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#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 2010-2016 California Institute of Technology.
# Copyright (c) 2016-2024 The Uncertainty Quantification Foundation.
# License: 3-clause BSD. The full license text is available at:
# - https://github.com/uqfoundation/mystic/blob/master/LICENSE
"""
an example of using an interpolator within a surface object
""" #XXX: use interpolator, plotter, and sampler (instead of Surface)?
from surface import Surface
import time
if __name__ == '__main__':
start = time.time()
"""
from mystic.models import griewangk
from mystic.termination import NormalizedChangeOverGeneration as NCOG
stop = NCOG(1e-4)
bounds = 2*[(-9.5,9.5)]
self = Surface(griewangk, maxpts=1000)
# self.doit(bounds, stop)
step=100; scale=False; shift=False; density=9; kwds={}
if not self.sampler.traj: self.sampler.UseTrajectories()
# get trajectories
self.Sample(bounds, stop)
# get interpolated function
self.Interpolate(**kwds)
# check extrema #XXX: put _min,_max in Interpolate? (downsampled)
f = lambda x,z: (z,self.surrogate(*x))
print("min: {}; min@f: {}".format(*f(*self._min())))
print("max: {}; max@f: {}".format(*f(*self._max())))
# plot surface
self.Plot(step=step, scale=scale, shift=shift, density=density)
"""
# parallel configuration
try:
from pathos.helpers import freeze_support, shutdown
freeze_support()
from pathos.pools import ProcessPool as Pool
#from pathos.pools import ThreadPool as Pool
#from pathos.pools import ParallelPool as Pool
except ImportError:
from mystic.pools import SerialPool as Pool
shutdown = lambda x=None:None
_map = Pool().map
# tools
from mystic.termination import VTR, ChangeOverGeneration as COG
from mystic.termination import NormalizedChangeOverGeneration as NCOG
from mystic.monitors import LoggingMonitor, VerboseMonitor, Monitor
from klepto.archives import dir_archive
stop = NCOG(1e-4)
disp = True # print optimization summary
monitor = True # use LoggingMonitor (uses much less memory)
archive = False # save an archive
all = True # use EvalMonitor (instead of StepMonitor only)
traj = not monitor # save all trajectories internally, if no logs
# cost function
from mystic.models import griewangk as model
ndim = 2 # model dimensionality
bounds = ndim * [(-9.5,9.5)] # griewangk
# the ensemble solvers
from mystic.solvers import BuckshotSolver, LatticeSolver, SparsitySolver
# the local solvers
from mystic.solvers import PowellDirectionalSolver
sprayer = BuckshotSolver
seeker = PowellDirectionalSolver
npts = 25 # number of solvers
retry = 1 # max consectutive iteration retries without a cache 'miss'
repeat = 0 # number of times to repeat the search
tol = 8 # rounding precision
mem = 1 # cache rounding precision
size = 0 # max in-memory cache size
#CUTE: 'configure' monitor and archive if they are desired
if monitor:
monitor = LoggingMonitor(1) # montor for all runs
costmon = LoggingMonitor(1, filename='inv.txt') #XXX: log.txt?
else:
monitor = costmon = None
if archive:
name = getattr(model,'__name__','model')
ar_name = '__%s_%sD_cache__' % (name,ndim)
archive = dir_archive(ar_name, serialized=True, cached=False)
ar_name = '__%s_%sD_invcache__' % (name,ndim)
ivcache = dir_archive(ar_name, serialized=True, cached=False)
else:
archive = ivcache = None
from mystic.search import Searcher #XXX: init w/ archive, then UseArchive?
expts,evals = (None,archive) if all else (archive, None)
#expts,evals = (archive, None) #XXX: don't override the sample archive
sampler = Searcher(npts, retry, tol, mem, size, _map, evals, expts, sprayer, seeker, repeat=repeat)
sampler.Verbose(disp)
sampler.UseTrajectories(traj)
### doit ###
maxpts = 1000. #10000.
surface = Surface(model, sampler, maxpts=maxpts, dim=ndim)
surface.UseMonitor(monitor, costmon)
surface.UseArchive(archive, ivcache)
density = 9
shift = 0
scale = 0
step = 200
args = {
#'smooth': 0,
'method': 'thin_plate',
'extrap': False,
'arrays': False,
}
#surface.doit(bounds, stop, step=step)
#'multiquadric','inverse','gaussian','linear','cubic','quintic','thin_plate'
# impose data filter (so X >= 0 and Y >= 0)
from mystic.filters import generate_mask, generate_filter
from mystic.constraints import impose_bounds
_bounds = impose_bounds((0.0, None))(lambda x:x)
filter = generate_filter(generate_mask(_bounds, _bounds))
# from numpy import round as _round
#############
# get trajectories
surface.Sample(bounds, stop, all=all, filter=filter)#, constraints=_round)
print("TOOK: %s" % (time.time() - start))
# exit()
# get interpolated function
surface.Interpolate(**args)
# check extrema #XXX: put _min,_max in Interpolate? (downsampled)
f = lambda x,z: (z,surface.surrogate(*x))
print("min: {}; min@f: {}".format(*f(*surface._min())))
print("max: {}; max@f: {}".format(*f(*surface._max())))
# shutdown worker pool
shutdown()
# print("TOOK: %s" % (time.time() - start))
# plot interpolated surface
axes = (0,1)
vals = () # use remaining minima as the fixed values
surface.Plot(step=step, scale=scale, shift=shift, density=density, axes=axes, vals=vals)
"""
try:
from klepto.archives import file_archive
archive = file_archive('models.pkl', serialized=True, cached=False)
archive[model.im_class.__name__.lower()] = surface.surrogate
except Exception:
print("serialization failed")
"""
# some testing of interpolated model
#import numpy as np
#actual = np.asarray(surface.z) # downsample?
#interp = surface.surrogate(*surface.x.T) # downsample? #NOTE: SLOW
#print("sum diff squares") #NOTE: is *worse* than with test_searcher.py
#print("actual and interp: %s" % np.sum((actual - interp)**2))
# EOF
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