# ___________________________________________________________________________
#
# Pyomo: Python Optimization Modeling Objects
# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
# Under the terms of Contract DE-NA0003525 with National Technology and
# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
# rights in this software.
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
from pyomo.common.config import ConfigBlock, ConfigList, ConfigValue
from pyomo.opt.base.problem import ProblemConfigFactory, BaseProblemConfig
@ProblemConfigFactory.register('default')
class Default_Config(BaseProblemConfig):
def config_block(self, init=False):
config, blocks = minlp_config_block(init=init)
return config, blocks
def minlp_config_block(init=False):
config = ConfigBlock("Configuration for a canonical model construction and optimization sequence")
blocks={}
#
# Data
#
data = config.declare('data', ConfigBlock())
data.declare('files', ConfigList(
[],
ConfigValue(None, str, 'Filename', None),
'Model data files',
None) )
data.declare('namespaces', ConfigList(
[],
ConfigValue(None, str, 'Namespace', None),
'A namespace that is used to select data in Pyomo data files.',
None) ).declare_as_argument('--namespace', dest='namespaces', action='append')
blocks['data'] = data
#
# Model
#
model = config.declare('model', ConfigBlock())
model.declare('filename', ConfigValue(
None,
str,
'The Python module that specifies the model',
None ) )
model.declare('object name', ConfigValue(
None,
str,
'The name of the model object that is created in the specified Pyomo module',
None ) ).declare_as_argument('--model-name', dest='model_name')
model.declare('type', ConfigValue(
None,
str,
'The problem type',
None ) )
model.declare('options', ConfigBlock(
implicit=True,
description='Options used to construct the model') )
model.declare('linearize expressions', ConfigValue(
False,
bool,
'An option intended for use on linear or mixed-integer models in which expression trees in a model (constraints or objectives) are compacted into a more memory-efficient and concise form.',
None) )
model.declare('save file', ConfigValue(
None,
str,
"The filename to which the model is saved. The suffix of this filename specifies the file format.",
None) )
model.declare('save format', ConfigValue(
None,
str,
"The format that the model is saved. When specified, this overrides the format implied by the 'save file' option.",
None) )
model.declare('symbolic solver labels', ConfigValue(
False,
bool,
'When interfacing with the solver, use symbol names derived from the model. For example, \"my_special_variable[1_2_3]\" instead of \"v1\". Useful for debugging. When using the ASL interface (--solver-io=nl), generates corresponding .row (constraints) and .col (variables) files. The ordering in these files provides a mapping from ASL index to symbolic model names.',
None) ).declare_as_argument(dest='symbolic_solver_labels')
model.declare('file determinism', ConfigValue(
1,
int,
'When interfacing with a solver using file based I/O, set the effort level for ensuring the file creation process is determistic. The default (1) sorts the index of components when transforming the model. Anything less than 1 disables index sorting. Anything greater than 1 additionaly sorts by component name to override declartion order.',
None) ).declare_as_argument(dest='file_determinism')
blocks['model'] = model
#
# Transform
#
transform = ConfigBlock()
transform.declare('name', ConfigValue(
None,
str,
'Name of the model transformation',
None ) )
transform.declare('options', ConfigBlock(
implicit=True,
description='Transformation options'
) )
blocks['transform'] = transform
#
transform_list = config.declare('transform', ConfigList(
[],
ConfigValue(None, str, 'Transformation', None),
'List of model transformations',
None) ).declare_as_argument( dest='transformations',
action='append' )
if init:
transform_list.append()
#
# Preprocess
#
config.declare('preprocess', ConfigList(
[],
ConfigValue(None, str, 'Module', None),
'Specify a Python module that gets immediately executed (before the optimization model is setup).',
None) ).declare_as_argument(dest='preprocess')
#
# Runtime
#
runtime = config.declare('runtime', ConfigBlock())
runtime.declare('logging', ConfigValue(
None,
str,
'Logging level: quiet, warning, info, verbose, debug',
None) ).declare_as_argument(dest="logging", metavar="LEVEL")
runtime.declare('logfile', ConfigValue(
None,
str,
'Redirect output to the specified file.',
None) ).declare_as_argument(dest="output", metavar="FILE")
runtime.declare('catch errors', ConfigValue(
False,
bool,
'Trigger failures for exceptions to print the program stack.',
None) ).declare_as_argument('-c', '--catch-errors', dest="catch")
runtime.declare('disable gc', ConfigValue(
False,
bool,
'Disable the garbage collecter.',
None) ).declare_as_argument('--disable-gc', dest='disable_gc')
runtime.declare('interactive', ConfigValue(
False,
bool,
'After executing Pyomo, launch an interactive Python shell. If IPython is installed, this shell is an IPython shell.',
None) )
runtime.declare('keep files', ConfigValue(
False,
bool,
'Keep temporary files',
None) ).declare_as_argument('-k', '--keepfiles', dest='keepfiles')
runtime.declare('paths', ConfigList(
[],
ConfigValue(None, str, 'Path', None),
'Give a path that is used to find the Pyomo python files.',
None) ).declare_as_argument('--path', dest='path')
runtime.declare('profile count', ConfigValue(
0,
int,
'Enable profiling of Python code. The value of this option is the number of functions that are summarized.',
None) ).declare_as_argument(dest='profile_count', metavar='COUNT')
runtime.declare('profile memory', ConfigValue(
0,
int,
"Report memory usage statistics for the generated instance and any associated processing steps. A value of 0 indicates disabled. A value of 1 forces the print of the total memory after major stages of the pyomo script. A value of 2 forces summary memory statistics after major stages of the pyomo script. A value of 3 forces detailed memory statistics during instance creation and various steps of preprocessing. Values equal to 4 and higher currently provide no additional information. Higher values automatically enable all functionality associated with lower values, e.g., 3 turns on detailed and summary statistics.",
None) )
runtime.declare('report timing', ConfigValue(
False,
bool,
'Report various timing statistics during model construction.',
None) ).declare_as_argument(dest='report_timing')
runtime.declare('tempdir', ConfigValue(
None,
str,
'Specify the directory where temporary files are generated.',
None) ).declare_as_argument(dest='tempdir')
blocks['runtime'] = runtime
#
return config, blocks