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Updates on printing and stopping criteria. These can be set and modified much easier without rewriting functions.

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This commit is contained in:
Rowan Cockett
2013-11-06 21:03:07 -08:00
parent 22f1ff9f74
commit f60017ff50
1 changed files with 121 additions and 35 deletions
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SimPEG/inverse/Optimize.py
+121 -35
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@@ -22,7 +22,7 @@ class Minimize(object):
"""
name = "GeneralOptimizationAlgorithm"
name = "General Optimization Algorithm"
maxIter = 20
maxIterLS = 10
@@ -36,6 +36,55 @@ class Minimize(object):
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),
"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),
"right": lambda M: 0 if M._iter==0 else M.tolX*(1+norm(M.x0)),
"stopType": "optimal"
},{
"str": "%d : |g| = %1.4e <= tolG = %1.4e",
"left": lambda M: norm(M.projection(M.g)),
"right": lambda M: M.tolG,
"stopType": "optimal"
},{
"str": "%d : |g| = %1.4e <= 1e3*eps = %1.4e",
"left": lambda M: norm(M.g),
"right": lambda M: 1e3*M.eps,
"stopType": "critical"
},{
"str": "%d : maxIter = %3d\t <= iter\t = %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.printers = [{
"title": "#",
"value": lambda M: M._iter,
"width": 5,
"format": "%3d"
},{
"title": "f",
"value": lambda M: self.f,
"width": 14,
"format": "%1.2e"
},{
"title": "|g|",
"value": lambda M: norm(M.g),
"width": 14,
"format": "%1.2e"
},{
"title": "LS",
"value": lambda M: M._iterLS,
"width": 5,
"format": "%d"
}]
self.setKwargs(**kwargs)
def setKwargs(self, **kwargs):
@@ -146,19 +195,29 @@ class Minimize(object):
xc = x0
_iter = _iterLS = 0
If you have things that also need to run on startup, you can create a method::
def _startup(self, x0):
pass
If present, _startup will be called at the start of the default startup call.
You may also completely overwrite this function.
:param numpy.ndarray x0: initial x
:rtype: None
:return: None
"""
if hasattr(self,'_startup'): self._startup(x0)
self._iter = 0
self._iterLS = 0
self._STOP = np.zeros((5,1),dtype=bool)
x0 = self.projection(x0) # ensure that we start of feasible.
self.x0 = x0
self.xc = x0
self.xOld = x0
def printInit(self):
"""
**printInit** is called at the beginning of the optimization routine.
@@ -171,9 +230,14 @@ class Minimize(object):
if self.parent is not None and hasattr(self.parent, 'printInit'):
self.parent.printInit()
return
print "%s %s %s" % ('='*22, self.name, '='*22)
print "iter\tJc\t\tnorm(dJ)\tLS"
print "%s" % '-'*57
titles = ''
widths = 0
for printer in self.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
def printIter(self):
"""
@@ -187,7 +251,12 @@ class Minimize(object):
if self.parent is not None and hasattr(self.parent, 'printIter'):
self.parent.printIter()
return
print "%3d\t%1.2e\t%1.2e\t%d" % (self._iter, self.f, norm(self.g), self._iterLS)
values = ''
for printer in self.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)
def printDone(self):
"""
@@ -198,30 +267,34 @@ class Minimize(object):
"""
if doPub: pub.sendMessage('Minimize.printDone', minimize=self)
if self.parent is not None and hasattr(self.parent, 'printDone'):
self.parent.printDone()
return
print "%s STOP! %s" % ('-'*25,'-'*25)
# TODO: put controls on gradient value, min model update, and function value
if self._iter > 0:
print "%d : |fc-fOld| = %1.4e <= tolF*(1+|fStop|) = %1.4e" % (self._STOP[0], abs(self.f-self.fOld), self.tolF*(1+abs(self.fStop)))
print "%d : |xc-xOld| = %1.4e <= tolX*(1+|x0|) = %1.4e" % (self._STOP[1], norm(self.xc-self.xOld), self.tolX*(1+norm(self.x0)))
print "%d : |g| = %1.4e <= tolG*(1+|fStop|) = %1.4e" % (self._STOP[2], norm(self.g), self.tolG*(1+abs(self.fStop)))
print "%d : |g| = %1.4e <= 1e3*eps = %1.4e" % (self._STOP[3], norm(self.g), 1e3*self.eps)
print "%d : iter = %3d\t <= maxIter\t = %3d" % (self._STOP[4], self._iter, self.maxIter)
for stopper in self.stoppers:
l = stopper['left'](self)
r = stopper['right'](self)
print stopper['str'] % (l<=r,l,r)
print "%s DONE! %s\n" % ('='*25,'='*25)
if self.parent is not None and hasattr(self.parent, 'printDone'): self.parent.printDone()
def stoppingCriteria(self):
if self._iter == 0:
self.fStop = self.f # Save this for stopping criteria
# Save this for stopping criteria
self.f0 = self.f
self.g0 = self.g
# check stopping rules
self._STOP[0] = self._iter > 0 and (abs(self.f-self.fOld) <= self.tolF*(1+abs(self.fStop)))
self._STOP[1] = self._iter > 0 and (norm(self.xc-self.xOld) <= self.tolX*(1+norm(self.x0)))
self._STOP[2] = norm(self.g) <= self.tolG*(1+abs(self.fStop))
self._STOP[3] = norm(self.g) <= 1e3*self.eps
self._STOP[4] = self._iter >= self.maxIter
return all(self._STOP[0:3]) | any(self._STOP[3:])
optimal = []
critical = []
for stopper in self.stoppers:
l = stopper['left'](self)
r = stopper['right'](self)
if stopper['stopType'] == 'optimal':
optimal.append(l <= r)
if stopper['stopType'] == 'critical':
critical.append(l <= r)
return all(optimal) | any(critical)
def projection(self, p):
"""
@@ -304,7 +377,7 @@ class Minimize(object):
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 + self.LSreduction*descent:
if ft < self.f + t*self.LSreduction*descent:
break
iterLS += 1
t = self.LSshorten*t
@@ -334,16 +407,29 @@ class Minimize(object):
def doEndIteration(self, xt):
"""
**doEndIteration** is called at the end of each minimize iteration.
**doEndIteration** is called at the end of each minimize iteration.
By default, function values and x locations are shuffled to store 1 past iteration in memory.
By default, function values and x locations are shuffled to store 1 past iteration in memory.
self.xc must be updated in this code.
self.xc must be updated in this code.
:param numpy.ndarray xt: tested new iterate that ensures a descent direction.
:rtype: None
:return: None
If you have things that also need to run at the end of every iteration, you can create a method::
def _doEndIteration(self, xt):
pass
If present, _doEndIteration will be called at the start of the default doEndIteration call.
You may also completely overwrite this function.
:param numpy.ndarray xt: tested new iterate that ensures a descent direction.
:rtype: None
:return: None
"""
if hasattr(self,'_doEndIteration'): self._doEndIteration(xt)
# store old values
self.fOld = self.f
self.xOld, self.xc = self.xc, xt
@@ -351,13 +437,13 @@ class Minimize(object):
class GaussNewton(Minimize):
name = 'GaussNewton'
name = 'Gauss Newton'
def findSearchDirection(self):
return Solver(self.H).solve(-self.g)
class InexactGaussNewton(Minimize):
name = 'InexactGaussNewton'
name = 'Inexact Gauss Newton'
maxIterCG = 10
tolCG = 1e-5
@@ -369,7 +455,7 @@ class InexactGaussNewton(Minimize):
class SteepestDescent(Minimize):
name = 'SteepestDescent'
name = 'Steepest Descent'
def findSearchDirection(self):
return -self.g
@@ -379,9 +465,9 @@ if __name__ == '__main__':
x0 = np.array([2.6, 3.7])
checkDerivative(Rosenbrock, x0, plotIt=False)
def listener1(minimize,p):
print 'hi: ', p
if doPub: pub.subscribe(listener1, 'Minimize.searchDirection')
# def listener1(minimize,p):
# print 'hi: ', p
# if doPub: pub.subscribe(listener1, 'Minimize.searchDirection')
xOpt = GaussNewton(maxIter=20,tolF=1e-10,tolX=1e-10,tolG=1e-10).minimize(Rosenbrock,x0)
print "xOpt=[%f, %f]" % (xOpt[0], xOpt[1])