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
lam_list = [10**n for n in range(-3, 5)]
c_list = [10**n for n in range(-3, 7)]
x = [-10**n for n in range(-1, 7)]
x.reverse()
c_list = x + c_list
if method == 'linear':
out_matrix_lin = np.zeros((len(c_list), len(lam_list)))
for c in range(len(c_list)):
for l in range(len(lam_list)):
print(f'c={c_list[c]}, lambda={lam_list[l]}', flush=True)
KRR = KernelRidgeRegression(type="linear")
KRR.set_var(c1=c_list[c], lambd=lam_list[l])
KRR.fit(X, Y, "error")
out = KRR.rmse
print(out, flush=True)
out_matrix_lin[c, l] = out
with open(folder + "out_matrix_lin_fixed_faulty_matrix", 'wb') as file:
pickle.dump([lam_list, c_list, out_matrix_lin], file)
lam_list = [10**n for n in range(-3, 3)]
c1_list = [10**n for n in range(-3, 3)]
c2_list = [10**n for n in range(-2, 3)]
x = [-10**n for n in range(-2, 3)]
elif Feature == "GP":
largeFeatureMatrixValidate = advanced_large_matrix(
path, featureMatrixFileValidate, atomicSymbolsListFileValidate)
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",
inistep = 1
i = 0
maxstep = 250
precision = 0.0001
steps = 0
diff = 100
while (abs(diff) > precision and steps < maxstep):
GoodDir = True
div = 2**i
step = inistep / div
while (GoodDir):
steps += 1
lambd = lambd + step * (-1)**i
if lambd <= 0: lambd = step * 1.1
KRR = KernelRidgeRegression(type="linear")
KRR.set_var(c1=-10**10, lambd=lambd)
KRR.fit(X, Y)
KRR.predict(Xv, Yv)
out2 = KRR.rmse
diff = prev - out2
if diff < 0:
GoodDir = False
prev = out2
output.append([lambd, out2])
print(str(lambd) + ", " + str(out2) + "\n", flush=True)
i += 1
with open(folder + "MINI_other_LIN", 'wb') as file:
pickle.dump([output], file)
c1_list = list(frange(-20, -5, 2))
