The following can save and restart from a previous x,
but I gather you want to save and restart more state, e.g. gradients, too; can you clarify that ?
See also basinhopping,
which has a nice-looking gui, pele-python .
#!/usr/bin/env python
""" Funcgradmn: wrap f() and grad(), save all x[] f[] grad[] to plot or restart """
from __future__ import division
import numpy as np
__version__ = "2016-10-18 oct denis"
class Funcgradmon(object):
""" Funcgradmn: wrap f() and grad(), save all x[] f[] grad[] to plot or restart
Example: minimize, save, restart --
fg = Funcgradmon( func, gradfunc, verbose=1 )
# fg(x): f(x), g(x) for minimize( jac=True )
# run 100 iter (if linesearch, 200-300 calls of fg()) --
options = dict( maxiter=100 ) # ...
min0 = minimize( fg, x0, jac=True, options=options )
fg.savez( "0.npz", paramstr="..." ) # to plot or restart
# restart from x[50] --
# (won't repeat the previous path from 50
# unless you save and restore the whole state of the optimizer)
x0 = fg.restart( 50 )
# change params ...
min50 = minimize( fg, x0, jac=True, options=options )
"""
def __init__( self, func, gradfunc, verbose=1 ):
self.func = func
self.gradfunc = gradfunc
self.verbose = verbose
self.x, self.f, self.g = [], [], [] # growing lists
self.t = 0
def __call__( self, x ):
""" f, g = func(x), gradfunc(x); save them; return f, g """
x = np.asarray_chkfinite( x ) # always
f = self.func(x)
g = self.gradfunc(x)
g = np.asarray_chkfinite( g )
self.x.append( np.copy(x) )
self.f.append( _copy( f ))
self.g.append( np.copy(g) )
if self.verbose:
print "%3d:" % self.t ,
fmt = "%-12g" if np.isscalar(f) else "%s\t"
print fmt % f ,
print "x: %s" % x , # with user's np.set_printoptions
print "\tgrad: %s" % g
# better df dx dg
# callback: plot
self.t += 1
return f, g
def restart( self, n ):
""" x0 = fg.restart( n ) returns x[n] to minimize( fg, x0 )
"""
x0 = self.x[n] # minimize from here
del self.x[:n]
del self.f[:n]
del self.g[:n]
self.t = n
if self.verbose:
print "Funcgradmon: restart from x[%d] %s" % (n, x0)
return x0
def savez( self, npzfile, **kw ):
""" np.savez( npzfile, x= f= g= ) """
x, f, g = map( np.array, [self.x, self.f, self.g] )
if self.verbose:
asum = "f: %s \nx: %s \ng: %s" % (
_asum(f), _asum(x), _asum(g) )
print "Funcgradmon: saving to %s: \n%s \n" % (npzfile, asum)
np.savez( npzfile, x=x, f=f, g=g, **kw )
def load( self, npzfile ):
load = np.load( npzfile )
x, f, g = load["x"], load["f"], load["g"]
if self.verbose:
asum = "f: %s \nx: %s \ng: %s" % (
_asum(f), _asum(x), _asum(g) )
print "Funcgradmon: load %s: \n%s \n" % (npzfile, asum)
self.x = list( x )
self.f = list( f )
self.g = list( g )
self.loaddict = load
return self.restart( len(x) - 1 )
def _asum( X ):
""" one-line array summary: "shape type min av max" """
if not hasattr( X, "dtype" ):
return str(X)
return "%s %s min av max %.3g %.3g %.3g" % (
X.shape, X.dtype, X.min(), X.mean(), X.max() )
def _copy( x ):
return x if x is None or np.isscalar(x) \
else np.copy( x )
#...............................................................................
if __name__ == "__main__":
import sys
from scipy.optimize import minimize, rosen, rosen_der
np.set_printoptions( threshold=20, edgeitems=10, linewidth=140,
formatter = dict( float = lambda x: "%.3g" % x )) # float arrays %.3g
dim = 3
method = "cg"
maxiter = 10 # 1 linesearch -> 2-3 calls of fg
# to change these params, run this.py a=1 b=None 'c = ...' in sh or ipython
for arg in sys.argv[1:]:
exec( arg )
print "\n", 80 * "-"
print "Funcgradmon: dim %d method %s maxiter %d \n" % (
dim, method, maxiter )
x0 = np.zeros( dim )
#...........................................................................
fg = Funcgradmon( rosen, rosen_der, verbose=1 )
options = dict( maxiter=maxiter ) # ...
min0 = minimize( fg, x0, jac=True, method=method, options=options )
fg.savez( "0.npz", paramstr="..." ) # to plot or restart
x0 = fg.restart( 5 ) # = fg.x[5]
# change params, print them all
min5 = minimize( fg, x0, jac=True, method=method, options=options )
fg.savez( "5.npz", paramstr="..." )
