def run(self, ref_data=None, restart=None):
"""
Runs the gradient optimization.
Ensure that the attributes in __init__ are set as you desire before
using this function.
Returns
-------
`datatypes.FF` (or subclass)
Contains the best parameters.
"""
# We need reference data if you didn't provide it.
if ref_data is None:
ref_data = opt.return_ref_data(self.args_ref)
# We need the initial FF data.
if self.ff.data is None:
logger.log(20, '~~ GATHERING INITIAL FF DATA ~~'.rjust(79, '~'))
# Is opt.Optimizer.ff_lines used anymore?
self.ff.export_ff()
self.ff.data = calculate.main(self.args_ff)
# Not 100% sure if this is necessary, but it certainly doesn't hurt.
compare.correlate_energies(ref_data, self.ff.data)
r_dict = compare.data_by_type(ref_data)
c_dict = compare.data_by_type(self.ff.data)
r_dict, c_dict = compare.trim_data(r_dict, c_dict)
if self.ff.score is None:
# Already zeroed reference and correlated the energies.
self.ff.score = compare.compare_data(r_dict, c_dict)
data_types = []
for typ in r_dict:
data_types.append(typ)
data_types.sort()
logger.log(20, '~~ GRADIENT OPTIMIZATION ~~'.rjust(79, '~'))
logger.log(20, 'INIT FF SCORE: {}'.format(self.ff.score))
opt.pretty_ff_results(self.ff, level=20)
logger.log(20, '~~ CENTRAL DIFFERENTIATION ~~'.rjust(79, '~'))
if restart:
par_file = restart
logger.log(
20, ' -- Restarting gradient from central '
'differentiation file {}.'.format(par_file))
else:
# We need a file to hold the differentiated parameter data.
par_files = glob.glob(os.path.join(self.direc, 'par_diff_???.txt'))
if par_files:
par_files.sort()
most_recent_par_file = par_files[-1]
most_recent_par_file = most_recent_par_file.split('/')[-1]
most_recent_num = most_recent_par_file[9:12]
num = int(most_recent_num) + 1
par_file = 'par_diff_{:03d}.txt'.format(num)
else:
par_file = 'par_diff_001.txt'
logger.log(
20, ' -- Generating central differentiation '
'file {}.'.format(par_file))
f = open(os.path.join(self.direc, par_file), 'w')
csv_writer = csv.writer(f)
# Row 1 - Labels
# Row 2 - Weights
# Row 3 - Reference data values
# Row 4 - Initial FF data values
## Deprecated -TR
#csv_writer.writerow([x.lbl for x in ref_data])
#csv_writer.writerow([x.wht for x in ref_data])
#csv_writer.writerow([x.val for x in ref_data])
#csv_writer.writerow([x.val for x in self.ff.data])
writerows = [[], [], [], []]
for data_type in data_types:
writerows[0].extend([x.lbl for x in r_dict[data_type]])
writerows[1].extend([x.wht for x in r_dict[data_type]])
writerows[2].extend([x.val for x in r_dict[data_type]])
writerows[3].extend([x.val for x in c_dict[data_type]])
for row in writerows:
csv_writer.writerow(row)
logger.log(20, '~~ DIFFERENTIATING PARAMETERS ~~'.rjust(79, '~'))
# Save many FFs, each with their own parameter sets.
ffs = opt.differentiate_ff(self.ff)
logger.log(
20, '~~ SCORING DIFFERENTIATED PARAMETERS ~~'.rjust(79, '~'))
for ff in ffs:
ff.export_ff(lines=self.ff.lines)
logger.log(20, ' -- Calculating {}.'.format(ff))
data = calculate.main(self.args_ff)
# Deprecated
#ff.score = compare.compare_data(ref_data, data)
c_data = compare.data_by_type(data)
r_dict, c_data = compare.trim_data(r_dict, c_data)
ff.score = compare.compare_data(r_dict, c_data)
opt.pretty_ff_results(ff)
# Write the data rather than storing it in memory. For large
# parameter sets, this could consume GBs of memory otherwise!
#csv_writer.writerow([x.val for x in data])
row = []
for data_type in data_types:
row.extend([x.val for x in c_data[data_type]])
csv_writer.writerow(row)
f.close()
# Make sure we have derivative information. Used for NR.
#
# The derivatives are useful for checking up on the progress of the
# optimization and for deciding which parameters to use in a
# subsequent simplex optimization.
#
# Still need a way to do this with the resatrt file.
opt.param_derivs(self.ff, ffs)
# Calculate the Jacobian, residual vector, matrix A and vector b.
# These aren't needed if you're only doing Newton-Raphson.
if self.do_lstsq or self.do_lagrange or self.do_levenberg or \
self.do_svd:
logger.log(20, '~~ JACOBIAN AND RESIDUAL VECTOR ~~'.rjust(79, '~'))
# Setup the residual vector.
# Deprecated - TR
#num_d = len(ref_data)
num_d = 0
for datatype in r_dict:
num_d += len(r_dict[datatype])
resid = np.empty((num_d, 1), dtype=float)
# Deprecated - TR
#for i in xrange(0, num_d):
# resid[i, 0] = ref_data[i].wht * \
# (ref_data[i].val - self.ff.data[i].val)
count = 0
for data_type in data_types:
for r, c in zip(r_dict[data_type], c_dict[data_type]):
resid[count, 0] = r.wht * (r.val - c.val)
count += 1
# logger.log(5, 'RESIDUAL VECTOR:\n{}'.format(resid))
logger.log(20,
' -- Formed {} residual vector.'.format(resid.shape))
# Setup the Jacobian.
num_p = len(self.ff.params)
# Maybe should be a part of the Jacobian function.
jacob = np.empty((num_d, num_p), dtype=float)
jacob = return_jacobian(jacob, os.path.join(self.direc, par_file))
# logger.log(5, 'JACOBIAN:\n{}'.format(jacob))
logger.log(20, ' -- Formed {} Jacobian.'.format(jacob.shape))
ma = jacob.T.dot(jacob)
vb = jacob.T.dot(resid)
# We need these for most optimization methods.
logger.log(5, ' MATRIX A AND VECTOR B '.center(79, '-'))
# logger.log(5, 'A:\n{}'.format(ma))
# logger.log(5, 'b:\n{}'.format(vb))
# Start coming up with new parameter sets.
if self.do_newton and not restart:
logger.log(20, '~~ NEWTON-RAPHSON ~~'.rjust(79, '~'))
# Moved the derivative section outside of here.
changes = do_newton(self.ff.params,
radii=self.newton_radii,
cutoffs=self.newton_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
if self.do_lstsq:
logger.log(20, '~~ LEAST SQUARES ~~'.rjust(79, '~'))
changes = do_lstsq(ma,
vb,
radii=self.lstsq_radii,
cutoffs=self.lstsq_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
if self.do_lagrange:
logger.log(20, '~~ LAGRANGE ~~'.rjust(79, '~'))
for factor in sorted(self.lagrange_factors):
changes = do_lagrange(ma,
vb,
factor,
radii=self.lagrange_radii,
cutoffs=self.lagrange_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
if self.do_levenberg:
logger.log(20, '~~ LEVENBERG ~~'.rjust(79, '~'))
for factor in sorted(self.levenberg_factors):
changes = do_levenberg(ma,
vb,
factor,
radii=self.levenberg_radii,
cutoffs=self.levenberg_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
if self.do_svd:
logger.log(20, '~~ SINGULAR VALUE DECOMPOSITION ~~'.rjust(79, '~'))
# J = U . s . VT
mu, vs, mvt = return_svd(jacob)
logger.log(1, '>>> mu.shape: {}'.format(mu.shape))
logger.log(1, '>>> vs.shape: {}'.format(vs.shape))
logger.log(1, '>>> mvt.shape: {}'.format(mvt.shape))
logger.log(1, '>>> vb.shape: {}'.format(vb.shape))
if self.svd_factors:
changes = do_svd_w_thresholds(mu,
vs,
mvt,
resid,
self.svd_factors,
radii=self.svd_radii,
cutoffs=self.svd_cutoffs)
else:
changes = do_svd_wo_thresholds(mu,
vs,
mvt,
resid,
radii=self.svd_radii,
cutoffs=self.svd_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
# Report how many trial FFs were generated.
logger.log(
20, ' -- Generated {} trial force field(s).'.format(
len(self.new_ffs)))
# If there are any trials, test them.
if self.new_ffs:
logger.log(20, '~~ EVALUATING TRIAL FF(S) ~~'.rjust(79, '~'))
for ff in self.new_ffs:
data = opt.cal_ff(ff, self.args_ff, parent_ff=self.ff)
# Shouldn't need to zero anymore.
# Deprecated
#ff.score = compare.compare_data(ref_data, data)
c_data = compare.data_by_type(data)
r_dict, c_data = compare.trim_data(r_dict, c_data)
ff.score = compare.compare_data(r_dict, c_data)
opt.pretty_ff_results(ff)
self.new_ffs = sorted(self.new_ffs, key=lambda x: x.score)
# Check for improvement.
if self.new_ffs[0].score < self.ff.score:
ff = self.new_ffs[0]
logger.log(
20,
'~~ GRADIENT FINISHED WITH IMPROVEMENTS ~~'.rjust(79, '~'))
opt.pretty_ff_results(self.ff, level=20)
opt.pretty_ff_results(ff, level=20)
# Copy parameter derivatives from original FF to save time in
# case we move onto simplex immediately after this.
copy_derivs(self.ff, ff)
else:
ff = self.ff
else:
ff = self.ff
ff.export_ff(ff.path)
return ff
def run(self, ref_data=None):
# We need reference data if you didn't provide it.
if ref_data is None:
ref_data = opt.return_ref_data(self.args_ref)
# We need the initial FF data.
if self.ff.data is None:
logger.log(20, '~~ GATHERING INITIAL FF DATA ~~'.rjust(79, '~'))
# Check whether this is efficient with the ff_lines.
self.ff.export_ff()
self.ff.data = calculate.main(self.args_ff)
# We could do this, but the zeroing of energies has already been
# done.
# self.ff.score = compare.compare_data(ref_data, self.ff.data)
# So instead we do this.
compare.correlate_energies(ref_data, self.ff.data)
if self.ff.score is None:
# Already zeroed reference and correlated the energies.
self.ff.score = compare.calculate_score(ref_data, self.ff.data)
logger.log(20, 'INITIAL FF SCORE: {}'.format(self.ff.fscore))
logger.log(20, '~~ GRADIENT OPTIMIZATION ~~'.rjust(79, '~'))
# We need a file to hold the differentiated parameter data.
par_files = glob.glob(os.path.join(self.direc, 'par_diff_???.txt'))
if par_files:
par_files.sort()
most_recent_par_file = par_files[-1]
most_recent_par_file = most_recent_par_file.split('/')[-1]
most_recent_num = most_recent_par_file[9:12]
num = int(most_recent_num) + 1
par_file = 'par_diff_{:03d}.txt'.format(num)
else:
par_file = 'par_diff_001.txt'
f = open(os.path.join(self.direc, par_file), 'w')
csv_writer = csv.writer(f)
# Row 1 - Labels
# Row 2 - Weights
# Row 3 - Reference data values
# Row 4 - Initial FF data values
csv_writer.writerow([x.lbl for x in ref_data])
csv_writer.writerow([x.wht for x in ref_data])
csv_writer.writerow([x.val for x in ref_data])
csv_writer.writerow([x.val for x in self.ff.data])
logger.log(20, '~~ DIFFERENTIATING PARAMETERS ~~'.rjust(79, '~'))
# Setup the residual vector.
# Perhaps move this closer to the Jacobian section.
num_d = len(ref_data)
resid = np.empty((num_d, 1), dtype=float)
for i in xrange(0, num_d):
resid[i, 0] = ref_data[i].wht * (ref_data[i].val - self.ff.data[i].val)
logger.log(5, 'RESIDUAL VECTOR:\n{}'.format(resid))
logger.log(20, ' -- Formed {} residual vector.'.format(resid.shape))
# Save many FFs, each with their own parameter sets.
ffs = opt.differentiate_ff(self.ff)
logger.log(20, '~~ SCORING DIFFERENTIATED PARAMETERS ~~'.rjust(79, '~'))
for ff in ffs:
ff.export_ff(lines=self.ff.lines)
logger.log(20, ' -- Calculating {}.'.format(ff))
data = calculate.main(self.args_ff)
compare.correlate_energies(ref_data, data)
ff.score = compare.calculate_score(ref_data, data)
opt.pretty_ff_results(ff)
# Write the data rather than storing it in memory. For large parameter
# sets, this could consume GBs of memory otherwise!
csv_writer.writerow([x.val for x in data])
f.close()
# Calculate the Jacobian, residual vector, matrix A and vector b.
# These aren't needed if you're only doing Newton-Raphson.
if self.do_lstsq or self.do_lagrange or self.do_levenberg or \
self.do_svd:
logger.log(20, '~~ JACOBIAN AND RESIDUAL VECTOR ~~'.rjust(79, '~'))
# Setup the Jacobian.
num_p = len(self.ff.params)
# Maybe should be a part of the Jacobian function.
jacob = np.empty((num_d, num_p), dtype=float)
jacob = return_jacobian(jacob, os.path.join(self.direc, par_file))
ma = jacob.T.dot(jacob)
vb = jacob.T.dot(resid)
# We need these for most optimization methods.
logger.log(5, ' MATRIX A AND VECTOR B '.center(79, '-'))
logger.log(5, 'A:\n{}'.format(ma))
logger.log(5, 'b:\n{}'.format(vb))
# Start coming up with new parameter sets.
if self.do_newton:
logger.log(20, '~~ NEWTON-RAPHSON ~~'.rjust(79, '~'))
# Make sure we have derivative information.
if self.ff.params[0].d1 is None:
opt.param_derivs(self.ff, ffs)
changes = do_newton(self.ff.params,
radii=self.newton_radii,
cutoffs=self.newton_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
if self.do_lstsq:
logger.log(20, '~~ LEAST SQUARES ~~'.rjust(79, '~'))
changes = do_lstsq(ma, vb,
radii=self.lstsq_radii,
cutoffs=self.lstsq_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
if self.do_lagrange:
logger.log(20, '~~ LAGRANGE ~~'.rjust(79, '~'))
for factor in sorted(self.lagrange_factors):
changes = do_lagrange(ma, vb, factor,
radii=self.lagrange_radii,
cutoffs=self.lagrange_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
if self.do_levenberg:
logger.log(20, '~~ LEVENBERG ~~'.rjust(79, '~'))
for factor in sorted(self.levenberg_factors):
changes = do_levenberg(ma, vb, factor,
radii=self.levenberg_radii,
cutoffs=self.levenberg_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
if self.do_svd:
logger.log(20, '~~ SINGULAR VALUE DECOMPOSITION ~~'.rjust(79, '~'))
mu, vs, mv = return_svd(ma)
if self.svd_factors:
changes = do_svd_w_thresholds(mu, vs, mv, vb, self.svd_factors,
radii=self.svd_radii,
cutoffs=self.svd_cutoffs)
else:
changes = do_svd_wo_thresholds(mu, vs, mv, vb,
radii=self.svd_radii,
cutoffs=self.svd_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
# Report how many trial FFs were generated.
logger.log(20, ' -- Generated {} trial force field(s).'.format(
len(self.new_ffs)))
# If there are any trials, test them.
if self.new_ffs:
logger.log(20, '~~ EVALUATING TRIAL FF(S) ~~'.rjust(79, '~'))
for ff in self.new_ffs:
data = opt.cal_ff(ff, self.args_ff, parent_ff=self.ff)
ff.score = compare.compare_data(ref_data, data, zero=False)
opt.pretty_ff_results(ff)
self.new_ffs = sorted(
self.new_ffs, key=lambda x: x.score)
# Check for improvement.
if self.new_ffs[0].score < self.ff.score:
ff = self.new_ffs[0]
logger.log(
20, '~~ GRADIENT FINISHED WITH IMPROVEMENTS ~~'.rjust(
79, '~'))
opt.pretty_ff_results(self.ff, level=20)
opt.pretty_ff_results(ff, level=20)
else:
ff = self.ff
else:
ff = self.ff
return ff
def run(self, ref_data=None, restart=None):
"""
Runs the gradient optimization.
Ensure that the attributes in __init__ are set as you desire before
using this function.
Returns
-------
`datatypes.FF` (or subclass)
Contains the best parameters.
"""
# We need reference data if you didn't provide it.
if ref_data is None:
ref_data = opt.return_ref_data(self.args_ref)
# We need the initial FF data.
if self.ff.data is None:
logger.log(20, '~~ GATHERING INITIAL FF DATA ~~'.rjust(79, '~'))
# Is opt.Optimizer.ff_lines used anymore?
self.ff.export_ff()
self.ff.data = calculate.main(self.args_ff)
# Not 100% sure if this is necessary, but it certainly doesn't hurt.
compare.correlate_energies(ref_data, self.ff.data)
r_dict = compare.data_by_type(ref_data)
c_dict = compare.data_by_type(self.ff.data)
r_dict, c_dict = compare.trim_data(r_dict,c_dict)
if self.ff.score is None:
# Already zeroed reference and correlated the energies.
self.ff.score = compare.compare_data(r_dict, c_dict)
data_types = []
for typ in r_dict:
data_types.append(typ)
data_types.sort()
logger.log(20, '~~ GRADIENT OPTIMIZATION ~~'.rjust(79, '~'))
logger.log(20, 'INIT FF SCORE: {}'.format(self.ff.score))
opt.pretty_ff_results(self.ff, level=20)
logger.log(20, '~~ CENTRAL DIFFERENTIATION ~~'.rjust(79, '~'))
if restart:
par_file = restart
logger.log(20, ' -- Restarting gradient from central '
'differentiation file {}.'.format(par_file))
else:
# We need a file to hold the differentiated parameter data.
par_files = glob.glob(os.path.join(self.direc, 'par_diff_???.txt'))
if par_files:
par_files.sort()
most_recent_par_file = par_files[-1]
most_recent_par_file = most_recent_par_file.split('/')[-1]
most_recent_num = most_recent_par_file[9:12]
num = int(most_recent_num) + 1
par_file = 'par_diff_{:03d}.txt'.format(num)
else:
par_file = 'par_diff_001.txt'
logger.log(20, ' -- Generating central differentiation '
'file {}.'.format(par_file))
f = open(os.path.join(self.direc, par_file), 'w')
csv_writer = csv.writer(f)
# Row 1 - Labels
# Row 2 - Weights
# Row 3 - Reference data values
# Row 4 - Initial FF data values
## Deprecated -TR
#csv_writer.writerow([x.lbl for x in ref_data])
#csv_writer.writerow([x.wht for x in ref_data])
#csv_writer.writerow([x.val for x in ref_data])
#csv_writer.writerow([x.val for x in self.ff.data])
writerows = [[],[],[],[]]
for data_type in data_types:
writerows[0].extend([x.lbl for x in r_dict[data_type]])
writerows[1].extend([x.wht for x in r_dict[data_type]])
writerows[2].extend([x.val for x in r_dict[data_type]])
writerows[3].extend([x.val for x in c_dict[data_type]])
for row in writerows:
csv_writer.writerow(row)
logger.log(20, '~~ DIFFERENTIATING PARAMETERS ~~'.rjust(79, '~'))
# Save many FFs, each with their own parameter sets.
ffs = opt.differentiate_ff(self.ff)
logger.log(20, '~~ SCORING DIFFERENTIATED PARAMETERS ~~'.rjust(
79, '~'))
for ff in ffs:
ff.export_ff(lines=self.ff.lines)
logger.log(20, ' -- Calculating {}.'.format(ff))
data = calculate.main(self.args_ff)
# Deprecated
#ff.score = compare.compare_data(ref_data, data)
c_data = compare.data_by_type(data)
r_dict, c_data = compare.trim_data(r_dict,c_data)
ff.score = compare.compare_data(r_dict, c_data)
opt.pretty_ff_results(ff)
# Write the data rather than storing it in memory. For large
# parameter sets, this could consume GBs of memory otherwise!
#csv_writer.writerow([x.val for x in data])
row = []
for data_type in data_types:
row.extend([x.val for x in c_data[data_type]])
csv_writer.writerow(row)
f.close()
# Make sure we have derivative information. Used for NR.
#
# The derivatives are useful for checking up on the progress of the
# optimization and for deciding which parameters to use in a
# subsequent simplex optimization.
#
# Still need a way to do this with the resatrt file.
opt.param_derivs(self.ff, ffs)
# Calculate the Jacobian, residual vector, matrix A and vector b.
# These aren't needed if you're only doing Newton-Raphson.
if self.do_lstsq or self.do_lagrange or self.do_levenberg or \
self.do_svd:
logger.log(20, '~~ JACOBIAN AND RESIDUAL VECTOR ~~'.rjust(79, '~'))
# Setup the residual vector.
# Deprecated - TR
#num_d = len(ref_data)
num_d = 0
for datatype in r_dict:
num_d += len(r_dict[datatype])
resid = np.empty((num_d, 1), dtype=float)
# Deprecated - TR
#for i in xrange(0, num_d):
# resid[i, 0] = ref_data[i].wht * \
# (ref_data[i].val - self.ff.data[i].val)
count = 0
for data_type in data_types:
for r,c in zip(r_dict[data_type],c_dict[data_type]):
resid[count, 0] = r.wht * (r.val - c.val)
count += 1
# logger.log(5, 'RESIDUAL VECTOR:\n{}'.format(resid))
logger.log(
20, ' -- Formed {} residual vector.'.format(resid.shape))
# Setup the Jacobian.
num_p = len(self.ff.params)
# Maybe should be a part of the Jacobian function.
jacob = np.empty((num_d, num_p), dtype=float)
jacob = return_jacobian(jacob, os.path.join(self.direc, par_file))
# logger.log(5, 'JACOBIAN:\n{}'.format(jacob))
logger.log(20, ' -- Formed {} Jacobian.'.format(jacob.shape))
ma = jacob.T.dot(jacob)
vb = jacob.T.dot(resid)
# We need these for most optimization methods.
logger.log(5, ' MATRIX A AND VECTOR B '.center(79, '-'))
# logger.log(5, 'A:\n{}'.format(ma))
# logger.log(5, 'b:\n{}'.format(vb))
# Start coming up with new parameter sets.
if self.do_newton and not restart:
logger.log(20, '~~ NEWTON-RAPHSON ~~'.rjust(79, '~'))
# Moved the derivative section outside of here.
changes = do_newton(self.ff.params,
radii=self.newton_radii,
cutoffs=self.newton_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
if self.do_lstsq:
logger.log(20, '~~ LEAST SQUARES ~~'.rjust(79, '~'))
changes = do_lstsq(ma, vb,
radii=self.lstsq_radii,
cutoffs=self.lstsq_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
if self.do_lagrange:
logger.log(20, '~~ LAGRANGE ~~'.rjust(79, '~'))
for factor in sorted(self.lagrange_factors):
changes = do_lagrange(ma, vb, factor,
radii=self.lagrange_radii,
cutoffs=self.lagrange_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
if self.do_levenberg:
logger.log(20, '~~ LEVENBERG ~~'.rjust(79, '~'))
for factor in sorted(self.levenberg_factors):
changes = do_levenberg(ma, vb, factor,
radii=self.levenberg_radii,
cutoffs=self.levenberg_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
if self.do_svd:
logger.log(20, '~~ SINGULAR VALUE DECOMPOSITION ~~'.rjust(79, '~'))
# J = U . s . VT
mu, vs, mvt = return_svd(jacob)
logger.log(1, '>>> mu.shape: {}'.format(mu.shape))
logger.log(1, '>>> vs.shape: {}'.format(vs.shape))
logger.log(1, '>>> mvt.shape: {}'.format(mvt.shape))
logger.log(1, '>>> vb.shape: {}'.format(vb.shape))
if self.svd_factors:
changes = do_svd_w_thresholds(mu, vs, mvt, resid, self.svd_factors,
radii=self.svd_radii,
cutoffs=self.svd_cutoffs)
else:
changes = do_svd_wo_thresholds(mu, vs, mvt, resid,
radii=self.svd_radii,
cutoffs=self.svd_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
# Report how many trial FFs were generated.
logger.log(20, ' -- Generated {} trial force field(s).'.format(
len(self.new_ffs)))
# If there are any trials, test them.
if self.new_ffs:
logger.log(20, '~~ EVALUATING TRIAL FF(S) ~~'.rjust(79, '~'))
for ff in self.new_ffs:
data = opt.cal_ff(ff, self.args_ff, parent_ff=self.ff)
# Shouldn't need to zero anymore.
# Deprecated
#ff.score = compare.compare_data(ref_data, data)
c_data = compare.data_by_type(data)
r_dict, c_data = compare.trim_data(r_dict,c_data)
ff.score = compare.compare_data(r_dict, c_data)
opt.pretty_ff_results(ff)
self.new_ffs = sorted(
self.new_ffs, key=lambda x: x.score)
# Check for improvement.
if self.new_ffs[0].score < self.ff.score:
ff = self.new_ffs[0]
logger.log(
20, '~~ GRADIENT FINISHED WITH IMPROVEMENTS ~~'.rjust(
79, '~'))
opt.pretty_ff_results(self.ff, level=20)
opt.pretty_ff_results(ff, level=20)
# Copy parameter derivatives from original FF to save time in
# case we move onto simplex immediately after this.
copy_derivs(self.ff, ff)
else:
ff = self.ff
else:
ff = self.ff
return ff
def run(self, ref_data=None, restart=None):
# We need reference data if you didn't provide it.
if ref_data is None:
ref_data = opt.return_ref_data(self.args_ref)
# We need the initial FF data.
if self.ff.data is None:
logger.log(20, '~~ GATHERING INITIAL FF DATA ~~'.rjust(79, '~'))
# Check whether this is efficient with the ff_lines.
self.ff.export_ff()
self.ff.data = calculate.main(self.args_ff)
# We could do this, but the zeroing of energies has already been
# done.
# self.ff.score = compare.compare_data(ref_data, self.ff.data)
# So instead we do this.
compare.correlate_energies(ref_data, self.ff.data)
if self.ff.score is None:
# Already zeroed reference and correlated the energies.
self.ff.score = compare.calculate_score(ref_data, self.ff.data)
logger.log(20, 'INITIAL FF SCORE: {}'.format(self.ff.score))
logger.log(20, '~~ GRADIENT OPTIMIZATION ~~'.rjust(79, '~'))
# We need a file to hold the differentiated parameter data.
logger.log(20, '~~ CENTRAL DIFFERENTIATION ~~'.rjust(79, '~'))
if restart:
par_file = restart
logger.log(
20, ' -- Restarting gradient from central '
'differentiation file {}.'.format(par_file))
else:
par_files = glob.glob(os.path.join(self.direc, 'par_diff_???.txt'))
if par_files:
par_files.sort()
most_recent_par_file = par_files[-1]
most_recent_par_file = most_recent_par_file.split('/')[-1]
most_recent_num = most_recent_par_file[9:12]
num = int(most_recent_num) + 1
par_file = 'par_diff_{:03d}.txt'.format(num)
else:
par_file = 'par_diff_001.txt'
logger.log(
20, ' -- Generating central differentiation '
'file {}.'.format(par_file))
f = open(os.path.join(self.direc, par_file), 'w')
csv_writer = csv.writer(f)
# Row 1 - Labels
# Row 2 - Weights
# Row 3 - Reference data values
# Row 4 - Initial FF data values
csv_writer.writerow([x.lbl for x in ref_data])
csv_writer.writerow([x.wht for x in ref_data])
csv_writer.writerow([x.val for x in ref_data])
csv_writer.writerow([x.val for x in self.ff.data])
logger.log(20, '~~ DIFFERENTIATING PARAMETERS ~~'.rjust(79, '~'))
# Save many FFs, each with their own parameter sets.
ffs = opt.differentiate_ff(self.ff)
logger.log(
20, '~~ SCORING DIFFERENTIATED PARAMETERS ~~'.rjust(79, '~'))
for ff in ffs:
ff.export_ff(lines=self.ff.lines)
logger.log(20, ' -- Calculating {}.'.format(ff))
data = calculate.main(self.args_ff)
compare.correlate_energies(ref_data, data)
ff.score = compare.calculate_score(ref_data, data)
opt.pretty_ff_results(ff)
# Write the data rather than storing it in memory. For large parameter
# sets, this could consume GBs of memory otherwise!
csv_writer.writerow([x.val for x in data])
f.close()
# Calculate the Jacobian, residual vector, matrix A and vector b.
# These aren't needed if you're only doing Newton-Raphson.
if self.do_lstsq or self.do_lagrange or self.do_levenberg or \
self.do_svd:
logger.log(20, '~~ JACOBIAN AND RESIDUAL VECTOR ~~'.rjust(79, '~'))
# Setup the residual vector.
num_d = len(ref_data)
resid = np.empty((num_d, 1), dtype=float)
for i in xrange(0, num_d):
resid[i, 0] = ref_data[i].wht * (ref_data[i].val -
self.ff.data[i].val)
# logger.log(5, 'RESIDUAL VECTOR:\n{}'.format(resid))
logger.log(20,
' -- Formed {} residual vector.'.format(resid.shape))
# Setup the Jacobian.
num_p = len(self.ff.params)
# Maybe should be a part of the Jacobian function.
jacob = np.empty((num_d, num_p), dtype=float)
jacob = return_jacobian(jacob, os.path.join(self.direc, par_file))
# logger.log(5, 'JACOBIAN:\n{}'.format(jacob))
logger.log(20, ' -- Formed {} Jacobian.'.format(jacob.shape))
ma = jacob.T.dot(jacob)
vb = jacob.T.dot(resid)
# We need these for most optimization methods.
logger.log(5, ' MATRIX A AND VECTOR B '.center(79, '-'))
# logger.log(5, 'A:\n{}'.format(ma))
# logger.log(5, 'b:\n{}'.format(vb))
# Start coming up with new parameter sets.
if self.do_newton and not restart:
logger.log(20, '~~ NEWTON-RAPHSON ~~'.rjust(79, '~'))
# Make sure we have derivative information.
if self.ff.params[0].d1 is None:
opt.param_derivs(self.ff, ffs)
changes = do_newton(self.ff.params,
radii=self.newton_radii,
cutoffs=self.newton_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
if self.do_lstsq:
logger.log(20, '~~ LEAST SQUARES ~~'.rjust(79, '~'))
changes = do_lstsq(ma,
vb,
radii=self.lstsq_radii,
cutoffs=self.lstsq_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
if self.do_lagrange:
logger.log(20, '~~ LAGRANGE ~~'.rjust(79, '~'))
for factor in sorted(self.lagrange_factors):
changes = do_lagrange(ma,
vb,
factor,
radii=self.lagrange_radii,
cutoffs=self.lagrange_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
if self.do_levenberg:
logger.log(20, '~~ LEVENBERG ~~'.rjust(79, '~'))
for factor in sorted(self.levenberg_factors):
changes = do_levenberg(ma,
vb,
factor,
radii=self.levenberg_radii,
cutoffs=self.levenberg_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
if self.do_svd:
logger.log(20, '~~ SINGULAR VALUE DECOMPOSITION ~~'.rjust(79, '~'))
# J = U . s . VT
mu, vs, mvt = return_svd(jacob)
logger.log(1, '>>> mu.shape: {}'.format(mu.shape))
logger.log(1, '>>> vs.shape: {}'.format(vs.shape))
logger.log(1, '>>> mvt.shape: {}'.format(mvt.shape))
logger.log(1, '>>> vb.shape: {}'.format(vb.shape))
if self.svd_factors:
changes = do_svd_w_thresholds(mu,
vs,
mvt,
resid,
self.svd_factors,
radii=self.svd_radii,
cutoffs=self.svd_cutoffs)
else:
changes = do_svd_wo_thresholds(mu,
vs,
mvt,
resid,
radii=self.svd_radii,
cutoffs=self.svd_cutoffs)
cleanup(self.new_ffs, self.ff, changes)
# Report how many trial FFs were generated.
logger.log(
20, ' -- Generated {} trial force field(s).'.format(
len(self.new_ffs)))
# If there are any trials, test them.
if self.new_ffs:
logger.log(20, '~~ EVALUATING TRIAL FF(S) ~~'.rjust(79, '~'))
for ff in self.new_ffs:
data = opt.cal_ff(ff, self.args_ff, parent_ff=self.ff)
# Shouldn't need to zero anymore.
ff.score = compare.compare_data(ref_data, data)
opt.pretty_ff_results(ff)
self.new_ffs = sorted(self.new_ffs, key=lambda x: x.score)
# Check for improvement.
if self.new_ffs[0].score < self.ff.score:
ff = self.new_ffs[0]
logger.log(
20,
'~~ GRADIENT FINISHED WITH IMPROVEMENTS ~~'.rjust(79, '~'))
opt.pretty_ff_results(self.ff, level=20)
opt.pretty_ff_results(ff, level=20)
# Copy parameter derivatives from original FF to save time in
# case we move onto simplex immediately after this.
copy_derivs(self.ff, ff)
else:
ff = self.ff
else:
ff = self.ff
return ff
