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synced 2026-09-11 12:44:29 +08:00
Simple profiling through decorator functions. (counting and timing of functions decorated with @count and @timeIt that have a SimPEG.utils.Counter)
e.g. output: ‘’’ Counters: InexactGaussNewton.doEndIteration : 6 InexactGaussNewton.printIter : 7 InexactGaussNewton.scaleSearchDirection : 6 Times: mean sum InexactGaussNewton.findSearchDirection : 1.55e-02, 9.29e-02, 6x InexactGaussNewton.minimize : 1.10e-01, 1.10e-01, 1x InexactGaussNewton.modifySearchDirection: 2.89e-04, 1.73e-03, 6x InexactGaussNewton.projection : 3.69e-06, 1.11e-04, 30x InexactGaussNewton.stoppingCriteria : 1.16e-04, 1.51e-03, 13x Inversion.dataObj : 6.60e-05, 8.58e-04, 13x Inversion.dataObj2Deriv : 1.03e-04, 6.20e-03, 60x Inversion.dataObjDeriv : 5.06e-05, 3.54e-04, 7x Inversion.evalFunction : 7.75e-04, 1.01e-02, 13x Inversion.run : 1.10e-01, 1.10e-01, 1x Regularization.modelObj : 3.56e-04, 4.63e-03, 13x Regularization.modelObj2Deriv : 1.29e-03, 7.76e-02, 60x Regularization.modelObjDeriv : 5.01e-04, 3.51e-03, 7x ‘’’
This commit is contained in:
Rowan Cockett
@@ -1,7 +1,7 @@
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import numpy as np
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import scipy.sparse as sp
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import SimPEG
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from SimPEG.utils import sdiag, mkvc, setKwargs, checkStoppers, printStoppers
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from SimPEG.utils import sdiag, mkvc, setKwargs, checkStoppers, printStoppers, count, timeIt
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from Optimize import Remember
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from BetaSchedule import Cooling
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@@ -13,6 +13,8 @@ class BaseInversion(object):
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debug = False
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beta0 = 1e4
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counter = None
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def __init__(self, prob, reg, opt, **kwargs):
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setKwargs(self, **kwargs)
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self.prob = prob
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@@ -56,6 +58,7 @@ class BaseInversion(object):
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def phi_d_target(self, value):
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self._phi_d_target = value
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@timeIt
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def run(self, m0):
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self.startup(m0)
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while True:
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@@ -131,7 +134,7 @@ class BaseInversion(object):
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"""
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printStoppers(self, self.stoppers)
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@timeIt
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def evalFunction(self, m, return_g=True, return_H=True):
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u = self.prob.field(m)
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@@ -162,7 +165,7 @@ class BaseInversion(object):
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out += (operator,)
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return out if len(out) > 1 else out[0]
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@timeIt
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def dataObj(self, m, u=None):
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"""
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:param numpy.array m: geophysical model
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@@ -184,6 +187,7 @@ class BaseInversion(object):
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R = mkvc(R)
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return 0.5*np.vdot(R, R)
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@timeIt
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def dataObjDeriv(self, m, u=None):
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"""
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:param numpy.array m: geophysical model
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@@ -224,6 +228,7 @@ class BaseInversion(object):
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return dmisfit
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@timeIt
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def dataObj2Deriv(self, m, v, u=None):
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"""
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:param numpy.array m: geophysical model
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@@ -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, checkStoppers
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from SimPEG.utils import mkvc, sdiag, setKwargs, printTitles, printLine, printStoppers, checkStoppers, count, timeIt
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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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@@ -106,6 +106,8 @@ class Minimize(object):
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debug = False
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debugLS = False
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counter = None
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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 = [StoppingCriteria.tolerance_f, StoppingCriteria.moving_x, StoppingCriteria.tolerance_g, StoppingCriteria.norm_g, StoppingCriteria.iteration]
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@@ -116,6 +118,7 @@ class Minimize(object):
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setKwargs(self, **kwargs)
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@timeIt
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def minimize(self, evalFunction, x0):
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"""
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Minimizes the function (evalFunction) starting at the location x0.
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@@ -263,6 +266,7 @@ class Minimize(object):
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name = self.name if not inLS else self.nameLS
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printTitles(self, self.printers if not inLS else self.printersLS, name, pad)
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@count
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def printIter(self, inLS=False):
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"""
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**printIter** is called directly after function evaluations.
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@@ -289,14 +293,14 @@ class Minimize(object):
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stoppers = self.stoppers if not inLS else self.stoppersLS
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printStoppers(self, stoppers, pad='', stop=stop, done=done)
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@timeIt
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def stoppingCriteria(self, inLS=False):
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if self._iter == 0:
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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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@timeIt
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def projection(self, p):
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"""
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projects the search direction.
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@@ -309,6 +313,7 @@ class Minimize(object):
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"""
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return p
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@timeIt
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def findSearchDirection(self):
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"""
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**findSearchDirection** should return an approximation of:
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@@ -338,6 +343,7 @@ class Minimize(object):
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"""
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return -self.g
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@count
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def scaleSearchDirection(self, p):
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"""
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**scaleSearchDirection** should scale the search direction if appropriate.
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@@ -355,6 +361,7 @@ class Minimize(object):
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nameLS = "Armijo linesearch"
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@timeIt
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def modifySearchDirection(self, p):
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"""
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**modifySearchDirection** changes the search direction based on some sort of linesearch or trust-region criteria.
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@@ -391,6 +398,7 @@ class Minimize(object):
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return self._LS_xt, self._iterLS < self.maxIterLS
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@count
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def modifySearchDirectionBreak(self, p):
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"""
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Code is called if modifySearchDirection fails
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@@ -411,6 +419,7 @@ class Minimize(object):
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print 'The linesearch got broken. Boo.'
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return p, False
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@count
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def doEndIteration(self, xt):
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"""
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**doEndIteration** is called at the end of each minimize iteration.
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@@ -529,18 +538,22 @@ class ProjectedGradient(Minimize, Remember):
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self.aSet_prev = self.activeSet(x0)
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@count
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def projection(self, x):
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"""Make sure we are feasible."""
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return np.median(np.c_[self.lower,x,self.upper],axis=1)
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@count
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def activeSet(self, x):
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"""If we are on a bound"""
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return np.logical_or(x == self.lower, x == self.upper)
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@count
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def inactiveSet(self, x):
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"""The free variables."""
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return np.logical_not(self.activeSet(x))
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@count
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def bindingSet(self, x):
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"""
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If we are on a bound and the negative gradient points away from the feasible set.
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@@ -552,6 +565,7 @@ class ProjectedGradient(Minimize, Remember):
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bind_low = np.logical_and(x == self.upper, self.g <= 0)
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return np.logical_or(bind_up, bind_low)
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@timeIt
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def findSearchDirection(self):
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self.aSet_prev = self.activeSet(self.xc)
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allBoundsAreActive = sum(self.aSet_prev) == self.xc.size
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@@ -592,6 +606,7 @@ class ProjectedGradient(Minimize, Remember):
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# aSet_after = self.activeSet(self.xc+p)
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return p
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@timeIt
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def _doEndIteration_ProjectedGradient(self, xt):
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aSet = self.activeSet(xt)
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bSet = self.bindingSet(xt)
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@@ -622,6 +637,8 @@ class ProjectedGradient(Minimize, Remember):
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class GaussNewton(Minimize, Remember):
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name = 'Gauss Newton'
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@timeIt
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def findSearchDirection(self):
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return Solver(self.H).solve(-self.g)
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@@ -632,6 +649,7 @@ class InexactGaussNewton(Minimize, Remember):
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maxIterCG = 10
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tolCG = 1e-5
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@timeIt
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def findSearchDirection(self):
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# TODO: use BFGS as a preconditioner or gauss sidel of the WtW or solve WtW directly
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p, info = sp.linalg.cg(self.H, -self.g, tol=self.tolCG, maxiter=self.maxIterCG)
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@@ -640,6 +658,8 @@ class InexactGaussNewton(Minimize, Remember):
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class SteepestDescent(Minimize, Remember):
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name = 'Steepest Descent'
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@timeIt
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def findSearchDirection(self):
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return -self.g
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