def fit(ctx, fxyz, design_matrix, use_atomic_descriptors, only_use_species, y,
normalized_by_size, prefix, test_ratio, learning_curve, lc_points):
"""
Fit a machine learning model to the design matrix and labels.
This command function evaluated before the specific ones,
we setup the general stuff here, such as read the files.
"""
if not fxyz and not design_matrix[0]:
return
if prefix is None: prefix = "ASAP-fit"
ctx.obj['fit_options'] = {
"prefix": prefix,
"learning_curve": learning_curve,
"lc_points": lc_points,
"test_ratio": test_ratio
}
asapxyz, desc, _ = read_xyz_n_dm(fxyz, design_matrix,
use_atomic_descriptors, only_use_species,
False)
try:
import numpy as np
y_all = np.genfromtxt(y, dtype=float)
except:
if use_atomic_descriptors:
y_all = asapxyz.get_atomic_property(y, normalized_by_size)
else:
y_all = asapxyz.get_property(y, normalized_by_size)
# print(y_all)
from asaplib.data import Design_Matrix
ctx.obj['dm'] = Design_Matrix(desc, y_all, True, test_ratio)
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()