main-6/Oct12_2023/res/plots/phase_transit_erdos_lr_1e-4.pdf")
plt.show()
#lr=1e-5
with open('/Users/nasimeh/Documents/distributed_GCN-main-6/Oct12_2023/log/maxind_rand_200_sl_tot.log') as f:
Log=f.readlines()
log_d={}
for lines in Log:
temp=lines.split(',')
temp1=temp[1].split(':')
temp2 = temp[2].split(':')
temp3 = temp[3].split(':')
name=temp[0][10:-1]
time=float(temp1[1][1:-1])
res=float(temp2[1][10:-4])
res_th=float(temp3[1][10:-4])
log_d[name]={}
log_d[name]['time']=time
log_d[name]['res'] = abs(int(res))
log_d[name]['res_th'] = abs(int(res_th))
n=200
for p in np.linspace(0.001,0.01,10):
name = 'Random_' + str(int(1000 * p)) + '_Over1000_' + str(n) + '.txt'
log_d[name]['p']=p
for p in np.linspace(0.01,0.1,10):
name = 'Random_' + str(int(1000 * p)) + '_Over1000_' + str(n) + '.txt'
log_d[name]['p']=p
y_axis=np.array([log_d[name]['res_th']/log_d[name]['res'] for name in log_d])
x_axis=np.array([np.log(log_d[name]['p']) for name in log_d])
nd=len(y_axis)
plotm=np.zeros([2,nd])
plotm[0,:]=x_axis
plotm[1,:]=y_axis
plotms=plotm[:, plotm[0].argsort()]
plt.plot(plotms[0,:],plotms[1,:], marker='o', label='N=200')
plt.ylabel('GNN outcome over HypOp outcome')
plt.xlabel('ln(p)')
plt.legend(loc='upper right')
#plt.show()
# with open('maxind_rand_1000_tot.log') as f:
# Log=f.readlines()
with open('/Users/nasimeh/Documents/distributed_GCN-main-6/Oct12_2023/log/maxind_rand_1000_sl_tot.log') as f:
Log=f.readlines()
log_d={}
for lines in Log:
temp=lines.split(',')
temp1=temp[1].split(':')
temp2 = temp[2].split(':')
temp3 = temp[3].split(':')
name=temp[0][10:-1]
time=float(temp1[1][1:-1])
res=float(temp2[1][10:-4])
res_th=float(temp3[1][10:-4])
log_d[name]={}
log_d[name]['time']=time
log_d[name]['res'] = abs(int(res))
log_d[name]['res_th'] = abs(int(res_th))
n=1000
for p in np.linspace(0.001,0.01,10):
name = 'Random_' + str(int(1000 * p)) + '_Over1000_' + str(n) + '.txt'
log_d[name]['p']=p
for p in np.linspace(0.01,0.1,10):
name = 'Random_' + str(int(1000 * p)) + '_Over1000_' + str(n) + '.txt'
log_d[name]['p']=p
y_axis=np.array([log_d[name]['res_th']/log_d[name]['res'] for name in log_d])
x_axis=np.array([np.log(log_d[name]['p']) for name in log_d])
nd=len(y_axis)
plotm=np.zeros([2,nd])
plotm[0,:]=x_axis
plotm[1,:]=y_axis
plotms=plotm[:, plotm[0].argsort()]
plt.plot(plotms[0,:],plotms[1,:], marker='o', label='N=1000')
plt.ylabel('GNN outcome over HypOp outcome')
plt.xlabel('ln(p)')
plt.legend(loc='upper right')
# with open('maxind_rand_2000.log') as f:
# Log=f.readlines()
with open('/Users/nasimeh/Documents/distributed_GCN-main-6/Oct12_2023/log/maxind_rand_2000_sl.log') as f:
Log=f.readlines()
log_d={}
for lines in Log:
temp=lines.split(',')
temp1=temp[1].split(':')
temp2 = temp[2].split(':')
temp3 = temp[3].split(':')
name=temp[0][10:-1]
time=float(temp1[1][1:-1])
res=float(temp2[1][10:-4])
res_th=float(temp3[1][10:-4])
log_d[name]={}
log_d[name]['time']=time
log_d[name]['res'] = abs(int(res))
log_d[name]['res_th'] = abs(int(res_th))
n=2000
for p in np.linspace(0.001,0.01,10):
name = 'Random_' + str(int(1000 * p)) + '_Over1000_' + str(n) + '.txt'
log_d[name]['p']=p
y_axis=np.array([log_d[name]['res_th']/log_d[name]['res'] for name in log_d])
x_axis=np.array([np.log(log_d[name]['p']) for name in log_d])
nd=len(y_axis)
plotm=np.zeros([2,nd])
plotm[0,:]=x_axis
plotm[1,:]=y_axis
plotms=plotm[:, plotm[0].argsort()]
plt.plot(plotms[0,:],plotms[1,:], marker='o', label='N=2000')
plt.ylabel('GNN outcome over HypOp outcome')
plt.xlabel('ln(p)')
plt.legend(loc='upper right')
# with open('maxind_rand_3000.log') as f:
# Log=f.readlines()
with open('/Users/nasimeh/Documents/distributed_GCN-main-6/Oct12_2023/log/maxind_rand_3000_sl.log') as f:
Log=f.readlines()
log_d={}
for lines in Log:
temp=lines.split(',')
temp1=temp[1].split(':')
temp2 = temp[2].split(':')
temp3 = temp[3].split(':')
name=temp[0][10:-1]
time=float(temp1[1][1:-1])
res=float(temp2[1][10:-4])
res_th=float(temp3[1][10:-4])
log_d[name]={}
log_d[name]['time']=time
log_d[name]['res'] = abs(int(res))
log_d[name]['res_th'] = abs(int(res_th))
n=3000
for p in np.linspace(0.001,0.01,10):
name = 'Random_' + str(int(1000 * p)) + '_Over1000_' + str(n) + '.txt'
log_d[name]['p']=p
y_axis=np.array([log_d[name]['res_th']/log_d[name]['res'] for name in log_d])
x_axis=np.array([np.log(log_d[name]['p']) for name in log_d])
nd=len(y_axis)
plotm=np.zeros([2,nd])
plotm[0,:]=x_axis
plotm[1,:]=y_axis
plotms=plotm[:, plotm[0].argsort()]
plt.plot(plotms[0,:],plotms[1,:], marker='o', label='N=3000')
plt.ylabel('GNN outcome over HypOp outcome')
plt.xlabel('ln(p)')
plt.legend(loc='upper right')
plt.savefig("/Users/nasimeh/Documents/distributed_GCN-main-6/Oct12_2023/res/plots/phase_transit_random_lr_1e-5.pdf")
plt.show()
#lr=1e-5
n_vec=[200, 1000, 2000, 3000]
p_phase=[0.03, 0.008, 0.004, 0.003]
p_erdos=[np.log(item)/item for item in n_vec]
plt.plot(n_vec,p_phase, marker='o', label='Phase Transition Curve')
plt.ylabel('Transition Probability')
plt.xlabel('n')
plt.legend(loc='upper right')
plt.plot(n_vec,p_erdos, marker='o', label='ln(N)/N Curve')
plt.ylabel('Transition Probability')
plt.xlabel('Number of Nodes')
plt.legend(loc='upper right')
plt.savefig("/Users/nasimeh/Documents/distributed_GCN-main-6/Oct12_2023/res/plots/phase_transit_erdos_lr_1e-5.pdf")
plt.show()