def main():
"""
Select frames from the supplied xyz file (fxyz) using one of the following algorithms:
1. random: random selection
2. fps: farthest point sampling selection. Need to supply a kernel matrix or descriptor matrix using -fmat
4. CUR decomposition
Parameters
----------
fxyz: Path to xyz file.
fmat: Path to the design matrix or name of the tags in ase xyz file
prefix: Filename prefix, default is ASAP
nkeep: The number of representative samples to select
algorithm: 'the algorithm for selecting frames ([random], [fps], [cur])')
fmat: Location of descriptor or kernel matrix file. Needed if you select [fps] or [cur].
"""
fxyz = os.path.join(os.path.split(__file__)[0], 'small_molecules-SOAP.xyz')
fmat = ['SOAP-n4-l3-c1.9-g0.23']
nkeep = 10
prefix = "test-frame-select"
# read the xyz file
asapxyz = ASAPXYZ(fxyz)
# for both algo we read in the descriptor matrix
desc, _ = asapxyz.get_descriptors(fmat)
print("shape of the descriptor matrix: ", np.shape(desc),
"number of descriptors: ", np.shape(desc[0]))
for algorithm in ['random', 'cur', 'fps']:
sparsifier = Sparsifier(algorithm)
sbs = sparsifier.sparsify(desc, nkeep)
# save
selection = np.zeros(asapxyz.get_num_frames(), dtype=int)
for i in sbs:
selection[i] = 1
np.savetxt(prefix + "-" + algorithm + "-n-" + str(nkeep) + '.index',
selection,
fmt='%d')
asapxyz.write(prefix + "-" + algorithm + "-n-" + str(nkeep), sbs)
def main(fxyz, fy, prefix, nkeep, algorithm, fmat, fkde, reweight_lambda):
"""
Select frames from the supplied xyz file (fxyz) using one of the following algorithms:
1. random: random selection
2. fps: farthest point sampling selection. Need to supply a kernel matrix or descriptor matrix using -fmat
3. sortmin/sortmax: select the frames with the largest/smallest value. Need to supply the vector of properties using
-fy
4. CUR decomposition
5. Reweight according to the re-weighted distribution exp(-f/\lambda),
where exp(-f) is the precomputed kernel density estimation of the original samples.
Parameters
----------
fxyz: Path to xyz file.
fy: Path to the list of properties (N floats) or name of the tags in ase xyz file
prefix: Filename prefix, default is ASAP
nkeep: The number of representative samples to select
algorithm: 'the algorithm for selecting frames ([random], [fps], [sort], [reweight])')
fmat: Location of descriptor or kernel matrix file. Needed if you select [fps].
You can use gen_kmat.py to compute it.
reweight_lambda: select samples according to the re-weighted distribution exp(-f/\lambda),
where exp(-f) is the kernel density estimation of the original samples.
"""
# read the xyz file
asapxyz = ASAPXYZ(fxyz)
nframes = asapxyz.get_num_frames()
if nkeep == 0:
nkeep = nframes
if fy != 'none':
y_all = []
try:
y_all = np.genfromtxt(fy, dtype=float)
except:
y_all = asapxyz.get_property(fy)
if len(y_all) != nframes:
raise ValueError(
'Length of the vector of properties is not the same as number of samples'
)
if algorithm == 'random' or algorithm == 'RANDOM':
idx = np.asarray(range(nframes))
sbs = np.random.choice(idx, nkeep, replace=False)
elif algorithm == 'sortmax' or algorithm == 'sortmin':
if fy == 'none':
raise ValueError(
'must supply the vector of properties for sorting')
idx = np.asarray(range(nframes))
if algorithm == 'sortmax':
sbs = [x for _, x in sorted(zip(y_all, idx))][:nkeep]
elif algorithm == 'sortmin':
sbs = [x for _, x in sorted(zip(y_all, idx))][nkeep:]
elif algorithm == 'fps' or algorithm == 'FPS' or algorithm == 'cur' or algorithm == 'CUR':
# for both algo we read in the descriptor matrix
desc, _ = asapxyz.get_descriptors(fmat)
if os.path.isfile(fmat):
try:
desc = np.genfromtxt(fmat, dtype=float)
except:
raise ValueError('Cannot load the kernel matrix')
print("shape of the descriptor matrix: ", np.shape(desc),
"number of descriptors: ", np.shape(desc[0]))
# FPS
if algorithm == 'fps' or algorithm == 'FPS':
sbs, dmax_remain = fps(desc, nkeep, 0)
print("Making farthest point sampling selection")
np.savetxt(prefix + "-" + algorithm + "-n-" + str(nkeep) +
'.error',
dmax_remain,
fmt='%4.8f',
header='the maximum remaining distance in FPS')
# CUR decomposition
if algorithm == 'cur' or algorithm == 'CUR':
desc = np.asmatrix(desc)
cov = np.dot(desc, desc.T)
print("Making CUR selection")
print("shape of the covariance matrix:", np.shape(cov))
sbs, rcov_error = CUR_deterministic(cov, nkeep)
np.savetxt(prefix + "-" + algorithm + "-n-" + str(nkeep) +
'.error',
rcov_error,
fmt='%4.8f',
header='the remaining error of the covariance matrix')
elif algorithm == 'reweight':
if os.path.isfile(fkde):
try:
logkde = np.genfromtxt(fkde, dtype=float)[:, 1]
except:
raise IOError(
'Cannot load the (log of) kernel density for each sample')
if len(logkde) != nframes:
raise ValueError(
'mismatch of number of frames and kernel densities')
else:
raise ValueError(
'must suply the (log of) kernel density for each sample')
new_kde = np.zeros(nframes)
for i in range(nframes):
new_kde[i] = np.exp(logkde[i] / reweight_lambda) / np.exp(
logkde[i])
# compute the normalization factor so we expect to select n samples in the end
normalization = nkeep / np.sum(new_kde)
new_kde *= normalization
sbs = []
randomchoice = np.random.rand(nframes)
for i in range(nframes):
if randomchoice[i] < new_kde[i]:
sbs.append(i)
algorithm = algorithm + "-lambda-" + str(reweight_lambda)
# save
selection = np.zeros(nframes, dtype=int)
for i in sbs:
selection[i] = 1
np.savetxt(prefix + "-" + algorithm + "-n-" + str(nkeep) + '.index',
selection,
fmt='%d')
if fy != 'none':
np.savetxt(prefix + "-" + algorithm + "-n-" + str(nkeep) + '-' + fy,
np.asarray(y_all)[sbs],
fmt='%4.8f')
asapxyz.write(prefix + "-" + algorithm + "-n-" + str(nkeep), sbs)