def freesolv_demo(n_configuration_samples=10, n_parameter_samples=10000):
"""Run toy 2D parameterization demo with all of freesolv"""
np.random.seed(0)
mols = []
hydrogens = []
for smiles in smiles_list:
mol = Molecule(smiles, vacuum_samples=[])
path_to_vacuum_samples = resource_filename(
'bayes_implicit_solvent',
'vacuum_samples/vacuum_samples_{}.h5'.format(
mol.mol_index_in_smiles_list))
vacuum_traj = md.load(path_to_vacuum_samples)
thinning = int(len(vacuum_traj) / n_configuration_samples)
mol.vacuum_traj = mdtraj_to_list_of_unitted_snapshots(
vacuum_traj[::thinning])
print('thinned vacuum_traj from {} to {}'.format(
len(vacuum_traj), len(mol.vacuum_traj)))
hydrogens.append(
np.array([a.element.symbol == 'H' for a in mol.top.atoms()]))
mols.append(mol)
def log_prob(radii):
logp = 0
for i in range(len(mols)):
mol = mols[i]
atomic_radii = np.ones(len(mol.pos)) * radii[0]
atomic_radii[hydrogens[i]] = radii[1]
# TODO: update this example to allow the scaling_factors to be variable also
default_scaling_factors = np.ones(len(mol.pos))
logp += mol.log_prob(atomic_radii, default_scaling_factors)
return logp
radii0 = np.array([0.1, 0.1])
scales0 = np.array([0.8, 0.8])
theta0 = pack(radii0, scales0)
stepsize = 0.002
traj, log_probs, acceptance_fraction = random_walk_mh(
theta0, log_prob, n_steps=n_parameter_samples, stepsize=stepsize)
np.savez(os.path.join(data_path, 'H_vs_not_freesolv.npz'),
traj=traj,
log_probs=log_probs,
acceptance_fraction=acceptance_fraction,
stepsize=stepsize,
n_steps=n_parameter_samples)
print("acceptance fraction: {:.4f}".format(acceptance_fraction))
def methane_demo(n_configuration_samples=10, n_parameter_samples=100000):
"""Run toy 2D parameterization demo with methane only"""
np.random.seed(0)
smiles = 'C'
mol = Molecule(smiles, vacuum_samples=[])
path_to_vacuum_samples = resource_filename(
'bayes_implicit_solvent', 'vacuum_samples/vacuum_samples_{}.h5'.format(
mol.mol_index_in_smiles_list))
vacuum_traj = md.load(path_to_vacuum_samples)
thinning = int(len(vacuum_traj) / n_configuration_samples)
mol.vacuum_traj = mdtraj_to_list_of_unitted_snapshots(
vacuum_traj[::thinning])
print('thinned vacuum_traj from {} to {}'.format(len(vacuum_traj),
len(mol.vacuum_traj)))
def log_prob(radii):
atomic_radii = np.zeros(len(mol.pos))
atomic_radii[0] = radii[0]
atomic_radii[1:] = radii[1]
# TODO: update this example to allow the scaling_factors to be variable also
default_scaling_factors = np.ones(len(radii))
return mol.log_prob(atomic_radii, default_scaling_factors)
radii0 = np.array([0.1, 0.1])
traj, log_probs, acceptance_fraction = random_walk_mh(
radii0, log_prob, n_steps=n_parameter_samples, stepsize=0.1)
np.save(
os.path.join(data_path,
'H_vs_not_radii_samples_{}.npy'.format(smiles)), traj)
print("acceptance fraction: {:.4f}".format(acceptance_fraction))
smiles_subset_fname = os.path.join(data_path,
'smiles_subset_{}.txt'.format(name))
with open(smiles_subset_fname, 'w') as f:
f.writelines(['{}\n'.format(s) for s in smiles_subset])
from bayes_implicit_solvent.utils import get_charges
from scipy.spatial.distance import pdist, squareform
for smiles in smiles_subset:
mol = Molecule(smiles, vacuum_samples=[])
path_to_vacuum_samples = resource_filename('bayes_implicit_solvent',
'vacuum_samples/vacuum_samples_{}.h5'.format(
mol.mol_index_in_smiles_list))
vacuum_traj = md.load(path_to_vacuum_samples)
thinning = int(len(vacuum_traj) / n_configuration_samples)
mol.vacuum_traj = mdtraj_to_list_of_unitted_snapshots(vacuum_traj[::thinning])
vacuum_trajs.append(mol.vacuum_traj)
print('thinned vacuum_traj from {} to {}'.format(len(vacuum_traj), len(mol.vacuum_traj)))
expt_means.append(mol.experimental_value)
expt_uncs.append(mol.experimental_uncertainty)
elements.append(np.array([a.element.atomic_number for a in mol.top.atoms()]))
charges.append(get_charges(mol.sys))
distance_matrices.append([squareform(pdist(snapshot / unit.nanometer)) for snapshot in mol.vacuum_traj])
mols.append(mol)
# 2. Define a likelihood function, including "type-assignment"
from autograd import numpy as np
from autograd.scipy.stats import norm
from autograd.scipy.stats import t as student_t
def quarter_freesolv_demo(n_configuration_samples=10,
n_parameter_samples=10000,
good_initialization=False):
"""Run toy 2D parameterization demo with one randomly-selected quarter of freesolv"""
np.random.seed(0)
inds = np.arange(len(smiles_list))
np.random.shuffle(inds)
inds = inds[:int(len(smiles_list) / 4)]
quarter_smiles = [smiles_list[i] for i in inds]
mols = []
hydrogens = []
for smiles in quarter_smiles:
mol = Molecule(smiles, vacuum_samples=[])
path_to_vacuum_samples = resource_filename(
'bayes_implicit_solvent',
'vacuum_samples/vacuum_samples_{}.h5'.format(
mol.mol_index_in_smiles_list))
vacuum_traj = md.load(path_to_vacuum_samples)
thinning = int(len(vacuum_traj) / n_configuration_samples)
mol.vacuum_traj = mdtraj_to_list_of_unitted_snapshots(
vacuum_traj[::thinning])
print('thinned vacuum_traj from {} to {}'.format(
len(vacuum_traj), len(mol.vacuum_traj)))
hydrogens.append(
np.array([a.element.symbol == 'H' for a in mol.top.atoms()]))
mols.append(mol)
def log_prob(theta):
radii, scales = unpack(theta)
logp = 0
for i in range(len(mols)):
mol = mols[i]
atomic_radii = np.ones(len(mol.pos)) * radii[0]
atomic_radii[hydrogens[i]] = radii[1]
atomic_scales = np.ones(len(mol.pos)) * scales[0]
atomic_scales[hydrogens[i]] = scales[1]
logp += mol.log_prob(atomic_radii, atomic_scales)
return logp
radii0 = np.array([0.1, 0.1])
scales0 = np.array([0.8, 0.8])
if good_initialization:
radii0 = np.array([0.28319081, 0.20943347])
scales0 = np.array([0.89298609, 0.67449963])
theta0 = pack(radii0, scales0)
stepsize = 0.0005
traj, log_probs, acceptance_fraction = random_walk_mh(
theta0, log_prob, n_steps=n_parameter_samples, stepsize=stepsize)
np.savez(os.path.join(
data_path,
'H_vs_not_freesolv_{}_dt={}.npz'.format(len(quarter_smiles),
stepsize)),
traj=traj,
log_probs=log_probs,
acceptance_fraction=acceptance_fraction,
stepsize=stepsize,
n_steps=n_parameter_samples,
smiles_subset=quarter_smiles,
n_configuration_samples=n_configuration_samples)
print("acceptance fraction: {:.4f}".format(acceptance_fraction))
