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InvProblem.py
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InvProblem.py
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from __future__ import print_function
from . import Utils
from . import Props
from . import DataMisfit
from . import Regularization
from . import ObjectiveFunction
from . import Optimization
import properties
import numpy as np
import scipy.sparse as sp
import gc
import dask
import dask.array as da
class BaseInvProblem(Props.BaseSimPEG):
"""BaseInvProblem(dmisfit, reg, opt)"""
#: Trade-off parameter
beta = 1.0
#: Print debugging information
debug = False
#: Set this to a SimPEG.Utils.Counter() if you want to count things
counter = None
#: DataMisfit
dmisfit = None
#: Regularization
reg = None
#: Optimization program
opt = None
#: Use BFGS
bfgs = True
#: List of strings, e.g. ['_MeSigma', '_MeSigmaI']
deleteTheseOnModelUpdate = []
model = Props.Model("Inversion model.")
@properties.observer('model')
def _on_model_update(self, value):
"""
Sets the current model, and removes dependent properties
"""
for prop in self.deleteTheseOnModelUpdate:
if hasattr(self, prop):
delattr(self, prop)
def __init__(self, dmisfit, reg, opt, **kwargs):
super(BaseInvProblem, self).__init__(**kwargs)
assert(
isinstance(dmisfit, DataMisfit.BaseDataMisfit) or
isinstance(dmisfit, ObjectiveFunction.BaseObjectiveFunction)
), 'dmisfit must be a DataMisfit or ObjectiveFunction class.'
assert(
isinstance(reg, Regularization.BaseRegularization) or
isinstance(reg, ObjectiveFunction.BaseObjectiveFunction)
), 'reg must be a Regularization or Objective Function class.'
self.dmisfit = dmisfit
self.reg = reg
self.opt = opt
# TODO: Remove: (and make iteration printers better!)
self.opt.parent = self
self.reg.parent = self
self.dmisfit.parent = self
@Utils.callHooks('startup')
def startup(self, m0):
"""startup(m0)
Called when inversion is first starting.
"""
if self.debug:
print('Calling InvProblem.startup')
if hasattr(self.reg, 'mref') and getattr(self.reg, 'mref', None) is None:
print('SimPEG.InvProblem will set Regularization.mref to m0.')
self.reg.mref = m0
if (
isinstance(self.reg, ObjectiveFunction.ComboObjectiveFunction) and
not isinstance(self.reg, Regularization.BaseComboRegularization)
):
for fct in self.reg.objfcts:
if hasattr(fct, 'mref') and getattr(fct, 'mref', None) is None:
print('SimPEG.InvProblem will set Regularization.mref to m0.')
fct.mref = m0
self.phi_d = np.nan
self.phi_m = np.nan
self.model = m0
# if self.bfgs:
# if isinstance(self.dmisfit, DataMisfit.BaseDataMisfit):
# print("""SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.
# ***Done using same Solver and solverOpts as the problem***"""
# )
# self.opt.bfgsH0 = self.dmisfit.prob.Solver(
# self.reg.deriv2(self.model), **self.dmisfit.prob.solverOpts
# )
# elif isinstance(self.dmisfit,
# ObjectiveFunction.BaseObjectiveFunction
# ):
# for objfct in self.dmisfit.objfcts:
# if isinstance(objfct, DataMisfit.BaseDataMisfit):
# print("""SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.
# ***Done using same Solver and solverOpts as the {} problem***""".format(
# objfct.prob.__class__.__name__
# )
# )
# self.opt.bfgsH0 = objfct.prob.Solver(
# self.reg.deriv2(self.model), **objfct.prob.solverOpts
# )
# break
@property
def warmstart(self):
return getattr(self, '_warmstart', [])
@warmstart.setter
def warmstart(self, value):
assert type(value) is list, 'warmstart must be a list.'
for v in value:
assert type(v) is tuple, 'warmstart must be a list of tuples (m, u).'
assert len(v) == 2, 'warmstart must be a list of tuples (m, u). YOURS IS NOT LENGTH 2!'
assert isinstance(v[0], np.ndarray), 'first warmstart value must be a model.'
self._warmstart = value
def getFields(self, m, store=False, deleteWarmstart=True):
f = None
for mtest, u_ofmtest in self.warmstart:
if m is mtest:
f = u_ofmtest
if self.debug:
print('InvProb is Warm Starting!')
break
if f is None:
if isinstance(self.dmisfit, DataMisfit.BaseDataMisfit):
f = self.dmisfit.prob.fields(m)
elif isinstance(self.dmisfit, ObjectiveFunction.BaseObjectiveFunction):
f = []
for objfct in self.dmisfit.objfcts:
if hasattr(objfct, 'prob'):
f += [objfct.prob.fields(m)]
else:
f += []
if deleteWarmstart:
self.warmstart = []
if store:
self.warmstart += [(m, f)]
return f
def get_dpred(self, m, f):
if isinstance(self.dmisfit, DataMisfit.BaseDataMisfit):
return self.dmisfit.survey.dpred(m, f=f)
elif isinstance(self.dmisfit, ObjectiveFunction.BaseObjectiveFunction):
dpred = []
index = []
for i, objfct in enumerate(self.dmisfit.objfcts):
if hasattr(objfct, 'survey'):
dpred += [objfct.survey.dpred(m, f=f[i])]
index += [np.where(objfct.survey.ind)]
else:
dpred += []
index += []
dpred = da.hstack(dpred).compute()
index = np.hstack(index)
return dpred[index]
@Utils.timeIt
def evalFunction(self, m, return_g=True, return_H=True):
"""evalFunction(m, return_g=True, return_H=True)
"""
self.model = m
gc.collect()
# Store fields if doing a line-search
f = self.getFields(m, store=(return_g is False and return_H is False))
# if isinstance(self.dmisfit, DataMisfit.BaseDataMisfit):
phi_d = self.dmisfit(m, f=f)
self.dpred = self.get_dpred(m, f=f)
phi_m = self.reg(m)
self.phi_d, self.phi_d_last = phi_d, self.phi_d
self.phi_m, self.phi_m_last = phi_m, self.phi_m
phi = phi_d + self.beta * phi_m
out = (phi,)
if return_g:
phi_dDeriv = np.squeeze(self.dmisfit.deriv(m, f=f))
phi_mDeriv = np.squeeze(self.reg.deriv(m))
g = phi_dDeriv + self.beta * phi_mDeriv
out += (g,)
if return_H:
def H_fun(v):
phi_m2Deriv = np.squeeze(self.reg.deriv2(m, v=v))
if isinstance(self.dmisfit.deriv2(m, v, f=f), dask.array.Array):
phi_d2Deriv = self.dmisfit.deriv2(m, v, f=f)
else:
phi_d2Deriv = np.squeeze(self.dmisfit.deriv2(m, v, f=f))
return phi_d2Deriv + self.beta * phi_m2Deriv
H = sp.linalg.LinearOperator( (m.size, m.size), H_fun, dtype=m.dtype )
out += (H,)
return out if len(out) > 1 else out[0]