from sklearn.linear_model import LinearRegression
from sklearn.kernel_ridge import KernelRidge
from qmlearn.model.model import QMModel
from qmlearn.io.hdf5 import DBHDF5
[docs]def db2qmmodel(filename, names = '*', mmodels = None):
db = DBHDF5(filename)
if isinstance(names, str):
prefix = names
names = dict.fromkeys(['qmmol', 'atoms', 'properties'])
names['qmmol'] = db.get_names(prefix + '/qmmol*')[0]
names['atoms'] = db.get_names(prefix + '/train_atoms*')[0]
names['properties'] = db.get_names(prefix + '/train_prop*')[0]
print(f'Guess DB names : {names}', flush = True)
refqmmol = db.read_qmmol(names['qmmol'])
train_atoms = db.read_images(names['atoms'])
properties = db.read_properties(names['properties'])
db.close()
#
X = properties['vext']
y = properties['gamma']
#
if mmodels is None :
mmodels={
'gamma': KernelRidge(alpha=0.1,kernel='linear'),
'd_gamma': LinearRegression(),
'd_energy': LinearRegression(),
'd_forces': LinearRegression(),
}
print(f'Guess mmodels: {mmodels}', flush = True)
model = QMModel(mmodels=mmodels, refqmmol = refqmmol)
model.fit(X, y)
#
if 'd_gamma' in mmodels :
shape = y[0].shape
gammas = []
for i, mol in enumerate(train_atoms):
# Do not rotate the molecule
gamma = model.predict(mol, refatoms=mol).reshape(shape)
gammas.append(gamma)
y = gammas
model.fit(y, properties['gamma'], method = 'd_gamma')
for k in mmodels :
if not k.startswith('d_') or k in ['d_gamma'] : continue
key = k[2:]
if key not in properties :
print(f"!WARN : '{key}' not in the database", flush = True)
model.fit(y, properties[key], method = k)
return model