def Pyomo2FuncDesigner(instance):
if not FD_available:
return None
ipoint = {}
vars = {}
sense = None
nobj = 0
smap = SymbolMap()
_f_name = []
_f = []
_c = []
for con in instance.component_data_objects(Constraint, active=True):
body = Pyomo2FD_expression(con.body, ipoint, vars, smap)
if not con.lower is None:
lower = Pyomo2FD_expression(con.lower, ipoint, vars, smap)
_c.append( body > lower )
if not con.upper is None:
upper = Pyomo2FD_expression(con.upper, ipoint, vars, smap)
_c.append( body < upper )
for var in instance.component_data_objects(Var, active=True):
body = Pyomo2FD_expression(var, ipoint, vars, smap)
if not var.lb is None:
lower = Pyomo2FD_expression(var.lb, ipoint, vars, smap)
_c.append( body > lower )
if not var.ub is None:
upper = Pyomo2FD_expression(var.ub, ipoint, vars, smap)
_c.append( body < upper )
for obj in instance.component_data_objects(Objective, active=True):
nobj += 1
if obj.is_minimizing():
_f.append( Pyomo2FD_expression(obj.expr, ipoint, vars, smap) )
else:
_f.append( - Pyomo2FD_expression(obj.expr, ipoint, vars, smap) )
_f_name.append(obj.name)
smap.getSymbol(obj, lambda objective: objective.name)
# TODO - use 0.0 for default values???
# TODO - create results map
S = FuncDesigner.oosystem()
S._symbol_map = smap
S.f = _f[0]
S._f_name = _f_name
S.constraints.update(_c)
S.initial_point = ipoint
S.sense = sense
return S
def Pyomo2FuncDesigner(instance):
if not FD_available:
return None
ipoint = {}
vars = {}
sense = None
nobj = 0
smap = SymbolMap()
_f_name = []
_f = []
_c = []
for con in instance.component_data_objects(Constraint, active=True):
body = Pyomo2FD_expression(con.body, ipoint, vars, smap)
if not con.lower is None:
lower = Pyomo2FD_expression(con.lower, ipoint, vars, smap)
_c.append( body > lower )
if not con.upper is None:
upper = Pyomo2FD_expression(con.upper, ipoint, vars, smap)
_c.append( body < upper )
for var in instance.component_data_objects(Var, active=True):
body = Pyomo2FD_expression(var, ipoint, vars, smap)
if not var.lb is None:
lower = Pyomo2FD_expression(var.lb, ipoint, vars, smap)
_c.append( body > lower )
if not var.ub is None:
upper = Pyomo2FD_expression(var.ub, ipoint, vars, smap)
_c.append( body < upper )
for obj in instance.component_data_objects(Objective, active=True):
nobj += 1
if obj.is_minimizing():
_f.append( Pyomo2FD_expression(obj.expr, ipoint, vars, smap) )
else:
_f.append( - Pyomo2FD_expression(obj.expr, ipoint, vars, smap) )
_f_name.append( obj.cname(True) )
smap.getSymbol(obj, lambda objective: objective.cname(True))
# TODO - use 0.0 for default values???
# TODO - create results map
S = FuncDesigner.oosystem()
S._symbol_map = smap
S.f = _f[0]
S._f_name = _f_name
S.constraints.update(_c)
S.initial_point = ipoint
S.sense = sense
return S
def __call__(self, model, output_filename, solver_capability, io_options):
# Make sure not to modify the user's dictionary, they may be
# reusing it outside of this call
io_options = dict(io_options)
# NOTE: io_options is a simple dictionary of keyword-value
# pairs specific to this writer.
symbolic_solver_labels = \
io_options.pop("symbolic_solver_labels", False)
labeler = io_options.pop("labeler", None)
# How much effort do we want to put into ensuring the
# LP file is written deterministically for a Pyomo model:
# 0 : None
# 1 : sort keys of indexed components (default)
# 2 : sort keys AND sort names (over declaration order)
file_determinism = io_options.pop("file_determinism", 1)
sorter = SortComponents.unsorted
if file_determinism >= 1:
sorter = sorter | SortComponents.indices
if file_determinism >= 2:
sorter = sorter | SortComponents.alphabetical
output_fixed_variable_bounds = \
io_options.pop("output_fixed_variable_bounds", False)
# Skip writing constraints whose body section is fixed (i.e.,
# no variables)
skip_trivial_constraints = \
io_options.pop("skip_trivial_constraints", False)
# Note: Baron does not allow specification of runtime
# option outside of this file, so we add support
# for them here
solver_options = io_options.pop("solver_options", {})
if len(io_options):
raise ValueError(
"ProblemWriter_baron_writer passed unrecognized io_options:\n\t"
+ "\n\t".join("%s = %s" % (k, v)
for k, v in iteritems(io_options)))
if symbolic_solver_labels and (labeler is not None):
raise ValueError("Baron problem writer: Using both the "
"'symbolic_solver_labels' and 'labeler' "
"I/O options is forbidden")
# Make sure there are no strange ActiveComponents. The expression
# walker will handle strange things in constraints later.
model_ctypes = model.collect_ctypes(active=True)
invalids = set()
for t in (model_ctypes - valid_active_ctypes_minlp):
if issubclass(t, ActiveComponent):
invalids.add(t)
if len(invalids):
invalids = [t.__name__ for t in invalids]
raise RuntimeError(
"Unallowable active component(s) %s.\nThe BARON writer cannot "
"export models with this component type." %
", ".join(invalids))
if output_filename is None:
output_filename = model.name + ".bar"
output_file = open(output_filename, "w")
# Process the options. Rely on baron to catch
# and reset bad option values
output_file.write("OPTIONS {\n")
summary_found = False
if len(solver_options):
for key, val in iteritems(solver_options):
if (key.lower() == 'summary'):
summary_found = True
if key.endswith("Name"):
output_file.write(key + ": \"" + str(val) + "\";\n")
else:
output_file.write(key + ": " + str(val) + ";\n")
if not summary_found:
# The 'summary option is defaulted to 0, so that no
# summary file is generated in the directory where the
# user calls baron. Check if a user explicitly asked for
# a summary file.
output_file.write("Summary: 0;\n")
output_file.write("}\n\n")
if symbolic_solver_labels:
# Note that the Var and Constraint labelers must use the
# same labeler, so that we can correctly detect name
# collisions (which can arise when we truncate the labels to
# the max allowable length. BARON requires all identifiers
# to start with a letter. We will (randomly) choose "s_"
# (for 'shortened')
v_labeler = c_labeler = ShortNameLabeler(15,
prefix='s_',
suffix='_',
caseInsensitive=True,
legalRegex='^[a-zA-Z]')
elif labeler is None:
v_labeler = NumericLabeler('x')
c_labeler = NumericLabeler('c')
else:
v_labeler = c_labeler = labeler
symbol_map = SymbolMap()
symbol_map.default_labeler = v_labeler
#sm_bySymbol = symbol_map.bySymbol
# Cache the list of model blocks so we don't have to call
# model.block_data_objects() many many times, which is slow
# for indexed blocks
all_blocks_list = list(
model.block_data_objects(active=True,
sort=sorter,
descend_into=True))
active_components_data_var = {}
#for block in all_blocks_list:
# tmp = active_components_data_var[id(block)] = \
# list(obj for obj in block.component_data_objects(Var,
# sort=sorter,
# descend_into=False))
# create_symbols_func(symbol_map, tmp, labeler)
# GAH: Not sure this is necessary, and also it would break for
# non-mutable indexed params so I am commenting out for now.
#for param_data in active_components_data(block, Param, sort=sorter):
#instead of checking if param_data._mutable:
#if not param_data.is_constant():
# create_symbol_func(symbol_map, param_data, labeler)
#symbol_map_variable_ids = set(symbol_map.byObject.keys())
#object_symbol_dictionary = symbol_map.byObject
#
# Go through the objectives and constraints and generate
# the output so that we can obtain the set of referenced
# variables.
#
equation_section_stream = StringIO()
referenced_variable_ids, branching_priorities_suffixes = \
self._write_equations_section(
model,
equation_section_stream,
all_blocks_list,
active_components_data_var,
symbol_map,
c_labeler,
output_fixed_variable_bounds,
skip_trivial_constraints,
sorter)
#
# BINARY_VARIABLES, INTEGER_VARIABLES, POSITIVE_VARIABLES, VARIABLES
#
BinVars = []
IntVars = []
PosVars = []
Vars = []
for vid in referenced_variable_ids:
name = symbol_map.byObject[vid]
var_data = symbol_map.bySymbol[name]()
if var_data.is_continuous():
if var_data.has_lb() and (value(var_data.lb) >= 0):
TypeList = PosVars
else:
TypeList = Vars
elif var_data.is_binary():
TypeList = BinVars
elif var_data.is_integer():
TypeList = IntVars
else:
assert False
TypeList.append(name)
if len(BinVars) > 0:
BinVars.sort()
output_file.write('BINARY_VARIABLES ')
output_file.write(", ".join(BinVars))
output_file.write(';\n\n')
if len(IntVars) > 0:
IntVars.sort()
output_file.write('INTEGER_VARIABLES ')
output_file.write(", ".join(IntVars))
output_file.write(';\n\n')
PosVars.append('ONE_VAR_CONST__')
PosVars.sort()
output_file.write('POSITIVE_VARIABLES ')
output_file.write(", ".join(PosVars))
output_file.write(';\n\n')
if len(Vars) > 0:
Vars.sort()
output_file.write('VARIABLES ')
output_file.write(", ".join(Vars))
output_file.write(';\n\n')
#
# LOWER_BOUNDS
#
lbounds = {}
for vid in referenced_variable_ids:
name = symbol_map.byObject[vid]
var_data = symbol_map.bySymbol[name]()
if var_data.fixed:
if output_fixed_variable_bounds:
var_data_lb = ftoa(var_data.value)
else:
var_data_lb = None
else:
var_data_lb = None
if var_data.has_lb():
var_data_lb = ftoa(var_data.lb)
if var_data_lb is not None:
name_to_output = symbol_map.getSymbol(var_data)
lbounds[name_to_output] = '%s: %s;\n' % (name_to_output,
var_data_lb)
if len(lbounds) > 0:
output_file.write("LOWER_BOUNDS{\n")
output_file.write("".join(lbounds[key]
for key in sorted(lbounds.keys())))
output_file.write("}\n\n")
lbounds = None
#
# UPPER_BOUNDS
#
ubounds = {}
for vid in referenced_variable_ids:
name = symbol_map.byObject[vid]
var_data = symbol_map.bySymbol[name]()
if var_data.fixed:
if output_fixed_variable_bounds:
var_data_ub = ftoa(var_data.value)
else:
var_data_ub = None
else:
var_data_ub = None
if var_data.has_ub():
var_data_ub = ftoa(var_data.ub)
if var_data_ub is not None:
name_to_output = symbol_map.getSymbol(var_data)
ubounds[name_to_output] = '%s: %s;\n' % (name_to_output,
var_data_ub)
if len(ubounds) > 0:
output_file.write("UPPER_BOUNDS{\n")
output_file.write("".join(ubounds[key]
for key in sorted(ubounds.keys())))
output_file.write("}\n\n")
ubounds = None
#
# BRANCHING_PRIORITIES
#
# Specifying priorities requires that the pyomo model has established an
# EXTERNAL, float suffix called 'branching_priorities' on the model
# object, indexed by the relevant variable
BranchingPriorityHeader = False
for suffix in branching_priorities_suffixes:
for var_data, priority in iteritems(suffix):
if id(var_data) not in referenced_variable_ids:
continue
if priority is not None:
if not BranchingPriorityHeader:
output_file.write('BRANCHING_PRIORITIES{\n')
BranchingPriorityHeader = True
name_to_output = symbol_map.getSymbol(var_data)
output_file.write(name_to_output + ': ' + str(priority) +
';\n')
if BranchingPriorityHeader:
output_file.write("}\n\n")
#
# Now write the objective and equations section
#
output_file.write(equation_section_stream.getvalue())
#
# STARTING_POINT
#
output_file.write('STARTING_POINT{\nONE_VAR_CONST__: 1;\n')
tmp = {}
for vid in referenced_variable_ids:
name = symbol_map.byObject[vid]
var_data = symbol_map.bySymbol[name]()
starting_point = var_data.value
if starting_point is not None:
var_name = symbol_map.getSymbol(var_data)
tmp[var_name] = "%s: %s;\n" % (var_name, ftoa(starting_point))
output_file.write("".join(tmp[key] for key in sorted(tmp.keys())))
output_file.write('}\n\n')
output_file.close()
return output_filename, symbol_map
class CPLEXPersistent(CPLEXDirect, PersistentSolver):
"""The CPLEX LP/MIP solver
"""
pyomo.util.plugin.alias('_cplex_persistent',
doc='Persistent Python interface to the CPLEX LP/MIP solver')
def __init__(self, **kwds):
#
# Call base class constructor
#
kwds['type'] = 'cplexpersistent'
CPLEXDirect.__init__(self, **kwds)
# maps pyomo var data labels to the corresponding CPLEX variable id.
self._cplex_variable_ids = {}
self._cplex_variable_names = None
#
# updates all variable bounds in the compiled model - handles
# fixed variables and related issues. re-does everything from
# scratch by default, ignoring whatever was specified
# previously. if the value associated with the keyword
# vars_to_update is a non-empty list (assumed to be variable name
# / index pairs), then only the bounds for those variables are
# updated. this function assumes that the variables themselves
# already exist in the compiled model.
#
def compile_variable_bounds(self, pyomo_instance, vars_to_update):
from pyomo.core.base import Var
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin "
"cannot compile variable bounds - no "
"instance is presently compiled")
# the bound update entries should be name-value pairs
new_lower_bounds = []
new_upper_bounds = []
# operates through side effects on the above lists!
def update_bounds_lists(var_name):
var_lb = None
var_ub = None
if var_data.fixed and self._output_fixed_variable_bounds:
var_lb = var_ub = var_data.value
elif var_data.fixed:
# if we've been directed to not deal with fixed
# variables, then skip - they should have been
# compiled out of any description of the constraints
return
else:
if not var_data.has_lb():
var_lb = -CPLEXDirect._cplex_module.infinity
else:
var_lb = value(var_data.lb)
if not var_data.has_ub():
var_ub = CPLEXDirect._cplex_module.infinity
else:
var_ub= value(var_data.ub)
var_cplex_id = self._cplex_variable_ids[var_name]
new_lower_bounds.append((var_cplex_id, var_lb))
new_upper_bounds.append((var_cplex_id, var_ub))
if len(vars_to_update) == 0:
for var_data in pyomo_instance.component_data_objects(Var, active=True):
var_name = self._symbol_map.getSymbol(var_data, self._labeler)
update_bounds_lists(var_name)
else:
for var_name, var_index in vars_to_update:
var = pyomo_instance.find_component(var_name)
# TBD - do some error checking!
var_data = var[var_index]
var_name = self._symbol_map.getSymbol(var_data, self._labeler)
update_bounds_lists(var_name)
self._active_cplex_instance.variables.set_lower_bounds(new_lower_bounds)
self._active_cplex_instance.variables.set_upper_bounds(new_upper_bounds)
#
# method to compile objective of the input pyomo instance.
# TBD:
# it may be smarter just to track the associated pyomo instance,
# and re-compile it automatically from a cached local attribute.
# this would ensure consistency, among other things!
#
def compile_objective(self, pyomo_instance):
from pyomo.core.base import Objective
from pyomo.repn import canonical_is_constant, LinearCanonicalRepn, canonical_degree
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin "
"cannot compile objective - no "
"instance is presently compiled")
cplex_instance = self._active_cplex_instance
self._has_quadratic_objective = False
cntr = 0
for block in pyomo_instance.block_data_objects(active=True):
gen_obj_canonical_repn = \
getattr(block, "_gen_obj_canonical_repn", True)
# Get/Create the ComponentMap for the repn
if not hasattr(block,'_canonical_repn'):
block._canonical_repn = ComponentMap()
block_canonical_repn = block._canonical_repn
for obj_data in block.component_data_objects(Objective,
active=True,
descend_into=False):
cntr += 1
if cntr > 1:
raise ValueError(
"Multiple active objectives found on Pyomo instance '%s'. "
"Solver '%s' will only handle a single active objective" \
% (pyomo_instance.name, self.type))
if obj_data.is_minimizing():
cplex_instance.objective.set_sense(
cplex_instance.objective.sense.minimize)
else:
cplex_instance.objective.set_sense(
cplex_instance.objective.sense.maximize)
cplex_instance.objective.set_name(
self._symbol_map.getSymbol(obj_data,
self._labeler))
if gen_obj_canonical_repn:
obj_repn = generate_canonical_repn(obj_data.expr)
block_canonical_repn[obj_data] = obj_repn
else:
obj_repn = block_canonical_repn[obj_data]
if (isinstance(obj_repn, LinearCanonicalRepn) and \
((obj_repn.linear == None) or \
(len(obj_repn.linear) == 0))) or \
canonical_is_constant(obj_repn):
print("Warning: Constant objective detected, replacing "
"with a placeholder to prevent solver failure.")
offset = obj_repn.constant
if offset is None:
offset = 0.0
objective_expression = [("ONE_VAR_CONSTANT",offset)]
cplex_instance.objective.set_linear(objective_expression)
else:
if isinstance(obj_repn, LinearCanonicalRepn):
objective_expression, offset = \
self._encode_constraint_body_linear_specialized(
obj_repn,
self._labeler,
use_variable_names=False,
cplex_variable_name_index_map=self._cplex_variable_ids,
as_pairs=True)
if offset != 0.0:
objective_expression.append((self._cplex_variable_ids["ONE_VAR_CONSTANT"],offset))
cplex_instance.objective.set_linear(objective_expression)
else:
#Linear terms
if 1 in obj_repn:
objective_expression, offset = \
self._encode_constraint_body_linear(
obj_repn,
self._labeler,
as_pairs=True)
if offset != 0.0:
objective_expression.append(("ONE_VAR_CONSTANT",offset))
cplex_instance.objective.set_linear(objective_expression)
#Quadratic terms
if 2 in obj_repn:
self._has_quadratic_objective = True
objective_expression = \
self._encode_constraint_body_quadratic(obj_repn,
self._labeler,
as_triples=True,
is_obj=2.0)
cplex_instance.objective.\
set_quadratic_coefficients(objective_expression)
degree = canonical_degree(obj_repn)
if (degree is None) or (degree > 2):
raise ValueError(
"CPLEXPersistent plugin does not support general nonlinear "
"objective expressions (only linear or quadratic).\n"
"Objective: %s" % (obj_data.name))
#
# method to populate the CPLEX problem instance (interface) from
# the supplied Pyomo problem instance.
#
def compile_instance(self,
pyomo_instance,
symbolic_solver_labels=False,
output_fixed_variable_bounds=False,
skip_trivial_constraints=False):
from pyomo.core.base import Var, Constraint, SOSConstraint
from pyomo.repn import canonical_is_constant, LinearCanonicalRepn, canonical_degree
self._symbolic_solver_labels = symbolic_solver_labels
self._output_fixed_variable_bounds = output_fixed_variable_bounds
self._skip_trivial_constraints = skip_trivial_constraints
self._has_quadratic_constraints = False
self._has_quadratic_objective = False
self._active_cplex_instance = CPLEXDirect._cplex_module.Cplex()
if self._symbolic_solver_labels:
labeler = self._labeler = TextLabeler()
else:
labeler = self._labeler = NumericLabeler('x')
self._symbol_map = SymbolMap()
self._instance = pyomo_instance
if isinstance(pyomo_instance, IBlockStorage):
# BIG HACK
if not hasattr(pyomo_instance, "._symbol_maps"):
setattr(pyomo_instance, "._symbol_maps", {})
getattr(pyomo_instance, "._symbol_maps")[id(self._symbol_map)] = \
self._symbol_map
else:
pyomo_instance.solutions.add_symbol_map(self._symbol_map)
self._smap_id = id(self._symbol_map)
# we use this when iterating over the constraints because it
# will have a much smaller hash table, we also use this for
# the warm start code after it is cleaned to only contain
# variables referenced in the constraints
self._variable_symbol_map = SymbolMap()
# cplex wants the caller to set the problem type, which is (for
# current purposes) strictly based on variable type counts.
self._num_binary_variables = 0
self._num_integer_variables = 0
self._num_continuous_variables = 0
self._used_sos_constraints = False
#############################################
# populate the variables in the cplex model #
#############################################
var_names = []
var_lbs = []
var_ubs = []
var_types = []
self._referenced_variable_ids.clear()
# maps pyomo var data labels to the corresponding CPLEX variable id.
self._cplex_variable_ids.clear()
# cached in the loop below - used to update the symbol map
# immediately following loop termination.
var_label_pairs = []
for var_data in pyomo_instance.component_data_objects(Var, active=True):
if var_data.fixed and not self._output_fixed_variable_bounds:
# if a variable is fixed, and we're preprocessing
# fixed variables (as in not outputting them), there
# is no need to add them to the compiled model.
continue
var_name = self._symbol_map.getSymbol(var_data, labeler)
var_names.append(var_name)
var_label_pairs.append((var_data, var_name))
self._cplex_variable_ids[var_name] = len(self._cplex_variable_ids)
if not var_data.has_lb():
var_lbs.append(-CPLEXDirect._cplex_module.infinity)
else:
var_lbs.append(value(var_data.lb))
if not var_data.has_ub():
var_ubs.append(CPLEXDirect._cplex_module.infinity)
else:
var_ubs.append(value(var_data.ub))
if var_data.is_integer():
var_types.append(self._active_cplex_instance.variables.type.integer)
self._num_integer_variables += 1
elif var_data.is_binary():
var_types.append(self._active_cplex_instance.variables.type.binary)
self._num_binary_variables += 1
elif var_data.is_continuous():
var_types.append(self._active_cplex_instance.variables.type.continuous)
self._num_continuous_variables += 1
else:
raise TypeError("Invalid domain type for variable with name '%s'. "
"Variable is not continuous, integer, or binary.")
self._active_cplex_instance.variables.add(names=var_names,
lb=var_lbs,
ub=var_ubs,
types=var_types)
self._active_cplex_instance.variables.add(lb=[1],
ub=[1],
names=["ONE_VAR_CONSTANT"])
self._cplex_variable_ids["ONE_VAR_CONSTANT"] = len(self._cplex_variable_ids)
self._variable_symbol_map.addSymbols(var_label_pairs)
self._cplex_variable_names = self._active_cplex_instance.variables.get_names()
########################################################
# populate the standard constraints in the cplex model #
########################################################
expressions = []
senses = []
rhss = []
range_values = []
names = []
qexpressions = []
qlinears = []
qsenses = []
qrhss = []
qnames = []
for block in pyomo_instance.block_data_objects(active=True):
gen_con_canonical_repn = \
getattr(block, "_gen_con_canonical_repn", True)
# Get/Create the ComponentMap for the repn
if not hasattr(block,'_canonical_repn'):
block._canonical_repn = ComponentMap()
block_canonical_repn = block._canonical_repn
for con in block.component_data_objects(Constraint,
active=True,
descend_into=False):
if (not con.has_lb()) and \
(not con.has_ub()):
assert not con.equality
continue # not binding at all, don't bother
con_repn = None
if con._linear_canonical_form:
con_repn = con.canonical_form()
elif isinstance(con, LinearCanonicalRepn):
con_repn = con
else:
if gen_con_canonical_repn:
con_repn = generate_canonical_repn(con.body)
block_canonical_repn[con] = con_repn
else:
con_repn = block_canonical_repn[con]
# There are conditions, e.g., when fixing variables, under which
# a constraint block might be empty. Ignore these, for both
# practical reasons and the fact that the CPLEX LP format
# requires a variable in the constraint body. It is also
# possible that the body of the constraint consists of only a
# constant, in which case the "variable" of
if isinstance(con_repn, LinearCanonicalRepn):
if self._skip_trivial_constraints and \
((con_repn.linear is None) or \
(len(con_repn.linear) == 0)):
continue
else:
# we shouldn't come across a constant canonical repn
# that is not LinearCanonicalRepn
assert not canonical_is_constant(con_repn)
name = self._symbol_map.getSymbol(con, labeler)
expr = None
qexpr = None
quadratic = False
if isinstance(con_repn, LinearCanonicalRepn):
expr, offset = \
self._encode_constraint_body_linear_specialized(con_repn,
labeler,
use_variable_names=False,
cplex_variable_name_index_map=self._cplex_variable_ids)
else:
degree = canonical_degree(con_repn)
if degree == 2:
quadratic = True
elif (degree != 0) or (degree != 1):
raise ValueError(
"CPLEXPersistent plugin does not support general nonlinear "
"constraint expression (only linear or quadratic).\n"
"Constraint: %s" % (con.name))
expr, offset = self._encode_constraint_body_linear(con_repn,
labeler)
if quadratic:
if expr is None:
expr = CPLEXDirect._cplex_module.SparsePair(ind=[0],val=[0.0])
self._has_quadratic_constraints = True
qexpr = self._encode_constraint_body_quadratic(con_repn,labeler)
qnames.append(name)
if con.equality:
# equality constraint.
qsenses.append('E')
qrhss.append(self._get_bound(con.lower) - offset)
elif con.has_lb() and con.has_ub():
raise RuntimeError(
"The CPLEXDirect plugin can not translate range "
"constraints containing quadratic expressions.")
elif con.has_lb():
assert not con.has_ub()
qsenses.append('G')
qrhss.append(self._get_bound(con.lower) - offset)
else:
assert con.has_ub()
qsenses.append('L')
qrhss.append(self._get_bound(con.upper) - offset)
qlinears.append(expr)
qexpressions.append(qexpr)
else:
names.append(name)
expressions.append(expr)
if con.equality:
# equality constraint.
senses.append('E')
rhss.append(self._get_bound(con.lower) - offset)
range_values.append(0.0)
elif con.has_lb() and con.has_ub():
# ranged constraint.
senses.append('R')
lower_bound = self._get_bound(con.lower) - offset
upper_bound = self._get_bound(con.upper) - offset
rhss.append(lower_bound)
range_values.append(upper_bound - lower_bound)
elif con.has_lb():
senses.append('G')
rhss.append(self._get_bound(con.lower) - offset)
range_values.append(0.0)
else:
assert con.has_ub()
senses.append('L')
rhss.append(self._get_bound(con.upper) - offset)
range_values.append(0.0)
###################################################
# populate the SOS constraints in the cplex model #
###################################################
# SOS constraints - largely taken from cpxlp.py so updates there,
# should be applied here
# TODO: Allow users to specify the variables coefficients for custom
# branching/set orders - refer to cpxlp.py
sosn = self._capabilities.sosn
sos1 = self._capabilities.sos1
sos2 = self._capabilities.sos2
modelSOS = ModelSOS()
for soscondata in pyomo_instance.component_data_objects(SOSConstraint,
active=True):
level = soscondata.level
if (level == 1 and not sos1) or \
(level == 2 and not sos2) or \
(level > 2 and not sosn):
raise Exception("Solver does not support SOS level %s constraints"
% (level,))
modelSOS.count_constraint(self._symbol_map,
labeler,
self._variable_symbol_map,
soscondata)
if modelSOS.sosType:
for key in modelSOS.sosType:
self._active_cplex_instance.SOS.add(type = modelSOS.sosType[key],
name = modelSOS.sosName[key],
SOS = [modelSOS.varnames[key],
modelSOS.weights[key]])
self._referenced_variable_ids.update(modelSOS.varids[key])
self._used_sos_constraints = True
self._active_cplex_instance.linear_constraints.add(
lin_expr=expressions,
senses=senses,
rhs=rhss,
range_values=range_values,
names=names)
for index in xrange(len(qexpressions)):
self._active_cplex_instance.quadratic_constraints.add(
lin_expr=qlinears[index],
quad_expr=qexpressions[index],
sense=qsenses[index],
rhs=qrhss[index],
name=qnames[index])
#############################################
# populate the objective in the cplex model #
#############################################
self.compile_objective(pyomo_instance)
#
# simple method to query whether a Pyomo instance has already been
# compiled.
#
def instance_compiled(self):
return self._active_cplex_instance is not None
#
# Override base class method to check for compiled instance
#
def _warm_start(self, instance):
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin "
"cannot warm start - no instance is "
"presently compiled")
# clear any existing warm starts.
self._active_cplex_instance.MIP_starts.delete()
# the iteration order is identical to that used in generating
# the cplex instance, so all should be well.
variable_ids = []
variable_values = []
# IMPT: the var_data returned is a weak ref!
for label, var_data in iteritems(self._variable_symbol_map.bySymbol):
cplex_id = self._cplex_variable_ids[label]
if var_data().fixed and not self._output_fixed_variable_bounds:
continue
elif var_data().value is not None:
variable_ids.append(cplex_id)
variable_values.append(var_data().value)
if len(variable_ids):
self._active_cplex_instance.MIP_starts.add(
[variable_ids, variable_values],
self._active_cplex_instance.MIP_starts.effort_level.auto)
#
# Override base class method to check for compiled instance
#
def _populate_cplex_instance(self, model):
assert model == self._instance
def _presolve(self, *args, **kwds):
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin"
" cannot presolve - no instance is "
"presently compiled")
# These must be passed in to the compile_instance method,
# but assert that any values here match those already supplied
if 'symbolic_solver_labels' in kwds:
assert self._symbolic_solver_labels == \
kwds['symbolic_solver_labels']
if 'output_fixed_variable_bounds' in kwds:
assert self._output_fixed_variable_bounds == \
kwds['output_fixed_variable_bounds']
if 'skip_trivial_constraints' in kwds:
assert self._skip_trivial_constraints == \
kwds["skip_trivial_constraints"]
if isinstance(self._instance, IBlockStorage):
# BIG HACK
if not hasattr(self._instance, "._symbol_maps"):
setattr(self._instance, "._symbol_maps", {})
getattr(self._instance, "._symbol_maps")[id(self._symbol_map)] = \
self._symbol_map
else:
if self._smap_id not in self._instance.solutions.symbol_map:
self._instance.solutions.add_symbol_map(self._symbol_map)
################################################
# populate the problem type in the cplex model #
################################################
# This gets rid of the annoying "Freeing MIP data." message.
def _filter_freeing_mip_data(val):
if val.strip() == 'Freeing MIP data.':
return ""
return val
self._active_cplex_instance.set_warning_stream(sys.stderr,
fn=_filter_freeing_mip_data)
if (self._has_quadratic_objective is True) or \
(self._has_quadratic_constraints is True):
if (self._num_integer_variables > 0) or \
(self._num_binary_variables > 0) or \
(self._used_sos_constraints):
if self._has_quadratic_constraints is True:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.MIQCP)
else:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.MIQP)
else:
if self._has_quadratic_constraints is True:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.QCP)
else:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.QP)
elif (self._num_integer_variables > 0) or \
(self._num_binary_variables > 0) or \
(self._used_sos_constraints):
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.MILP)
else:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.LP)
# restore the warning stream without our filter function
self._active_cplex_instance.set_warning_stream(sys.stderr)
CPLEXDirect._presolve(self, *args, **kwds)
# like other solver plugins, persistent solver plugins can
# take an instance as an input argument. the only context in
# which this instance is used, however, is for warm-starting.
if len(args) > 2:
raise ValueError("The CPLEXPersistent plugin method "
"'_presolve' can be supplied at most "
"one problem instance - %s were "
"supplied" % len(args))
# Re-add the symbol map id if it was cleared
# after a previous solution load
if id(self._symbol_map) not in args[0].solutions.symbol_map:
args[0].solutions.add_symbol_map(self._symbol_map)
self._smap_id = id(self._symbol_map)
#
# invoke the solver on the currently compiled instance!!!
#
def _apply_solver(self):
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin cannot "
"apply solver - no instance is presently compiled")
# NOTE:
# CPLEX maintains the pool of feasible solutions from the
# prior solve as the set of mip starts for the next solve.
# and evaluating multiple mip starts (and there can be many)
# is expensive. so if the warm_start method is not invoked,
# there will potentially be a lot of time wasted.
return CPLEXDirect._apply_solver(self)
def _postsolve(self):
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin "
"cannot postsolve - no instance is "
"presently compiled")
active_cplex_instance = self._active_cplex_instance
variable_symbol_map = self._variable_symbol_map
instance = self._instance
ret = CPLEXDirect._postsolve(self)
#
# These get reset to None by the base class method
#
self._active_cplex_instance = active_cplex_instance
self._variable_symbol_map = variable_symbol_map
self._instance = instance
return ret
class NLWriter(PersistentBase):
def __init__(self):
super(NLWriter, self).__init__()
self._config = WriterConfig()
self._writer = None
self._symbol_map = SymbolMap()
self._var_labeler = None
self._con_labeler = None
self._param_labeler = None
self._pyomo_var_to_solver_var_map = dict()
self._pyomo_con_to_solver_con_map = dict()
self._solver_var_to_pyomo_var_map = dict()
self._solver_con_to_pyomo_con_map = dict()
self._pyomo_param_to_solver_param_map = dict()
self._walker = PyomoToCModelWalker(
self._pyomo_var_to_solver_var_map,
self._pyomo_param_to_solver_param_map)
@property
def config(self):
return self._config
@config.setter
def config(self, val: WriterConfig):
self._config = val
@property
def symbol_map(self):
return self._symbol_map
def set_instance(self, model):
saved_config = self.config
saved_update_config = self.update_config
self.__init__()
self.config = saved_config
self.update_config = saved_update_config
self._model = model
if self.config.symbolic_solver_labels:
self._var_labeler = TextLabeler()
self._con_labeler = TextLabeler()
self._param_labeler = TextLabeler()
else:
self._var_labeler = NumericLabeler('x')
self._con_labeler = NumericLabeler('c')
self._param_labeler = NumericLabeler('p')
self._writer = cmodel.NLWriter()
self.add_block(model)
if self._objective is None:
self.set_objective(None)
def _add_variables(self, variables: List[_GeneralVarData]):
cvars = cmodel.create_vars(len(variables))
for ndx, v in enumerate(variables):
cv = cvars[ndx]
cv.name = self._symbol_map.getSymbol(v, self._var_labeler)
if not v.is_continuous():
raise NotImplementedError(
'NLWriter currently only supports continuous variables')
lb = value(v.lb)
ub = value(v.ub)
if lb is not None:
cv.lb = lb
if ub is not None:
cv.ub = ub
if v.value is not None:
cv.value = v.value
if v.is_fixed():
cv.fixed = True
self._pyomo_var_to_solver_var_map[id(v)] = cv
self._solver_var_to_pyomo_var_map[cv] = v
def _add_params(self, params: List[_ParamData]):
cparams = cmodel.create_params(len(params))
for ndx, p in enumerate(params):
cp = cparams[ndx]
cp.name = self._symbol_map.getSymbol(p, self._param_labeler)
cp.value = p.value
self._pyomo_param_to_solver_param_map[id(p)] = cp
def _add_constraints(self, cons: List[_GeneralConstraintData]):
for c in cons:
cname = self._symbol_map.getSymbol(c, self._con_labeler)
repn = generate_standard_repn(c.body,
compute_values=False,
quadratic=False)
const = self._walker.dfs_postorder_stack(repn.constant)
lin_vars = [
self._pyomo_var_to_solver_var_map[id(i)]
for i in repn.linear_vars
]
lin_coef = [
self._walker.dfs_postorder_stack(i) for i in repn.linear_coefs
]
if repn.nonlinear_expr is None:
nonlin = self._walker.dfs_postorder_stack(0)
else:
nonlin = self._walker.dfs_postorder_stack(repn.nonlinear_expr)
cc = cmodel.NLConstraint(const, lin_coef, lin_vars, nonlin)
lb = c.lower
ub = c.upper
if lb is not None:
cc.lb = self._walker.dfs_postorder_stack(lb)
if ub is not None:
cc.ub = self._walker.dfs_postorder_stack(ub)
self._writer.add_constraint(cc)
self._pyomo_con_to_solver_con_map[c] = cc
self._solver_con_to_pyomo_con_map[cc] = c
def _add_sos_constraints(self, cons: List[_SOSConstraintData]):
if len(cons) != 0:
raise NotImplementedError(
'NL writer does not support SOS constraints')
def _remove_constraints(self, cons: List[_GeneralConstraintData]):
for c in cons:
cc = self._pyomo_con_to_solver_con_map.pop(c)
self._writer.remove_constraint(cc)
self._symbol_map.removeSymbol(c)
self._con_labeler.remove_obj(c)
del self._solver_con_to_pyomo_con_map[cc]
def _remove_sos_constraints(self, cons: List[_SOSConstraintData]):
if len(cons) != 0:
raise NotImplementedError(
'NL writer does not support SOS constraints')
def _remove_variables(self, variables: List[_GeneralVarData]):
for v in variables:
cvar = self._pyomo_var_to_solver_var_map.pop(id(v))
del self._solver_var_to_pyomo_var_map[cvar]
self._symbol_map.removeSymbol(v)
self._var_labeler.remove_obj(v)
def _remove_params(self, params: List[_ParamData]):
for p in params:
del self._pyomo_param_to_solver_param_map[id(p)]
self._symbol_map.removeSymbol(p)
self._param_labeler.remove_obj(p)
def _update_variables(self, variables: List[_GeneralVarData]):
for v in variables:
cv = self._pyomo_var_to_solver_var_map[id(v)]
if not v.is_continuous():
raise NotImplementedError(
'NLWriter currently only supports continuous variables')
lb = value(v.lb)
ub = value(v.ub)
if lb is None:
cv.lb = -cmodel.inf
else:
cv.lb = lb
if ub is None:
cv.ub = cmodel.inf
else:
cv.ub = ub
if v.value is not None:
cv.value = v.value
if v.is_fixed():
cv.fixed = True
else:
cv.fixed = False
def update_params(self):
for p_id, p in self._params.items():
cp = self._pyomo_param_to_solver_param_map[p_id]
cp.value = p.value
def _set_objective(self, obj: _GeneralObjectiveData):
if obj is None:
const = cmodel.Constant(0)
lin_vars = list()
lin_coef = list()
nonlin = cmodel.Constant(0)
sense = 0
else:
repn = generate_standard_repn(obj.expr,
compute_values=False,
quadratic=False)
const = self._walker.dfs_postorder_stack(repn.constant)
lin_vars = [
self._pyomo_var_to_solver_var_map[id(i)]
for i in repn.linear_vars
]
lin_coef = [
self._walker.dfs_postorder_stack(i) for i in repn.linear_coefs
]
if repn.nonlinear_expr is None:
nonlin = cmodel.Constant(0)
else:
nonlin = self._walker.dfs_postorder_stack(repn.nonlinear_expr)
if obj.sense is minimize:
sense = 0
else:
sense = 1
cobj = cmodel.NLObjective(const, lin_coef, lin_vars, nonlin)
cobj.sense = sense
self._writer.objective = cobj
def write(self,
model: _BlockData,
filename: str,
timer: HierarchicalTimer = None):
if timer is None:
timer = HierarchicalTimer()
if model is not self._model:
timer.start('set_instance')
self.set_instance(model)
timer.stop('set_instance')
else:
timer.start('update')
self.update(timer=timer)
for cv, v in self._solver_var_to_pyomo_var_map.items():
if v.value is not None:
cv.value = v.value
timer.stop('update')
timer.start('write file')
self._writer.write(filename)
timer.stop('write file')
def get_ordered_vars(self):
return [
self._solver_var_to_pyomo_var_map[i]
for i in self._writer.get_solve_vars()
]
def get_ordered_cons(self):
return [
self._solver_con_to_pyomo_con_map[i]
for i in self._writer.get_solve_cons()
]
def get_active_objective(self):
return self._objective
class LPWriter(PersistentBase):
def __init__(self):
super(LPWriter, self).__init__()
self._config = WriterConfig()
self._writer = None
self._symbol_map = SymbolMap()
self._var_labeler = None
self._con_labeler = None
self._param_labeler = None
self._obj_labeler = None
self._pyomo_var_to_solver_var_map = dict()
self._pyomo_con_to_solver_con_map = dict()
self._solver_var_to_pyomo_var_map = dict()
self._solver_con_to_pyomo_con_map = dict()
self._pyomo_param_to_solver_param_map = dict()
self._walker = PyomoToCModelWalker(
self._pyomo_var_to_solver_var_map,
self._pyomo_param_to_solver_param_map)
@property
def config(self):
return self._config
@config.setter
def config(self, val: WriterConfig):
self._config = val
def set_instance(self, model):
saved_config = self.config
saved_update_config = self.update_config
self.__init__()
self.config = saved_config
self.update_config = saved_update_config
self._model = model
if self.config.symbolic_solver_labels:
self._var_labeler = TextLabeler()
self._con_labeler = TextLabeler()
self._param_labeler = TextLabeler()
self._obj_labeler = TextLabeler()
else:
self._var_labeler = NumericLabeler('x')
self._con_labeler = NumericLabeler('c')
self._param_labeler = NumericLabeler('p')
self._obj_labeler = NumericLabeler('obj')
self._writer = cmodel.LPWriter()
self.add_block(model)
if self._objective is None:
self.set_objective(None)
def _add_variables(self, variables: List[_GeneralVarData]):
cvars = cmodel.create_vars(len(variables))
for ndx, v in enumerate(variables):
cv = cvars[ndx]
cv.name = self._symbol_map.getSymbol(v, self._var_labeler)
if v.is_binary():
cv.domain = 'binary'
elif v.is_integer():
cv.domain = 'integer'
else:
assert v.is_continuous(
), 'LP writer only supports continuous, binary, and integer variables'
cv.domain = 'continuous'
_, lb, ub, v_is_fixed, v_domain, v_value = self._vars[id(v)]
if lb is not None:
cv.lb = lb
if ub is not None:
cv.ub = ub
if v_value is not None:
cv.value = v_value
if v_is_fixed:
cv.fixed = True
self._pyomo_var_to_solver_var_map[id(v)] = cv
self._solver_var_to_pyomo_var_map[cv] = v
def _add_params(self, params: List[_ParamData]):
cparams = cmodel.create_params(len(params))
for ndx, p in enumerate(params):
cp = cparams[ndx]
cp.name = self._symbol_map.getSymbol(p, self._param_labeler)
cp.value = p.value
self._pyomo_param_to_solver_param_map[id(p)] = cp
def _add_constraints(self, cons: List[_GeneralConstraintData]):
cmodel.process_lp_constraints(cons, self)
def _add_sos_constraints(self, cons: List[_SOSConstraintData]):
if len(cons) != 0:
raise NotImplementedError(
'LP writer does not yet support SOS constraints')
def _remove_constraints(self, cons: List[_GeneralConstraintData]):
for c in cons:
cc = self._pyomo_con_to_solver_con_map.pop(c)
self._writer.remove_constraint(cc)
self._symbol_map.removeSymbol(c)
self._con_labeler.remove_obj(c)
del self._solver_con_to_pyomo_con_map[cc]
def _remove_sos_constraints(self, cons: List[_SOSConstraintData]):
if len(cons) != 0:
raise NotImplementedError(
'LP writer does not yet support SOS constraints')
def _remove_variables(self, variables: List[_GeneralVarData]):
for v in variables:
cvar = self._pyomo_var_to_solver_var_map.pop(id(v))
del self._solver_var_to_pyomo_var_map[cvar]
self._symbol_map.removeSymbol(v)
self._var_labeler.remove_obj(v)
def _remove_params(self, params: List[_ParamData]):
for p in params:
del self._pyomo_param_to_solver_param_map[id(p)]
self._symbol_map.removeSymbol(p)
self._param_labeler.remove_obj(p)
def update_variables(self, variables: List[_GeneralVarData]):
for v in variables:
cv = self._pyomo_var_to_solver_var_map[id(v)]
if v.is_binary():
cv.domain = 'binary'
elif v.is_integer():
cv.domain = 'integer'
else:
assert v.is_continuous(
), 'LP writer only supports continuous, binary, and integer variables'
cv.domain = 'continuous'
lb = value(v.lb)
ub = value(v.ub)
if lb is None:
cv.lb = -cmodel.inf
else:
cv.lb = lb
if ub is None:
cv.ub = cmodel.inf
else:
cv.ub = ub
if v.value is not None:
cv.value = v.value
if v.is_fixed():
cv.fixed = True
else:
cv.fixed = False
def update_params(self):
for p_id, p in self._params.items():
cp = self._pyomo_param_to_solver_param_map[p_id]
cp.value = p.value
def _set_objective(self, obj: _GeneralObjectiveData):
if obj is None:
const = cmodel.Constant(0)
lin_coef = list()
lin_vars = list()
quad_coef = list()
quad_vars_1 = list()
quad_vars_2 = list()
sense = 0
else:
repn = generate_standard_repn(obj.expr,
compute_values=False,
quadratic=True)
const = self._walker.dfs_postorder_stack(repn.constant)
lin_coef = [
self._walker.dfs_postorder_stack(i) for i in repn.linear_coefs
]
lin_vars = [
self._pyomo_var_to_solver_var_map[id(i)]
for i in repn.linear_vars
]
quad_coef = [
self._walker.dfs_postorder_stack(i)
for i in repn.quadratic_coefs
]
quad_vars_1 = [
self._pyomo_var_to_solver_var_map[id(i[0])]
for i in repn.quadratic_vars
]
quad_vars_2 = [
self._pyomo_var_to_solver_var_map[id(i[1])]
for i in repn.quadratic_vars
]
if obj.sense is minimize:
sense = 0
else:
sense = 1
cobj = cmodel.LPObjective(const, lin_coef, lin_vars, quad_coef,
quad_vars_1, quad_vars_2)
cobj.sense = sense
if obj is None:
cname = 'objective'
else:
cname = self._symbol_map.getSymbol(obj, self._obj_labeler)
cobj.name = cname
self._writer.objective = cobj
def write(self,
model: _BlockData,
filename: str,
timer: HierarchicalTimer = None):
if timer is None:
timer = HierarchicalTimer()
if model is not self._model:
timer.start('set_instance')
self.set_instance(model)
timer.stop('set_instance')
else:
timer.start('update')
self.update(timer=timer)
timer.stop('update')
timer.start('write file')
self._writer.write(filename)
timer.stop('write file')
def get_vars(self):
return [
self._solver_var_to_pyomo_var_map[i]
for i in self._writer.get_solve_vars()
]
def get_ordered_cons(self):
return [
self._solver_con_to_pyomo_con_map[i]
for i in self._writer.get_solve_cons()
]
def get_active_objective(self):
return self._objective
@property
def symbol_map(self):
return self._symbol_map
class NLWriter(PersistentBase):
def __init__(self):
super(NLWriter, self).__init__()
self._config = WriterConfig()
self._writer = None
self._symbol_map = SymbolMap()
self._var_labeler = None
self._con_labeler = None
self._param_labeler = None
self._pyomo_var_to_solver_var_map = dict()
self._pyomo_con_to_solver_con_map = dict()
self._solver_var_to_pyomo_var_map = dict()
self._solver_con_to_pyomo_con_map = dict()
self._pyomo_param_to_solver_param_map = dict()
self._expr_types = None
@property
def config(self):
return self._config
@config.setter
def config(self, val: WriterConfig):
self._config = val
@property
def symbol_map(self):
return self._symbol_map
def set_instance(self, model):
saved_config = self.config
saved_update_config = self.update_config
self.__init__()
self.config = saved_config
self.update_config = saved_update_config
self._model = model
self._expr_types = cmodel.PyomoExprTypes()
if self.config.symbolic_solver_labels:
self._var_labeler = TextLabeler()
self._con_labeler = TextLabeler()
self._param_labeler = TextLabeler()
else:
self._var_labeler = NumericLabeler('x')
self._con_labeler = NumericLabeler('c')
self._param_labeler = NumericLabeler('p')
self._writer = cmodel.NLWriter()
self.add_block(model)
if self._objective is None:
self.set_objective(None)
self._set_pyomo_amplfunc_env()
def _add_variables(self, variables: List[_GeneralVarData]):
cmodel.process_pyomo_vars(self._expr_types, variables,
self._pyomo_var_to_solver_var_map,
self._pyomo_param_to_solver_param_map,
self._vars,
self._solver_var_to_pyomo_var_map, False,
None, None, False)
def _add_params(self, params: List[_ParamData]):
cparams = cmodel.create_params(len(params))
for ndx, p in enumerate(params):
cp = cparams[ndx]
cp.name = self._symbol_map.getSymbol(p, self._param_labeler)
cp.value = p.value
self._pyomo_param_to_solver_param_map[id(p)] = cp
def _add_constraints(self, cons: List[_GeneralConstraintData]):
cmodel.process_nl_constraints(self._writer, self._expr_types, cons,
self._pyomo_var_to_solver_var_map,
self._pyomo_param_to_solver_param_map,
self._active_constraints,
self._pyomo_con_to_solver_con_map,
self._solver_con_to_pyomo_con_map)
def _add_sos_constraints(self, cons: List[_SOSConstraintData]):
if len(cons) != 0:
raise NotImplementedError(
'NL writer does not support SOS constraints')
def _remove_constraints(self, cons: List[_GeneralConstraintData]):
for c in cons:
cc = self._pyomo_con_to_solver_con_map.pop(c)
self._writer.remove_constraint(cc)
self._con_labeler.remove_obj(c)
del self._solver_con_to_pyomo_con_map[cc]
def _remove_sos_constraints(self, cons: List[_SOSConstraintData]):
if len(cons) != 0:
raise NotImplementedError(
'NL writer does not support SOS constraints')
def _remove_variables(self, variables: List[_GeneralVarData]):
for v in variables:
cvar = self._pyomo_var_to_solver_var_map.pop(id(v))
del self._solver_var_to_pyomo_var_map[cvar]
# self._symbol_map.removeSymbol(v)
self._var_labeler.remove_obj(v)
def _remove_params(self, params: List[_ParamData]):
for p in params:
del self._pyomo_param_to_solver_param_map[id(p)]
self._symbol_map.removeSymbol(p)
self._param_labeler.remove_obj(p)
def _update_variables(self, variables: List[_GeneralVarData]):
cmodel.process_pyomo_vars(self._expr_types, variables,
self._pyomo_var_to_solver_var_map,
self._pyomo_param_to_solver_param_map,
self._vars,
self._solver_var_to_pyomo_var_map, False,
None, None, True)
def update_params(self):
for p_id, p in self._params.items():
cp = self._pyomo_param_to_solver_param_map[p_id]
cp.value = p.value
def _set_objective(self, obj: _GeneralObjectiveData):
if obj is None:
const = cmodel.Constant(0)
lin_vars = list()
lin_coef = list()
nonlin = cmodel.Constant(0)
sense = 0
else:
pyomo_expr_types = cmodel.PyomoExprTypes()
repn = generate_standard_repn(obj.expr,
compute_values=False,
quadratic=False)
const = cmodel.appsi_expr_from_pyomo_expr(
repn.constant, self._pyomo_var_to_solver_var_map,
self._pyomo_param_to_solver_param_map, pyomo_expr_types)
lin_vars = [
self._pyomo_var_to_solver_var_map[id(i)]
for i in repn.linear_vars
]
lin_coef = [
cmodel.appsi_expr_from_pyomo_expr(
i, self._pyomo_var_to_solver_var_map,
self._pyomo_param_to_solver_param_map, pyomo_expr_types)
for i in repn.linear_coefs
]
if repn.nonlinear_expr is None:
nonlin = cmodel.appsi_expr_from_pyomo_expr(
0, self._pyomo_var_to_solver_var_map,
self._pyomo_param_to_solver_param_map, pyomo_expr_types)
else:
nonlin = cmodel.appsi_expr_from_pyomo_expr(
repn.nonlinear_expr, self._pyomo_var_to_solver_var_map,
self._pyomo_param_to_solver_param_map, pyomo_expr_types)
if obj.sense is minimize:
sense = 0
else:
sense = 1
cobj = cmodel.NLObjective(const, lin_coef, lin_vars, nonlin)
cobj.sense = sense
self._writer.objective = cobj
def write(self,
model: _BlockData,
filename: str,
timer: HierarchicalTimer = None):
if timer is None:
timer = HierarchicalTimer()
if model is not self._model:
timer.start('set_instance')
self.set_instance(model)
timer.stop('set_instance')
else:
timer.start('update')
self.update(timer=timer)
for cv, v in self._solver_var_to_pyomo_var_map.items():
if v.value is not None:
cv.value = v.value
timer.stop('update')
timer.start('write file')
self._writer.write(filename)
timer.stop('write file')
def update(self, timer: HierarchicalTimer = None):
super(NLWriter, self).update(timer=timer)
self._set_pyomo_amplfunc_env()
def get_ordered_vars(self):
return [
self._solver_var_to_pyomo_var_map[i]
for i in self._writer.get_solve_vars()
]
def get_ordered_cons(self):
return [
self._solver_con_to_pyomo_con_map[i]
for i in self._writer.get_solve_cons()
]
def get_active_objective(self):
return self._objective
def _set_pyomo_amplfunc_env(self):
if self._external_functions:
external_Libs = OrderedSet()
for con, ext_funcs in self._external_functions.items():
external_Libs.update([i._fcn._library for i in ext_funcs])
set_pyomo_amplfunc_env(external_Libs)
elif "PYOMO_AMPLFUNC" in os.environ:
del os.environ["PYOMO_AMPLFUNC"]
def solve(self,
model: _BlockData,
tee: bool = False,
load_solutions: bool = True,
logfile: Optional[str] = None,
solnfile: Optional[str] = None,
timelimit: Optional[float] = None,
report_timing: bool = False,
solver_io: Optional[str] = None,
suffixes: Optional[Sequence] = None,
options: Optional[Dict] = None,
keepfiles: bool = False,
symbolic_solver_labels: bool = False):
original_config = self.config
self.config = self.config()
self.config.stream_solver = tee
self.config.load_solution = load_solutions
self.config.symbolic_solver_labels = symbolic_solver_labels
self.config.time_limit = timelimit
self.config.report_timing = report_timing
if solver_io is not None:
raise NotImplementedError('Still working on this')
if suffixes is not None:
raise NotImplementedError('Still working on this')
if logfile is not None:
raise NotImplementedError('Still working on this')
if 'keepfiles' in self.config:
self.config.keepfiles = keepfiles
if solnfile is not None:
if 'filename' in self.config:
filename = os.path.splitext(solnfile)[0]
self.config.filename = filename
original_options = self.options
if options is not None:
self.options = options
results: Results = super(LegacySolverInterface, self).solve(model)
legacy_results = LegacySolverResults()
legacy_soln = LegacySolution()
legacy_results.solver.status = legacy_solver_status_map[results.termination_condition]
legacy_results.solver.termination_condition = legacy_termination_condition_map[results.termination_condition]
legacy_soln.status = legacy_solution_status_map[results.termination_condition]
legacy_results.solver.termination_message = str(results.termination_condition)
obj = get_objective(model)
legacy_results.problem.sense = obj.sense
if obj.sense == minimize:
legacy_results.problem.lower_bound = results.best_objective_bound
legacy_results.problem.upper_bound = results.best_feasible_objective
else:
legacy_results.problem.upper_bound = results.best_objective_bound
legacy_results.problem.lower_bound = results.best_feasible_objective
if results.best_feasible_objective is not None and results.best_objective_bound is not None:
legacy_soln.gap = abs(results.best_feasible_objective - results.best_objective_bound)
else:
legacy_soln.gap = None
symbol_map = SymbolMap()
symbol_map.byObject = dict(self.symbol_map.byObject)
symbol_map.bySymbol = {symb: weakref.ref(obj()) for symb, obj in self.symbol_map.bySymbol.items()}
symbol_map.aliases = {symb: weakref.ref(obj()) for symb, obj in self.symbol_map.aliases.items()}
symbol_map.default_labeler = self.symbol_map.default_labeler
model.solutions.add_symbol_map(symbol_map)
legacy_results._smap_id = id(symbol_map)
delete_legacy_soln = True
if load_solutions:
if hasattr(model, 'dual') and model.dual.import_enabled():
for c, val in results.solution_loader.get_duals().items():
model.dual[c] = val
if hasattr(model, 'slack') and model.slack.import_enabled():
for c, val in results.solution_loader.get_slacks().items():
model.slack[c] = val
if hasattr(model, 'rc') and model.rc.import_enabled():
for v, val in results.solution_loader.get_reduced_costs().items():
model.rc[v] = val
elif results.best_feasible_objective is not None:
delete_legacy_soln = False
for v, val in results.solution_loader.get_primals().items():
legacy_soln.variable[symbol_map.getSymbol(v)] = {'Value': val}
if hasattr(model, 'dual') and model.dual.import_enabled():
for c, val in results.solution_loader.get_duals().items():
legacy_soln.constraint[symbol_map.getSymbol(c)] = {'Dual': val}
if hasattr(model, 'slack') and model.slack.import_enabled():
for c, val in results.solution_loader.get_slacks().items():
symbol = symbol_map.getSymbol(c)
if symbol in legacy_soln.constraint:
legacy_soln.constraint[symbol]['Slack'] = val
if hasattr(model, 'rc') and model.rc.import_enabled():
for v, val in results.solution_loader.get_reduced_costs().items():
legacy_soln.variable['Rc'] = val
legacy_results.solution.insert(legacy_soln)
if delete_legacy_soln:
legacy_results.solution.delete(0)
self.config = original_config
self.options = original_options
return legacy_results
class CPLEXPersistent(CPLEXDirect, PersistentSolver):
"""The CPLEX LP/MIP solver
"""
pyomo.util.plugin.alias('_cplex_persistent',
doc='Persistent Python interface to the CPLEX LP/MIP solver')
def __init__(self, **kwds):
#
# Call base class constructor
#
kwds['type'] = 'cplexpersistent'
CPLEXDirect.__init__(self, **kwds)
# maps pyomo var data labels to the corresponding CPLEX variable id.
self._cplex_variable_ids = {}
self._cplex_variable_names = None
#
# updates all variable bounds in the compiled model - handles
# fixed variables and related issues. re-does everything from
# scratch by default, ignoring whatever was specified
# previously. if the value associated with the keyword
# vars_to_update is a non-empty list (assumed to be variable name
# / index pairs), then only the bounds for those variables are
# updated. this function assumes that the variables themselves
# already exist in the compiled model.
#
def compile_variable_bounds(self, pyomo_instance, vars_to_update):
from pyomo.core.base import Var
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin "
"cannot compile variable bounds - no "
"instance is presently compiled")
# the bound update entries should be name-value pairs
new_lower_bounds = []
new_upper_bounds = []
# operates through side effects on the above lists!
def update_bounds_lists(var_name):
var_lb = None
var_ub = None
if var_data.fixed and self._output_fixed_variable_bounds:
var_lb = var_ub = var_data.value
elif var_data.fixed:
# if we've been directed to not deal with fixed
# variables, then skip - they should have been
# compiled out of any description of the constraints
return
else:
if var_data.lb is None:
var_lb = -cplex.infinity
else:
var_lb = value(var_data.lb)
if var_data.ub is None:
var_ub = cplex.infinity
else:
var_ub= value(var_data.ub)
var_cplex_id = self._cplex_variable_ids[var_name]
new_lower_bounds.append((var_cplex_id, var_lb))
new_upper_bounds.append((var_cplex_id, var_ub))
if len(vars_to_update) == 0:
for var_data in pyomo_instance.component_data_objects(Var, active=True):
var_name = self._symbol_map.getSymbol(var_data, self._labeler)
update_bounds_lists(var_name)
else:
for var_name, var_index in vars_to_update:
var = pyomo_instance.find_component(var_name)
# TBD - do some error checking!
var_data = var[var_index]
var_name = self._symbol_map.getSymbol(var_data, self._labeler)
update_bounds_lists(var_name)
self._active_cplex_instance.variables.set_lower_bounds(new_lower_bounds)
self._active_cplex_instance.variables.set_upper_bounds(new_upper_bounds)
#
# method to compile objective of the input pyomo instance.
# TBD:
# it may be smarter just to track the associated pyomo instance,
# and re-compile it automatically from a cached local attribute.
# this would ensure consistency, among other things!
#
def compile_objective(self, pyomo_instance):
from pyomo.core.base import Objective
from pyomo.repn import canonical_is_constant, LinearCanonicalRepn, canonical_degree
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin "
"cannot compile objective - no "
"instance is presently compiled")
cplex_instance = self._active_cplex_instance
cntr = 0
for block in pyomo_instance.block_data_objects(active=True):
gen_obj_canonical_repn = \
getattr(block, "_gen_obj_canonical_repn", True)
# Get/Create the ComponentMap for the repn
if not hasattr(block,'_canonical_repn'):
block._canonical_repn = ComponentMap()
block_canonical_repn = block._canonical_repn
for obj_data in block.component_data_objects(Objective,
active=True,
descend_into=False):
cntr += 1
if cntr > 1:
raise ValueError(
"Multiple active objectives found on Pyomo instance '%s'. "
"Solver '%s' will only handle a single active objective" \
% (pyomo_instance.cname(True), self.type))
if obj_data.is_minimizing():
cplex_instance.objective.set_sense(
cplex_instance.objective.sense.minimize)
else:
cplex_instance.objective.set_sense(
cplex_instance.objective.sense.maximize)
cplex_instance.objective.set_name(
self._symbol_map.getSymbol(obj_data,
self._labeler))
if gen_obj_canonical_repn:
obj_repn = generate_canonical_repn(obj_data.expr)
block_canonical_repn[obj_data] = obj_repn
else:
obj_repn = block_canonical_repn[obj_data]
if (isinstance(obj_repn, LinearCanonicalRepn) and \
(obj_repn.linear == None)) or \
canonical_is_constant(obj_repn):
print("Warning: Constant objective detected, replacing "
"with a placeholder to prevent solver failure.")
offset = obj_repn.constant
if offset is None:
offset = 0.0
objective_expression = [("ONE_VAR_CONSTANT",offset)]
cplex_instance.objective.set_linear(objective_expression)
else:
if isinstance(obj_repn, LinearCanonicalRepn):
objective_expression, offset = \
self._encode_constraint_body_linear_specialized(
obj_repn,
self._labeler,
use_variable_names=False,
cplex_variable_name_index_map=self._cplex_variable_ids,
as_pairs=True)
if offset != 0.0:
objective_expression.append((self._cplex_variable_ids["ONE_VAR_CONSTANT"],offset))
cplex_instance.objective.set_linear(objective_expression)
else:
#Linear terms
if 1 in obj_repn:
objective_expression, offset = \
self._encode_constraint_body_linear(
obj_repn,
self._labeler,
as_pairs=True)
if offset != 0.0:
objective_expression.append(("ONE_VAR_CONSTANT",offset))
cplex_instance.objective.set_linear(objective_expression)
#Quadratic terms
if 2 in obj_repn:
self._has_quadratic_objective = True
objective_expression = \
self._encode_constraint_body_quadratic(obj_repn,
self._labeler,
as_triples=True,
is_obj=2.0)
cplex_instance.objective.\
set_quadratic_coefficients(objective_expression)
degree = canonical_degree(obj_repn)
if (degree is None) or (degree > 2):
raise ValueError(
"CPLEXPersistent plugin does not support general nonlinear "
"objective expressions (only linear or quadratic).\n"
"Objective: %s" % (obj_data.cname(True)))
#
# method to populate the CPLEX problem instance (interface) from
# the supplied Pyomo problem instance.
#
def compile_instance(self,
pyomo_instance,
symbolic_solver_labels=False,
output_fixed_variable_bounds=False,
skip_trivial_constraints=False):
from pyomo.core.base import Var, Constraint, SOSConstraint
from pyomo.repn import canonical_is_constant, LinearCanonicalRepn, canonical_degree
self._symbolic_solver_labels = symbolic_solver_labels
self._output_fixed_variable_bounds = output_fixed_variable_bounds
self._skip_trivial_constraints = skip_trivial_constraints
self._has_quadratic_constraints = False
self._has_quadratic_objective = False
used_sos_constraints = False
self._active_cplex_instance = cplex.Cplex()
if self._symbolic_solver_labels:
labeler = self._labeler = TextLabeler()
else:
labeler = self._labeler = NumericLabeler('x')
self._symbol_map = SymbolMap()
self._instance = pyomo_instance
pyomo_instance.solutions.add_symbol_map(self._symbol_map)
self._smap_id = id(self._symbol_map)
# we use this when iterating over the constraints because it
# will have a much smaller hash table, we also use this for
# the warm start code after it is cleaned to only contain
# variables referenced in the constraints
self._variable_symbol_map = SymbolMap()
# cplex wants the caller to set the problem type, which is (for
# current purposes) strictly based on variable type counts.
num_binary_variables = 0
num_integer_variables = 0
num_continuous_variables = 0
#############################################
# populate the variables in the cplex model #
#############################################
var_names = []
var_lbs = []
var_ubs = []
var_types = []
self._referenced_variable_ids.clear()
# maps pyomo var data labels to the corresponding CPLEX variable id.
self._cplex_variable_ids.clear()
# cached in the loop below - used to update the symbol map
# immediately following loop termination.
var_label_pairs = []
for var_data in pyomo_instance.component_data_objects(Var, active=True):
if var_data.fixed and not self._output_fixed_variable_bounds:
# if a variable is fixed, and we're preprocessing
# fixed variables (as in not outputting them), there
# is no need to add them to the compiled model.
continue
var_name = self._symbol_map.getSymbol(var_data, labeler)
var_names.append(var_name)
var_label_pairs.append((var_data, var_name))
self._cplex_variable_ids[var_name] = len(self._cplex_variable_ids)
if (var_data.lb is None) or (var_data.lb == -infinity):
var_lbs.append(-cplex.infinity)
else:
var_lbs.append(value(var_data.lb))
if (var_data.ub is None) or (var_data.ub == infinity):
var_ubs.append(cplex.infinity)
else:
var_ubs.append(value(var_data.ub))
if var_data.is_integer():
var_types.append(self._active_cplex_instance.variables.type.integer)
num_integer_variables += 1
elif var_data.is_binary():
var_types.append(self._active_cplex_instance.variables.type.binary)
num_binary_variables += 1
elif var_data.is_continuous():
var_types.append(self._active_cplex_instance.variables.type.continuous)
num_continuous_variables += 1
else:
raise TypeError("Invalid domain type for variable with name '%s'. "
"Variable is not continuous, integer, or binary.")
self._active_cplex_instance.variables.add(names=var_names,
lb=var_lbs,
ub=var_ubs,
types=var_types)
self._active_cplex_instance.variables.add(lb=[1],
ub=[1],
names=["ONE_VAR_CONSTANT"])
self._cplex_variable_ids["ONE_VAR_CONSTANT"] = len(self._cplex_variable_ids)
self._variable_symbol_map.addSymbols(var_label_pairs)
self._cplex_variable_names = self._active_cplex_instance.variables.get_names()
########################################################
# populate the standard constraints in the cplex model #
########################################################
expressions = []
senses = []
rhss = []
range_values = []
names = []
qexpressions = []
qlinears = []
qsenses = []
qrhss = []
qnames = []
for block in pyomo_instance.block_data_objects(active=True):
gen_con_canonical_repn = \
getattr(block, "_gen_con_canonical_repn", True)
# Get/Create the ComponentMap for the repn
if not hasattr(block,'_canonical_repn'):
block._canonical_repn = ComponentMap()
block_canonical_repn = block._canonical_repn
for con in block.component_data_objects(Constraint,
active=True,
descend_into=False):
if (con.lower is None) and \
(con.upper is None):
continue # not binding at all, don't bother
con_repn = None
if isinstance(con, LinearCanonicalRepn):
con_repn = con
else:
if gen_con_canonical_repn:
con_repn = generate_canonical_repn(con.body)
block_canonical_repn[con] = con_repn
else:
con_repn = block_canonical_repn[con]
# There are conditions, e.g., when fixing variables, under which
# a constraint block might be empty. Ignore these, for both
# practical reasons and the fact that the CPLEX LP format
# requires a variable in the constraint body. It is also
# possible that the body of the constraint consists of only a
# constant, in which case the "variable" of
if isinstance(con_repn, LinearCanonicalRepn):
if (con_repn.linear is None) and \
self._skip_trivial_constraints:
continue
else:
# we shouldn't come across a constant canonical repn
# that is not LinearCanonicalRepn
assert not canonical_is_constant(con_repn)
name = self._symbol_map.getSymbol(con, labeler)
expr = None
qexpr = None
quadratic = False
if isinstance(con_repn, LinearCanonicalRepn):
expr, offset = \
self._encode_constraint_body_linear_specialized(con_repn,
labeler,
use_variable_names=False,
cplex_variable_name_index_map=self._cplex_variable_ids)
else:
degree = canonical_degree(con_repn)
if degree == 2:
quadratic = True
elif (degree != 0) or (degree != 1):
raise ValueError(
"CPLEXPersistent plugin does not support general nonlinear "
"constraint expression (only linear or quadratic).\n"
"Constraint: %s" % (con.cname(True)))
expr, offset = self._encode_constraint_body_linear(con_repn,
labeler)
if quadratic:
if expr is None:
expr = cplex.SparsePair(ind=[0],val=[0.0])
self._has_quadratic_constraints = True
qexpr = self._encode_constraint_body_quadratic(con_repn,labeler)
qnames.append(name)
if con.equality:
# equality constraint.
qsenses.append('E')
qrhss.append(self._get_bound(con.lower) - offset)
elif (con.lower is not None) and (con.upper is not None):
raise RuntimeError(
"The CPLEXDirect plugin can not translate range "
"constraints containing quadratic expressions.")
elif con.lower is not None:
assert con.upper is None
qsenses.append('G')
qrhss.append(self._get_bound(con.lower) - offset)
else:
qsenses.append('L')
qrhss.append(self._get_bound(con.upper) - offset)
qlinears.append(expr)
qexpressions.append(qexpr)
else:
names.append(name)
expressions.append(expr)
if con.equality:
# equality constraint.
senses.append('E')
rhss.append(self._get_bound(con.lower) - offset)
range_values.append(0.0)
elif (con.lower is not None) and (con.upper is not None):
# ranged constraint.
senses.append('R')
lower_bound = self._get_bound(con.lower) - offset
upper_bound = self._get_bound(con.upper) - offset
rhss.append(lower_bound)
range_values.append(upper_bound - lower_bound)
elif con.lower is not None:
senses.append('G')
rhss.append(self._get_bound(con.lower) - offset)
range_values.append(0.0)
else:
senses.append('L')
rhss.append(self._get_bound(con.upper) - offset)
range_values.append(0.0)
###################################################
# populate the SOS constraints in the cplex model #
###################################################
# SOS constraints - largely taken from cpxlp.py so updates there,
# should be applied here
# TODO: Allow users to specify the variables coefficients for custom
# branching/set orders - refer to cpxlp.py
sosn = self._capabilities.sosn
sos1 = self._capabilities.sos1
sos2 = self._capabilities.sos2
modelSOS = ModelSOS()
for soscondata in pyomo_instance.component_data_objects(SOSConstraint,
active=True):
level = soscondata.level
if (level == 1 and not sos1) or \
(level == 2 and not sos2) or \
(level > 2 and not sosn):
raise Exception("Solver does not support SOS level %s constraints"
% (level,))
modelSOS.count_constraint(self._symbol_map,
labeler,
self._variable_symbol_map,
soscondata)
if modelSOS.sosType:
for key in modelSOS.sosType:
self._active_cplex_instance.SOS.add(type = modelSOS.sosType[key],
name = modelSOS.sosName[key],
SOS = [modelSOS.varnames[key],
modelSOS.weights[key]])
self._referenced_variable_ids.update(modelSOS.varids[key])
used_sos_constraints = True
self._active_cplex_instance.linear_constraints.add(
lin_expr=expressions,
senses=senses,
rhs=rhss,
range_values=range_values,
names=names)
for index in xrange(len(qexpressions)):
self._active_cplex_instance.quadratic_constraints.add(
lin_expr=qlinears[index],
quad_expr=qexpressions[index],
sense=qsenses[index],
rhs=qrhss[index],
name=qnames[index])
#############################################
# populate the objective in the cplex model #
#############################################
self.compile_objective(pyomo_instance)
################################################
# populate the problem type in the cplex model #
################################################
# This gets rid of the annoying "Freeing MIP data." message.
def _filter_freeing_mip_data(val):
if val.strip() == 'Freeing MIP data.':
return ""
return val
self._active_cplex_instance.set_warning_stream(sys.stderr,
fn=_filter_freeing_mip_data)
if (self._has_quadratic_objective is True) or \
(self._has_quadratic_constraints is True):
if (num_integer_variables > 0) or \
(num_binary_variables > 0) or \
(used_sos_constraints):
if self._has_quadratic_constraints is True:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.MIQCP)
else:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.MIQP)
else:
if self._has_quadratic_constraints is True:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.QCP)
else:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.QP)
elif (num_integer_variables > 0) or \
(num_binary_variables > 0) or \
(used_sos_constraints):
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.MILP)
else:
self._active_cplex_instance.set_problem_type(
self._active_cplex_instance.problem_type.LP)
# restore the warning stream without our filter function
self._active_cplex_instance.set_warning_stream(sys.stderr)
#
# simple method to query whether a Pyomo instance has already been
# compiled.
#
def instance_compiled(self):
return self._active_cplex_instance is not None
#
# Override base class method to check for compiled instance
#
def _warm_start(self, instance):
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin "
"cannot warm start - no instance is "
"presently compiled")
# clear any existing warm starts.
self._active_cplex_instance.MIP_starts.delete()
# the iteration order is identical to that used in generating
# the cplex instance, so all should be well.
variable_ids = []
variable_values = []
# IMPT: the var_data returned is a weak ref!
for label, var_data in iteritems(self._variable_symbol_map.bySymbol):
cplex_id = self._cplex_variable_ids[label]
if var_data().fixed and not self._output_fixed_variable_bounds:
continue
elif var_data().value is not None:
variable_ids.append(cplex_id)
variable_values.append(var_data().value)
if len(variable_ids):
self._active_cplex_instance.MIP_starts.add(
[variable_ids, variable_values],
self._active_cplex_instance.MIP_starts.effort_level.auto)
#
# Override base class method to check for compiled instance
#
def _populate_cplex_instance(self, model):
assert model == self._instance
def _presolve(self, *args, **kwds):
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin"
" cannot presolve - no instance is "
"presently compiled")
# These must be passed in to the compile_instance method,
# but assert that any values here match those already supplied
if 'symbolic_solver_labels' in kwds:
assert self._symbolic_solver_labels == \
kwds['symbolic_solver_labels']
if 'output_fixed_variable_bounds' in kwds:
assert self._output_fixed_variable_bounds == \
kwds['output_fixed_variable_bounds']
if 'skip_trivial_constraints' in kwds:
assert self._skip_trivial_constraints == \
kwds["skip_trivial_constraints"]
if self._smap_id not in self._instance.solutions.symbol_map:
self._instance.solutions.add_symbol_map(self._symbol_map)
CPLEXDirect._presolve(self, *args, **kwds)
# like other solver plugins, persistent solver plugins can
# take an instance as an input argument. the only context in
# which this instance is used, however, is for warm-starting.
if len(args) > 2:
raise ValueError("The CPLEXPersistent plugin method "
"'_presolve' can be supplied at most "
"one problem instance - %s were "
"supplied" % len(args))
# Re-add the symbol map id if it was cleared
# after a previous solution load
if id(self._symbol_map) not in args[0].solutions.symbol_map:
args[0].solutions.add_symbol_map(self._symbol_map)
self._smap_id = id(self._symbol_map)
#
# invoke the solver on the currently compiled instance!!!
#
def _apply_solver(self):
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin cannot "
"apply solver - no instance is presently compiled")
# NOTE:
# CPLEX maintains the pool of feasible solutions from the
# prior solve as the set of mip starts for the next solve.
# and evaluating multiple mip starts (and there can be many)
# is expensive. so if the warm_start method is not invoked,
# there will potentially be a lot of time wasted.
return CPLEXDirect._apply_solver(self)
def _postsolve(self):
if self._active_cplex_instance is None:
raise RuntimeError("***The CPLEXPersistent solver plugin "
"cannot postsolve - no instance is "
"presently compiled")
active_cplex_instance = self._active_cplex_instance
variable_symbol_map = self._variable_symbol_map
instance = self._instance
ret = CPLEXDirect._postsolve(self)
#
# These get reset to None by the base class method
#
self._active_cplex_instance = active_cplex_instance
self._variable_symbol_map = variable_symbol_map
self._instance = instance
return ret
class IntervalTightener(PersistentBase):
def __init__(self):
super(IntervalTightener, self).__init__()
self._config = IntervalConfig()
self._cmodel = None
self._var_map = dict()
self._con_map = dict()
self._param_map = dict()
self._rvar_map = dict()
self._rcon_map = dict()
self._pyomo_expr_types = cmodel.PyomoExprTypes()
self._symbolic_solver_labels: bool = False
self._symbol_map = SymbolMap()
self._var_labeler = None
self._con_labeler = None
self._param_labeler = None
self._obj_labeler = None
self._objective = None
@property
def config(self):
return self._config
@config.setter
def config(self, val: IntervalConfig):
self._config = val
def set_instance(self,
model,
symbolic_solver_labels: Optional[bool] = None):
saved_config = self.config
saved_update_config = self.update_config
self.__init__()
self.config = saved_config
self.update_config = saved_update_config
self._expr_types = cmodel.PyomoExprTypes()
if symbolic_solver_labels is not None:
self._symbolic_solver_labels = symbolic_solver_labels
if self._symbolic_solver_labels:
self._var_labeler = TextLabeler()
self._con_labeler = TextLabeler()
self._param_labeler = TextLabeler()
self._obj_labeler = TextLabeler()
self._model = model
self._cmodel = cmodel.FBBTModel()
self.add_block(model)
if self._objective is None:
self.set_objective(None)
def _add_variables(self, variables: List[_GeneralVarData]):
if self._symbolic_solver_labels:
set_name = True
symbol_map = self._symbol_map
labeler = self._var_labeler
else:
set_name = False
symbol_map = None
labeler = None
cmodel.process_pyomo_vars(self._pyomo_expr_types, variables,
self._var_map, self._param_map, self._vars,
self._rvar_map, set_name, symbol_map,
labeler, False)
def _add_params(self, params: List[_ParamData]):
cparams = cmodel.create_params(len(params))
for ndx, p in enumerate(params):
cp = cparams[ndx]
cp.value = p.value
self._param_map[id(p)] = cp
if self._symbolic_solver_labels:
for ndx, p in enumerate(params):
cp = cparams[ndx]
cp.name = self._symbol_map.getSymbol(p, self._param_labeler)
def _add_constraints(self, cons: List[_GeneralConstraintData]):
cmodel.process_fbbt_constraints(self._cmodel, self._pyomo_expr_types,
cons, self._var_map, self._param_map,
self._active_constraints,
self._con_map, self._rcon_map)
if self._symbolic_solver_labels:
for c, cc in self._con_map.items():
cc.name = self._symbol_map.getSymbol(c, self._con_labeler)
def _add_sos_constraints(self, cons: List[_SOSConstraintData]):
if len(cons) != 0:
raise NotImplementedError(
'IntervalTightener does not support SOS constraints')
def _remove_constraints(self, cons: List[_GeneralConstraintData]):
if self._symbolic_solver_labels:
for c in cons:
self._symbol_map.removeSymbol(c)
for c in cons:
cc = self._con_map.pop(c)
self._cmodel.remove_constraint(cc)
del self._rcon_map[cc]
def _remove_sos_constraints(self, cons: List[_SOSConstraintData]):
if len(cons) != 0:
raise NotImplementedError(
'IntervalTightener does not support SOS constraints')
def _remove_variables(self, variables: List[_GeneralVarData]):
if self._symbolic_solver_labels:
for v in variables:
self._symbol_map.removeSymbol(v)
for v in variables:
cvar = self._var_map.pop(id(v))
del self._rvar_map[cvar]
def _remove_params(self, params: List[_ParamData]):
if self._symbolic_solver_labels:
for p in params:
self._symbol_map.removeSymbol(p)
for p in params:
del self._param_map[id(p)]
def _update_variables(self, variables: List[_GeneralVarData]):
cmodel.process_pyomo_vars(self._pyomo_expr_types, variables,
self._var_map, self._param_map, self._vars,
self._rvar_map, False, None, None, True)
def update_params(self):
for p_id, p in self._params.items():
cp = self._param_map[p_id]
cp.value = p.value
def set_objective(self, obj: _GeneralObjectiveData):
if self._symbolic_solver_labels:
if self._objective is not None:
self._symbol_map.removeSymbol(self._objective)
super().set_objective(obj)
def _set_objective(self, obj: _GeneralObjectiveData):
if obj is None:
ce = cmodel.Constant(0)
sense = 0
else:
ce = cmodel.appsi_expr_from_pyomo_expr(obj.expr, self._var_map,
self._param_map,
self._pyomo_expr_types)
if obj.sense is minimize:
sense = 0
else:
sense = 1
cobj = cmodel.FBBTObjective(ce)
cobj.sense = sense
self._cmodel.objective = cobj
self._objective = obj
if self._symbolic_solver_labels and obj is not None:
cobj.name = self._symbol_map.getSymbol(obj, self._obj_labeler)
def _update_pyomo_var_bounds(self):
for cv, v in self._rvar_map.items():
cv_lb = cv.get_lb()
cv_ub = cv.get_ub()
if -cmodel.inf < cv_lb:
v.setlb(cv_lb)
v_id = id(v)
_v, _lb, _ub, _fixed, _domain, _value = self._vars[v_id]
self._vars[v_id] = (_v, cv_lb, _ub, _fixed, _domain, _value)
if cv_ub < cmodel.inf:
v.setub(cv_ub)
v_id = id(v)
_v, _lb, _ub, _fixed, _domain, _value = self._vars[v_id]
self._vars[v_id] = (_v, _lb, cv_ub, _fixed, _domain, _value)
def _deactivate_satisfied_cons(self):
cons_to_deactivate = list()
if self.config.deactivate_satisfied_constraints:
for c, cc in self._con_map.items():
if not cc.active:
cons_to_deactivate.append(c)
self.remove_constraints(cons_to_deactivate)
for c in cons_to_deactivate:
c.deactivate()
def perform_fbbt(self,
model: _BlockData,
symbolic_solver_labels: Optional[bool] = None):
if model is not self._model:
self.set_instance(model,
symbolic_solver_labels=symbolic_solver_labels)
else:
if symbolic_solver_labels is not None and symbolic_solver_labels != self._symbolic_solver_labels:
raise RuntimeError(
'symbolic_solver_labels can only be changed through the set_instance method. '
'Please either use set_instance or create a new instance of IntervalTightener.'
)
self.update()
try:
n_iter = self._cmodel.perform_fbbt(
self.config.feasibility_tol, self.config.integer_tol,
self.config.improvement_tol, self.config.max_iter,
self.config.deactivate_satisfied_constraints)
finally:
# we want to make sure the pyomo model and cmodel stay in sync
# even if an exception is raised and caught
self._update_pyomo_var_bounds()
self._deactivate_satisfied_cons()
return n_iter
def perform_fbbt_with_seed(self, model: _BlockData,
seed_var: _GeneralVarData):
if model is not self._model:
self.set_instance(model)
else:
self.update()
try:
n_iter = self._cmodel.perform_fbbt_with_seed(
self._var_map[id(seed_var)], self.config.feasibility_tol,
self.config.integer_tol, self.config.improvement_tol,
self.config.max_iter,
self.config.deactivate_satisfied_constraints)
finally:
# we want to make sure the pyomo model and cmodel stay in sync
# even if an exception is raised and caught
self._update_pyomo_var_bounds()
self._deactivate_satisfied_cons()
return n_iter
class LPWriter(PersistentBase):
def __init__(self):
super(LPWriter, self).__init__()
self._config = WriterConfig()
self._writer = None
self._symbol_map = SymbolMap()
self._var_labeler = None
self._con_labeler = None
self._param_labeler = None
self._obj_labeler = None
self._pyomo_var_to_solver_var_map = dict()
self._pyomo_con_to_solver_con_map = dict()
self._solver_var_to_pyomo_var_map = dict()
self._solver_con_to_pyomo_con_map = dict()
self._pyomo_param_to_solver_param_map = dict()
self._expr_types = None
@property
def config(self):
return self._config
@config.setter
def config(self, val: WriterConfig):
self._config = val
def set_instance(self, model):
saved_config = self.config
saved_update_config = self.update_config
self.__init__()
self.config = saved_config
self.update_config = saved_update_config
self._model = model
self._expr_types = cmodel.PyomoExprTypes()
if self.config.symbolic_solver_labels:
self._var_labeler = TextLabeler()
self._con_labeler = TextLabeler()
self._param_labeler = TextLabeler()
self._obj_labeler = TextLabeler()
else:
self._var_labeler = NumericLabeler('x')
self._con_labeler = NumericLabeler('c')
self._param_labeler = NumericLabeler('p')
self._obj_labeler = NumericLabeler('obj')
self._writer = cmodel.LPWriter()
self.add_block(model)
if self._objective is None:
self.set_objective(None)
def _add_variables(self, variables: List[_GeneralVarData]):
cmodel.process_pyomo_vars(self._expr_types, variables, self._pyomo_var_to_solver_var_map,
self._pyomo_param_to_solver_param_map, self._vars,
self._solver_var_to_pyomo_var_map, True, self._symbol_map,
self._var_labeler, False)
def _add_params(self, params: List[_ParamData]):
cparams = cmodel.create_params(len(params))
for ndx, p in enumerate(params):
cp = cparams[ndx]
cp.name = self._symbol_map.getSymbol(p, self._param_labeler)
cp.value = p.value
self._pyomo_param_to_solver_param_map[id(p)] = cp
def _add_constraints(self, cons: List[_GeneralConstraintData]):
cmodel.process_lp_constraints(cons, self)
def _add_sos_constraints(self, cons: List[_SOSConstraintData]):
if len(cons) != 0:
raise NotImplementedError('LP writer does not yet support SOS constraints')
def _remove_constraints(self, cons: List[_GeneralConstraintData]):
for c in cons:
cc = self._pyomo_con_to_solver_con_map.pop(c)
self._writer.remove_constraint(cc)
self._symbol_map.removeSymbol(c)
self._con_labeler.remove_obj(c)
del self._solver_con_to_pyomo_con_map[cc]
def _remove_sos_constraints(self, cons: List[_SOSConstraintData]):
if len(cons) != 0:
raise NotImplementedError('LP writer does not yet support SOS constraints')
def _remove_variables(self, variables: List[_GeneralVarData]):
for v in variables:
cvar = self._pyomo_var_to_solver_var_map.pop(id(v))
del self._solver_var_to_pyomo_var_map[cvar]
self._symbol_map.removeSymbol(v)
self._var_labeler.remove_obj(v)
def _remove_params(self, params: List[_ParamData]):
for p in params:
del self._pyomo_param_to_solver_param_map[id(p)]
self._symbol_map.removeSymbol(p)
self._param_labeler.remove_obj(p)
def _update_variables(self, variables: List[_GeneralVarData]):
cmodel.process_pyomo_vars(self._expr_types, variables, self._pyomo_var_to_solver_var_map,
self._pyomo_param_to_solver_param_map, self._vars,
self._solver_var_to_pyomo_var_map, False, None,
None, True)
def update_params(self):
for p_id, p in self._params.items():
cp = self._pyomo_param_to_solver_param_map[p_id]
cp.value = p.value
def _set_objective(self, obj: _GeneralObjectiveData):
cobj = cmodel.process_lp_objective(self._expr_types, obj, self._pyomo_var_to_solver_var_map,
self._pyomo_param_to_solver_param_map)
if obj is None:
sense = 0
cname = 'objective'
else:
cname = self._symbol_map.getSymbol(obj, self._obj_labeler)
if obj.sense is minimize:
sense = 0
else:
sense = 1
cobj.sense = sense
cobj.name = cname
self._writer.objective = cobj
def write(self, model: _BlockData, filename: str, timer: HierarchicalTimer = None):
if timer is None:
timer = HierarchicalTimer()
if model is not self._model:
timer.start('set_instance')
self.set_instance(model)
timer.stop('set_instance')
else:
timer.start('update')
self.update(timer=timer)
timer.stop('update')
timer.start('write file')
self._writer.write(filename)
timer.stop('write file')
def get_vars(self):
return [self._solver_var_to_pyomo_var_map[i] for i in self._writer.get_solve_vars()]
def get_ordered_cons(self):
return [self._solver_con_to_pyomo_con_map[i] for i in self._writer.get_solve_cons()]
def get_active_objective(self):
return self._objective
@property
def symbol_map(self):
return self._symbol_map
def __call__(self,
model,
output_filename,
solver_capability,
io_options):
# Make sure not to modify the user's dictionary, they may be
# reusing it outside of this call
io_options = dict(io_options)
# NOTE: io_options is a simple dictionary of keyword-value
# pairs specific to this writer.
symbolic_solver_labels = \
io_options.pop("symbolic_solver_labels", False)
labeler = io_options.pop("labeler", None)
# How much effort do we want to put into ensuring the
# LP file is written deterministically for a Pyomo model:
# 0 : None
# 1 : sort keys of indexed components (default)
# 2 : sort keys AND sort names (over declaration order)
file_determinism = io_options.pop("file_determinism", 1)
sorter = SortComponents.unsorted
if file_determinism >= 1:
sorter = sorter | SortComponents.indices
if file_determinism >= 2:
sorter = sorter | SortComponents.alphabetical
output_fixed_variable_bounds = \
io_options.pop("output_fixed_variable_bounds", False)
# Skip writing constraints whose body section is fixed (i.e.,
# no variables)
skip_trivial_constraints = \
io_options.pop("skip_trivial_constraints", False)
# Note: Baron does not allow specification of runtime
# option outside of this file, so we add support
# for them here
solver_options = io_options.pop("solver_options", {})
if len(io_options):
raise ValueError(
"ProblemWriter_baron_writer passed unrecognized io_options:\n\t" +
"\n\t".join("%s = %s" % (k,v) for k,v in iteritems(io_options)))
if symbolic_solver_labels and (labeler is not None):
raise ValueError("Baron problem writer: Using both the "
"'symbolic_solver_labels' and 'labeler' "
"I/O options is forbidden")
# Make sure there are no strange ActiveComponents. The expression
# walker will handle strange things in constraints later.
model_ctypes = model.collect_ctypes(active=True)
invalids = set()
for t in (model_ctypes - valid_active_ctypes_minlp):
if issubclass(t, ActiveComponent):
invalids.add(t)
if len(invalids):
invalids = [t.__name__ for t in invalids]
raise RuntimeError(
"Unallowable active component(s) %s.\nThe BARON writer cannot "
"export models with this component type." %
", ".join(invalids))
if output_filename is None:
output_filename = model.name + ".bar"
output_file=open(output_filename, "w")
# Process the options. Rely on baron to catch
# and reset bad option values
output_file.write("OPTIONS {\n")
summary_found = False
if len(solver_options):
for key, val in iteritems(solver_options):
if (key.lower() == 'summary'):
summary_found = True
if key.endswith("Name"):
output_file.write(key+": \""+str(val)+"\";\n")
else:
output_file.write(key+": "+str(val)+";\n")
if not summary_found:
# The 'summary option is defaulted to 0, so that no
# summary file is generated in the directory where the
# user calls baron. Check if a user explicitly asked for
# a summary file.
output_file.write("Summary: 0;\n")
output_file.write("}\n\n")
if symbolic_solver_labels:
v_labeler = AlphaNumericTextLabeler()
c_labeler = AlphaNumericTextLabeler()
elif labeler is None:
v_labeler = NumericLabeler('x')
c_labeler = NumericLabeler('c')
symbol_map = SymbolMap()
symbol_map.default_labeler = v_labeler
#sm_bySymbol = symbol_map.bySymbol
# Cache the list of model blocks so we don't have to call
# model.block_data_objects() many many times, which is slow
# for indexed blocks
all_blocks_list = list(model.block_data_objects(active=True,
sort=sorter,
descend_into=True))
active_components_data_var = {}
#for block in all_blocks_list:
# tmp = active_components_data_var[id(block)] = \
# list(obj for obj in block.component_data_objects(Var,
# sort=sorter,
# descend_into=False))
# create_symbols_func(symbol_map, tmp, labeler)
# GAH: Not sure this is necessary, and also it would break for
# non-mutable indexed params so I am commenting out for now.
#for param_data in active_components_data(block, Param, sort=sorter):
#instead of checking if param_data._mutable:
#if not param_data.is_constant():
# create_symbol_func(symbol_map, param_data, labeler)
#symbol_map_variable_ids = set(symbol_map.byObject.keys())
#object_symbol_dictionary = symbol_map.byObject
#
# Go through the objectives and constraints and generate
# the output so that we can obtain the set of referenced
# variables.
#
equation_section_stream = StringIO()
referenced_variable_ids, branching_priorities_suffixes = \
self._write_equations_section(
model,
equation_section_stream,
all_blocks_list,
active_components_data_var,
symbol_map,
c_labeler,
output_fixed_variable_bounds,
skip_trivial_constraints,
sorter)
#
# BINARY_VARIABLES, INTEGER_VARIABLES, POSITIVE_VARIABLES, VARIABLES
#
BinVars = []
IntVars = []
PosVars = []
Vars = []
for vid in referenced_variable_ids:
name = symbol_map.byObject[vid]
var_data = symbol_map.bySymbol[name]()
if var_data.is_continuous():
if var_data.has_lb() and \
(self._get_bound(var_data.lb) >= 0):
TypeList = PosVars
else:
TypeList = Vars
elif var_data.is_binary():
TypeList = BinVars
elif var_data.is_integer():
TypeList = IntVars
else:
assert False
TypeList.append(name)
if len(BinVars) > 0:
BinVars.sort()
output_file.write('BINARY_VARIABLES ')
output_file.write(", ".join(BinVars))
output_file.write(';\n\n')
if len(IntVars) > 0:
IntVars.sort()
output_file.write('INTEGER_VARIABLES ')
output_file.write(", ".join(IntVars))
output_file.write(';\n\n')
PosVars.append('ONE_VAR_CONST__')
PosVars.sort()
output_file.write('POSITIVE_VARIABLES ')
output_file.write(", ".join(PosVars))
output_file.write(';\n\n')
if len(Vars) > 0:
Vars.sort()
output_file.write('VARIABLES ')
output_file.write(", ".join(Vars))
output_file.write(';\n\n')
#
# LOWER_BOUNDS
#
lbounds = {}
for vid in referenced_variable_ids:
name = symbol_map.byObject[vid]
var_data = symbol_map.bySymbol[name]()
if var_data.fixed:
if output_fixed_variable_bounds:
var_data_lb = var_data.value
else:
var_data_lb = None
else:
var_data_lb = None
if var_data.has_lb():
var_data_lb = self._get_bound(var_data.lb)
if var_data_lb is not None:
name_to_output = symbol_map.getSymbol(var_data)
lb_string_template = '%s: %'+self._precision_string+';\n'
lbounds[name_to_output] = lb_string_template % (name_to_output, var_data_lb)
if len(lbounds) > 0:
output_file.write("LOWER_BOUNDS{\n")
output_file.write("".join( lbounds[key] for key in sorted(lbounds.keys()) ) )
output_file.write("}\n\n")
lbounds = None
#
# UPPER_BOUNDS
#
ubounds = {}
for vid in referenced_variable_ids:
name = symbol_map.byObject[vid]
var_data = symbol_map.bySymbol[name]()
if var_data.fixed:
if output_fixed_variable_bounds:
var_data_ub = var_data.value
else:
var_data_ub = None
else:
var_data_ub = None
if var_data.has_ub():
var_data_ub = self._get_bound(var_data.ub)
if var_data_ub is not None:
name_to_output = symbol_map.getSymbol(var_data)
ub_string_template = '%s: %'+self._precision_string+';\n'
ubounds[name_to_output] = ub_string_template % (name_to_output, var_data_ub)
if len(ubounds) > 0:
output_file.write("UPPER_BOUNDS{\n")
output_file.write("".join( ubounds[key] for key in sorted(ubounds.keys()) ) )
output_file.write("}\n\n")
ubounds = None
#
# BRANCHING_PRIORITIES
#
# Specifying priorities requires that the pyomo model has established an
# EXTERNAL, float suffix called 'branching_priorities' on the model
# object, indexed by the relevant variable
BranchingPriorityHeader = False
for suffix in branching_priorities_suffixes:
for var_data, priority in iteritems(suffix):
if id(var_data) not in referenced_variable_ids:
continue
if priority is not None:
if not BranchingPriorityHeader:
output_file.write('BRANCHING_PRIORITIES{\n')
BranchingPriorityHeader = True
name_to_output = symbol_map.getSymbol(var_data)
output_file.write(name_to_output+': '+str(priority)+';\n')
if BranchingPriorityHeader:
output_file.write("}\n\n")
#
# Now write the objective and equations section
#
output_file.write(equation_section_stream.getvalue())
#
# STARTING_POINT
#
output_file.write('STARTING_POINT{\nONE_VAR_CONST__: 1;\n')
tmp = {}
string_template = '%s: %'+self._precision_string+';\n'
for vid in referenced_variable_ids:
name = symbol_map.byObject[vid]
var_data = symbol_map.bySymbol[name]()
starting_point = var_data.value
if starting_point is not None:
var_name = symbol_map.getSymbol(var_data)
tmp[var_name] = string_template % (var_name, starting_point)
output_file.write("".join( tmp[key] for key in sorted(tmp.keys()) ))
output_file.write('}\n\n')
output_file.close()
return output_filename, symbol_map