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predict_maxval.py
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predict_maxval.py
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import numpy as np
from matplotlib import pyplot as plt
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C ,WhiteKernel as Wht,Matern as matk
from sklearn.gaussian_process.kernels import RationalQuadratic as expker
from sklearn.metrics import mean_squared_error as MSError
inputmap=dict()
ninputmap=dict()
totfea_atom=2 #total number of atoms per layer
n_3layer_atoms=6 # number of atoms in 3 layer
natom_layer=n_3layer_atoms*totfea_atom #total number of features
#input parameters
inputfile_name="3-layer-band_gap.txt" #file name of the input data
train_test_split=0.60 #split between training and test set
Nrun = 1
#create input feature vector of the given n-layer heterostructure
def createinputmap(inputmap,ninputmap,totfea_atom):
#define the eletronegetivity and ionization potential of each atoms
inputmap['Mo'] = [2.16,684.3]
inputmap['W'] = [2.36,770.0]
inputmap['S'] = [2.58,999.6]
inputmap['Se'] = [2.55,941.0]
inputmap['Te'] = [2.10,869.3]
#normalize the input features by (tt-xmax)/(xmax-xmin)
Xmax = np.empty(totfea_atom,dtype=float)
Xmin = np.empty(totfea_atom, dtype=float)
Xmean= np.empty(totfea_atom,dtype=float)
Xstd = np.empty(totfea_atom,dtype=float)
Xmax.fill(0.0)
Xmin.fill(10000.0)
Xmean.fill(0.0)
Xstd.fill(0.0)
nfeatures=0
for keys in inputmap:
nfeatures+=1
for ii in range(0,totfea_atom):
if Xmax[ii] < inputmap[keys][ii]: Xmax[ii]=inputmap[keys][ii]
if Xmin[ii] > inputmap[keys][ii]: Xmin[ii]=inputmap[keys][ii]
Xmean[ii]+=inputmap[keys][ii]
for ii in range(0,totfea_atom):
Xmean[ii]=Xmean[ii]/float(nfeatures)
for keys in inputmap:
for ii in range(0, totfea_atom):
Xstd[ii]+=(inputmap[keys][ii]- Xmean[ii])*(inputmap[keys][ii]- Xmean[ii])
for ii in range(0, totfea_atom):
Xstd[ii]=np.sqrt(Xstd[ii]/float(nfeatures))
print("Xmax and Xmin: ",Xmax,Xmin)
print("Xmean and Xstd: ",Xmean,Xstd)
for keys in inputmap:
ninputmap[keys]=list()
for ii in range(0, totfea_atom):
ninputmap[keys].append((inputmap[keys][ii]-Xmean[ii])/Xstd[ii])
#print the final keys:
# for keys in inputmap:
# print("key :", keys,inputmap[keys])
# for keys in ninputmap:
# print("nkey :", keys, ninputmap[keys])
#read input data
def readinput(filename,natom_layer):
inputfile=open(filename,'r')
dataset=list()
itag=0
count=-1
ndata=0
for lines in inputfile:
if itag==0:
ndata=int(lines)
Xdata = np.ndarray(shape=(ndata, natom_layer), dtype=float)
Ydata = np.empty(ndata,dtype=float)
itag=1
else :
lines = lines.replace("\n", "").split()
count+=1
for ii in range(0,lines.__len__()-1):
jj=lines[ii]
Xdata[count][2 * ii] = ninputmap[jj][0]
Xdata[count][2 * ii + 1] = ninputmap[jj][1]
Ydata[count] = float(lines[lines.__len__() - 1])
#print the entire dataset
# for ii in range(0,ndata):
# print("data: ",ii,Xdata[ii][:],Ydata[ii])
return Xdata,Ydata,ndata
#building a gaussian process regression model
def gpregression(Xtrain,Ytrain,Xtest,Ytest,ntrain,ntest):
print("regression")
cmean=[1.0]*12
cbound=[[1e-3, 1000]]*12
kernel = C(1.0, (1e-3, 1e3)) * matk(cmean, cbound, 1.5)+ Wht(1.0, (1e-3, 1e3))
# kernel = C(1.0, (1e-3, 1e3)) * matk(1, (1e-05, 1000.0), 2.5) + Wht(1.0, (1e-3, 1e3))+ C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))
gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=100, normalize_y=False)
gp.fit(Xtrain, Ytrain)
print("initial parameters:", kernel)
print("optimal parameters:", gp.kernel_, "likelihood:", gp.log_marginal_likelihood(gp.kernel_.theta))
y_pred, sigma = gp.predict(Xtest, return_std=True)
dataorder=np.argsort(Ytest)
tYest=Ytest[dataorder]
ty_pred=y_pred[dataorder]
tsigma=sigma[dataorder]
del Ytest,y_pred,sigma
Ytest=tYest
y_pred=ty_pred
sigma=tsigma
toterr=0.0
for val in range(0,ntest):
# print("Prediction: ",Ytest[val]," ",y_pred[val]," ",sigma[val])
toterr+=np.abs(Ytest[val]-y_pred[val])
print("toterr prediction loss : ",toterr,toterr/float(ntest))
fig = plt.figure(figsize=(14,10))
plt.rc('xtick', labelsize=20)
plt.rc('ytick', labelsize=20)
plt.rc('font', weight='bold')
xxdummy=range(ntest)
plt.plot(xxdummy, Ytest, 'r-', linewidth=3.5, label=u'True Value')
plt.plot(xxdummy, Ytest, 'r.', markersize=20)
plt.plot(xxdummy, y_pred, 'b--', linewidth=3.5, label=u'Prediction')
plt.plot(xxdummy, y_pred, 'b.', markersize=20)
plt.fill(np.concatenate([xxdummy, xxdummy[::-1]]),np.concatenate([y_pred - 1.9600 * sigma,(y_pred + 1.9600 * sigma)[::-1]]),alpha=.5, fc='y', ec='None', label='95% confidence interval')
plt.xlabel('tri-layer structure',fontsize=40, fontweight='bold')
plt.ylabel('Band GAP',fontsize=40, fontweight='bold')
plt.legend(loc='upper left', ncol=1, fancybox=True, shadow=True, prop={'size': 20})
# plt.legend(loc='upper left')
plt.title("TEST DATA",fontsize=40,fontweight='bold')
#-----training set-----
yt_pred, tsigma = gp.predict(Xtrain, return_std=True)
# for val in range(0,ntrain):
# print("Training set: ",Ytrain[val]," ",yt_pred[val]," ",tsigma[val])
print("Total training errror: ",np.sqrt(MSError(Ytrain,yt_pred)))
print("Total prediction errror: ", np.sqrt(MSError(Ytest,y_pred)))
# xxtdummy=range(ntrain)
# plt.plot(xxtdummy, Ytrain, 'r-', markersize=10, label=u'Observations')
# plt.plot(xxtdummy, Ytrain, 'r.', markersize=10)
# plt.plot(xxtdummy, yt_pred, 'b-', markersize=10, label=u'Prediction')
# plt.plot(xxtdummy, yt_pred, 'b.', markersize=10)
# plt.fill(np.concatenate([xxtdummy, xxtdummy[::-1]]),np.concatenate([yt_pred - 1.9600 * tsigma,(yt_pred + 1.9600 * tsigma)[::-1]]),alpha=.8, fc='b', ec='None', label='95% confidence interval')
# plt.xlabel('$x$')
# plt.ylabel('$f(x)$')
# plt.legend(loc='upper left')
# plt.title("Training data")
plt.ylim(-0.6,1.6)
plt.show()
# plt.savefig('fig1a.png')
# plt.close()
return
#------- Main Program -------------
createinputmap(inputmap,ninputmap,totfea_atom)
Xdata,Ydata,ndata=readinput(inputfile_name,natom_layer)
print("Original Training and Y :",np.shape(Xdata),np.shape(Ydata))
print("Transpose Training and Y : ",np.shape(np.transpose(Xdata)),np.shape(np.transpose(Ydata)))
print("Original Training and Y :",np.shape(Xdata),np.shape(Ydata))
ntrain=int(train_test_split*ndata)
ntest=ndata-ntrain
print("Total training and Test Data: ",ntrain,ntest)
for ii in range(0,Nrun):
dataset=np.random.permutation(ndata)
a1data=np.empty(ntrain, dtype=int)
a2data=np.empty(ntest, dtype=int)
a1data[:]=dataset[0:ntrain]
a2data[:]=dataset[ntrain:ndata]
Xtrain=np.ndarray(shape=(ntrain, natom_layer), dtype=float)
Ytrain = np.empty(ntrain, dtype=float)
Xtest = np.ndarray(shape=(ntest, natom_layer), dtype=float)
Ytest = np.empty(ntest, dtype=float)
for itrain in range(0,ntrain):
mm=a1data[itrain]
Xtrain[itrain][:]=Xdata[mm][:]
Ytrain[itrain]=Ydata[mm]
for itest in range(0,ntest):
mm = a2data[itest]
Xtest[itest][:]=Xdata[mm][:]
Ytest[itest]=Ydata[mm]
gpregression(Xtrain,Ytrain,Xtest,Ytest,ntrain,ntest)
del Xtrain,Ytrain
del Xtest,Ytest