mirror of
https://github.com/wassname/simpeg.git
synced 2026-07-07 18:21:55 +08:00
Combined stopping criteria and printers into a class, for easy reuse.
This commit is contained in:
Rowan Cockett
+64
-121
@@ -1,6 +1,6 @@
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import numpy as np
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import matplotlib.pyplot as plt
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from SimPEG.utils import mkvc, sdiag, setKwargs, printTitles, printLine, printStoppers
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from SimPEG.utils import mkvc, sdiag, setKwargs, printTitles, printLine, printStoppers, checkStoppers
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norm = np.linalg.norm
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import scipy.sparse as sp
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from SimPEG import Solver
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@@ -12,7 +12,62 @@ except Exception, e:
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print 'Warning: you may not have the required pubsub installed, use pypubsub. You will not be able to listen to events.'
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doPub = False
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class StoppingCriteria(object):
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"""docstring for StoppingCriteria"""
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iteration = { "str": "%d : maxIter = %3d <= iter = %3d",
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"left": lambda M: M.maxIter, "right": lambda M: M._iter,
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"stopType": "critical"}
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iterationLS = { "str": "%d : maxIterLS = %3d <= iterLS = %3d",
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"left": lambda M: M.maxIterLS, "right": lambda M: M._iterLS,
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"stopType": "critical"}
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armijoGoldstein = { "str": "%d : ft = %1.4e <= alp*descent = %1.4e",
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"left": lambda M: M._LS_ft, "right": lambda M: M.f + M.LSreduction * M._LS_descent,
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"stopType": "optimal"}
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tolerance_f = { "str": "%d : |fc-fOld| = %1.4e <= tolF*(1+|f0|) = %1.4e",
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"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)),
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"stopType": "optimal"}
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moving_x = { "str": "%d : |xc-x_last| = %1.4e <= tolX*(1+|x0|) = %1.4e",
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"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)),
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"stopType": "optimal"}
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tolerance_g = { "str": "%d : |g| = %1.4e <= tolG = %1.4e",
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"left": lambda M: norm(M.projection(M.g)), "right": lambda M: M.tolG,
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"stopType": "optimal"}
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norm_g = { "str": "%d : |g| = %1.4e <= 1e3*eps = %1.4e",
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"left": lambda M: norm(M.g), "right": lambda M: 1e3*M.eps,
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"stopType": "critical"}
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bindingSet = { "str": "%d : probSize = %3d <= bindingSet = %3d",
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"left": lambda M: M.xc.size, "right": lambda M: np.sum(M.bindingSet(M.xc)),
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"stopType": "critical"}
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bindingSet_LS = { "str": "%d : probSize = %3d <= bindingSet = %3d",
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"left": lambda M: M._LS_xt.size, "right": lambda M: np.sum(M.bindingSet(M._LS_xt)),
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"stopType": "critical"}
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class IterationPrinters(object):
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"""docstring for IterationPrinters"""
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iteration = {"title": "#", "value": lambda M: M._iter, "width": 5, "format": "%3d"}
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f = {"title": "f", "value": lambda M: M.f, "width": 10, "format": "%1.2e"}
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norm_g = {"title": "|g|", "value": lambda M: norm(M.g), "width": 10, "format": "%1.2e"}
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totalLS = {"title": "LS", "value": lambda M: M._iterLS, "width": 5, "format": "%d"}
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iterationLS = {"title": "#", "value": lambda M: (M._iter, M._iterLS), "width": 5, "format": "%3d.%d"}
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LS_ft = {"title": "ft", "value": lambda M: M._LS_ft, "width": 10, "format": "%1.2e"}
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LS_t = {"title": "t", "value": lambda M: M._LS_t, "width": 10, "format": "%0.5f"}
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LS_armijoGoldstein = {"title": "f + alp*g.T*p", "value": lambda M: M.f + M.LSreduction*M._LS_descent, "width": 16, "format": "%1.2e"}
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itType = {"title": "itType", "value": lambda M: M._itType, "width": 8, "format": "%s"}
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aSet = {"title": "aSet", "value": lambda M: np.sum(M.activeSet(M.xc)), "width": 8, "format": "%d"}
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bSet = {"title": "bSet", "value": lambda M: np.sum(M.bindingSet(M.xc)), "width": 8, "format": "%d"}
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comment = {"title": "Comment", "value": lambda M: M.projComment, "width": 7, "format": "%s"}
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class Minimize(object):
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"""
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@@ -39,88 +94,11 @@ class Minimize(object):
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def __init__(self, **kwargs):
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self._id = int(np.random.rand()*1e6) # create a unique identifier to this program to be used in pubsub
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self.stoppers = [{
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"str": "%d : |fc-fOld| = %1.4e <= tolF*(1+|f0|) = %1.4e",
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"left": lambda M: 1 if M._iter==0 else abs(M.f-M.f_last),
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"right": lambda M: 0 if M._iter==0 else M.tolF*(1+abs(M.f0)),
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"stopType": "optimal"
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},{
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"str": "%d : |xc-x_last| = %1.4e <= tolX*(1+|x0|) = %1.4e",
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"left": lambda M: 1 if M._iter==0 else norm(M.xc-M.x_last),
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"right": lambda M: 0 if M._iter==0 else M.tolX*(1+norm(M.x0)),
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"stopType": "optimal"
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},{
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"str": "%d : |g| = %1.4e <= tolG = %1.4e",
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"left": lambda M: norm(M.projection(M.g)),
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"right": lambda M: M.tolG,
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"stopType": "optimal"
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},{
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"str": "%d : |g| = %1.4e <= 1e3*eps = %1.4e",
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"left": lambda M: norm(M.g),
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"right": lambda M: 1e3*M.eps,
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"stopType": "critical"
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},{
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"str": "%d : maxIter = %3d <= iter = %3d",
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"left": lambda M: M.maxIter,
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"right": lambda M: M._iter,
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"stopType": "critical"
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}]
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self.stoppers = [StoppingCriteria.tolerance_f, StoppingCriteria.moving_x, StoppingCriteria.tolerance_g, StoppingCriteria.norm_g, StoppingCriteria.iteration]
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self.stoppersLS = [StoppingCriteria.armijoGoldstein, StoppingCriteria.iterationLS]
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self.stoppersLS = [{
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"str": "%d : ft = %1.4e <= alp*descent = %1.4e",
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"left": lambda M: M._LS_ft,
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"right": lambda M: M.f + self.LSreduction * M._LS_descent,
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"stopType": "optimal"
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},{
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"str": "%d : maxIterLS = %3d <= iterLS = %3d",
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"left": lambda M: M.maxIterLS,
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"right": lambda M: M._iterLS,
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"stopType": "critical"
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}]
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self.printers = [{
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"title": "#",
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"value": lambda M: M._iter,
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"width": 5,
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"format": "%3d"
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},{
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"title": "f",
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"value": lambda M: self.f,
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"width": 10,
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"format": "%1.2e"
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},{
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"title": "|g|",
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"value": lambda M: norm(M.g),
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"width": 10,
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"format": "%1.2e"
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},{
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"title": "LS",
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"value": lambda M: M._iterLS,
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"width": 5,
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"format": "%d"
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}]
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self.printersLS = [{
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"title": "#",
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"value": lambda M: (M._iter, M._iterLS),
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"width": 5,
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"format": "%3d.%d"
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},{
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"title": "t",
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"value": lambda M: M._LS_t,
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"width": 10,
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"format": "%0.5f"
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},{
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"title": "ft",
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"value": lambda M: M._LS_ft,
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"width": 10,
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"format": "%1.2e"
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},{
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"title": "f + alp*g.T*p",
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"value": lambda M: M.f + M.LSreduction*M._LS_descent,
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"width": 16,
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"format": "%1.2e"
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}]
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self.printers = [IterationPrinters.iteration, IterationPrinters.f, IterationPrinters.norm_g, IterationPrinters.totalLS]
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self.printersLS = [IterationPrinters.iterationLS, IterationPrinters.LS_ft, IterationPrinters.LS_t, IterationPrinters.LS_armijoGoldstein]
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setKwargs(self, **kwargs)
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@@ -300,23 +278,10 @@ class Minimize(object):
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def stoppingCriteria(self, inLS=False):
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if self._iter == 0:
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# Save this for stopping criteria
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self.f0 = self.f
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self.g0 = self.g
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return checkStoppers(self, self.stoppers if not inLS else self.stoppersLS)
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# check stopping rules
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optimal = []
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critical = []
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stoppers = self.stoppers if not inLS else self.stoppersLS
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for stopper in stoppers:
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l = stopper['left'](self)
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r = stopper['right'](self)
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if stopper['stopType'] == 'optimal':
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optimal.append(l <= r)
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if stopper['stopType'] == 'critical':
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critical.append(l <= r)
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return all(optimal) | any(critical)
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def projection(self, p):
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"""
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@@ -524,32 +489,10 @@ class ProjectedGradient(Minimize, Remember):
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def __init__(self,**kwargs):
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super(ProjectedGradient, self).__init__(**kwargs)
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self.stoppers.append({
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"str": "%d : probSize = %3d <= bindingSet = %3d",
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"left": lambda M: M.xc.size,
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"right": lambda M: np.sum(M.bindingSet(M.xc)),
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"stopType": "critical"
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})
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self.stoppers.append(StoppingCriteria.bindingSet)
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self.stoppersLS.append(StoppingCriteria.bindingSet_LS)
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self.stoppersLS.append({
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"str": "%d : probSize = %3d <= bindingSet = %3d",
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"left": lambda M: M._LS_xt.size,
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"right": lambda M: np.sum(M.bindingSet(M._LS_xt)),
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"stopType": "critical"
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})
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self.printers.append({"title": "itType",
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"value": lambda M: M._itType,
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"width": 8, "format": "%s"})
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self.printers.append({"title": "aSet",
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"value": lambda M: np.sum(M.activeSet(M.xc)),
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"width": 8, "format": "%d"})
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self.printers.append({"title": "bSet",
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"value": lambda M: np.sum(M.bindingSet(M.xc)),
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"width": 8, "format": "%d"})
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self.printers.append({"title": "Comment",
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"value": lambda M: M.projComment,
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"width": 7, "format": "%s"})
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self.printers.extend([ IterationPrinters.itType, IterationPrinters.aSet, IterationPrinters.bSet, IterationPrinters.comment ])
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def _startup(self, x0):
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@@ -36,6 +36,19 @@ def printLine(obj, printers, pad=''):
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values += ('{:^%i}'%printer['width']).format(printer['format'] % printer['value'](obj))
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print pad + values
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def checkStoppers(obj, stoppers):
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# check stopping rules
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optimal = []
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critical = []
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for stopper in stoppers:
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l = stopper['left'](obj)
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r = stopper['right'](obj)
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if stopper['stopType'] == 'optimal':
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optimal.append(l <= r)
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if stopper['stopType'] == 'critical':
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critical.append(l <= r)
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return all(optimal) | any(critical)
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def printStoppers(obj, stoppers, pad='', stop='STOP!', done='DONE!'):
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print pad + "%s%s%s" % ('-'*25,stop,'-'*25)
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for stopper in stoppers:
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