import os
import pickle
from gprMax.optimisation_taguchi import plot_optimisation_history
"""Plots the results (pickled to file) from a Taguchi optimisation process."""
# Parse command line arguments
parser = argparse.ArgumentParser(description='Plots the results (pickled to file) from a Taguchi optimisation process.', usage='cd gprMax; python -m user_libs.optimisation_taguchi.plot_results picklefile')
parser.add_argument('picklefile', help='name of file including path')
args = parser.parse_args()
f = open(args.picklefile, 'rb')
optparamshist = pickle.load(f)
fitnessvalueshist = pickle.load(f)
optparamsinit = pickle.load(f)
print('Optimisations summary for: {}'.format(os.path.split(args.picklefile)[1]))
print('Number of iterations: {:g}'.format(len(fitnessvalueshist)))
print('History of fitness values: {}'.format(fitnessvalueshist))
print('Initial parameter values:')
for item in optparamsinit:
print('\t{}: {}'.format(item[0], item[1]))
print('History of parameter values:')
for key, value in optparamshist.items():
print('\t{}: {}'.format(key, value))
# Plot the history of fitness values and each optimised parameter values for the optimisation
plot_optimisation_history(fitnessvalueshist, optparamshist, optparamsinit)
import os
import pickle
from gprMax.optimisation_taguchi import plot_optimisation_history
"""Plots the results (pickled to file) from a Taguchi optimisation process."""
# Parse command line arguments
parser = argparse.ArgumentParser(description='Plots the results (pickled to file) from a Taguchi optimisation process.', usage='cd gprMax; python -m user_libs.optimisation_taguchi.plot_results picklefile')
parser.add_argument('picklefile', help='name of file including path')
args = parser.parse_args()
f = open(args.picklefile, 'rb')
optparamshist = pickle.load(f)
fitnessvalueshist = pickle.load(f)
optparamsinit = pickle.load(f)
print('Optimisations summary for: {}'.format(os.path.split(args.picklefile)[1]))
print('Number of iterations: {:g}'.format(len(fitnessvalueshist)))
print('History of fitness values: {}'.format(fitnessvalueshist))
print('Initial parameter values:')
for item in optparamsinit:
print('\t{}: {}'.format(item[0], item[1]))
print('History of parameter values:')
for key, value in optparamshist.items():
print('\t{}: {}'.format(key, value))
# Plot the history of fitness values and each optimised parameter values for the optimisation
plot_optimisation_history(fitnessvalueshist, optparamshist, optparamsinit)
def main():
"""This is the main function for gprMax."""
# Print gprMax logo, version, and licencing/copyright information
logo(gprMax.__version__ + ' (Bowmore)')
# Parse command line arguments
parser = argparse.ArgumentParser(prog='gprMax', description='Electromagnetic modelling software based on the Finite-Difference Time-Domain (FDTD) method')
parser.add_argument('inputfile', help='path to and name of inputfile')
parser.add_argument('-n', default=1, type=int, help='number of times to run the input file')
parser.add_argument('-mpi', action='store_true', default=False, help='switch on MPI')
parser.add_argument('--geometry-only', action='store_true', default=False, help='only build model and produce geometry file(s)')
parser.add_argument('--write-python', action='store_true', default=False, help='write an input file after any Python code blocks in the original input file have been processed')
parser.add_argument('--opt-taguchi', action='store_true', default=False, help='optimise parameters using the Taguchi optimisation method')
args = parser.parse_args()
numbermodelruns = args.n
inputdirectory = os.path.dirname(os.path.abspath(args.inputfile)) + os.sep
inputfile = inputdirectory + os.path.basename(args.inputfile)
inputfileparts = os.path.splitext(inputfile)
# Create a separate namespace that users can access in any Python code blocks in the input file
usernamespace = {'c': c, 'e0': e0, 'm0': m0, 'z0': z0, 'number_model_runs': numbermodelruns, 'inputdirectory': inputdirectory}
if args.opt_taguchi and numbermodelruns > 1:
raise CmdInputError('When a Taguchi optimisation is being carried out the number of model runs argument is not required')
########################################
# Process for Taguchi optimisation #
########################################
if args.opt_taguchi:
from gprMax.optimisation_taguchi import taguchi_code_blocks, construct_OA, calculate_ranges_experiments, calculate_optimal_levels, plot_optimisation_history
# Default maximum number of iterations of optimisation to perform (used if the stopping criterion is not achieved)
maxiterations = 20
# Process Taguchi code blocks in the input file; pass in ordered dictionary to hold parameters to optimise
tmp = usernamespace.copy()
tmp.update({'optparams': OrderedDict()})
taguchinamespace = taguchi_code_blocks(inputfile, tmp)
# Extract dictionaries and variables containing initialisation parameters
optparams = taguchinamespace['optparams']
fitness = taguchinamespace['fitness']
if 'maxiterations' in taguchinamespace:
maxiterations = taguchinamespace['maxiterations']
# Store initial parameter ranges
optparamsinit = list(optparams.items())
# Dictionary to hold history of optmised values of parameters
optparamshist = OrderedDict((key, list()) for key in optparams)
# Import specified fitness function
fitness_metric = getattr(importlib.import_module('user_libs.optimisation_taguchi_fitness'), fitness['name'])
# Select OA
OA, N, cols, k, s, t = construct_OA(optparams)
print('\n{}\n\nTaguchi optimisation: orthogonal array with {} experiments, {} parameters ({} used), {} levels, and strength {} will be used.'.format(68*'*', N, cols, k, s, t))
# Initialise arrays and lists to store parameters required throughout optimisation
# Lower, central, and upper values for each parameter
levels = np.zeros((s, k), dtype=floattype)
# Optimal lower, central, or upper value for each parameter
levelsopt = np.zeros(k, dtype=floattype)
# Difference used to set values for levels
levelsdiff = np.zeros(k, dtype=floattype)
# History of fitness values from each confirmation experiment
fitnessvalueshist = []
i = 0
while i < maxiterations:
# Set number of model runs to number of experiments
numbermodelruns = N
usernamespace['number_model_runs'] = numbermodelruns
# Fitness values for each experiment
fitnessvalues = []
# Set parameter ranges and define experiments
optparams, levels, levelsdiff = calculate_ranges_experiments(optparams, optparamsinit, levels, levelsopt, levelsdiff, OA, N, k, s, i)
# Mixed mode MPI/OpenMP - task farm for model runs with MPI; each model parallelised with OpenMP
if args.mpi:
from mpi4py import MPI
# Define MPI message tags
tags = Enum('tags', {'READY': 0, 'DONE': 1, 'EXIT': 2, 'START': 3})
# Initializations and preliminaries
comm = MPI.COMM_WORLD # get MPI communicator object
size = comm.size # total number of processes
rank = comm.rank # rank of this process
status = MPI.Status() # get MPI status object
name = MPI.Get_processor_name() # get name of processor/host
if rank == 0:
# Master process
modelrun = 1
numworkers = size - 1
closedworkers = 0
print('Master: PID {} on {} using {} workers.'.format(os.getpid(), name, numworkers))
while closedworkers < numworkers:
data = comm.recv(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG, status=status)
source = status.Get_source()
tag = status.Get_tag()
if tag == tags.READY.value:
# Worker is ready, so send it a task
if modelrun < numbermodelruns + 1:
comm.send(modelrun, dest=source, tag=tags.START.value)
print('Master: sending model {} to worker {}.'.format(modelrun, source))
modelrun += 1
else:
comm.send(None, dest=source, tag=tags.EXIT.value)
elif tag == tags.DONE.value:
print('Worker {}: completed.'.format(source))
elif tag == tags.EXIT.value:
print('Worker {}: exited.'.format(source))
closedworkers += 1
else:
# Worker process
print('Worker {}: PID {} on {} requesting {} OpenMP threads.'.format(rank, os.getpid(), name, os.environ.get('OMP_NUM_THREADS')))
while True:
comm.send(None, dest=0, tag=tags.READY.value)
# Receive a model number to run from the master
modelrun = comm.recv(source=0, tag=MPI.ANY_TAG, status=status)
tag = status.Get_tag()
if tag == tags.START.value:
# Run a model
# Add specific value for each parameter to optimise for each experiment to user accessible namespace
optnamespace = usernamespace.copy()
tmp = {}
tmp.update((key, value[modelrun - 1]) for key, value in optparams.items())
optnamespace.update({'optparams': tmp})
run_model(args, modelrun, numbermodelruns, inputfile, usernamespace)
comm.send(None, dest=0, tag=tags.DONE.value)
elif tag == tags.EXIT.value:
break
comm.send(None, dest=0, tag=tags.EXIT.value)
# Standard behaviour - models run serially; each model parallelised with OpenMP
else:
tsimstart = perf_counter()
for modelrun in range(1, numbermodelruns + 1):
# Add specific value for each parameter to optimise, for each experiment to user accessible namespace
optnamespace = usernamespace.copy()
tmp = {}
tmp.update((key, value[modelrun - 1]) for key, value in optparams.items())
optnamespace.update({'optparams': tmp})
run_model(args, modelrun, numbermodelruns, inputfile, optnamespace)
tsimend = perf_counter()
print('\nTotal simulation time [HH:MM:SS]: {}'.format(datetime.timedelta(seconds=int(tsimend - tsimstart))))
# Calculate fitness value for each experiment
for exp in range(1, numbermodelruns + 1):
outputfile = inputfileparts[0] + str(exp) + '.out'
fitnessvalues.append(fitness_metric(outputfile, fitness['args']))
os.remove(outputfile)
print('\nTaguchi optimisation, iteration {}: completed initial {} experiments completed with fitness values {}.'.format(i + 1, numbermodelruns, fitnessvalues))
# Calculate optimal levels from fitness values by building a response table; update dictionary of parameters with optimal values
optparams, levelsopt = calculate_optimal_levels(optparams, levels, levelsopt, fitnessvalues, OA, N, k)
# Run a confirmation experiment with optimal values
numbermodelruns = 1
usernamespace['number_model_runs'] = numbermodelruns
tsimstart = perf_counter()
for modelrun in range(1, numbermodelruns + 1):
# Add specific value for each parameter to optimise, for each experiment to user accessible namespace
optnamespace = usernamespace.copy()
tmp = {}
for key, value in optparams.items():
tmp[key] = value[modelrun - 1]
optparamshist[key].append(value[modelrun - 1])
optnamespace.update({'optparams': tmp})
run_model(args, modelrun, numbermodelruns, inputfile, optnamespace)
tsimend = perf_counter()
print('\nTotal simulation time [HH:MM:SS]: {}'.format(datetime.timedelta(seconds=int(tsimend - tsimstart))))
# Calculate fitness value for confirmation experiment
outputfile = inputfileparts[0] + '.out'
fitnessvalueshist.append(fitness_metric(outputfile, fitness['args']))
# Rename confirmation experiment output file so that it is retained for each iteraction
os.rename(outputfile, os.path.splitext(outputfile)[0] + '_final' + str(i + 1) + '.out')
print('\nTaguchi optimisation, iteration {} completed. History of optimal parameter values {} and of fitness values {}'.format(i + 1, dict(optparamshist), fitnessvalueshist, 68*'*'))
i += 1
# Stop optimisation if stopping criterion has been reached
if fitnessvalueshist[i - 1] > fitness['stop']:
print('\nTaguchi optimisation stopped as fitness criteria reached')
break
# Stop optimisation if successive fitness values are within 0.5%
# if i > 2:
# fitnessvaluesclose = (np.abs(fitnessvalueshist[i - 2] - fitnessvalueshist[i - 1]) / fitnessvalueshist[i - 1]) * 100
# fitnessvaluesthres = 0.1
# if fitnessvaluesclose < fitnessvaluesthres:
# print('\nTaguchi optimisation stopped as successive fitness values within {}%'.format(fitnessvaluesthres))
# break
# Save optimisation parameters history and fitness values history to file
opthistfile = inputfileparts[0] + '_hist'
np.savez(opthistfile, dict(optparamshist), fitnessvalueshist)
print('\n{}\nTaguchi optimisation completed after {} iteration(s).\nHistory of optimal parameter values {} and of fitness values {}\n{}\n'.format(68*'*', i, dict(optparamshist), fitnessvalueshist, 68*'*'))
# Plot the history of fitness values and each optimised parameter values for the optimisation
plot_optimisation_history(fitnessvalueshist, optparamshist, optparamsinit)
#######################################
# Process for standard simulation #
#######################################
else:
if args.mpi and numbermodelruns == 1:
raise CmdInputError('MPI is not beneficial when there is only one model to run')
# Mixed mode MPI/OpenMP - task farm for model runs with MPI; each model parallelised with OpenMP
if args.mpi:
from mpi4py import MPI
# Define MPI message tags
tags = Enum('tags', {'READY': 0, 'DONE': 1, 'EXIT': 2, 'START': 3})
# Initializations and preliminaries
comm = MPI.COMM_WORLD # get MPI communicator object
size = comm.size # total number of processes
rank = comm.rank # rank of this process
status = MPI.Status() # get MPI status object
name = MPI.Get_processor_name() # get name of processor/host
if rank == 0:
# Master process
modelrun = 1
numworkers = size - 1
closedworkers = 0
print('Master: PID {} on {} using {} workers.'.format(os.getpid(), name, numworkers))
while closedworkers < numworkers:
data = comm.recv(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG, status=status)
source = status.Get_source()
tag = status.Get_tag()
if tag == tags.READY.value:
# Worker is ready, so send it a task
if modelrun < numbermodelruns + 1:
comm.send(modelrun, dest=source, tag=tags.START.value)
print('Master: sending model {} to worker {}.'.format(modelrun, source))
modelrun += 1
else:
comm.send(None, dest=source, tag=tags.EXIT.value)
elif tag == tags.DONE.value:
print('Worker {}: completed.'.format(source))
elif tag == tags.EXIT.value:
print('Worker {}: exited.'.format(source))
closedworkers += 1
else:
# Worker process
print('Worker {}: PID {} on {} requesting {} OpenMP threads.'.format(rank, os.getpid(), name, os.environ.get('OMP_NUM_THREADS')))
while True:
comm.send(None, dest=0, tag=tags.READY.value)
# Receive a model number to run from the master
modelrun = comm.recv(source=0, tag=MPI.ANY_TAG, status=status)
tag = status.Get_tag()
if tag == tags.START.value:
# Run a model
run_model(args, modelrun, numbermodelruns, inputfile, usernamespace)
comm.send(None, dest=0, tag=tags.DONE.value)
elif tag == tags.EXIT.value:
break
comm.send(None, dest=0, tag=tags.EXIT.value)
# Standard behaviour - models run serially; each model parallelised with OpenMP
else:
tsimstart = perf_counter()
for modelrun in range(1, numbermodelruns + 1):
run_model(args, modelrun, numbermodelruns, inputfile, usernamespace)
tsimend = perf_counter()
print('\nTotal simulation time [HH:MM:SS]: {}'.format(datetime.timedelta(seconds=int(tsimend - tsimstart))))
print('\nSimulation completed.\n{}\n'.format(68*'*'))
