def optimise( function, fitting_parameters, opts ):
tot_vars = ( fitting_parameters.to_fit + fitting_parameters.fixed ).strings
pot_vars = fitting_parameters.to_fit.strings
const_vars = fitting_parameters.fixed.strings
const_values = np.asarray( fitting_parameters.fixed.initial_values )
tot_values_min = ( fitting_parameters.fixed + fitting_parameters.to_fit ).min_bounds
tot_values_max = ( fitting_parameters.fixed + fitting_parameters.to_fit ).max_bounds
all_step_sizes = ( fitting_parameters.fixed + fitting_parameters.to_fit ).max_deltas
step_sizes = np.asarray( fitting_parameters.to_fit.max_deltas )
to_fit_and_not = ( fitting_parameters.fixed + fitting_parameters.to_fit ).is_fixed
pot_values_min = fitting_parameters.to_fit.min_bounds
pot_values_max = fitting_parameters.to_fit.max_bounds
pot_values = np.asarray( fitting_parameters.to_fit.initial_values )
# Choose the calculation order
if ( 'use_basin_hopping' not in opts.keys() or
opts[ 'use_basin_hopping' ] == False or
opts[ 'basin_hopping' ][ 'calc_order' ] == 0 ):
# what happens if we are not using basin hopping?
if opts[ 'method' ] == 'L-BFGS-B':
minimizer = LBFGSB_Minimizer( opts )
results_min = minimizer.minimize( function, pot_values, bounds = fitting_parameters.to_fit.bounds )
elif opts[ 'method' ] == 'CG':
minimizer = CG_Minimizer( opts )
results_min = minimizer.minimize( function, pot_values )
elif opts[ 'method' ] == 'Nelder-Mead':
minimizer = Nelder_Mead_Minimizer( opts )
results_min = minimizer.minimize( function, pot_values )
else:
sys.exit( 'minimization method {} not supported'.format( opts[ 'method' ] ) )
output( results_min.message )
tot_values = np.concatenate((const_values,results_min.x),axis=0)
# Write a results file
write_Results_min = WriteRestart(tot_vars,const_values,to_fit_and_not,tot_values_min,tot_values_max,all_step_sizes,'RESULTS_min')
write_Results_min(results_min.x,results_min.fun,accepted=1)
function.evaluate( results_min.x, plot = True )
mkdir_p('./min-errors-pdfs')
os.system('mv *.pdf ./min-errors-pdfs')
########################################################################################
# basin hopping part using the minimization parameters as a starting guess #
# The temperature should be a fraction of the final function value from the minimizer #
########################################################################################
# Define variables for BH RESTART file
write_restart = WriteRestart(tot_vars,const_values,to_fit_and_not,tot_values_min,tot_values_max,all_step_sizes,'RESTART')
#
if opts[ 'use_basin_hopping' ] == True:
print( 'Basin Hopping' )
if opts[ 'basin_hopping' ]['calc_order' ] == 0:
# Pass the optimized values to BH
pot_values = results_min.x
# Temperature parameter for BH
temperature = results_min.fun * opts[ 'temperature' ]
elif opts[ 'basin_hopping' ]['calc_order' ] == 1:
# Temperature parameter for BH
temperature = function(pot_values) * opts[ 'basin_hopping' ][ 'temperature' ]
else:
exit( 'not recognised as basin hopping calculation order: {}'.format( opts[ 'basin_hopping' ][ 'calc_order' ] ) )
# Step sizes for BH
output( 'The temperature is set to: '+str(temperature)+'\n' )
# Set the options for the minimization algo in BH
if opts[ 'basin_hopping' ][ 'method' ] in ('L-BFGS-B','CG'):
options = { 'ftol': opts[ 'basin_hopping' ][ 'tolerance' ][ 'ftol' ],
'gtol': opts[ 'basin_hopping' [ 'tolerance' ]][ 'gtol' ],
'disp': opts[ 'verbose' ],
'maxiter': opts[ 'basin_hopping' ][ 'maxiter' ],
'eps': opts[ 'basin_hopping' ][ 'stepsize' ] }
elif opts[ 'basin_hopping' ][ 'method' ] == 'Nelder-Mead':
options = { 'ftol': opts[ 'basin_hopping' ][ 'tolerance' ][ 'ftol' ],
'xtol': opts[ 'basin_hopping' ][ 'tolerance' ][ 'xtol' ],
'disp': opts[ 'verbose' ],
'maxiter': opts[ 'basin_hopping' ][ 'maxiter' ] }
else:
sys.exit('Minimization method {} not supported'.format( opts[ 'basin_hopping' ][ 'method' ] ) )
# Bounds for BH
mybounds = MyBounds(pot_values_max,pot_values_min)
if opts[ 'basin_hopping' ][ 'method' ] in ('L-BFGS-B'):
minimizer_kwargs = { 'method': opts[ 'basin_hopping' ][ 'method' ],
'bounds': fitting_parameters.to_fit.bounds,
'options': options}
else:
minimizer_kwargs = { 'method': opts[ 'basin_hopping' ][ 'method' ],
'options': options}
results_BH = basinhopping( function,
x0 = pot_values,
niter = opts[ 'basin_hopping' ][ 'maxiter' ], # TODO check this
T = temperature,
stepsize = opts[ 'basin_hopping'][ 'timestep' ],
minimizer_kwargs = minimizer_kwargs,
take_step = MyTakeStep( step_sizes ),
disp = opts[ 'verbose' ],
accept_test = mybounds,
callback = write_restart,
niter_success = opts[ 'basin_hopping' ][ 'niter_success' ] )
output( results_BH.message[0] )
tot_values = np.concatenate((const_values,results_BH.x),axis=0)
#
## Write a results file for the BH part
write_Results_BH = WriteRestart(tot_vars,const_values,to_fit_and_not,tot_values_min,tot_values_max,all_step_sizes,'RESULTS_BH')
write_Results_BH( results_BH.x, results_BH.fun, accepted = 1 )
# plot should take target filenames as arguments to save having to move afterwards
function.evaluate( results_BH.x, plot = True )
mkdir_p('./BH-errors-pdfs')
os.system('mv *.pdf ./BH-errors-pdfs')
def optimise(function, fitting_parameters, opts):
tot_vars = (fitting_parameters.to_fit + fitting_parameters.fixed).strings
pot_vars = fitting_parameters.to_fit.strings
const_vars = fitting_parameters.fixed.strings
const_values = np.asarray(fitting_parameters.fixed.initial_values)
tot_values_min = (fitting_parameters.fixed +
fitting_parameters.to_fit).min_bounds
tot_values_max = (fitting_parameters.fixed +
fitting_parameters.to_fit).max_bounds
all_step_sizes = (fitting_parameters.fixed +
fitting_parameters.to_fit).max_deltas
step_sizes = np.asarray(fitting_parameters.to_fit.max_deltas)
to_fit_and_not = (fitting_parameters.fixed +
fitting_parameters.to_fit).is_fixed
pot_values_min = fitting_parameters.to_fit.min_bounds
pot_values_max = fitting_parameters.to_fit.max_bounds
pot_values = np.asarray(fitting_parameters.to_fit.initial_values)
# Choose the calculation order
if ('use_basin_hopping' not in opts.keys()
or opts['use_basin_hopping'] == False
or opts['basin_hopping']['calc_order'] == 0):
# what happens if we are not using basin hopping?
if opts['method'] == 'L-BFGS-B':
minimizer = LBFGSB_Minimizer(opts)
results_min = minimizer.minimize(
function, pot_values, bounds=fitting_parameters.to_fit.bounds)
elif opts['method'] == 'CG':
minimizer = CG_Minimizer(opts)
results_min = minimizer.minimize(function, pot_values)
elif opts['method'] == 'Nelder-Mead':
minimizer = Nelder_Mead_Minimizer(opts)
results_min = minimizer.minimize(function, pot_values)
else:
sys.exit('minimization method {} not supported'.format(
opts['method']))
output(results_min.message)
tot_values = np.concatenate((const_values, results_min.x), axis=0)
# Write a results file
write_Results_min = WriteRestart(tot_vars, const_values,
to_fit_and_not, tot_values_min,
tot_values_max, all_step_sizes,
'RESULTS_min')
write_Results_min(results_min.x, results_min.fun, accepted=1)
function.evaluate(results_min.x, plot=True)
mkdir_p('./min-errors-pdfs')
os.system('mv *.pdf ./min-errors-pdfs')
########################################################################################
# basin hopping part using the minimization parameters as a starting guess #
# The temperature should be a fraction of the final function value from the minimizer #
########################################################################################
# Define variables for BH RESTART file
write_restart = WriteRestart(tot_vars, const_values, to_fit_and_not,
tot_values_min, tot_values_max,
all_step_sizes, 'RESTART')
#
if opts['use_basin_hopping'] == True:
print('Basin Hopping')
if opts['basin_hopping']['calc_order'] == 0:
# Pass the optimized values to BH
pot_values = results_min.x
# Temperature parameter for BH
temperature = results_min.fun * opts['temperature']
elif opts['basin_hopping']['calc_order'] == 1:
# Temperature parameter for BH
temperature = function(
pot_values) * opts['basin_hopping']['temperature']
else:
exit(
'not recognised as basin hopping calculation order: {}'.format(
opts['basin_hopping']['calc_order']))
# Step sizes for BH
output('The temperature is set to: ' + str(temperature) + '\n')
# Set the options for the minimization algo in BH
if opts['basin_hopping']['method'] in ('L-BFGS-B', 'CG'):
options = {
'ftol': opts['basin_hopping']['tolerance']['ftol'],
'gtol': opts['basin_hopping'['tolerance']]['gtol'],
'disp': opts['verbose'],
'maxiter': opts['basin_hopping']['maxiter'],
'eps': opts['basin_hopping']['stepsize']
}
elif opts['basin_hopping']['method'] == 'Nelder-Mead':
options = {
'ftol': opts['basin_hopping']['tolerance']['ftol'],
'xtol': opts['basin_hopping']['tolerance']['xtol'],
'disp': opts['verbose'],
'maxiter': opts['basin_hopping']['maxiter']
}
else:
sys.exit('Minimization method {} not supported'.format(
opts['basin_hopping']['method']))
# Bounds for BH
mybounds = MyBounds(pot_values_max, pot_values_min)
if opts['basin_hopping']['method'] in ('L-BFGS-B'):
minimizer_kwargs = {
'method': opts['basin_hopping']['method'],
'bounds': fitting_parameters.to_fit.bounds,
'options': options
}
else:
minimizer_kwargs = {
'method': opts['basin_hopping']['method'],
'options': options
}
results_BH = basinhopping(
function,
x0=pot_values,
niter=opts['basin_hopping']['maxiter'], # TODO check this
T=temperature,
stepsize=opts['basin_hopping']['timestep'],
minimizer_kwargs=minimizer_kwargs,
take_step=MyTakeStep(step_sizes),
disp=opts['verbose'],
accept_test=mybounds,
callback=write_restart,
niter_success=opts['basin_hopping']['niter_success'])
output(results_BH.message[0])
tot_values = np.concatenate((const_values, results_BH.x), axis=0)
#
## Write a results file for the BH part
write_Results_BH = WriteRestart(tot_vars, const_values, to_fit_and_not,
tot_values_min, tot_values_max,
all_step_sizes, 'RESULTS_BH')
write_Results_BH(results_BH.x, results_BH.fun, accepted=1)
# plot should take target filenames as arguments to save having to move afterwards
function.evaluate(results_BH.x, plot=True)
mkdir_p('./BH-errors-pdfs')
os.system('mv *.pdf ./BH-errors-pdfs')
def evaluate( self, test_values, plot = False ):
if type( test_values ) is not np.ndarray:
raise TypeError
self.potential_file.write_with_parameters( test_values )
ran_okay = self.training_set.run()
if not ran_okay:
totalChi = 1E10
output('Error: likely due to unphysical parameter value\n')
return totalChi
ff_forces = self.training_set.new_forces.T
ff_dipoles = self.training_set.new_dipoles.T
ff_stresses = self.training_set.new_stresses.T
chiSq = {}
chiSq[ 'forces' ] = chi_squared( self.ai_forces, ff_forces, plot, 'forces-errors' )
chiSq[ 'dipoles' ] = chi_squared( self.ai_dipoles, ff_dipoles, plot, 'dipoles-errors' )
chiSq[ 'stresses' ] = chi_squared( self.ai_stresses, ff_stresses, plot, 'stresses-errors' )
factorTot = sum( self.scaling.values() )
totalChi = sum( [ self.scaling[ k ] * chiSq[ k ] for k in chiSq.keys() ] ) / factorTot
output( 'Forces chi sq: ' + fmt.format(chiSq[ 'forces' ])+'\n')
output( 'Dipoles chi sq: ' + fmt.format(chiSq[ 'dipoles' ]) +'\n')
output( 'Stresses chi sq: ' + fmt.format(chiSq[ 'stresses' ]) + '\n')
output( 'Total chi sq (no factors): ' + fmt.format( np.mean( list( chiSq.values() ) ) ) + '\n' )
output('Total chi sq: '+fmt.format(totalChi)+'\n')
output('\n')
return totalChi
def evaluate(self, test_values, plot=False):
if type(test_values) is not np.ndarray:
raise TypeError
self.potential_file.write_with_parameters(test_values)
ran_okay = self.training_set.run()
if not ran_okay:
totalChi = 1E10
output('Error: likely due to unphysical parameter value\n')
return totalChi
ff_forces = self.training_set.new_forces.T
ff_dipoles = self.training_set.new_dipoles.T
ff_stresses = self.training_set.new_stresses.T
chiSq = {}
chiSq['forces'] = chi_squared(self.ai_forces, ff_forces, plot,
'forces-errors')
chiSq['dipoles'] = chi_squared(self.ai_dipoles, ff_dipoles, plot,
'dipoles-errors')
chiSq['stresses'] = chi_squared(self.ai_stresses, ff_stresses, plot,
'stresses-errors')
factorTot = sum(self.scaling.values())
totalChi = sum([self.scaling[k] * chiSq[k]
for k in chiSq.keys()]) / factorTot
output('Forces chi sq: ' + fmt.format(chiSq['forces']) + '\n')
output('Dipoles chi sq: ' + fmt.format(chiSq['dipoles']) + '\n')
output('Stresses chi sq: ' + fmt.format(chiSq['stresses']) + '\n')
output('Total chi sq (no factors): ' +
fmt.format(np.mean(list(chiSq.values()))) + '\n')
output('Total chi sq: ' + fmt.format(totalChi) + '\n')
output('\n')
return totalChi
