From e842eaa4d7a16a43fd5df97b330621404c27e9cf Mon Sep 17 00:00:00 2001
From: BOULANGEOT Nathan <nathan.boulangeot@univ-lorraine.fr>
Date: Tue, 28 May 2024 12:36:45 +0000
Subject: [PATCH] Delete run.py
---
codes/run.py | 58 ----------------------------------------------------
1 file changed, 58 deletions(-)
delete mode 100644 codes/run.py
diff --git a/codes/run.py b/codes/run.py
deleted file mode 100644
index 0237477..0000000
--- a/codes/run.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import os
-import sys
-import ase.io
-import numpy as np
-from ase import Atoms, Atom
-from ase.io import Trajectory
-from sklearn.metrics import mean_squared_error
-from dscribe.descriptors import SOAP
-from scipy.interpolate import UnivariateSpline
-from sklearn.gaussian_process import GaussianProcessRegressor
-from sklearn.gaussian_process.kernels import RBF
-from sklearn.model_selection import train_test_split, learning_curve, validation_curve, GridSearchCV
-from joblib import Parallel, delayed
-from ase import neighborlist
-from sklearn.preprocessing import StandardScaler
-from create_descriptor import *
-from tool import *
-from zopt import *
-
-if __name__ == '__main__':
- #debut de l initialisation
- E = -808.18103420 #slab without O
- mu = -9.85391418/2 #O2 sur 2
- atomseul='H' #atome deposé
- traj='data/final.traj'
- data_folder='data'
- poscar_file='POSCAR' #slab with no adsorbate
- os.chdir('/home/boulang31/Documents/codes/zopt')
- train_pos_indices=[1,25,36]
- atoms_train,y_train,species=get_train_data(traj,data_folder,train_pos_indices)
- xx,yy,zz=pos_to_relax(poscar_file)
- print(zz)
- params={'species':species,'l_max':2,'n_max':2,'r_cut':7}
- desc=create_descriptor(method='soap',params=params,ats=0)
- X_train=desc.create(atoms_train,load=True,save_file=data_folder)
-
- y_mean=np.mean(y_train)
- y_train=y_train-y_mean
- scaler=StandardScaler()
- X_train=scaler.fit_transform(X_train)
-
- #machine
- kernel=1**2 * RBF(length_scale=200,length_scale_bounds=(1/np.sqrt(2*1e-0),1/np.sqrt(2*1e-8)))
- GP = GaussianProcessRegressor(kernel=kernel)
- title='GP'
- parameters = {'alpha':[1e-3,]}
- g_search = GridSearchCV(GP, parameters)
- g_search.fit(X_train, y_train)
- print('best estimator',g_search.best_estimator_.kernel_)
- np.save('GP_alpha.npy',g_search.best_estimator_.alpha_)
- #recherche mini
- #for i in range(len(xx)):
- # x_soap,zetap,ind,zlim=searchEminzgrid(x=xx[i],y=yy[i],ind=i,z=17,posDFT=posDFT,atomseul='O')
- # x_soap=scaler.transform(x_soap)
- # Eprd,Estd=g_search.best_estimator_.predict(x_soap,return_std=True)
- # pos_reduc(np.array(zetap),Eprd,Estd,ind,zlim,xx[i],yy[i])
-
- Parallel(n_jobs=10)(delayed(run_para)([xx[i],yy[i],zz[i]],i,desc,scaler,g_search) for i in range(len(xx)))
\ No newline at end of file
--
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