def func(a,*args):
if a[1]<=0:
return 10000
KRR=KernelRidgeRegression(type="laplace")
KRR.set_var(sigma=a[0], lambd=a[1])
KRR.fit(X,Y, "error")
out1=KRR.rmse
KRR.predict(Xv,Yv)
out2=KRR.rmse
print(str(a[1])+", "+str(a[0])+", "+str(out1)+", "+str(out2)+"\n", flush=True)
return out2
def func(a,*args):
if a[1]<=0:
return 10000
KRR=KernelRidgeRegression(type="laplace")
KRR.set_var(sigma=a[0], lambd=a[1])
KRR.fit(X,Y, "error")
out1=KRR.rmse
KRR.predict(Xv,Yv)
out2=KRR.rmse
with open(folder+filename, "a") as myfile:
myfile.write(str(size)+", "+str(out1)+", "+str(out2)+"\n")
return out2
def func(a, *args):
if a[3]<=0:
return 10000
try:
KRR=KernelRidgeRegression(type="poly")
KRR.set_var(c1=a[0],c2=a[1],d=a[2], lambd=a[3])
KRR.fit(X,Y, "error")
out1=KRR.rmse
KRR.predict(Xv,Yv)
out2=KRR.rmse
print(str(a[3])+", "+str(a[0])+", "+str(a[1])+", "+str(a[2])+", "+str(out1)+", "+str(out2)+"\n", flush=True)
except numpy.linalg.linalg.LinAlgError:
out1=10**9
out2=10**9
return out2
with open(path + energiesFileValidate, "rb") as pickleFile:
energiesValidate = pickle.load(pickleFile)
largeFeatureMatrixValidate.shape = (largeFeatureMatrixValidate.shape[0], -1)
Xv = largeFeatureMatrixValidate
Yv = np.array(energiesValidate)
if method == 'linear':
folder = folder + "lin/"
KRR = KernelRidgeRegression(type="linear")
KRR.set_var(c1=Args, lambd=lambd)
KRR.fit(X, Y, "error")
out1 = KRR.rmse
KRR.predict(Xv, Yv)
out2 = KRR.rmse
print("\nTrain: " + str(out1) + " Validation: " + str(out2) + "\n",
flush=True)
elif method == 'polynomial':
try:
KRR = KernelRidgeRegression(type="poly")
KRR.set_var(c1=c1_list[c1], c2=c2_list[c2], d=d_list[d], lambd=lambd)
KRR.fit(X, Y, "error")
out1 = KRR.rmse
KRR.predict(Xv, Yv)
out2 = KRR.rmse
print("\nTrain: " + str(out1) + " Validation: " + str(out2) + "\n",
flush=True)
except numpy.linalg.linalg.LinAlgError:
out1 = -1
#%% Load validation data
featureMatrixFileValidate = "validate_featureMatrix.npy"
atomicSymbolsListFileValidate = "validate_pickledAtomicSymbolsList.txt"
energiesFileValidate = "validate_pickledEnergies.txt"
largeFeatureMatrixValidate, mappedAtomicNumberValidate = simpleLargeMatrix(path,featureMatrixFileValidate, atomicSymbolsListFileValidate)
with open(path+energiesFileValidate, "rb") as pickleFile:
energiesValidate = pickle.load(pickleFile)
#Flatten description
largeFeatureMatrixValidate.shape = (largeFeatureMatrixValidate.shape[0], -1)
X_v = largeFeatureMatrixValidate
Y_v = np.array(energiesValidate)
import time
start=time.time()
KRR=KernelRidgeRegression(type="laplace")
KRR.fit(X,Y)
Y_predict_val=KRR.predict(X_v,Y_v)
print(KRR.rmse)
rmse_class=np.sqrt(np.mean(np.square(Y_predict_val-Y_v)))
print(rmse_class)
end=time.time()
print(end-start)