diff --git a/rrompy/reduction_methods/pivoted/generic_pivoted_approximant.py b/rrompy/reduction_methods/pivoted/generic_pivoted_approximant.py index b4f37cc..b58b187 100644 --- a/rrompy/reduction_methods/pivoted/generic_pivoted_approximant.py +++ b/rrompy/reduction_methods/pivoted/generic_pivoted_approximant.py @@ -1,718 +1,698 @@ # Copyright (C) 2018 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 copy import deepcopy as copy from rrompy.reduction_methods.base.generic_approximant import ( GenericApproximant) from rrompy.utilities.base.data_structures import purgeDict from rrompy.sampling import SamplingEngineStandard, SamplingEngineStandardPOD -from rrompy.sampling import SamplingEnginePivoted, SamplingEnginePivotedPOD from rrompy.utilities.poly_fitting.polynomial import polybases as ppb from rrompy.utilities.poly_fitting.radial_basis import polybases as rbpb from rrompy.utilities.poly_fitting.piecewise_linear import sparsekinds as sk from rrompy.utilities.base.types import Np2D, paramList, ListAny from rrompy.utilities.base import verbosityManager as vbMng from rrompy.utilities.numerical import dot from rrompy.utilities.numerical.degree import reduceDegreeN from rrompy.utilities.exception_manager import (RROMPyException, RROMPyAssert, RROMPyWarning) from rrompy.parameter import checkParameterList __all__ = ['GenericPivotedApproximantNoMatch', 'GenericPivotedApproximant'] class GenericPivotedApproximantBase(GenericApproximant): - def __init__(self, directionPivot:ListAny, *args, - noSampleMemory : bool = False, **kwargs): + def __init__(self, directionPivot:ListAny, *args, **kwargs): self._preInit() if len(directionPivot) > 1: raise RROMPyException(("Exactly 1 pivot parameter allowed in pole " "matching.")) from rrompy.parameter.parameter_sampling import QuadratureSampler as QS from rrompy.parameter.parameter_sampling import SparseGridSampler as SG QSBase = QS([[0.], [1.]], "UNIFORM") SGBase = SG([[0.], [1.]], "UNIFORM") self._addParametersToList(["cutOffTolerance", "radialDirectionalWeightsMarginal"], [np.inf, [1.]], ["samplerPivot", "SMarginal", "samplerMarginal"], [QSBase, 1, SGBase]) del QS, SG self._directionPivot = directionPivot - self._noSampleMemory = noSampleMemory super().__init__(*args, **kwargs) self._postInit() def setupSampling(self): """Setup sampling engine.""" RROMPyAssert(self._mode, message = "Cannot setup sampling engine.") if not hasattr(self, "_POD") or self._POD is None: return - if self._noSampleMemory: - if self.POD: - SamplingEngine = SamplingEngineStandardPOD - else: - SamplingEngine = SamplingEngineStandard - self.samplingEngine = SamplingEngine(self.HFEngine, - sample_state = self.approx_state, - verbosity = self.verbosity) + if self.POD: + SamplingEngine = SamplingEngineStandardPOD else: - if self.POD: - SamplingEngine = SamplingEnginePivotedPOD - else: - SamplingEngine = SamplingEnginePivoted - self.samplingEngine = SamplingEngine(self.HFEngine, - self.directionPivot, - sample_state = self.approx_state, - verbosity = self.verbosity) + SamplingEngine = SamplingEngineStandard + self.samplingEngine = SamplingEngine(self.HFEngine, + sample_state = self.approx_state, + verbosity = self.verbosity) def initializeModelData(self, datadict): if "directionPivot" in datadict.keys(): from .trained_model.trained_model_pivoted_data import ( TrainedModelPivotedData) return (TrainedModelPivotedData(datadict["mu0"], datadict["mus"], datadict.pop("projMat"), datadict["scaleFactor"], datadict.pop("rescalingExp"), datadict["directionPivot"]), ["mu0", "scaleFactor", "directionPivot", "mus"]) else: return super().initializeModelData(datadict) @property def npar(self): """Number of parameters.""" if hasattr(self, "_temporaryPivot"): return self.nparPivot return super().npar @property def mus(self): """Value of mus. Its assignment may reset snapshots.""" return self._mus @mus.setter def mus(self, mus): mus = checkParameterList(mus)[0] musOld = copy(self.mus) if hasattr(self, '_mus') else None if (musOld is None or len(mus) != len(musOld) or not mus == musOld): self.resetSamples() self._mus = mus @property def musMarginal(self): """Value of musMarginal. Its assignment may reset snapshots.""" return self._musMarginal @musMarginal.setter def musMarginal(self, musMarginal): musMarginal = checkParameterList(musMarginal)[0] if hasattr(self, '_musMarginal'): musMOld = copy(self.musMarginal) else: musMOld = None if (musMOld is None or len(musMarginal) != len(musMOld) or not musMarginal == musMOld): self.resetSamples() self._musMarginal = musMarginal @property def cutOffTolerance(self): """Value of cutOffTolerance.""" return self._cutOffTolerance @cutOffTolerance.setter def cutOffTolerance(self, cutOffTolerance): self._cutOffTolerance = cutOffTolerance self._approxParameters["cutOffTolerance"] = self.cutOffTolerance @property def SMarginal(self): """Value of SMarginal.""" return self._SMarginal @SMarginal.setter def SMarginal(self, SMarginal): if SMarginal <= 0: raise RROMPyException("SMarginal must be positive.") if hasattr(self, "_SMarginal") and self._SMarginal is not None: Sold = self.SMarginal else: Sold = -1 self._SMarginal = SMarginal self._approxParameters["SMarginal"] = self.SMarginal if Sold != self.SMarginal: self.resetSamples() @property def radialDirectionalWeightsMarginal(self): """Value of radialDirectionalWeightsMarginal.""" return self._radialDirectionalWeightsMarginal @radialDirectionalWeightsMarginal.setter def radialDirectionalWeightsMarginal(self, radialDirWeightsMarginal): if hasattr(radialDirWeightsMarginal, "__len__"): radialDirWeightsMarginal = list(radialDirWeightsMarginal) else: radialDirWeightsMarginal = [radialDirWeightsMarginal] self._radialDirectionalWeightsMarginal = radialDirWeightsMarginal self._approxParameters["radialDirectionalWeightsMarginal"] = ( self.radialDirectionalWeightsMarginal) @property def directionPivot(self): """Value of directionPivot. Its assignment may reset snapshots.""" return self._directionPivot @directionPivot.setter def directionPivot(self, directionPivot): if hasattr(self, '_directionPivot'): directionPivotOld = copy(self.directionPivot) else: directionPivotOld = None if (directionPivotOld is None or len(directionPivot) != len(directionPivotOld) or not directionPivot == directionPivotOld): self.resetSamples() self._directionPivot = directionPivot @property def directionMarginal(self): return [x for x in range(self.HFEngine.npar) \ if x not in self.directionPivot] @property def nparPivot(self): return len(self.directionPivot) @property def nparMarginal(self): return self.npar - self.nparPivot @property def rescalingExpPivot(self): return [self.HFEngine.rescalingExp[x] for x in self.directionPivot] @property def rescalingExpMarginal(self): return [self.HFEngine.rescalingExp[x] for x in self.directionMarginal] @property def muBounds(self): """Value of muBounds.""" return self.samplerPivot.lims @property def muBoundsMarginal(self): """Value of muBoundsMarginal.""" return self.samplerMarginal.lims @property def sampler(self): """Proxy of samplerPivot.""" return self._samplerPivot @property def samplerPivot(self): """Value of samplerPivot.""" return self._samplerPivot @samplerPivot.setter def samplerPivot(self, samplerPivot): if 'generatePoints' not in dir(samplerPivot): raise RROMPyException("Pivot sampler type not recognized.") if hasattr(self, '_samplerPivot') and self._samplerPivot is not None: samplerOld = self.samplerPivot self._samplerPivot = samplerPivot self._approxParameters["samplerPivot"] = self.samplerPivot if not 'samplerOld' in locals() or samplerOld != self.samplerPivot: self.resetSamples() @property def samplerMarginal(self): """Value of samplerMarginal.""" return self._samplerMarginal @samplerMarginal.setter def samplerMarginal(self, samplerMarginal): if 'generatePoints' not in dir(samplerMarginal): raise RROMPyException("Marginal sampler type not recognized.") if (hasattr(self, '_samplerMarginal') and self._samplerMarginal is not None): samplerOld = self.samplerMarginal self._samplerMarginal = samplerMarginal self._approxParameters["samplerMarginal"] = self.samplerMarginal if not 'samplerOld' in locals() or samplerOld != self.samplerMarginal: self.resetSamples() def setSamples(self, samplingEngine): """Copy samplingEngine and samples.""" self.mus = copy(samplingEngine.mus[0]) for sEj in samplingEngine.mus[1:]: self.mus.append(sEj) super().setSamples(samplingEngine) def computeScaleFactor(self): """Compute parameter rescaling factor.""" self.scaleFactorPivot = .5 * np.abs( self.muBounds[0] ** self.rescalingExpPivot - self.muBounds[1] ** self.rescalingExpPivot) self.scaleFactorMarginal = .5 * np.abs( self.muBoundsMarginal[0] ** self.rescalingExpMarginal - self.muBoundsMarginal[1] ** self.rescalingExpMarginal) self.scaleFactor = np.empty(self.npar) self.scaleFactor[self.directionPivot] = self.scaleFactorPivot self.scaleFactor[self.directionMarginal] = self.scaleFactorMarginal - def _finalizeSnapshots(self, samplingEngs): - if self._noSampleMemory: return - self.setupSampling() - self.samplingEngine.resetHistory() - for muM, sEN in zip(self.musMarginal, samplingEngs): - self.samplingEngine.setpickleableStuff((muM,) + sEN, False) - def _setupTrainedModel(self, pMat:Np2D, pMatUpdate : bool = False, forceNew : bool = False): pMatEff = dot(self.HFEngine.C, pMat) if self.approx_state else pMat if forceNew or self.trainedModel is None: self.trainedModel = self.tModelType() self.trainedModel.verbosity = self.verbosity self.trainedModel.timestamp = self.timestamp datadict = {"mu0": self.mu0, "mus": copy(self.mus), "projMat": pMatEff, "scaleFactor": self.scaleFactor, "rescalingExp": self.HFEngine.rescalingExp, "directionPivot": self.directionPivot} self.trainedModel.data = self.initializeModelData(datadict)[0] else: self.trainedModel = self.trainedModel if pMatUpdate: self.trainedModel.data.projMat = np.hstack( (self.trainedModel.data.projMat, pMatEff)) else: self.trainedModel.data.projMat = copy(pMatEff) self.trainedModel.data.mus = copy(self.mus) self.trainedModel.data.musMarginal = copy(self.musMarginal) def normApprox(self, mu:paramList) -> float: _PODOld = self.POD self._POD = False result = super().normApprox(mu) self._POD = _PODOld return result def loadTrainedModel(self, filename:str): """Load trained reduced model from file.""" super().loadTrainedModel(filename) self._musMarginal = self.trainedModel.data.musMarginal class GenericPivotedApproximantNoMatch(GenericPivotedApproximantBase): """ ROM pivoted approximant (without pole matching) computation for parametric problems (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': whether to compute POD of snapshots; defaults to True; - 'scaleFactorDer': scaling factors for derivative computation; defaults to 'AUTO'; - 'cutOffTolerance': tolerance for ignoring parasitic poles; defaults to np.inf; - '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; - 'radialDirectionalWeightsMarginal': radial basis weights for marginal interpolant; defaults to 1. Defaults to empty dict. approx_state(optional): Whether to approximate state. Defaults to False. 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': whether to compute POD of snapshots; - 'scaleFactorDer': scaling factors for derivative computation; - 'cutOffTolerance': tolerance for ignoring parasitic poles; - '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. approx_state: Whether to approximate state. verbosity: Verbosity level. POD: Whether to compute POD of snapshots. scaleFactorDer: Scaling factors for derivative computation. cutOffTolerance: Tolerance for ignoring parasitic poles. 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. 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. """ @property def tModelType(self): from .trained_model.trained_model_pivoted_rational_nomatch import ( TrainedModelPivotedRationalNoMatch) return TrainedModelPivotedRationalNoMatch def _finalizeMarginalization(self): vbMng(self, "INIT", "Recompressing by cut off.", 10) msg = self.trainedModel.recompressByCutOff(self.cutOffTolerance, self.samplerPivot.normalFoci(), self.samplerPivot.groundPotential()) vbMng(self, "DEL", "Done recompressing." + msg, 10) self.trainedModel.setupMarginalInterp( self.radialDirectionalWeightsMarginal) self.trainedModel.data.approxParameters = copy(self.approxParameters) class GenericPivotedApproximant(GenericPivotedApproximantBase): """ ROM pivoted approximant (with pole matching) computation for parametric problems (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': whether to compute POD of snapshots; defaults to True; - 'scaleFactorDer': scaling factors for derivative computation; defaults to 'AUTO'; - 'matchingWeight': weight for pole matching optimization; defaults to 1; - 'matchingMode': mode for pole matching optimization; allowed values include 'NONE' and 'SHIFT'; defaults to 'NONE'; - 'cutOffTolerance': tolerance for ignoring parasitic poles; defaults to np.inf; - 'cutOffSharedRatio': required ratio of marginal points to share resonance in cut off strategy; defaults to 1.; - '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; - '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' or 'PIECEWISE_LINEAR_*'; . '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'. - 'radialDirectionalWeightsMarginal': radial basis weights for marginal interpolant; defaults to 1. Defaults to empty dict. approx_state(optional): Whether to approximate state. Defaults to False. 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': whether to compute POD of snapshots; - 'scaleFactorDer': scaling factors for derivative computation; - 'matchingWeight': weight for pole matching optimization; - 'matchingMode': mode for pole matching optimization; - 'cutOffTolerance': tolerance for ignoring parasitic poles; - 'cutOffSharedRatio': required ratio of marginal points to share resonance in cut off strategy; - '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. - '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. approx_state: Whether to approximate state. verbosity: Verbosity level. POD: Whether to compute POD of snapshots. scaleFactorDer: Scaling factors for derivative computation. matchingWeight: Weight for pole matching optimization. matchingMode: Mode for pole matching optimization. cutOffTolerance: Tolerance for ignoring parasitic poles. cutOffSharedRatio: Required ratio of marginal points to share resonance in cut off strategy. 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. polybasisMarginal: Type of polynomial basis for marginal interpolation. paramsMarginal: Dictionary of parameters for marginal interpolation. 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 __init__(self, *args, **kwargs): self._preInit() self._addParametersToList(["matchingWeight", "matchingMode", "cutOffSharedRatio", "polybasisMarginal", "paramsMarginal"], [1., "NONE", 1., "MONOMIAL", {}]) self.parameterMarginalList = ["MMarginal", "nNeighborsMarginal", "polydegreetypeMarginal", "interpRcondMarginal"] super().__init__(*args, **kwargs) self._postInit() @property def tModelType(self): from .trained_model.trained_model_pivoted_rational import ( TrainedModelPivotedRational) return TrainedModelPivotedRational @property def matchingWeight(self): """Value of matchingWeight.""" return self._matchingWeight @matchingWeight.setter def matchingWeight(self, matchingWeight): self._matchingWeight = matchingWeight self._approxParameters["matchingWeight"] = self.matchingWeight @property def matchingMode(self): """Value of matchingMode.""" return self._matchingMode @matchingMode.setter def matchingMode(self, matchingMode): matchingMode = matchingMode.upper().strip().replace(" ", "") if matchingMode != "NONE" and matchingMode[: 5] != "SHIFT": raise RROMPyException("Prescribed matching mode not recognized.") self._matchingMode = matchingMode self._approxParameters["matchingMode"] = self.matchingMode @property def cutOffSharedRatio(self): """Value of cutOffSharedRatio.""" return self._cutOffSharedRatio @cutOffSharedRatio.setter def cutOffSharedRatio(self, cutOffSharedRatio): if cutOffSharedRatio > 1.: RROMPyWarning("Cut off shared ratio too large. Clipping to 1.") cutOffSharedRatio = 1. elif cutOffSharedRatio < 0.: RROMPyWarning("Cut off shared ratio too small. Clipping to 0.") cutOffSharedRatio = 0. self._cutOffSharedRatio = cutOffSharedRatio self._approxParameters["cutOffSharedRatio"] = self.cutOffSharedRatio @property def polybasisMarginal(self): """Value of polybasisMarginal.""" return self._polybasisMarginal @polybasisMarginal.setter def polybasisMarginal(self, polybasisMarginal): try: polybasisMarginal = polybasisMarginal.upper().strip().replace(" ", "") if polybasisMarginal not in ppb + rbpb + ["NEARESTNEIGHBOR"] + sk: raise RROMPyException( "Prescribed marginal polybasis not recognized.") self._polybasisMarginal = polybasisMarginal except: RROMPyWarning(("Prescribed marginal polybasis not recognized. " "Overriding to 'MONOMIAL'.")) self._polybasisMarginal = "MONOMIAL" self._approxParameters["polybasisMarginal"] = self.polybasisMarginal @property def paramsMarginal(self): """Value of paramsMarginal.""" return self._paramsMarginal @paramsMarginal.setter def paramsMarginal(self, paramsMarginal): paramsMarginal = purgeDict(paramsMarginal, self.parameterMarginalList, dictname = self.name() + ".paramsMarginal", baselevel = 1) keyList = list(paramsMarginal.keys()) if not hasattr(self, "_paramsMarginal"): self._paramsMarginal = {} if "MMarginal" in keyList: MMarg = paramsMarginal["MMarginal"] elif ("MMarginal" in self.paramsMarginal and not hasattr(self, "_MMarginal_isauto")): MMarg = self.paramsMarginal["MMarginal"] else: MMarg = "AUTO" if isinstance(MMarg, str): MMarg = MMarg.strip().replace(" ","") if "-" not in MMarg: MMarg = MMarg + "-0" self._MMarginal_isauto = True self._MMarginal_shift = int(MMarg.split("-")[-1]) MMarg = 0 if MMarg < 0: raise RROMPyException("MMarginal must be non-negative.") self._paramsMarginal["MMarginal"] = MMarg if "nNeighborsMarginal" in keyList: self._paramsMarginal["nNeighborsMarginal"] = max(1, paramsMarginal["nNeighborsMarginal"]) elif "nNeighborsMarginal" not in self.paramsMarginal: self._paramsMarginal["nNeighborsMarginal"] = 1 if "polydegreetypeMarginal" in keyList: try: polydegtypeM = paramsMarginal["polydegreetypeMarginal"]\ .upper().strip().replace(" ","") if polydegtypeM not in ["TOTAL", "FULL"]: raise RROMPyException(("Prescribed polydegreetypeMarginal " "not recognized.")) self._paramsMarginal["polydegreetypeMarginal"] = polydegtypeM except: RROMPyWarning(("Prescribed polydegreetypeMarginal not " "recognized. Overriding to 'TOTAL'.")) self._paramsMarginal["polydegreetypeMarginal"] = "TOTAL" elif "polydegreetypeMarginal" not in self.paramsMarginal: self._paramsMarginal["polydegreetypeMarginal"] = "TOTAL" if "interpRcondMarginal" in keyList: self._paramsMarginal["interpRcondMarginal"] = ( paramsMarginal["interpRcondMarginal"]) elif "interpRcondMarginal" not in self.paramsMarginal: self._paramsMarginal["interpRcondMarginal"] = -1 self._approxParameters["paramsMarginal"] = self.paramsMarginal def _setMMarginalAuto(self): if (self.polybasisMarginal not in ppb + rbpb or "MMarginal" not in self.paramsMarginal or "polydegreetypeMarginal" not in self.paramsMarginal): raise RROMPyException(("Cannot set MMarginal if " "polybasisMarginal does not allow it.")) self.paramsMarginal["MMarginal"] = max(0, reduceDegreeN( len(self.musMarginal), len(self.musMarginal), self.nparMarginal, self.paramsMarginal["polydegreetypeMarginal"]) - self._MMarginal_shift) vbMng(self, "MAIN", ("Automatically setting MMarginal to {}.").format( self.paramsMarginal["MMarginal"]), 25) def purgeparamsMarginal(self): self.paramsMarginal = {} paramsMbadkeys = [] if self.polybasisMarginal in ppb + rbpb + sk: paramsMbadkeys += ["nNeighborsMarginal"] if self.polybasisMarginal in ["NEARESTNEIGHBOR"] + sk: paramsMbadkeys += ["MMarginal", "polydegreetypeMarginal"] if hasattr(self, "_MMarginal_isauto"): del self._MMarginal_isauto if hasattr(self, "_MMarginal_shift"): del self._MMarginal_shift if self.polybasisMarginal == "NEARESTNEIGHBOR": paramsMbadkeys += ["interpRcondMarginal"] for key in paramsMbadkeys: if key in self._paramsMarginal: del self._paramsMarginal[key] self._approxParameters["paramsMarginal"] = self.paramsMarginal def _finalizeMarginalization(self): vbMng(self, "INIT", "Recompressing by cut off.", 10) msg = self.trainedModel.recompressByCutOff(self.cutOffTolerance, self.cutOffSharedRatio, self.samplerPivot.normalFoci(), self.samplerPivot.groundPotential()) vbMng(self, "DEL", "Done recompressing." + msg, 10) if self.polybasisMarginal == "NEARESTNEIGHBOR": interpPars = [self.paramsMarginal["nNeighborsMarginal"]] else: interpPars = [{"rcond":self.paramsMarginal["interpRcondMarginal"]}] if self.polybasisMarginal in ppb + rbpb: interpPars = [self.verbosity >= 5, self.paramsMarginal["polydegreetypeMarginal"] == "TOTAL", {}] + interpPars extraPar = hasattr(self, "_reduceDegreeNNoWarn") if self.polybasisMarginal in ppb: rDWMEff = None else: #if self.polybasisMarginal in rbpb + ["NEARESTNEIGHBOR"] + sk: self.computeScaleFactor() rDWMEff = [w * f for w, f in zip( self.radialDirectionalWeightsMarginal, self.scaleFactorMarginal)] if self.polybasisMarginal in sk: idxEff = [x for x in range(self.samplerMarginal.npoints) if not hasattr(self.trainedModel, "_idxExcl") or x not in self.trainedModel._idxExcl] extraPar = self.samplerMarginal.depth[idxEff] self.trainedModel.setupMarginalInterp(self, interpPars, hasattr(self, "_MMarginal_isauto"), rDWMEff, extraPar) self.trainedModel.data.approxParameters = copy(self.approxParameters) 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 f81fa9a..632dec7 100644 --- a/rrompy/reduction_methods/pivoted/greedy/generic_pivoted_greedy_approximant.py +++ b/rrompy/reduction_methods/pivoted/greedy/generic_pivoted_greedy_approximant.py @@ -1,858 +1,836 @@ # Copyright (C) 2018 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.reduction_methods.pivoted.generic_pivoted_approximant import ( GenericPivotedApproximantBase, GenericPivotedApproximantNoMatch, GenericPivotedApproximant) 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, chordalMetricAdjusted, potential) from rrompy.utilities.exception_manager import (RROMPyException, RROMPyAssert, RROMPyWarning) from rrompy.parameter import checkParameterList, emptyParameterList from rrompy.utilities.parallel import (COMM, poolRank, masterCore, indicesScatter, listGather, arrayGatherv, matrixGatherv) __all__ = ['GenericPivotedGreedyApproximantNoMatch', 'GenericPivotedGreedyApproximant'] class GenericPivotedGreedyApproximantBase(GenericPivotedApproximantBase): _allowedEstimatorKindsMarginal = ["LEAVE_ONE_OUT", "LOOK_AHEAD", "LOOK_AHEAD_RECOVER", "NONE"] def __init__(self, *args, **kwargs): self._preInit() self._addParametersToList(["matchingWeightError", "cutOffToleranceError", "errorEstimatorKindMarginal", "greedyTolMarginal", "maxIterMarginal"], [0., "AUTO", "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 hasattr(scaleFactorDer, "__len__"): 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 cutOffToleranceError(self): """Value of cutOffToleranceError.""" return self._cutOffToleranceError @cutOffToleranceError.setter def cutOffToleranceError(self, cutOffToleranceError): if isinstance(cutOffToleranceError, (str,)): cutOffToleranceError = cutOffToleranceError.upper()\ .strip().replace(" ","") if cutOffToleranceError != "AUTO": RROMPyWarning(("String value of cutOffToleranceError not " "recognized. Overriding to 'AUTO'.")) cutOffToleranceError == "AUTO" self._cutOffToleranceError = cutOffToleranceError self._approxParameters["cutOffToleranceError"] = ( self.cutOffToleranceError) @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 _getPolesResExact(self, HITest, foci:Tuple[float, float], ground:float) -> Tuple[Np1D, Np2D]: if self.cutOffToleranceError == "AUTO": cutOffTolErr = self.cutOffTolerance else: cutOffTolErr = self.cutOffToleranceError polesEx = copy(HITest.poles) idxExEff = np.where(potential(polesEx, foci) - ground <= cutOffTolErr * ground)[0] if self.matchingWeightError != 0: resEx = HITest.coeffs[idxExEff] else: resEx = None return polesEx[idxExEff], resEx def _getDistanceApp(self, polesEx:Np1D, resEx:Np2D, muTest:paramVal, foci:Tuple[float, float], ground:float) -> float: if self.cutOffToleranceError == "AUTO": cutOffTolErr = self.cutOffTolerance else: cutOffTolErr = self.cutOffToleranceError polesAp = self.trainedModel.interpolateMarginalPoles(muTest)[0] idxApEff = np.where(potential(polesAp, foci) - ground <= cutOffTolErr * ground)[0] polesAp = polesAp[idxApEff] if self.matchingWeightError != 0: resAp = self.trainedModel.interpolateMarginalCoeffs(muTest)[0][ idxApEff, :] resEx = self.trainedModel.data.projMat[:, : resEx.shape[1]].dot(resEx.T) resAp = self.trainedModel.data.projMat[:, : resAp.shape[1]].dot(resAp.T) else: resAp = None dist = chordalMetricAdjusted(polesEx, polesAp, self.matchingWeightError, resEx, resAp, self.HFEngine, False) pmR, pmC = pointMatching(dist) return np.mean(dist[pmR, pmC]) def getErrorEstimatorMarginalLeaveOneOut(self) -> Np1D: nTest = len(self.trainedModel.data.musMarginal) self._musMarginalTestIdxs = np.arange(nTest) if nTest <= 1: err = np.empty(nTest) err[:] = np.inf return err idx, sizes = indicesScatter(nTest, return_sizes = True) err = [] if len(idx) > 0: _tMdataFull = copy(self.trainedModel.data) _musMExcl = None self.verbosity -= 35 self.trainedModel.verbosity -= 35 foci = self.samplerPivot.normalFoci() ground = self.samplerPivot.groundPotential() for i, j in enumerate(idx): jEff = j - (i > 0) muTest = self.trainedModel.data.musMarginal[jEff] polesEx, resEx = self._getPolesResExact( self.trainedModel.data.HIs[jEff], foci, ground) if i > 0: self.musMarginal.insert(_musMExcl, j - 1) _musMExcl = self.musMarginal[j] self.musMarginal.pop(j) if len(polesEx) == 0: err += [0.] continue self._updateTrainedModelMarginalSamples([j]) self._finalizeMarginalization() err += [self._getDistanceApp(polesEx, resEx, muTest, foci, ground)] self._updateTrainedModelMarginalSamples() self.musMarginal.insert(_musMExcl, idx[-1]) self.verbosity += 35 self.trainedModel.verbosity += 35 self.trainedModel.data = _tMdataFull return arrayGatherv(np.array(err), sizes) 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 -= 35 self.trainedModel.verbosity -= 35 foci = self.samplerPivot.normalFoci() ground = self.samplerPivot.groundPotential() for j in idx: muTest = self.trainedModel._musMExcl[j] HITest = self.trainedModel._HIsExcl[j] polesEx, resEx = self._getPolesResExact(HITest, foci, ground) if len(polesEx) == 0: err += [0.] continue err += [self._getDistanceApp(polesEx, resEx, muTest, foci, ground)] self.verbosity += 35 self.trainedModel.verbosity += 35 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 == "LEAVE_ONE_OUT": err = self.getErrorEstimatorMarginalLeaveOneOut() elif self.errorEstimatorKindMarginal[: 10] == "LOOK_AHEAD": err = self.getErrorEstimatorMarginalLookAhead() else:#if self.errorEstimatorKindMarginal == "NONE": err = self.getErrorEstimatorMarginalNone() 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()): fig = plt.figure(figsize = plt.figaspect(1. / self.nparMarginal)) for jpar in range(self.nparMarginal): ax = fig.add_subplot(1, self.nparMarginal, 1 + jpar) if self.errorEstimatorKindMarginal == "LEAVE_ONE_OUT": musre = copy(self.trainedModel.data.musMarginal.re.data) else:#if self.errorEstimatorKindMarginal[: 10] == "LOOK_AHEAD": if not hasattr(self.trainedModel, "_musMExcl"): return 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.grid() plt.tight_layout() plt.show() def _addMarginalSample(self, mus:paramList): mus = checkParameterList(mus, self.nparMarginal)[0] if len(mus) == 0: return nmusOld, nmus = len(self.musMarginal), len(mus) if (hasattr(self, "trainedModel") and self.trainedModel is not None and hasattr(self.trainedModel, "_musMExcl")): nmusOld += len(self.trainedModel._musMExcl) vbMng(self, "MAIN", ("Adding marginal sample point{} no. {}{} at {} to training " "set.").format("s" * (nmus > 1), nmusOld + 1, "--{}".format(nmusOld + nmus) * (nmus > 1), mus), 3) self.musMarginal.append(mus) self.setupApproxPivoted(mus) self._poleMatching() 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.") 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) self._addMarginalSample(self.samplerMarginal.points[idxAdded]) errorEstTest, muidx, maxErrorEst = self.errorEstimatorMarginal(True) if plotEst == "ALL": self.plotEstimatorMarginal(errorEstTest, muidx, maxErrorEst) return (errorEstTest, self._musMarginalTestIdxs[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 _finalizeSnapshots(self, mus:paramList, samplingEngs:ListAny): - if self._noSampleMemory: return - self.samplingEngine = self._samplingEngineOld - for muM, sEN in zip(mus, samplingEngs): - self.samplingEngine.setpickleableStuff((muM,) + sEN, False) - del self._samplingEngineOld - 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 - if self._noSampleMemory: - self._musLoc = copy(self.mus) - else: - self._samplingEngineOld = copy(self.samplingEngine) + self._musLoc = copy(self.mus) idx, sizes = indicesScatter(len(mus), return_sizes = True) emptyCores = np.where(np.logical_not(sizes))[0] self.verbosity -= 15 return idx, sizes, emptyCores def _postSetupApproxPivoted(self, mus:paramList, data:ListAny, pMat:Np2D, sizes:ListAny): self.scaleFactor = self._scaleFactorOldPivot del self._scaleFactorOldPivot, self._temporaryPivot data = listGather(data) - if self._noSampleMemory: - _mus = [x[2] for x in data] - if len(self._musLoc) > 0: - self._mus = checkParameterList(self._musLoc, self.npar)[0] - self._mus.append(_mus[0]) - else: - self._mus = checkParameterList(_mus[0], self.npar)[0] - nsamples, sizesEff, idx = [], [], 0 - for size in sizes: - sizesEff += [0] - for _ in range(size): - _m = _mus[idx] - if idx > 0: self._mus.append(_m) - nsamples += [len(_m)] - sizesEff[-1] += nsamples[-1] - idx += 1 - pMat = matrixGatherv(pMat, sizesEff, False) + if len(self._musLoc) > 0: + self._mus = checkParameterList(self._musLoc, self.npar)[0] + self._mus.append(data[0][2]) else: - self._finalizeSnapshots([x[2] for x in data]) - self._mus = self.samplingEngine.musCoalesced + self._mus = checkParameterList(data[0][2], self.npar)[0] + nsamples, sizesEff, idx = [], [], 0 + for size in sizes: + sizesEff += [0] + for _ in range(size): + _m = data[idx][2] + if idx > 0: self._mus.append(_m) + nsamples += [len(_m)] + sizesEff[-1] += nsamples[-1] + idx += 1 + pMat = matrixGatherv(pMat, sizesEff, False) self.trainedModel = self._trainedModelOld del self._trainedModelOld padLeft = self.trainedModel.data.projMat.shape[1] - if not self._noSampleMemory: - pMat = self.samplingEngine.samplesCoalesced.data[:, padLeft :] - nsamples = self.samplingEngine.nsamples[- len(data) :] suppNew = np.append(0, np.cumsum(nsamples)) self._setupTrainedModel(pMat, padLeft > 0) self.trainedModel.data.Qs += [x[0] for x in data] self.trainedModel.data.Ps += [x[1] for x in data] self.trainedModel.data.Psupp += list(padLeft + suppNew[: -1]) self.trainedModel.data.approxParameters = copy(self.approxParameters) self.verbosity += 15 - def _localPivotedMatrix(self, pMat:Np2D, req:ListAny, emptyCores): - if self._noSampleMemory: - if pMat is None: - pMat = copy(self.samplingEngine.samples.data) - if masterCore(): - for dest in emptyCores: - req += [COMM.isend((len(pMat), pMat.dtype), - dest = dest, tag = dest)] - else: - pMat = np.hstack((pMat, - self.samplingEngine.samples.data)) - return pMat, req - - def _localPivotedResult(self): - if self._noSampleMemory: - return copy(self.samplingEngine.mus) + def _localPivotedResult(self, pMat:Np2D, req:ListAny, + emptyCores:ListAny) -> Tuple[Np2D, ListAny, + paramList]: + if pMat is None: + pMat = copy(self.samplingEngine.samples.data) + if masterCore(): + for dest in emptyCores: + req += [COMM.isend((len(pMat), pMat.dtype), + dest = dest, tag = dest)] else: - return copy(self.samplingEngine.getpickleableStuff()) + pMat = np.hstack((pMat, + self.samplingEngine.samples.data)) + #FIXME + return pMat, req, copy(self.samplingEngine.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) idx = 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[: 10] == "LOOK_AHEAD": muidx = np.arange(len(self.trainedModel.data.musMarginal)) else:#if self.errorEstimatorKindMarginal in ["LEAVE_ONE_OUT", "NONE"]: 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) vbMng(self, "MAIN", ("Uniform testing error estimate " "{:.4e}.").format(max2ErrorEst), 3) else: max2ErrorEst = 0. 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 GenericPivotedGreedyApproximantNoMatch( GenericPivotedGreedyApproximantBase, GenericPivotedApproximantNoMatch): """ ROM pivoted greedy interpolant computation for parametric problems (without 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': whether to compute POD of snapshots; defaults to True; - 'scaleFactorDer': scaling factors for derivative computation; defaults to 'AUTO'; - 'cutOffTolerance': tolerance for ignoring parasitic poles; defaults to np.inf; - 'matchingWeightError': weight for pole matching optimization in error estimation; defaults to 0; - 'cutOffToleranceError': tolerance for ignoring parasitic poles in error estimation; defaults to 'AUTO', i.e. cutOffTolerance; - '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 'LEAVE_ONE_OUT', 'LOOK_AHEAD', 'LOOK_AHEAD_RECOVER', and 'NONE'; defaults to 'NONE'; - '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. approx_state(optional): Whether to approximate state. Defaults to False. 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': whether to compute POD of snapshots; - 'scaleFactorDer': scaling factors for derivative computation; - 'cutOffTolerance': tolerance for ignoring parasitic poles; - 'matchingWeightError': weight for pole matching optimization in error estimation; - 'cutOffToleranceError': tolerance for ignoring parasitic poles in error estimation; - 'errorEstimatorKindMarginal': kind of marginal error estimator; - '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. approx_state: Whether to approximate state. verbosity: Verbosity level. POD: Whether to compute POD of snapshots. scaleFactorDer: Scaling factors for derivative computation. cutOffTolerance: Tolerance for ignoring parasitic poles. matchingWeightError: Weight for pole matching optimization in error estimation. cutOffToleranceError: Tolerance for ignoring parasitic poles 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. 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 _poleMatching(self): vbMng(self, "INIT", "Compressing poles.", 10) self.trainedModel.initializeFromRational() vbMng(self, "DEL", "Done compressing poles.", 10) def _updateTrainedModelMarginalSamples(self, idx : ListAny = []): self.trainedModel.updateEffectiveSamples(idx) class GenericPivotedGreedyApproximant(GenericPivotedGreedyApproximantBase, GenericPivotedApproximant): """ 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': whether to compute POD of snapshots; defaults to True; - 'scaleFactorDer': scaling factors for derivative computation; defaults to 'AUTO'; - 'matchingWeight': weight for pole matching optimization; defaults to 1; - 'matchingMode': mode for pole matching optimization; allowed values include 'NONE' and 'SHIFT'; defaults to 'NONE'; - 'cutOffTolerance': tolerance for ignoring parasitic poles; defaults to np.inf; - 'cutOffSharedRatio': required ratio of marginal points to share resonance in cut off strategy; defaults to 1.; - 'matchingWeightError': weight for pole matching optimization in error estimation; defaults to 0; - 'cutOffToleranceError': tolerance for ignoring parasitic poles in error estimation; defaults to 'AUTO', i.e. cutOffTolerance; - '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 'LEAVE_ONE_OUT', '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' or 'PIECEWISE_LINEAR_*'; . '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'. - '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. approx_state(optional): Whether to approximate state. Defaults to False. 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': whether to compute POD of snapshots; - 'scaleFactorDer': scaling factors for derivative computation; - 'matchingWeight': weight for pole matching optimization; - 'matchingMode': mode for pole matching optimization; - 'cutOffTolerance': tolerance for ignoring parasitic poles; - 'cutOffSharedRatio': required ratio of marginal points to share resonance in cut off strategy; - 'matchingWeightError': weight for pole matching optimization in error estimation; - 'cutOffToleranceError': tolerance for ignoring parasitic poles 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. - '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. approx_state: Whether to approximate state. verbosity: Verbosity level. POD: Whether to compute POD of snapshots. scaleFactorDer: Scaling factors for derivative computation. matchingWeight: Weight for pole matching optimization. matchingMode: Mode for pole matching optimization. cutOffTolerance: Tolerance for ignoring parasitic poles. cutOffSharedRatio: Required ratio of marginal points to share resonance in cut off strategy. matchingWeightError: Weight for pole matching optimization in error estimation. cutOffToleranceError: Tolerance for ignoring parasitic poles 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 _poleMatching(self): vbMng(self, "INIT", "Compressing and matching poles.", 10) self.trainedModel.initializeFromRational(self.matchingWeight, self.matchingMode, self.HFEngine, False) vbMng(self, "DEL", "Done compressing and matching poles.", 10) def _updateTrainedModelMarginalSamples(self, idx : ListAny = []): self.trainedModel.updateEffectiveSamples(idx, self.matchingWeight, self.matchingMode, self.HFEngine, False) def getErrorEstimatorMarginalLeaveOneOut(self) -> Np1D: if self.polybasisMarginal != "NEARESTNEIGHBOR": if not hasattr(self, "_MMarginal_isauto"): if not hasattr(self, "_MMarginalOriginal"): self._MMarginalOriginal = self.paramsMarginal["MMarginal"] self.paramsMarginal["MMarginal"] = self._MMarginalOriginal self._reduceDegreeNNoWarn = 1 err = super().getErrorEstimatorMarginalLeaveOneOut() if self.polybasisMarginal != "NEARESTNEIGHBOR": del self._reduceDegreeNNoWarn return err def setupApprox(self, *args, **kwargs) -> int: if self.checkComputedApprox(): return -1 self.purgeparamsMarginal() return super().setupApprox(*args, **kwargs) diff --git a/rrompy/reduction_methods/pivoted/greedy/rational_interpolant_greedy_pivoted_greedy.py b/rrompy/reduction_methods/pivoted/greedy/rational_interpolant_greedy_pivoted_greedy.py index 27a72e1..cb04ba0 100644 --- a/rrompy/reduction_methods/pivoted/greedy/rational_interpolant_greedy_pivoted_greedy.py +++ b/rrompy/reduction_methods/pivoted/greedy/rational_interpolant_greedy_pivoted_greedy.py @@ -1,530 +1,524 @@ #Copyright (C) 2018 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 .generic_pivoted_greedy_approximant import ( GenericPivotedGreedyApproximantBase, GenericPivotedGreedyApproximantNoMatch, GenericPivotedGreedyApproximant) from rrompy.reduction_methods.standard.greedy import RationalInterpolantGreedy from rrompy.reduction_methods.standard.greedy.generic_greedy_approximant \ import pruneSamples from rrompy.reduction_methods.pivoted import ( RationalInterpolantGreedyPivotedNoMatch, RationalInterpolantGreedyPivoted) from rrompy.utilities.base.types import Np1D, Tuple, paramVal, paramList from rrompy.utilities.base import verbosityManager as vbMng from rrompy.utilities.exception_manager import RROMPyAssert from rrompy.parameter import emptyParameterList from rrompy.utilities.parallel import COMM, poolRank __all__ = ['RationalInterpolantGreedyPivotedGreedyNoMatch', 'RationalInterpolantGreedyPivotedGreedy'] class RationalInterpolantGreedyPivotedGreedyBase( GenericPivotedGreedyApproximantBase): @property def sampleBatchSize(self): """Value of sampleBatchSize.""" return 1 @property def sampleBatchIdx(self): """Value of sampleBatchIdx.""" return self.S 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.data[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 _setSampleBatch(self, maxS:int): return self.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.resetSamples() self.samplingEngine.scaleFactor = self.scaleFactorDer musPivot = self.trainSetGenerator.generatePoints(self.S) while len(musPivot) > self.S: musPivot.pop() muTestBasePivot = self.samplerPivot.generatePoints(self.nTestPoints, False) idxPop = pruneSamples( muTestBasePivot ** self.HFEngine.rescalingExp[self.directionPivot[0]], musPivot ** self.HFEngine.rescalingExp[self.directionPivot[0]], 1e-10 * self.scaleFactor[0]) muTestBasePivot.pop(idxPop) self.mus = emptyParameterList() self.mus.reset((self.S - 1, self.HFEngine.npar)) self.muTest = emptyParameterList() self.muTest.reset((len(muTestBasePivot) + 1, self.HFEngine.npar)) for k in range(self.S - 1): self.mus.data[k, self.directionPivot] = musPivot[k].data self.mus.data[k, self.directionMarginal] = self.muMargLoc for k in range(len(muTestBasePivot)): self.muTest.data[k, self.directionPivot] = muTestBasePivot[k].data self.muTest.data[k, self.directionMarginal] = self.muMargLoc self.muTest.data[-1, self.directionPivot] = musPivot[-1].data self.muTest.data[-1, self.directionMarginal] = self.muMargLoc 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.M, self.N = ("AUTO",) * 2 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) if not hasattr(self, "_plotEstPivot"): self._plotEstPivot = "NONE" idx, sizes, emptyCores = self._preSetupApproxPivoted(mus) S0 = copy(self.S) data, pMat, req = [], None, [] if len(idx) == 0: vbMng(self, "MAIN", "Idling.", 45) - if self._noSampleMemory: - pL, pT = COMM.recv(source = 0, tag = poolRank()) - pMat = np.empty((pL, 0), dtype = pT) + pL, pT = COMM.recv(source = 0, tag = poolRank()) + pMat = np.empty((pL, 0), dtype = pT) else: for i in idx: self.muMargLoc = mus[i] vbMng(self, "MAIN", "Building marginal model no. {} at " "{}.".format(i + 1, self.muMargLoc), 25) - if self._noSampleMemory: - self.samplingEngine.resetHistory() - else: - RationalInterpolantGreedy.setupSampling(self) + self.samplingEngine.resetHistory() self.trainedModel = None self.verbosity -= 5 self.samplingEngine.verbosity -= 5 RationalInterpolantGreedy.setupApprox(self, self._plotEstPivot) self.verbosity += 5 self.samplingEngine.verbosity += 5 - if self._noSampleMemory: - pMat, req = self._localPivotedMatrix(pMat, req, emptyCores) + pMat, req, _m = self._localPivotedResult(pMat, req, emptyCores) data += [(copy(self.trainedModel.data.Q), - copy(self.trainedModel.data.P), - self._localPivotedResult())] + copy(self.trainedModel.data.P), _m)] self._S = S0 del self.muMargLoc for r in req: r.wait() self._postSetupApproxPivoted(mus, data, pMat, sizes) vbMng(self, "DEL", "Done setting up pivoted approximant.", 10) return 0 def setupApprox(self, plotEst : str = "NONE") -> int: if self.checkComputedApprox(): return -1 if '_' not in plotEst: plotEst = plotEst + "_NONE" plotEstM, self._plotEstPivot = plotEst.split("_") val = super().setupApprox(plotEstM) return val class RationalInterpolantGreedyPivotedGreedyNoMatch( RationalInterpolantGreedyPivotedGreedyBase, GenericPivotedGreedyApproximantNoMatch, RationalInterpolantGreedyPivotedNoMatch): """ ROM greedy pivoted greedy rational interpolant computation for parametric problems (without pole matching). 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': whether to compute POD of snapshots; defaults to True; - 'scaleFactorDer': scaling factors for derivative computation; defaults to 'AUTO'; - 'cutOffTolerance': tolerance for ignoring parasitic poles; defaults to np.inf; - 'matchingWeightError': weight for pole matching optimization in error estimation; defaults to 0; - 'cutOffToleranceError': tolerance for ignoring parasitic poles in error estimation; defaults to 'AUTO', i.e. cutOffTolerance; - '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 'LEAVE_ONE_OUT', 'LOOK_AHEAD', and 'LOOK_AHEAD_RECOVER'; defaults to 'LEAVE_ONE_OUT'; - 'polybasis': type of polynomial basis for pivot interpolation; defaults to 'MONOMIAL'; - '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; - 'errorEstimatorKind': kind of error estimator; available values include 'AFFINE', 'DISCREPANCY', 'LOOK_AHEAD', 'LOOK_AHEAD_RES', and 'NONE'; defaults to 'NONE'; - '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; - 'interpRcond': tolerance for pivot interpolation; defaults to None; - 'robustTol': tolerance for robust rational denominator management; defaults to 0; - 'correctorForce': whether corrector should forcefully delete bad poles; defaults to False; - 'correctorTol': tolerance for corrector step; defaults to 0., i.e. no bad poles; - 'correctorMaxIter': maximum number of corrector iterations; defaults to 1. Defaults to empty dict. approx_state(optional): Whether to approximate state. Defaults to False. 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': whether to compute POD of snapshots; - 'scaleFactorDer': scaling factors for derivative computation; - 'cutOffTolerance': tolerance for ignoring parasitic poles; - 'matchingWeightError': weight for pole matching optimization in error estimation; - 'cutOffToleranceError': tolerance for ignoring parasitic poles in error estimation; - 'errorEstimatorKindMarginal': kind of marginal error estimator; - 'polybasis': type of polynomial basis for pivot interpolation; - '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; - 'greedyTolMarginal': uniform error tolerance for marginal greedy algorithm; - 'maxIterMarginal': maximum number of marginal greedy steps; - 'radialDirectionalWeightsMarginal': radial basis weights for marginal interpolant; - 'interpRcond': tolerance for pivot interpolation; - 'robustTol': tolerance for robust rational denominator management; - 'correctorForce': whether corrector should forcefully delete bad poles; - 'correctorTol': tolerance for corrector step; - 'correctorMaxIter': maximum number of corrector iterations. 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. approx_state: Whether to approximate state. verbosity: Verbosity level. POD: Whether to compute POD of snapshots. scaleFactorDer: Scaling factors for derivative computation. cutOffTolerance: Tolerance for ignoring parasitic poles. matchingWeightError: Weight for pole matching optimization in error estimation. cutOffToleranceError: Tolerance for ignoring parasitic poles 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. polybasis: Type of polynomial basis for pivot interpolation. 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. errorEstimatorKind: kind of error estimator. greedyTolMarginal: Uniform error tolerance for marginal greedy algorithm. maxIterMarginal: Maximum number of marginal greedy steps. radialDirectionalWeightsMarginal: Radial basis weights for marginal interpolant. interpRcond: Tolerance for pivot interpolation. robustTol: Tolerance for robust rational denominator management. correctorForce: Whether corrector should forcefully delete bad poles. correctorTol: Tolerance for corrector step. correctorMaxIter: Maximum number of corrector iterations. 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. """ class RationalInterpolantGreedyPivotedGreedy( RationalInterpolantGreedyPivotedGreedyBase, GenericPivotedGreedyApproximant, RationalInterpolantGreedyPivoted): """ ROM greedy pivoted greedy rational interpolant computation for parametric problems (with pole matching). 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': whether to compute POD of snapshots; defaults to True; - 'scaleFactorDer': scaling factors for derivative computation; defaults to 'AUTO'; - 'matchingWeight': weight for pole matching optimization; defaults to 1; - 'matchingMode': mode for pole matching optimization; allowed values include 'NONE' and 'SHIFT'; defaults to 'NONE'; - 'cutOffTolerance': tolerance for ignoring parasitic poles; defaults to np.inf; - 'cutOffSharedRatio': required ratio of marginal points to share resonance in cut off strategy; defaults to 1.; - 'matchingWeightError': weight for pole matching optimization in error estimation; defaults to 0; - 'cutOffToleranceError': tolerance for ignoring parasitic poles in error estimation; defaults to 'AUTO', i.e. cutOffTolerance; - '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 'LEAVE_ONE_OUT', 'LOOK_AHEAD', and 'LOOK_AHEAD_RECOVER'; defaults to 'LEAVE_ONE_OUT'; - 'polybasis': type of polynomial basis for pivot interpolation; defaults to 'MONOMIAL'; - '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' or 'PIECEWISE_LINEAR_*'; . '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'. - '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; - 'errorEstimatorKind': kind of error estimator; available values include 'AFFINE', 'DISCREPANCY', 'LOOK_AHEAD', 'LOOK_AHEAD_RES', and 'NONE'; defaults to 'NONE'; - '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; - 'interpRcond': tolerance for pivot interpolation; defaults to None; - 'robustTol': tolerance for robust rational denominator management; defaults to 0; - 'correctorForce': whether corrector should forcefully delete bad poles; defaults to False; - 'correctorTol': tolerance for corrector step; defaults to 0., i.e. no bad poles; - 'correctorMaxIter': maximum number of corrector iterations; defaults to 1. Defaults to empty dict. approx_state(optional): Whether to approximate state. Defaults to False. 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': whether to compute POD of snapshots; - 'scaleFactorDer': scaling factors for derivative computation; - 'matchingWeight': weight for pole matching optimization; - 'matchingMode': mode for pole matching optimization; - 'cutOffTolerance': tolerance for ignoring parasitic poles; - 'cutOffSharedRatio': required ratio of marginal points to share resonance in cut off strategy; - 'matchingWeightError': weight for pole matching optimization in error estimation; - 'cutOffToleranceError': tolerance for ignoring parasitic poles in error estimation; - 'errorEstimatorKindMarginal': kind of marginal error estimator; - 'polybasis': type of polynomial basis for pivot interpolation; - '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. - '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; - 'greedyTolMarginal': uniform error tolerance for marginal greedy algorithm; - 'maxIterMarginal': maximum number of marginal greedy steps; - 'radialDirectionalWeightsMarginal': radial basis weights for marginal interpolant; - 'interpRcond': tolerance for pivot interpolation; - 'robustTol': tolerance for robust rational denominator management; - 'correctorForce': whether corrector should forcefully delete bad poles; - 'correctorTol': tolerance for corrector step; - 'correctorMaxIter': maximum number of corrector iterations. 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. approx_state: Whether to approximate state. verbosity: Verbosity level. POD: Whether to compute POD of snapshots. scaleFactorDer: Scaling factors for derivative computation. matchingWeight: Weight for pole matching optimization. matchingMode: Mode for pole matching optimization. cutOffTolerance: Tolerance for ignoring parasitic poles. cutOffSharedRatio: Required ratio of marginal points to share resonance in cut off strategy. matchingWeightError: Weight for pole matching optimization in error estimation. cutOffToleranceError: Tolerance for ignoring parasitic poles 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. polybasis: Type of polynomial basis for pivot interpolation. polybasisMarginal: Type of polynomial basis for marginal interpolation. paramsMarginal: Dictionary of parameters for marginal interpolation. 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. errorEstimatorKind: kind of error estimator. greedyTolMarginal: Uniform error tolerance for marginal greedy algorithm. maxIterMarginal: Maximum number of marginal greedy steps. radialDirectionalWeightsMarginal: Radial basis weights for marginal interpolant. interpRcond: Tolerance for pivot interpolation. robustTol: Tolerance for robust rational denominator management. correctorForce: Whether corrector should forcefully delete bad poles. correctorTol: Tolerance for corrector step. correctorMaxIter: Maximum number of corrector iterations. 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. """ diff --git a/rrompy/reduction_methods/pivoted/greedy/rational_interpolant_pivoted_greedy.py b/rrompy/reduction_methods/pivoted/greedy/rational_interpolant_pivoted_greedy.py index 671e521..6aff0f9 100644 --- a/rrompy/reduction_methods/pivoted/greedy/rational_interpolant_pivoted_greedy.py +++ b/rrompy/reduction_methods/pivoted/greedy/rational_interpolant_pivoted_greedy.py @@ -1,448 +1,442 @@ # Copyright (C) 2018 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 from numpy import empty from .generic_pivoted_greedy_approximant import ( GenericPivotedGreedyApproximantBase, GenericPivotedGreedyApproximantNoMatch, GenericPivotedGreedyApproximant) from rrompy.reduction_methods.standard import RationalInterpolant from rrompy.reduction_methods.pivoted import ( RationalInterpolantPivotedNoMatch, RationalInterpolantPivoted) from rrompy.utilities.base.types import paramList from rrompy.utilities.base import verbosityManager as vbMng from rrompy.utilities.exception_manager import RROMPyAssert from rrompy.parameter import checkParameterList, emptyParameterList from rrompy.utilities.parallel import COMM, poolRank __all__ = ['RationalInterpolantPivotedGreedyNoMatch', 'RationalInterpolantPivotedGreedy'] class RationalInterpolantPivotedGreedyBase( GenericPivotedGreedyApproximantBase): def computeSnapshots(self): """Compute snapshots of solution map.""" RROMPyAssert(self._mode, message = "Cannot start snapshot computation.") vbMng(self, "INIT", "Starting computation of snapshots.", 5) self.samplingEngine.scaleFactor = self.scaleFactorDer if not hasattr(self, "musPivot") or len(self.musPivot) != self.S: self.musPivot = self.samplerPivot.generatePoints(self.S) while len(self.musPivot) > self.S: self.musPivot.pop() musLoc = emptyParameterList() musLoc.reset((self.S, self.HFEngine.npar)) self.samplingEngine.resetHistory() for k in range(self.S): musLoc.data[k, self.directionPivot] = self.musPivot[k].data musLoc.data[k, self.directionMarginal] = self.muMargLoc self.samplingEngine.iterSample(musLoc) vbMng(self, "DEL", "Done computing snapshots.", 5) self._m_selfmus = copy(musLoc) self._mus = self.musPivot self._m_mu0 = copy(self.mu0) self._m_HFErescalingExp = copy(self.HFEngine.rescalingExp) self._mu0 = checkParameterList(self.mu0(self.directionPivot), 1)[0] self.HFEngine.rescalingExp = [self.HFEngine.rescalingExp[ self.directionPivot[0]]] 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) idx, sizes, emptyCores = self._preSetupApproxPivoted(mus) data, pMat, req = [], None, [] if len(idx) == 0: vbMng(self, "MAIN", "Idling.", 45) - if self._noSampleMemory: - pL, pT = COMM.recv(source = 0, tag = poolRank()) - pMat = empty((pL, 0), dtype = pT) + pL, pT = COMM.recv(source = 0, tag = poolRank()) + pMat = empty((pL, 0), dtype = pT) else: for i in idx: self.muMargLoc = mus[i] vbMng(self, "MAIN", "Building marginal model no. {} at " "{}.".format(i + 1, self.muMargLoc), 25) - if self._noSampleMemory: - self.samplingEngine.resetHistory() - else: - RationalInterpolant.setupSampling(self) + self.samplingEngine.resetHistory() self.trainedModel = None self.verbosity -= 5 self.samplingEngine.verbosity -= 5 RationalInterpolant.setupApprox(self) self.verbosity += 5 self.samplingEngine.verbosity += 5 self._mu0 = self._m_mu0 self._mus = self._m_selfmus self.HFEngine.rescalingExp = self._m_HFErescalingExp del self._m_mu0, self._m_selfmus, self._m_HFErescalingExp - if self._noSampleMemory: - pMat, req = self._localPivotedMatrix(pMat, req, emptyCores) + pMat, req, _m = self._localPivotedResult(pMat, req, emptyCores) data += [(copy(self.trainedModel.data.Q), - copy(self.trainedModel.data.P), - self._localPivotedResult())] + copy(self.trainedModel.data.P), _m)] del self.muMargLoc for r in req: r.wait() self._postSetupApproxPivoted(mus, data, pMat, sizes) vbMng(self, "DEL", "Done setting up pivoted approximant.", 10) return 0 class RationalInterpolantPivotedGreedyNoMatch( RationalInterpolantPivotedGreedyBase, GenericPivotedGreedyApproximantNoMatch, RationalInterpolantPivotedNoMatch): """ ROM pivoted greedy rational interpolant computation for parametric problems (without pole matching). 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': whether to compute POD of snapshots; defaults to True; - 'scaleFactorDer': scaling factors for derivative computation; defaults to 'AUTO'; - 'cutOffTolerance': tolerance for ignoring parasitic poles; defaults to np.inf; - 'matchingWeightError': weight for pole matching optimization in error estimation; defaults to 0; - 'cutOffToleranceError': tolerance for ignoring parasitic poles in error estimation; defaults to 'AUTO', i.e. cutOffTolerance; - '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 'LEAVE_ONE_OUT', 'LOOK_AHEAD', and 'LOOK_AHEAD_RECOVER'; defaults to 'LEAVE_ONE_OUT'; - 'polybasis': type of polynomial basis for pivot interpolation; defaults to 'MONOMIAL'; - 'M': degree of rational interpolant numerator; defaults to 'AUTO', i.e. maximum allowed; - 'N': degree of rational interpolant denominator; defaults to 'AUTO', i.e. maximum allowed; - 'greedyTolMarginal': uniform error tolerance for marginal greedy algorithm; defaults to 1e-1; - 'maxIterMarginal': maximum number of marginal greedy steps; defaults to 1e2; - 'radialDirectionalWeights': radial basis weights for pivot numerator; defaults to 1; - 'radialDirectionalWeightsMarginal': radial basis weights for marginal interpolant; defaults to 1; - 'interpRcond': tolerance for pivot interpolation; defaults to None; - 'robustTol': tolerance for robust rational denominator management; defaults to 0; - 'correctorForce': whether corrector should forcefully delete bad poles; defaults to False; - 'correctorTol': tolerance for corrector step; defaults to 0., i.e. no bad poles; - 'correctorMaxIter': maximum number of corrector iterations; defaults to 1. Defaults to empty dict. approx_state(optional): Whether to approximate state. Defaults to False. verbosity(optional): Verbosity level. Defaults to 10. Attributes: HFEngine: HF problem solver. mu0: Default parameter. directionPivot: Pivot components. mus: Array of snapshot parameters. musPivot: Array of pivot 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': whether to compute POD of snapshots; - 'scaleFactorDer': scaling factors for derivative computation; - 'cutOffTolerance': tolerance for ignoring parasitic poles; - 'matchingWeightError': weight for pole matching optimization in error estimation; - 'cutOffToleranceError': tolerance for ignoring parasitic poles in error estimation; - 'errorEstimatorKindMarginal': kind of marginal error estimator; - 'polybasis': type of polynomial basis for pivot interpolation; - 'M': degree of rational interpolant numerator; - 'N': degree of rational interpolant denominator; - 'greedyTolMarginal': uniform error tolerance for marginal greedy algorithm; - 'maxIterMarginal': maximum number of marginal greedy steps; - 'radialDirectionalWeights': radial basis weights for pivot numerator; - 'radialDirectionalWeightsMarginal': radial basis weights for marginal interpolant; - 'interpRcond': tolerance for pivot interpolation; - 'robustTol': tolerance for robust rational denominator management; - 'correctorForce': whether corrector should forcefully delete bad poles; - 'correctorTol': tolerance for corrector step; - 'correctorMaxIter': maximum number of corrector iterations. 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. approx_state: Whether to approximate state. verbosity: Verbosity level. POD: Whether to compute POD of snapshots. scaleFactorDer: Scaling factors for derivative computation. cutOffTolerance: Tolerance for ignoring parasitic poles. matchingWeightError: Weight for pole matching optimization in error estimation. cutOffToleranceError: Tolerance for ignoring parasitic poles 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. polybasis: Type of polynomial basis for pivot interpolation. M: Degree of rational interpolant numerator. N: Degree of rational interpolant denominator. greedyTolMarginal: Uniform error tolerance for marginal greedy algorithm. maxIterMarginal: Maximum number of marginal greedy steps. radialDirectionalWeights: Radial basis weights for pivot numerator. radialDirectionalWeightsMarginal: Radial basis weights for marginal interpolant. interpRcond: Tolerance for pivot interpolation. robustTol: Tolerance for robust rational denominator management. correctorForce: Whether corrector should forcefully delete bad poles. correctorTol: Tolerance for corrector step. correctorMaxIter: Maximum number of corrector iterations. 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. """ class RationalInterpolantPivotedGreedy(RationalInterpolantPivotedGreedyBase, GenericPivotedGreedyApproximant, RationalInterpolantPivoted): """ ROM pivoted greedy rational interpolant computation for parametric problems (with pole matching). 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': whether to compute POD of snapshots; defaults to True; - 'scaleFactorDer': scaling factors for derivative computation; defaults to 'AUTO'; - 'matchingWeight': weight for pole matching optimization; defaults to 1; - 'matchingMode': mode for pole matching optimization; allowed values include 'NONE' and 'SHIFT'; defaults to 'NONE'; - 'cutOffTolerance': tolerance for ignoring parasitic poles; defaults to np.inf; - 'cutOffSharedRatio': required ratio of marginal points to share resonance in cut off strategy; defaults to 1.; - 'matchingWeightError': weight for pole matching optimization in error estimation; defaults to 0; - 'cutOffToleranceError': tolerance for ignoring parasitic poles in error estimation; defaults to 'AUTO', i.e. cutOffTolerance; - '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 'LEAVE_ONE_OUT', 'LOOK_AHEAD', and 'LOOK_AHEAD_RECOVER'; defaults to 'LEAVE_ONE_OUT'; - 'polybasis': type of polynomial basis for pivot interpolation; defaults to 'MONOMIAL'; - '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' or 'PIECEWISE_LINEAR_*'; . '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'. - 'M': degree of rational interpolant numerator; defaults to 'AUTO', i.e. maximum allowed; - 'N': degree of rational interpolant denominator; defaults to 'AUTO', i.e. maximum allowed; - 'greedyTolMarginal': uniform error tolerance for marginal greedy algorithm; defaults to 1e-1; - 'maxIterMarginal': maximum number of marginal greedy steps; defaults to 1e2; - 'radialDirectionalWeights': radial basis weights for pivot numerator; defaults to 1; - 'radialDirectionalWeightsMarginal': radial basis weights for marginal interpolant; defaults to 1; - 'interpRcond': tolerance for pivot interpolation; defaults to None; - 'robustTol': tolerance for robust rational denominator management; defaults to 0; - 'correctorForce': whether corrector should forcefully delete bad poles; defaults to False; - 'correctorTol': tolerance for corrector step; defaults to 0., i.e. no bad poles; - 'correctorMaxIter': maximum number of corrector iterations; defaults to 1. Defaults to empty dict. approx_state(optional): Whether to approximate state. Defaults to False. verbosity(optional): Verbosity level. Defaults to 10. Attributes: HFEngine: HF problem solver. mu0: Default parameter. directionPivot: Pivot components. mus: Array of snapshot parameters. musPivot: Array of pivot 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': whether to compute POD of snapshots; - 'scaleFactorDer': scaling factors for derivative computation; - 'matchingWeight': weight for pole matching optimization; - 'matchingMode': mode for pole matching optimization; - 'cutOffTolerance': tolerance for ignoring parasitic poles; - 'cutOffSharedRatio': required ratio of marginal points to share resonance in cut off strategy; - 'matchingWeightError': weight for pole matching optimization in error estimation; - 'cutOffToleranceError': tolerance for ignoring parasitic poles in error estimation; - 'errorEstimatorKindMarginal': kind of marginal error estimator; - 'polybasis': type of polynomial basis for pivot interpolation; - '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. - 'M': degree of rational interpolant numerator; - 'N': degree of rational interpolant denominator; - 'greedyTolMarginal': uniform error tolerance for marginal greedy algorithm; - 'maxIterMarginal': maximum number of marginal greedy steps; - 'radialDirectionalWeights': radial basis weights for pivot numerator; - 'radialDirectionalWeightsMarginal': radial basis weights for marginal interpolant; - 'interpRcond': tolerance for pivot interpolation; - 'robustTol': tolerance for robust rational denominator management; - 'correctorForce': whether corrector should forcefully delete bad poles; - 'correctorTol': tolerance for corrector step; - 'correctorMaxIter': maximum number of corrector iterations. 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. approx_state: Whether to approximate state. verbosity: Verbosity level. POD: Whether to compute POD of snapshots. scaleFactorDer: Scaling factors for derivative computation. matchingWeight: Weight for pole matching optimization. matchingMode: Mode for pole matching optimization. cutOffTolerance: Tolerance for ignoring parasitic poles. cutOffSharedRatio: Required ratio of marginal points to share resonance in cut off strategy. matchingWeightError: Weight for pole matching optimization in error estimation. cutOffToleranceError: Tolerance for ignoring parasitic poles 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. polybasis: Type of polynomial basis for pivot interpolation. polybasisMarginal: Type of polynomial basis for marginal interpolation. paramsMarginal: Dictionary of parameters for marginal interpolation. M: Degree of rational interpolant numerator. N: Degree of rational interpolant denominator. greedyTolMarginal: Uniform error tolerance for marginal greedy algorithm. maxIterMarginal: Maximum number of marginal greedy steps. radialDirectionalWeights: Radial basis weights for pivot numerator. radialDirectionalWeightsMarginal: Radial basis weights for marginal interpolant. interpRcond: Tolerance for pivot interpolation. robustTol: Tolerance for robust rational denominator management. correctorForce: Whether corrector should forcefully delete bad poles. correctorTol: Tolerance for corrector step. correctorMaxIter: Maximum number of corrector iterations. 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. """ diff --git a/rrompy/reduction_methods/pivoted/rational_interpolant_greedy_pivoted.py b/rrompy/reduction_methods/pivoted/rational_interpolant_greedy_pivoted.py index 1c2b10c..5e536e8 100644 --- a/rrompy/reduction_methods/pivoted/rational_interpolant_greedy_pivoted.py +++ b/rrompy/reduction_methods/pivoted/rational_interpolant_greedy_pivoted.py @@ -1,641 +1,627 @@ # Copyright (C) 2018 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 .generic_pivoted_approximant import (GenericPivotedApproximantBase, GenericPivotedApproximantNoMatch, GenericPivotedApproximant) from rrompy.reduction_methods.standard.greedy.rational_interpolant_greedy \ import RationalInterpolantGreedy from rrompy.reduction_methods.standard.greedy.generic_greedy_approximant \ import pruneSamples from rrompy.utilities.base.types import Np1D from rrompy.utilities.base import verbosityManager as vbMng from rrompy.utilities.poly_fitting.polynomial import polyvander as pv from rrompy.utilities.exception_manager import RROMPyAssert, RROMPyWarning from rrompy.parameter import emptyParameterList, checkParameterList from rrompy.utilities.parallel import (COMM, poolRank, indicesScatter, listGather, matrixGatherv) __all__ = ['RationalInterpolantGreedyPivotedNoMatch', 'RationalInterpolantGreedyPivoted'] class RationalInterpolantGreedyPivotedBase(GenericPivotedApproximantBase, RationalInterpolantGreedy): def __init__(self, *args, **kwargs): self._preInit() self._addParametersToList(toBeExcluded = ["sampler"]) super().__init__(*args, **kwargs) self._postInit() @property def tModelType(self): if hasattr(self, "_temporaryPivot"): return RationalInterpolantGreedy.tModelType.fget(self) return super().tModelType @property def polybasis0(self): if "_" in self.polybasis: return self.polybasis.split("_")[0] return self.polybasis @property def correctorTol(self): """Value of correctorTol.""" return self._correctorTol @correctorTol.setter def correctorTol(self, correctorTol): if correctorTol < 0. or (correctorTol > 0. and self.nparPivot > 1): RROMPyWarning(("Overriding prescribed corrector tolerance " "to 0.")) correctorTol = 0. self._correctorTol = correctorTol self._approxParameters["correctorTol"] = self.correctorTol @property def correctorMaxIter(self): """Value of correctorMaxIter.""" return self._correctorMaxIter @correctorMaxIter.setter def correctorMaxIter(self, correctorMaxIter): if correctorMaxIter < 1 or (correctorMaxIter > 1 and self.nparPivot > 1): RROMPyWarning(("Overriding prescribed max number of corrector " "iterations to 1.")) correctorMaxIter = 1 self._correctorMaxIter = correctorMaxIter self._approxParameters["correctorMaxIter"] = self.correctorMaxIter def _polyvanderAuxiliary(self, mus, deg, *args): degEff = [0] * self.npar degEff[self.directionPivot[0]] = deg return pv(mus, degEff, *args) def _marginalizeMiscellanea(self, forward:bool): if forward: self._m_mu0 = copy(self.mu0) self._m_selfmus = copy(self.mus) self._m_HFErescalingExp = copy(self.HFEngine.rescalingExp) self._mu0 = checkParameterList(self.mu0(self.directionPivot), 1)[0] self._mus = checkParameterList(self.mus(self.directionPivot), 1)[0] self.HFEngine.rescalingExp = [self.HFEngine.rescalingExp[ self.directionPivot[0]]] else: self._mu0 = self._m_mu0 self._mus = self._m_selfmus self.HFEngine.rescalingExp = self._m_HFErescalingExp del self._m_mu0, self._m_selfmus, self._m_HFErescalingExp def _marginalizeTrainedModel(self, forward:bool): if forward: del self._temporaryPivot self.trainedModel.data.mu0 = self.mu0 self.trainedModel.data.scaleFactor = [1.] * self.npar self.trainedModel.data.scaleFactor[self.directionPivot[0]] = ( self.scaleFactor[0]) self.trainedModel.data.rescalingExp = self.HFEngine.rescalingExp Qc = np.zeros((1,) * self.directionPivot[0] + (len(self.trainedModel.data.Q.coeffs),) + (1,) * (self.npar - self.directionPivot[0] - 1), dtype = self.trainedModel.data.Q.coeffs.dtype) Pc = np.zeros((1,) * self.directionPivot[0] + (len(self.trainedModel.data.P.coeffs),) + (1,) * (self.npar - self.directionPivot[0] - 1) + (self.trainedModel.data.P.coeffs.shape[1],), dtype = self.trainedModel.data.P.coeffs.dtype) for j in range(len(self.trainedModel.data.Q.coeffs)): Qc[(0,) * self.directionPivot[0] + (j,) + (0,) * (self.npar - self.directionPivot[0] - 1)] = ( self.trainedModel.data.Q.coeffs[j]) for j in range(len(self.trainedModel.data.P.coeffs)): for k in range(self.trainedModel.data.P.coeffs.shape[1]): Pc[(0,) * self.directionPivot[0] + (j,) + (0,) * (self.npar - self.directionPivot[0] - 1) + (k,)] = self.trainedModel.data.P.coeffs[j, k] self.trainedModel.data.Q.coeffs = Qc self.trainedModel.data.P.coeffs = Pc self._m_musUniqueCN = copy(self._musUniqueCN) musUniqueCNAux = np.zeros((self.S, self.npar), dtype = self._musUniqueCN.dtype) musUniqueCNAux[:, self.directionPivot[0]] = self._musUniqueCN(0) self._musUniqueCN = checkParameterList(musUniqueCNAux, self.npar)[0] self._m_derIdxs = copy(self._derIdxs) for j in range(len(self._derIdxs)): for l in range(len(self._derIdxs[j])): derjl = self._derIdxs[j][l][0] self._derIdxs[j][l] = [0] * self.npar self._derIdxs[j][l][self.directionPivot[0]] = derjl else: self._temporaryPivot = 1 self.trainedModel.data.mu0 = checkParameterList( self.mu0(self.directionPivot), 1)[0] self.trainedModel.data.scaleFactor = self.scaleFactor self.trainedModel.data.rescalingExp = self.HFEngine.rescalingExp[ self.directionPivot[0]] self.trainedModel.data.Q.coeffs = self.trainedModel.data.Q.coeffs[ (0,) * self.directionPivot[0] + (slice(None),) + (0,) * (self.HFEngine.npar - 1 - self.directionPivot[0])] self.trainedModel.data.P.coeffs = self.trainedModel.data.P.coeffs[ (0,) * self.directionPivot[0] + (slice(None),) + (0,) * (self.HFEngine.npar - 1 - self.directionPivot[0])] self._musUniqueCN = copy(self._m_musUniqueCN) self._derIdxs = copy(self._m_derIdxs) del self._m_musUniqueCN, self._m_derIdxs self.trainedModel.data.npar = self.npar self.trainedModel.data.Q.npar = self.npar self.trainedModel.data.P.npar = self.npar def errorEstimator(self, mus:Np1D, return_max : bool = False) -> Np1D: """Standard residual-based error estimator.""" self._marginalizeMiscellanea(True) setupOK = self.setupApproxLocal() self._marginalizeMiscellanea(False) if setupOK > 0: err = np.empty(len(mus)) err[:] = np.nan if not return_max: return err return err, [- setupOK], np.nan self._marginalizeTrainedModel(True) errRes = super().errorEstimator(mus, return_max) self._marginalizeTrainedModel(False) return errRes def _preliminaryTraining(self): """Initialize starting snapshots of solution map.""" RROMPyAssert(self._mode, message = "Cannot start greedy algorithm.") self._S = self._setSampleBatch(self.S) self.resetSamples() self.samplingEngine.scaleFactor = self.scaleFactorDer musPivot = self.trainSetGenerator.generatePoints(self.S) while len(musPivot) > self.S: musPivot.pop() muTestPivot = self.samplerPivot.generatePoints(self.nTestPoints, False) idxPop = pruneSamples(muTestPivot ** self.HFEngine.rescalingExp[ self.directionPivot[0]], musPivot ** self.HFEngine.rescalingExp[ self.directionPivot[0]], 1e-10 * self.scaleFactor[0]) self.mus = emptyParameterList() self.mus.reset((self.S, self.npar + len(self.musMargLoc))) muTestBase = emptyParameterList() muTestBase.reset((len(muTestPivot), self.npar + len(self.musMargLoc))) for k in range(self.S): self.mus.data[k, self.directionPivot] = musPivot[k].data self.mus.data[k, self.directionMarginal] = self.musMargLoc.data for k in range(len(muTestPivot)): muTestBase.data[k, self.directionPivot] = muTestPivot[k].data muTestBase.data[k, self.directionMarginal] = self.musMargLoc.data 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.data[: -1] = muTestBase.data self.muTest.data[-1] = muLast.data self.M, self.N = ("AUTO",) * 2 def setupApprox(self, *args, **kwargs) -> int: """Compute rational interpolant.""" if self.checkComputedApprox(): return -1 RROMPyAssert(self._mode, message = "Cannot setup approximant.") vbMng(self, "INIT", "Setting up {}.". format(self.name()), 5) self.computeScaleFactor() self._musMarginal = self.samplerMarginal.generatePoints(self.SMarginal) while len(self.musMarginal) > self.SMarginal: self.musMarginal.pop() S0 = copy(self.S) idx, sizes = indicesScatter(len(self.musMarginal), return_sizes = True) data, pMat = [], None req, emptyCores = [], np.where(np.logical_not(sizes))[0] if len(idx) == 0: vbMng(self, "MAIN", "Idling.", 25) - if self._noSampleMemory: - pL, pT = COMM.recv(source = 0, tag = poolRank()) - pMat = np.empty((pL, 0), dtype = pT) + pL, pT = COMM.recv(source = 0, tag = poolRank()) + pMat = np.empty((pL, 0), dtype = pT) else: _scaleFactorOldPivot = copy(self.scaleFactor) self.scaleFactor = self.scaleFactorPivot self._temporaryPivot = 1 for i in idx: self.musMargLoc = self.musMarginal[i] vbMng(self, "MAIN", "Building marginal model no. {} at {}.".format(i + 1, self.musMargLoc), 5) - if self._noSampleMemory: - self.samplingEngine.resetHistory() - else: - RationalInterpolantGreedy.setupSampling(self) + self.samplingEngine.resetHistory() self.trainedModel = None self.verbosity -= 5 self.samplingEngine.verbosity -= 5 super().setupApprox(*args, **kwargs) self.verbosity += 5 self.samplingEngine.verbosity += 5 - dat0 = (copy(self.trainedModel.data.Q), - copy(self.trainedModel.data.P)) - if self._noSampleMemory: - if pMat is None: - pMat = copy(self.samplingEngine.samples.data) - if i == 0: - for dest in emptyCores: - req += [COMM.isend((len(pMat), pMat.dtype), - dest = dest, tag = dest)] - else: - pMat = np.hstack((pMat, - self.samplingEngine.samples.data)) - dat0 += (copy(self.samplingEngine.mus.data),) + if pMat is None: + pMat = copy(self.samplingEngine.samples.data) + if i == 0: + for dest in emptyCores: + req += [COMM.isend((len(pMat), pMat.dtype), + dest = dest, tag = dest)] else: - dat0 += (copy(self.samplingEngine.getpickleableStuff()),) - data += [dat0] + pMat = np.hstack((pMat, + self.samplingEngine.samples.data)) + data += [(copy(self.trainedModel.data.Q), + copy(self.trainedModel.data.P), + copy(self.samplingEngine.mus.data))] + #FIXME self._S = S0 del self._temporaryPivot, self.musMargLoc self.scaleFactor = _scaleFactorOldPivot for r in req: r.wait() data = listGather(data) - if self._noSampleMemory: - _mus = [x[2] for x in data] - self._mus = checkParameterList(_mus[0], self.npar)[0] - nsamples, sizesEff, idx = [], [], 0 - for size in sizes: - sizesEff += [0] - for _ in range(size): - _m = _mus[idx] - if idx > 0: self._mus.append(_m) - nsamples += [len(_m)] - sizesEff[-1] += nsamples[-1] - idx += 1 - pMat = matrixGatherv(pMat, sizesEff, False) - else: - self._finalizeSnapshots([x[2] for x in data]) - self._mus = self.samplingEngine.musCoalesced - pMat = self.samplingEngine.samplesCoalesced.data - nsamples = self.samplingEngine.nsamples + self._mus = checkParameterList(data[0][2], self.npar)[0] + nsamples, sizesEff, idx = [], [], 0 + for size in sizes: + sizesEff += [0] + for _ in range(size): + _m = data[idx][2] + if idx > 0: self._mus.append(_m) + nsamples += [len(_m)] + sizesEff[-1] += nsamples[-1] + idx += 1 + pMat = matrixGatherv(pMat, sizesEff, False) Psupp = np.append(0, np.cumsum(nsamples)) self._setupTrainedModel(pMat, forceNew = True) self.trainedModel.data.Qs = [x[0] for x in data] self.trainedModel.data.Ps = [x[1] for x in data] self.trainedModel.data.Psupp = list(Psupp[: -1]) self._poleMatching() self._finalizeMarginalization() vbMng(self, "DEL", "Done setting up approximant.", 5) return 0 class RationalInterpolantGreedyPivotedNoMatch( RationalInterpolantGreedyPivotedBase, GenericPivotedApproximantNoMatch): """ ROM pivoted rational interpolant (without pole matching) computation for parametric problems. 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': whether to compute POD of snapshots; defaults to True; - 'scaleFactorDer': scaling factors for derivative computation; defaults to 'AUTO'; - 'cutOffTolerance': tolerance for ignoring parasitic poles; defaults to np.inf; - '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; - 'polybasis': type of polynomial basis for pivot interpolation; defaults to 'MONOMIAL'; - '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; - 'errorEstimatorKind': kind of error estimator; available values include 'AFFINE', 'DISCREPANCY', 'LOOK_AHEAD', 'LOOK_AHEAD_RES', and 'NONE'; defaults to 'NONE'; - 'radialDirectionalWeightsMarginal': radial basis weights for marginal interpolant; defaults to 1; - 'interpRcond': tolerance for pivot interpolation; defaults to None; - 'robustTol': tolerance for robust rational denominator management; defaults to 0; - 'correctorForce': whether corrector should forcefully delete bad poles; defaults to False; - 'correctorTol': tolerance for corrector step; defaults to 0., i.e. no bad poles; - 'correctorMaxIter': maximum number of corrector iterations; defaults to 1. Defaults to empty dict. approx_state(optional): Whether to approximate state. Defaults to False. 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': whether to compute POD of snapshots; - 'scaleFactorDer': scaling factors for derivative computation; - 'cutOffTolerance': tolerance for ignoring parasitic poles; - 'polybasis': type of polynomial basis for pivot interpolation; - '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; - 'radialDirectionalWeightsMarginal': radial basis weights for marginal interpolant; - 'interpRcond': tolerance for pivot interpolation; - 'robustTol': tolerance for robust rational denominator management; - 'correctorForce': whether corrector should forcefully delete bad poles; - 'correctorTol': tolerance for corrector step; - 'correctorMaxIter': maximum number of corrector iterations. 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. approx_state: Whether to approximate state. verbosity: Verbosity level. POD: Whether to compute POD of snapshots. scaleFactorDer: Scaling factors for derivative computation. cutOffTolerance: Tolerance for ignoring parasitic poles. 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. polybasis: Type of polynomial basis for pivot interpolation. 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. errorEstimatorKind: kind of error estimator. radialDirectionalWeightsMarginal: Radial basis weights for marginal interpolant. interpRcond: Tolerance for pivot interpolation. robustTol: Tolerance for robust rational denominator management. correctorForce: Whether corrector should forcefully delete bad poles. correctorTol: Tolerance for corrector step. correctorMaxIter: Maximum number of corrector iterations. 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. Q: Numpy 1D vector containing complex coefficients of approximant denominator. P: Numpy 2D vector whose columns are FE dofs of coefficients of approximant numerator. """ def _poleMatching(self): vbMng(self, "INIT", "Compressing poles.", 10) self.trainedModel.initializeFromRational() vbMng(self, "DEL", "Done compressing poles.", 10) class RationalInterpolantGreedyPivoted(RationalInterpolantGreedyPivotedBase, GenericPivotedApproximant): """ ROM pivoted rational interpolant (with pole matching) computation for parametric problems. 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': whether to compute POD of snapshots; defaults to True; - 'scaleFactorDer': scaling factors for derivative computation; defaults to 'AUTO'; - 'matchingWeight': weight for pole matching optimization; defaults to 1; - 'matchingMode': mode for pole matching optimization; allowed values include 'NONE' and 'SHIFT'; defaults to 'NONE'; - 'cutOffTolerance': tolerance for ignoring parasitic poles; defaults to np.inf; - 'cutOffSharedRatio': required ratio of marginal points to share resonance in cut off strategy; defaults to 1.; - '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; - 'polybasis': type of polynomial basis for pivot interpolation; defaults to 'MONOMIAL'; - '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' or 'PIECEWISE_LINEAR_*'; . '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'. - '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; - 'errorEstimatorKind': kind of error estimator; available values include 'AFFINE', 'DISCREPANCY', 'LOOK_AHEAD', 'LOOK_AHEAD_RES', and 'NONE'; defaults to 'NONE'; - 'radialDirectionalWeightsMarginal': radial basis weights for marginal interpolant; defaults to 1; - 'interpRcond': tolerance for pivot interpolation; defaults to None; - 'robustTol': tolerance for robust rational denominator management; defaults to 0; - 'correctorForce': whether corrector should forcefully delete bad poles; defaults to False; - 'correctorTol': tolerance for corrector step; defaults to 0., i.e. no bad poles; - 'correctorMaxIter': maximum number of corrector iterations; defaults to 1. Defaults to empty dict. approx_state(optional): Whether to approximate state. Defaults to False. 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': whether to compute POD of snapshots; - 'scaleFactorDer': scaling factors for derivative computation; - 'matchingWeight': weight for pole matching optimization; - 'matchingMode': mode for pole matching optimization; - 'cutOffTolerance': tolerance for ignoring parasitic poles; - 'cutOffSharedRatio': required ratio of marginal points to share resonance in cut off strategy; - 'polybasis': type of polynomial basis for pivot interpolation; - '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. - '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; - 'radialDirectionalWeightsMarginal': radial basis weights for marginal interpolant; - 'interpRcond': tolerance for pivot interpolation; - 'robustTol': tolerance for robust rational denominator management; - 'correctorForce': whether corrector should forcefully delete bad poles; - 'correctorTol': tolerance for corrector step; - 'correctorMaxIter': maximum number of corrector iterations. 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. approx_state: Whether to approximate state. verbosity: Verbosity level. POD: Whether to compute POD of snapshots. scaleFactorDer: Scaling factors for derivative computation. matchingWeight: Weight for pole matching optimization. matchingMode: Mode for pole matching optimization. cutOffTolerance: Tolerance for ignoring parasitic poles. cutOffSharedRatio: Required ratio of marginal points to share resonance in cut off strategy. 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. polybasis: Type of polynomial basis for pivot interpolation. polybasisMarginal: Type of polynomial basis for marginal interpolation. paramsMarginal: Dictionary of parameters for marginal interpolation. 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. errorEstimatorKind: kind of error estimator. radialDirectionalWeightsMarginal: Radial basis weights for marginal interpolant. interpRcond: Tolerance for pivot interpolation. robustTol: Tolerance for robust rational denominator management. correctorForce: Whether corrector should forcefully delete bad poles. correctorTol: Tolerance for corrector step. correctorMaxIter: Maximum number of corrector iterations. 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. Q: Numpy 1D vector containing complex coefficients of approximant denominator. P: Numpy 2D vector whose columns are FE dofs of coefficients of approximant numerator. """ def _poleMatching(self): vbMng(self, "INIT", "Compressing and matching poles.", 10) self.trainedModel.initializeFromRational(self.matchingWeight, self.matchingMode, self.HFEngine, False) vbMng(self, "DEL", "Done compressing and matching poles.", 10) def setupApprox(self, *args, **kwargs) -> int: if self.checkComputedApprox(): return -1 self.purgeparamsMarginal() return super().setupApprox(*args, **kwargs) diff --git a/rrompy/reduction_methods/pivoted/rational_interpolant_pivoted.py b/rrompy/reduction_methods/pivoted/rational_interpolant_pivoted.py index 91a8c8e..3cec481 100644 --- a/rrompy/reduction_methods/pivoted/rational_interpolant_pivoted.py +++ b/rrompy/reduction_methods/pivoted/rational_interpolant_pivoted.py @@ -1,519 +1,508 @@ # Copyright (C) 2018 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 .generic_pivoted_approximant import (GenericPivotedApproximantBase, GenericPivotedApproximantNoMatch, GenericPivotedApproximant) from rrompy.reduction_methods.standard.rational_interpolant import ( RationalInterpolant) from rrompy.utilities.base import verbosityManager as vbMng from rrompy.utilities.numerical.hash_derivative import nextDerivativeIndices from rrompy.utilities.exception_manager import RROMPyAssert, RROMPyWarning from rrompy.parameter import emptyParameterList from rrompy.utilities.parallel import (COMM, poolRank, indicesScatter, listGather, matrixGatherv) __all__ = ['RationalInterpolantPivotedNoMatch', 'RationalInterpolantPivoted'] class RationalInterpolantPivotedBase(GenericPivotedApproximantBase, RationalInterpolant): def __init__(self, *args, **kwargs): self._preInit() self._addParametersToList(toBeExcluded = ["polydegreetype", "sampler"]) 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.scaleFactorPivot return self._scaleFactorDer @scaleFactorDer.setter def scaleFactorDer(self, scaleFactorDer): if isinstance(scaleFactorDer, (str,)): scaleFactorDer = scaleFactorDer.upper() elif hasattr(scaleFactorDer, "__len__"): scaleFactorDer = list(scaleFactorDer) self._scaleFactorDer = scaleFactorDer self._approxParameters["scaleFactorDer"] = self._scaleFactorDer @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 polybasis0(self): if "_" in self.polybasis: return self.polybasis.split("_")[0] return self.polybasis @property def correctorTol(self): """Value of correctorTol.""" return self._correctorTol @correctorTol.setter def correctorTol(self, correctorTol): if correctorTol < 0. or (correctorTol > 0. and self.nparPivot > 1): RROMPyWarning(("Overriding prescribed corrector tolerance " "to 0.")) correctorTol = 0. self._correctorTol = correctorTol self._approxParameters["correctorTol"] = self.correctorTol @property def correctorMaxIter(self): """Value of correctorMaxIter.""" return self._correctorMaxIter @correctorMaxIter.setter def correctorMaxIter(self, correctorMaxIter): if correctorMaxIter < 1 or (correctorMaxIter > 1 and self.nparPivot > 1): RROMPyWarning(("Overriding prescribed max number of corrector " "iterations to 1.")) correctorMaxIter = 1 self._correctorMaxIter = correctorMaxIter self._approxParameters["correctorMaxIter"] = self.correctorMaxIter def _setupInterpolationIndices(self): """Setup parameters for polyvander.""" RROMPyAssert(self._mode, message = "Cannot setup interpolation indices.") if (self._musUniqueCN is None or len(self._reorder) != len(self.musPivot)): try: muPC = self.trainedModel.centerNormalizePivot(self.musPivot) except: muPC = self.trainedModel.centerNormalize(self.musPivot) self._musUniqueCN, musIdxsTo, musIdxs, musCount = (muPC.unique( return_index = True, return_inverse = True, return_counts = True)) self._musUnique = self.musPivot[musIdxsTo] self._derIdxs = [None] * len(self._musUniqueCN) self._reorder = np.empty(len(musIdxs), dtype = int) filled = 0 for j, cnt in enumerate(musCount): self._derIdxs[j] = nextDerivativeIndices([], self.nparPivot, cnt) jIdx = np.nonzero(musIdxs == j)[0] self._reorder[jIdx] = np.arange(filled, filled + cnt) filled += cnt def setupApprox(self) -> int: """Compute rational interpolant.""" if self.checkComputedApprox(): return -1 RROMPyAssert(self._mode, message = "Cannot setup approximant.") vbMng(self, "INIT", "Setting up {}.". format(self.name()), 5) self.computeScaleFactor() self.resetSamples() self.samplingEngine.scaleFactor = self.scaleFactorDer self.musPivot = self.samplerPivot.generatePoints(self.S) while len(self.musPivot) > self.S: self.musPivot.pop() self._musMarginal = self.samplerMarginal.generatePoints(self.SMarginal) while len(self.musMarginal) > self.SMarginal: self.musMarginal.pop() self.mus = emptyParameterList() self.mus.reset((self.S * self.SMarginal, self.HFEngine.npar)) for j, muMarg in enumerate(self.musMarginal): for k in range(j * self.S, (j + 1) * self.S): self.mus.data[k, self.directionPivot] = ( self.musPivot[k - j * self.S].data) self.mus.data[k, self.directionMarginal] = muMarg.data N0 = copy(self.N) self._setupTrainedModel(np.zeros((0, 0)), forceNew = True) idx, sizes = indicesScatter(len(self.musMarginal), return_sizes = True) data, pMat = [], None req, emptyCores = [], np.where(np.logical_not(sizes))[0] if len(idx) == 0: vbMng(self, "MAIN", "Idling.", 30) - if self._noSampleMemory: - pL, pT = COMM.recv(source = 0, tag = poolRank()) - pMat = np.empty((pL, 0), dtype = pT) + pL, pT = COMM.recv(source = 0, tag = poolRank()) + pMat = np.empty((pL, 0), dtype = pT) else: _scaleFactorOldPivot = copy(self.scaleFactor) self.scaleFactor = self.scaleFactorPivot self._temporaryPivot = 1 for i in idx: vbMng(self, "MAIN", "Building marginal model no. {} at {}.".format(i + 1, self.musMarginal[i]), 5) vbMng(self, "INIT", "Starting computation of snapshots.", 10) - if self._noSampleMemory: - self.samplingEngine.resetHistory() - else: - RationalInterpolant.setupSampling(self) + self.samplingEngine.resetHistory() self.samplingEngine.iterSample( self.mus.data[self.S * i : self.S * (i + 1)]) vbMng(self, "DEL", "Done computing snapshots.", 10) self.verbosity -= 5 self.samplingEngine.verbosity -= 5 self._iterCorrector() self.verbosity += 5 self.samplingEngine.verbosity += 5 - dat0 = (copy(self.trainedModel.data.Q), - copy(self.trainedModel.data.P)) - del self.trainedModel.data.Q, self.trainedModel.data.P - if self._noSampleMemory: - if pMat is None: - pMat = copy(self.samplingEngine.samples.data) - if i == 0: - for dest in emptyCores: - req += [COMM.isend((len(pMat), pMat.dtype), - dest = dest, tag = dest)] - else: - pMat = np.hstack((pMat, - self.samplingEngine.samples.data)) + if pMat is None: + pMat = copy(self.samplingEngine.samples.data) + if i == 0: + for dest in emptyCores: + req += [COMM.isend((len(pMat), pMat.dtype), + dest = dest, tag = dest)] else: - dat0 += (copy(self.samplingEngine.getpickleableStuff()),) - data += [dat0] + pMat = np.hstack((pMat, + self.samplingEngine.samples.data)) + data += [(copy(self.trainedModel.data.Q), + copy(self.trainedModel.data.P))] + del self.trainedModel.data.Q, self.trainedModel.data.P + #FIXME self.N = N0 del self._temporaryPivot self.scaleFactor = _scaleFactorOldPivot for r in req: r.wait() data = listGather(data) - if self._noSampleMemory: - pMat = matrixGatherv(pMat, [self.S * s for s in sizes], False) - else: - self._finalizeSnapshots([x[2] for x in data]) - pMat = self.samplingEngine.samplesCoalesced.data + pMat = matrixGatherv(pMat, [self.S * s for s in sizes], False) self._setupTrainedModel(pMat) self.trainedModel.data.Qs = [x[0] for x in data] self.trainedModel.data.Ps = [x[1] for x in data] Psupp = np.arange(0, len(self.musMarginal) * self.S, self.S) self.trainedModel.data.Psupp = list(Psupp) self._poleMatching() self._finalizeMarginalization() vbMng(self, "DEL", "Done setting up approximant.", 5) return 0 class RationalInterpolantPivotedNoMatch(RationalInterpolantPivotedBase, GenericPivotedApproximantNoMatch): """ ROM pivoted rational interpolant (without pole matching) computation for parametric problems. 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': whether to compute POD of snapshots; defaults to True; - 'scaleFactorDer': scaling factors for derivative computation; defaults to 'AUTO'; - 'cutOffTolerance': tolerance for ignoring parasitic poles; defaults to np.inf; - '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; - 'polybasis': type of polynomial basis for pivot interpolation; defaults to 'MONOMIAL'; - 'M': degree of rational interpolant numerator; defaults to 'AUTO', i.e. maximum allowed; - 'N': degree of rational interpolant denominator; defaults to 'AUTO', i.e. maximum allowed; - 'radialDirectionalWeights': radial basis weights for pivot numerator; defaults to 1; - 'radialDirectionalWeightsMarginal': radial basis weights for marginal interpolant; defaults to 1; - 'interpRcond': tolerance for pivot interpolation; defaults to None; - 'robustTol': tolerance for robust rational denominator management; defaults to 0; - 'correctorForce': whether corrector should forcefully delete bad poles; defaults to False; - 'correctorTol': tolerance for corrector step; defaults to 0., i.e. no bad poles; - 'correctorMaxIter': maximum number of corrector iterations; defaults to 1. Defaults to empty dict. approx_state(optional): Whether to approximate state. Defaults to False. verbosity(optional): Verbosity level. Defaults to 10. Attributes: HFEngine: HF problem solver. mu0: Default parameter. directionPivot: Pivot components. mus: Array of snapshot parameters. musPivot: Array of pivot 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': whether to compute POD of snapshots; - 'scaleFactorDer': scaling factors for derivative computation; - 'cutOffTolerance': tolerance for ignoring parasitic poles; - 'polybasis': type of polynomial basis for pivot interpolation; - 'M': degree of rational interpolant numerator; - 'N': degree of rational interpolant denominator; - 'radialDirectionalWeights': radial basis weights for pivot numerator; - 'radialDirectionalWeightsMarginal': radial basis weights for marginal interpolant; - 'interpRcond': tolerance for pivot interpolation; - 'robustTol': tolerance for robust rational denominator management; - 'correctorForce': whether corrector should forcefully delete bad poles; - 'correctorTol': tolerance for corrector step; - 'correctorMaxIter': maximum number of corrector iterations. 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. approx_state: Whether to approximate state. verbosity: Verbosity level. POD: Whether to compute POD of snapshots. scaleFactorDer: Scaling factors for derivative computation. cutOffTolerance: Tolerance for ignoring parasitic poles. 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. polybasis: Type of polynomial basis for pivot interpolation. M: Numerator degree of approximant. N: Denominator degree of approximant. radialDirectionalWeights: Radial basis weights for pivot numerator. radialDirectionalWeightsMarginal: Radial basis weights for marginal interpolant. interpRcond: Tolerance for pivot interpolation. robustTol: Tolerance for robust rational denominator management. correctorForce: Whether corrector should forcefully delete bad poles. correctorTol: Tolerance for corrector step. correctorMaxIter: Maximum number of corrector iterations. 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. Q: Numpy 1D vector containing complex coefficients of approximant denominator. P: Numpy 2D vector whose columns are FE dofs of coefficients of approximant numerator. """ def _poleMatching(self): vbMng(self, "INIT", "Compressing poles.", 10) self.trainedModel.initializeFromRational() vbMng(self, "DEL", "Done compressing poles.", 10) class RationalInterpolantPivoted(RationalInterpolantPivotedBase, GenericPivotedApproximant): """ ROM pivoted rational interpolant (with pole matching) computation for parametric problems. 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': whether to compute POD of snapshots; defaults to True; - 'scaleFactorDer': scaling factors for derivative computation; defaults to 'AUTO'; - 'matchingWeight': weight for pole matching optimization; defaults to 1; - 'matchingMode': mode for pole matching optimization; allowed values include 'NONE' and 'SHIFT'; defaults to 'NONE'; - 'cutOffTolerance': tolerance for ignoring parasitic poles; defaults to np.inf; - 'cutOffSharedRatio': required ratio of marginal points to share resonance in cut off strategy; defaults to 1.; - '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; - 'polybasis': type of polynomial basis for pivot interpolation; defaults to 'MONOMIAL'; - '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' or 'PIECEWISE_LINEAR_*'; . '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'. - 'M': degree of rational interpolant numerator; defaults to 'AUTO', i.e. maximum allowed; - 'N': degree of rational interpolant denominator; defaults to 'AUTO', i.e. maximum allowed; - 'radialDirectionalWeights': radial basis weights for pivot numerator; defaults to 1; - 'radialDirectionalWeightsMarginal': radial basis weights for marginal interpolant; defaults to 1; - 'interpRcond': tolerance for pivot interpolation; defaults to None; - 'robustTol': tolerance for robust rational denominator management; defaults to 0; - 'correctorForce': whether corrector should forcefully delete bad poles; defaults to False; - 'correctorTol': tolerance for corrector step; defaults to 0., i.e. no bad poles; - 'correctorMaxIter': maximum number of corrector iterations; defaults to 1. Defaults to empty dict. approx_state(optional): Whether to approximate state. Defaults to False. verbosity(optional): Verbosity level. Defaults to 10. Attributes: HFEngine: HF problem solver. mu0: Default parameter. directionPivot: Pivot components. mus: Array of snapshot parameters. musPivot: Array of pivot 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': whether to compute POD of snapshots; - 'scaleFactorDer': scaling factors for derivative computation; - 'matchingWeight': weight for pole matching optimization; - 'matchingMode': mode for pole matching optimization; - 'cutOffTolerance': tolerance for ignoring parasitic poles; - 'cutOffSharedRatio': required ratio of marginal points to share resonance in cut off strategy; - 'polybasis': type of polynomial basis for pivot interpolation; - '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. - 'M': degree of rational interpolant numerator; - 'N': degree of rational interpolant denominator; - 'radialDirectionalWeights': radial basis weights for pivot numerator; - 'radialDirectionalWeightsMarginal': radial basis weights for marginal interpolant; - 'interpRcond': tolerance for pivot interpolation; - 'robustTol': tolerance for robust rational denominator management; - 'correctorForce': whether corrector should forcefully delete bad poles; - 'correctorTol': tolerance for corrector step; - 'correctorMaxIter': maximum number of corrector iterations. 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. approx_state: Whether to approximate state. verbosity: Verbosity level. POD: Whether to compute POD of snapshots. scaleFactorDer: Scaling factors for derivative computation. matchingWeight: Weight for pole matching optimization. matchingMode: Mode for pole matching optimization. cutOffTolerance: Tolerance for ignoring parasitic poles. cutOffSharedRatio: Required ratio of marginal points to share resonance in cut off strategy. 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. polybasis: Type of polynomial basis for pivot interpolation. polybasisMarginal: Type of polynomial basis for marginal interpolation. paramsMarginal: Dictionary of parameters for marginal interpolation. M: Numerator degree of approximant. N: Denominator degree of approximant. radialDirectionalWeights: Radial basis weights for pivot numerator. radialDirectionalWeightsMarginal: Radial basis weights for marginal interpolant. interpRcond: Tolerance for pivot interpolation. robustTol: Tolerance for robust rational denominator management. correctorForce: Whether corrector should forcefully delete bad poles. correctorTol: Tolerance for corrector step. correctorMaxIter: Maximum number of corrector iterations. 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. Q: Numpy 1D vector containing complex coefficients of approximant denominator. P: Numpy 2D vector whose columns are FE dofs of coefficients of approximant numerator. """ def _poleMatching(self): vbMng(self, "INIT", "Compressing and matching poles.", 10) self.trainedModel.initializeFromRational(self.matchingWeight, self.matchingMode, self.HFEngine, False) vbMng(self, "DEL", "Done compressing and matching poles.", 10) def setupApprox(self, *args, **kwargs) -> int: if self.checkComputedApprox(): return -1 self.purgeparamsMarginal() return super().setupApprox(*args, **kwargs) diff --git a/rrompy/reduction_methods/standard/greedy/generic_greedy_approximant.py b/rrompy/reduction_methods/standard/greedy/generic_greedy_approximant.py index ef9cb30..9f98c40 100644 --- a/rrompy/reduction_methods/standard/greedy/generic_greedy_approximant.py +++ b/rrompy/reduction_methods/standard/greedy/generic_greedy_approximant.py @@ -1,646 +1,646 @@ # Copyright (C) 2018 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, normEng, 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.solver import normEngine from rrompy.utilities.exception_manager import (RROMPyException, RROMPyAssert, RROMPyWarning) from rrompy.parameter import checkParameterList, emptyParameterList 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 len(badmus) == 0: return mus proximity = np.min(localL2Distance(mus.data, badmus.data), axis = 1) return np.arange(len(mus))[proximity <= tol] 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': whether to compute POD of snapshots; defaults to True; - '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. approx_state(optional): Whether to approximate state. Defaults to False. 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': whether to compute POD of snapshots; - '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. approx_state: Whether to approximate state. verbosity: Verbosity level. POD: whether to compute POD of snapshots. 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. estimatorNormEngine: Engine for estimator norm computation. 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() self._addParametersToList(["greedyTol", "collinearityTol", "maxIter", "nTestPoints"], [1e-2, 0., 1e2, 5e2], ["trainSetGenerator"], ["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 initEstimatorNormEngine(self, normEngn : normEng = None): """Initialize estimator norm engine.""" if (normEngn is not None or not hasattr(self, "estimatorNormEngine") or self.estimatorNormEngine is None): if normEngn is None: if self.approx_state: if not hasattr(self.HFEngine, "energyNormDualMatrix"): self.HFEngine.buildEnergyNormDualForm() estimatorEnergyMatrix = self.HFEngine.energyNormDualMatrix else: estimatorEnergyMatrix = self.HFEngine.outputNormMatrix else: if hasattr(normEngn, "buildEnergyNormDualForm"): if not hasattr(normEngn, "energyNormDualMatrix"): normEngn.buildEnergyNormDualForm() estimatorEnergyMatrix = normEngn.energyNormDualMatrix else: estimatorEnergyMatrix = normEngn self.estimatorNormEngine = normEngine(estimatorEnergyMatrix) 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") self.HFEngine.buildA() self.HFEngine.buildb() mus = checkParameterList(mus, self.npar)[0] tMverb, self.trainedModel.verbosity = self.trainedModel.verbosity, 0 uApproxRs = self.getApproxReduced(mus) self.trainedModel.verbosity = tMverb muTestEff = mus ** self.HFEngine.rescalingExp 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.data, 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 = checkParameterList(mus, self.npar)[0] 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) 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: reff = self.samplingEngine.RPOD[:, -1] else: RROMPyWarning(("Repeated orthogonalization of the samples for " "collinearity check. Consider setting POD to " "True.")) if not hasattr(self, "_PODEngine"): - from rrompy.sampling.base.pod_engine import 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 = copy(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.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.data[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.") self.computeScaleFactor() if self.samplingEngine.nsamples > 0: return self.resetSamples() 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(muTestBase ** self.HFEngine.rescalingExp, self.mus ** self.HFEngine.rescalingExp, 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: self.mus.pop(-1) self.samplingEngine.popSample() if muidx[0] < 0: self.trainedModel = None raise RROMPyException(("Instability in approximant " "computation. Aborting greedy " "iterations.")) else: self._approxParameters = ( trainedModelOld.data.approxParameters) self._S = trainedModelOld.data.approxParameters["S"] self._approxParameters["S"] = self.S self.trainedModel.data = copy(trainedModelOld.data) 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 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: self._S = self.samplingEngine.nsamples self._approxParameters["S"] = self.S 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) vbMng(self, "DEL", ("Done computing snapshots (final snapshot count: " "{}).").format(self.samplingEngine.nsamples), 3) return 0 def assembleReducedResidualGramian(self, pMat:sampList): """ Build residual gramian of reduced linear system through projections. """ self.initEstimatorNormEngine() if (not hasattr(self.trainedModel.data, "gramian") or self.trainedModel.data.gramian is None): gramian = self.estimatorNormEngine.innerProduct(pMat, pMat) 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.estimatorNormEngine.innerProduct(pMat(idxNew), pMat(idxOld))) gramian[Sold :, : Sold] = gramian[: Sold, Sold :].T.conj() gramian[Sold :, Sold :] = ( self.estimatorNormEngine.innerProduct(pMat(idxNew), pMat(idxNew))) self.trainedModel.data.gramian = gramian def assembleReducedResidualBlocksbb(self, bs:List[Np1D]): """ Build blocks (of type bb) of reduced linear system through projections. """ self.initEstimatorNormEngine() 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.estimatorNormEngine.innerProduct(Mbi, Mbi) for j in range(i): Mbj = bs[j] resbb[i, j] = self.estimatorNormEngine.innerProduct(Mbj, Mbi) 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. """ self.initEstimatorNormEngine() 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 not isinstance(pMat, (np.ndarray,)): 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.estimatorNormEngine.innerProduct(MAj, Mbi) else: Sold = self.trainedModel.data.resAb.shape[1] if Sold == S: return if Sold > S: resAb = self.trainedModel.data.resAb[:, : S, :] else: if not isinstance(pMat, (np.ndarray,)): 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.estimatorNormEngine.innerProduct(MAj, Mbi)) self.trainedModel.data.resAb = resAb def assembleReducedResidualBlocksAA(self, As:List[Np2D], pMat:sampList): """ Build blocks (of type AA) of reduced linear system through projections. """ self.initEstimatorNormEngine() nAs = len(As) S = len(self.mus) if (not hasattr(self.trainedModel.data, "resAA") or self.trainedModel.data.resAA is None): if not isinstance(pMat, (np.ndarray,)): 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.estimatorNormEngine.innerProduct(MAi, MAi)) for j in range(i): MAj = dot(As[j], pMat) resAA[:, i, :, j] = ( self.estimatorNormEngine.innerProduct(MAj, MAi)) 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 not isinstance(pMat, (np.ndarray,)): 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.estimatorNormEngine.innerProduct(MAi[:, Sold :], MAi[:, : Sold])) resAA[Sold :, i, : Sold, i] = resAA[: Sold, i, Sold :, i].T.conj() resAA[Sold :, i, Sold :, i] = ( self.estimatorNormEngine.innerProduct(MAi[:, Sold :], MAi[:, Sold :])) for j in range(i): MAj = dot(As[j], pMat) resAA[: Sold, i, Sold :, j] = ( self.estimatorNormEngine.innerProduct(MAj[:, Sold :], MAi[:, : Sold])) resAA[Sold :, i, : Sold, j] = ( self.estimatorNormEngine.innerProduct(MAj[:, : Sold], MAi[:, Sold :])) resAA[Sold :, i, Sold :, j] = ( self.estimatorNormEngine.innerProduct(MAj[:, Sold :], MAi[:, Sold :])) 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") else: checkIfAffine(self.HFEngine, "assemble reduced RHS blocks", True) self.HFEngine.buildb() self.assembleReducedResidualBlocksbb(self.HFEngine.bs) if full: pMat = self.samplingEngine.samples self.HFEngine.buildA() self.assembleReducedResidualBlocksAb(self.HFEngine.As, self.HFEngine.bs, pMat) self.assembleReducedResidualBlocksAA(self.HFEngine.As, pMat)