def main():
"""
Test if Ridge regression is working.
Parameters
----------
fxyz: string giving location of xyz file
prefix: string giving the filename prefix
"""
fxyz = os.path.join(os.path.split(__file__)[0], 'small_molecules-SOAP.xyz')
fmat = ['SOAP-n4-l3-c1.9-g0.23']
fy = 'dft_formation_energy_per_atom_in_eV'
prefix = "test-skrr"
test_ratio = 0.05
lc_points = 8
lc_repeats = 8
# try to read the xyz file
asapxyz = ASAPXYZ(fxyz)
desc, _ = asapxyz.get_descriptors(fmat, False)
y_all = asapxyz.get_property(fy)
# print(desc)
dm = Design_Matrix(X=desc, y=y_all, whiten=True, test_ratio=test_ratio)
# kernel, jitter, delta, sigma, sparse_mode="fps", n_sparse=None
k_spec = {
'k0': {
"type": "linear"
}
} # { 'k1': {"type": "polynomial", "d": power}}
# if sigma is not set...
sigma = 0.001 * np.std(y_all)
krr = KRRSparse(0., None, sigma)
skrr = SPARSE_KRR_Wrapper(k_spec, krr, sparse_mode="fps", n_sparse=-1)
# fit the model
dm.compute_fit(skrr, 'skrr', store_results=True, plot=True)
# learning curve
if lc_points > 1:
dm.compute_learning_curve(skrr,
'ridge_regression',
lc_points=lc_points,
lc_repeats=lc_repeats,
randomseed=42,
verbose=False)
dm.save_state(prefix)
plt.show()
def main():
"""
Test if Ridge regression is working.
Parameters
----------
fxyz: string giving location of xyz file
prefix: string giving the filename prefix
"""
fxyz = os.path.join(os.path.split(__file__)[0], 'small_molecules-SOAP.xyz')
fmat = ['SOAP-n4-l3-c1.9-g0.23']
fy = 'dft_formation_energy_per_atom_in_eV'
prefix = "test-rr"
test_ratio = 0.05
lc_points = 8
lc_repeats = 8
# try to read the xyz file
asapxyz = ASAPXYZ(fxyz)
desc, _ = asapxyz.get_descriptors(fmat, False)
y_all = asapxyz.get_property(fy)
# print(desc)
dm = Design_Matrix(X=desc, y=y_all, whiten=True, test_ratio=test_ratio)
# if sigma is not set...
sigma = 0.001 * np.std(y_all)
rr = RidgeRegression(sigma)
# fit the model
dm.compute_fit(rr, 'ridge_regression', store_results=True, plot=True)
# learning curve
if lc_points > 1:
dm.compute_learning_curve(rr,
'ridge_regression',
lc_points=lc_points,
lc_repeats=lc_repeats,
randomseed=42,
verbose=False)
dm.save_state(prefix)
plt.show()
def main(fmat, fxyz, fy, prefix, scale, test_ratio, sigma, lc_points,
lc_repeats):
"""
Parameters
----------
fmat: Location of descriptor matrix file or name of the tags in ase xyz file. You can use gen_descriptors.py to compute it.
fxyz: Location of xyz file for reading the properties.
fy: Location of property list (1D-array of floats)
prefix: filename prefix for learning curve figure
scale: Scale the coordinates (True/False). Scaling highly recommanded.
test_ratio: train/test ratio
sigma: noise level in kernel ridge regression, default is 0.1% of the standard deviation of the data.
lc_points : number of points on the learning curve
lc_repeats : number of sub-sampling when compute the learning curve
Returns
-------
Learning curve.
"""
scale = bool(scale)
# try to read the xyz file
if fxyz != 'none':
asapxyz = ASAPXYZ(fxyz)
desc, _ = asapxyz.get_descriptors(fmat)
# we can also load the descriptor matrix from a standalone file
if os.path.isfile(fmat[0]):
try:
desc = np.genfromtxt(fmat[0], dtype=float)
print("loaded the descriptor matrix from file: ", fmat)
except:
raise ValueError('Cannot load the descriptor matrix from file')
if len(desc) == 0:
raise ValueError(
'Please supply descriptor in a xyz file or a standlone descriptor matrix'
)
print("shape of the descriptor matrix: ", np.shape(desc),
"number of descriptors: ", np.shape(desc[0]))
# read in the properties to be predicted
y_all = []
try:
y_all = np.genfromtxt(fy, dtype=float)
except:
y_all = asapxyz.get_property(fy)
dm = Design_Matrix(X=desc, y=y_all, whiten=True, test_ratio=test_ratio)
# if sigma is not set...
if sigma < 0:
sigma = 0.001 * np.std(y_all)
rr = RidgeRegression(sigma)
# fit the model
dm.compute_fit(rr, 'ridge_regression', store_results=True, plot=True)
# learning curve
if lc_points > 1:
lc_scores = dm.compute_learning_curve(rr,
'ridge_regression',
lc_points=lc_points,
lc_repeats=lc_repeats,
randomseed=42,
verbose=False)
# make plot
lc_scores.plot_learning_curve()
plt.show()