def fit_transform(self, X, y=None):
self.fit(X)
# Compute the kernel matrix by only computing the lower half
kernel = get_local_symmetric_kernels(self.train_points)[0]
# If specified, average it
if self.average:
_compute_average(kernel, X, X)
return kernel
def test_krr_fchl_atomic():
test_dir = os.path.dirname(os.path.realpath(__file__))
# Parse file containing PBE0/def2-TZVP heats of formation and xyz filenames
data = get_energies(test_dir + "/data/hof_qm7.txt")
# Generate a list of qml.Compound() objects"
mols = []
for xyz_file in sorted(data.keys())[:10]:
# Initialize the qml.Compound() objects
mol = qml.Compound(xyz=test_dir + "/qm7/" + xyz_file)
# Associate a property (heat of formation) with the object
mol.properties = data[xyz_file]
# This is a Molecular Coulomb matrix sorted by row norm
mol.representation = generate_representation(mol.coordinates, \
mol.nuclear_charges, cut_distance=1e6)
mols.append(mol)
X = np.array([mol.representation for mol in mols])
# Set hyper-parameters
sigma = 2.5
K = get_local_symmetric_kernels(X, [sigma])[0]
K_test = np.zeros((len(mols), len(mols)))
for i, Xi in enumerate(X):
for j, Xj in enumerate(X):
K_atomic = get_atomic_kernels(Xi[:mols[i].natoms],
Xj[:mols[j].natoms], [sigma])[0]
K_test[i, j] = np.sum(K_atomic)
assert np.invert(np.all(
np.isnan(K_atomic))), "FCHL atomic kernel contains NaN"
if (i == j):
K_atomic_symmetric = get_atomic_symmetric_kernels(
Xi[:mols[i].natoms], [sigma])[0]
assert np.allclose(K_atomic, K_atomic_symmetric
), "Error in FCHL symmetric atomic kernels"
assert np.invert(np.all(np.isnan(K_atomic_symmetric))
), "FCHL atomic symmetric kernel contains NaN"
assert np.allclose(K, K_test), "Error in FCHL atomic kernels"
def test_krr_fchl_local():
# Test that all kernel arguments work
kernel_args = {
"cut_distance": 1e6,
"cut_start": 0.5,
"two_body_width": 0.1,
"two_body_scaling": 2.0,
"two_body_power": 6.0,
"three_body_width": 3.0,
"three_body_scaling": 2.0,
"three_body_power": 3.0,
"alchemy": "periodic-table",
"alchemy_period_width": 1.0,
"alchemy_group_width": 1.0,
"fourier_order": 2,
}
test_dir = os.path.dirname(os.path.realpath(__file__))
# Parse file containing PBE0/def2-TZVP heats of formation and xyz filenames
data = get_energies(test_dir + "/data/hof_qm7.txt")
# Generate a list of qml.Compound() objects"
mols = []
for xyz_file in sorted(data.keys())[:100]:
# Initialize the qml.Compound() objects
mol = qml.Compound(xyz=test_dir + "/qm7/" + xyz_file)
# Associate a property (heat of formation) with the object
mol.properties = data[xyz_file]
# This is a Molecular Coulomb matrix sorted by row norm
mol.generate_fchl_representation(cut_distance=1e6)
mols.append(mol)
# Shuffle molecules
np.random.seed(666)
np.random.shuffle(mols)
# Make training and test sets
n_test = len(mols) // 3
n_train = len(mols) - n_test
training = mols[:n_train]
test = mols[-n_test:]
X = np.array([mol.representation for mol in training])
Xs = np.array([mol.representation for mol in test])
# List of properties
Y = np.array([mol.properties for mol in training])
Ys = np.array([mol.properties for mol in test])
# Set hyper-parameters
sigma = 2.5
llambda = 1e-8
K_symmetric = get_local_symmetric_kernels(X, [sigma], **kernel_args)[0]
K = get_local_kernels(X, X, [sigma], **kernel_args)[0]
assert np.allclose(K, K_symmetric), "Error in FCHL symmetric local kernels"
assert np.invert(np.all(
np.isnan(K_symmetric))), "FCHL local symmetric kernel contains NaN"
assert np.invert(np.all(np.isnan(K))), "FCHL local kernel contains NaN"
# Solve alpha
K[np.diag_indices_from(K)] += llambda
alpha = cho_solve(K, Y)
# Calculate prediction kernel
Ks = get_local_kernels(Xs, X, [sigma], **kernel_args)[0]
assert np.invert(np.all(
np.isnan(Ks))), "FCHL local testkernel contains NaN"
Yss = np.dot(Ks, alpha)
mae = np.mean(np.abs(Ys - Yss))
assert abs(2 - mae) < 1.0, "Error in FCHL local kernel-ridge regression"
def test_fchl_local_periodic():
nuclear_charges = [
np.array([
13, 13, 58, 58, 58, 58, 58, 58, 16, 16, 16, 16, 16, 16, 16, 16, 16,
16, 16, 16, 16, 16, 23, 23
]),
np.array([
34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 73, 73, 73, 73, 81,
81, 81, 81
]),
np.array([48, 8, 8, 8, 8, 8, 8, 51, 51]),
np.array([
16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,
16, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30,
30, 30
]),
np.array([58, 58, 8, 8, 8, 8, 8, 8, 8, 8, 23, 23])
]
cells = np.array([[[1.01113290e+01, -1.85000000e-04, 0.00000000e+00],
[-5.05582400e+00, 8.75745400e+00, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 6.15100100e+00]],
[[9.672168, 0., 0.], [0., 3.643786, 0.],
[0., 0., 14.961818]],
[[5.28208000e+00, -1.20000000e-05, 1.50000000e-05],
[-2.64105000e+00, 4.57443400e+00, -3.00000000e-05],
[1.40000000e-05, -2.40000000e-05, 4.77522000e+00]],
[[-1.917912, 3.321921, 0.], [3.835824, 0., 0.],
[1.917912, -1.107307, -56.423542]],
[[3.699168, 3.699168, -3.255938],
[3.699168, -3.699168, 3.255938],
[-3.699168, -3.699168, -3.255938]]])
fractional_coordinates = [
[[0.6666706, 0.33333356, 0.15253127],
[0.33332896, 0.66666655, 0.65253119],
[0.14802736, 0.375795, 0.23063888],
[0.62422269, 0.77225019, 0.23063888],
[0.22775607, 0.85196133, 0.23063888],
[0.77224448, 0.14803879, 0.7306388],
[0.37577687, 0.22774993,
0.7306388], [0.8519722, 0.62420512, 0.7306388],
[0.57731818, 0.47954083, 0.0102715],
[0.52043884, 0.09777799, 0.01028288],
[0.90211803, 0.4226259, 0.01032677],
[0.33335216, 0.6666734, 0.01482035],
[0.90959766, 0.77585201, 0.30637615],
[0.86626106, 0.09040873, 0.3063794],
[0.22415808, 0.13374794, 0.30638265],
[0.42268138, 0.52045928, 0.51027142],
[0.47956114, 0.90222098, 0.5102828],
[0.09788153, 0.57737421, 0.51032669],
[0.6666474, 0.33332671, 0.51482027],
[0.09040133, 0.22414696, 0.80637769],
[0.13373793, 0.90959025, 0.80637932],
[0.77584247, 0.86625217, 0.80638257], [0., 0., 0.05471142],
[0., 0., 0.55471134]],
[[0.81615001, 0.75000014, 0.00116296],
[0.52728096, 0.25000096, 0.0993275],
[0.24582596, 0.75000014, 0.2198563],
[0.74582658, 0.75000014, 0.28014376],
[0.02728137, 0.25000096, 0.40067257],
[0.31615042, 0.75000014, 0.49883711],
[0.68384978, 0.25000096, 0.50116236],
[0.97271884, 0.75000014, 0.59932757],
[0.25417362, 0.25000096, 0.71985637],
[0.75417321, 0.25000096, 0.78014383],
[0.47271925, 0.75000014, 0.90067263],
[0.1838502, 0.25000096, 0.99883717],
[0.33804831, 0.75000014, 0.07120258],
[0.83804789, 0.75000014, 0.42879749],
[0.16195232, 0.25000096, 0.57120198],
[0.6619519, 0.25000096, 0.92879756],
[0.98245812, 0.25000096, 0.17113829],
[0.48245853, 0.25000096, 0.32886177],
[0.51754167, 0.75000014, 0.67113836],
[0.01754209, 0.75000014, 0.82886184]],
[[0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[3.66334233e-01, 1.96300000e-07, 2.70922493e-01],
[6.33665197e-01, 6.33666177e-01, 2.70923540e-01],
[3.62000000e-08, 3.66333081e-01, 2.70923851e-01],
[6.70000000e-09, 6.33664733e-01, 7.29076149e-01],
[6.33664135e-01, 9.99998055e-01, 7.29076460e-01],
[3.66336157e-01, 3.66334260e-01, 7.29077507e-01],
[3.33333635e-01, 6.66667395e-01, 4.99998953e-01],
[6.66667720e-01, 3.33333042e-01, 5.00000000e-01]],
[[0.3379644, 0.66203644, 0.01389048],
[0.02316309, 0.97683587, 0.06948926],
[0.70833843, 0.29165976, 0.12501892],
[0.39352259, 0.60647824, 0.18056506],
[0.74538243, 0.25461577, 0.2361509],
[0.09722803, 0.90277092, 0.2916841],
[0.44907919, 0.55092165,
0.34723485], [0.8009281, 0.1990701, 0.4027879],
[0.15278103, 0.84721793, 0.45834308],
[0.83797345, 0.16202475, 0.51392396],
[0.52315813, 0.4768427, 0.56947169],
[0.20833916, 0.7916598, 0.62501748],
[0.89352691, 0.10647128, 0.68058436],
[0.57870427, 0.42129656, 0.73611012],
[0.93056329, 0.06943491, 0.79169347],
[0.28241704, 0.71758191, 0.84725114],
[0.63426956, 0.36573128, 0.90280596],
[0.98611817, 0.01388002, 0.95835813], [0., 0., 0.],
[0.35185434, 0.64814649, 0.05556032],
[0.03704151, 0.96295744, 0.11112454],
[0.72221887, 0.27777932, 0.16666022],
[0.40741437, 0.59258647, 0.22224039],
[0.75926009, 0.24073811, 0.27778387],
[0.11111195, 0.888887, 0.33333586],
[0.46296234, 0.53703849, 0.38888431],
[0.81480954, 0.18518866, 0.44443222],
[0.16667233, 0.83332662, 0.500017],
[0.85185117, 0.14814703, 0.55555711],
[0.53704217, 0.46295866, 0.61112381],
[0.22222196, 0.777777, 0.66666587],
[0.90740847, 0.09258972, 0.72222903],
[0.59259328, 0.40740756,
0.77777712], [0.94444213, 0.05555607, 0.83333],
[0.29630132, 0.70369764, 0.88890396],
[0.64815247, 0.35184836, 0.94445471]],
[[0., 0., 0.], [0.75000042, 0.50000027, 0.25000015],
[0.15115386, 0.81961403, 0.33154037],
[0.51192691, 0.18038651, 0.3315404],
[0.08154025, 0.31961376, 0.40115401],
[0.66846017, 0.81961403, 0.48807366],
[0.08154025, 0.68038678, 0.76192703],
[0.66846021, 0.18038651, 0.84884672],
[0.23807355, 0.31961376, 0.91846033],
[0.59884657, 0.68038678, 0.91846033], [0.50000031, 0., 0.50000031],
[0.25000015, 0.50000027, 0.75000042]]
]
n = 5
X = np.array([
generate_representation(fractional_coordinates[i],
nuclear_charges[i],
cell=cells[i],
max_size=36,
neighbors=200,
cut_distance=7.0) for i in range(5)
])
sigmas = [2.5]
K = get_local_symmetric_kernels(X, sigmas, cut_distance=7.0, cut_start=0.7)
K_ref = np.array([
[530.03184304, 435.65196293, 198.61245535, 782.49428327, 263.53562172],
[435.65196293, 371.35281119, 163.83766549, 643.99777576, 215.04338938],
[198.61245535, 163.83766549, 76.12134823, 295.02739281, 99.89595704],
[
782.49428327, 643.99777576, 295.02739281, 1199.61736141,
389.31169487
],
[263.53562172, 215.04338938, 99.89595704, 389.31169487, 133.36920188]
])
assert np.allclose(K, K_ref), "Error in periodic FCHL"
def test_krr_fchl_alchemy():
test_dir = os.path.dirname(os.path.realpath(__file__))
# Parse file containing PBE0/def2-TZVP heats of formation and xyz filenames
data = get_energies(test_dir + "/data/hof_qm7.txt")
# Generate a list of qml.Compound() objects"
mols = []
for xyz_file in sorted(data.keys())[:20]:
# Initialize the qml.Compound() objects
mol = qml.Compound(xyz=test_dir + "/qm7/" + xyz_file)
# Associate a property (heat of formation) with the object
mol.properties = data[xyz_file]
# This is a Molecular Coulomb matrix sorted by row norm
mol.generate_fchl_representation(cut_distance=1e6)
mols.append(mol)
# Shuffle molecules
np.random.seed(666)
np.random.shuffle(mols)
X = np.array([mol.representation for mol in mols])
sigma = 2.5
np.set_printoptions(edgeitems=16, linewidth=6666)
overlap = np.array(
[[
1., 0.00835282, 0.90696062, 0.82257756, 0.61368025, 0.37660345,
0.19010927, 0.07894037, 0.02696323, 0.00757568, 0.67663385,
0.61368025, 0.45783336, 0.28096329, 0.14183016, 0.05889311,
0.02011579, 0.0056518, 0.41523683, 0.37660345
],
[
0.00835282, 1., 0.00757568, 0.02696323, 0.07894037, 0.19010927,
0.37660345, 0.61368025, 0.82257756, 0.90696062, 0.0056518,
0.02011579, 0.05889311, 0.14183016, 0.28096329, 0.45783336,
0.61368025, 0.67663385, 0.0034684, 0.01234467
],
[
0.90696062, 0.00757568, 1., 0.90696062, 0.67663385, 0.41523683,
0.20961139, 0.08703837, 0.02972922, 0.00835282, 0.90696062,
0.82257756, 0.61368025, 0.37660345, 0.19010927, 0.07894037,
0.02696323, 0.00757568, 0.67663385, 0.61368025
],
[
0.82257756, 0.02696323, 0.90696062, 1., 0.90696062, 0.67663385,
0.41523683, 0.20961139, 0.08703837, 0.02972922, 0.82257756,
0.90696062, 0.82257756, 0.61368025, 0.37660345, 0.19010927,
0.07894037, 0.02696323, 0.61368025, 0.67663385
],
[
0.61368025, 0.07894037, 0.67663385, 0.90696062, 1., 0.90696062,
0.67663385, 0.41523683, 0.20961139, 0.08703837, 0.61368025,
0.82257756, 0.90696062, 0.82257756, 0.61368025, 0.37660345,
0.19010927, 0.07894037, 0.45783336, 0.61368025
],
[
0.37660345, 0.19010927, 0.41523683, 0.67663385, 0.90696062, 1.,
0.90696062, 0.67663385, 0.41523683, 0.20961139, 0.37660345,
0.61368025, 0.82257756, 0.90696062, 0.82257756, 0.61368025,
0.37660345, 0.19010927, 0.28096329, 0.45783336
],
[
0.19010927, 0.37660345, 0.20961139, 0.41523683, 0.67663385,
0.90696062, 1., 0.90696062, 0.67663385, 0.41523683, 0.19010927,
0.37660345, 0.61368025, 0.82257756, 0.90696062, 0.82257756,
0.61368025, 0.37660345, 0.14183016, 0.28096329
],
[
0.07894037, 0.61368025, 0.08703837, 0.20961139, 0.41523683,
0.67663385, 0.90696062, 1., 0.90696062, 0.67663385, 0.07894037,
0.19010927, 0.37660345, 0.61368025, 0.82257756, 0.90696062,
0.82257756, 0.61368025, 0.05889311, 0.14183016
],
[
0.02696323, 0.82257756, 0.02972922, 0.08703837, 0.20961139,
0.41523683, 0.67663385, 0.90696062, 1., 0.90696062, 0.02696323,
0.07894037, 0.19010927, 0.37660345, 0.61368025, 0.82257756,
0.90696062, 0.82257756, 0.02011579, 0.05889311
],
[
0.00757568, 0.90696062, 0.00835282, 0.02972922, 0.08703837,
0.20961139, 0.41523683, 0.67663385, 0.90696062, 1., 0.00757568,
0.02696323, 0.07894037, 0.19010927, 0.37660345, 0.61368025,
0.82257756, 0.90696062, 0.0056518, 0.02011579
],
[
0.67663385, 0.0056518, 0.90696062, 0.82257756, 0.61368025,
0.37660345, 0.19010927, 0.07894037, 0.02696323, 0.00757568, 1.,
0.90696062, 0.67663385, 0.41523683, 0.20961139, 0.08703837,
0.02972922, 0.00835282, 0.90696062, 0.82257756
],
[
0.61368025, 0.02011579, 0.82257756, 0.90696062, 0.82257756,
0.61368025, 0.37660345, 0.19010927, 0.07894037, 0.02696323,
0.90696062, 1., 0.90696062, 0.67663385, 0.41523683, 0.20961139,
0.08703837, 0.02972922, 0.82257756, 0.90696062
],
[
0.45783336, 0.05889311, 0.61368025, 0.82257756, 0.90696062,
0.82257756, 0.61368025, 0.37660345, 0.19010927, 0.07894037,
0.67663385, 0.90696062, 1., 0.90696062, 0.67663385, 0.41523683,
0.20961139, 0.08703837, 0.61368025, 0.82257756
],
[
0.28096329, 0.14183016, 0.37660345, 0.61368025, 0.82257756,
0.90696062, 0.82257756, 0.61368025, 0.37660345, 0.19010927,
0.41523683, 0.67663385, 0.90696062, 1., 0.90696062, 0.67663385,
0.41523683, 0.20961139, 0.37660345, 0.61368025
],
[
0.14183016, 0.28096329, 0.19010927, 0.37660345, 0.61368025,
0.82257756, 0.90696062, 0.82257756, 0.61368025, 0.37660345,
0.20961139, 0.41523683, 0.67663385, 0.90696062, 1., 0.90696062,
0.67663385, 0.41523683, 0.19010927, 0.37660345
],
[
0.05889311, 0.45783336, 0.07894037, 0.19010927, 0.37660345,
0.61368025, 0.82257756, 0.90696062, 0.82257756, 0.61368025,
0.08703837, 0.20961139, 0.41523683, 0.67663385, 0.90696062, 1.,
0.90696062, 0.67663385, 0.07894037, 0.19010927
],
[
0.02011579, 0.61368025, 0.02696323, 0.07894037, 0.19010927,
0.37660345, 0.61368025, 0.82257756, 0.90696062, 0.82257756,
0.02972922, 0.08703837, 0.20961139, 0.41523683, 0.67663385,
0.90696062, 1., 0.90696062, 0.02696323, 0.07894037
],
[
0.0056518, 0.67663385, 0.00757568, 0.02696323, 0.07894037,
0.19010927, 0.37660345, 0.61368025, 0.82257756, 0.90696062,
0.00835282, 0.02972922, 0.08703837, 0.20961139, 0.41523683,
0.67663385, 0.90696062, 1., 0.00757568, 0.02696323
],
[
0.41523683, 0.0034684, 0.67663385, 0.61368025, 0.45783336,
0.28096329, 0.14183016, 0.05889311, 0.02011579, 0.0056518,
0.90696062, 0.82257756, 0.61368025, 0.37660345, 0.19010927,
0.07894037, 0.02696323, 0.00757568, 1., 0.90696062
],
[
0.37660345, 0.01234467, 0.61368025, 0.67663385, 0.61368025,
0.45783336, 0.28096329, 0.14183016, 0.05889311, 0.02011579,
0.82257756, 0.90696062, 0.82257756, 0.61368025, 0.37660345,
0.19010927, 0.07894037, 0.02696323, 0.90696062, 1.
]])
K_alchemy = get_local_symmetric_kernels(X, [sigma],
alchemy="periodic-table")[0]
K_custom = get_local_symmetric_kernels(X, [sigma], alchemy=overlap)[0]
assert np.allclose(K_alchemy, K_custom), "Error in alchemy"
nooverlap = np.eye(100)
K_noalchemy = get_local_symmetric_kernels(X, [sigma], alchemy="off")[0]
K_custom = get_local_symmetric_kernels(X, [sigma], alchemy=nooverlap)[0]
assert np.allclose(K_noalchemy, K_custom), "Error in no-alchemy"
def fit_transform(self, X, y=None):
self.fit(X)
return np.squeeze(get_local_symmetric_kernels(self.train_points))