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Stopping Criteria and Printers generalized in Optimize.

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This commit is contained in:
Rowan Cockett
2013-11-07 01:20:03 -08:00
parent f60017ff50
commit 318ca555c8
3 changed files with 665 additions and 121 deletions
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SimPEG/inverse/Inversion.py
+23 -7
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@@ -9,11 +9,27 @@ class Inversion(object):
name = 'SimPEG Inversion'
def __init__(self, prob, reg, opt, **kwargs):
self.setKwargs(**kwargs)
self.prob = prob
self.reg = reg
self.opt = opt
self.opt.parent = self
self.setKwargs(**kwargs)
# Check if we have inserted printers into the optimization
haveInserted = False
for printer in self.opt.printers:
haveInserted = haveInserted or printer["title"] == 'phi_d'
if not haveInserted:
self.opt.printers.insert(1,{"title": "beta",
"value": lambda M: M.parent._beta,
"width": 13, "format": "%1.2e"})
self.opt.printers.insert(2,{"title": "phi_d",
"value": lambda M: M.parent._phi_d_last,
"width": 13, "format": "%1.2e"})
self.opt.printers.insert(3,{"title": "phi_m",
"value": lambda M: M.parent._phi_m_last,
"width": 13, "format": "%1.2e"})
def setKwargs(self, **kwargs):
"""Sets key word arguments (kwargs) that are present in the object, throw an error if they don't exist."""
@@ -23,13 +39,13 @@ class Inversion(object):
else:
raise Exception('%s attr is not recognized' % attr)
def printInit(self):
print "%s %s %s" % ('='*22, self.name, '='*22)
print " # beta phi_d phi_m f norm(dJ) #LS"
print "%s" % '-'*62
# def printInit(self):
# print "%s %s %s" % ('='*22, self.name, '='*22)
# print " # beta phi_d phi_m f norm(dJ) #LS"
# print "%s" % '-'*62
def printIter(self):
print "%3d %1.2e %1.2e %1.2e %1.2e %1.2e %3d" % (self.opt._iter, self._beta, self._phi_d_last, self._phi_m_last, self.opt.f, np.linalg.norm(self.opt.g), self.opt._iterLS)
# def printIter(self):
# print "%3d %1.2e %1.2e %1.2e %1.2e %1.2e %3d" % (self.opt._iter, self._beta, self._phi_d_last, self._phi_m_last, self.opt.f, np.linalg.norm(self.opt.g), self.opt._iterLS)
@property
def Wd(self):
SimPEG/inverse/Optimize.py
+94 -50
View File
@@ -33,17 +33,18 @@ class Minimize(object):
tolX = 1e-1
tolG = 1e-1
eps = 1e-5
debug = True
def __init__(self, **kwargs):
self._id = int(np.random.rand()*1e6) # create a unique identifier to this program to be used in pubsub
self.stoppers = [{
"str": "%d : |fc-fOld| = %1.4e <= tolF*(1+|f0|) = %1.4e",
"left": lambda M: 1 if M._iter==0 else abs(M.f-M.fOld),
"left": lambda M: 1 if M._iter==0 else abs(M.f-M.f_last),
"right": lambda M: 0 if M._iter==0 else M.tolF*(1+abs(M.f0)),
"stopType": "optimal"
},{
"str": "%d : |xc-xOld| = %1.4e <= tolX*(1+|x0|) = %1.4e",
"left": lambda M: 1 if M._iter==0 else norm(M.xc-M.xOld),
"str": "%d : |xc-x_last| = %1.4e <= tolX*(1+|x0|) = %1.4e",
"left": lambda M: 1 if M._iter==0 else norm(M.xc-M.x_last),
"right": lambda M: 0 if M._iter==0 else M.tolX*(1+norm(M.x0)),
"stopType": "optimal"
},{
@@ -57,17 +58,28 @@ class Minimize(object):
"right": lambda M: 1e3*M.eps,
"stopType": "critical"
},{
"str": "%d : maxIter = %3d\t <= iter\t = %3d",
"str": "%d : maxIter = %3d <= iter = %3d",
"left": lambda M: M.maxIter,
"right": lambda M: M._iter,
"stopType": "critical"
}]
# print "%3d\t%1.2e\t%1.2e\t%d" % (self._iter, self.f, norm(self.g), self._iterLS)
self.stoppersLS = [{
"str": "%d : ft = %1.4e <= alp*descent = %1.4e",
"left": lambda M: M._LS_ft,
"right": lambda M: M.f + self.LSreduction * M._LS_descent,
"stopType": "optimal"
},{
"str": "%d : maxIterLS = %3d <= iterLS = %3d",
"left": lambda M: M.maxIterLS,
"right": lambda M: M._iterLS,
"stopType": "critical"
}]
self.printers = [{
"title": "#",
"value": lambda M: M._iter,
"width": 5,
"width": 10,
"format": "%3d"
},{
"title": "f",
@@ -85,6 +97,29 @@ class Minimize(object):
"width": 5,
"format": "%d"
}]
self.printersLS = [{
"title": "#",
"value": lambda M: (M._iter, M._iterLS),
"width": 10,
"format": "%3d.%d"
},{
"title": "t",
"value": lambda M: M._LS_t,
"width": 14,
"format": "%0.5f"
},{
"title": "ft",
"value": lambda M: M._LS_ft,
"width": 14,
"format": "%1.2e"
},{
"title": "f + alp*g.T*p",
"value": lambda M: M.f + M.LSreduction*M._LS_descent,
"width": 16,
"format": "%1.2e"
}]
self.setKwargs(**kwargs)
def setKwargs(self, **kwargs):
@@ -157,7 +192,6 @@ class Minimize(object):
while True:
self.f, self.g, self.H = evalFunction(self.xc, return_g=True, return_H=True)
if doPub: pub.sendMessage('Minimize.evalFunction', minimize=self, f=self.f, g=self.g, H=self.H)
self.printIter()
if self.stoppingCriteria(): break
p = self.findSearchDirection()
if doPub: pub.sendMessage('Minimize.searchDirection', minimize=self, p=p)
@@ -170,6 +204,7 @@ class Minimize(object):
if not caught: return self.xc
self.doEndIteration(xt)
if doPub: pub.sendMessage('Minimize.endIteration', minimize=self, xt=xt)
self.printIter()
self.printDone()
@@ -215,10 +250,11 @@ class Minimize(object):
x0 = self.projection(x0) # ensure that we start of feasible.
self.x0 = x0
self.xc = x0
self.xOld = x0
self.f_last = np.nan
self.x_last = x0
def printInit(self):
def printInit(self, inLS=False):
"""
**printInit** is called at the beginning of the optimization routine.
@@ -226,20 +262,21 @@ class Minimize(object):
parent.printInit function and call that.
"""
if doPub: pub.sendMessage('Minimize.printInit', minimize=self)
if self.parent is not None and hasattr(self.parent, 'printInit'):
self.parent.printInit()
return
if doPub and not inLS: pub.sendMessage('Minimize.printInit', minimize=self)
pad = ' '*10 if inLS else ''
printers = self.printers if not inLS else self.printersLS
name = self.name if not inLS else self.nameLS
titles = ''
widths = 0
for printer in self.printers:
for printer in printers:
titles += ('{:^%i}'%printer['width']).format(printer['title']) + ''
widths += printer['width']
print "{0} {1} {0}".format('='*((widths-1-len(self.name))/2), self.name)
print titles
print "%s" % '-'*widths
print pad + "{0} {1} {0}".format('='*((widths-1-len(name))/2), name)
print pad + titles
print pad + "%s" % '-'*widths
def printIter(self):
def printIter(self, inLS=False):
"""
**printIter** is called directly after function evaluations.
@@ -247,18 +284,17 @@ class Minimize(object):
parent.printIter function and call that.
"""
if doPub: pub.sendMessage('Minimize.printIter', minimize=self)
if self.parent is not None and hasattr(self.parent, 'printIter'):
self.parent.printIter()
return
if doPub and not inLS: pub.sendMessage('Minimize.printIter', minimize=self)
pad = ' '*10 if inLS else ''
printers = self.printers if not inLS else self.printersLS
values = ''
for printer in self.printers:
for printer in printers:
values += ('{:^%i}'%printer['width']).format(printer['format'] % printer['value'](self))
print values
# print "%3d\t%1.2e\t%1.2e\t%d" % (self._iter, self.f, norm(self.g), self._iterLS)
print pad + values
# print pad + "%3d\t%1.2e\t%1.2e\t%d" % (self._iter, self.f, norm(self.g), self._iterLS)
def printDone(self):
def printDone(self, inLS=False):
"""
**printDone** is called at the end of the optimization routine.
@@ -266,19 +302,21 @@ class Minimize(object):
parent.printDone function and call that.
"""
if doPub: pub.sendMessage('Minimize.printDone', minimize=self)
print "%s STOP! %s" % ('-'*25,'-'*25)
# TODO: put controls on gradient value, min model update, and function value
for stopper in self.stoppers:
if doPub and not inLS: pub.sendMessage('Minimize.printDone', minimize=self)
pad = ' '*10 if inLS else ''
stop, done = (' STOP! ', ' DONE! ') if not inLS else ('----------------', ' End Linesearch ')
print pad + "%s%s%s" % ('-'*25,stop,'-'*25)
stoppers = self.stoppers if not inLS else self.stoppersLS
for stopper in stoppers:
l = stopper['left'](self)
r = stopper['right'](self)
print stopper['str'] % (l<=r,l,r)
print pad + stopper['str'] % (l<=r,l,r)
print "%s DONE! %s\n" % ('='*25,'='*25)
print pad + "%s%s%s" % ('-'*25,done,'-'*25)
if self.parent is not None and hasattr(self.parent, 'printDone'): self.parent.printDone()
def stoppingCriteria(self):
def stoppingCriteria(self, inLS=False):
if self._iter == 0:
# Save this for stopping criteria
self.f0 = self.f
@@ -287,7 +325,9 @@ class Minimize(object):
# check stopping rules
optimal = []
critical = []
for stopper in self.stoppers:
stoppers = self.stoppers if not inLS else self.stoppersLS
for stopper in stoppers:
l = stopper['left'](self)
r = stopper['right'](self)
if stopper['stopType'] == 'optimal':
@@ -352,6 +392,8 @@ class Minimize(object):
p = self.maxStep*p/np.abs(p.max())
return p
nameLS = "Armijo linesearch"
def modifySearchDirection(self, p):
"""
**modifySearchDirection** changes the search direction based on some sort of linesearch or trust-region criteria.
@@ -371,20 +413,22 @@ class Minimize(object):
:return: (xt, passLS)
"""
# Projected Armijo linesearch
t = 1
iterLS = 0
while iterLS < self.maxIterLS:
xt = self.projection(self.xc + t*p)
ft = self.evalFunction(xt, return_g=False, return_H=False)
descent = np.inner(self.g, xt - self.xc) # this takes into account multiplying by t, but is important for projection.
if ft < self.f + t*self.LSreduction*descent:
break
iterLS += 1
t = self.LSshorten*t
# TODO: Check if t is tooo small.
self._LS_t = 1
self._iterLS = 0
while self._iterLS < self.maxIterLS:
self._LS_xt = self.projection(self.xc + self._LS_t*p)
self._LS_ft = self.evalFunction(self._LS_xt, return_g=False, return_H=False)[0]
self._LS_descent = np.inner(self.g, self._LS_xt - self.xc) # this takes into account multiplying by t, but is important for projection.
if self.stoppingCriteria(inLS=True): break
self._iterLS += 1
self._LS_t = self.LSshorten*self._LS_t
if self.debug:
if self._iterLS == 1: self.printInit(inLS=True)
self.printIter(inLS=True)
self._iterLS = iterLS
return xt, iterLS < self.maxIterLS
if self.debug and self._iterLS > 0: self.printDone(inLS=True)
return self._LS_xt, self._iterLS < self.maxIterLS
def modifySearchDirectionBreak(self, p):
"""
@@ -431,8 +475,8 @@ class Minimize(object):
# store old values
self.fOld = self.f
self.xOld, self.xc = self.xc, xt
self.f_last = self.f
self.x_last, self.xc = self.xc, xt
self._iter += 1
notebooks/Bound Constraint Problem.ipynb
+548 -64
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