Source code for TransportMaps.CLI.AvailableOptions

#!/usr/bin/env python

#
# This file is part of TransportMaps.
#
# TransportMaps 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.
#
# TransportMaps 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 TransportMaps.  If not, see <http://www.gnu.org/licenses/>.
#
# Transport Maps Library
# Copyright (C) 2015-2018 Massachusetts Institute of Technology
# Uncertainty Quantification group
# Department of Aeronautics and Astronautics
#
# Author: Transport Map Team
# Website: transportmaps.mit.edu
# Support: transportmaps.mit.edu/qa/
#

__all__ = ['AVAIL_MONOTONE', 'AVAIL_SPAN', 'AVAIL_BTYPE', 'AVAIL_SPARSITY',
           'AVAIL_QTYPE', 'AVAIL_DERS',
           'AVAIL_VALIDATOR', 'AVAIL_COST_FUNCTION',
           'AVAIL_PRECONDITIONING',
           'AVAIL_ADAPTIVITY', 'AVAIL_ADAPT_TRUNC',
           'AVAIL_REGRESSION_ADAPTIVITY',
           'AVAIL_MCMC_ALGORITHMS', 'AVAIL_LOGGING',
           'AVAIL_DISTRIBUTIONS',
           'print_avail_options']


# MONOTONE APPROXIMATION
# 'intexp': Integrated Exponential
# 'linspan': Constrained Polynomial

[docs]AVAIL_MONOTONE = {'linspan': 'monotone linear span',
                  'intexp': 'integrated exponential',
                  'intsq': 'integrated square'}


# SPAN APPROXIMATION
# 'full': Full order approximation
# 'total': Total order approximation

[docs]AVAIL_SPAN = {'full': 'full span',
              'total': 'total order span'}


# BASIS TYPES
# 'poly': Hermite polynomials
# 'rbf': Radial basis functions

[docs]AVAIL_BTYPE = {'poly': 'polynomial basis',
               'rbf': 'radial basis functions (requires SPAN=full)'}


# SPARSITY PATTERNS

[docs]AVAIL_SPARSITY = {'tri': 'lower triangular map',
                  'diag': 'diagonal map'}


# QUADRATURES
# 0: Monte Carlo
# 3: Gauss quadrature

[docs]AVAIL_QTYPE = {0: 'Monte Carlo',
               3: 'Gauss quadrature'}


# DERS
# 1: BFGS
# 2: Newton-CG

[docs]AVAIL_DERS = {0: 'BFGS (gradient free)',
              1: 'BFGS (gradient needed)',
              2: 'Newton-CG (Hessian needed)'}


# VALIDATORS

[docs]AVAIL_VALIDATOR = {'none': 'no validator used',
                   'saa': 'Sample Average Approximation',
                   'gradboot': 'Gradient bootstrap'}


# COST FUNCTIONS

[docs]AVAIL_COST_FUNCTION = {'tot-time': 'total elapsed time'}


# PRECONDITIONING
# 'none': no preconditioning
# 'lr': low-rank preconditioning

[docs]AVAIL_PRECONDITIONING = {
    'none': 'no preconditioning',
    'lr': 'low-rank preconditioning'
}


# ADAPTIVITY
# 'none': no adaptivity performed
# 'sequential': a sequence of maps of increasing order are used

[docs]AVAIL_ADAPTIVITY = {
    'none': 'no adaptivity',
    'sequential': 'a prefix sequence of maps is used',
    'tol-sequential': 'a sequence of maps is used until tolerance is met',
    'fv': 'first variation [FV] adaptivity'
}



[docs]AVAIL_ADAPT_TRUNC = {
    'manual': 'the user is queried for every truncation (matplotlib required)',
    'percentage': 'the number of basis is increase/decreased by a percentage value',
    'constant': 'the number of basis is increase/decreased by a constant value',
}


# REGRESSION ADAPTIVITY
# 'none': no adaptivity performed
# 'tol-sequential': a sequence of maps of increasing order are used

[docs]AVAIL_REGRESSION_ADAPTIVITY = {
    'none': 'no adaptivity',
    'tol-sequential': 'meet a tolerance using a prefix sequence of maps'}


# MCMC ALGORITHMS

[docs]AVAIL_MCMC_ALGORITHMS = {
    'mh': 'Metropolis Hastings',
    'mhind': 'Metropolis-Hastings with independent proposals',
    'hmc': "Hamiltonian Monte Carlo",
    }


AVAIL_MCMC_ESS_METHODS = {
    'acor': 'autocorrelation function and variance bars',
    'uw': 'Ulli Wolff effective sample size'
}

# LOGGING

[docs]AVAIL_LOGGING = {10: 'debug',
                 20: 'info',
                 30: 'warning',
                 40: 'error',
                 50: 'critical'}



[docs]AVAIL_DISTRIBUTIONS = {'exact-target': 'exact target density',
                       'approx-target': 'approximate target density',
                       'exact-base': 'exact base density',
                       'approx-base': 'approximate base density'}


AVAIL_LAPLACE_X0 = {
    "rnd": "will sample randomly from the prior (if available)",
    "zero": "will start with a zero initial guess"
}

AVAIL_LAPLACE_SQRT = {
    "sym": "uses a symmetrized eigenvalue square root",
    "tri": "uses a Cholesky square root",
    "kl": "uses a Karuenen-Loeve square root",
    "lis": "if using low-rank approximation of the square root, then "
           "it uses the square root which cluster the likelihood informed "
           "directions in the first column of the square root"
}

AVAIL_LAPLACE_HESS_APPROX = {
    "low-rank": "build the optimal low-rank approximation",
    "fd": "use a finite difference approximation"
}