def do_newton(params):
"""
Do a Newton-Raphson type parameter change prediction.
Parameters
----------
params : list of `datatypes.Param` (or subclass)
The instances of `datatypes.Param` must already have
their first and second derivative attributes populated.
"""
changes = []
for param in params:
if param.d1 != 0.:
if param.d2 > 0.00000001:
changes.append(-param.d1 / param.d2)
else:
logger.warning(' -- 2nd derivative of {} is {:.4f}.'.format(
param, param.d2))
logger.warning(' -- 1st derivative of {} is {:.4f}.'.format(
param, param.d1))
if param.d1 > 0.:
changes.append(-1.)
logger.warning(
' -- Change for {} set to -1.'.format(param))
else:
changes.append(1.)
logger.warning(
' -- Change for {} set to 1.'.format(param))
else:
raise opt.OptError(
'1st derivative of {} is {}. Skipping Newton-Raphson.'.format(
param, param.d1))
return [('NR', changes)]
def run(self, r_data=None):
"""
Once all attributes are setup as you so desire, run this method to
optimize the parameters.
Returns
-------
`datatypes.FF` (or subclass)
Contains the best parameters.
"""
if r_data is None:
r_data = opt.return_ref_data(self.args_ref)
if self.ff.score is None:
logger.log(20, '~~ CALCULATING INITIAL FF SCORE ~~'.rjust(79, '~'))
self.ff.export_ff()
# Could store data on self.ff.data if we wanted. Not necessary for
# simplex. If simplex yielded no improvements, it would return this
# FF, and then we might want the data such taht we don't have to
# recalculate it in gradient. Let's hope simplex generally yields
# improvements.
data = calculate.main(self.args_ff)
#deprecated
#self.ff.score = compare.compare_data(r_data, data)
r_dict = compare.data_by_type(r_data)
c_dict = compare.data_by_type(data)
r_dict, c_dict = compare.trim_data(r_dict, c_dict)
self.ff.score = compare.compare_data(r_dict, c_dict)
else:
logger.log(20,
' -- Reused existing score and data for initial FF.')
logger.log(20, '~~ SIMPLEX OPTIMIZATION ~~'.rjust(79, '~'))
logger.log(20, 'INIT FF SCORE: {}'.format(self.ff.score))
opt.pretty_ff_results(self.ff, level=20)
# Here's what we do if there are too many parameters.
if self.max_params and len(self.ff.params) > self.max_params:
logger.log(20, ' -- More parameters than the maximum allowed.')
logger.log(5, 'CURRENT PARAMS: {}'.format(len(self.ff.params)))
logger.log(5, 'MAX PARAMS: {}'.format(self.max_params))
# Here we select the parameters that have the lowest 2nd
# derivatives.
# Could fail when simplex finds improvements but restores other
# parameters.
# if self.ff.params[0].d1:
if None in [x.d1 for x in self.ff.params]:
logger.log(15, ' -- Calculating new parameter derivatives.')
# Do central differentiation so we can calculate derivatives.
# Another option would be to write code to determine
# derivatives only from forward differentiation.
ffs = opt.differentiate_ff(self.ff, central=True)
# We have to score to get the derivatives.
for ff in ffs:
ff.export_ff(path=self.ff.path, lines=self.ff_lines)
logger.log(20, ' -- Calculating {}.'.format(ff))
data = calculate.main(self.args_ff)
#deprecated
#ff.score = compare.compare_data(r_data, data)
r_dict = compare.data_by_type(r_data)
c_dict = compare.data_by_type(data)
r_dict, c_dict = compare.trim_data(r_dict, c_dict)
ff.score = compare.compare_data(r_dict, c_dict)
opt.pretty_ff_results(ff)
# Add the derivatives to your original FF.
opt.param_derivs(self.ff, ffs)
# Only keep the forward differentiated FFs.
ffs = opt.extract_forward(ffs)
logger.log(
5, ' -- Keeping {} forward differentiated '
'FFs.'.format(len(ffs)))
else:
logger.log(15, ' -- Reusing existing parameter derivatives.')
# Differentiate all parameters forward. Yes, I know this is
# counter-intuitive because we are going to only use subset of
# the forward differentiated FFs. However, this is very
# computationally inexpensive because we're not scoring them
# now. We will remove the forward differentiated FFs we don't
# want before scoring.
ffs = opt.differentiate_ff(self.ff, central=False)
# This sorts the parameters based upon their 2nd derivative.
# It keeps the ones with lowest 2nd derivatives.
# SCHEDULED FOR CHANGES. NOT A GOOD SORTING CRITERION.
params = select_simp_params_on_derivs(self.ff.params,
max_params=self.max_params)
# From the entire list of forward differentiated FFs, pick
# out the ones that have the lowest 2nd derivatives.
self.new_ffs = opt.extract_ff_by_params(ffs, params)
logger.log(1,
'>>> len(self.new_ffs): {}'.format(len(self.new_ffs)))
# Reduce number of parameters.
# Will need an option that's not MM3* specific in the future.
ff_rows = [x.mm3_row for x in params]
ff_cols = [x.mm3_col for x in params]
for ff in self.new_ffs:
new_params = []
for param in ff.params:
if param.mm3_row in ff_rows and param.mm3_col in ff_cols:
new_params.append(param)
ff.params = new_params
# Make a copy of your original FF that has less parameters.
ff_copy = copy.deepcopy(self.ff)
new_params = []
for param in ff.params:
if param.mm3_row in ff_rows and param.mm3_col in ff_cols:
new_params.append(param)
ff_copy.params = new_params
else:
# In this case it's simple. Just forward differentiate each
# parameter.
self.new_ffs = opt.differentiate_ff(self.ff, central=False)
logger.log(1,
'>>> len(self.new_ffs): {}'.format(len(self.new_ffs)))
# Still make that FF copy.
ff_copy = copy.deepcopy(self.ff)
# Double check and make sure they're all scored.
for ff in self.new_ffs:
if ff.score is None:
ff.export_ff(path=self.ff.path, lines=self.ff_lines)
logger.log(20, ' -- Calculating {}.'.format(ff))
data = calculate.main(self.args_ff)
#deprecated
#ff.score = compare.compare_data(r_data, data)
r_dict = compare.data_by_type(r_data)
c_dict = compare.data_by_type(data)
r_dict, c_dict = compare.trim_data(r_dict, c_dict)
ff.score = compare.compare_data(r_dict, c_dict)
opt.pretty_ff_results(ff)
# Add your copy of the orignal to FF to the forward differentiated FFs.
self.new_ffs = sorted(self.new_ffs + [ff_copy], key=lambda x: x.score)
# Allow 3 cycles w/o change for each parameter present. Remember that
# the initial FF was added here, hence the minus one.
self._max_cycles_wo_change = 3 * (len(self.new_ffs) - 1)
wrapper = textwrap.TextWrapper(width=79)
# Shows all FFs parameters.
opt.pretty_ff_params(self.new_ffs)
# Start the simplex cycles.
current_cycle = 0
cycles_wo_change = 0
while current_cycle < self.max_cycles \
and cycles_wo_change < self._max_cycles_wo_change:
current_cycle += 1
# Save the last best in case some accidental sort goes on.
# Plus it makes reading the code a litle easier.
last_best_ff = copy.deepcopy(self.new_ffs[0])
logger.log(
20, '~~ START SIMPLEX CYCLE {} ~~'.format(current_cycle).rjust(
79, '~'))
logger.log(20, 'ORDERED FF SCORES:')
logger.log(
20,
wrapper.fill('{}'.format(' '.join('{:15.4f}'.format(x.score)
for x in self.new_ffs))))
inv_ff = self.ff.__class__()
if self.do_weighted_reflection:
inv_ff.method = 'WEIGHTED INVERSION'
else:
inv_ff.method = 'INVERSION'
inv_ff.params = copy.deepcopy(last_best_ff.params)
ref_ff = self.ff.__class__()
ref_ff.method = 'REFLECTION'
ref_ff.params = copy.deepcopy(last_best_ff.params)
# Need score difference sum for weighted inversion.
# Calculate this value before going into loop.
if self.do_weighted_reflection:
# If zero, should break.
score_diff_sum = sum([
x.score - self.new_ffs[-1].score for x in self.new_ffs[:-1]
])
if score_diff_sum == 0.:
logger.warning('No difference between force field scores. '
'Exiting simplex.')
# We want to raise opt.OptError such that
# opt.catch_run_errors will write the best FF obtained thus
# far.
raise opt.OptError(
'No difference between force field scores. '
'Exiting simplex.')
for i in range(0, len(last_best_ff.params)):
if self.do_weighted_reflection:
inv_val = (sum([
x.params[i].value * (x.score - self.new_ffs[-1].score)
for x in self.new_ffs[:-1]
]) / score_diff_sum)
else:
inv_val = (
sum([x.params[i].value for x in self.new_ffs[:-1]]) /
len(self.new_ffs[:-1]))
inv_ff.params[i].value = inv_val
ref_ff.params[i].value = (2 * inv_val -
self.new_ffs[-1].params[i].value)
# The inversion point does not need to be scored.
# Calculate score for reflected parameters.
ref_ff.export_ff(path=self.ff.path, lines=self.ff.lines)
data = calculate.main(self.args_ff)
#deprecated
#ref_ff.score = compare.compare_data(r_data, data)
r_dict = compare.data_by_type(r_data)
c_dict = compare.data_by_type(data)
r_dict, c_dict = compare.trim_data(r_dict, c_dict)
ref_ff.score = compare.compare_data(r_dict, c_dict)
opt.pretty_ff_results(ref_ff)
if ref_ff.score < last_best_ff.score:
logger.log(20, '~~ ATTEMPTING EXPANSION ~~'.rjust(79, '~'))
exp_ff = self.ff.__class__()
exp_ff.method = 'EXPANSION'
exp_ff.params = copy.deepcopy(last_best_ff.params)
for i in range(0, len(last_best_ff.params)):
exp_ff.params[i].value = (
3 * inv_ff.params[i].value -
2 * self.new_ffs[-1].params[i].value)
exp_ff.export_ff(path=self.ff.path, lines=self.ff.lines)
data = calculate.main(self.args_ff)
#deprecated
#exp_ff.score = compare.compare_data(r_data, data)
r_dict = compare.data_by_type(r_data)
c_dict = compare.data_by_type(data)
r_dict, c_dict = compare.trim_data(r_dict, c_dict)
exp_ff.score = compare.compare_data(r_dict, c_dict)
opt.pretty_ff_results(exp_ff)
if exp_ff.score < ref_ff.score:
self.new_ffs[-1] = exp_ff
logger.log(
20, ' -- Expansion succeeded. Keeping expanded '
'parameters.')
else:
self.new_ffs[-1] = ref_ff
logger.log(
20,
' -- Expansion failed. Keeping reflected parameters.')
elif ref_ff.score < self.new_ffs[-2].score:
logger.log(20, ' -- Keeping reflected parameters.')
self.new_ffs[-1] = ref_ff
else:
logger.log(20, '~~ ATTEMPTING CONTRACTION ~~'.rjust(79, '~'))
con_ff = self.ff.__class__()
con_ff.method = 'CONTRACTION'
con_ff.params = copy.deepcopy(last_best_ff.params)
for i in range(0, len(last_best_ff.params)):
if ref_ff.score > self.new_ffs[-1].score:
con_val = ((inv_ff.params[i].value +
self.new_ffs[-1].params[i].value) / 2)
else:
con_val = ((3 * inv_ff.params[i].value -
self.new_ffs[-1].params[i].value) / 2)
con_ff.params[i].value = con_val
self.ff.export_ff(params=con_ff.params)
data = calculate.main(self.args_ff)
#deprecated
#con_ff.score = compare.compare_data(r_data, data)
r_dict = compare.data_by_type(r_data)
c_dict = compare.data_by_type(data)
r_dict, c_dict = compare.trim_data(r_dict, c_dict)
con_ff.score = compare.compare_data(r_dict, c_dict)
opt.pretty_ff_results(con_ff)
# This change was made to reflect the 1998 Q2MM publication.
# if con_ff.score < self.new_ffs[-1].score:
if con_ff.score < self.new_ffs[-2].score:
logger.log(20, ' -- Contraction succeeded.')
self.new_ffs[-1] = con_ff
elif self.do_massive_contraction:
logger.log(
20, '~~ DOING MASSIVE CONTRACTION ~~'.rjust(79, '~'))
for ff_num, ff in enumerate(self.new_ffs[1:]):
for i in range(0, len(last_best_ff.params)):
ff.params[i].value = (
(ff.params[i].value +
self.new_ffs[0].params[i].value) / 2)
self.ff.export_ff(params=ff.params)
data = calculate.main(self.args_ff)
#deprecated
#ff.score = compare.compare_data(r_data, data)
r_dict = compare.data_by_type(r_data)
c_dict = compare.data_by_type(data)
r_dict, c_dict = compare.trim_data(r_dict, c_dict)
ff.score = compare.compare_data(r_dict, c_dict)
ff.method += ' MC'
opt.pretty_ff_results(ff)
else:
logger.log(
20, ' -- Contraction failed. Keeping parmaeters '
'anyway.')
self.new_ffs[-1] = con_ff
self.new_ffs = sorted(self.new_ffs, key=lambda x: x.score)
# Keep track of the number of cycles without change. If there's
# improvement, reset the counter.
if self.new_ffs[0].score < last_best_ff.score:
cycles_wo_change = 0
else:
cycles_wo_change += 1
logger.log(
20, ' -- {} cycles without improvement out of {} '
'allowed.'.format(cycles_wo_change,
self._max_cycles_wo_change))
logger.log(20, 'BEST:')
opt.pretty_ff_results(self.new_ffs[0], level=20)
logger.log(
20, '~~ END SIMPLEX CYCLE {} ~~'.format(current_cycle).rjust(
79, '~'))
# This sort is likely unnecessary because it should be done at the end
# of the last loop cycle, but I put it here just in case.
self.new_ffs = sorted(self.new_ffs, key=lambda x: x.score)
best_ff = self.new_ffs[0]
if best_ff.score < self.ff.score:
logger.log(
20, '~~ SIMPLEX FINISHED WITH IMPROVEMENTS ~~'.rjust(79, '~'))
best_ff = restore_simp_ff(best_ff, self.ff)
else:
logger.log(
20,
'~~ SIMPLEX FINISHED WITHOUT IMPROVEMENTS ~~'.rjust(79, '~'))
# This restores the inital parameters, so no need to use
# restore_simp_ff here.
best_ff = self.ff
opt.pretty_ff_results(self.ff, level=20)
opt.pretty_ff_results(best_ff, level=20)
logger.log(20, ' -- Writing best force field from simplex.')
best_ff.export_ff(best_ff.path)
return best_ff