diff --git a/examples/9_active_remeshing/active_remeshing.py b/examples/9_active_remeshing/active_remeshing.py
index 3071410..6c033c4 100755
--- a/examples/9_active_remeshing/active_remeshing.py
+++ b/examples/9_active_remeshing/active_remeshing.py
@@ -1,119 +1,118 @@
import numpy as np
from pickle import load
from matplotlib import pyplot as plt
from active_remeshing_engine import ActiveRemeshingEngine
from rrompy.reduction_methods import (
RationalInterpolantGreedyPivotedNoMatch as RIGPNM,
RationalInterpolantGreedyPivotedGreedyPoleMatch as RIGPG)
from rrompy.parameter.parameter_sampling import (QuadratureSampler as QS,
SparseGridSampler as SGS)
zs, ts = [0., 100.], [0., .5]
-z0, t0, n = np.mean(zs), np.mean(ts), 15
+z0, t0, n = np.mean(zs), np.mean(ts), 150
solver = ActiveRemeshingEngine(z0, t0, n)
mus = [[z0, ts[0]], [z0, ts[1]]]
for mu in mus:
u = solver.solve(mu, return_state = True)[0]
Y = solver.applyC(u, mu)[0][0]
_ = solver.plot(u, what = "REAL", name = "u(z={}, t={})".format(*mu),
is_state = True, figsize = (12, 4))
print("Y(z={}, t={}) = {} (solution norm squared)".format(*mu, Y))
fighandles = []
with open("./active_remeshing_hf_samples.pkl", "rb") as f:
zspace, tspace, Yex = load(f)
# zspace = np.linspace(zs[0], zs[-1], 198)
# tspace = np.linspace(ts[0], ts[-1], 50)
# Yex = np.log10([[solver.solve([z, t]) for t in tspace] for z in zspace])
-# (from a ~2h45m simulation on one node of the EPFL Helvetios cluster)
+# (from a ~2.5h simulation on one node of the EPFL Helvetios cluster)
-for match in [1]:
-#for match in [0, 1]:
+for match in [0, 1]:
params = {"POD": True, "S": 5, "greedyTol": 1e-4, "nTestPoints": 500,
"polybasis": "LEGENDRE", "trainSetGenerator": QS(zs, "UNIFORM"),
"samplerPivot":QS(zs, "CHEBYSHEV"), "samplerMarginal":SGS(ts),
"errorEstimatorKind": "LOOK_AHEAD_OUTPUT"}
if match:
print("\nTesting output-based matching with weight 1.")
params["SMarginal"] = 3
params["maxIterMarginal"] = 25
params["greedyTolMarginal"] = 1e-2
params["matchingWeight"] = 1.
params["sharedRatio"] = .75
params["polybasisMarginal"] = "PIECEWISE_LINEAR_UNIFORM"
params["errorEstimatorKindMarginal"] = "LOOK_AHEAD_RECOVER"
algo = RIGPG
else:
print("\nTesting matching-free approach.")
params["SMarginal"] = 5
algo = RIGPNM
approx = algo([0], solver, mu0 = [z0, t0], approxParameters = params,
verbosity = 15)
approx.setupApprox("ALL" * match)
verb, verbTM = approx.verbosity, approx.trainedModel.verbosity
approx.verbosity, approx.trainedModel.verbosity = 0, 0
for j, t in enumerate(tspace):
out = approx.getApprox(np.pad(zspace.reshape(-1, 1), [(0, 0), (0, 1)],
"constant", constant_values = t))
pls = approx.getPoles([None, t])
pls[np.abs(np.imag(pls)) > 1e-5] = np.nan
if j == 0:
Ys = np.empty((len(zspace), len(tspace)))
poles = np.empty((len(tspace), len(pls)))
Ys[:, j] = np.log10(out.data)
if len(pls) > poles.shape[1]:
poles = np.pad(poles, [(0, 0), (0, len(pls) - poles.shape[1])],
"constant", constant_values = np.nan)
poles[j, : len(pls)] = np.real(pls)
approx.verbosity, approx.trainedModel.verbosity = verb, verbTM
Ymin, Ymax = min(np.min(Ys), np.min(Yex)), max(np.max(Ys), np.max(Yex))
fighandles += [plt.figure(figsize = (15, 5))]
ax1 = fighandles[-1].add_subplot(1, 2, 1)
ax2 = fighandles[-1].add_subplot(1, 2, 2)
if match:
ax1.plot(poles, tspace)
else:
- ax1.plot(poles, tspace, 'k.')
+ ax1.plot(poles, tspace, "k.")
ax1.set_ylim(ts)
- ax1.set_xlabel('z')
- ax1.set_ylabel('t')
+ ax1.set_xlabel("z")
+ ax1.set_ylabel("t")
ax1.grid()
if match:
ax2.plot(poles, tspace)
else:
- ax2.plot(poles, tspace, 'k.')
+ ax2.plot(poles, tspace, "k.")
for mm in approx.musMarginal:
- ax2.plot(zs, [mm[0, 0]] * 2, 'k--', linewidth = 1)
+ ax2.plot(zs, [mm[0, 0]] * 2, "k--", linewidth = 1)
ax2.set_xlim(zs)
ax2.set_ylim(ts)
- ax2.set_xlabel('z')
- ax2.set_ylabel('t')
+ ax2.set_xlabel("z")
+ ax2.set_ylabel("t")
ax2.grid()
plt.show()
print("Approximate poles")
fighandles += [plt.figure(figsize = (15, 5))]
ax1 = fighandles[-1].add_subplot(1, 2, 1)
ax2 = fighandles[-1].add_subplot(1, 2, 2)
p = ax1.contourf(np.repeat(zspace.reshape(-1, 1), len(tspace), axis = 1),
np.repeat(tspace.reshape(1, -1), len(zspace), axis = 0),
Ys, vmin = Ymin, vmax = Ymax, cmap = "gray_r",
levels = np.linspace(Ymin, Ymax, 50))
plt.colorbar(p, ax = ax1)
- ax1.set_xlabel('z')
- ax1.set_ylabel('t')
+ ax1.set_xlabel("z")
+ ax1.set_ylabel("t")
ax1.grid()
p = ax2.contourf(np.repeat(zspace.reshape(-1, 1), len(tspace), axis = 1),
np.repeat(tspace.reshape(1, -1), len(zspace), axis = 0),
Yex, vmin = Ymin, vmax = Ymax, cmap = "gray_r",
levels = np.linspace(Ymin, Ymax, 50))
- ax2.set_xlabel('z')
- ax2.set_ylabel('t')
+ ax2.set_xlabel("z")
+ ax2.set_ylabel("t")
ax2.grid()
plt.colorbar(p, ax = ax2)
plt.show()
print("Approximate and exact output\n")
diff --git a/rrompy/reduction_methods/base/generic_approximant.py b/rrompy/reduction_methods/base/generic_approximant.py
index 65430f4..7eafcc1 100644
--- a/rrompy/reduction_methods/base/generic_approximant.py
+++ b/rrompy/reduction_methods/base/generic_approximant.py
@@ -1,909 +1,889 @@
# Copyright (C) 2018-2020 by the RROMPy authors
#
# This file is part of RROMPy.
#
# RROMPy is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# RROMPy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with RROMPy. If not, see .
#
from abc import abstractmethod
import numpy as np
from collections.abc import Iterable
from itertools import product as iterprod
from copy import deepcopy as copy
from os import remove as osrm
from rrompy.sampling import (SamplingEngine, SamplingEngineNormalize,
SamplingEnginePOD)
from rrompy.utilities.base.types import (Np1D, DictAny, HFEng, List, Tuple,
ListAny, strLst, paramVal, paramList,
sampList)
from rrompy.utilities.base.data_structures import purgeDict, getNewFilename
from rrompy.utilities.base import verbosityManager as vbMng
from rrompy.utilities.exception_manager import (RROMPyException, RROMPyAssert,
RROMPy_READY, RROMPy_FRAGILE)
from rrompy.utilities.base.pickle_utilities import pickleDump, pickleLoad
from rrompy.parameter import (emptyParameterList, checkParameter,
checkParameterList)
from rrompy.sampling import sampleList, emptySampleList
from rrompy.utilities.parallel import (bcast, masterCore, listGather,
listScatter)
__all__ = ['GenericApproximant']
def addNormFieldToClass(self, fieldName):
def objFunc(self, mu:paramList, *args, **kwargs) -> Np1D:
uV = getattr(self.__class__, "get" + fieldName)(self, mu)
kwargs["is_state"] = False
val = self.HFEngine.norm(uV, *args, **kwargs)
return val
setattr(self.__class__, "norm" + fieldName, objFunc)
def addNormDualFieldToClass(self, fieldName):
def objFunc(self, mu:paramList, *args, **kwargs) -> Np1D:
uV = getattr(self.__class__, "get" + fieldName)(self, mu)
kwargs["is_state"] = True
if "dual" not in kwargs.keys(): kwargs["dual"] = True
val = self.HFEngine.norm(uV, *args, **kwargs)
return val
setattr(self.__class__, "norm" + fieldName, objFunc)
def addPlotFieldToClass(self, fieldName):
def objFunc(self, mu:paramList, *args, **kwargs):
uV = getattr(self.__class__, "get" + fieldName)(self, mu)
uV = listScatter(uV)[0].T
filesOut = []
if len(uV) > 0:
if "name" in kwargs.keys(): nameBase = copy(kwargs["name"])
for j, u in enumerate(uV):
if "name" in kwargs.keys(): kwargs["name"] = nameBase + str(j)
filesOut += [self.HFEngine.plot(u, *args, **kwargs)]
if "name" in kwargs.keys(): kwargs["name"] = nameBase
filesOut = listGather(filesOut)
if filesOut[0] is None: return None
return filesOut
setattr(self.__class__, "plot" + fieldName, objFunc)
def addPlotDualFieldToClass(self, fieldName):
def objFunc(self, mu:paramList, *args, **kwargs):
uV = getattr(self.__class__, "get" + fieldName)(self, mu)
uV = listScatter(uV)[0].T
filesOut = []
if len(uV) > 0:
if "name" in kwargs.keys(): nameBase = copy(kwargs["name"])
for j, u in enumerate(uV):
if "name" in kwargs.keys(): kwargs["name"] = nameBase + str(j)
filesOut += [self.HFEngine.plot(u, *args, **kwargs)]
if "name" in kwargs.keys(): kwargs["name"] = nameBase
filesOut = listGather(filesOut)
if filesOut[0] is None: return None
return filesOut
setattr(self.__class__, "plot" + fieldName, objFunc)
def addOutParaviewFieldToClass(self, fieldName):
def objFunc(self, mu:paramVal, *args, **kwargs):
if not hasattr(self.HFEngine, "outParaview"):
raise RROMPyException(("High fidelity engine cannot output to "
"Paraview."))
uV = getattr(self.__class__, "get" + fieldName)(self, mu)
uV = listScatter(uV)[0].T
filesOut = []
if len(uV) > 0:
if "name" in kwargs.keys(): nameBase = copy(kwargs["name"])
for j, u in enumerate(uV):
if "name" in kwargs.keys(): kwargs["name"] = nameBase + str(j)
filesOut += [self.HFEngine.outParaview(u, *args, **kwargs)]
if "name" in kwargs.keys(): kwargs["name"] = nameBase
filesOut = listGather(filesOut)
if filesOut[0] is None: return None
return filesOut
setattr(self.__class__, "outParaview" + fieldName, objFunc)
def addOutParaviewTimeDomainFieldToClass(self, fieldName):
def objFunc(self, mu:paramVal, *args, **kwargs):
if not hasattr(self.HFEngine, "outParaviewTimeDomain"):
raise RROMPyException(("High fidelity engine cannot output to "
"Paraview."))
uV = getattr(self.__class__, "get" + fieldName)(self, mu)
uV = listScatter(uV)[0].T
filesOut = []
if len(uV) > 0:
omega = args.pop(0) if len(args) > 0 else np.real(mu)
if "name" in kwargs.keys(): nameBase = copy(kwargs["name"])
filesOut = []
for j, u in enumerate(uV):
if "name" in kwargs.keys(): kwargs["name"] = nameBase + str(j)
filesOut += [self.HFEngine.outParaviewTimeDomain(u, omega,
*args,
**kwargs)]
if "name" in kwargs.keys(): kwargs["name"] = nameBase
filesOut = listGather(filesOut)
if filesOut[0] is None: return None
return filesOut
setattr(self.__class__, "outParaviewTimeDomain" + fieldName, objFunc)
class GenericApproximant:
"""
ABSTRACT
ROM approximant computation for parametric problems.
Args:
HFEngine: HF problem solver.
mu0(optional): Default parameter. Defaults to 0.
approxParameters(optional): Dictionary containing values for main
parameters of approximant. Recognized keys are:
- 'POD': kind of snapshots orthogonalization; allowed values
include 0, 1/2, and 1; defaults to 1, i.e. full POD;
- 'scaleFactorDer': scaling factors for derivative computation;
defaults to 'AUTO';
- 'S': total number of samples current approximant relies upon.
Defaults to empty dict.
verbosity(optional): Verbosity level. Defaults to 10.
Attributes:
HFEngine: HF problem solver.
trainedModel: Trained model evaluator.
mu0: Default parameter.
approxParameters: Dictionary containing values for main parameters of
approximant. Recognized keys are in parameterList{Soft,Critical}.
parameterListSoft: Recognized keys of soft approximant parameters:
- 'POD': kind of snapshots orthogonalization;
- 'scaleFactorDer': scaling factors for derivative computation.
parameterListCritical: Recognized keys of critical approximant
parameters:
- 'S': total number of samples current approximant relies upon.
verbosity: Verbosity level.
POD: Kind of snapshots orthogonalization.
scaleFactorDer: Scaling factors for derivative computation.
S: Number of solution snapshots over which current approximant is
based upon.
samplingEngine: Sampling engine.
uHF: High fidelity solution(s) with parameter(s) lastSolvedHF as
sampleList.
lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
solution(s) as parameterList.
uApproxReduced: Reduced approximate solution(s) with parameter(s)
lastSolvedApprox as sampleList.
lastSolvedApproxReduced: Parameter(s) corresponding to last computed
reduced approximate solution(s) as parameterList.
uApprox: Approximate solution(s) with parameter(s) lastSolvedApprox as
sampleList.
lastSolvedApprox: Parameter(s) corresponding to last computed
approximate solution(s) as parameterList.
"""
__all__ += [ftype + dtype for ftype, dtype in iterprod(
["norm", "plot", "outParaview", "outParaviewTimeDomain"],
["HF", "RHS", "Approx", "Res", "Err"])]
def __init__(self, HFEngine:HFEng, mu0 : paramVal = None,
approxParameters : DictAny = {}, verbosity : int = 10,
timestamp : bool = True):
self._preInit()
self._mode = RROMPy_READY
self.verbosity = verbosity
self.timestamp = timestamp
if not hasattr(self, "_output_lvl"): self._output_lvl = []
self._output_lvl += [1]
vbMng(self, "INIT",
"Initializing engine of type {}.".format(self.name()), 10)
self._HFEngine = HFEngine
self.trainedModel = None
self.lastSolvedHF = emptyParameterList()
self.uHF = emptySampleList()
self._addParametersToList(["POD", "scaleFactorDer"], [1, "AUTO"],
["S"], [1.])
if mu0 is None:
if hasattr(self.HFEngine, "mu0"):
self.mu0 = checkParameter(self.HFEngine.mu0)
else:
raise RROMPyException(("Center of approximation cannot be "
"inferred from HF engine. Parameter "
"required"))
else:
self.mu0 = checkParameter(mu0, self.HFEngine.npar)
self.resetSamples()
self.approxParameters = approxParameters
self._postInit()
### add norm{HF,Err} methods
"""
Compute norm of * at arbitrary parameter.
Args:
mu: Target parameter.
Returns:
Target norm of *.
"""
for objName in ["HF", "Err"]:
addNormFieldToClass(self, objName)
### add norm{RHS,Res} methods
"""
Compute norm of * at arbitrary parameter.
Args:
mu: Target parameter.
Returns:
Target norm of *.
"""
for objName in ["RHS", "Res"]:
addNormDualFieldToClass(self, objName)
### add plot{HF,Approx,Err} methods
"""
Do some nice plots of * at arbitrary parameter.
Args:
mu: Target parameter.
name(optional): Name to be shown as title of the plots. Defaults to
'u'.
what(optional): Which plots to do. If list, can contain 'ABS',
'PHASE', 'REAL', 'IMAG'. If str, same plus wildcard 'ALL'.
Defaults to 'ALL'.
save(optional): Where to save plot(s). Defaults to None, i.e. no
saving.
saveFormat(optional): Format for saved plot(s). Defaults to "eps".
saveDPI(optional): DPI for saved plot(s). Defaults to 100.
show(optional): Whether to show figure. Defaults to True.
figspecs(optional key args): Optional arguments for matplotlib
figure creation.
"""
for objName in ["HF", "Approx", "Err"]:
addPlotFieldToClass(self, objName)
### add plot{RHS,Res} methods
"""
Do some nice plots of * at arbitrary parameter.
Args:
mu: Target parameter.
name(optional): Name to be shown as title of the plots. Defaults to
'u'.
what(optional): Which plots to do. If list, can contain 'ABS',
'PHASE', 'REAL', 'IMAG'. If str, same plus wildcard 'ALL'.
Defaults to 'ALL'.
save(optional): Where to save plot(s). Defaults to None, i.e. no
saving.
saveFormat(optional): Format for saved plot(s). Defaults to "eps".
saveDPI(optional): DPI for saved plot(s). Defaults to 100.
show(optional): Whether to show figure. Defaults to True.
figspecs(optional key args): Optional arguments for matplotlib
figure creation.
"""
for objName in ["RHS", "Res"]:
addPlotDualFieldToClass(self, objName)
### add outParaview{HF,RHS,Approx,Res,Err} methods
"""
Output * to ParaView file.
Args:
mu: Target parameter.
name(optional): Base name to be used for data output.
filename(optional): Name of output file.
time(optional): Timestamp.
what(optional): Which plots to do. If list, can contain 'MESH',
'ABS', 'PHASE', 'REAL', 'IMAG'. If str, same plus wildcard
'ALL'. Defaults to 'ALL'.
forceNewFile(optional): Whether to create new output file.
filePW(optional): Fenics File entity (for time series).
"""
for objName in ["HF", "RHS", "Approx", "Res", "Err"]:
addOutParaviewFieldToClass(self, objName)
### add outParaviewTimeDomain{HF,RHS,Approx,Res,Err} methods
"""
Output * to ParaView file, converted to time domain.
Args:
mu: Target parameter.
omega(optional): frequency.
timeFinal(optional): final time of simulation.
periodResolution(optional): number of time steps per period.
name(optional): Base name to be used for data output.
filename(optional): Name of output file.
forceNewFile(optional): Whether to create new output file.
"""
for objName in ["HF", "RHS", "Approx", "Res", "Err"]:
addOutParaviewTimeDomainFieldToClass(self, objName)
def _preInit(self):
if not hasattr(self, "depth"): self.depth = 0
else: self.depth += 1
@property
def tModelType(self):
raise RROMPyException("No trainedModel type assigned.")
def initializeModelData(self, datadict):
from .trained_model.trained_model_data import TrainedModelData
data = TrainedModelData(datadict["mu0"], datadict["mus"],
datadict.pop("projMat"),
datadict["scaleFactor"],
datadict.pop("parameterMap"))
if hasattr(self.HFEngine, "_is_C_quadratic"):
data._is_C_quadratic = self.HFEngine._is_C_quadratic
return (data, ["mu0", "scaleFactor", "mus"])
@property
def parameterList(self):
"""Value of parameterListSoft + parameterListCritical."""
return self.parameterListSoft + self.parameterListCritical
def _addParametersToList(self, whatSoft : strLst = [],
defaultSoft : ListAny = [],
whatCritical : strLst = [],
defaultCritical : ListAny = [],
toBeExcluded : strLst = []):
if not hasattr(self, "parameterToBeExcluded"):
self.parameterToBeExcluded = []
self.parameterToBeExcluded = toBeExcluded + self.parameterToBeExcluded
if not hasattr(self, "parameterListSoft"):
self.parameterListSoft = []
if not hasattr(self, "parameterDefaultSoft"):
self.parameterDefaultSoft = {}
if not hasattr(self, "parameterListCritical"):
self.parameterListCritical = []
if not hasattr(self, "parameterDefaultCritical"):
self.parameterDefaultCritical = {}
for j, what in enumerate(whatSoft):
if what not in self.parameterToBeExcluded:
self.parameterListSoft = [what] + self.parameterListSoft
self.parameterDefaultSoft[what] = defaultSoft[j]
for j, what in enumerate(whatCritical):
if what not in self.parameterToBeExcluded:
self.parameterListCritical = ([what]
+ self.parameterListCritical)
self.parameterDefaultCritical[what] = defaultCritical[j]
def _postInit(self):
if self.depth == 0:
vbMng(self, "DEL", "Done initializing.", 10)
del self.depth
else: self.depth -= 1
def name(self) -> str:
return self.__class__.__name__
def __str__(self) -> str:
return self.name()
def __repr__(self) -> str:
return self.__str__() + " at " + hex(id(self))
def setupSampling(self, reset_samples : bool = True):
"""Setup sampling engine."""
RROMPyAssert(self._mode, message = "Cannot setup sampling engine.")
if not hasattr(self, "_POD") or self._POD is None: return
if self.POD == 1:
sEng = SamplingEnginePOD
elif self.POD == 1/2:
sEng = SamplingEngineNormalize
else:
sEng = SamplingEngine
self.samplingEngine = sEng(self.HFEngine, verbosity = self.verbosity)
if reset_samples: self.resetSamples()
@property
def HFEngine(self):
"""Value of HFEngine."""
return self._HFEngine
@HFEngine.setter
def HFEngine(self, HFEngine):
raise RROMPyException("Cannot change HFEngine.")
@property
def mu0(self):
"""Value of mu0."""
return self._mu0
@mu0.setter
def mu0(self, mu0):
mu0 = checkParameter(mu0)
if not hasattr(self, "_mu0") or mu0 != self.mu0:
self.resetSamples()
self._mu0 = mu0
@property
def npar(self):
"""Number of parameters."""
return self.mu0.shape[1]
def checkParameterList(self, mu:paramList,
check_if_single : bool = False) -> paramList:
return checkParameterList(mu, self.npar, check_if_single)
def mapParameterList(self, *args, **kwargs):
return self.HFEngine.mapParameterList(*args, **kwargs)
@property
def approxParameters(self):
"""Value of approximant parameters."""
return self._approxParameters
@approxParameters.setter
def approxParameters(self, approxParams):
if not hasattr(self, "approxParameters"):
self._approxParameters = {}
approxParameters = purgeDict(approxParams, self.parameterList,
dictname = self.name() + ".approxParameters",
baselevel = 1)
keyList = list(approxParameters.keys())
for key in self.parameterListCritical:
if key in keyList:
setattr(self, "_" + key, self.parameterDefaultCritical[key])
for key in self.parameterListSoft:
if key in keyList:
setattr(self, "_" + key, self.parameterDefaultSoft[key])
fragile = False
for key in self.parameterListCritical:
if key in keyList:
val = approxParameters[key]
else:
val = getattr(self, "_" + key, None)
if val is None:
fragile = True
val = self.parameterDefaultCritical[key]
if self._mode == RROMPy_FRAGILE:
setattr(self, "_" + key, val)
self.approxParameters[key] = val
else:
getattr(self.__class__, key, None).fset(self, val)
for key in self.parameterListSoft:
if key in keyList:
val = approxParameters[key]
else:
val = getattr(self, "_" + key, None)
if val is None:
val = self.parameterDefaultSoft[key]
if self._mode == RROMPy_FRAGILE:
setattr(self, "_" + key, val)
self.approxParameters[key] = val
else:
getattr(self.__class__, key, None).fset(self, val)
if fragile: self._mode = RROMPy_FRAGILE
@property
def POD(self):
"""Value of POD."""
return self._POD
@POD.setter
def POD(self, POD):
if hasattr(self, "_POD"): PODold = self.POD
else: PODold = -1
if POD not in [0, 1/2, 1]:
raise RROMPyException("POD must be either 0, 1/2, or 1.")
self._POD = POD
self._approxParameters["POD"] = self.POD
if PODold != self.POD:
self.samplingEngine = None
self.resetSamples()
@property
def scaleFactorDer(self):
"""Value of scaleFactorDer."""
if self._scaleFactorDer == "NONE": return 1.
if self._scaleFactorDer == "AUTO": return self.scaleFactor
return self._scaleFactorDer
@scaleFactorDer.setter
def scaleFactorDer(self, scaleFactorDer):
if isinstance(scaleFactorDer, (str,)):
scaleFactorDer = scaleFactorDer.upper()
elif isinstance(scaleFactorDer, Iterable):
scaleFactorDer = list(scaleFactorDer)
self._scaleFactorDer = scaleFactorDer
self._approxParameters["scaleFactorDer"] = self._scaleFactorDer
@property
def scaleFactorRel(self):
"""Value of scaleFactorDer / scaleFactor."""
if self._scaleFactorDer == "AUTO": return None
try:
return np.divide(self.scaleFactorDer, self.scaleFactor)
except:
raise RROMPyException(("Error in computation of relative scaling "
"factor. Make sure that scaleFactor is "
"properly initialized.")) from None
@property
def S(self):
"""Value of S."""
return self._S
@S.setter
def S(self, S):
if S <= 0: raise RROMPyException("S must be positive.")
if hasattr(self, "_S") and self._S is not None: Sold = self.S
else: Sold = -1
self._S = S
self._approxParameters["S"] = self.S
if Sold != self.S: self.resetSamples()
@property
def trainedModel(self):
"""Value of trainedModel."""
return self._trainedModel
@trainedModel.setter
def trainedModel(self, trainedModel):
self._trainedModel = trainedModel
if self._trainedModel is not None:
self._trainedModel.reset()
self.lastSolvedApproxReduced = emptyParameterList()
self.lastSolvedApprox = emptyParameterList()
self.uApproxReduced = emptySampleList()
self.uApprox = emptySampleList()
def resetSamples(self):
if hasattr(self, "samplingEngine") and self.samplingEngine is not None:
self.samplingEngine.resetHistory()
else:
self.setupSampling()
self._mode = RROMPy_READY
def plotSamples(self, *args, **kwargs) -> List[str]:
"""
Do some nice plots of the samples.
Returns:
Output filenames.
"""
RROMPyAssert(self._mode, message = "Cannot plot samples.")
return self.samplingEngine.plotSamples(*args, **kwargs)
def outParaviewSamples(self, *args, **kwargs) -> List[str]:
"""
Output samples to ParaView file.
Returns:
Output filenames.
"""
RROMPyAssert(self._mode, message = "Cannot output samples.")
return self.samplingEngine.outParaviewSamples(*args, **kwargs)
def outParaviewTimeDomainSamples(self, *args, **kwargs) -> List[str]:
"""
Output samples to ParaView file, converted to time domain.
Returns:
Output filenames.
"""
RROMPyAssert(self._mode, message = "Cannot output samples.")
return self.samplingEngine.outParaviewTimeDomainSamples(*args,
**kwargs)
def setTrainedModel(self, model):
"""Deepcopy approximation from trained model."""
if hasattr(model, "storeTrainedModel"):
verb = model.verbosity
model.verbosity = 0
fileOut = model.storeTrainedModel()
model.verbosity = verb
else:
try:
fileOut = getNewFilename("trained_model", "pkl")
pickleDump(model.data.__dict__, fileOut)
except:
raise RROMPyException(("Failed to store model data. Parameter "
"model must have either "
"storeTrainedModel or "
"data.__dict__ properties.")) from None
self.loadTrainedModel(fileOut)
osrm(fileOut)
@abstractmethod
def setupApprox(self) -> int:
"""
Setup approximant. (ABSTRACT)
Any specialization should include something like
self.trainedModel = ...
self.trainedModel.data = ...
self.trainedModel.data.approxParameters = copy(
self.approxParameters)
Returns > 0 if error was encountered, < 0 if no computation was
necessary.
"""
if self.checkComputedApprox(): return -1
RROMPyAssert(self._mode, message = "Cannot setup approximant.")
vbMng(self, "INIT", "Setting up {}.". format(self.name()), 5)
pass
vbMng(self, "DEL", "Done setting up approximant.", 5)
return 0
def checkComputedApprox(self) -> bool:
"""
Check if setup of new approximant is not needed.
Returns:
True if new setup is not needed. False otherwise.
"""
return self._mode == RROMPy_FRAGILE or (self.trainedModel is not None
and self.trainedModel.data.approxParameters == self.approxParameters
and len(self.mus) == len(self.trainedModel.data.mus))
def _pruneBeforeEval(self, mu:paramList, field:str, append:bool,
prune:bool) -> Tuple[paramList, Np1D, Np1D, bool]:
mu = self.checkParameterList(mu)
idx = np.empty(len(mu), dtype = np.int)
if prune:
jExtra = np.zeros(len(mu), dtype = bool)
muExtra = emptyParameterList()
lastSolvedMus = getattr(self, "lastSolved" + field)
if (len(mu) > 0 and len(mu) == len(lastSolvedMus)
and mu == lastSolvedMus):
idx = np.arange(len(mu), dtype = np.int)
return muExtra, jExtra, idx, True
muKeep = copy(muExtra)
for j in range(len(mu)):
jPos = lastSolvedMus.find(mu[j])
if jPos is not None:
idx[j] = jPos
muKeep.append(mu[j])
else:
jExtra[j] = True
muExtra.append(mu[j])
if len(muKeep) > 0 and not append:
lastSolvedu = getattr(self, "u" + field)
idx[~jExtra] = getattr(self.__class__, "set" + field)(self,
muKeep, lastSolvedu[idx[~jExtra]], append)
append = True
else:
jExtra = np.ones(len(mu), dtype = bool)
muExtra = mu
return muExtra, jExtra, idx, append
def _setObject(self, mu:paramList, field:str, object:sampList,
append:bool) -> List[int]:
newMus = self.checkParameterList(mu)
newObj = sampleList(object)
if append:
getattr(self, "lastSolved" + field).append(newMus)
getattr(self, "u" + field).append(newObj)
Ltot = len(getattr(self, "u" + field))
return list(range(Ltot - len(newObj), Ltot))
setattr(self, "lastSolved" + field, copy(newMus))
setattr(self, "u" + field, copy(newObj))
return list(range(len(getattr(self, "u" + field))))
def setHF(self, muHF:paramList, uHF:sampleList,
append : bool = False) -> List[int]:
"""Assign high fidelity solution."""
return self._setObject(muHF, "HF", uHF, append)
def evalHF(self, mu:paramList, append : bool = False,
prune : bool = True) -> List[int]:
"""
Find high fidelity solution with original parameters and arbitrary
parameter.
Args:
mu: Target parameter.
append(optional): Whether to append new HF solutions to old ones.
prune(optional): Whether to remove duplicates of already appearing
HF solutions.
"""
muExtra, jExtra, idx, append = self._pruneBeforeEval(mu, "HF", append,
prune)
if len(muExtra) > 0:
vbMng(self, "INIT", "Solving HF model for mu = {}.".format(mu),
15)
if (hasattr(self.HFEngine, "_is_C_quadratic")
and self.HFEngine._is_C_quadratic):
newuHFs = [self.HFEngine.solve(muE) for muE in muExtra]
else:
newuHFs = self.HFEngine.solve(muExtra)
vbMng(self, "DEL", "Done solving HF model.", 15)
idx[jExtra] = self.setHF(muExtra, newuHFs, append)
return list(idx)
def setApproxReduced(self, muApproxR:paramList, uApproxR:sampleList,
append : bool = False) -> List[int]:
"""Assign high fidelity solution."""
return self._setObject(muApproxR, "ApproxReduced", uApproxR, append)
def evalApproxReduced(self, mu:paramList, append : bool = False,
- prune : bool = True) -> List[int]:
+ prune : bool = False) -> List[int]:
"""
Evaluate reduced representation of approximant at arbitrary parameter.
Args:
mu: Target parameter.
append(optional): Whether to append new HF solutions to old ones.
prune(optional): Whether to remove duplicates of already appearing
HF solutions.
"""
self.setupApprox()
muExtra, jExtra, idx, append = self._pruneBeforeEval(mu,
"ApproxReduced",
append, prune)
if len(muExtra) > 0:
newuApproxs = self.trainedModel.getApproxReduced(muExtra)
idx[jExtra] = self.setApproxReduced(muExtra, newuApproxs, append)
return list(idx)
def setApprox(self, muApprox:paramList, uApprox:sampleList,
append : bool = False) -> List[int]:
"""Assign high fidelity solution."""
return self._setObject(muApprox, "Approx", uApprox, append)
def evalApprox(self, mu:paramList, append : bool = False,
- prune : bool = True) -> List[int]:
+ prune : bool = False) -> List[int]:
"""
Evaluate approximant at arbitrary parameter.
Args:
mu: Target parameter.
append(optional): Whether to append new HF solutions to old ones.
prune(optional): Whether to remove duplicates of already appearing
HF solutions.
"""
self.setupApprox()
muExtra, jExtra, idx, append = self._pruneBeforeEval(mu, "Approx",
append, prune)
if len(muExtra) > 0:
newuApproxs = self.trainedModel.getApprox(muExtra)
idx[jExtra] = self.setApprox(muExtra, newuApproxs, append)
return list(idx)
- def getHF(self, mu:paramList, append : bool = False,
- prune : bool = True) -> sampList:
+ def getHF(self, *args, **kwargs) -> sampList:
"""
Get HF solution at arbitrary parameter.
-
- Args:
- mu: Target parameter.
-
+
Returns:
HFsolution.
"""
- mu = self.checkParameterList(mu)
- idx = self.evalHF(mu, append = append, prune = prune)
+ idx = self.evalHF(*args, **kwargs)
return self.uHF(idx)
def getRHS(self, mu:paramList) -> sampList:
"""
Get linear system RHS at arbitrary parameter.
Args:
mu: Target parameter.
Returns:
Linear system RHS.
"""
return self.HFEngine.residual(mu, None)
- def getApproxReduced(self, mu:paramList, append : bool = False,
- prune : bool = True) -> sampList:
+ def getApproxReduced(self, *args, **kwargs) -> sampList:
"""
Get approximant at arbitrary parameter.
- Args:
- mu: Target parameter.
-
Returns:
Reduced approximant.
"""
- mu = self.checkParameterList(mu)
- idx = self.evalApproxReduced(mu, append = append, prune = prune)
+ idx = self.evalApproxReduced(*args, **kwargs)
return self.uApproxReduced(idx)
- def getApprox(self, mu:paramList, append : bool = False,
- prune : bool = True) -> sampList:
+ def getApprox(self, *args, **kwargs) -> sampList:
"""
Get approximant at arbitrary parameter.
- Args:
- mu: Target parameter.
-
Returns:
Approximant.
"""
- mu = self.checkParameterList(mu)
- idx = self.evalApprox(mu, append = append, prune = prune)
+ idx = self.evalApprox(*args, **kwargs)
return self.uApprox(idx)
- def getRes(self, mu:paramList) -> sampList:
+ def getRes(self, mu:paramList, *args, **kwargs) -> sampList:
"""
Get residual at arbitrary parameter.
Args:
mu: Target parameter.
Returns:
Approximant residual.
"""
if not self.HFEngine.isCEye:
raise RROMPyException(("Residual of solution with non-scalar C "
"not computable."))
- return self.HFEngine.residual(mu,
- self.getApprox(mu) / self.HFEngine.C(mu))
+ return self.HFEngine.residual(mu, self.getApprox(mu, *args, **kwargs)
+ / self.HFEngine.C(mu))
- def getErr(self, mu:paramList, append : bool = False,
- prune : bool = True) -> sampList:
+ def getErr(self, *args, **kwargs) -> sampList:
"""
Get error at arbitrary parameter.
- Args:
- mu: Target parameter.
-
Returns:
Approximant error.
"""
- return (self.getApprox(mu, append = append, prune =prune)
- - self.getHF(mu, append = append, prune = prune))
+ return self.getApprox(*args, **kwargs) - self.getHF(*args, **kwargs)
- def normApprox(self, mu:paramList) -> float:
+ def normApprox(self, mu:paramList, *args, **kwargs) -> float:
"""
Compute norm of approximant at arbitrary parameter.
Args:
mu: Target parameter.
Returns:
Target norm of approximant.
"""
- return self.HFEngine.norm(self.getApprox(mu), is_state = False)
+ return self.HFEngine.norm(self.getApprox(mu), *args, **kwargs)
def getPoles(self, *args, **kwargs) -> Np1D:
"""
Obtain approximant poles.
Returns:
Numpy complex vector of poles.
"""
self.setupApprox()
vbMng(self, "INIT", "Computing poles of model.", 20)
poles = self.trainedModel.getPoles(*args, **kwargs)
vbMng(self, "DEL", "Done computing poles.", 20)
return poles
def storeSamples(self, filenameBase : str = "samples",
forceNewFile : bool = True) -> str:
"""Store samples to file."""
filename = filenameBase + "_" + self.name()
if forceNewFile: filename = getNewFilename(filename, "pkl")[: - 4]
return self.samplingEngine.store(filename, False)
def storeTrainedModel(self, filenameBase : str = "trained_model",
forceNewFile : bool = True) -> str:
"""Store trained reduced model to file."""
self.setupApprox()
filename = None
if masterCore():
vbMng(self, "INIT", "Storing trained model to file.", 20)
if forceNewFile:
filename = getNewFilename(filenameBase, "pkl")
else:
filename = "{}.pkl".format(filenameBase)
pickleDump(self.trainedModel.data.__dict__, filename)
vbMng(self, "DEL", "Done storing trained model.", 20)
filename = bcast(filename)
return filename
def loadTrainedModel(self, filename:str):
"""Load trained reduced model from file."""
vbMng(self, "INIT", "Loading pre-trained model from file.", 20)
datadict = pickleLoad(filename)
self.mu0 = datadict["mu0"]
self.scaleFactor = datadict["scaleFactor"]
self.mus = datadict["mus"]
self.trainedModel = self.tModelType()
self.trainedModel.verbosity = self.verbosity
self.trainedModel.timestamp = self.timestamp
data, selfkeys = self.initializeModelData(datadict)
for key in selfkeys: setattr(self, key, datadict.pop(key))
approxParameters = datadict.pop("approxParameters")
data.approxParameters = copy(approxParameters)
for apkey in data.approxParameters.keys():
self._approxParameters[apkey] = approxParameters.pop(apkey)
setattr(self, "_" + apkey, self._approxParameters[apkey])
for key in datadict: setattr(data, key, datadict[key])
self.trainedModel.data = data
self._mode = RROMPy_FRAGILE
vbMng(self, "DEL", "Done loading pre-trained model.", 20)
diff --git a/rrompy/reduction_methods/pivoted/greedy/generic_pivoted_greedy_approximant.py b/rrompy/reduction_methods/pivoted/greedy/generic_pivoted_greedy_approximant.py
index 3790dbf..74cdf02 100644
--- a/rrompy/reduction_methods/pivoted/greedy/generic_pivoted_greedy_approximant.py
+++ b/rrompy/reduction_methods/pivoted/greedy/generic_pivoted_greedy_approximant.py
@@ -1,654 +1,654 @@
# Copyright (C) 2018-2020 by the RROMPy authors
#
# This file is part of RROMPy.
#
# RROMPy is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# RROMPy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with RROMPy. If not, see .
#
from abc import abstractmethod
from copy import deepcopy as copy
import numpy as np
from collections.abc import Iterable
from matplotlib import pyplot as plt
from rrompy.reduction_methods.pivoted.generic_pivoted_approximant import (
GenericPivotedApproximantBase,
GenericPivotedApproximantPoleMatch)
from rrompy.reduction_methods.pivoted.gather_pivoted_approximant import (
gatherPivotedApproximant)
from rrompy.utilities.base.types import (Np1D, Np2D, Tuple, List, paramVal,
paramList, ListAny)
from rrompy.utilities.base import verbosityManager as vbMng
from rrompy.utilities.numerical.point_matching import (pointMatching,
buildResiduesForDistance)
from rrompy.utilities.numerical.point_distances import chordalMetricAngleMatrix
from rrompy.utilities.exception_manager import (RROMPyException, RROMPyAssert,
RROMPyWarning)
from rrompy.parameter import emptyParameterList
from rrompy.utilities.parallel import (masterCore, indicesScatter,
arrayGatherv, isend)
__all__ = ['GenericPivotedGreedyApproximantPoleMatch']
class GenericPivotedGreedyApproximantBase(GenericPivotedApproximantBase):
_allowedEstimatorKindsMarginal = ["LOOK_AHEAD", "LOOK_AHEAD_RECOVER",
"NONE"]
def __init__(self, *args, **kwargs):
self._preInit()
self._addParametersToList(["matchingWeightError",
"errorEstimatorKindMarginal",
"greedyTolMarginal", "maxIterMarginal"],
[0., "NONE", 1e-1, 1e2])
super().__init__(*args, **kwargs)
self._postInit()
@property
def scaleFactorDer(self):
"""Value of scaleFactorDer."""
if self._scaleFactorDer == "NONE": return 1.
if self._scaleFactorDer == "AUTO": return self._scaleFactorOldPivot
return self._scaleFactorDer
@scaleFactorDer.setter
def scaleFactorDer(self, scaleFactorDer):
if isinstance(scaleFactorDer, (str,)):
scaleFactorDer = scaleFactorDer.upper()
elif isinstance(scaleFactorDer, Iterable):
scaleFactorDer = list(scaleFactorDer)
self._scaleFactorDer = scaleFactorDer
self._approxParameters["scaleFactorDer"] = self._scaleFactorDer
@property
def samplerMarginal(self):
"""Value of samplerMarginal."""
return self._samplerMarginal
@samplerMarginal.setter
def samplerMarginal(self, samplerMarginal):
if 'refine' not in dir(samplerMarginal):
raise RROMPyException("Marginal sampler type not recognized.")
GenericPivotedApproximantBase.samplerMarginal.fset(self,
samplerMarginal)
@property
def errorEstimatorKindMarginal(self):
"""Value of errorEstimatorKindMarginal."""
return self._errorEstimatorKindMarginal
@errorEstimatorKindMarginal.setter
def errorEstimatorKindMarginal(self, errorEstimatorKindMarginal):
errorEstimatorKindMarginal = errorEstimatorKindMarginal.upper()
if errorEstimatorKindMarginal not in (
self._allowedEstimatorKindsMarginal):
RROMPyWarning(("Marginal error estimator kind not recognized. "
"Overriding to 'NONE'."))
errorEstimatorKindMarginal = "NONE"
self._errorEstimatorKindMarginal = errorEstimatorKindMarginal
self._approxParameters["errorEstimatorKindMarginal"] = (
self.errorEstimatorKindMarginal)
@property
def matchingWeightError(self):
"""Value of matchingWeightError."""
return self._matchingWeightError
@matchingWeightError.setter
def matchingWeightError(self, matchingWeightError):
self._matchingWeightError = matchingWeightError
self._approxParameters["matchingWeightError"] = (
self.matchingWeightError)
@property
def greedyTolMarginal(self):
"""Value of greedyTolMarginal."""
return self._greedyTolMarginal
@greedyTolMarginal.setter
def greedyTolMarginal(self, greedyTolMarginal):
if greedyTolMarginal < 0:
raise RROMPyException("greedyTolMarginal must be non-negative.")
if (hasattr(self, "_greedyTolMarginal")
and self.greedyTolMarginal is not None):
greedyTolMarginalold = self.greedyTolMarginal
else:
greedyTolMarginalold = -1
self._greedyTolMarginal = greedyTolMarginal
self._approxParameters["greedyTolMarginal"] = self.greedyTolMarginal
if greedyTolMarginalold != self.greedyTolMarginal:
self.resetSamples()
@property
def maxIterMarginal(self):
"""Value of maxIterMarginal."""
return self._maxIterMarginal
@maxIterMarginal.setter
def maxIterMarginal(self, maxIterMarginal):
if maxIterMarginal <= 0:
raise RROMPyException("maxIterMarginal must be positive.")
if (hasattr(self, "_maxIterMarginal")
and self.maxIterMarginal is not None):
maxIterMarginalold = self.maxIterMarginal
else:
maxIterMarginalold = -1
self._maxIterMarginal = maxIterMarginal
self._approxParameters["maxIterMarginal"] = self.maxIterMarginal
if maxIterMarginalold != self.maxIterMarginal:
self.resetSamples()
def resetSamples(self):
"""Reset samples."""
super().resetSamples()
if not hasattr(self, "_temporaryPivot"):
self._mus = emptyParameterList()
self._musMarginal = emptyParameterList()
if hasattr(self, "samplerMarginal"): self.samplerMarginal.reset()
if hasattr(self, "samplingEngine") and self.samplingEngine is not None:
self.samplingEngine.resetHistory()
def _getDistanceApp(self, polesEx:Np1D, resEx:Np2D, muTest:paramVal,
foci:Tuple[float, float], ground:float) -> float:
polesAp = self.trainedModel.interpolateMarginalPoles(muTest)[0]
if self.matchingWeightError != 0:
resAp = self.trainedModel.interpolateMarginalCoeffs(muTest)[0][
: len(polesAp), :]
if (hasattr(self.trainedModel.data, "_is_C_quadratic")
and self.trainedModel.data._is_C_quadratic):
self.trainedModel._setupQuadMapping()
projMapping = self.quad_mapping
projMappingReal = self.data._is_C_quadratic == 2
else:
projMapping, projMappingReal = None, False
resEx = buildResiduesForDistance(resEx,
self.trainedModel.data.projMat,
0, projMapping, projMappingReal)
resAp = buildResiduesForDistance(resAp,
self.trainedModel.data.projMat,
0, projMapping, projMappingReal)
else:
resAp = None
dist = chordalMetricAngleMatrix(polesEx, polesAp,
self.matchingWeightError, resEx, resAp,
self.HFEngine, False)
pmR, pmC = pointMatching(dist)
return np.mean(dist[pmR, pmC])
def getErrorEstimatorMarginalLookAhead(self) -> Np1D:
if not hasattr(self.trainedModel, "_musMExcl"):
err = np.zeros(0)
err[:] = np.inf
self._musMarginalTestIdxs = np.zeros(0, dtype = int)
return err
self._musMarginalTestIdxs = np.array(self.trainedModel._idxExcl,
dtype = int)
idx, sizes = indicesScatter(len(self.trainedModel._musMExcl),
return_sizes = True)
err = []
if len(idx) > 0:
self.verbosity -= 25
self.trainedModel.verbosity -= 25
foci = self.samplerPivot.normalFoci()
ground = self.samplerPivot.groundPotential()
for j in idx:
muTest = self.trainedModel._musMExcl[j]
HITest = self.trainedModel._HIsExcl[j]
polesEx = HITest.poles
idxGood = np.logical_not(np.logical_or(np.isinf(polesEx),
np.isnan(polesEx)))
polesEx = polesEx[idxGood]
if self.matchingWeightError != 0:
resEx = HITest.coeffs[np.where(idxGood)[0]]
else:
resEx = None
if len(polesEx) == 0:
err += [0.]
continue
err += [self._getDistanceApp(polesEx, resEx, muTest,
foci, ground)]
self.verbosity += 25
self.trainedModel.verbosity += 25
return arrayGatherv(np.array(err), sizes)
def getErrorEstimatorMarginalNone(self) -> Np1D:
nErr = len(self.trainedModel.data.musMarginal)
self._musMarginalTestIdxs = np.arange(nErr)
return (1. + self.greedyTolMarginal) * np.ones(nErr)
def errorEstimatorMarginal(self, return_max : bool = False) -> Np1D:
vbMng(self.trainedModel, "INIT",
"Evaluating error estimator at mu = {}.".format(
self.trainedModel.data.musMarginal), 10)
if self.errorEstimatorKindMarginal == "NONE":
nErr = len(self.trainedModel.data.musMarginal)
self._musMarginalTestIdxs = np.arange(nErr)
err = (1. + self.greedyTolMarginal) * np.ones(nErr)
else:#if self.errorEstimatorKindMarginal[: 10] == "LOOK_AHEAD":
err = self.getErrorEstimatorMarginalLookAhead()
- vbMng(self.trainedModel, "DEL", "Done evaluating error estimator", 10)
+ vbMng(self.trainedModel, "DEL", "Done evaluating error estimator.", 10)
if not return_max: return err
idxMaxEst = np.where(err > self.greedyTolMarginal)[0]
maxErr = err[idxMaxEst]
if self.errorEstimatorKindMarginal == "NONE": maxErr = None
return err, idxMaxEst, maxErr
def plotEstimatorMarginal(self, est:Np1D, idxMax:List[int],
estMax:List[float]):
if self.errorEstimatorKindMarginal == "NONE": return
if (not (np.any(np.isnan(est)) or np.any(np.isinf(est)))
and masterCore() and hasattr(self.trainedModel, "_musMExcl")):
fig = plt.figure(figsize = plt.figaspect(1. / self.nparMarginal))
for jpar in range(self.nparMarginal):
ax = fig.add_subplot(1, self.nparMarginal, 1 + jpar)
musre = np.real(self.trainedModel._musMExcl)
if len(idxMax) > 0 and estMax is not None:
maxrej = musre[idxMax, jpar]
errCP = copy(est)
idx = np.delete(np.arange(self.nparMarginal), jpar)
while len(musre) > 0:
if self.nparMarginal == 1:
currIdx = np.arange(len(musre))
else:
currIdx = np.where(np.isclose(np.sum(
np.abs(musre[:, idx] - musre[0, idx]), 1), 0.))[0]
currIdxSorted = currIdx[np.argsort(musre[currIdx, jpar])]
ax.semilogy(musre[currIdxSorted, jpar],
errCP[currIdxSorted], 'k.-', linewidth = 1)
musre = np.delete(musre, currIdx, 0)
errCP = np.delete(errCP, currIdx)
ax.semilogy(self.musMarginal.re(jpar),
(self.greedyTolMarginal,) * len(self.musMarginal),
'*m')
if len(idxMax) > 0 and estMax is not None:
ax.semilogy(maxrej, estMax, 'xr')
ax.set_xlim(*list(self.samplerMarginal.lims.re(jpar)))
ax.grid()
plt.tight_layout()
plt.show()
def _addMarginalSample(self, mus:paramList):
mus = self.checkParameterListMarginal(mus)
if len(mus) == 0: return
self._nmusOld, nmus = len(self.musMarginal), len(mus)
if (hasattr(self, "trainedModel") and self.trainedModel is not None
and hasattr(self.trainedModel, "_musMExcl")):
self._nmusOld += len(self.trainedModel._musMExcl)
vbMng(self, "MAIN",
("Adding marginal sample point{} no. {}{} at {} to training "
"set.").format("s" * (nmus > 1), self._nmusOld + 1,
"--{}".format(self._nmusOld + nmus) * (nmus > 1),
mus), 3)
self.musMarginal.append(mus)
self.setupApproxPivoted(mus)
self._preliminaryMarginalFinalization()
del self._nmusOld
if (self.errorEstimatorKindMarginal[: 10] == "LOOK_AHEAD"
and not self.firstGreedyIterM):
ubRange = len(self.trainedModel.data.musMarginal)
if hasattr(self.trainedModel, "_idxExcl"):
shRange = len(self.trainedModel._musMExcl)
else:
shRange = 0
testIdxs = list(range(ubRange + shRange - len(mus),
ubRange + shRange))
for j in testIdxs[::-1]:
self.musMarginal.pop(j - shRange)
if hasattr(self.trainedModel, "_idxExcl"):
testIdxs = self.trainedModel._idxExcl + testIdxs
self._updateTrainedModelMarginalSamples(testIdxs)
self._finalizeMarginalization()
self._SMarginal = len(self.musMarginal)
self._approxParameters["SMarginal"] = self.SMarginal
self.trainedModel.data.approxParameters["SMarginal"] = self.SMarginal
def greedyNextSampleMarginal(self, muidx:List[int],
plotEst : str = "NONE") \
-> Tuple[Np1D, List[int], float, paramVal]:
RROMPyAssert(self._mode, message = "Cannot add greedy sample.")
muidx = self._musMarginalTestIdxs[muidx]
if (self.errorEstimatorKindMarginal[: 10] == "LOOK_AHEAD"
and not self.firstGreedyIterM):
if not hasattr(self.trainedModel, "_idxExcl"):
raise RROMPyException(("Sample index to be added not present "
"in trained model."))
testIdxs = copy(self.trainedModel._idxExcl)
skippedIdx = 0
for cj, j in enumerate(self.trainedModel._idxExcl):
if j in muidx:
testIdxs.pop(skippedIdx)
self.musMarginal.insert(self.trainedModel._musMExcl[cj],
j - skippedIdx)
else:
skippedIdx += 1
if len(self.trainedModel._idxExcl) < (len(muidx)
+ len(testIdxs)):
raise RROMPyException(("Sample index to be added not present "
"in trained model."))
self._updateTrainedModelMarginalSamples(testIdxs)
self._SMarginal = len(self.musMarginal)
self._approxParameters["SMarginal"] = self.SMarginal
self.trainedModel.data.approxParameters["SMarginal"] = (
self.SMarginal)
self.firstGreedyIterM = False
idxAdded = self.samplerMarginal.refine(muidx)[0]
self._addMarginalSample(self.samplerMarginal.points[idxAdded])
errorEstTest, muidx, maxErrorEst = self.errorEstimatorMarginal(True)
if plotEst == "ALL":
self.plotEstimatorMarginal(errorEstTest, muidx, maxErrorEst)
return (errorEstTest, muidx, maxErrorEst,
self.samplerMarginal.points[muidx])
def _preliminaryTrainingMarginal(self):
"""Initialize starting snapshots of solution map."""
RROMPyAssert(self._mode, message = "Cannot start greedy algorithm.")
if np.sum(self.samplingEngine.nsamples) > 0: return
self.resetSamples()
self._addMarginalSample(self.samplerMarginal.generatePoints(
self.SMarginal))
def _preSetupApproxPivoted(self, mus:paramList) \
-> Tuple[ListAny, ListAny, ListAny]:
self.computeScaleFactor()
if self.trainedModel is None:
self._setupTrainedModel(np.zeros((0, 0)))
self.trainedModel.data.Qs, self.trainedModel.data.Ps = [], []
self.trainedModel.data.Psupp = []
self._trainedModelOld = copy(self.trainedModel)
self._scaleFactorOldPivot = copy(self.scaleFactor)
self.scaleFactor = self.scaleFactorPivot
self._temporaryPivot = 1
self._musLoc = copy(self.mus)
idx, sizes = indicesScatter(len(mus), return_sizes = True)
emptyCores = np.where(np.logical_not(sizes))[0]
self.verbosity -= 10
self.samplingEngine.verbosity -= 10
return idx, sizes, emptyCores
def _postSetupApproxPivoted(self, mus:Np2D, pMat:Np2D, Ps:ListAny,
Qs:ListAny, sizes:ListAny):
self.scaleFactor = self._scaleFactorOldPivot
del self._scaleFactorOldPivot, self._temporaryPivot
pMat, Ps, Qs, mus, nsamples = gatherPivotedApproximant(pMat, Ps, Qs,
mus, sizes,
self.polybasis)
if len(self._musLoc) > 0:
self._mus = self.checkParameterList(self._musLoc)
self._mus.append(mus)
else:
self._mus = self.checkParameterList(mus)
self.trainedModel = self._trainedModelOld
del self._trainedModelOld
padLeft = self.trainedModel.data.projMat.shape[1]
suppNew = np.append(0, np.cumsum(nsamples))
self._setupTrainedModel(pMat, padLeft > 0)
self.trainedModel.data.Qs += Qs
self.trainedModel.data.Ps += Ps
self.trainedModel.data.Psupp += list(padLeft + suppNew[: -1])
self.trainedModel.data.approxParameters = copy(self.approxParameters)
self.verbosity += 10
self.samplingEngine.verbosity += 10
def _localPivotedResult(self, pMat:Np2D, req:ListAny, emptyCores:ListAny,
mus:Np2D) -> Tuple[Np2D, ListAny, Np2D]:
pMati = self.samplingEngine.projectionMatrix
musi = self.samplingEngine.mus
if not hasattr(self, "matchState") or not self.matchState:
if self.POD == 1 and not (
hasattr(self.HFEngine.C, "is_mu_independent")
and self.HFEngine.C.is_mu_independent in self._output_lvl):
raise RROMPyException(("Cannot apply mu-dependent C "
"to orthonormalized samples."))
vbMng(self, "INIT", "Extracting system output from state.", 35)
if (hasattr(self.HFEngine, "_is_C_quadratic")
and self.HFEngine._is_C_quadratic):
pMati = self.HFEngine.applyC(pMati, musi)
else:
pMatiEff = None
for j, mu in enumerate(musi):
pMij = np.expand_dims(self.HFEngine.applyC(pMati[:, j],
mu), -1)
if pMatiEff is None:
pMatiEff = np.array(pMij)
else:
pMatiEff = np.append(pMatiEff, pMij, axis = 1)
pMati = pMatiEff
vbMng(self, "DEL", "Done extracting system output.", 35)
if pMat is None:
mus = copy(musi.data)
pMat = copy(pMati)
if masterCore():
for dest in emptyCores:
req += [isend((len(pMat), pMat.dtype, mus.dtype),
dest = dest, tag = dest)]
else:
mus = np.vstack((mus, musi.data))
pMat = np.hstack((pMat, pMati))
return pMat, req, mus
@abstractmethod
def setupApproxPivoted(self, mus:paramList) -> int:
if self.checkComputedApproxPivoted(): return -1
RROMPyAssert(self._mode, message = "Cannot setup approximant.")
vbMng(self, "INIT", "Setting up pivoted approximant.", 10)
self._preSetupApproxPivoted()
data = []
pass
self._postSetupApproxPivoted(mus, data)
vbMng(self, "DEL", "Done setting up pivoted approximant.", 10)
return 0
def setupApprox(self, plotEst : str = "NONE") -> int:
"""Compute greedy snapshots of solution map."""
if self.checkComputedApprox(): return -1
RROMPyAssert(self._mode, message = "Cannot start greedy algorithm.")
vbMng(self, "INIT", "Starting computation of snapshots.", 3)
max2ErrorEst, self.firstGreedyIterM = np.inf, True
self._preliminaryTrainingMarginal()
if self.errorEstimatorKindMarginal == "NONE":
muidx = []
else:#if self.errorEstimatorKindMarginal[: 10] == "LOOK_AHEAD":
muidx = np.arange(len(self.trainedModel.data.musMarginal))
self._musMarginalTestIdxs = np.array(muidx)
while self.firstGreedyIterM or (max2ErrorEst > self.greedyTolMarginal
and self.samplerMarginal.npoints < self.maxIterMarginal):
errorEstTest, muidx, maxErrorEst, mu = \
self.greedyNextSampleMarginal(muidx, plotEst)
if maxErrorEst is None:
max2ErrorEst = 1. + self.greedyTolMarginal
else:
if len(maxErrorEst) > 0:
max2ErrorEst = np.max(maxErrorEst)
else:
max2ErrorEst = np.max(errorEstTest)
vbMng(self, "MAIN", ("Uniform testing error estimate "
"{:.4e}.").format(max2ErrorEst), 3)
if plotEst == "LAST":
self.plotEstimatorMarginal(errorEstTest, muidx, maxErrorEst)
vbMng(self, "DEL", ("Done computing snapshots (final snapshot count: "
"{}).").format(len(self.mus)), 3)
if (self.errorEstimatorKindMarginal == "LOOK_AHEAD_RECOVER"
and hasattr(self.trainedModel, "_idxExcl")
and len(self.trainedModel._idxExcl) > 0):
vbMng(self, "INIT", "Recovering {} test models.".format(
len(self.trainedModel._idxExcl)), 7)
for j, mu in zip(self.trainedModel._idxExcl,
self.trainedModel._musMExcl):
self.musMarginal.insert(mu, j)
self._updateTrainedModelMarginalSamples()
self._finalizeMarginalization()
self._SMarginal = len(self.musMarginal)
self._approxParameters["SMarginal"] = self.SMarginal
self.trainedModel.data.approxParameters["SMarginal"] = (
self.SMarginal)
vbMng(self, "DEL", "Done recovering test models.", 7)
return 0
def checkComputedApproxPivoted(self) -> bool:
return (super().checkComputedApprox()
and len(self.musMarginal) == len(self.trainedModel.data.musMarginal))
class GenericPivotedGreedyApproximantPoleMatch(
GenericPivotedGreedyApproximantBase,
GenericPivotedApproximantPoleMatch):
"""
ROM pivoted greedy interpolant computation for parametric problems (with
pole matching) (ABSTRACT).
Args:
HFEngine: HF problem solver.
mu0(optional): Default parameter. Defaults to 0.
directionPivot(optional): Pivot components. Defaults to [0].
approxParameters(optional): Dictionary containing values for main
parameters of approximant. Recognized keys are:
- 'POD': kind of snapshots orthogonalization; allowed values
include 0, 1/2, and 1; defaults to 1, i.e. POD;
- 'scaleFactorDer': scaling factors for derivative computation;
defaults to 'AUTO';
- 'matchState': whether to match the system state rather than the
system output; defaults to False;
- 'matchingWeight': weight for pole matching optimization; defaults
to 1;
- 'sharedRatio': required ratio of marginal points to share
resonance; defaults to 1.;
- 'matchingWeightError': weight for pole matching optimization in
error estimation; defaults to 0;
- 'S': total number of pivot samples current approximant relies
upon;
- 'samplerPivot': pivot sample point generator;
- 'SMarginal': number of starting marginal samples;
- 'samplerMarginal': marginal sample point generator via sparse
grid;
- 'errorEstimatorKindMarginal': kind of marginal error estimator;
available values include 'LOOK_AHEAD', 'LOOK_AHEAD_RECOVER',
and 'NONE'; defaults to 'NONE';
- 'polybasisMarginal': type of polynomial basis for marginal
interpolation; allowed values include 'MONOMIAL_*',
'CHEBYSHEV_*', 'LEGENDRE_*', 'NEARESTNEIGHBOR', and
'PIECEWISE_LINEAR_*'; defaults to 'MONOMIAL';
- 'paramsMarginal': dictionary of parameters for marginal
interpolation; include:
. 'MMarginal': degree of marginal interpolant; defaults to
'AUTO', i.e. maximum allowed; not for 'NEARESTNEIGHBOR' or
'PIECEWISE_LINEAR_*';
. 'nNeighborsMarginal': number of marginal nearest neighbors;
defaults to 1; only for 'NEARESTNEIGHBOR';
. 'polydegreetypeMarginal': type of polynomial degree for
marginal; defaults to 'TOTAL'; not for 'NEARESTNEIGHBOR' or
'PIECEWISE_LINEAR_*';
. 'interpRcondMarginal': tolerance for marginal interpolation;
defaults to None; not for 'NEARESTNEIGHBOR' or
'PIECEWISE_LINEAR_*';
. 'radialDirectionalWeightsMarginalAdapt': bounds for adaptive
rescaling of marginal radial basis weights; only for
radial basis.
- 'greedyTolMarginal': uniform error tolerance for marginal greedy
algorithm; defaults to 1e-1;
- 'maxIterMarginal': maximum number of marginal greedy steps;
defaults to 1e2;
- 'radialDirectionalWeightsMarginal': radial basis weights for
marginal interpolant; defaults to 1.
Defaults to empty dict.
verbosity(optional): Verbosity level. Defaults to 10.
Attributes:
HFEngine: HF problem solver.
mu0: Default parameter.
directionPivot: Pivot components.
mus: Array of snapshot parameters.
musMarginal: Array of marginal snapshot parameters.
approxParameters: Dictionary containing values for main parameters of
approximant. Recognized keys are in parameterList.
parameterListSoft: Recognized keys of soft approximant parameters:
- 'POD': kind of snapshots orthogonalization;
- 'scaleFactorDer': scaling factors for derivative computation;
- 'matchState': whether to match the system state rather than the
system output;
- 'matchingWeight': weight for pole matching optimization;
- 'sharedRatio': required ratio of marginal points to share
resonance;
- 'matchingWeightError': weight for pole matching optimization in
error estimation;
- 'errorEstimatorKindMarginal': kind of marginal error estimator;
- 'polybasisMarginal': type of polynomial basis for marginal
interpolation;
- 'paramsMarginal': dictionary of parameters for marginal
interpolation; include:
. 'MMarginal': degree of marginal interpolant;
. 'nNeighborsMarginal': number of marginal nearest neighbors;
. 'polydegreetypeMarginal': type of polynomial degree for
marginal;
. 'interpRcondMarginal': tolerance for marginal interpolation;
. 'radialDirectionalWeightsMarginalAdapt': bounds for adaptive
rescaling of marginal radial basis weights.
- 'greedyTolMarginal': uniform error tolerance for marginal greedy
algorithm;
- 'maxIterMarginal': maximum number of marginal greedy steps;
- 'radialDirectionalWeightsMarginal': radial basis weights for
marginal interpolant.
parameterListCritical: Recognized keys of critical approximant
parameters:
- 'S': total number of pivot samples current approximant relies
upon;
- 'samplerPivot': pivot sample point generator;
- 'SMarginal': total number of marginal samples current approximant
relies upon;
- 'samplerMarginal': marginal sample point generator via sparse
grid.
verbosity: Verbosity level.
POD: Kind of snapshots orthogonalization.
scaleFactorDer: Scaling factors for derivative computation.
matchState: Whether to match the system state rather than the system
output.
matchingWeight: Weight for pole matching optimization.
sharedRatio: Required ratio of marginal points to share resonance.
matchingWeightError: Weight for pole matching optimization in error
estimation.
S: Total number of pivot samples current approximant relies upon.
samplerPivot: Pivot sample point generator.
SMarginal: Total number of marginal samples current approximant relies
upon.
samplerMarginal: Marginal sample point generator via sparse grid.
errorEstimatorKindMarginal: Kind of marginal error estimator.
polybasisMarginal: Type of polynomial basis for marginal interpolation.
paramsMarginal: Dictionary of parameters for marginal interpolation.
greedyTolMarginal: Uniform error tolerance for marginal greedy
algorithm.
maxIterMarginal: Maximum number of marginal greedy steps.
radialDirectionalWeightsMarginal: Radial basis weights for marginal
interpolant.
muBounds: list of bounds for pivot parameter values.
muBoundsMarginal: list of bounds for marginal parameter values.
samplingEngine: Sampling engine.
uHF: High fidelity solution(s) with parameter(s) lastSolvedHF as
sampleList.
lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
solution(s) as parameterList.
uApproxReduced: Reduced approximate solution(s) with parameter(s)
lastSolvedApprox as sampleList.
lastSolvedApproxReduced: Parameter(s) corresponding to last computed
reduced approximate solution(s) as parameterList.
uApprox: Approximate solution(s) with parameter(s) lastSolvedApprox as
sampleList.
lastSolvedApprox: Parameter(s) corresponding to last computed
approximate solution(s) as parameterList.
"""
def _updateTrainedModelMarginalSamples(self, idx : ListAny = []):
self.trainedModel.updateEffectiveSamples(idx, self.matchingWeight,
self.HFEngine, False)
def setupApprox(self, *args, **kwargs) -> int:
if self.checkComputedApprox(): return -1
self.purgeparamsMarginal()
_polybasisMarginal = self.polybasisMarginal
self._polybasisMarginal = ("PIECEWISE_LINEAR_"
+ self.samplerMarginal.kind)
setupOK = super().setupApprox(*args, **kwargs)
self._polybasisMarginal = _polybasisMarginal
self._finalizeMarginalization()
if self.matchState: self._postApplyC()
return setupOK
diff --git a/rrompy/reduction_methods/pivoted/trained_model/trained_model_pivoted_rational_nomatch.py b/rrompy/reduction_methods/pivoted/trained_model/trained_model_pivoted_rational_nomatch.py
index 4eacc69..e9aa8ba 100644
--- a/rrompy/reduction_methods/pivoted/trained_model/trained_model_pivoted_rational_nomatch.py
+++ b/rrompy/reduction_methods/pivoted/trained_model/trained_model_pivoted_rational_nomatch.py
@@ -1,284 +1,293 @@
# Copyright (C) 2018-2020 by the RROMPy authors
#
# This file is part of RROMPy.
#
# RROMPy is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# RROMPy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with RROMPy. If not, see .
#
import numpy as np
from collections.abc import Iterable
from rrompy.reduction_methods.standard.trained_model.trained_model_rational \
import TrainedModelRational
from rrompy.utilities.base.types import (Np1D, Np2D, List, ListAny, paramVal,
paramList, sampList)
from rrompy.utilities.base import verbosityManager as vbMng, freepar as fp
from rrompy.utilities.numerical import dot
from rrompy.utilities.numerical.compress_matrix import compressMatrix
from rrompy.utilities.poly_fitting.heaviside import rational2heaviside
from rrompy.utilities.poly_fitting.nearest_neighbor import (
NearestNeighborInterpolator as NNI)
from rrompy.utilities.exception_manager import (RROMPyException, RROMPyAssert,
RROMPyWarning)
from rrompy.parameter import checkParameterList
from rrompy.sampling import emptySampleList
__all__ = ['TrainedModelPivotedRationalNoMatch']
class TrainedModelPivotedRationalNoMatch(TrainedModelRational):
"""
ROM approximant evaluation for pivoted approximants based on interpolation
of rational approximants (without pole matching).
Attributes:
Data: dictionary with all that can be pickled.
"""
@property
def supportEnd(self):
+ # for a quadratic output, it is different from data.projMat.shape[1]
lookAtExcl = hasattr(self, "_PsuppExcl") and len(self._PsuppExcl) > 0
idxIncl = np.argmax(self.data.Psupp)
if lookAtExcl: idxExcl = np.argmax(self._PsuppExcl)
if (not lookAtExcl
or self.data.Psupp[idxIncl] >= self._PsuppExcl[idxExcl]):
return self.data.Psupp[idxIncl] + self.data.Ps[idxIncl].shape[0]
return self._PsuppExcl[idxExcl] + self._PsExcl[idxExcl].shape[0]
def checkParameterListPivot(self, mu:paramList,
check_if_single : bool = False) -> paramList:
return checkParameterList(mu, self.data.nparPivot, check_if_single)
def checkParameterListMarginal(self, mu:paramList,
check_if_single : bool = False) -> paramList:
return checkParameterList(mu, self.data.nparMarginal, check_if_single)
def compress(self, collapse : bool = False, tol : float = 0.,
returnRMat : bool = False, **compressMatrixkwargs):
if not collapse and tol <= 0.: return
if hasattr(self.data, "_is_C_quadratic") and self.data._is_C_quadratic:
raise RROMPyException(("Cannot compress model with quadratic "
"output."))
RMat = self.data.projMat
if not collapse:
if hasattr(self.data, "_compressTol"):
RROMPyWarning(("Recompressing already compressed model is "
"ineffective. Aborting."))
return
self.data.projMat, RMat, _ = compressMatrix(RMat, tol,
**compressMatrixkwargs)
if hasattr(self.data, "Ps"):
for obj, suppj in zip(self.data.Ps, self.data.Psupp):
obj.postmultiplyTensorize(RMat.T[suppj : suppj + obj.shape[0]])
if hasattr(self, "_PsExcl"):
for obj, suppj in zip(self._PsExcl, self._PsuppExcl):
obj.postmultiplyTensorize(RMat.T[suppj : suppj + obj.shape[0]])
self._PsuppExcl = [0] * len(self._PsuppExcl)
self.data.Psupp = [0] * len(self.data.Psupp)
super(TrainedModelRational, self).compress(collapse, tol)
if returnRMat: return RMat
def centerNormalizePivot(self, mu : paramList = [],
mu0 : paramVal = None) -> paramList:
"""
Compute normalized parameter to be plugged into approximant.
Args:
mu: Parameter(s) 1.
mu0: Parameter(s) 2. If None, set to self.data.mu0Pivot.
Returns:
Normalized parameter.
"""
mu = self.checkParameterListPivot(mu)
if mu0 is None:
mu0 = self.checkParameterListPivot(
self.data.mu0(0, self.data.directionPivot))
return (self.mapParameterList(mu, idx = self.data.directionPivot)
- self.mapParameterList(mu0, idx = self.data.directionPivot)
) / [self.data.scaleFactor[x] for x in self.data.directionPivot]
def setupMarginalInterp(self, interpPars:ListAny):
self.data.marginalInterp = NNI()
self.data.marginalInterp.setupByInterpolation(self.data.musMarginal,
np.arange(len(self.data.musMarginal)),
1, *interpPars)
def updateEffectiveSamples(self, exclude:List[int]):
if hasattr(self, "_idxExcl"):
for j, excl in enumerate(self._idxExcl):
self.data.musMarginal.insert(self._musMExcl[j], excl)
self.data.Ps.insert(excl, self._PsExcl[j])
self.data.Qs.insert(excl, self._QsExcl[j])
self.data.Psupp.insert(excl, self._PsuppExcl[j])
self._idxExcl, self._musMExcl = list(np.sort(exclude)), []
self._PsExcl, self._QsExcl, self._PsuppExcl = [], [], []
for excl in self._idxExcl[::-1]:
self._musMExcl = [self.data.musMarginal[excl]] + self._musMExcl
self.data.musMarginal.pop(excl)
self._PsExcl = [self.data.Ps.pop(excl)] + self._PsExcl
self._QsExcl = [self.data.Qs.pop(excl)] + self._QsExcl
self._PsuppExcl = [self.data.Psupp.pop(excl)] + self._PsuppExcl
def _setupQuadMapping(self, N2 : List[int] = None):
if not (hasattr(self.data, "_is_C_quadratic")
and self.data._is_C_quadratic):
RROMPyWarning(("Quadratic mapping is useless if output is not "
"quadratic. Aborting."))
return
if N2 is None:
N2 = np.array([P.shape[0] for P in self.data.Ps], dtype = int)
if self.data._is_C_quadratic == 1:
N2 = N2 ** 2
else: # if self.data._is_C_quadratic == 2:
N2 = N2 * (N2 + 1) // 2
if (hasattr(self, "quad_mapping")
and self.quad_mapping.shape[1] == np.sum(N2)): return
quad_mapping = np.empty((2, 0), dtype = int)
for Nloc, suppj in zip(N2, self.data.Psupp):
self.quad_mapping = np.empty((0, 0))
super()._setupQuadMapping(Nloc)
quad_mapping = np.append(quad_mapping, self.quad_mapping + suppj,
axis = 1)
self.quad_mapping = quad_mapping
def getPVal(self, mu : paramList = []) -> sampList:
"""
Evaluate rational numerator at arbitrary parameter.
Args:
mu: Target parameter.
"""
RROMPyAssert(self.data.nparPivot, 1, "Number of pivot parameters")
mu = self.checkParameterList(mu)
muP = self.centerNormalizePivot(mu(self.data.directionPivot))
muM = self.checkParameterListMarginal(mu(self.data.directionMarginal))
- intM = np.array(self.data.marginalInterp(muM), dtype = int)
+ idxMUnique, idxMmap = np.unique(self.data.marginalInterp(muM),
+ return_inverse = True)
+ idxMUnique = np.array(idxMUnique, dtype = int)
p = emptySampleList()
vbMng(self, "INIT", "Evaluating numerator at mu = {}.".format(mu), 17)
- for i, iM in enumerate(intM):
- Pval, supp = self.data.Ps[iM](muP[i]), self.data.Psupp[iM]
+ for i, iM in enumerate(idxMUnique):
+ idx = np.where(idxMmap == i)[0]
+ Pval, supp = self.data.Ps[iM](muP[idx]), self.data.Psupp[iM]
if i == 0:
p.reset((self.supportEnd, len(mu)), dtype = Pval.dtype)
p.data[:] = 0.
- p.data[supp : supp + len(Pval), i] = Pval[:, 0]
+ p.data[supp : supp + len(Pval), idx] = Pval
vbMng(self, "DEL", "Done evaluating numerator.", 17)
return p
def getQVal(self, mu:Np1D, der : List[int] = None,
scl : Np1D = None) -> Np1D:
"""
Evaluate rational denominator at arbitrary parameter.
Args:
mu: Target parameter.
der(optional): Derivatives to take before evaluation.
"""
RROMPyAssert(self.data.nparPivot, 1, "Number of pivot parameters")
mu = self.checkParameterList(mu)
muP = self.centerNormalizePivot(mu(self.data.directionPivot))
muM = self.checkParameterListMarginal(mu(self.data.directionMarginal))
if der is None:
derP, derM = 0, [0]
else:
derP = der[self.data.directionPivot[0]]
derM = [der[x] for x in self.data.directionMarginal]
if np.any(np.array(derM) != 0):
raise RROMPyException(("Derivatives of Q with respect to marginal "
"parameters not allowed."))
sclP = 1 if scl is None else scl[self.data.directionPivot[0]]
- intM = np.array(self.data.marginalInterp(muM), dtype = int)
+ idxMUnique, idxMmap = np.unique(self.data.marginalInterp(muM),
+ return_inverse = True)
+ idxMUnique = np.array(idxMUnique, dtype = int)
vbMng(self, "INIT", "Evaluating denominator at mu = {}.".format(mu),
17)
- q = np.array([self.data.Qs[iM](mP, derP, sclP)[0]
- for iM, mP in zip(intM, muP)])
+ for i, iM in enumerate(idxMUnique):
+ idx = np.where(idxMmap == i)[0]
+ Qval = self.data.Qs[iM](muP[idx], derP, sclP)
+ if i == 0: q = np.empty(len(mu), dtype = Qval.dtype)
+ q[idx] = Qval
vbMng(self, "DEL", "Done evaluating denominator.", 17)
return q
def getPoles(self, *args, **kwargs) -> Np1D:
"""
Obtain approximant poles.
Returns:
Numpy complex vector of poles.
"""
RROMPyAssert(self.data.nparPivot, 1, "Number of pivot parameters")
if len(args) + len(kwargs) > 1:
raise RROMPyException(("Wrong number of parameters passed. "
"Only 1 available."))
elif len(args) + len(kwargs) == 1:
if len(args) == 1:
mVals = args[0]
else:
mVals = kwargs["marginalVals"]
if not isinstance(mVals, Iterable): mVals = [mVals]
mVals = list(mVals)
else:
mVals = [fp]
rDim = mVals.index(fp)
if rDim < len(mVals) - 1 and fp in mVals[rDim + 1 :]:
raise RROMPyException(("Exactly 1 'freepar' entry in "
"marginalVals must be provided."))
if rDim != self.data.directionPivot[0]:
raise RROMPyException(("'freepar' entry in marginalVals must "
"coincide with pivot direction."))
mVals[rDim] = self.data.mu0(rDim)[0]
muM = self.checkParameterListMarginal([mVals[j]
for j in range(len(mVals)) if j != rDim])
iM = int(self.data.marginalInterp(muM))
roots = self.data.scaleFactor[rDim] * self.data.Qs[iM].roots()
return self.mapParameterList(self.mapParameterList(self.data.mu0(rDim),
idx = [rDim])(0, 0)
+ roots, "B", [rDim])(0)
def getResidues(self, *args, **kwargs) -> Np2D:
"""
Obtain approximant residues.
Returns:
Numpy matrix with residues as columns.
"""
RROMPyAssert(self.data.nparPivot, 1, "Number of pivot parameters")
if len(args) + len(kwargs) > 1:
raise RROMPyException(("Wrong number of parameters passed. "
"Only 1 available."))
elif len(args) + len(kwargs) == 1:
if len(args) == 1:
mVals = args[0]
else:
mVals = kwargs["marginalVals"]
if not isinstance(mVals, Iterable): mVals = [mVals]
mVals = list(mVals)
else:
mVals = [fp]
rDim = mVals.index(fp)
if rDim < len(mVals) - 1 and fp in mVals[rDim + 1 :]:
raise RROMPyException(("Exactly 1 'freepar' entry in "
"marginalVals must be provided."))
if rDim != self.data.directionPivot[0]:
raise RROMPyException(("'freepar' entry in marginalVals must "
"coincide with pivot direction."))
mVals[rDim] = self.data.mu0(rDim)[0]
muM = self.checkParameterListMarginal([mVals[j]
for j in range(len(mVals)) if j != rDim])
iM = int(self.data.marginalInterp(muM))
res, pls, basis = rational2heaviside(self.data.Ps[iM],
self.data.Qs[iM])
res = res[: len(pls), :].T
if hasattr(self.data, "_is_C_quadratic") and self.data._is_C_quadratic:
self._setupQuadMapping()
res = res[self.quad_mapping[0]] * res[self.quad_mapping[1]].conj()
if not self.data._collapsed: res = dot(self.data.projMat, res).T
if (hasattr(self.data, "_is_C_quadratic")
and self.data._is_C_quadratic == 2):
res = np.real(res)
return pls, res
diff --git a/rrompy/reduction_methods/standard/greedy/generic_greedy_approximant.py b/rrompy/reduction_methods/standard/greedy/generic_greedy_approximant.py
index fe2881a..e50cd40 100644
--- a/rrompy/reduction_methods/standard/greedy/generic_greedy_approximant.py
+++ b/rrompy/reduction_methods/standard/greedy/generic_greedy_approximant.py
@@ -1,620 +1,620 @@
# Copyright (C) 2018-2020 by the RROMPy authors
#
# This file is part of RROMPy.
#
# RROMPy is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# RROMPy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with RROMPy. If not, see .
#
from abc import abstractmethod
from copy import deepcopy as copy
import numpy as np
from matplotlib import pyplot as plt
from rrompy.hfengines.base.linear_affine_engine import checkIfAffine
from rrompy.reduction_methods.standard.generic_standard_approximant import (
GenericStandardApproximant)
from rrompy.utilities.base.types import (Np1D, Np2D, Tuple, List, paramVal,
paramList, sampList)
from rrompy.utilities.base import verbosityManager as vbMng
from rrompy.utilities.numerical import dot
from rrompy.utilities.expression import expressionEvaluator
from rrompy.utilities.exception_manager import (RROMPyException, RROMPyAssert,
RROMPyWarning)
from rrompy.sampling.sample_list import sampleList
from rrompy.parameter import emptyParameterList, parameterList
from rrompy.utilities.parallel import masterCore
__all__ = ['GenericGreedyApproximant']
def localL2Distance(mus:Np2D, badmus:Np2D) -> Np2D:
return np.linalg.norm(np.tile(mus[..., np.newaxis], [1, 1, len(badmus)])
- badmus[..., np.newaxis].T, axis = 1)
def pruneSamples(mus:paramList, badmus:paramList,
tol : float = 1e-8) -> Np1D:
"""Remove from mus all the elements which are too close to badmus."""
if isinstance(mus, (parameterList, sampleList)): mus = mus.data
if isinstance(badmus, (parameterList, sampleList)): badmus = badmus.data
if len(badmus) == 0: return np.arange(len(mus))
proximity = np.min(localL2Distance(mus, badmus), axis = 1)
return np.where(proximity <= tol)[0]
class GenericGreedyApproximant(GenericStandardApproximant):
"""
ROM greedy interpolant computation for parametric problems
(ABSTRACT).
Args:
HFEngine: HF problem solver.
mu0(optional): Default parameter. Defaults to 0.
approxParameters(optional): Dictionary containing values for main
parameters of approximant. Recognized keys are:
- 'POD': kind of snapshots orthogonalization; allowed values
include 0, 1/2, and 1; defaults to 1, i.e. POD;
- 'scaleFactorDer': scaling factors for derivative computation;
defaults to 'AUTO';
- 'S': number of starting training points;
- 'sampler': sample point generator;
- 'greedyTol': uniform error tolerance for greedy algorithm;
defaults to 1e-2;
- 'collinearityTol': collinearity tolerance for greedy algorithm;
defaults to 0.;
- 'maxIter': maximum number of greedy steps; defaults to 1e2;
- 'nTestPoints': number of test points; defaults to 5e2;
- 'trainSetGenerator': training sample points generator; defaults
to sampler.
Defaults to empty dict.
verbosity(optional): Verbosity level. Defaults to 10.
Attributes:
HFEngine: HF problem solver.
mu0: Default parameter.
mus: Array of snapshot parameters.
approxParameters: Dictionary containing values for main parameters of
approximant. Recognized keys are in parameterList.
parameterListSoft: Recognized keys of soft approximant parameters:
- 'POD': kind of snapshots orthogonalization;
- 'scaleFactorDer': scaling factors for derivative computation;
- 'greedyTol': uniform error tolerance for greedy algorithm;
- 'collinearityTol': collinearity tolerance for greedy algorithm;
- 'maxIter': maximum number of greedy steps;
- 'nTestPoints': number of test points;
- 'trainSetGenerator': training sample points generator.
parameterListCritical: Recognized keys of critical approximant
parameters:
- 'S': total number of samples current approximant relies upon;
- 'sampler': sample point generator.
verbosity: Verbosity level.
POD: Kind of snapshots orthogonalization.
scaleFactorDer: Scaling factors for derivative computation.
S: number of test points.
sampler: Sample point generator.
greedyTol: Uniform error tolerance for greedy algorithm.
collinearityTol: Collinearity tolerance for greedy algorithm.
maxIter: maximum number of greedy steps.
nTestPoints: number of starting training points.
trainSetGenerator: training sample points generator.
muBounds: list of bounds for parameter values.
samplingEngine: Sampling engine.
uHF: High fidelity solution(s) with parameter(s) lastSolvedHF as
sampleList.
lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
solution(s) as parameterList.
uApproxReduced: Reduced approximate solution(s) with parameter(s)
lastSolvedApprox as sampleList.
lastSolvedApproxReduced: Parameter(s) corresponding to last computed
reduced approximate solution(s) as parameterList.
uApprox: Approximate solution(s) with parameter(s) lastSolvedApprox as
sampleList.
lastSolvedApprox: Parameter(s) corresponding to last computed
approximate solution(s) as parameterList.
"""
def __init__(self, *args, **kwargs):
self._preInit()
if not hasattr(self, "_affine_lvl"): self._affine_lvl = []
self._affine_lvl += [1]
self._addParametersToList(["greedyTol", "collinearityTol", "maxIter",
"nTestPoints", "trainSetGenerator"],
[1e-2, 0., 1e2, 5e2, "AUTO"])
super().__init__(*args, **kwargs)
self._postInit()
@property
def greedyTol(self):
"""Value of greedyTol."""
return self._greedyTol
@greedyTol.setter
def greedyTol(self, greedyTol):
if greedyTol < 0:
raise RROMPyException("greedyTol must be non-negative.")
if hasattr(self, "_greedyTol") and self.greedyTol is not None:
greedyTolold = self.greedyTol
else:
greedyTolold = -1
self._greedyTol = greedyTol
self._approxParameters["greedyTol"] = self.greedyTol
if greedyTolold != self.greedyTol:
self.resetSamples()
@property
def collinearityTol(self):
"""Value of collinearityTol."""
return self._collinearityTol
@collinearityTol.setter
def collinearityTol(self, collinearityTol):
if collinearityTol < 0:
raise RROMPyException("collinearityTol must be non-negative.")
if (hasattr(self, "_collinearityTol")
and self.collinearityTol is not None):
collinearityTolold = self.collinearityTol
else:
collinearityTolold = -1
self._collinearityTol = collinearityTol
self._approxParameters["collinearityTol"] = self.collinearityTol
if collinearityTolold != self.collinearityTol:
self.resetSamples()
@property
def maxIter(self):
"""Value of maxIter."""
return self._maxIter
@maxIter.setter
def maxIter(self, maxIter):
if maxIter <= 0: raise RROMPyException("maxIter must be positive.")
if hasattr(self, "_maxIter") and self.maxIter is not None:
maxIterold = self.maxIter
else:
maxIterold = -1
self._maxIter = maxIter
self._approxParameters["maxIter"] = self.maxIter
if maxIterold != self.maxIter:
self.resetSamples()
@property
def nTestPoints(self):
"""Value of nTestPoints."""
return self._nTestPoints
@nTestPoints.setter
def nTestPoints(self, nTestPoints):
if nTestPoints <= 0:
raise RROMPyException("nTestPoints must be positive.")
if not np.isclose(nTestPoints, np.int(nTestPoints)):
raise RROMPyException("nTestPoints must be an integer.")
nTestPoints = np.int(nTestPoints)
if hasattr(self, "_nTestPoints") and self.nTestPoints is not None:
nTestPointsold = self.nTestPoints
else:
nTestPointsold = -1
self._nTestPoints = nTestPoints
self._approxParameters["nTestPoints"] = self.nTestPoints
if nTestPointsold != self.nTestPoints:
self.resetSamples()
@property
def trainSetGenerator(self):
"""Value of trainSetGenerator."""
return self._trainSetGenerator
@trainSetGenerator.setter
def trainSetGenerator(self, trainSetGenerator):
if (isinstance(trainSetGenerator, (str,))
and trainSetGenerator.upper() == "AUTO"):
trainSetGenerator = self.sampler
if 'generatePoints' not in dir(trainSetGenerator):
raise RROMPyException("trainSetGenerator type not recognized.")
if (hasattr(self, '_trainSetGenerator')
and self.trainSetGenerator not in [None, "AUTO"]):
trainSetGeneratorOld = self.trainSetGenerator
self._trainSetGenerator = trainSetGenerator
self._approxParameters["trainSetGenerator"] = self.trainSetGenerator
if (not 'trainSetGeneratorOld' in locals()
or trainSetGeneratorOld != self.trainSetGenerator):
self.resetSamples()
def resetSamples(self):
"""Reset samples."""
super().resetSamples()
self._mus = emptyParameterList()
def _affineResidualMatricesContraction(self, rb:Np2D, rA : Np2D = None) \
-> Tuple[Np1D, Np1D, Np1D]:
self.assembleReducedResidualBlocks(full = rA is not None)
# 'ij,jk,ik->k', resbb, radiusb, radiusb.conj()
ff = np.sum(self.trainedModel.data.resbb.dot(rb) * rb.conj(), axis = 0)
if rA is None: return ff
# 'ijk,jkl,il->l', resAb, radiusA, radiusb.conj()
Lf = np.sum(np.tensordot(self.trainedModel.data.resAb, rA, 2)
* rb.conj(), axis = 0)
# 'ijkl,klt,ijt->t', resAA, radiusA, radiusA.conj()
LL = np.sum(np.tensordot(self.trainedModel.data.resAA, rA, 2)
* rA.conj(), axis = (0, 1))
return ff, Lf, LL
def getErrorEstimatorAffine(self, mus:Np1D) -> Np1D:
"""Standard residual estimator."""
checkIfAffine(self.HFEngine, "apply affinity-based error estimator",
False, self._affine_lvl)
self.HFEngine.buildA()
self.HFEngine.buildb()
mus = self.checkParameterList(mus)
tMverb, self.trainedModel.verbosity = self.trainedModel.verbosity, 0
uApproxRs = self.getApproxReduced(mus).data
self.trainedModel.verbosity = tMverb
muTestEff = self.mapParameterList(mus)
radiusA = np.empty((len(self.HFEngine.thAs), len(mus)),
dtype = np.complex)
radiusb = np.empty((len(self.HFEngine.thbs), len(mus)),
dtype = np.complex)
for j, thA in enumerate(self.HFEngine.thAs):
radiusA[j] = expressionEvaluator(thA[0], muTestEff)
for j, thb in enumerate(self.HFEngine.thbs):
radiusb[j] = expressionEvaluator(thb[0], muTestEff)
radiusA = np.expand_dims(uApproxRs, 1) * radiusA
ff, Lf, LL = self._affineResidualMatricesContraction(radiusb, radiusA)
err = np.abs((LL - 2. * np.real(Lf) + ff) / ff) ** .5
return err
def errorEstimator(self, mus:Np1D, return_max : bool = False) -> Np1D:
setupOK = self.setupApproxLocal()
if setupOK > 0:
err = np.empty(len(mus))
err[:] = np.nan
if not return_max: return err
return err, [- setupOK], np.nan
mus = self.checkParameterList(mus)
vbMng(self.trainedModel, "INIT",
"Evaluating error estimator at mu = {}.".format(mus), 10)
err = self.getErrorEstimatorAffine(mus)
- vbMng(self.trainedModel, "DEL", "Done evaluating error estimator", 10)
+ vbMng(self.trainedModel, "DEL", "Done evaluating error estimator.", 10)
if not return_max: return err
idxMaxEst = [np.argmax(err)]
return err, idxMaxEst, err[idxMaxEst]
def _isLastSampleCollinear(self) -> bool:
"""Check collinearity of last sample."""
if self.collinearityTol <= 0.: return False
if self.POD == 1:
reff = self.samplingEngine.Rscale[:, -1]
else:
RROMPyWarning(("Repeated orthogonalization of the samples for "
"collinearity check. Consider setting POD to "
"True."))
if not hasattr(self, "_PODEngine"):
from rrompy.sampling import PODEngine
self._PODEngine = PODEngine(self.HFEngine)
reff = self._PODEngine.generalizedQR(self.samplingEngine.samples,
only_R = True,
is_state = True)[:, -1]
cLevel = np.abs(reff[-1]) / np.linalg.norm(reff)
cLevel = np.inf if np.isclose(cLevel, 0.) else cLevel ** -1.
vbMng(self, "MAIN", "Collinearity indicator {:.4e}.".format(cLevel), 3)
return cLevel > self.collinearityTol
def plotEstimator(self, est:Np1D, idxMax:List[int], estMax:List[float]):
if (not (np.any(np.isnan(est)) or np.any(np.isinf(est)))
and masterCore()):
fig = plt.figure(figsize = plt.figaspect(1. / self.npar))
for jpar in range(self.npar):
ax = fig.add_subplot(1, self.npar, 1 + jpar)
musre = np.array(self.muTest.re.data)
errCP = copy(est)
idx = np.delete(np.arange(self.npar), jpar)
while len(musre) > 0:
if self.npar == 1:
currIdx = np.arange(len(musre))
else:
currIdx = np.where(np.isclose(np.sum(
np.abs(musre[:, idx] - musre[0, idx]), 1), 0.))[0]
ax.semilogy(musre[currIdx, jpar], errCP[currIdx], 'k',
linewidth = 1)
musre = np.delete(musre, currIdx, 0)
errCP = np.delete(errCP, currIdx)
ax.semilogy([self.muBounds.re(0, jpar),
self.muBounds.re(-1, jpar)],
[self.greedyTol] * 2, 'r--')
ax.semilogy(self.mus.re(jpar),
2. * self.greedyTol * np.ones(len(self.mus)), '*m')
if len(idxMax) > 0 and estMax is not None:
ax.semilogy(self.muTest.re(idxMax, jpar), estMax, 'xr')
ax.set_xlim(*list(self.sampler.lims.re(jpar)))
ax.grid()
plt.tight_layout()
plt.show()
def greedyNextSample(self, muidx:int, plotEst : str = "NONE")\
-> Tuple[Np1D, int, float, paramVal]:
"""Compute next greedy snapshot of solution map."""
RROMPyAssert(self._mode, message = "Cannot add greedy sample.")
mus = copy(self.muTest[muidx])
self.muTest.pop(muidx)
for j, mu in enumerate(mus):
vbMng(self, "MAIN",
("Adding sample point no. {} at {} to training "
"set.").format(len(self.mus) + 1, mu), 3)
self.mus.append(mu)
self._S = len(self.mus)
self._approxParameters["S"] = self.S
if (self.samplingEngine.nsamples <= len(mus) - j - 1
or not np.allclose(mu, self.samplingEngine.mus[j - len(mus)])):
self.samplingEngine.nextSample(mu)
if self._isLastSampleCollinear():
vbMng(self, "MAIN",
("Collinearity above tolerance detected. Starting "
"preemptive greedy loop termination."), 3)
self._collinearityFlag = 1
errorEstTest = np.empty(len(self.muTest))
errorEstTest[:] = np.nan
return errorEstTest, [-1], np.nan, np.nan
errorEstTest, muidx, maxErrorEst = self.errorEstimator(self.muTest,
True)
if plotEst == "ALL":
self.plotEstimator(errorEstTest, muidx, maxErrorEst)
return errorEstTest, muidx, maxErrorEst, self.muTest[muidx]
def _preliminaryTraining(self):
"""Initialize starting snapshots of solution map."""
RROMPyAssert(self._mode, message = "Cannot start greedy algorithm.")
if self.samplingEngine.nsamples > 0: return
self.resetSamples()
self.computeScaleFactor()
self.samplingEngine.scaleFactor = self.scaleFactorDer
self.mus = self.trainSetGenerator.generatePoints(self.S)
while len(self.mus) > self.S: self.mus.pop()
muTestBase = self.sampler.generatePoints(self.nTestPoints, False)
idxPop = pruneSamples(self.mapParameterList(muTestBase),
self.mapParameterList(self.mus),
1e-10 * self.scaleFactor[0])
muTestBase.pop(idxPop)
muLast = copy(self.mus[-1])
self.mus.pop()
if len(self.mus) > 0:
vbMng(self, "MAIN",
("Adding first {} sample point{} at {} to training "
"set.").format(self.S - 1, "" + "s" * (self.S > 2),
self.mus), 3)
self.samplingEngine.iterSample(self.mus)
self._S = len(self.mus)
self._approxParameters["S"] = self.S
self.muTest = emptyParameterList()
self.muTest.reset((len(muTestBase) + 1, self.mus.shape[1]))
self.muTest[: -1] = muTestBase.data
self.muTest[-1] = muLast.data
@abstractmethod
def setupApproxLocal(self) -> int:
if self.checkComputedApprox(): return -1
RROMPyAssert(self._mode, message = "Cannot setup approximant.")
vbMng(self, "INIT", "Setting up local approximant.", 5)
pass
vbMng(self, "DEL", "Done setting up local approximant.", 5)
return 0
def setupApprox(self, plotEst : str = "NONE") -> int:
"""Compute greedy snapshots of solution map."""
if self.checkComputedApprox(): return -1
RROMPyAssert(self._mode, message = "Cannot start greedy algorithm.")
vbMng(self, "INIT", "Starting computation of snapshots.", 3)
self._collinearityFlag = 0
self._preliminaryTraining()
muidx, self.firstGreedyIter = [len(self.muTest) - 1], True
errorEstTest, maxErrorEst = [np.inf], np.inf
max2ErrorEst, trainedModelOld = np.inf, None
while self.firstGreedyIter or (len(self.muTest) > 0
and (maxErrorEst is None or max2ErrorEst > self.greedyTol)
and self.samplingEngine.nsamples < self.maxIter):
muTestOld, errorEstTestOld = self.muTest, errorEstTest
muidxOld, maxErrorEstOld = muidx, maxErrorEst
errorEstTest, muidx, maxErrorEst, mu = self.greedyNextSample(muidx,
plotEst)
if maxErrorEst is not None and (np.any(np.isnan(maxErrorEst))
or np.any(np.isinf(maxErrorEst))):
if self._collinearityFlag == 0 and not self.firstGreedyIter:
RROMPyWarning(("Instability in a posteriori "
"estimator. Starting preemptive greedy "
"loop termination."))
self.muTest, errorEstTest = muTestOld, errorEstTestOld
if self.firstGreedyIter and muidx[0] < 0:
self.trainedModel = None
raise RROMPyException(("Instability in approximant "
"computation. Aborting greedy "
"iterations."))
self._S = trainedModelOld.data.approxParameters["S"]
self._approxParameters["S"] = self.S
while self.samplingEngine.nsamples > self.S:
self.samplingEngine.popSample()
while len(self.mus) > self.S: self.mus.pop(-1)
muidx, maxErrorEst = muidxOld, maxErrorEstOld
break
if maxErrorEst is not None:
max2ErrorEst = np.max(maxErrorEst)
vbMng(self, "MAIN", ("Uniform testing error estimate "
"{:.4e}.").format(max2ErrorEst), 3)
if self.firstGreedyIter:
trainedModelOld = copy(self.trainedModel)
else:
trainedModelOld.data = copy(self.trainedModel.data)
self.firstGreedyIter = False
vbMng(self, "DEL", ("Done computing snapshots (final snapshot count: "
"{}).").format(len(self.mus)), 3)
if (maxErrorEst is None or max2ErrorEst <= self.greedyTol
or np.any(np.isnan(maxErrorEst)) or np.any(np.isinf(maxErrorEst))):
while self.samplingEngine.nsamples > self.S:
self.samplingEngine.popSample()
while len(self.mus) > self.S: self.mus.pop(-1)
else:
while len(self.mus) < self.S:
self.mus.append(self.samplingEngine.mus[len(self.mus)])
self.setupApproxLocal()
if plotEst == "LAST":
self.plotEstimator(errorEstTest, muidx, maxErrorEst)
return 0
def assembleReducedResidualGramian(self, pMat:sampList):
"""
Build residual gramian of reduced linear system through projections.
"""
if (not hasattr(self.trainedModel.data, "gramian")
or self.trainedModel.data.gramian is None):
gramian = self.HFEngine.innerProduct(pMat, pMat, dual = True)
else:
Sold = self.trainedModel.data.gramian.shape[0]
S = len(self.mus)
if Sold > S:
gramian = self.trainedModel.data.gramian[: S, : S]
else:
idxOld = list(range(Sold))
idxNew = list(range(Sold, S))
gramian = np.empty((S, S), dtype = np.complex)
gramian[: Sold, : Sold] = self.trainedModel.data.gramian
gramian[: Sold, Sold :] = self.HFEngine.innerProduct(
pMat(idxNew), pMat(idxOld), dual = True)
gramian[Sold :, : Sold] = gramian[: Sold, Sold :].T.conj()
gramian[Sold :, Sold :] = self.HFEngine.innerProduct(
pMat(idxNew), pMat(idxNew), dual = True)
self.trainedModel.data.gramian = gramian
def assembleReducedResidualBlocksbb(self, bs:List[Np1D]):
"""
Build blocks (of type bb) of reduced linear system through projections.
"""
nbs = len(bs)
if (not hasattr(self.trainedModel.data, "resbb")
or self.trainedModel.data.resbb is None):
resbb = np.empty((nbs, nbs), dtype = np.complex)
for i in range(nbs):
Mbi = bs[i]
resbb[i, i] = self.HFEngine.innerProduct(Mbi, Mbi, dual = True)
for j in range(i):
Mbj = bs[j]
resbb[i, j] = self.HFEngine.innerProduct(Mbj, Mbi,
dual = True)
for i in range(nbs):
for j in range(i + 1, nbs):
resbb[i, j] = resbb[j, i].conj()
self.trainedModel.data.resbb = resbb
def assembleReducedResidualBlocksAb(self, As:List[Np2D], bs:List[Np1D],
pMat:sampList):
"""
Build blocks (of type Ab) of reduced linear system through projections.
"""
nAs = len(As)
nbs = len(bs)
S = len(self.mus)
if (not hasattr(self.trainedModel.data, "resAb")
or self.trainedModel.data.resAb is None):
if isinstance(pMat, (parameterList, sampleList)): pMat = pMat.data
resAb = np.empty((nbs, S, nAs), dtype = np.complex)
for j in range(nAs):
MAj = dot(As[j], pMat)
for i in range(nbs):
Mbi = bs[i]
resAb[i, :, j] = self.HFEngine.innerProduct(MAj, Mbi,
dual = True)
else:
Sold = self.trainedModel.data.resAb.shape[1]
if Sold == S: return
if Sold > S:
resAb = self.trainedModel.data.resAb[:, : S, :]
else:
if isinstance(pMat, (parameterList, sampleList)):
pMat = pMat.data
resAb = np.empty((nbs, S, nAs), dtype = np.complex)
resAb[:, : Sold, :] = self.trainedModel.data.resAb
for j in range(nAs):
MAj = dot(As[j], pMat[:, Sold :])
for i in range(nbs):
Mbi = bs[i]
resAb[i, Sold :, j] = self.HFEngine.innerProduct(
MAj, Mbi, dual = True)
self.trainedModel.data.resAb = resAb
def assembleReducedResidualBlocksAA(self, As:List[Np2D], pMat:sampList):
"""
Build blocks (of type AA) of reduced linear system through projections.
"""
nAs = len(As)
S = len(self.mus)
if (not hasattr(self.trainedModel.data, "resAA")
or self.trainedModel.data.resAA is None):
if isinstance(pMat, (parameterList, sampleList)): pMat = pMat.data
resAA = np.empty((S, nAs, S, nAs), dtype = np.complex)
for i in range(nAs):
MAi = dot(As[i], pMat)
resAA[:, i, :, i] = self.HFEngine.innerProduct(MAi, MAi,
dual = True)
for j in range(i):
MAj = dot(As[j], pMat)
resAA[:, i, :, j] = self.HFEngine.innerProduct(MAj, MAi,
dual = True)
for i in range(nAs):
for j in range(i + 1, nAs):
resAA[:, i, :, j] = resAA[:, j, :, i].T.conj()
else:
Sold = self.trainedModel.data.resAA.shape[0]
if Sold == S: return
if Sold > S:
resAA = self.trainedModel.data.resAA[: S, :, : S, :]
else:
if isinstance(pMat, (parameterList, sampleList)):
pMat = pMat.data
resAA = np.empty((S, nAs, S, nAs), dtype = np.complex)
resAA[: Sold, :, : Sold, :] = self.trainedModel.data.resAA
for i in range(nAs):
MAi = dot(As[i], pMat)
resAA[: Sold, i, Sold :, i] = self.HFEngine.innerProduct(
MAi[:, Sold :], MAi[:, : Sold], dual = True)
resAA[Sold :, i, : Sold, i] = resAA[: Sold, i,
Sold :, i].T.conj()
resAA[Sold :, i, Sold :, i] = self.HFEngine.innerProduct(
MAi[:, Sold :], MAi[:, Sold :], dual = True)
for j in range(i):
MAj = dot(As[j], pMat)
resAA[: Sold, i, Sold :, j] = (
self.HFEngine.innerProduct(MAj[:, Sold :],
MAi[:, : Sold],
dual = True))
resAA[Sold :, i, : Sold, j] = (
self.HFEngine.innerProduct(MAj[:, : Sold],
MAi[:, Sold :],
dual = True))
resAA[Sold :, i, Sold :, j] = (
self.HFEngine.innerProduct(MAj[:, Sold :],
MAi[:, Sold :],
dual = True))
for i in range(nAs):
for j in range(i + 1, nAs):
resAA[: Sold, i, Sold :, j] = (
resAA[Sold :, j, : Sold, i].T.conj())
resAA[Sold :, i, : Sold, j] = (
resAA[: Sold, j, Sold :, i].T.conj())
resAA[Sold :, i, Sold :, j] = (
resAA[Sold :, j, Sold :, i].T.conj())
self.trainedModel.data.resAA = resAA
def assembleReducedResidualBlocks(self, full : bool = False):
"""Build affine blocks of affine decomposition of residual."""
if full:
checkIfAffine(self.HFEngine, "assemble reduced residual blocks",
False, self._affine_lvl)
else:
checkIfAffine(self.HFEngine, "assemble reduced RHS blocks", True,
self._affine_lvl)
self.HFEngine.buildb()
self.assembleReducedResidualBlocksbb(self.HFEngine.bs)
if full:
pMat = self.samplingEngine.projectionMatrix
self.HFEngine.buildA()
self.assembleReducedResidualBlocksAb(self.HFEngine.As,
self.HFEngine.bs, pMat)
self.assembleReducedResidualBlocksAA(self.HFEngine.As, pMat)
diff --git a/rrompy/reduction_methods/standard/greedy/rational_interpolant_greedy.py b/rrompy/reduction_methods/standard/greedy/rational_interpolant_greedy.py
index 2251b78..d8fa57d 100644
--- a/rrompy/reduction_methods/standard/greedy/rational_interpolant_greedy.py
+++ b/rrompy/reduction_methods/standard/greedy/rational_interpolant_greedy.py
@@ -1,531 +1,531 @@
# Copyright (C) 2018-2020 by the RROMPy authors
#
# This file is part of RROMPy.
#
# RROMPy is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# RROMPy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with RROMPy. If not, see .
#
from copy import deepcopy as copy
import numpy as np
from rrompy.hfengines.base.linear_affine_engine import checkIfAffine
from .generic_greedy_approximant import GenericGreedyApproximant
from rrompy.utilities.poly_fitting.polynomial import (polybases, polyfitname,
PolynomialInterpolator as PI,
polyvander)
from rrompy.utilities.numerical import dot
from rrompy.utilities.numerical.degree import totalDegreeN
from rrompy.utilities.expression import expressionEvaluator
from rrompy.reduction_methods.standard import RationalInterpolant
from rrompy.utilities.base.types import Np1D, Tuple, paramVal, List
from rrompy.utilities.base.verbosity_depth import (verbosityManager as vbMng,
getVerbosityDepth, setVerbosityDepth)
from rrompy.utilities.poly_fitting import customFit
from rrompy.utilities.exception_manager import (RROMPyWarning, RROMPyException,
RROMPyAssert, RROMPy_FRAGILE)
from rrompy.sampling import sampleList, emptySampleList
__all__ = ['RationalInterpolantGreedy']
class RationalInterpolantGreedy(GenericGreedyApproximant, RationalInterpolant):
"""
ROM greedy rational interpolant computation for parametric problems.
Args:
HFEngine: HF problem solver.
mu0(optional): Default parameter. Defaults to 0.
approxParameters(optional): Dictionary containing values for main
parameters of approximant. Recognized keys are:
- 'POD': kind of snapshots orthogonalization; allowed values
include 0, 1/2, and 1; defaults to 1, i.e. POD;
- 'scaleFactorDer': scaling factors for derivative computation;
defaults to 'AUTO';
- 'S': number of starting training points;
- 'sampler': sample point generator;
- 'greedyTol': uniform error tolerance for greedy algorithm;
defaults to 1e-2;
- 'collinearityTol': collinearity tolerance for greedy algorithm;
defaults to 0.;
- 'maxIter': maximum number of greedy steps; defaults to 1e2;
- 'nTestPoints': number of test points; defaults to 5e2;
- 'trainSetGenerator': training sample points generator; defaults
to sampler;
- 'polybasis': type of basis for interpolation; defaults to
'MONOMIAL';
- 'errorEstimatorKind': kind of error estimator; available values
include 'AFFINE', 'DISCREPANCY', 'LOOK_AHEAD',
'LOOK_AHEAD_RES', 'LOOK_AHEAD_OUTPUT', and 'NONE'; defaults to
'NONE';
- 'functionalSolve': strategy for minimization of denominator
functional; allowed values include 'NORM', 'DOMINANT', 'NODAL',
'BARYCENTRIC_NORM', and 'BARYCENTRIC[_AVERAGE]' (check pdf in
main folder for meaning); defaults to 'NORM';
- 'interpRcond': tolerance for interpolation; defaults to None;
- 'robustTol': tolerance for robust rational denominator
management; defaults to 0.
Defaults to empty dict.
verbosity(optional): Verbosity level. Defaults to 10.
Attributes:
HFEngine: HF problem solver.
mu0: Default parameter.
mus: Array of snapshot parameters.
approxParameters: Dictionary containing values for main parameters of
approximant. Recognized keys are in parameterList.
parameterListSoft: Recognized keys of soft approximant parameters:
- 'POD': kind of snapshots orthogonalization;
- 'scaleFactorDer': scaling factors for derivative computation;
- 'greedyTol': uniform error tolerance for greedy algorithm;
- 'collinearityTol': collinearity tolerance for greedy algorithm;
- 'maxIter': maximum number of greedy steps;
- 'nTestPoints': number of test points;
- 'trainSetGenerator': training sample points generator;
- 'errorEstimatorKind': kind of error estimator;
- 'functionalSolve': strategy for minimization of denominator
functional;
- 'interpRcond': tolerance for interpolation;
- 'robustTol': tolerance for robust rational denominator
management.
parameterListCritical: Recognized keys of critical approximant
parameters:
- 'S': total number of samples current approximant relies upon;
- 'sampler': sample point generator.
verbosity: Verbosity level.
POD: Kind of snapshots orthogonalization.
scaleFactorDer: Scaling factors for derivative computation.
S: number of test points.
sampler: Sample point generator.
greedyTol: uniform error tolerance for greedy algorithm.
collinearityTol: Collinearity tolerance for greedy algorithm.
maxIter: maximum number of greedy steps.
nTestPoints: number of starting training points.
trainSetGenerator: training sample points generator.
robustTol: tolerance for robust rational denominator management.
errorEstimatorKind: kind of error estimator.
functionalSolve: Strategy for minimization of denominator functional.
interpRcond: tolerance for interpolation.
robustTol: tolerance for robust rational denominator management.
muBounds: list of bounds for parameter values.
samplingEngine: Sampling engine.
uHF: High fidelity solution(s) with parameter(s) lastSolvedHF as
sampleList.
lastSolvedHF: Parameter(s) corresponding to last computed high fidelity
solution(s) as parameterList.
uApproxReduced: Reduced approximate solution(s) with parameter(s)
lastSolvedApprox as sampleList.
lastSolvedApproxReduced: Parameter(s) corresponding to last computed
reduced approximate solution(s) as parameterList.
uApprox: Approximate solution(s) with parameter(s) lastSolvedApprox as
sampleList.
lastSolvedApprox: Parameter(s) corresponding to last computed
approximate solution(s) as parameterList.
"""
_allowedEstimatorKinds = ["AFFINE", "DISCREPANCY", "LOOK_AHEAD",
"LOOK_AHEAD_RES", "LOOK_AHEAD_OUTPUT", "NONE"]
def __init__(self, *args, **kwargs):
self._preInit()
self._addParametersToList(["errorEstimatorKind"], ["DISCREPANCY"],
toBeExcluded = ["M", "N", "polydegreetype",
"radialDirectionalWeights"])
super().__init__(*args, **kwargs)
self._postInit()
@property
def E(self):
"""Value of E."""
self._E = self.sampleBatchIdx - 1
return self._E
@E.setter
def E(self, E):
RROMPyWarning(("E is used just to simplify inheritance, and its value "
"cannot be changed from that of sampleBatchIdx - 1."))
def _setMAuto(self):
self.M = self.E
def _setNAuto(self):
self.N = self.E
@property
def polydegreetype(self):
"""Value of polydegreetype."""
return "TOTAL"
@polydegreetype.setter
def polydegreetype(self, polydegreetype):
RROMPyWarning(("polydegreetype is used just to simplify inheritance, "
"and its value cannot be changed from 'TOTAL'."))
@property
def polybasis(self):
"""Value of polybasis."""
return self._polybasis
@polybasis.setter
def polybasis(self, polybasis):
try:
polybasis = polybasis.upper().strip().replace(" ","")
if polybasis not in polybases:
raise RROMPyException("Sample type not recognized.")
self._polybasis = polybasis
except:
RROMPyWarning(("Prescribed polybasis not recognized. Overriding "
"to 'MONOMIAL'."))
self._polybasis = "MONOMIAL"
self._approxParameters["polybasis"] = self.polybasis
@property
def errorEstimatorKind(self):
"""Value of errorEstimatorKind."""
return self._errorEstimatorKind
@errorEstimatorKind.setter
def errorEstimatorKind(self, errorEstimatorKind):
errorEstimatorKind = errorEstimatorKind.upper()
if errorEstimatorKind not in self._allowedEstimatorKinds:
RROMPyWarning(("Error estimator kind not recognized. Overriding "
"to 'NONE'."))
errorEstimatorKind = "NONE"
self._errorEstimatorKind = errorEstimatorKind
self._approxParameters["errorEstimatorKind"] = self.errorEstimatorKind
def _polyvanderAuxiliary(self, mus, deg, *args):
return polyvander(mus, deg, *args)
def getErrorEstimatorDiscrepancy(self, mus:Np1D) -> Np1D:
"""Discrepancy-based residual estimator."""
checkIfAffine(self.HFEngine, "apply discrepancy-based error estimator",
False, self._affine_lvl)
mus = self.checkParameterList(mus)
muCTest = self.trainedModel.centerNormalize(mus)
tMverb, self.trainedModel.verbosity = self.trainedModel.verbosity, 0
QTest = self.trainedModel.getQVal(mus)
QTzero = np.where(QTest == 0.)[0]
if len(QTzero) > 0:
RROMPyWarning(("Adjusting estimator to avoid division by "
"numerically zero denominator."))
QTest[QTzero] = np.finfo(np.complex).eps / (1. + self.N)
self.HFEngine.buildA()
self.HFEngine.buildb()
nAs, nbs = self.HFEngine.nAs, self.HFEngine.nbs
muTrainEff = self.mapParameterList(self.mus)
muTestEff = self.mapParameterList(mus)
PTrain = self.trainedModel.getPVal(self.mus).data.T
QTrain = self.trainedModel.getQVal(self.mus)
QTzero = np.where(QTrain == 0.)[0]
if len(QTzero) > 0:
RROMPyWarning(("Adjusting estimator to avoid division by "
"numerically zero denominator."))
QTrain[QTzero] = np.finfo(np.complex).eps / (1. + self.N)
PTest = self.trainedModel.getPVal(mus).data
self.trainedModel.verbosity = tMverb
radiusAbTrain = np.empty((self.S, nAs * self.S + nbs),
dtype = np.complex)
radiusA = np.empty((self.S, nAs, len(mus)), dtype = np.complex)
radiusb = np.empty((nbs, len(mus)), dtype = np.complex)
for j, thA in enumerate(self.HFEngine.thAs):
idxs = j * self.S + np.arange(self.S)
radiusAbTrain[:, idxs] = expressionEvaluator(thA[0], muTrainEff,
(self.S, 1)) * PTrain
radiusA[:, j] = PTest * expressionEvaluator(thA[0], muTestEff,
(len(mus),))
for j, thb in enumerate(self.HFEngine.thbs):
idx = nAs * self.S + j
radiusAbTrain[:, idx] = QTrain * expressionEvaluator(thb[0],
muTrainEff, (self.S,))
radiusb[j] = QTest * expressionEvaluator(thb[0], muTestEff,
(len(mus),))
QRHSNorm2 = self._affineResidualMatricesContraction(radiusb)
vanTrain = self._polyvanderAuxiliary(self._musUniqueCN, self.E,
self.polybasis0, self._derIdxs,
self._reorder)
interpPQ = customFit(vanTrain, radiusAbTrain,
rcond = self.interpRcond)
vanTest = self._polyvanderAuxiliary(muCTest, self.E, self.polybasis0)
DradiusAb = vanTest.dot(interpPQ)
radiusA = (radiusA
- DradiusAb[:, : - nbs].reshape(len(mus), -1, self.S).T)
radiusb = radiusb - DradiusAb[:, - nbs :].T
ff, Lf, LL = self._affineResidualMatricesContraction(radiusb, radiusA)
err = np.abs((LL - 2. * np.real(Lf) + ff) / QRHSNorm2) ** .5
return err
def getErrorEstimatorLookAhead(self, mus:Np1D,
what : str = "") -> Tuple[Np1D, List[int]]:
"""Residual estimator based on look-ahead idea."""
errTest, QTest, idxMaxEst = self._EIMStep(mus)
mu_muTestS = mus[idxMaxEst]
app_muTestSample = self.getApproxReduced(mu_muTestS)
if self._mode == RROMPy_FRAGILE:
if what == "RES" and not self.HFEngine.isCEye:
raise RROMPyException(("Cannot compute LOOK_AHEAD_RES "
"estimator in fragile mode for "
"non-scalar C."))
app_muTestSample = dot(self.trainedModel.data.projMat[:,
: app_muTestSample.shape[0]],
app_muTestSample)
else:
app_muTestSample = dot(self.samplingEngine.projectionMatrix,
app_muTestSample)
app_muTestSample = sampleList(app_muTestSample)
if what == "RES":
errmu = self.HFEngine.residual(mu_muTestS, app_muTestSample,
post_c = False)
solmu = self.HFEngine.residual(mu_muTestS, None, post_c = False)
normSol = self.HFEngine.norm(solmu, dual = True)
normErr = self.HFEngine.norm(errmu, dual = True)
else:
applyCglob = (hasattr(self.HFEngine, "_is_C_quadratic")
and self.HFEngine._is_C_quadratic)
for j, mu in enumerate(mu_muTestS):
uEx = self.samplingEngine.nextSample(mu)
if what == "OUTPUT" and not applyCglob:
uEx = self.HFEngine.applyC(uEx, mu)
app_muTS = self.HFEngine.applyC(app_muTestSample[j], mu)
if j == 0:
app_muTestS = emptySampleList()
app_muTestS.reset((len(app_muTS), len(mu_muTestS)),
dtype = app_muTS.dtype)
app_muTestS[j] = app_muTS
if j == 0:
solmu = emptySampleList()
solmu.reset((len(uEx), len(mu_muTestS)), dtype = uEx.dtype)
solmu[j] = uEx
if what == "OUTPUT":
if applyCglob:
solmu = sampleList(self.HFEngine.applyC(solmu, mu_muTestS))
app_muTestS = sampleList(self.HFEngine.applyC(
app_muTestSample, mu_muTestS))
app_muTestSample = app_muTestS
errmu = solmu - app_muTestSample
normSol = self.HFEngine.norm(solmu, is_state = what != "OUTPUT")
normErr = self.HFEngine.norm(errmu, is_state = what != "OUTPUT")
errsamples = normErr / normSol
musT = copy(self.mus)
musT.append(mu_muTestS)
musT = self.trainedModel.centerNormalize(musT)
musC = self.trainedModel.centerNormalize(mus)
errT = np.zeros((len(musT), len(mu_muTestS)), dtype = np.complex)
errT[np.arange(len(self.mus), len(musT)),
np.arange(len(mu_muTestS))] = errsamples * QTest[idxMaxEst]
vanT = self._polyvanderAuxiliary(musT, self.E + 1, self.polybasis)
fitOut = customFit(vanT, errT, full = True, rcond = self.interpRcond)
vbMng(self, "MAIN",
("Fitting {} samples with degree {} through {}... Conditioning "
"of LS system: {:.4e}.").format(len(vanT), self.E + 1,
polyfitname(self.polybasis),
fitOut[1][2][0] / fitOut[1][2][-1]), 15)
vanC = self._polyvanderAuxiliary(musC, self.E + 1, self.polybasis)
err = np.sum(np.abs(vanC.dot(fitOut[0])), axis = -1) / QTest
return err, idxMaxEst
def getErrorEstimatorNone(self, mus:Np1D) -> Np1D:
"""EIM-based residual estimator."""
err = np.max(self._EIMStep(mus, True), axis = 1)
err *= self.greedyTol / np.mean(err)
return err
def _EIMStep(self, mus:Np1D,
only_one : bool = False) -> Tuple[Np1D, Np1D, List[int]]:
"""Residual estimator based on look-ahead idea."""
mus = self.checkParameterList(mus)
tMverb, self.trainedModel.verbosity = self.trainedModel.verbosity, 0
QTest = self.trainedModel.getQVal(mus)
QTzero = np.where(QTest == 0.)[0]
if len(QTzero) > 0:
RROMPyWarning(("Adjusting estimator to avoid division by "
"numerically zero denominator."))
QTest[QTzero] = np.finfo(np.complex).eps / (1. + self.N)
QTest = np.abs(QTest)
muCTest = self.trainedModel.centerNormalize(mus)
muCTrain = self.trainedModel.centerNormalize(self.mus)
self.trainedModel.verbosity = tMverb
vanTest = self._polyvanderAuxiliary(muCTest, self.E, self.polybasis)
vanTestNext = self._polyvanderAuxiliary(muCTest, self.E + 1,
self.polybasis)[:,
vanTest.shape[1] :]
idxsTest = np.arange(vanTestNext.shape[1])
basis = np.zeros((len(idxsTest), 0), dtype = float)
idxMaxEst = []
while len(idxsTest) > 0:
vanTrial = self._polyvanderAuxiliary(muCTrain, self.E,
self.polybasis)
vanTrialNext = self._polyvanderAuxiliary(muCTrain, self.E + 1,
self.polybasis)[:,
vanTrial.shape[1] :]
vanTrial = np.hstack((vanTrial, vanTrialNext.dot(basis).reshape(
len(vanTrialNext), basis.shape[1])))
valuesTrial = vanTrialNext[:, idxsTest]
vanTestEff = np.hstack((vanTest, vanTestNext.dot(basis).reshape(
len(vanTestNext), basis.shape[1])))
vanTestNextEff = vanTestNext[:, idxsTest]
coeffTest = np.linalg.solve(vanTrial, valuesTrial)
errTest = (np.abs(vanTestNextEff - vanTestEff.dot(coeffTest))
/ np.expand_dims(QTest, 1))
if only_one: return errTest
idxMaxErr = np.unravel_index(np.argmax(errTest), errTest.shape)
idxMaxEst += [idxMaxErr[0]]
muCTrain.append(muCTest[idxMaxErr[0]])
basis = np.pad(basis, [(0, 0), (0, 1)], "constant")
basis[idxsTest[idxMaxErr[1]], -1] = 1.
idxsTest = np.delete(idxsTest, idxMaxErr[1])
return errTest, QTest, idxMaxEst
def errorEstimator(self, mus:Np1D, return_max : bool = False) -> Np1D:
"""Standard residual-based error estimator."""
setupOK = self.setupApproxLocal()
if setupOK > 0:
err = np.empty(len(mus))
err[:] = np.nan
if not return_max: return err
return err, [- setupOK], np.nan
mus = self.checkParameterList(mus)
vbMng(self.trainedModel, "INIT",
"Evaluating error estimator at mu = {}.".format(mus), 10)
if self.errorEstimatorKind == "AFFINE":
err = self.getErrorEstimatorAffine(mus)
else:
self._setupInterpolationIndices()
if self.errorEstimatorKind == "DISCREPANCY":
err = self.getErrorEstimatorDiscrepancy(mus)
elif self.errorEstimatorKind[: 10] == "LOOK_AHEAD":
err, idxMaxEst = self.getErrorEstimatorLookAhead(mus,
self.errorEstimatorKind[11 :])
else: #if self.errorEstimatorKind == "NONE":
err = self.getErrorEstimatorNone(mus)
- vbMng(self.trainedModel, "DEL", "Done evaluating error estimator", 10)
+ vbMng(self.trainedModel, "DEL", "Done evaluating error estimator.", 10)
if not return_max: return err
if self.errorEstimatorKind[: 10] != "LOOK_AHEAD":
idxMaxEst = np.empty(self.sampleBatchSize, dtype = int)
errCP = copy(err)
for j in range(self.sampleBatchSize):
k = np.argmax(errCP)
idxMaxEst[j] = k
if j + 1 < self.sampleBatchSize:
musZero = self.trainedModel.centerNormalize(mus, mus[k])
errCP *= np.linalg.norm(musZero.data, axis = 1)
return err, idxMaxEst, err[idxMaxEst]
def plotEstimator(self, *args, **kwargs):
super().plotEstimator(*args, **kwargs)
if self.errorEstimatorKind == "NONE":
vbMng(self, "MAIN",
("Warning! Error estimator has been arbitrarily normalized "
"before plotting."), 15)
def greedyNextSample(self, *args,
**kwargs) -> Tuple[Np1D, int, float, paramVal]:
"""Compute next greedy snapshot of solution map."""
RROMPyAssert(self._mode, message = "Cannot add greedy sample.")
self.sampleBatchIdx += 1
self.sampleBatchSize = totalDegreeN(self.npar - 1, self.sampleBatchIdx)
err, muidx, maxErr, muNext = super().greedyNextSample(*args, **kwargs)
if maxErr is not None and (np.any(np.isnan(maxErr))
or np.any(np.isinf(maxErr))):
self.sampleBatchIdx -= 1
self.sampleBatchSize = totalDegreeN(self.npar - 1,
self.sampleBatchIdx)
if (self.errorEstimatorKind == "NONE" and not np.isnan(maxErr)
and not np.isinf(maxErr)):
maxErr = None
return err, muidx, maxErr, muNext
def _setSampleBatch(self, maxS:int):
self.sampleBatchIdx, self.sampleBatchSize, S = -1, 0, 0
nextBatchSize = 1
while S + nextBatchSize <= maxS:
self.sampleBatchIdx += 1
self.sampleBatchSize = nextBatchSize
S += self.sampleBatchSize
nextBatchSize = totalDegreeN(self.npar - 1,
self.sampleBatchIdx + 1)
return S
def _preliminaryTraining(self):
"""Initialize starting snapshots of solution map."""
RROMPyAssert(self._mode, message = "Cannot start greedy algorithm.")
if self.samplingEngine.nsamples > 0: return
self._S = self._setSampleBatch(self.S)
super()._preliminaryTraining()
self.M, self.N = ("AUTO",) * 2
def setupApproxLocal(self) -> int:
"""Compute rational interpolant."""
if self.checkComputedApprox(): return -1
RROMPyAssert(self._mode, message = "Cannot setup approximant.")
self.verbosity -= 10
vbMng(self, "INIT", "Setting up local approximant.", 5)
pMatOld, pMat = None, self.samplingEngine.projectionMatrix
firstRun = self.trainedModel is None
applyCglob = (hasattr(self.HFEngine, "_is_C_quadratic")
and self.HFEngine._is_C_quadratic)
if not firstRun:
Sold = len(self.trainedModel.data.mus)
if applyCglob: pMatOld = pMat[:, : Sold]
pMat = pMat[:, Sold :]
self._setupTrainedModel(pMat, not firstRun, pMatOld)
self.catchInstability = 2
vbDepth = getVerbosityDepth()
unstable = 0
if self.E > 0:
try:
Q = self._setupDenominator()
except RROMPyException as RE:
if RE.critical: raise RE from None
setVerbosityDepth(vbDepth)
RROMPyWarning("Downgraded {}: {}".format(RE.__class__.__name__,
RE))
unstable = 1
else:
Q = PI()
Q.coeffs = np.ones((1,) * self.npar, dtype = np.complex)
Q.npar = self.npar
Q.polybasis = self.polybasis
if not unstable:
self.trainedModel.data.Q = copy(Q)
try:
P = copy(self._setupNumerator())
except RROMPyException as RE:
if RE.critical: raise RE from None
setVerbosityDepth(vbDepth)
RROMPyWarning("Downgraded {}: {}".format(RE.__class__.__name__,
RE))
unstable = 1
if not unstable:
self.trainedModel.data.P = copy(P)
self.trainedModel.data.approxParameters = copy(
self.approxParameters)
vbMng(self, "DEL", "Done setting up local approximant.", 5)
self.catchInstability = 0
self.verbosity += 10
return unstable
def setupApprox(self, plotEst : str = "NONE") -> int:
val = super().setupApprox(plotEst)
if val == 0:
if (self.errorEstimatorKind[:10] == "LOOK_AHEAD"
and len(self.mus) < self.samplingEngine.nsamples):
while len(self.mus) < self.samplingEngine.nsamples:
self.mus.append(self.samplingEngine.mus[len(self.mus)])
self.trainedModel = None
self._S = self._setSampleBatch(len(self.mus) + 1)
self.setupApproxLocal()
self._setupRational(self.trainedModel.data.Q,
self.trainedModel.data.P)
self.trainedModel.data.approxParameters = copy(
self.approxParameters)
return val
def loadTrainedModel(self, filename:str):
"""Load trained reduced model from file."""
super().loadTrainedModel(filename)
self._setSampleBatch(self.S + 1)
diff --git a/rrompy/reduction_methods/standard/trained_model/trained_model_nearest_neighbor.py b/rrompy/reduction_methods/standard/trained_model/trained_model_nearest_neighbor.py
index 0f3de8d..3b8d49f 100644
--- a/rrompy/reduction_methods/standard/trained_model/trained_model_nearest_neighbor.py
+++ b/rrompy/reduction_methods/standard/trained_model/trained_model_nearest_neighbor.py
@@ -1,97 +1,106 @@
# Copyright (C) 2018-2020 by the RROMPy authors
#
# This file is part of RROMPy.
#
# RROMPy is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# RROMPy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with RROMPy. If not, see .
#
import numpy as np
from rrompy.reduction_methods.base.trained_model.trained_model import (
TrainedModel)
from rrompy.utilities.numerical.compress_matrix import compressMatrix
from rrompy.utilities.base.types import Np1D, paramList, sampList
from rrompy.utilities.base import verbosityManager as vbMng
from rrompy.utilities.exception_manager import RROMPyException, RROMPyWarning
-from rrompy.sampling import sampleList
+from rrompy.sampling import emptySampleList
__all__ = ['TrainedModelNearestNeighbor']
class TrainedModelNearestNeighbor(TrainedModel):
"""
ROM approximant evaluation for nearest neighbor approximant.
Attributes:
Data: dictionary with all that can be pickled.
"""
+ @property
+ def supportEnd(self):
+ # for a quadratic output, it is different from data.projMat.shape[1]
+ idxIncl = np.argmax(self.data.supp)
+ return self.data.supp[idxIncl] + len(self.data.vals[idxIncl])
+
def compress(self, collapse : bool = False, tol : float = 0., *args,
**kwargs):
if not collapse and tol <= 0.: return
if hasattr(self.data, "_is_C_quadratic") and self.data._is_C_quadratic:
raise RROMPyException(("Cannot compress model with quadratic "
"output."))
RMat = self.data.projMat
if not collapse:
if hasattr(self.data, "_compressTol"):
RROMPyWarning(("Recompressing already compressed model is "
"ineffective. Aborting."))
return
self.data.projMat, RMat, _ = compressMatrix(RMat, tol, *args,
**kwargs)
for j, suppj in enumerate(self.data.supp):
self.data.vals[j] = RMat[:, suppj : suppj + len(self.data.vals[j])
].dot(self.data.vals[j])
self.data.supp[j] = [0]
super().compress(collapse, tol)
def getNearestNeighbor(self, mu : paramList = []) -> Np1D:
"""
Find nearest neighbor to arbitrary parameter.
Args:
mu: Target parameter.
"""
mu = self.checkParameterList(mu)
+ idxUnique, idxmap = np.unique(self.data.NN(mu), return_inverse = True)
+ idxUnique = np.array(idxUnique, dtype = int)
vbMng(self, "INIT", "Finding nearest neighbor to mu = {}.".format(mu),
22)
- nn = []
- intM = np.array(self.data.NN(mu), dtype = int)
- for iM in intM:
+ nn = emptySampleList()
+ for i, iM in enumerate(idxUnique):
+ idx = np.where(idxmap == i)[0]
val, supp = self.data.vals[iM], self.data.supp[iM]
- nn += [np.pad(val, (supp, self.data.projMat.shape[1]
- - supp - len(val)), 'constant')]
- nn = sampleList(nn)
+ if i == 0:
+ nn.reset((self.supportEnd, len(mu)), dtype = val.dtype)
+ nn.data[:] = 0.
+ for i in idx: nn.data[supp : supp + len(val), i] = val
vbMng(self, "DEL", "Done finding nearest neighbor.", 22)
return nn
def getApproxReduced(self, mu : paramList = []) -> sampList:
"""
Evaluate reduced representation of approximant at arbitrary parameter.
Args:
mu: Target parameter.
"""
mu = self.checkParameterList(mu)
if (not hasattr(self, "lastSolvedApproxReduced")
or self.lastSolvedApproxReduced != mu):
vbMng(self, "INIT",
"Evaluating approximant at mu = {}.".format(mu), 12)
self.uApproxReduced = self.getNearestNeighbor(mu)
vbMng(self, "DEL", "Done evaluating approximant.", 12)
self.lastSolvedApproxReduced = mu
return self.uApproxReduced
def getPoles(self, *args, **kwargs) -> Np1D:
"""Obtain approximant poles."""
return np.empty(0)