def test_enhance_atoms():
p_table = Get_periodic_table()
#####
mols = dmy.get_rndethane_mols(distance=True)
atoms = GNR.make_atom_df(mols)
structure_dict = GNR.make_struc_dict(atoms)
BCAI.enhance_structure_dict(structure_dict)
###########
BCAI.enhance_atoms(atoms, structure_dict)
for i, idx in enumerate(atoms['atom_index'].values):
molid = atoms['molecule_name'][i]
mol = 0
for ml_fnd in mols:
if ml_fnd.molid == molid:
mol = ml_fnd
atid = atoms['atom_index'][i]
assert p_table.index(atoms['typestr'][i]) == mol.types[atid]
assert np.array_equal(atoms['conn'][i], mol.conn[atid])
assert np.array_equal(atoms['distance'][i], mol.dist[atid])
def test_get_dummy_features():
mol = dmy.get_ethane_mol()
mols = [mol]
x, y, r = GNR.get_dummy_features(mols, targetflag='CCS')
assert x == []
assert np.array_equal(y, mol.shift[np.where(mol.types==6)])
assert np.array_equal(r, [['ethane', 0], ['ethane', 1]])
x, y, r = GNR.get_dummy_features(mols, targetflag='HCS')
assert x == []
assert np.array_equal(y, mol.shift[np.where(mol.types==1)])
assert np.array_equal(r, [['ethane', 2], ['ethane', 3], ['ethane', 4],
['ethane', 5], ['ethane', 6], ['ethane', 7]])
x, y, r = GNR.get_dummy_features(mols, targetflag='1JCH')
for i, index in enumerate(r):
assert y[i] == mol.coupling[index[1]][index[2]]
assert np.array_equal(r, [['ethane', 0, 2], ['ethane', 0, 3], ['ethane', 0, 4],
['ethane', 1, 5], ['ethane', 1, 6], ['ethane', 1, 7]])
x, y, r = GNR.get_dummy_features(mols, targetflag='3JHH')
for i, index in enumerate(r):
assert y[i] == mol.coupling[index[1]][index[2]]
assert np.array_equal(r, [['ethane', 2, 5], ['ethane', 2, 6], ['ethane', 2, 7],
['ethane', 3, 5], ['ethane', 3, 6], ['ethane', 3, 7],
['ethane', 4, 5], ['ethane', 4, 6], ['ethane', 4, 7]])
def test_make_triplets():
p_table = Get_periodic_table()
#####
mols = dmy.get_rndethane_mols(distance=True)
atoms = GNR.make_atom_df(mols)
structure_dict = GNR.make_struc_dict(atoms)
BCAI.enhance_structure_dict(structure_dict)
BCAI.enhance_atoms(atoms, structure_dict)
bonds = GNR.make_bonds_df(mols)
BCAI.enhance_bonds(bonds, structure_dict, flag='3JHH')
############
triplets = BCAI.make_triplets(bonds["molecule_name"].unique(),
structure_dict)
assert len(triplets["molecule_name"].unique()) == len(mols)
count = 0
for mol in mols:
for atom1, type1 in enumerate(mol.types):
for atom2, type2 in enumerate(mol.types):
if atom1 == atom2:
continue
for atom3, type3 in enumerate(mol.types):
if atom3 in [atom1, atom2] or atom3 < atom2:
continue
if mol.conn[atom1][atom2] != 1 or mol.conn[atom1][
atom3] != 1:
continue
row = triplets.loc[(triplets.molecule_name == mol.molid)
& (triplets.atom_index_0 == atom1)
& (triplets.atom_index_1 == atom2)
& (triplets.atom_index_2 == atom3)]
assert len(row.index) == 1
ba = mol.xyz[atom2] - mol.xyz[atom1]
bc = mol.xyz[atom3] - mol.xyz[atom1]
angle = np.sum(
ba * bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))
angle = np.arccos(np.clip(angle, -1.0, 1.0))
assert angle == row.angle.values
count += 1
assert count == len(triplets.index)
def test_get_atomic_ACSF():
mols = dmy.get_rndethane_mols()
atoms = GNR.make_atom_df(mols)
struc = GNR.make_struc_dict(atoms)
bonds = GNR.make_bonds_df(mols)
atoms = QML.get_atomic_qml_features(atoms,
bonds,
struc,
featureflag='ACSF')
def get_features_frommols(self,
args,
params={},
molcheck_run=False,
training=True,
max=200):
self.params = params
target = flag_to_target(args['targetflag'])
self.remove_mols(target)
if molcheck_run:
return
for mol in self.mols:
if len(mol.types) > max:
max = len(mol.types) + 1
print('WARNING, setting max atoms to ', max)
self.params['max'] = max
self.atoms = GNR.make_atom_df(self.mols)
self.struc = GNR.make_struc_dict(self.atoms)
if len(args['targetflag']) == 4:
self.bonds = GNR.make_bonds_df(self.mols)
if args['featureflag'] in ['aSLATM', 'CMAT', 'FCHL', 'ACSF']:
from autoenrich.ml.features import QML_features
self.atoms = QML_features.get_atomic_qml_features(
self.atoms,
self.bonds,
self.struc,
featureflag=args['featureflag'],
cutoff=params['cutoff'],
max=max)
elif args['featureflag'] == 'BCAI':
from autoenrich.ml.features import TFM_features
self.BCAI, self.atoms, self.bonds, self.struc, xfiles, rfiles, yfiles = TFM_features.get_BCAI_features(
self.atoms,
self.bonds,
self.struc,
targetflag=args['targetflag'],
training=training)
elif args['featureflag'] != 'dummy':
return
else:
print('Feature flag not recognised, no feature flag: ',
args['featureflag'])
return 0
def test_make_struc_dict():
p_table = Get_periodic_table()
mols = dmy.get_rndethane_mols()
atoms = GNR.make_atom_df(mols)
structure_dict = GNR.make_struc_dict(atoms)
assert len(structure_dict.keys()) == len(mols)
for mol in mols:
assert structure_dict[mol.molid]['typesstr'] == [p_table[type] for type in mol.types]
assert np.array_equal(structure_dict[mol.molid]['positions'], mol.xyz)
assert np.array_equal(structure_dict[mol.molid]['conn'], mol.conn)
def test_get_atomic_FCHL():
mols = dmy.get_rndethane_mols()
atoms = GNR.make_atom_df(mols)
struc = GNR.make_struc_dict(atoms)
bonds = GNR.make_bonds_df(mols)
atoms = QML.get_atomic_qml_features(atoms,
bonds,
struc,
featureflag='FCHL')
assert atoms['atomic_rep'].values[0].shape == (5, 50)
def test_get_atomic_aSLATM():
mols = dmy.get_rndethane_mols()
atoms = GNR.make_atom_df(mols)
struc = GNR.make_struc_dict(atoms)
bonds = GNR.make_bonds_df(mols)
atoms = QML.get_atomic_qml_features(atoms,
bonds,
struc,
featureflag='aSLATM')
assert len(atoms['atomic_rep'].values[0]) == 20105
def test_enhance_bonds():
p_table = Get_periodic_table()
#####
mols = dmy.get_rndethane_mols(distance=True)
atoms = GNR.make_atom_df(mols)
structure_dict = GNR.make_struc_dict(atoms)
BCAI.enhance_structure_dict(structure_dict)
BCAI.enhance_atoms(atoms, structure_dict)
bonds = GNR.make_bonds_df(mols)
############
BCAI.enhance_bonds(bonds, structure_dict, flag='3JHH')
for idx, bond in enumerate(bonds):
molid = bonds['molecule_name'][idx]
at1 = bonds['atom_index_0'][idx]
at2 = bonds['atom_index_1'][idx]
mol = 0
for ml_fnd in mols:
if ml_fnd.molid == molid:
mol = ml_fnd
assert mol.coupling_len[at1][at2] == int(bonds['type'][idx][0])
assert mol.coupling[at1][at2] == bonds['scalar_coupling_constant'][idx]
if bonds['labeled_type'][idx] == '3JHH':
assert bonds['predict'][idx] == 1
else:
assert bonds['predict'][idx] == 0
for mol in mols:
for atom1, type1 in enumerate(mol.types):
for atom2, type2 in enumerate(mol.types):
if atom1 == atom2:
continue
row = bonds.loc[(bonds['molecule_name'] == mol.molid)
& (bonds['atom_index_0'] == atom1)
& (bonds['atom_index_1'] == atom2)]
cpl = row['scalar_coupling_constant'].values
if type1 == 1 and type2 == 1 and mol.coupling_len[atom1][
atom2] == 3:
assert row.predict.values == 1
assert mol.coupling[atom1][atom2] == cpl[0]
def test_get_atomic_cmat():
mols = dmy.get_rndethane_mols()
atoms = GNR.make_atom_df(mols)
struc = GNR.make_struc_dict(atoms)
bonds = GNR.make_bonds_df(mols)
atoms = QML.get_atomic_qml_features(atoms,
bonds,
struc,
featureflag='CMAT')
assert len(atoms['atomic_rep'].values[0]) == 50 * (50 + 1) / 2
assert len(atoms['atomic_rep'].values[0].nonzero()[0]) == len(
mols[0].types) * (len(mols[0].types) + 1) / 2
def test_get_scaling():
#####
mols = dmy.get_rndethane_mols(distance=True)
atoms = GNR.make_atom_df(mols)
structure_dict = GNR.make_struc_dict(atoms)
BCAI.enhance_structure_dict(structure_dict)
BCAI.enhance_atoms(atoms, structure_dict)
bonds = GNR.make_bonds_df(mols)
BCAI.enhance_bonds(bonds, structure_dict, flag='3JHH')
#####
means, stds = BCAI.get_scaling(bonds)
def test_make_bonds_df():
p_table = Get_periodic_table()
#####
mols = dmy.get_rndethane_mols()
#####
bonds = GNR.make_bonds_df(mols)
assert len(bonds["molecule_name"].unique()) == len(mols)
for idx, bond in enumerate(bonds):
molid = bonds['molecule_name'][idx]
at1 = bonds['atom_index_0'][idx]
at2 = bonds['atom_index_1'][idx]
mol = 0
for ml_fnd in mols:
if ml_fnd.molid == molid:
mol = ml_fnd
assert mol.coupling_len[at1][at2] == int(bonds['type'][idx][0])
assert mol.coupling[at1][at2] == bonds['scalar_coupling_constant'][idx]
def test_enhance_structure_dict():
p_table = Get_periodic_table()
#####
mols = dmy.get_rndethane_mols(distance=True)
atoms = GNR.make_atom_df(mols)
structure_dict = GNR.make_struc_dict(atoms)
#####
BCAI.enhance_structure_dict(structure_dict)
for mol in mols:
assert structure_dict[mol.molid]['typesstr'] == [
p_table[type] for type in mol.types
]
assert np.array_equal(structure_dict[mol.molid]['positions'], mol.xyz)
assert np.array_equal(structure_dict[mol.molid]['conn'], mol.conn)
assert np.array_equal(structure_dict[mol.molid]['distances'], mol.dist)
def test_add_embedding():
p_table = Get_periodic_table()
#####
mols = dmy.get_rndethane_mols(distance=True)
atoms = GNR.make_atom_df(mols)
structure_dict = GNR.make_struc_dict(atoms)
BCAI.enhance_structure_dict(structure_dict)
BCAI.enhance_atoms(atoms, structure_dict)
bonds = GNR.make_bonds_df(mols)
BCAI.enhance_bonds(bonds, structure_dict, flag='3JHH')
triplets = BCAI.make_triplets(bonds["molecule_name"].unique(),
structure_dict)
#####
embeddings, atoms, bonds, triplets = BCAI.add_embedding(
atoms, bonds, triplets)
def test_make_atom_df():
mols = dmy.get_rndethane_mols()
ats = 0
for mol in mols:
ats += len(mol.types)
atoms = GNR.make_atom_df(mols)
assert len(atoms["molecule_name"].unique()) == len(mols)
counted = 0
for i, idx in enumerate(atoms['atom_index'].values):
for mol in mols:
if atoms['molecule_name'][i] == mol.molid:
counted += 1
assert np.array_equal(mol.conn[idx], atoms['conn'][i])
assert mol.xyz[idx][0] == atoms['x'][i]
assert mol.xyz[idx][1] == atoms['y'][i]
assert mol.xyz[idx][2] == atoms['z'][i]
assert counted == ats
def get_features_frommols(self,
args,
params={},
molcheck_run=False,
training=True):
featureflag = args['featureflag']
targetflag = args['targetflag']
try:
max = args['max_size']
except:
max = 200
for mol in self.mols:
if len(mol.types) > max:
max = len(mol.types)
print('WARNING, SETTING MAXIMUM MOLECULE SIZE TO, ', max)
if 'cutoff' in params:
if params['cutoff'] < 0.1:
params['cutoff'] = 0.1
else:
params['cutoff'] = 5.0
x = []
y = []
r = []
self.params = params
target = flag_to_target(targetflag)
self.remove_mols(target)
if molcheck_run:
return
if featureflag in ['aSLATM', 'CMAT', 'FCHL', 'ACSF']:
import qml
elif featureflag in ['BCAI']:
from autoenrich.ml.features import TFM_features
_, y, r = GNR_features.get_dummy_features(self.mols, targetflag)
if featureflag == 'aSLATM':
mbtypes = [[1], [1, 1], [1, 1, 1], [1, 1, 6], [1, 1, 7], [1, 1, 8],
[1, 1, 9], [1, 6], [1, 6, 1], [1, 6, 6], [1, 6, 7],
[1, 6, 8], [1, 6, 9], [1, 7], [1, 7, 1], [1, 7, 6],
[1, 7, 7], [1, 7, 8], [1, 7, 9], [1, 8], [1, 8, 1],
[1, 8, 6], [1, 8, 7], [1, 8, 8], [1, 8, 9], [1, 9],
[1, 9, 1], [1, 9, 6], [1, 9, 7], [1, 9, 8], [1, 9, 9],
[6], [6, 1], [6, 1, 1], [6, 1, 6], [6, 1, 7], [6, 1, 8],
[6, 1, 9], [6, 6], [6, 6, 1], [6, 6, 6], [6, 6, 7],
[6, 6, 8], [6, 6, 9], [6, 7], [6, 7, 1], [6, 7, 6],
[6, 7, 7], [6, 7, 8], [6, 7, 9], [6, 8], [6, 8, 1],
[6, 8, 6], [6, 8, 7], [6, 8, 8], [6, 8, 9], [6, 9],
[6, 9, 1], [6, 9, 6], [6, 9, 7], [6, 9, 8], [6, 9, 9],
[7], [7, 1], [7, 1, 1], [7, 1, 6], [7, 1, 7], [7, 1, 8],
[7, 1, 9], [7, 6], [7, 6, 1], [7, 6, 6], [7, 6, 7],
[7, 6, 8], [7, 6, 9], [7, 7], [7, 7, 1], [7, 7, 6],
[7, 7, 7], [7, 7, 8], [7, 7, 9], [7, 8], [7, 8, 1],
[7, 8, 6], [7, 8, 7], [7, 8, 8], [7, 8, 9], [7, 9],
[7, 9, 1], [7, 9, 6], [7, 9, 7], [7, 9, 8], [7, 9, 9],
[8], [8, 1], [8, 1, 1], [8, 1, 6], [8, 1, 7], [8, 1, 8],
[8, 1, 9], [8, 6], [8, 6, 1], [8, 6, 6], [8, 6, 7],
[8, 6, 8], [8, 6, 9], [8, 7], [8, 7, 1], [8, 7, 6],
[8, 7, 7], [8, 7, 8], [8, 7, 9], [8, 8], [8, 8, 1],
[8, 8, 6], [8, 8, 7], [8, 8, 8], [8, 8, 9], [8, 9],
[8, 9, 1], [8, 9, 6], [8, 9, 7], [8, 9, 8], [8, 9, 9],
[9], [9, 1], [9, 1, 1], [9, 1, 6], [9, 1, 7], [9, 1, 8],
[9, 1, 9], [9, 6], [9, 6, 1], [9, 6, 6], [9, 6, 7],
[9, 6, 8], [9, 6, 9], [9, 7], [9, 7, 1], [9, 7, 6],
[9, 7, 7], [9, 7, 8], [9, 7, 9], [9, 8], [9, 8, 1],
[9, 8, 6], [9, 8, 7], [9, 8, 8], [9, 8, 9], [9, 9],
[9, 9, 1], [9, 9, 6], [9, 9, 7], [9, 9, 8], [9, 9, 9]]
'''
nuclear_charges = []
for tmp_mol in mols:
nuclear_charges.append(tmp_mol.types)
mbtypes = qml.representations.get_slatm_mbtypes(nuclear_charges)
'''
reps = qml.representations.generate_slatm(mol.xyz,
mol.types,
mbtypes,
rcut=cutoff)
x = np.asarray(reps)
elif featureflag == 'CMAT':
reps = qml.representations.generate_atomic_coulomb_matrix(
mol.types, mol.xyz, size=max, central_cutoff=cutoff)
x = np.asarray(reps)
elif featureflag == 'FCHL':
reps = qml.fchl.generate_representation(mol.xyz,
mol.types,
max,
cut_distance=cutoff)
x = np.asarray(reps)
elif featureflag == 'ACSF':
reps = qml.representations.generate_acsf(
mol.types,
mol.xyz,
elements=[1, 6, 7, 8, 9, 14, 15, 16, 17, 35],
nRs2=int(nRs2),
nRs3=int(nRs3),
nTs=int(nTs),
eta2=eta2,
eta3=eta3,
zeta=zeta,
rcut=cutoff,
acut=acut,
bin_min=0.0,
gradients=False)
x = np.asarray(reps)
elif featureflag == 'BCAI':
_x, _y, _r, mol_order = TFM_features.get_BCAI_features(
self.mols, targetflag, training=training)
x.extend(_x)
y.extend(_y)
r.extend(_r)
batch_mols = []
else:
print('Feature flag not recognised, no feature flag: ',
featureflag)
if featureflag == 'BCAI':
self.x = x
self.y = y
self.r = r
self.mol_order = mol_order
else:
self.x = np.asarray(x)
self.y = np.asarray(y)
self.r = r
if featureflag not in ['dummy', 'BCAI']:
print('Reps generated, shape: ', self.x.shape)