print('Shpae of y: {}'.format(y.shape))
print('Shpae of y_sc: {}'.format(sc_y.shape))
for trr in tr_ratio:
for rs in r_state:
print(' Working on random state: {}'.format(rs))
X_train, X_test, y_train, y_test, scaling_y_train, \
scaling_y_test, metadata_train, metadata_test = \
train_test_split(X, y, sc_y, metadata, \
train_size=trr, random_state=rs)
MLGP = OMGP(X_train=X_train,
X_test=X_test,
y_train=y_train,
y_test=y_test,
kernel_recipe=kernel_recipe,
scaling=True,
scaling_params={
'alpha': sc.slope,
'gamma': sc.intercept
},
scaling_y_train=scaling_y_train,
scaling_y_test=scaling_y_test)
MLGP.run_GP()
trr_data[trr][rs] = MLGP.__dict__
trr_data[trr][rs]['metadata_train'] = metadata_train
trr_data[trr][rs]['metadata_test'] = metadata_test
if not os.path.exists('gp_scaling_{0}_{1}'.format(ads1, ads2)):
os.mkdir('gp_scaling_{0}_{1}'.format(ads1, ads2))
np.save('gp_scaling_{0}_{1}/{1}_rsdata.npy'.format(ads1, ads2), trr_data)
r_data = {rs: {} for rs in r_state}
print('Shpae of y: {}'.format(y.shape))
print('Shpae of y_sc: {}'.format(sc_y.shape))
for rs in r_state:
print(' Working on random state: {}'.format(rs))
X_train, X_test, y_train, y_test, scaling_y_train, scaling_y_test, \
metadata_train, metadata_test = \
train_test_split(X, y, sc_y, metadata, \
train_size=0.80, random_state=rs)
MLGP = OMGP(X_train=X_train,
X_test=X_test,
y_train=y_train,
y_test=y_test,
kernel_recipe=kernel_recipe,
scaling=True,
scaling_params={
'alpha': sc.slope,
'gamma': sc.intercept
},
scaling_y_train=scaling_y_train,
scaling_y_test=scaling_y_test)
MLGP.run_GP()
r_data[rs] = MLGP.__dict__
r_data[rs]['metadata_train'] = metadata_train
r_data[rs]['metadata_test'] = metadata_test
np.save('gp_scaling_{0}_{1}/{1}_rsdata.npy'.format(ads1, ads2), r_data)
#LC = MLGP.plot_learning_curve()
#LC.savefig('gp_scaling_{0}_{1}/{1}_learning_curve.png'.format(ads1, ads2))
PP = MLGP.parity_plot(data='train')
'noise_level': 0.1,
'noise_level_bounds': (1e-5, 1e5)
}
}
r_state = [10, 20, 42, 80, 150, 200, 300, 400]
#r_state = [42]
r_data = {rs: {} for rs in r_state}
for rs in r_state:
print(' Working on random state: {}'.format(rs))
X_train, X_test, y_train, y_test, metadata_train, metadata_test = \
train_test_split(X, y, metadata, train_size=0.80, random_state=rs)
MLGP = OMGP(X_train=X_train,
X_test=X_test,
y_train=y_train,
y_test=y_test,
kernel_recipe=kernel_recipe)
MLGP.run_GP()
r_data[rs] = MLGP.__dict__
r_data[rs]['metadata_train'] = metadata_train
r_data[rs]['metadata_test'] = metadata_test
if not os.path.exists('run_{}'.format(ads)):
os.mkdir('run_{}'.format(ads))
np.save('run_{0}/{0}_rsdata.npy'.format(ads), r_data)
#LC = MLGP.plot_learning_curve()
#LC.savefig('run_{0}/{0}_learning_curve.png'.format(ads))
PP = MLGP.parity_plot(data='train')
'WhiteKernel': {
'noise_level': 0.1,
'noise_level_bounds': (1e-5, 1e5)
}
}
r_state = [15, 25, 45]
trr_data = {tr: {rs: {} for rs in r_state} for tr in tr_ratio}
for trr in tr_ratio:
for rs in r_state:
print(' Working on random state: {}'.format(rs))
X_train, X_test, y_train, y_test, metadata_train, metadata_test = \
train_test_split(X, y, metadata, train_size=trr, random_state=rs)
MLGP = OMGP(X_train=X_train,
X_test=X_test,
y_train=y_train,
y_test=y_test,
kernel_recipe=kernel_recipe)
MLGP.run_GP()
trr_data[trr][rs] = MLGP.__dict__
trr_data[trr][rs]['metadata_train'] = metadata_train
trr_data[trr][rs]['metadata_test'] = metadata_test
prev_data = np.load('run_{0}/{0}_rsdata.npy'.format(adsorbate))[()]
for trr in tr_ratio:
prev_data[trr] = trr_data[trr]
np.save('run_{0}/{0}_rsdata.npy'.format(adsorbate), prev_data)