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ErrorContours_gamma2.py
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import numpy as np
from tqdm import tqdm
from Model_MFML import ModelMFML as MFML
from sklearn.utils import shuffle
def TZVP_countour_generation(X_train:np.ndarray, X_test:np.ndarray, X_val:np.ndarray,
y_trains:np.ndarray,y_test:np.ndarray, y_val:np.ndarray,
indexes:np.ndarray, k_type:str='laplacian',
sigma:float=200.0, reg:float=1e-10, navg:int=10):
nmax=9 #maximum of 512 samples at TZVP
full_MAEs_OLS = np.zeros((nmax,nmax),dtype=float) #for OLS MFML
for n in tqdm(range(navg),desc='Average run',leave=True):
MAEs_OLS = np.zeros((nmax,nmax),dtype=float)
for i in tqdm(range(1,nmax+1),desc='upper fidelity loop',leave = False):
for j in tqdm(range(i+1,nmax+2),desc='lower fidelity loop',leave=False):
n_trains = np.asarray([2**(j+3),2**(j+2),2**(j+1),2**j,2**i])
model = MFML(reg=reg, kernel=k_type,
order=1, metric='l2', #only used for matern kernel
sigma=sigma, p_bar=False)
model.train(X_train_parent=X_train,
y_trains=y_trains, indexes=indexes,
shuffle=True, n_trains=n_trains, seed=n)
##########OLS##########
_ = model.predict(X_test = X_test, y_test = y_test,
X_val = X_val, y_val = y_val,
optimiser='OLS', copy_X= True,
fit_intercept= False)
MAEs_OLS[i-1,j-2] = np.copy(model.mae)
full_MAEs_OLS[:,:] += MAEs_OLS
return full_MAEs_OLS/navg
def SVP_countour_generation(X_train:np.ndarray, X_test:np.ndarray, X_val:np.ndarray,
y_trains:np.ndarray,y_test:np.ndarray, y_val:np.ndarray,
indexes:np.ndarray, k_type:str='laplacian',
sigma:float=200.0, reg:float=1e-10, navg:int=10):
nmax=10 #maximum of 512 samples at TZVP
full_MAEs_OLS = np.zeros((nmax-1,nmax-1),dtype=float) #for OLS MFML
for n in tqdm(range(navg),desc='Average run',leave=True):
MAEs_OLS = np.zeros((nmax-1,nmax-1),dtype=float)
for i in tqdm(range(1,nmax+1),desc='upper fidelity loop',leave = False):
for j in tqdm(range(i+1,nmax+1),desc='lower fidelity loop',leave=False):
n_trains = np.asarray([2**(j+3),2**(j+2),2**(j+1),2**(i+1),2])
model = MFML(reg=reg, kernel=k_type,
order=1, metric='l2', #only used for matern kernel
sigma=sigma, p_bar=False)
model.train(X_train_parent=X_train,
y_trains=y_trains, indexes=indexes,
shuffle=True, n_trains=n_trains, seed=n)
##########OLS##########
_ = model.predict(X_test = X_test, y_test = y_test,
X_val = X_val, y_val = y_val,
optimiser='OLS', copy_X= True,
fit_intercept= False)
MAEs_OLS[i-1,j-2] = np.copy(model.mae)
full_MAEs_OLS[:,:] += MAEs_OLS
return full_MAEs_OLS/navg
def G631_countour_generation(X_train:np.ndarray, X_test:np.ndarray, X_val:np.ndarray,
y_trains:np.ndarray,y_test:np.ndarray, y_val:np.ndarray,
indexes:np.ndarray, k_type:str='laplacian',
sigma:float=200.0, reg:float=1e-10, navg:int=10):
nmax = 11 #maximum of 512 samples at TZVP
full_MAEs_OLS = np.zeros((nmax-2,nmax-2),dtype=float) #for OLS MFML
for n in tqdm(range(navg),desc='Average run',leave=True):
MAEs_OLS = np.zeros((nmax-2,nmax-2),dtype=float)
for i in tqdm(range(1,nmax+1),desc='upper fidelity loop',leave = False):
for j in tqdm(range(i+1,nmax),desc='lower fidelity loop',leave=False):
n_trains = np.asarray([2**(j+3),2**(j+2),2**(i+2),4,2])
model = MFML(reg=reg, kernel=k_type,
order=1, metric='l2', #only used for matern kernel
sigma=sigma, p_bar=False)
model.train(X_train_parent=X_train,
y_trains=y_trains, indexes=indexes,
shuffle=True, n_trains=n_trains, seed=n)
##########OLS##########
_ = model.predict(X_test = X_test, y_test = y_test,
X_val = X_val, y_val = y_val,
optimiser='OLS', copy_X= True,
fit_intercept= False)
MAEs_OLS[i-1,j-2] = np.copy(model.mae)
full_MAEs_OLS[:,:] += MAEs_OLS
return full_MAEs_OLS/navg
def G321_countour_generation(X_train:np.ndarray, X_test:np.ndarray, X_val:np.ndarray,
y_trains:np.ndarray,y_test:np.ndarray, y_val:np.ndarray,
indexes:np.ndarray, k_type:str='laplacian',
sigma:float=200.0, reg:float=1e-10, navg:int=10):
nmax = 12 #maximum of 512 samples at TZVP
full_MAEs_OLS = np.zeros((nmax-3,nmax-3),dtype=float) #for OLS MFML
for n in tqdm(range(navg),desc='Average run',leave=True):
MAEs_OLS = np.zeros((nmax-3,nmax-3),dtype=float)
for i in tqdm(range(1,nmax+1),desc='upper fidelity loop',leave = False):
for j in tqdm(range(i+1,nmax-1),desc='lower fidelity loop',leave=False):
n_trains = np.asarray([2**(j+3),2**(i+3),8,4,2])
model = MFML(reg=reg, kernel=k_type,
order=1, metric='l2', #only used for matern kernel
sigma=sigma, p_bar=False)
model.train(X_train_parent=X_train,
y_trains=y_trains, indexes=indexes,
shuffle=True, n_trains=n_trains, seed=n)
##########OLS##########
_ = model.predict(X_test = X_test, y_test = y_test,
X_val = X_val, y_val = y_val,
optimiser='OLS', copy_X= True,
fit_intercept= False)
MAEs_OLS[i-1,j-2] = np.copy(model.mae)
full_MAEs_OLS[:,:] += MAEs_OLS
return full_MAEs_OLS/navg
def main():
X_train = np.load(f'Data/{rep}_train.npy')
X_test = np.load(f'Data/{rep}_test.npy')
X_val = np.load(f'Data/{rep}_val.npy')
y_train = np.load(f'Data/{prop}_train.npy',allow_pickle=True)
y_test = np.load(f'Data/{prop}_test.npy')
y_val = np.load(f'Data/{prop}_val.npy')
indexes = np.load('Data/indexes.npy',allow_pickle=True)
contour = TZVP_countour_generation(X_train=X_train, X_test=X_test, X_val=X_val,
y_trains=y_train, y_test=y_test, y_val=y_val,
indexes=indexes, k_type=ker,
sigma=sig, reg=reg, navg=10)
np.save(f'outs/{factor}_contour_TZVP_SVP.npy',contour)
contour = SVP_countour_generation(X_train=X_train, X_test=X_test, X_val=X_val,
y_trains=y_train, y_test=y_test, y_val=y_val,
indexes=indexes, k_type=ker,
sigma=sig, reg=reg, navg=10)
np.save(f'outs/{factor}_contour_SVP_631G.npy',contour)
contour = G631_countour_generation(X_train=X_train, X_test=X_test, X_val=X_val,
y_trains=y_train, y_test=y_test, y_val=y_val,
indexes=indexes, k_type=ker,
sigma=sig, reg=reg, navg=10)
np.save(f'outs/{factor}_contour_631G_321G.npy',contour)
contour = G321_countour_generation(X_train=X_train, X_test=X_test, X_val=X_val,
y_trains=y_train, y_test=y_test, y_val=y_val,
indexes=indexes, k_type=ker,
sigma=sig, reg=reg, navg=10)
np.save(f'outs/{factor}_contour_321G_STO3G.npy',contour)
if __name__=='__main__':
prop='EV'
rep='CM'
ker='matern' #matern usually; gaussian for SLATM SCF
reg=1e-10
sig=200.0 #200 for EV(CM) 2200 for SCF(CM) 650 for SCF(SLATM)
navg=10
factor=2 #3,4,5,6
main()