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RatioTimeBasedScalingFactor.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 2 14:58:13 2024
@author: vvinod
"""
import numpy as np
from tqdm import tqdm
from Model_MFML import ModelMFML as MFML
def LC_routine(y_trains:np.ndarray, indexes:np.ndarray, X_train:np.ndarray,
X_test:np.ndarray, X_val:np.ndarray,
y_test:np.ndarray, y_val:np.ndarray,
k_type:str='laplacian', sigma:float=200.0,
reg:float=1e-10, navg:int=10,
factor:np.ndarray=None, nmax:int=10):
'''
Function to generate the learnign curves of MFML and o-MFML for a given baseline fidelities
Parameters
----------
X_train : np.ndarray
Training reps.
X_val : np.ndarray
validation reps.
X_test : np.ndarray
test reps.
y_train : np.ndarray
training energies across all fidelities.
y_val : np.ndarray
validaiton energies at target fidelity.
y_test : np.ndarray
Test energies at target fidelity.
indexes : np.ndarray
Indexes of training reps and corresponding energy locations across fidelities.
ker : str, optional
Type of kernel to be used. The default is 'laplacian'.
sig : float, optional
Kernel width. The default is 200.0.
reg : float, optional
Lavrentiev regularizer. The default is 1-10.
navg : int, optional
Number of times to avg across the training set. The default is 1.
factor : np.ndarray, optional
Scaling factor between fidelities as returned based on Time cost of fidelities. The default is None.
nmax : int, optional
Log2 of maximum number of training samples to be used at the target fidelity keeping in mind the scaling factor. The default is 10.
Returns
-------
MAEs_OLS : np.ndarray
MAEs from o-MFML.
MAEs_def : np.ndarray
MAEs from standard MFML.
'''
nfids = y_trains.shape[0]
MAEs_OLS = np.zeros((nmax-1),dtype=float) #for OLS MFML
MAEs_def = np.zeros((nmax-1),dtype=float) # for default MFML
for i in tqdm(range(navg),desc='avg run',leave=False):
mae_ntr_OLS = []
mae_ntr_def = []
for j in range(1,nmax):
n_trains = (2**j)*np.asarray([factor[0]*factor[1]*factor[2]*factor[3],
factor[1]*factor[2]*factor[3],
factor[2]*factor[3], factor[3],
1])+np.asarray([0,1,0,1,0])
n_trains = n_trains[5-nfids:]
###TRAINING######
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=i)
######default#########
_ = model.predict(X_test = X_test, y_test = y_test,
X_val = X_val, y_val = y_val,
optimiser='default')
mae_ntr_def.append(model.mae)
##########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)
mae_ntr_OLS.append(model.mae)
#store each avg run MAE
mae_ntr_OLS = np.asarray(mae_ntr_OLS)
mae_ntr_def = np.asarray(mae_ntr_def)
MAEs_OLS += mae_ntr_OLS
MAEs_def += mae_ntr_def
#return averaged MAE
MAEs_OLS = MAEs_OLS/navg
MAEs_def = MAEs_def/navg
return MAEs_OLS, MAEs_def
def varying_baselines(X_train:np.ndarray, X_val:np.ndarray, X_test:np.ndarray,
y_train:np.ndarray, y_val:np.ndarray, y_test:np.ndarray,
indexes:np.ndarray,
ker:str='laplacian', sig:float=200.0, reg:float=1-10,
navg:int=1, factor:np.ndarray=None, nmax:int=10):
'''
Function to generate the learnign curves of MFML and o-MFML for different baseline fidelities
Parameters
----------
X_train : np.ndarray
Training reps.
X_val : np.ndarray
validation reps.
X_test : np.ndarray
test reps.
y_train : np.ndarray
training energies across all fidelities.
y_val : np.ndarray
validaiton energies at target fidelity.
y_test : np.ndarray
Test energies at target fidelity.
indexes : np.ndarray
Indexes of training reps and corresponding energy locations across fidelities.
ker : str, optional
Type of kernel to be used. The default is 'laplacian'.
sig : float, optional
Kernel width. The default is 200.0.
reg : float, optional
Lavrentiev regularizer. The default is 1-10.
navg : int, optional
Number of times to avg across the training set. The default is 1.
factor : np.ndarray, optional
Scaling factor between fidelities as deterined by the Time cost. The default is None.
nmax : int, optional
Log2 of maximum number of training samples to be used at the target fidelity keeping in mind the scaling factor. The default is 10.
Returns
-------
None.
All outputs are saved locally.
'''
maeols = np.zeros((4),dtype=object)
maedef = np.zeros((4),dtype=object)
for fb in tqdm(range(4),desc='Baseline loop...'):
maeols[fb],maedef[fb] = LC_routine(y_trains=y_train[fb:], indexes=indexes[fb:],
X_train=X_train, X_test=X_test,
X_val=X_val, y_test=y_test, y_val=y_val, k_type=ker,
sigma=sig, reg=reg, navg=navg,
factor=factor, nmax=nmax)
np.save(f'outs/def_mae_{prop}_{rep}_ffm1ratio.npy',maedef)
np.save(f'outs/ols_mae_{prop}_{rep}_ffm1ratio.npy',maeols)
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)
factors = np.asarray([3,1,2,1])
varying_baselines(X_train, X_val, X_test, y_train, y_val, y_test,
indexes=indexes,
ker=ker, sig=sig, reg=reg,
navg=navg, factor=factors, nmax=13)
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
main()